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tag:blogger.com,1999:blog-90934361613261553592016-07-23T18:22:17.579+01:00Variable VariabilityVictor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-37731128243975647212016-07-14T17:56:00.000+01:002016-07-14T18:20:52.149+01:00Do not ban homoeopathy in the name of Science

The BBC reports on a petition to ban homoeopathy for pets, which is already singed by a 1000 British vets. The justification for this is that homoeopathy does not work. The vet who started the petition said: "It's been shown that homeopathy doesn't work, so it probably shouldn't be offered any more even if it is offered with good intentions."

Classical homoeopathy naturally does not work. It works with strong dilutions, some are actually so strongly diluted that you can show that most bottles do not contain anything.

Furthermore, homoeopathy is taken as pills or drops. This means that you can make a double blind randomized trail. You randomly give half the people the "medicine" and half a fake medicine (placebo). The patient and doctor both do not know who got what (double blind). At the end you analyse whether there was a difference between the medicine and the placebo. If you pass this trial, your medicine works, no matter how it was made, whether is was conventional or homoeopathic. Also for many traditional medicines it is not known (well) how they work, but the double-blind randomized trail shows that it does. Classical homoeopathic medicine has failed this foundational test.

However, that homoeopathy does not work is not sufficient reason to ban it. It also does no harm and in doubt we should always chose the side of freedom. Government action is there to fix important wrongs, not for stuff that just makes us uncomfortable.

Just sleep
Even if it does not work, homoeopathy may still do some good. Also placebos work. Patients heal faster with a placebo than without treatment. Medical science should work much more on how to optimize the placebo effect. The number of times a day you take the pill, its colour, taste, size, ...

In The Netherlands doctors often tell their patients to simply go home, sleep and if the symptoms do not go away come back in a week. I like that, I am Dutch. Isn't it a relief when the doctor tells you it is not something serious and you should just go to bed? Sounds fine to me.

Foreigners mostly hate it. If they do not get a pill they do not feel taken seriously and they can even get quite aggressive. I would not be surprised if their anger leads to an anti-placebo effect. Why not give them something homoeopathic? That is better than unnecessary medicine, which may do harm, and lot better than over-prescribing on antibiotics.

Homoeopathy or classical homoeopathy?
The vet in the BBC article gives the impression he really believes in homoeopathy. That could be problematic. I would avoid such doctors because they clearly do not understand how to evaluate evidence. But a doctor who pragmatically prescribes it in case someone should just go to bed would be fine.

It could also be that the medicines this vet prescribes are not classical homoeopathy, but herbal medicine, which is often marketed as homoeopathic. Those could naturally work, many conventional medicines come from substances that were used in traditional cures in healing traditions from all over the world. The active ingredient of aspirin famously comes from willow bark.

That is why I do not like intolerance directed against traditional medicine; we should study it, see if it works, try to understand why it works, how to reduce side effects and how to improve their effectiveness. In many cases we do not have clear evidence against it, like we have for classical homoeopathy, and even if the "explanation" of how the cures work makes no sense we should keep an open mind that the actual treatment does bring benefits.

If a "homoeopathic" herbal medicine would pass the double blind trial, you could sell it as a normal medicine if it works better than existing medicines. Thus for me it is a bad sign when a pill is marketed as homoeopathic. Although, I do not know whether it is true, but I once heard an ironic story about a pill that went through all the tests and was approved as conventional medicine, but then the marketing department decided it was better to sell it as a herbal cure.

Science and politics
There is a parallel to the climate debate. Science can tell you some of the consequences of continued fossil fuel use. Science can tell you classical homoeopathy does not work. Science cannot tell society how to respond to climate change. Science cannot tell whether we should ban homoeopathy.

As a citizen I feel we should transition to renewable energy. As a citizen I feel we can be tolerant towards homoeopathy.

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Related reading
BBC News, Science & Environment: Vets: Ban the use of homeopathy in animals

Steven Novella, MD, academic clinical neurologist at Yale University School of Medicine and president and co-founder of the New England Skeptical Society want to ban homoeopathy for pets: Should We Ban Homeopathy for Animals?

* Top photo: Cats by Abdullah AlBargan, used with a Creative Commons Attribution-NoDerivs 2.0 Generic (CC BY-ND 2.0) license.
* Middle photo: Cat, MacDuff the cat by Kevin Dooley, used with a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.
* This post was a wonderful excuse to finally write a popular post with cute cat photos.Victor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-49416434491393798592016-07-07T14:28:00.000+01:002016-07-09T21:12:37.110+01:00Is it time to freak out about the climate sensitivity estimates from energy budget models?Estimates of climate sensitivity using simple energy budget models tended to produce lower values than many other methods. Consequently they were loved by the mitigation sceptical movement, who seemed to regard these as the most robust of all methods. Part of their argument is the claim that these are "empirical" estimates, conveniently forgetting the simple statistical model the method uses, that they still require information from physical global climate models for the forcings, and that global climate models output also fit the "empirical" temperature change (and many other observed changes).

Before the last IPCC report the estimate for equilibrium climate sensitivity was between 2°C and 4.5°C with a best estimate of 3°C. I do not know of any explicit statement, but I have the feeling that the new studies with low estimates from energy budget models were the reason why the last IPCC report reduced the lower bound to 1.5°C. Since the reasons for the discrepancies were not understood the last IPCC report no longer gave a best estimate for equilibrium climate sensitivity.

The equilibrium climate sensitivity is defined as the equilibrium change in global mean near-surface air temperature after doubling the atmospheric concentration of carbon dioxide.

A Nature News and Views by Kyle Armour (2016) showed this week that three assumptions made in the simple energy budget models lead to strong biases.

1. This week Mark Richardson and colleagues (2016) showed that the temperature change is underestimated because we have few measurements in regions where the change is large, especially the Arctic. This masking problem creates a bias of 15%.

Furthermore, over the ocean, empirical estimates do not use the air temperature, but use the sea surface temperature instead; the water temperature is a much smoother field and can thus be estimated using many fewer samples, which is good because observations over the oceans are sparse. Above sea ice the air temperature is used. Thus this also means that the decrease in the ice cover need to be taken into account. The temperature trend of the air temperature over the ocean is also higher than the trend of the sea surface temperature. Both effects make the "observed" trend 9% smaller.*

2. Climate change is mainly due to increases in carbon dioxide concentrations, but also warming due to increases in methane concentrations, cooling due to increases in aerosols (small airborne particles) and changing due to land use changes. Half a year ago Kate Marvel and colleagues showed that these forcings do not have the same global effect as carbon dioxide and that, as a consequence, the energy balance models are biased low. Marvel and colleagues estimate that this makes the estimates of energy balance models 30% too low.

3. Kyle Armour and colleagues (2013) previous work showed that in the early warming phase climate sensitivity appears smaller than the true value you would get if you would wait till the system has returned to equilibrium. This leads to an underestimate of 25%.

Taking all three biases into account the best estimate from the energy balance models from around 2°C estimate becomes 4.6°C**; see Figure 1b of Armour (2016) reproduced below.

Climate sensitivity estimated from observations1 (black), and its revision following Richardson et al. (blue) then following Marvel et al. (green), and in red the revision for the time dependence (Armour). The grey histogram shows climate model values.

The equilibrium climate sensitivity from global climate models is about 3.5°C***, which is close to the best estimate from all lines of evidence of about 3°C. The "empirical" estimate of 4.6°C is now thus clearly larger than the ones of the global climate models.

Is that a reason to freak out? Have we severely underestimated the severity of the problem?

Probably not, there are many different lines of evidence that support an equilibrium climate sensitivity around 3, with a likely range from around 2 to about 4.5. That the simple energy balance models might now suggest a best estimate of around 4.6°C does not really influence this overall assessment. It is just one line of evidence.

That the energy balance climate sensitivity is minimally above the upper bound does not change this. These energy balance models have not been studied much and the biases are so large that the correction need to very accurate, while they are currently mostly based on single studies. It is quite likely that this value will still change the coming years. If this value still holds after a dozen more studies you may want to consider freaking out a little. How uncertain this bias corrected climate sensitivity is is illustrated by its wide distribution in the above graph with a 95% uncertainty range of 2.5-12.8°C.

[UPDATE. Gavin Schmidt mentions on twitter that it should also be studied whether these three factors are fully independent. While they seem to relate to different aspects there could be a link because spatial patterns and forcing efficacy are strongly related. Thus it would be valuable to make a study that considers all three biases in combination.]

The promotion of the cherry picked climate sensitivity of 2°C, or lower, was disingenuous. A similar promotion of a value of 4.6°C would be no better. (Someone promoting a climate sensitivity of 12.8°C deserves a place in statistical Purgatory.)

There are many other lines of evidence for an equilibrium climate sensitivity around 3, from basic physics, to global climate models, various climatic changes in the deep past and the climate response to volcanoes. Before accepting values far away from 3 we would need to understand the physics of the feedbacks that produce such deviations.

Figure 1 Ranges and best estimates of ECS based on different lines of evidence. Bars show 5-95% uncertainty ranges with the best estimates marked by dots. Dashed lines give alternative estimates within one study. The grey shaded range marks the likely 1.5°C to 4.5°C range as reported in AR5, and the grey solid line the extremely unlikely less than 1°C, the grey dashed line the very unlikely greater than 6°C. Figure taken from figure 1 of Box 12.2 in the IPCC 5th assessment report (AR5). Unlabeled ranges refer to studies cited in AR4. The figure in the review article by Knutti and Hegerl (2008) presented by Skeptical Science is also a very insightful overview.

The likely range of possible climate sensitivity values has been between 2°C and 4.5°C since the 1990s. That does not sound like much progress. However, we now have many more lines of evidence and those lines have been much better vetted. Thus we can be more sure nowadays that this range is about right. A large part of the uncertainty comes from cloud and vegetation feedbacks. Having worked on clouds myself, I know that these are very difficult problems. Thus I am not hopeful that the uncertainty range will strongly decrease the coming decade or maybe even decades.

We will have to make decisions in the face of this uncertainty. Like any decision in a complex world.

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Notes
* The temperature trend of the air temperature over the ocean is 9% higher than the trend of the sea surface temperature in the CMIP5 models. For most models the top layer is 10 m deep. For those models with a higher vertical resolution the trend is only 8% higher. The difference is small and not statistically significant, but the effective resolution of numerical models is normally larger than the nominal resolution, thus I would not be surprised if studies with dedicated high resolution models may lead to estimates that are a few percent points lower.

** If we simply combine all these biases: 1.24 (Richardson) * 1.30 (Marvel) * 1.25 (Armour) we get that the simple energy balance models are biased by as much as a factor 2. Taking this into account could suggest increasing the best estimate from the energy balance models from around 2oC to around 4oC. Because of the uncertainty around the estimates and the thick tails, the estimate becomes 4.6°C. See Figure 1b of Armour (2016).

*** The ensemble of global climate models of the CMIP5 project have an average climate sensitivity of 3.5°C with a 95% uncertainty range of 2.0-5.6°C (Geoffroy, et al. 2013).

**** Many thanks to Kyle Armour and And Then There's Physics for many helpful hints and comments. Any errors are naturally mine.

Related reading
Skeptical Science: How sensitive is our climate?

Climate dialogue: Climate Sensitivity and Transient Climate Response

Fans of Judith Curry: the uncertainty monster is not your friend

Tough, but interesting for scientists: Andrew Dessler talk at Ringberg15 on why the equilibrium climate sensitivity exceeds 2°C.

References
Armour, Kyle C., 2016: Projection and prediction: Climate sensitivity on the rise. Nature Climate Change, News and Views, doi: 10.1038/nclimate3079.

Armour, Kyle C., Cecilia M. Bitz and Gerard H. Roe, 2013: Time-Varying Climate Sensitivity from Regional Feedbacks. Journal of Climate, doi: 10.1175/JCLI-D-12-00544.1

Geoffroy, O., D. Saint-Martin, G. Bellon, A. Voldoire, D.J.L. Olivié and S. Tytéca, 2013: Transient Climate Response in a Two-Layer Energy-Balance Model. Part II: Representation of the Efficacy of Deep-Ocean Heat Uptake and Validation for CMIP5 AOGCMs. Journal of Climate, 26, pp. 1859- 1876, doi: 10.1175/JCLI-D-12-00196.1.

Marvel, K., G.A. Schmidt, R.L. Miller and L.S. Nazarenko, 2015: Implications for climate sensitivity from the response to individual forcings, Nature Climate Change, 6, pp. 386-389. 10.1038/nclimate2888.

Richardson, Mark, Kevin Cowtan, Ed Hawkins and Martin B. Stolpe, 2016: Reconciled climate response estimates from climate models and the energy budget of Earth. Nature Climate Change, doi: 10.1038/nclimate3066. If you cannot read this article at Nature, you can go there via The Guardian, which has a special link that allows everyone to read (not download) the article. See also the News and Views on this article by Kyle Armour.

Otto, A., F.E.L. Otto, O. Boucher, J. Church, G. Hegerl, P.M. Forster, N.P. Gillett, J. Gregory, G.C. Johnson, R. Knutti, N. Lewis, U. Lohmann, J. Marotzke, G. Myhre, D. Shindell, B. Stevens, and M.R. Allen, 2013: Energy budget constraints on climate response", Nature Geoscience, 6, pp. 415-416. 10.1038/ngeo1836.Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-6506864521295578192016-06-30T08:02:00.000+01:002016-07-04T08:59:49.283+01:00The EU, refugees and migrationAn alternative to Brexit that helps refugees and workers

Summary. This post proposes an alternative to Brexit that makes all EU citizens better off and helps refugees better. Let's add to the refugee convention the condition that if you help refugees in the region, you do not have to house them at home. The real problem of migration is not the migration itself, but the reduction in bargaining power of the average worked. Rather than restrict the freedom of EU citizens to work elsewhere, we could also improve the bargaining power of the workers in other ways. If we then also stop the neo-liberal projects TTIP, CETA and Euro, the EU becomes an attractive way to collaborate for all European citizens.

Brexit, Geert Wilders, Nigel Farage, Marine Le Pen and thousands of refugees drowning in the Mediterranean. We should talk about the EU, refugees and migration.

Europe
The European Union started as a peace project, as a collaboration based on two industries that were crucial for war: coal and iron. It is often still sold as a peace project. That is certainly an aspect, but I think that this part is oversold when people point to Europe's violent past. The EU sure helps. However, also outside of the EU the frequency of international conflicts is decreasing. The benefits of war have decreased, most capital is nowadays in humans and organisation and cannot be easily plundered. The costs of war have also increased with nuclear and chemical weapons. The spread of democracy and the absence of war itself makes war less likely.

If leaders and countries acted rationally the EU might no longer be necessary for peace in Europe. Marine Le Pen in France, Geert Wilders in The Netherlands, Nigel Farage in England and Donald Trump in the USA make clear that we should not count on every leader making a cost benefit analysis. The wars in Yugoslavia and Ukraine also warn us that war is possible in Europe. Peace is one of the benefits of the EU and one reason why right-wing extremists do not like it.

The main benefit of the EU is that is allows the citizens of Europe to collaborate and stand up against economic powers. Environmental problems belong to this category. Where powerful companies pollute to make more money, while people with less power have to deal with the consequences. This power abuse increases inequality. On a national level, it is the role of the government to solve such problems. The polluter can, however, threaten to go to another country. International collaboration by setting environmental standards make such threats less credible and makes it easier for governments to serve their populations.

Many environmental problems are naturally also international, for instance, pollution of large rivers and acid rain, and natural candidates for collaboration to avoid international conflicts. A study just out this week tried to estimate the impact of European political measures to reduce air pollutants. It found that:

The reduction in PM2.5 concentrations [very small particles in the air] is calculated to have prevented 80 000 (37 000–116 000, at 95% confidence intervals) premature deaths annually across the European Union, resulting in a perceived financial benefit to society of US$232 billion annually (1.4% of 2010 EU GDP).
Those 80 thousand bodies and 1.4% of GDP is for small particles alone. Add to this all the other pollutants, workers rights and consumer protection. National laws would on average be less strict because firms would nationally have a stronger negotiation position. More people would die, more economic damage would be done. Socializing losses is a money making machine. In this case avoid investments in cleaner technology or selling more cheaper lower quality products increase the private gains at our costs.

People need to collaborate to reduce tax competition between countries. The rich and especially their money are more mobile and they can threaten democracies to pay their taxes elsewhere if the rates for the rich and large companies do not go down. That means that the lower 99%, you and me, will have to pay more, which is why incomes did not increase for most groups in the last decades, while most of the new wealth went to the super rich. The EU should coordinate taxes much more and especially get rid of national tax tricks to allow foreigners to pay less taxes than local people. The EU does this too little, but without the EU we can stop dreaming of achieving more justice here.

What amazes me most about the Leave campaign in the UK is that they managed to portrait the EU as the establishment and themselves as the defenders of the normal man. In reality both campaigns had their elites behind them and leaving the EU would make the UK establishment more powerful. Rupert Murdoch supported the Leave campaign because politicians in London do what he tells them to do. Last time I looked Rupert Murdoch was member of the establishment and not a working man trying to get by.

Rupert Murdoch supported the Leave campaign because politicians in London do what he tells them to do

Yes, the EU also does terrible things. A democratic institution will not always follow your preference; if you like that, try to become a dictator. The establishment naturally also sees that the EU is their main opponent and lobbies to make the EU do what they would like. This is facilitated by the fact that the media does not report much on the EU and much can thus be done behind the back of the people, which means that politicians do not have to fear losing their jobs for doing the bidding of the establishment. We should pay more attention and organize to make sure that our lobbies are also in Brussels.

Well know examples of terrible neo-liberal EU projects are the trade agreements TTIP and CETA and the Euro. Europe is not a banana republic, our courts do their job well and there is no need for special TTIP private courts so that corporations can threaten governments who want to improve living conditions. It is an assault on our democracies. If the EU presses through TTIP or CETA, I will stop being reasonable and from then on I will be anti-EU.

The Euro is a mess, it has many, many problems. We should slowly move out of it.

Refugees
The increase in the number of refugees is not just an EU problem. Improved information and travel possibilities means that more people are travelling further to find a save home.

It is not just an EU problem, but currently the consequences of the Bush-Blair war against Iraq are producing large refugee streams into the EU and tensions within the union. A closer EU foreign policy could have prevented this mess. (The misinformation campaign for the Iraq war is comparable with the Brexit campaign; in both cases the Anglo-American population did not do their due diligence.)

Most countries on Earth have signed the [[Geneva Convention relating to the Status of Refugees]], which obliges them to act humanely and accept refugees. The duty to protect refugees is international law.

While most people we have an empathetic side that wants to help people in need, we also have a tribal side and many do not like too many people from other groups coming to stay with us. If you put yourself in the position of a native American that instinct can make sense. In retrospect it was a monumental mistake to let Columbus and Co. get away alive.

In response to the growing numbers, the refugee convention has been hollowed out by making it very difficult to enter a country and ask for asylum, as well as by the principle of secure third countries and send refugees away without investigating their case. As a consequence many thousands of people drown in the Mediterranean trying to reach Europe and Australia has set up an disgusting system of lawless concentration camps.

I would propose to add two principles to the convention:
1. That when a country helps refugees in the region they come from, they are no longer obliged to house them in their own country. But only then.
2. That refugees can also send a request for asylum by mail, so that people are no longer dying trying to cross the border.

The current refugee crisis started with insufficient aid for the refugee camps along the Syrian border where people were literally hungering. This new legal principle would make such cases of neglect less likely, because it would have consequences. If we do our part to help locally, it should be no problem that asylum can be requested by mail, because they could then be rejected.

Helping refugees locally is also better for them. We may be rich, but a refugee who is used to a normal culture will have a hard time accustoming to the cold and impersonal European societies. Next to all the other culture shocks. Even without considering cultural differences, staying in the region makes it easier to maintain social ties and to go back home when the problems are over.

Staying in the region makes it easier to maintain social ties and to go back home

We will not be able to help everyone locally, especially in case of small groups or individuals. A gay man who is threatened in Russia is helped most easily by granting him asylum in Europe.

These two new principles would strengthen the right of asylum, help refugees better and reduce the number of refugees coming to Europe. Racists will not like this solution, but for the majority who experience a mix of empathy and concern, this should be a good solution. For people in favour of a multi-cultural society this is a good solution because it helps refugees better (and there will be enough diversity left).

Immigration
Refugees and migrants are often seen as one category, but they are fundamentally different. A refugee needs our help. To allow the partner and children of a refugee to live together would be migration, but seems to be a no-brainer as well for people with some empathy.

Economic migration is a different case. Even when it is good for a country, it may not be good for all segments of society. One reason we have democracy is to make sure that all interests are represented.

For the elite migration is mostly nice. By definition the migrants see migration as a benefit. And the elite has other options, thus if they migrate that is normally because they see clear benefits. Even before EU citizens could work everywhere in Europe it was normally possible for scientists to work elsewhere because of the importance of migration for science. Science is highly specialized; there is no labour market in Germany for my specialization.

Many other professions are similarly specialised and professionals with high salaries were normally allowed to work in another country. Also a sufficiently wealthy pensioner will be happy to be allowed to migrate to another (warmer) country. Living in another country a few years can be very enriching.

If you are less well off, the possibility of migration of cheap labour can be used by firms to reduce your bargaining power and you may end up with an even lower salary or without a job. The region the migrant comes from looses a valuable labourer. Migration can thus be used to increase inequality even more. Even for scientists from wealthy countries migration makes the negotiation positions weaker and thus labour conditions worse. But it is good for science and for scientists from poorer countries.

Salaries are determined by bargaining power, not by productivity, which is undefined for individuals in nonlinear production processes

Especially within the EU, I would be in favour of allowing everyone to work where they would like to. Freedom should be our default. In return for this benefit, the elite should compensate the disadvantages for the rest of society by improving the negotiation position of workers. One may think of migration restrictions for some professions, stronger unions, redistribution of wealth, programs for retraining, job guarantees for the unemployed and humane treatment of unemployed people.

A new European Union
There are many benefits of collaboration. The people of Europe need to collaborate to have the power to stand up against ever larger economic powers. There is no reason why this collaboration needs to be so intensive that the EU would become a nation itself. People's interests and customs differ and power is best exercised close to the people. We should only collaborate on large scales where this has a clear benefit.

The Euro makes inequality worse and it creates a lot of negative political energy in the EU that avoids other positive changes. Let's get rid of it. Now that the worst financial crisis is over, this is a good time to start a slow transition.

If we change the refugee convention to alternatively help refugees in the region where they come from, we can help them better than now, less will die on their way to Europe and another problem that creates bad blood in Europe would be gone.

Because refugees and migration are often seen as one problem, a reduction in the number of refugees may also reduce problems people have with migration. Still we should not be blind to the large difference in interests between the elites and the rest of society when it comes to migration. A compromise between the groups may be improving the negotiation position of workers.

Overarching above it all: You are not more pro Europe the more you would like the EU to replace the old nations. When Juncker sees the Brexit as a great opportunity to build a European nation and force the fast introduction of the Euro in every country, he is pro Europe. When I reject that uncreative vision of Europe and see the EU as a way for the people of Europe to collaborate, I am also pro Europe. Just like people argue nationally what the role of government is, we should have an open discussion in the EU about where collaboration is fruitful and possible given our differences.

For me, the most valuable innovation of the EU is actually that it is a twitter. That is the reason why nations all over the world are building similar regional collaborations. They would not if the aim would the end of their nations and only building a larger more anonymous nation. Europe should be proud of its queer identity.

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Related reading
Brexit is great news for the rest of the EU. Britain has not yet come to terms with its own irrelevance, and would only have got in the way of plans to create a more democratic pooling of sovereignty.

* Top photo: EU Grunge Flag, Attribution 2.0 Generic (CC BY 2.0).

Photo Auschwitz: Arbeit Macht Frei, Attribution-ShareAlike 2.0 Generic (CC BY-SA 2.0)

Map of Regional Organizations: CC BY-SA 3.0.Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-12332117878025093762016-06-23T23:41:00.001+01:002016-07-06T21:28:42.501+01:00Four wonderful climate science podcasts you need to know[UPDATE: How is this Buzzfeed headline?]

For some years I was a regular listener of EconTalk, where the economist Russ Roberts would interview a colleague, typically about a recent book or article. Roberts is a staunch libertarian and the interviews with fellow libertarians are worse than listening in on drunk men agreeing with each other in a bar at 4am, but many other interviews with economists who went out in the world and had studied reality were wonderful. I learned a lot about the world view of this special tribe and something about the limits on how we organize society.

Podcasts are a nice way to learn. The debate makes otherwise maybe more boring engaging and you can listen to it while commuting, walking or doing the household chores.

I tried to get a few people enthusiastic about doing such a podcast for climate science and even in the end considered doing it myself. But there is no need any more. Suddenly a wealth of really good climate science podcasts has sprung up.

Warm Regards
The newest podcast is by Eric Holthaus, journalist at Slate. It is called Warm Regards. He has as co-hosts climate scientist and Ice Age ecologist Jacquelyn Gill and New York Times science blogger Andrew Revkin. They are so new, in fact, that I could not listen to their first podcast yet: "How Do We Talk About Climate Change?". They are now also on iTunes.

[UPDATE. While preparing my diner, I listened to the podcast. Really enjoyed it. Good voices. Good sound. Professionally made. They introduced themselves, what they work on and why they care about climate change. The main topic was science communication and they emphasised that a good relationship with the listener is much more important than details that are quickly forgotten. Revkin liked talking to mitigation sceptics, the other two take the more productive route of trying to talk as much as possible to people who are wiling to listen and consider the arguments. The app Block Together is a good way to keep lines of communication open with decent people on twitter by very efficiently blocking harassing accounts. I also use it and can highly recommend it.

Personally I would add that we should not overestimate the importance of science communication. Outside of Anglo-America scientists are much less active in communicating climate change, but we have nearly no problems with mitigation sceptical movements. The difference is a working political system and better press. Talking about climate and science is what I do best, but if you have the option, it is probably better to invest your time in getting money out of US politics and building up a free and democratic press, for example by supporting membership supported media channels.]

Climate History Podcast
The Climate History Podcast is hosted by Dr. Dagomar Degroot, the founder of HistoricalClimatology.com and co-founder of the Climate History Network. Even if society has changed a lot, we can learn from how humans have responded to the small climatic changes in the past. This initiative is just three podcasts old and the one I listened to, on the little ice age, is really interesting. First three titles are:

1. Climate Change and Crisis: Lessons from the Past
2. The History of Climate Change with Professor Sam White
3. Archaeology in the Arctic: Reconstructing the Consequences of Climate Change in the Far North

You can listen to it on iTunes project and download and listen to the podcast at SoundCloud.

Mostly Weather
The UK MetOffice produces the podcasts Mostly Weather. So officially it is about weather, but most topics are actually dual-use science, important for both weather and climate. (Mitigation sceptics often do not seem to know that meteorology is bigger than climatology.) It is made with love by climate scientists Doug McNeall, Niall Robinson and Claire Witham.

It is aimed at a general audience, but also a scientist can still learn something. I learned from their first podcasts on the history of weather forecasting that this started over the ocean: there it is most important and easier to do. Other podcasts were on the elements: clouds, snow, lightning and about the structure of atmosphere and they just had a series on weather forecasting.

Forecast
For me as a scientist, the clear favorite is Forecast. It is made by Michael White, editor for climate topics at the scientific journal Nature. He makes it as a private project, but naturally he has access to the best and the brightest as editor and a good understanding of the climate system. This makes for in depth interviews on the science, but he also talks a lot about the serendipitous personal and scientific histories of the scientists. I have the feeling, many non-scientists will be able to understand the interviews, but admit I am not a very good judge of this.

The last interview was with Gabi Hegerl, the woman who discovered climate change (the first to do an attribution study). Other names people may recognize are: Reto Knutti, astronaut Piers Sellers, Chris Field, Bjorn Stevens, Kim Cobb, Mat Collins. Oh and a modeller from NASA GISS. Gavin Schmidt.

Have fun listening. Let me know if I missed something and which podcasts you like most.

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Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-4207190196427890262016-05-08T09:12:00.000+01:002016-06-20T16:21:46.401+01:00Grassroots scientific publishingThese were the weeks of peer review. Sophie Lewis wrote her farewell to peer reviewing. Climate Feedback is making it easy for scientists to review journalistic articles with nifty new annotation technology. And Carbon Brief showed that while there is a grey area, it is pretty easy to distinguish between science and nonsense in the climate "debate", which is one of the functions of peer review. And John Christy and Richard McNider managed to get an article published, which I would have advised to reject as reviewer. A little longer ago we had the open review of the Hansen sea level rise paper, where the publicity circus resulted in a-scientific elements spraying their graffiti on the journal wall.

Sophie Lewis writes about two recent reviews she was asked to make. One where the reviewers were negative, but the article was published anyway by the volunteer editor and one case where the reviewers were quite positive, but the manuscript was rejected by a salaried editor.

I have had similar experiences. As reviewer you invest your time and heart in a manuscript and root for the ones you like to make it in print. Making the final decision naturally is the task of the editor, but it is very annoying as a reviewer to have the feeling your review is ignored. There are many interesting things you could have done in that time. At least nowadays you get to see the other reviews and hear the final decision more often, which is motivating.

The European Geophysical Union has a range of journals with open review, where you can see the first round of reviews and anyone can contribute reviews. This kind of open review could benefit from the annotation system used by Climate Feedback to review journalistic articles; it makes reviewing easier and the reader can immediately see the text the review revers to. The open annotation system allows you to add comments to any webpage or PDF article or manuscript. You can see it as an extra layer on top of the web.

The reviewer can select a part of the text and add comments, including figures and links to references. Here is an annotated article in the New York Times that Climate Feedback found to be scientifically very credible, where you can see the annotate system in action. You can click on the text with a yellow background to see the corresponding comment or click on the small symbol at the top right to see all comments. (Examples of articles with low scientific credibility are somehow mostly pay-walled; one would think that the dark money behind these articles would want them to be read widely.)

I got to know annotation via Climate Feedback. We use the annotation system of Hypothes.is and this system was actually not developed to annotate journalistic articles, but for reviewing scientific articles.

The annotation system makes writing a review easier for the reviewer and makes it easier to read reviews. The difference between writing some notes on an article for yourself and a peer review becomes gradual this way. It cannot take away having to read the manuscript and trying to understand it. That takes most time, but this is the fun part, reducing time time for the tedious part makes it more attractive to review.

Publishing and peer review
Is there a better way to review and publish? The difficult part is no longer the publishing. The central part that remains is the trust of a reader in a source.

It starts to become ironic that the owners of the scientific journals are called "scientific publishers" because the main task of a publisher is nowadays no longer the publishing. Everyone can do that nowadays with a (free) word processor and a (free) web page. The publishers and their journals are mostly brands nowadays. The scientific publisher, the journal is a trusted name. Trust is slow to build up (and easy to lose), producing huge barriers to entry and leading to near monopoly profits of scientific publishing houses of 30 to 40%. That is tax-payer money that is not spend on science and promotes organization that prefer to keep science unused behind pay-walls.

Peer review performs various functions. It helps to give a manuscript the initial credibility that makes people trust it, that makes people willing to invest time in it to study its ideas. If the scientific literature would be as abominable as the mitigation skeptical blog Watts Up With That (WUWT) scientific progress would slow down enormously. At WUWT the unqualified readers are supposed to find out themselves whether they are being conned or not. Even if they would do so: having every reader do a thorough review is wasteful; it is much more efficient to ask a few experts to first vet manuscripts.

Without peer review it would be harder for new people to get others to read their work, especially if they would make a spectacular claim and use unfamiliar methods. My colleagues will likely be happy to read my homogenization papers without peer review. Gavin Schmidt's colleagues will be happy to read his climate modelling papers and Michel Mann's colleagues his papers on climate reconstructions. But for new people it would be harder to be heard, for me it would be harder to be heard if I would publish something about another topic and for outsiders it would be harder to judge who is credible. The latter is increasingly important the more interdisciplinary sciences becomes.

Improving peer review
When I was dreaming of a future review system where scientific articles were all in one global database, I used to think of a system without journals or editors. The readers would simply judge the articles and comments, like on Ars Technica or Slashdot. The very active open science movement in Spain has implemented such a peer review system for institutional repositories, where the manuscripts and reviews are judged and reputation metrics are estimated. Let me try to explain why I changed my mind and how important editors and journals are for science.

One of my main worries for a flat database would be that there would be many manuscripts that never got any review. In the current system the editor makes sure that every reasonable manuscript gets a review. Without an editor explicitly asking a scientist to write a review, I would expect that many articles would never get a review. Personal relations are important.

Science is not a democracy, but a meritocracy. Just voting an article up or down does not do the job. It is important that this decision is made carefully. You could try to statistically determine which readers are good at predicting the quality of an article, where quality could be determined by later votes or citations. This would be difficult, however, because it is important that the assessment is made by people with the right expertise, often by people from multiple backgrounds; we have seen how much even something as basic as the scientific consensus on climate change depends on expertise. Try determining expertise algorithmically. The editor knows the reviewers.

While it is not a democracy, the scientific enterprise should naturally be open. Everyone is welcome to submit manuscripts. But editors and reviewers need to be trusted and level headed individuals.

More openness in publishing could in future come from everyone being able to start a "journal" by becoming editor (or better by organization a group of editors) and try to convince their colleagues that they do a good job. The fun thing about the annotation system is that you can demonstrate that you do a good job using existing articles and manuscripts.

This could provide real value for the reader. Not only would the reviews be visible, but it would also be possible to explain why an article was accepted, was it speculative, but really interesting if true (something for experts) or was it simply solid (something for outsiders). Which parts do the experts debate about. The debate would also continue after acceptance.

The code and the data of every "journal" should be open so that everyone can start a new "journal" with reviewed articles. So that when Heartland offers me a nice amount of dark money to start accepting WUWT-quality articles, a group of colleagues can start a new journal and fix my dark-money "mistakes", but otherwise have a complete portfolio from the beginning. If they would have to start from scratch that would be a large barrier to entry, which like the traditional system encourages sloppy work, corruption and power abuse.

Peer review is also not just for selecting articles, but also to help making them better. Theoretically the author can also ask colleagues to do so, but in practice reviewers are better in finding errors. Maybe because the colleagues who will put in most effort are your friends who have to same blind spots? These improvements of the manuscript would also be missing in a pure voting system of "finished" articles. Having a manuscript phase is helpful.

Finally, an editor makes anonymous reviews a lot less problematic because the editor could delete comment where the anonymity seduced people into inappropriate behavior. Anonymity could be abused to make false attacks with impunity. On the other hand anonymity can also provide protection in case of large power differences in case of real problems.

The advantage of internet publishing is that there is no need for an editor to reject technically correct manuscripts. If the contribution to science is small or if the result is very speculative and quite likely to be found to be wrong in future, the manuscript can still be accepted but simply be given a corresponding grade.

This also points to a main disadvantage of the current dead-tree-inspired system: you get either a yes or a no. There is a bit more information in the journal the author chooses, but that is about it. A digital system can communicate much more subtly with a prospective reader. A speculative article is interesting for experts, but may be best avoided by outsiders until the issues are better understood. Some articles mainly review the state-of-the-art, others provide original research. Some articles have a specific audience: for example the users of a specific dataset or model. Some articles are expected to be more important for scientific progress than others or discuss issues that are more urgent than others. And so on. This information can be communicated to the reader.

The nice thing about the open annotate system is that we can begin reviewing articles before authors start submitting their articles. We can simply review existing articles as well as manuscripts, such as the ones uploaded to ArXiv. The editors could reject articles that should not have been published in the traditional journals and accept manuscripts from archives. I would judge this assessment of a knowledgeable editor (team) more than the acceptance by a traditional journal.

In this way we can produce collections of existing articles. If the new system provides a better reviewing service to science, the authors at some moment can stop submitting their manuscripts to traditional journals and submit them directly to the editors of a collection. Then we have real grassroots scientific journals that serve science.

For colleagues in the communities it would be clear which of these collections have credibility. However, for outsiders we would also need some system that communicates this, which would traditionally be the role of publishing houses and the high barriers to entry. This could be assessed where collections have overlap. Preferably again by humans and not by algorithms. For some articles there may be legitimate reasons why there are differences (hard to assess, other topic of collection), for other articles an editor not having noticed problems may be a sign of bad editorship. This problem is likely not too hard, in a recent analysis of twitter discussions on climate change there was a very clear distinction between science and nonsense.

There is still a lot to do, but with the ease of modern publishing and the open annotate system a lot of software is already there. Larger improvements would be tools for editors to moderate review comments (or at least to collapse less valuable comments); Hypothes.is is working on it. A grassroots journal would need a grading system; standardized when possible. More practical tools would include some help in tracking the manuscripts under review and for sending reminders, and the editors of one collection should be able to communicate with each other. The grassroots journal should remain visible even if the editor team stops; that will need collaboration with libraries or science societies.

If we get this working

we can say goodbye to frustrated reviewers (well mostly),

goodbye to pay-walled journals in which publicly financed research is hidden for the public and many scientists alike and

goodbye to wasting limited research money on monopolistic profits by publishing houses, while

we can welcoming better review and selection and

we are building a system that inherently allows for post-publication peer review.

What do you think?

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Related reading
There is now an "arXiv overlay journal", Discrete Analysis. Articles are published/hosted by ArXiv, otherwise traditional peer review. The announcement mentions three software initiative that make starting a digital journal easy: Scholastica, Episciences.org and Open Journal Systems.

Annotating the scholarly web

A coalition to Annotating All Knowledge A new open layer is being created over all knowledge

Brian A. Nosek and Yoav Bar-Anan describe a scientific utopia: Scientific Utopia: I. Opening scientific communication. I hope the ideas in the above post makes this transition possible.

Climate Feedback has started a crowed funding campaign to be able to review more media articles on climate science

Farewell peer reviewing

7 Crazy Realities of Scientific Publishing (The Director's Cut!)

Mapped: The climate change conversation on Twitter

I would trust most scientists to use annotation responsibly, but it can also be used to harass vulnerable voices on the web. Genius Web Annotator vs. One Young Woman With a Blog

Nature Chemistry blog: Post-publication peer review is a reality, so what should the rules be?

Report from the Knowledge Exchange event: Pathways to open scholarship gives an overview of the different initiative to make science more open.

Magnificent BBC Reith lecture: A question of trust
Victor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-59861173058910609782016-05-01T19:15:00.000+01:002016-05-03T16:41:44.828+01:00Christy and McNider: Time Series Construction of Summer Surface Temperatures for AlabamaJohn Christy and Richard McNider have a new paper in the AMS Journal of Applied Meteorology and Climatology called "Time Series Construction of Summer Surface Temperatures for Alabama, 1883–2014, and Comparisons with Tropospheric Temperature and Climate Model Simulations". Link: Christy and McNider (2016).

This post gives just few quick notes on the methodological aspects of the paper.
1. They select data with a weak climatic temperature trend.
2. They select data with a large cooling bias due to improvements in radiation protection of thermometers.
3. They developed a new homogenization method using an outdated design and did not test it.

Weak climatic trend
Christy and McNider wrote: "This is important because the tropospheric layer represents a region where responses to forcing (i.e., enhanced greenhouse concentrations) should be most easily detected relative to the natural background."

The trend in the troposphere should a few percent stronger than at the surface; mainly in the tropics. However, it is mainly interesting that they see a strong trend as a reason to prefer tropospheric temperatures, because when it comes to the surface they select the period and temperature with the smallest temperature trend: the daily maximum temperatures in summer.

The trend in winter due to global warming should be 1.5 times the trend in summer and the trend in the night time minimum temperatures is stronger than the trend in the day time maximum temperatures, as discussed here. Thus Christy and McNider select the data with the smallest trend for the surface. Using their reasoning for the tropospheric temperatures they should prefer night time winter temperatures.

(And their claim on the tropospheric temperatures is not right because whether a trend can be detected does not only depend on the signal, but also on the noise. The weather noise due to El Nino is much stronger in the troposphere and the instrumental uncertainties are also much larger. Thus the signal to noise ratio is smaller for the tropospheric temperatures, even if the signal were as long as the surface observations.

Furthermore, I am somewhat amused that there are still people interested in the question whether global warming can be detected.)

[UPDATE. Tamino shows that within the USA, Alabama happens to be the region with the least warming. The more so for the maximum temperature. The more so for the summer temperature.]

Cooling bias
Then they used data with a very large cooling bias due to improvements in the protection of the thermometer for (solar and infra-red) radiation. Early thermometers were not protected as well against solar radiation and typically record too high temperatures. Early thermometers also recorded too cool minimum temperatures; the thermometer should not see the cold sky, otherwise it radiates out to it and cools. The warming bias in the maximum temperature is larger than the cooling bias in the minimum temperature, thus the mean temperature still has some bias, but less than the maximum temperature.

Due to this reduction in the radiation error summer temperatures have a stronger cooling bias than winter temperatures.

The warming effect of early measurements on the annual means is probably about 0.2 to 0.3°C. In the maximum temperature is will be a lot higher and in the summer temperature it will again be a lot higher.

That is why most climatologists use the annual means. Homogenization can improve climate data, but it cannot remove all biases. Thus it is good to start with data that has least bias. Much better than starting with a highly biased dataset like Christy and McNider did.

Statistical homogenization removes biases by comparing a candidate station to its neighbour. The stations need to be close enough together so that the regional climate can be assumed to be similar in both stations. The difference between two stations is then weather noise and inhomogeneities (non-climatic changes due to changes in the way temperature was measured).

If you want to be able to see the inhomogeneities you thus need to have well correlated neighbors that have as little weather noise as possible. By using only the maximum temperature, rather than the mean temperature, you increase the weather noise. But using the monthly means in summer, rather than the annual means or at the very least the summer means, you increase the weather noise. By going back in time more than a century you increase the noise because we had less stations to compare with at the time.

They keyed part of the the data themselves mainly for the period before 1900 from the paper records. It sounds as if they performed no quality control of these values (to detect measurement errors). This will also increase the noise.

With such a low signal to noise ratio (inhomogeneities that are small relative to the weather noise in the difference time series), the estimated date of the breaks they still found will have a large uncertainty. It is thus a pity that they purposefully did not use information from station histories (metadata) to get the date of the breaks right.

Homogenization method
They developed their own homogenization method and only tested it on a noise signal with one break in the middle. Real series have multiple breaks; in the USA typically every 15 years. Furthermore also the reference series has breaks.

The method uses the detection equation from the Standard Normal Homogeneity Test (SNHT), but then starts using different significance levels. Furthermore for some reason it does not use the hierarchical splitting of SNHT to deal with multiple breaks, but it detects on a window, in which it is assumed there is only one break. However, if you select the window too long it will contain more than one break and if you select the window too short the method will have no detection power. You would thus theoretically expect the use of a window for detection to perform very badly and this is also what we found in a numerical validation study.

I see no real excuse not to use better homogenization methods (ACMANT, PRODIGE, HOMER, MASH, Craddock). These are build to take into account that also the reference station has breaks and that a series will have multiple breaks; no need for ad-hoc windows.

If you design your own homogenization method, it is good scientific practice to test it first, to study whether it does what you hope it does. There is, for example, the validation dataset of the COST Action HOME. Using that immediately allows you to compare your skill to the other methods. Given the outdated design principles, I am not hopeful the Christy and McNider homogenization method would score above average.

Conclusions
These are my first impressions on the homogenization method used. Unfortunately I do not have the time at the moment to comment on the non-methodological parts of the paper.

If there are no knowledgeable reviewers available in the USA, it would be nice if the AMS would ask European researchers, rather than some old professor who in the 1960s once removed an inhomogeneity from his dataset. Homogenization is a specialization, it is not trivial to make data better and it really would not hurt if the AMS would ask for expertise from Europe when American experts are busy.

Hitler is gone. The EGU general assembly has a session on homogenization, the AGU does not. The EMS has a session on homogenization, the AMS does not. EUMETNET organizes data management workshops, a large part of which is about homogenization; I do not know of an American equivalent. And we naturally have the Budapest seminars on homogenization and quality control. Not Budapest, Georgia, nor Budapest, Missouri, but Budapest, Hungary, Europe.

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Related reading
Tamino: Cooling America. Alabama compared to the rest of contiguous USA.

HotWhopper discusses further aspects of this paper and some differences between the paper and the press release. Why nights can warm faster than days - Christy & McNider vs Davy 2016

Early global warming

Statistical homogenisation for dummiesVictor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-67908003027595588822016-04-26T15:57:00.000+01:002016-04-26T16:23:49.441+01:00Climate scientists are now grading climate journalism

Guest post by Daniel Nethery and Emmanuel Vincent Daniel Nethery is the associate editor and Emmanuel Vincent is the founder of Climate Feedback. Climate Feedback is launching a crowdfunding campaign today.

The internet represents an extraordinary opportunity for democracy. Never before has it been possible for people from all over the world to access the latest information and collectively seek solutions to the challenges which face our planet, and not a moment too soon: the year 2015 was the hottest in human history, and the Great Barrier Reef is suffering the consequences of warming oceans right now.

Yet despite the scientific consensus that global warming is real and primarily due to human activity, studies show that only about half the population in some countries with among the highest CO2 emissions per capita understand that human beings are the driving force of our changing climate. Even fewer people are aware of the scientific consensus on this question. We live in an information age, but the information isn't getting through. How can this be?

While the internet puts information at our fingertips, it has also allowed misinformation to sow doubt and confusion in the minds of many of those whose opinions and votes will determine the future of the planet. And up to now scientists have been on the back foot in countering the spread of this misinformation and pointing the public to trustworthy sources of information on climate change.

Climate Feedback intends to change that. It brings together a global network of scientists who use a new web-annotation platform to provide feedback on climate change reporting. Their comments, which bring context and insights from the latest research, and point out factual and logical errors where they exist, remain layered over the target article in the public domain. You can read them for yourself, right in your browser. The scientists also provide a score on a five-point scale to let you know whether the article is consistent with the science. For the first time, Climate Feedback allows you to check whether you can trust the latest breaking story on climate change.

An example of Climate Feedback in action. Scientists' comments and ratings appear as a layer over the article. Text annotated with Hypothesis is highlighted in yellow in the web browser and scientists' comments appear in a sidebar next to the article. Illustration: Climate Feedback

Last year the scientists looked at some influential content. Take the Pope's encyclical, for instance. The scientists gave those parts of the encyclical relating to climate science a stamp of approval. Other "feedbacks," as we call them, have made a lasting impact. When the scientists found that an article in The Telegraph misrepresented recent research by claiming that the world faced an impending ice age, the newspaper issued a public correction and substantially modified the online text.

But there's more work to be done. Toward the end of the year the scientists carried out a series of evaluations of some of Forbes magazine's reporting on climate change. The results give an idea of the scale of the problem we're tackling. Two of the magazine's most popular articles for 2015, one of which attracted almost one million hits, turned out to be profoundly inaccurate and misleading. Both articles, reviewed by nine and twelve scientists, unanimously received the lowest possible scientific credibility rating. This rarely occurs, and just in case you're wondering, yes, the scientists do score articles independently: ratings are only revealed once all scientists have completed their review.

We argue that scientists have a moral duty to speak up when they see misinformation masquerading as science. Up to now scientists have however had little choice but to engage in time-consuming op-ed exchanges, which result in one or two high-profile scientists arguing against the views of an individual who may have no commitment to scientific accuracy at all. Climate Feedback takes a different approach. Our collective reviews allow scientists from all over the world to provide feedback in a timely, effective manner. We then publish an accessible synthesis of their responses, and provide feedback to editors so that they can improve the accuracy of their reporting.

We've got proof of concept. Now we need to scale up, and for that we need the support of everyone who values accuracy in reporting on one of the most critical challenges facing our planet. Climate Feedback won't reach its full potential until we start measuring the credibility of news outlets in a systematic way. We want to be in a position to carry out an analysis of any influential internet article on climate change. We want to develop a 'Scientific Trust Tracker' – an index of how credible major news sources are when it comes to climate change.

We're all increasingly relying on the internet to get our news. But the internet has engendered a competitive media environment where in the race to attract the most hits, sensational headlines can trump sober facts. We're building into the system a new incentive for journalists with integrity to get ahead. Some journalists are already coming to us, asking our network of scientists to look at their work. We want readers to know which sources they can trust. We want editors to think twice before they publish ideological rather than evidence-based reporting on global warming.

On Friday 22 May 2016, more than 170 countries signed the Paris climate agreement. But this unprecedented international treaty will lead to real action only if the leaders of those countries can garner popular support for the measures needed to curb greenhouse gas emissions. The fate of the Paris deal lies largely in the hands of voters in democratic countries, and we cannot expect democracies to produce good policy responses to challenges of climate change if voters have a confused understanding of reality.

Scientists from all over the world are standing up for better informed democracies. You can help them make their voices heard. We invite you to stand with us for a better internet. We invite you to stand with science.

Victor Venema: I am also part of the Climate Feedback community and have annotated several journalistic articles when they made claims about climate data quality. It is a very effective way to combat misinformation. Just click on the text and add a short comment; Climate Feedback will take care of spreading your contribution. If you are a publishing scientist, do consider to join.

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* Photo at the top. Severe suburban flooding in New Orleans, USA. Aftermath of Hurricane Katrina. Photo by ark Moran, NOAA Corps, NMAO/AOC (CC BY 2.0) Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-84092785015570539882016-03-29T16:55:00.000+01:002016-03-29T16:56:44.096+01:00Upcoming meetings on climate data quality

It looks like 2016 will be a year full of interesting conferences. I already pointed you to EGU, IMSC and EMS before. Here is an update with three upcoming European meetings, including two close deadlines.

The marine climate data community will hold its main workshop this July (18 to 22). The deadline has just been prolonged to the 6th of April, Wednesday next week.

The metrologists (no typo) organize a meeting on climate data, MMC2016. It will take place from 26 to 30 September and will be organized together with the WMO TECO conference.

And naturally we will have the European Meteorological Society meeting in Autumn. This year is an ECAC year (European Conference on Applied Climatology). The abstract submission deadline is in about three weeks, 21 Apr 2016, during EGU. So start writing soon. As always we will have a session on "Climate monitoring; data rescue, management, quality and homogenization" for the homogenization addicted readers of this blog.

If you know of more interesting conferences, do add them in the comments.

MARCDAT-IV
The Fourth International Workshop on the Advances in the Use of Historical Marine Climate Data (MARCDAT-IV) will be held at the National Oceanography Centre, Southampton, UK between the 18th and 22nd July 2016. The workshop will be arranged around the following themes:

Data homogenization (benchmarking, bias adjustments, step change analysis, metadata)

Quantification and estimation of uncertainty

Data management, recovery and reprocessing (digitisation efforts and reprocessing of previously digitised data)

Reconstructing past climates

Integrating In-situ / satellite data sources

Consistency of the climate across domain boundaries (land, ocean, surface, subsurface, atmosphere)

The role of ICOADS and applications of marine climate data

Review of the 10-year action plan

This looks like an invitation of people working on land data to also participate. I just asked some colleagues on the homogenization list and it looks like there are a decent number of weather stations near the coast. We could compare them to marine observations, I would especially be interested in comparing sea surface temperature observations.

This workshop is free to attend, but participants must register.

Key Dates:
Abstract submission deadline: 8th April 2016
Registration closes: 31st May 2016

MMC-2016
International workshop on Metrology for Meteorology and Climate in conjunction with WMO TECO 2016 conference & Meteorological Technology World Expo 2016. It will be held in Madrid Spain from 26 to 30 September 2016.

During the last years an increasing collaboration has been established between the Metrology and Meteorology communities. EURAMET, the European association of metrology Institutes, is funding several projects aiming at delivering results of valuable impact for the meteorology and climatology science. The key aspect of such projects is the traceability of measurements and uncertainties of measured physical and chemical quantities describing the earth atmosphere. The MMC conference aims to give an opportunity to those two communities to present and discuss needs, methods, expertise and devices for cooperating in producing better data. The invitation is addressed to the metrology, meteorology and climate scientific communities and operators. Starting with the first MMC 2014 in Brdo, Slovenia, this time the MMC 2016 is organized Madrid, Spain, in conjunction with CIMO-TECO conference.
As far as I know the abstract deadline has not been determined yet, but write the date into your agenda, bookmark the homepage and contact the organizers to give you a notice once the deadline is known.

EMS2016
The conference theme of the Annual Meeting of the European Meteorological Society is: Where atmosphere, sea and land meet: bridging between sciences, applications and stakeholders. It will be held from 12 to 16 September 2016 in Trieste, Italy. The abstract submission deadline is 21 April 2016, during EGU2016.

MC1 Climate monitoring; data rescue, management, quality and homogenization
Convener: Manola Brunet-India
Co-Conveners: Hermann Mächel, Victor Venema, Ingeborg Auer, Dan Hollis

Robust and reliable climatic studies, particularly those assessments dealing with climate variability and change, greatly depend on availability and accessibility to high-quality/high-resolution and long-term instrumental climate data. At present, a restricted availability and accessibility to long-term and high-quality climate records and datasets is still limiting our ability to better understand, detect, predict and respond to climate variability and change at lower spatial scales than global. In addition, the need for providing reliable, opportune and timely climate services deeply relies on the availability and accessibility to high-quality and high-resolution climate data, which also requires further research and innovative applications in the areas of data rescue techniques and procedures, data management systems, climate monitoring, climate time-series quality control and homogenisation.

In this session, we welcome contributions (oral and poster) in the following major topics:

Climate monitoring , including early warning systems and improvements in the quality of the observational meteorological networks

More efficient transfer of the data rescued into the digital format by means of improving the current state-of-the-art on image enhancement, image segmentation and post-correction techniques, innovating on adaptive Optical Character Recognition and Speech Recognition technologies and their application to transfer data, defining best practices about the operational context for digitisation, improving techniques for inventorying, organising, identifying and validating the data rescued, exploring crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records

Climate data and metadata processing, including climate data flow management systems, from improved database models to better data extraction, development of relational metadata databases and data exchange platforms and networks interoperability

Innovative, improved and extended climate data quality controls (QC), including both near real-time and time-series QCs: from gross-errors and tolerance checks to temporal and spatial coherence tests, statistical derivation and machine learning of QC rules, and extending tailored QC application to monthly, daily and sub-daily data and to all essential climate variables

Improvements to the current state-of-the-art of climate data homogeneity and homogenisation methods, including methods intercomparison and evaluation, along with other topics such as climate time-series inhomogeneities detection and correction techniques/algorithms (either absolute or relative approaches), using parallel measurements to study inhomogeneities and extending approaches to detect/adjust monthly and, especially, daily and sub-daily time-series and to homogenise all essential climate variables

Fostering evaluation of the uncertainty budget in reconstructed time-series, including the influence of the various data processes steps, and analytical work and numerical estimates using realistic benchmarking datasets

Next to this session, readers of this blog may also like: Climate change detection, assessment of trends, variability and extremes. And I personally like the session on Spatial Climatology, which has a lot to do with structure and variability.

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Top photo by Martin Duggan, which has a CC BY 2.0 license.Victor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-76481903602038591962016-03-21T10:03:00.000+00:002016-03-21T10:03:18.871+00:00Cooling moves of urban stations

It has been studied over and over again, in very many ways: in global temperature datasets urban stations have about the same temperature trend as surrounding rural stations.

There is also massive evidence that urban areas are typically warmer than their surroundings. For large urban areas the Urban Heat Island (UHI) effect can increase the temperature by several degrees Celsius.

A constant higher temperature due to the UHI does not influence temperature changes. However, when cities grow around a weather station, this produces an artificial warming trend.

Why don't we see this in the urban stations of the global temperature collections? There are several reasons; the one I want to focus on in this post is that stations do not stay at the same place.

Urban stations are often relocated to better locations, more outside of town. It is common for urban stations to be moved to airports, especially when meteorological offices are moved to the airport to assist in airport safety. Also when meteorological offices can no longer pay the rent in the city center, they are forced to move out and take the station with them. When urban development makes the surrounding unsuited or when a volunteer observer retires, the station has to move, it makes sense to then search for a better location, which will likely be in a less urban area.

Relocations are nearly always the most frequent reason for inhomogeneities. For example, Manola Brunet and colleagues (2006) write about Spain:

"Changes in location and setting are the main cause of inhomogeneities (about 56% of stations). Station relocations have been common during the longest Spanish temperature records. Stations were moved from one place to another within the same city/town (i.e. from the city centre to outskirts in the distant past and, more recently, from outskirts to airfields and airports far away from urban influence) and from one setting (roofs) to another (courtyards)."
Since relocations of that kind are likely to result in a cooling, the Parallel Observations Science Team (ISTI-POST) wants to have a look at how large this effect is. As far as we know there is no overview study yet, but papers on the homogenization of a station network often report on adjustments made for specific inhomogeneities.

We, that is mainly Jenny Linden of Mainz University, had a look in the scientific literature. Let's start in China were urbanization is strong and can be clearly seen in the raw data of many stations. They also have strong cooling relocations. The graph below from Wenhui Xu and colleagues (2013) shows the distribution of breaks that were detected (and corrected) with statistical homogenization for which the station history indicated that they were caused by relocations. Both the minimum and the maximum temperature cool by a few tenth of a degree Celsius due to the relocations.

The distribution of the breaks that were due to relocations for the maximum temperature (left) and minimum temperature (right). The red line is a Gaussian distribution for comparison.

Going more in detail, Zhongwei Yan (2010) and colleagues studied two relocations in Beijing. They found that the relocations cooled the observations by −0.81°C and −0.69°C. Yuan-Jian Yang and colleagues (2013) find a cooling relocation of 0.7°C in the data of Hefei. Clearly for single urban stations, relocations can have a large influence.

Fatemeh Rahimzadeh and Mojtaba Nassaji Zavareh (2014) homogenized the Iranian temperature observations and observed that relocations were frequent:

"The main non-climatic reasons for non-homogeneity of temperature series measured in Iran are relocation and changes in the measuring site, especially a move from town to higher elevations, due to urbanization and expansion of the city, construction of buildings beside the stations, and changes in vegetation."
They show an example with 5 stations where one station (Khoramabad) has a relocation in 1980 and another station (Shahrekord) has two relocation in 1980 and 2002. These relocations have a strong cooling effect of 1 to 3 degrees Celsius.

Temperature in 5 stations in Iran, including their adjusted series.

The relocations do not always have a strong effect. Margarita Syrakova and Milena Stefanova (2009) do not find any influence of the inhomogeneities on the annual mean temperature averaged over Bulgaria. This while "Most of the inhomogeneities were caused by station relocations… As there were no changes of the type of thermometers, shelters and the calculation of the daily mean temperatures, the main reasons of inhomogeneities could be station relocations, changes of the environment or changes of the station type (class)."

In Finland, Norway, Sweden and the UK the relocations produced a cooling bias of -0.11°C and relocations appear to be the most common cause of inhomogeneities (Tuomenvirta, 2001). The table below summarises the breaks that were found and what the reasons for them were if this was known from the station histories. They write:

"[Station histories suggest] that during the 1930s, 1940s and 1950s, there has been a tendency to move stations from closed areas in growing towns to more open sites, for example, to airports. This can be seen as a counter-action to increasing urbanization."

Table with the average bias of inhomogeneities found in Finland, Sweden, Norway and the UK in winter (DJF), spring (MAM), summer (JJA) and autumn (SON) and in the yearly average. Changes in the surrounding, such as urbanization or micro-siting changes, made the temperatures higher. This was counteracted by more frequent cooling biases from changes in the thermometers and the screens used to protect the thermometers, by relocations and by changes in the formula used to compute the daily mean temperature.

Concluding, relocations are a frequent type of inhomogeneity. They produce a cooling bias. For urban stations the cooling can be very large. For the average over a region, the values are smaller, but especially because they are so common, they will have most likely a clear influence on global warming in raw temperature observations.

Future research
One problem with studying relocations is that they are frequently accompanied by other changes. Thus you can study them in two ways: study only relocations where you know that no other changes were made or study all historical relocations whether there was another change or not.

The first set-up allows us to characterize the relocations directly, to understand the physical consequences to move for example a station from the center of a city / village to the airport. In this way the differences are not subject to other changes specific to a network. So, the results can be easily compared between regions. The problem is that only a part of the parallel measurements available satisfy these strict conditions.

Conversely, for the second design (taking all historical relocations, also when they have another change) the characterization of the bias will be limited to the datasets studied and we will need a large sample to say something about the global climate record. But on the other hand, we can also analyze more data this way.

There are also two possible sources of information. The above studies relied on statistical homogenization comparing a candidate station to its neighbors. All you need to know for this is which inhomogeneities belong to a relocation. A more direct way to study these relocations is by using parallel measurements at both locations. This is especially helpful to study changes in the variability around the mean and in weather extremes. That is where the Parallel Observation Science Team (ISTI-POST) comes into play.

It is also possible to study specific relocations. The relocation of stations to airports was an important transition, especially around the 1940s. This temperature change is likely large and this transition quite frequent and well documented. One could focus on urban stations or on village stations, rather than studying all stations.

One could make a classification of the micro and macro siting before and after the relocation. For micro-siting the Michel Leroy (2010) classification could be interesting; as far as I know this classification has not been validated yet, we do not know how large the biases of the 5 categories are and how well-defined these biases are. Ian Stewart and Tim Oke (2012) have made a beautiful classification of the local climate zones of (urban) areas, which can also be used to classify the surrounding of stations.

Example of various combinations of building and land use of the local climate zones of Stewart and Oke.

There are many options and what we choose will also depend on what kind of data we can get. Currently our preference is to study parallel data with identical instrumentation at two locations, to understand the influence of the relocation itself as well as possible. In addition to study the influence on the mean, we are gathering data on the break sizes found by statistical homogenization for breaks due to relocations. The station histories (metadata) are crucial for this in order to clearly assign breakpoints to relocation activities. It will also be interesting to compare those two information sources where possible. This may become one study or two depending on how involved the analysis will become.

This POST study is coordinated by Alba Guilabert and Jenny Linden and Manuel Dienst are very active. Please contact one of us if you would like to be involved in a global study like this and tell us what kind of data you would have. Also if anyone knows of more studies reporting the size of inhomogeneities due to relocations, please let us know. I certainly have seen more such tables at conferences, but they may not have been published.

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Related reading
Parallel Observations Science Team (POST) of the International Surface Temperature Initiative (ISTI).

The transition to automatic weather stations. We'd better study it now.

Changes in screen design leading to temperature trend biases.

Early global warming.

Why raw temperatures show too little global warming.

References
Brunet M., O. Saladie, P. Jones, J. Sigró, E. Aguilar, et al., 2006: The development of a new daily adjusted temperature dataset for Spain (SDATS) (1850–2003). International Journal of Climatology, 26, pp. 1777–1802, doi: 10.1002/joc.1338.
See also: a case-study/guidance on the development of long-term daily adjusted temperature datasets.

Leroy, M., 2010: Siting classifications for surface observing stations on land. In WMO Guide to Meteorological Instruments and Methods of Observation. "CIMO Guide", WMO-No. 8, Part I, Chapter 1, Annex 1B.

Rahimzadeh, F. and M.N. Zavareh, 2014: Effects of adjustment for non‐climatic discontinuities on determination of temperature trends and variability over Iran. International Journal of Climatology, 34, pp. 2079-2096, doi: 10.1002/joc.3823.

Stewart, I.D. and T.R. Oke, 2012: Local climate zones for urban temperature studies. Bulletin American Meteorological Society, 93, pp. 1879–1900, doi: 10.1175/BAMS-D-11-00019.1.
See also the World Urban Database.

Tuomenvirta, H., 2001: Homogeneity adjustments of temperature and precipitation series - Finnish and Nordic data. International Journal of Climatology, 21, pp. 495-506, doi: 10.1002/joc.616.

Xu, W., Q. Li, X.L. Wang, S. Yang, L. Cao, and Y. Feng, 2013: Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices. Journal Geophysical Research Atmospheres, 118, doi: 10.1002/jgrd.50791.

Syrakova M. and Stefanova M., 2009: Homogenization of Bulgarian temperature series. International. Journal Climatology, 29, pp. 1835-1849, doi: 10.1002/jov.1829.

Yan ZW; Li Z; Xia JJ. 2014. Homogenisation of climate series: The basis for assessing climate changes. Science China: Earth Sciences, 57, pp 2891-2900, doi: 10.1007/s11430-014-4945-x.

* Photo at the top "High Above Sydney" by Taro Taylor used with a Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) license. Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-64451952867794178362016-03-07T20:35:00.001+00:002016-03-29T21:10:39.901+01:00Bernie Sanders is more electable

The bias in the American mass media are driving me crazy. So let me get onto my little soap box.

You can see the bias when they add the super-delegates to the pledged delegates to pretend that Clinton has a higher lead. If these super-delegates would actually vote against the wish of the primary voters, the disgust will be large enough to make sure that the Democrats would lose.

You can see the bias in that the media hardly speaks about money in politics. A main topic in both primaries.

What I want to talk about today is that the media generally assumes that Hillary Clinton is more electable in the general presidential election than Bernie Sanders. Often in the dismissive implicit way of the establishment. That can be a real opinion, but the evidence goes into another direction.

Money in politics
I am happy to admit that I am also biased. For me Bernie Sanders is clearly the best candidate, mainly because he wants to get money out of politics. Money in politics is bad for the public debate, for democracy, for the environment and for the economy.

Politicians who have to do what their donors tell them have no flexibility to compromise and get things done; they have to defend indefensible positions. This leads to a childish political debate. Nearly all Republican representatives in Washington reject basic scientific findings on climate change. This is childish. It is equivalent to putting your fingers in your ears and singing la, la, la, la. It matches the donor's preferences, but does not match their voter base. Half of the Republican voters support policies to reduce greenhouse gasses.

Just look around the globe, democracies have cleaner air and water. Progress in climate policies will be very slow as long as money determines US politics. On so many topics the positions of the politicians do not fit to what the population wants according to the polls. Even if money had no influence, the appearance of influence alone makes people cynical and hurts the American democracy.

The evidence
So okay, I have a bias. Let me give you the evidence I have. Do your due diligence and check them. Compare that to the lack of evidence provided by the media.

It would be great if everyone could just vote for the candidate of their liking, but as long as the election system is what it is, it is reasonable to want to elect a Democrat that can beat Donald Drumpf and make sure that 2016 is not the last election. It is reasonable to want to elect someone who can beat Ted Dogma Cruz who is beholden to four donors from the fossil fuel industry.

The most quantitative evidence is from the head to head polls. Clinton beats Trump by a few percent, but Sanders beats him clearly. Clinton would lose an election with Ted Cruz, which would be a catastrophe, while Sanders would beat him by 10 percent. Marco Rubio would beat Clinton by 5%, while Sanders would win.

Polls are not elections and it is still early, but the difference is clear. At the very least these polls certainly do not show that Clinton is more electable.

Polls also show much better favorability and honesty ratings for Sanders. In fact, Sanders is the only candidate on both sides that has positive a favorability. People do not only vote for policies, they also vote for personality. These ratings are a big help in the general elections.

More in general, the American people is fed up with establishment politics, with bought politicians that do not represent the population. This is clear in the approval ratings of Congress and government, which is at historical lows. You can see this clearly on the Republican side were people vote for candidates without any political competence because they are at least not establishment politicians (Donald Trump, Ben Carson). That category includes Ted Cruz, who is actually a life-long politician and worked for Bush, but in this election benefits from being able to piss off every politician he ever worked with.

There are people who argue that the American people is not ready for a political revolution. I would argue they were already proven wrong eight years ago by the campaign of Barack Obama. He campaign for change and won. Obama did not deliver, but he won the election promising change. With the long track record of Sanders people can be sure the will try to get change. It won't be easy and you never get all you want, but at least he will try.

In such a climate, it is not helpful to have an establishment candidate on the Democratic side. Count on Trump and Cruz emphasizing this.

If you look at the demographics supporting the candidates, Sanders also looks more electable. Clinton does well because of the African Americans. I see no reason why they would not vote for Sanders as well; his policies and past record are fine, if not better. Sanders does well with independents, they may otherwise go to the Republicans. Sanders does well with people who are fed up with bribed politicians; they may well not vote if Clinton becomes the candidate. There are also conservatives for Bernie, who like that he will not start wars quickly and will fight the symbiosis of state and corporations. I have yet to see a group "conservatives for Hillary". Although noting is too crazy, to not exist in the USA.

In 2016 there are seven super-swingy states: Colorado, Florida, Nevada, Ohio, Virginia, Iowa and New Hampshire.

Sanders won Colorado and New Hampshire. Iowa was a tie. Clinton won Virginia and Nevada.

These battle ground states are important in a general election.

People will have to turn out and actually vote. I may be biased, but I see more enthusiasm of the supporters of Sanders. Partially you can give numbers for this. The rallies of Sanders are a lot bigger and Sanders has twice as many donors.

The longer the election runs the more people get to know Bernie Sanders. For the general election even more people will inform themselves about Sanders and a lot of evidence shows that people who know him, like him and his policies.

He started with no name recognition in 2015 and made up a difference of 60%. I have the impression that Sanders election results more often than not beat the expectations based on polling, but could not find numbers on that. Also informative is that Clinton won her home state Arkansas by 66.3% over 29.7%, but in his home state Vermont Sanders won much bigger by 86.1% over 13.6%. A more than 70% difference. If you see New York as Clinton's home state, in the polls she only has a lead of 21% there.

Imagine how the debates will go. Trump will surely point out that Clinton took money from him. If only to demonstrate power. Trump will use his position as an outsider that is beholden to no one. Sanders would be able to answer that he is his own man and wants to get money out of politics.

Both candidates have their liabilities. Sanders will be attacked for being a socialist. I do not think that scares many people any more. And Sanders can simply refer to successful social democratic market economies in Europe. Socialism or capitalism is not black and white, many popular policies in America are already socialist. In every election the Republicans accuse the Democrats of being socialists; the Democrats are clearly more social and it may look better own it than to be apologetic about it.

I am not sure if Clinton has less liabilities that would be attacked in the general election. Next to the Republican Clinton evergreens, we have a possible indictment over her private email server. The content of those emails may be more important. Also the money of foreign governments and shady corporations flowing into the Clinton Foundation while she was secretary of state can be made into a scandal. Clinton keeps on refusing to release the transcripts of the speeches she gave to Goldman-Sachs for 200,0000 dollar a piece. She seems to think there is something in these speeches that would hurt her. Cruz' wife works at Goldman-Sachs; you can be sure these transcripts will be leaked in the general election.

Does anyone have evidence that Hillary Clinton is more electable? More than just a gut feeling?

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Related reading
English professor Seth Abramson explains the above so much better than I could and even comes to 12 reasons why Bernie Sanders is more electable than Hillary Clinton. And why the media is not telling you the truth about this. A Dozen Reasons Sanders Voters Are Justifiably Angry at the Media Right Now

Salon: Hillary Clinton just can't win: Democrats need to accept that only Bernie Sanders can defeat the GOP.

Many polls can be found on Real Clear Politics.

FiveThirtyEight also gathers many polls and tries to predict primary outcomes.

The Hillary Clinton-Bernie Sanders clash over the auto bailout, explained. I am not sure whether Clinton's flexibility with the truth is appreciated by Democrats.

The difference between Bernie Sanders and Hillary Clinton on climate change? I don't care. The difference on climate change is small, especially compared to Republican positions. The real difference is about ending crony capitalism and preventing the next banking recession.

* Caricature at the top by DonkeyHotey, which has a CC BY-SA 2.0 license.Victor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-51351025974070974132016-02-24T01:33:00.000+00:002016-02-24T01:33:25.812+00:00Australia flying blind into unchartered climatic conditionsThe Australian Climate Council wrote a report explaining how the gutting of climate science at the main Australian research institute, CSIRO, will harm climate science in Australia: Flying Blind: Navigating Climate Change Without CSIRO. The Australian climate research program is the most important one in the Southern Hemisphere and seriously hurts humanities understanding of the climatic changes there, which are anyway already understudied because most research happens in the global north.

The report also shows that these planned cuts to CSIRO's climate science division would breach Australia's commitments under the Paris agreement.

While destroying research can be done quickly with such a decision, building up a research program takes decades. A next Australian government will not be able to get to the same level again by renewing funding. That will take a long, long time.

It is very ironic that this decision comes from a right wing government that does everything to increase the use of coal and thus make climate change worse. It comes from a group that often advocates an adaption-only strategy to climate change. If you want to adapt, you need to know what climatic changes you need to adapt to. For that you need to know what will happen at a local scale, often with respect to changes in extreme weather, often what will happen with precipitation and storms. It is cleat that climate change is real, that the global mean temperature will keep on increasing if humanity refuses to act, but all those "details" we need to adapt are far from clear. Australians needed that information, farmers, engineers, companies, governors and majors, will find that CSIRO will not be able to answer their telephone calls.

Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-16657692444240106202016-02-17T22:20:00.003+00:002016-02-17T22:26:48.881+00:00The global warming conspiracy would be huge
The concept of global warming was created by and for the Chinese in order to make US manufacturing non-competitive.
Republican front runner Donald Trump on Twitter

Snowing in Texas and Louisiana, record setting freezing temperatures throughout the country and beyond. Global warming is an expensive hoax!
Republican front runner Donald Trump on Twitter

How do you know the climate didn't actually cool?
Eric Worrall, the main contributor to WUWT

Why use discredited surface data which everyone knows is fraudulent?
"Scottish" "Sceptic"

I am working on a study to compare nationally homogenized temperature data with the temperatures in the large international collections (GHCN, CRUTEM, etc.). Looking for such national datasets, I found many graphs in the scientific literature showing national temperature increases, which I want to share with you.

Mitigation skeptics like to talk about "The Team", as if a small group of people would be "in charge". That makes their conspiracy theories a little less absurd, although even small conspiracies typically do not last for decades. The national temperature series show that hundreds of national weather services and numerous universities would also need to be in the conspiracy of science against mankind. To me that sounds unrealistic.

The mitigation skeptics have a rough time and nowadays more often claim that they do not dispute the greenhouse effect or the warming of the Earth at all, but only bla, bla, bla. Which is why I thought I would show that this post is not fighting strawmen by citing some of the main bloggers and political leaders of the mitigation skeptical movement at the top of this post.

Anthony Watts, the weather presenter hosting Watts Up With That (WUWT), typically claims that only half of the warming is real, although he recently softened his stance for the USA and now only claims that a third is not real. If half of the warming in the global collections were not real, many scientists would have noticed that the global data does not fit to their local observations.

Plot idea: 97% of the world's scientists contrive an environmental crisis, but are exposed by a plucky band of billionaires & oil companies.
Scott Westerfeld

And do not forget all the other scientists studying other parts of the climate system, the upper air, ground temperatures, sea surface temperature, ocean heat content, precipitation, glaciers, ice sheets, lake temperatures, sea ice, lake and river freezing, snow, birds, plants, insects, agriculture. One really wonders with Eric Worrall how on Earth science knows the climate didn't actually cool.

Another reason to write this post is to ask for help. For this comparison study, I have datasets or first contacts for the countries below. If you know of more homogenized datasets, please, please let me know. Even if it is "only" a reference. Also if you have a dataset from one of the countries below: multiple datasets from one country are very much welcome.

Countries: Albania, Argentina, Armenia, Australia, Austria, Belgium, Benin, Bolivia, Bulgaria, Canada, Chile, China, Congo Brazzaville, Croatia, Czech Republic, Denmark, Ecuador, Estonia, Finland, France, Germany, Greece, Hungary, Iran, Israel, Italy, Latvia, Libya, Macedonia, Morocco, Netherlands, New Zealand, Norway, Peru, Philippines, Portugal, Romania, Russia, Serbia, Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Tanzania, Uganda, Ukraine, United Kingdom, United States of America.
Regions: Catalonia, Carpathian basin, Central England Temperature, Greater Alpine Region.

Alpine region

The temperature for the Greater Alpine Region from the HISTALP project (Ingeborg Auer and colleagues, 2007). The lower panel shows the temperature for four low altitude regions. The top panel their average (black) and the signal for the high altitude stations (grey). All series are smoothed over 10 years.

Armenia

The increase in the annual temperatures and the decrease in annual precipitation in Armenia. From Levon Vardanyan and colleagues (2013), see also Artur Gevorgyan and colleagues (2016).

Australia

The temperature signal over Australia for the day-time maximum temperature (red), the mean temperature (green) and the night-time minimum temperature. Figure from Fawcett and colleagues (2012).

Canada

From Lucie Vincent of Environment Canada and colleagues (2012).

The Czech Republic

Changes in mean annual and seasonal temperature time series for the Czech Lands in the period 1800–2010. The part of series calculated from only two stations is expressed by a dashed line. Figure by Petr Stepanek of the Global Change Research Institute CAS, Brno, Czech Republic.

China

The temperature change in China over the last 106 years, the annual mean temperature and the seasonal temperatures from QingXiang Li and colleagues (2010).

England

The famous Central England Temperature series of the Hadley Centre.

Finland

The annual average temperature in Finland. National averages are more noisy than global averages. Thus to show the trend better the graph adds the decadal average temperature. From Mikkonen and colleagues (2015).

India

The temperature signal since 1900 in India according to Kothawale et al. (2010) of the Indian Institute of Tropical Meteorology (IITM), Pune.

Italy

The temperature series of Italy since 1800 according to Michele Brunetti and colleagues (2006).

Middle America and Northern South America

These graphs show the change in the number of warm days (maximum temperature) and warm night (minimum temperature) and the number of cold days and cold nights computed from daily data from several countries in Middle America and in the North of South America. Figures from Enric Aguilar and colleagues (2005).

The Netherlands

Annual mean temperatures of the actual observations at De Bilt (red), the De Bilt homogenised series (dark blue), the previous version of the Central Netherlands Temperature series (CNT2,7; light blue) and the current Central Netherlands Temperature series (CNT4,6; pink). Gerard van der Schrier and colleagues (2009) from the Dutch weather service, KNMI. De Bilt is a city in the middle of The Netherlands were the KNMI main office is. The Central Netherlands series is for a larger region in the middle of The Netherlands.

New Zealand

The famous New Zealand 7-stations series.

Philippines

Observed annual mean temperature anomalies in the Philippines during the period 1951–2010 computed by Thelma A. Cincoa and colleagues (2014).

Russia

Temperature averaged over Russia from the annual climate report of ROSHYDROMET (2014). The top panel are the annual averages, the four lower panels the seasons (winter, spring, summer and autumn). No homogenization.

The variability in winter is very high. According to mitigation sceptic Anthony Watts this is due to Russian Steam Pipes:

I do know this: neither I nor NOAA has a good handle on the siting characteristics of Russian weather stations. I do know one thing though, the central heating schemes for many Russian cities puts a lot of waste heat into the air from un-insulated steam pipes.
Then it would be surprising that such large regions are affected in the same way and that the steam pipe years are also hot in the analysis of global weather prediction models and satellite temperature datasets.

Spain

Temperature trends computed by José Antonio Guijaro (2015) of the Spanish State Meteorological Agency (AEMET) for 12 river catchments within Spain. Homogenization with CLIMATOL.

The Spanish temperature dataset of the URV University in Tarragona. The panels on the left show the minimum temperature, the panels on the right the maximum temperature. The top panels show raw data before homogenization, the lower panels the homogenized data. The maximum temperature before 1910 had to be corrected strongly because of the use of a French screen before this time.

United States of America

The minimum and maximum temperature of the lower 48 states of the United States of America computed by NOAA. You can see it is an original American-made graph because it is in [[Fahrenheit]].

Switzerland

The temperature signal in Switzerland computed by Michael Begert and colleagues of the MeteoSchweiz. The top panel show original station time series, the lower panel shows them after removal of non-climatic changes.

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Related reading
Climatologists have manipulated data to REDUCE global warming

Charges of conspiracy, collusion and connivance. What to do when confronted by conspiracy theories?

If you're thinking of creating a massive conspiracy, you may be better scaling back your plans, according to an Oxford University researcher.

References
Aguilar, E., et al., 2005: Changes in precipitation and temperature extremes in Central America and northern South America, 1961–2003. Journal Geophysical Research, 110, D23107, doi: 10.1029/2005JD006119.

Auer, I., Böhm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M., Müller-Westermeier, G., Kveton, V., Bochnicek, O., Stastny, P., Lapin, M., Szalai, S., Szentimrey, T., Cegnar, T., Dolinar, M., Gajic-Capka, M., Zaninovic, K., Majstorovic, Z. and Nieplova, E., 2007: HISTALP—historical instrumental climatological surface time series of the Greater Alpine Region. International Journal of Climatology, 27, pp. 17–46. doi: 10.1002/joc.1377.

Begert, M., Schlegel, T. and Kirchhofer, W., 2005: Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000. International Journal of Climatology, 25, pp. 65–80. doi: 10.1002/joc.1118.

Brunetti, M., Maugeri, M., Monti, F. and Nanni, T., 2006: Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. International Journal of Climatology, 26, pp. 345–381, doi: 10.1002/joc.1251.

Cincoa, Thelma A., Rosalina G. de Guzmana, Flaviana D. Hilarioa, David M. Wilson, 2014: Long-term trends and extremes in observed daily precipitation and near surface air temperature in the Philippines for the period 1951–2010. Atmospheric Research, 145–146, pp. 12–26, j.atmosres.2014.03.025.

Fawcett, R.J.B., B.C. Trewin, K. Braganza, R.J Smalley, B. Jovanovic and D.A. Jones, 2012: On the sensitivity of Australian temperature trends and variability to analysis methods and observation networks. CAWCR Technical Report No. 050.

Gevorgyan, A., H. Melkonyan, T. Aleksanyan, A. Iritsyan and Y. Khalatyan, 2016: An assessment of observed and projected temperature changes in Armenia. Arabian Journal of Geosciences, 9, pp. 1-9, DOI 10.1007/s12517-015-2167-y.

Guijaro, J.A., 2015: Temperature trends. AEMET Report.

Jain, Sharad K. and Vijay Kumar, 2012: Trend analysis of rainfall and temperature data for India. Current Science, 102.

Kothawale, D.R., A.A. Munot, K. Krishna Kumar, 2010: Surface air temperature variability over India during 1901–2007, and its association with ENSO. Climate Research, 42, pp. 89-104.

Li Q X, Dong W J, Li W, et al. Assessment of the uncertainties in temperature change in China during the last century. Chinese Sci Bull, 2010, 55: 1974−1982, doi: 10.1007/s11434-010-3209-1

Mikkonen, S., M. Laine, H. M. Mäkelä, H. Gregow, H. Tuomenvirta, M. Lahtinen, A. Laaksonen, 2015: Trends in the average temperature in Finland, 1847–2013. Stochastic Environmental Research and Risk Assessment, 29, Issue 6, pp 1521-1529, doi: 10.1007/s00477-014-0992-2.

Schrier, van der, G., A. van Ulden, and G. J. van Oldenborgh, 2011: The construction of a Central Netherlands temperature. Climate of the Past, 7, 527–542, doi: 10.5194/cp-7-527-2011

Ulden, van, Aad, Geert Jan van Oldenborgh, and Gerard van der Schrier, 2009: The Construction of a Central Netherlands Temperature. Scientific report, WR2009-03. See also Van der Schrier et al. (2011).

Vardanyan, L., H. Melkonyan, A. Hovsepyan, 2013: Current status and perspectives for development of climate services in Armenia. Report, ISBN 978-9939-69-050-6.

Vincent, L. A., X. L. Wang, E. J. Milewska, H. Wan, F. Yang, and V. Swail (2012), A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis, J. Geophys. Res., 117, D18110, doi: 10.1029/2012JD017859.Victor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-5007269240338771742016-02-11T20:20:00.000+00:002016-04-15T22:35:35.473+01:00Early global warmingHow much did the world warm during the transition to Stevenson screens around 1900?

Stevenson screen in Poland.

The main global temperature datasets show little or no warming in the land surface temperature and the sea surface temperature for the period between 1850 and 1920. I am wondering whether this is right or whether we do not correct the temperatures enough for the warm bias of screens that were used before the Stevenson screen was introduced. This transition mostly happened in this period.

This is gonna be a long story, but it is worth it. We start with the current estimates of warming in this period. There is not much data on how large the artificial cooling due to the introduction of Stevenson screens is, thus we need to understand why thermometers in Stevenson screens record lower temperatures than before to estimate how much warming this transition may have hidden. Then we compare this to the corrections NOAA makes for the introduction of the Stevenson screen. Also other changes in the climate system suggest there was warming in this period. It is naturally interesting to speculate what this stronger early warming may mean for the causes of global warming.

No global warming in main datasets
The figure below with the temperature estimates of the four main groups show no warming for the land temperature between 1850 and 1920. Only Berkeley and CRUTEM start in 1850, the other two later.

If you look at the land temperatures plotted by Berkeley Earth themselves there is actually a hint of warming. The composite figure below shows all four temperature estimates for their common area for the best comparison, while the Berkeley Earth figure is interpolated over the entire world and thus sees Arctic warming more, which was strong in this period, like it again was strong in recent times. Thus there was likely some warming in this period, mainly due to the warming Arctic.

The temperature changes of the land according to the last IPCC report. My box.

In the same period the sea surface temperature was even cooling a little according to HadSST3 shown below.

The sea surface temperature of the four main groups and night marine air temperature from the last IPCC report. I added the red box to mark the period of interest.

Also the large number of climate models runs produced by the Coupled Model Intercomparison Project (CIMP5), colloquial called IPCC models, do not show much warming in our period of interest.

CMIP5 climate model ensemble (yellow lines) and its mean (red line) plotted together with several instrumental temperature estimates (black lines). Figure from Jones et al. (2013) with our box added to emphasize the period.

Transition to Stevenson screens
In early times temperature observations were often made in unheated rooms or in window screens of such rooms facing poleward. These window screens protected the expensive thermometers against the weather and increasingly also against direct sun light, but a lot of sun could get onto the instrument or the sun could heat the wall beneath the thermometer and warm air would rise up.

A Wild screen (left) and a Stevenson screen in Basel, Switzerland.
When it was realised that these measurements have a bias, a period with much experimentation ensued. Scientists tried stands (free standing vertical boards with a little roof that often had to be rotated to avoid sun during sunrise and -fall), shelters of various sizes that were open to the poles and to the bottom, screens of various sizes, sometimes near the shade of a wall, but mostly in gardens and pagoda huts that could have been used for a tea party.

The more open a screen is, the better the ventilation, which likely motived earlier more open designs, but this also leads to radiation errors. In the end the Stevenson screen became the standard, which protects the instrument from radiation from all sides. It is made of white painted wood and has a measurement chamber mounted on a wood frame, it typically has a double board roof and double Louvred walls to all sides. Initially it sometimes did not have a bottom, but later had slanted boards at the bottom.

The first version [[Stevenson screen]] was crafted in 1864 in the UK, the final version designed in 1884. It is thought that most countries switched to Stevenson screens before 1920, but some countries were later. For example, Switzerland made the transition from Wild screens to Stevenson screens in the 1960s. The Belgium Station Uccle changed their half open shelter to a Stevenson screen in 1983. The rest of Belgium in the 1920s.

Open shelter (at the front) and two Stevenson screens (in the back) at the main office of the Belgium weather service in Uccle.

Radiation error
The schematic below shows the main factors influencing the radiation error. Solar radiation makes the observed maximum temperatures too warm. This can be direct radiation or radiation scattered via clouds or the (snow covered) ground. The sun can also heat the outside of a not perfectly white screen, which then warms the air flowing in. Similarly the sun can heat the ground, which then may radiate towards the thermometer and screen. However, the lack of radiation shielding also makes the minimum temperature too low when the thermometer radiates infrared radiation into the cold sky. This error is largest on dry cloudless nights and small when the sky radiates back to the thermometer, which happens when the sky is cloudy and the absolute humidity is high, which reduces the net infrared radiative cooling. The radiation error is largest when there is not much ventilation, which in most cases need wind. The direct radiation effects are smaller for smaller thermometers.

Schematic showing the various factors that can influence the radiation error of a temperature sensor.

From our understanding of the radiation error, we would thus expect the bias in the day-time maximum temperature to be large where the sun is strong, the wind is calm, the soil is dry and heats up fast. The minimum temperature at night has the largest cooling bias when the sky is cloudless and dry.

This means that we expect the radiation errors for the mean temperature to be largest in the tropics (strong sun and high humidity) and subtropics (sun, hot soil), while it is likely smallest in the mid and high latitudes (not much sun, low specific humidity), especially near the coast (wind). Continental climates are the question mark; they have dry soils and not much wind, but also not as much sun and low absolute humidity.

Parallel measurements
These theoretical expectations fit to the limited number of temperature differences found in the literature; see table below. For the mid-latitudes, David Parker (1994) found that the difference was less than 0.2°C, but his data mainly came from maritime climates in north-west Europe. Other differences found in the mid-latitudes are about 0.2°C (Kremsmünster, Austria; Adelaide, Australia; Basel, Switzerland). While in the sub-tropics we have one parallel measurement showing a difference of 0.35°C and the two tropical parallel measurements show have a difference of 0.4°C. We are missing information from continental climates.

Table with the differences found for various climates and early screen1. Temperature difference in Basel is about zero using 3 fixed hour measurements to compute mean temperature, which was the local standard, but about 0.25 when using minimum and maximum temperature as is used most for global studies.

 Region	 Screen	 Temperature difference

 North-West Europe	 Various; Parker (1994)	 < 0.2°C

 Basel, Switzerland	 Wild screen; Auchmann & Brönnimann (2012)	 ˜0 (0.25)°C 1

 Kremsmünster, Austria	 North-wall window screen; Böhm et al. (2010)	 0.2°C

 Adelaide, South Australia	 Glaisher stand; Nicholls et al. (1996)	 0.2°C

 Spain	 French screen; Brunet et al. (2011)	 0.35 °C

 Sri Lanka	 Tropical screen; in Parker (1994)	 0.37°C

 India	 Tropical screen; in Parker (1994)	 0.42°C

Most of the measurements we have are in North West Europe and do not show much bias. However, theoretically we would not expect much radiation errors here. The small number of estimates showing large biases come from tropical and sub-tropical climates and may well be representative for large parts of the globe.

Information on continental climates is missing, while they also make up a large part of the Earth. The bias could be high here because of calm winds and dry soils, but the sun is on average not as strong and the humidity low.

Next to the climatic susceptibility to radiation errors also the designs of the screens used before the Stevenson screen could be important. In the numbers in the table we do not see much influence of the designs, but maybe we will see it when we get more data.

Global Historical Climate Network temperatures
The radiation error and thus the introduction of Stevenson screens affected the summer temperatures more than the winter temperatures. Thus it is interesting that the trend in winter is 3 times stronger in the (Northern Hemisphere, GHCNv3). In winter it is 1.2°C per century, in summer it is 0.4°C per century over the period 1881-1920; see figure below2.

Also without measurement errors, the trend in winter is expected to be larger than in summer because the enhanced greenhouse effect affects winter temperatures more. In the CMIP5 climate model average the winter trend is about 1.5 times the summer trend3, but not 3 times.

Temperature anomalies in winter and summer over land in NOAA's GHCNv3. The light lines are the data, the thick striped lines the linear trend estimates.

The adjustments made by the pairwise homogenization algorithm of NOAA for the study period are small. The left panel of the figure below shows the original and adjusted temperature anomalies of GHCNv3. The right panel shows the difference, which shows that there are adjustments in the 1940s and around 1970. The official GHCN global average starts in 1880. Zeke Hausfather kindly provided me with his estimate starting in 1850. During our period of interest the adjustments are about 0.1°C; a large part of which was before 1880.

These adjustments are smaller than the jump expected due to the introduction of the Stevenson screens. However, they should also be smaller because many stations will have started as Stevenson screens. It is not known how large percentage this is, but the adjustments seem small and early.

Other climatic changes
So far for the temperature record. What do other datasets say about warming in our period?

Water freezing
Lake and river freeze and breakup times have been observed for a very long time. Lakes and rivers are warming at a surprisingly fast rate. They show a clear shortening of the freezing period between 1850 and 1920; the freezing started later and ice break-up started. The figure below shows that this was already going on in 1845.

Time series of freeze and breakup dates from selected Northern Hemisphere lakes and rivers (1846 to 1995). Data were smoothed with a 10-year moving average. Figure 1 from Magnuson et al. (2002).

Magnuson has updated his dataset regularly: when you take the current dataset and average over all rivers and lakes that have data over our period you get the clear signal shown below.

The average change in the freezing date in days and the ice break-up date (flipped) is shown as red dots and smoothed as a red line. The smoothed series for individual lakes and rivers freezing or breaking up is shown in the background as light grey lines.

Glaciers
Most of the glaciers for which we have data from this period show reductions in their lengths, which signals clear warming. Oerlemans (2005) used this information for a temperature reconstruction, which is tricky because glaciers respond slowly and are also influenced by precipitation changes.

Temperature estimate of Oerlemans (2005) from glacier data. (My red boxes.)

Proxies
Temperature reconstructions from proxies show warming. For example the NTREND dataset based on tree proxies from the Northern Hemisphere as plotted below by Tamino.

Temperature reconstruction of the non-tropical Northern Hemisphere.

Paleo Model Intercomparison project
While the CMIP5 climate model runs did not show much warming in our period, the runs for the last millennium of the PMIP3 project do show some warming, although it strongly depends on the exact period; see below. The difference between CMIP5 and PMIP3 is likely that in the beginning of the 19th century there was much volcanic activity, which decreased the ocean temperature to below its equilibrium and it took some decades for it to return to its equilibrium. CMIP5 starts in 1850 and modelers try to start their models in equilibrium.

Simulated Northern Hemisphere mean temperature anomalies from PMIP3 for last millennium. CCSM4 shows the simulated Northern Hemisphere mean temperature anomalies (annual values in light gray, 30-yr Gaussian smoothed in black). For comparison various smoothed reconstructions (colored lines) are included which come from a variety of proxies, including tree ring width and density, boreholes, ice cores, speleothems, documentary evidence, and coral growth.

Sea surface temperature
Land surface warming is important for us, but does not change the global mean temperature that much. The Earth is a blue dot; 70% of our planet is ocean. Thus is we had a bias in the station data our period of 0.3°C, that would be a bias global temperature of 0.1°C. However, larger warming of land temperatures are difficult if the sea surface is not also warming and currently the data shows a slight cooling over our period. I have no expertise here, but wonder if such a large difference would be reasonable.

Thus maybe we overlooked a source of bias in the sea surface temperature as well. It was a period in which sailing ships were replaced by steamships, which was a large change. The sea surface temperature was measured by sampling a bucket of water and measuring its temperature. During the measurement, the water would evaporate and cool. On a steamship there is more wind than on a sailing ship and thus maybe more evaporation. The shipping routes have also changed.

I must mention that it is a small scandal how few scientists work on the sea surface temperature. It would be about a dozen and most of them only part-time. Not only is the ocean 2/3 of the Earth, the sea surface temperature is also often used to drive atmospheric climate models and to study climate modes. The group is small, while the detection of trend biases in sea surface temperature is much more difficult than in station data because they cannot detect unknown changes by comparing stations with each other. The maritime climate data community deserves more support. There are more scientists working on climate impacts for wine; this is absurd.

A French (Montsouri) screen and two Stevenson screens in Spain. The introduction of the Stevenson screen went fast in Spain and was hard to correct using statistical homogenization alone. Thus a modern replica of the original French screen build for an experiment, which was part of the SCREEN project.

Causes of global warming
Let's speculate a bit more and assume that the sea surface temperature increase was also larger than currently thought. Then it would be interesting to study why the models show less warming. An obvious candidate would be aerosols, small particles in the air, which have also increased with the burning of fossil fuels. Maybe models overestimate how much they cool the climate.

The figure from the last IPCC report below shows the various forcings of the climate system. These estimates suggest that the cooling of aerosols and the warming of greenhouse gases is similar in climate models until 1900. However, with less influence of aerosols, the warming would start earlier.

Stevens (2015) argues that we have overestimated the importance of aerosols. I do not find Stevens' arguments particularly convincing, but everyone in the field agrees that there are at least huge uncertainties. The CMIP5 figure gives the error bars at the right and it is within the confidence interval that there is effectively nearly no net influence of aerosols (ochre bar at the right).

There is direct cooling of aerosols due to scattering of solar radiation. This is indicated in red as "Aer-Rad int." This is uncertain because we do not have good estimates on the amount and size of the aerosols. Even larger uncertainties are in how aerosols influence the radiative properties of clouds, marked in ochre as "Aer-Cld int."

Some of the warming in our period was also due to less natural volcanic aerosols at the end. Their influence on climate is also uncertain because of lack of observations on the size of the eruptions and the spatial pattern of the aerosols.

Forcing estimate for the IPPC AR5 report.

The article mentioned in the beginning (Jones et al. 2013) showing the CMIP5 global climate model ensemble temperatures for all forcings, which did not show much warming in our period, also gives results for model runs that only include greenhouse gases, which shows a warming of about 0.2°C; see below. If we interpret this difference as the influence of aerosols, (there is also a natural part) then aerosols would be responsible for 0.2°C cooling in our period in the current model runs. In the limit of the confidence interval were aerosols do not have a net influence, an additional warming of 0.2°C could thus be explained by aerosols.

CMIP5 climate model ensemble (yellow lines) and its mean (red line) plotted together with several instrumental temperature estimates (black lines). Figure from Jones et al. (2013) with our box added to estimate the temperature increase.

Conclusion on early global warming
Several lines of evidence suggest that the Earth's surface actually was warming during this period. Every line of evidence by itself is currently not compelling, but the [[consilience]] of evidence at least makes a good case for further research and especially to revisit the warming bias of early instrumental observations.

To make a good case, one would have to make sure that all datasets cover the same regions/locations. With the modest warming during this period, the analysis should be very careful. It would also need an expert for each of the different measurement types to understand the uncertainties in their trends. Anyone interested in make a real publishable study out of this please contact me.

Austrian Hann screen (a large screen build close to a northern wall) and a Stevenson screen in Graz, Austria.

Collaboration on studying the bias
To study the transition to Stevenson screens, we are collecting data from parallel measurements of early instrumentation with Stevenson screens.

We have located the data for the first seven sources listed below.

Australia, Adelaide, Glaisher stand
Austria, Kremsmünster, North Wall
Austria, Hann screen in Vienna and Graz
Spain, SCREEN project, Montsouris (French) screen in Murcia and La Coruña
Switzerland, Wild screen in Basel and Zurich
Northern Ireland, North wall in Armagh
Norway, North wall

Most are historical datasets, but there are also two modern experiments with historical screens (Spain and Kremsmünster). Such experiments with replicas is something I hope will be done more in future. It could also be an interesting project for an enthusiastic weather observer with an interest in history.

From the literature we know of a number of further parallel measurements all over the world; listed below. If you have contacts to people who may know where these datasets are, please let us know.

Belgium, Uccle, open screen
Denmark, Bovbjerg Fyr, Skjoldnñs, Keldsnor, Rudkùbing, Spodsbjerg Fyr, Gedser Fyr, North wall.
France, Paris, Montsouris (French) screen
Germany, Hohenpeissenberg, North wall
Germany, Berlin, Montsouris screen
Iceland, 8 stations, North wall
Northern Ireland, a thermograph in North wall screen in Valentia
Norway, Fredriksberg observatory, Glomfjord, Dombas, North wall
Samoa, tropic screen
South Africa, Window screen, French and Stevenson screens
Sweden, Karlstadt, Free standing shelter
Sweden, Stockholm Observatory
UK, Strathfield Turgiss, Lawson stand
UK, Greenwich, London, Glaisher stand
UK, Croydon, Glaisher stand
UK, London, Glaisher stand

To get a good estimate of the bias we need many parallel measurements, from as many early screens as possible and from many different climatic regions, especially continental, tropical and sub-tropical climates. Measurements made outside of Europe are lacking most and would be extremely valuable.

If you know of any further parallel measurements, please get in touch. It does not have to be a dataset, also a literature reference is a great hint and a starting point for a search. If your twitter followers or facebook friends may have parallel datasets please post this post on POST.

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Related reading
Parallel Observations Science Team (POST) of the International Surface Temperature Initiative (ISTI).

The transition to automatic weather stations. We'd better study it now.

Why raw temperatures show too little global warming.

Changes in screen design leading to temperature trend biases.

Notes

1) The difference in Basel is nearly zero if you use the local way to compute the mean temperature from fixed hour measurements, but it is about 0.25°C if you use the maximum and minimum temperature, which is mostly used in climatology.

2) Note that GHCNv3 only homogenizes the annual means, that is, every month gets the same corrections. Thus the difference in trends between summer and winter shown in the figure is like it is in the raw data.

3) The winter trend is 1.5 times the summer trend in the mean temperature of the CMIP5 ensemble for the Northern Hemisphere (ocean and land). The factor three we found in for GHCN was only for land. Thus a more careful analysis may find somewhat different values.

References
Auchmann, R. and S. Brönnimann, 2012: A physics-based correction model for homogenizing sub-daily temperature series. Journal Geophysical Research Atmospheres., 117, art. no. D17119, doi: 10.1029/2012JD018067.

Bjorn Stevens, 2015: Rethinking the Lower Bound on Aerosol Radiative Forcing. Journal of Climate, 28, pp. 4794–4819, doi: 10.1175/JCLI-D-14-00656.1.

Böhm, R., P.D. Jones, J. Hiebl, D. Frank, et al., 2010: The early instrumental warm-bias: a solution for long central European temperature series 1760–2007. Climatic Change, 101, pp. 41–67, doi: 10.1007/s10584-009-9649-4.

Brunet, M., J. Asin, J. Sigró, M. Bañón, F. García, E. Aguilar, J. Esteban Palenzuela, T.C. Peterson, P. Jones, 2011: The minimization of the screen bias from ancient Western Mediterranean air temperature records: an exploratory statistical analysis. International Journal Climatololgy, 31, 1879–1895, doi: 10.1002/joc.2192.

Jones, G. S., P. A. Stott, and N. Christidis, 2013: Attribution of observed historical near‒surface temperature variations to anthropogenic and natural causes using CMIP5 simulations. Journal Geophysical Research Atmospheres, 118, 4001–4024, doi: 10.1002/jgrd.50239.

Magnuson, John J., Dale M. Robertson, Barbara J. Benson, Randolf H. Wynne, David M. Livingstone, Tadashi Arai, Raymond A. Assel, Roger B. Barry, Virginia Card, Esko Kuusisto, Nick G. Granin, Terry D. Prowse, Kenton M. Stewart, and Valery S. Vuglinski, 2000: Historical trends in lake and river ice cover in the Northern Hemisphere. Science, 289, pp. 1743-1746, doi: 10.1126/science.289.5485.1743

Nicholls, N., R. Tapp, K. Burrows, and D. Richards, 1996: Historical thermometer exposures in Australia. International Journal of Climatology, 16, pp. 705-710, doi: 10.1002/(SICI)1097-0088(199606)16:6<705::AID-JOC30>3.0.CO;2-S.

Oerlemans, J., 2005: Extracting a Climate Signal from 169 Glacier Records. Science, 308, no. 5722, pp. 675-677, doi: 10.1126/science.1107046.

Parker, D.E., 1994: Effects of changing exposure of thermometers at land stations. International Journal Climatology, 14, pp. 1–31, doi: 10.1002/joc.3370140102.

Photo at the top a Stevenson screen of the amateur weather station near Czarny Dunajec, Poland. Photographer: Arnold Jakubczyk.
Photos of Wild screen and Stevenson screen in Basel by Paul Della Marta.
Photo of open shelter in Belgium by Belgium weather service.
Photo of French screen in Spain courtesy of SCREEN project.
Photo of Hann screen and Stevenson screen in Graz courtesy of the University of Graz.
Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-69846846995767004622016-02-05T03:25:00.001+00:002016-02-11T16:37:21.200+00:00Malcolm Turnbull, how should Australia adapt to climate change without science?Adapting to climate change needs information on local changes in the mean, weather variability and extremes. Observed changes in the means are not enough.

If you don't like what #climate science is telling you, just fire all the climate scientists

Miles Grant

The "conservative" government of Australia plans to gut its climate research and kill the groups doing climate research at Australia's main research institute, CSIRO. Australia's opposition leader rightly said the Prime Minister Malcolm Turnbull should "hang his head in shame".

The destruction is not for lack of quality of the research. CSIRO's new chief Larry Marshall send an email to its employees stating:

"CSIRO pioneered climate research ... Our climate models are among the best in the world and our measurements honed those models to prove global climate change.
From this the strange conclusion is drawn:

That question has been answered, and the new question is what do we do about it, and how can we find solutions for the climate we will be living with?"
[UPDATE. Judith Curry agrees with this strange sentiment: "Now that the UN's community of nations has accepted consensus climate science to drive international energy and carbon policy, what is the point of heavy government funding of climate research, particularly global climate modelling?"]

Just because we know climate change is real, does not mean that we understand everything. Projecting increases in the global mean temperature is easy. Saying something about the changes in the hydrological cycle is much harder. We know how much the global mean precipitation will increase because we can estimate the additional evaporation and what goes up must go down, but say where and how it goes down is hard. These assessments naturally have their uncertainties and it certainly pays to reduce them to make better political decisions.

Much more important than the uncertainties in the changes in the global means, for "solutions for the climate we will be living with" (adapting to climate change) we will need local predictions. That is a lot harder and very uncertain. Locally the changes can be very different from the global change. As Roger Pielke Jr. writes about storms on the US East Coast: "So those who argue for a simple relationship between increasing water content of the atmosphere and storm strength, data do not support such a claim over this multi-decadal period, in this region." (my emphasis)

Much more important than the uncertainties in the changes in the global means, for adaptation we need information on changes in weather variability and extremes. Especially for a country like Australia that knows very large variations due, for example, to El Nino.

One of the strategies of the mitigation skeptics is to pretend that adaptation is straightforward and cheap. When the sea level goes up 1 mm, just make the dikes 1 mm higher. However, the sea dikes will break during spring tide and a strong storm. Thus we also need to understand the storms to know how much stronger the dikes need to be. They will break during a once in a century storm. Or at least during what used to be a once in a century storm. Try to estimate from observations during a changing climate whether the 100-year storms are getting worse.

"With climate change, we can't drive by looking in the rear view mirror. We're in a new normal."
Climate scientists Berrien Moore and Katharine Hayhoe

Was the flooding of New Orleans due to [[Hurricane Katrina]] a unique event or the "new normal"? During the flooding last year in South Carolina in some locations the rain amounted to a 1,000-year event (in a given year there is a 1 in 1,000 chance of observing rainfall of that magnitude of more). Does South Carolina have to adapt to this because this will happen more often or will this remain an outlier? Parts of the United Kingdom were hit three times by 100 year rain events the last few years. How often will they have to suffer this before we know from waiting and seeing that the weather has changed, people will have to move and the infrastructure needs to be more more robust?

"There's no point putting in flood defenses that respond to mean climate change if you haven't thought of what a one-in-a-hundred-year event will look like in a warmer world... They don't want to know what the climate will be like, they want to know what the weather will be like in 20, 30, 50 years time."

Chief Scientist UK MetOffice Dame Julia Slingo on adaptation

The same goes less visibly for droughts. When your farm takes a hit due to a drought, do you build it up again when the rain comes back or is your land no longer profitable. Do you want to do this blindly? Or do you prefer some scientific guidance? For planning crops and managing reservoirs during droughts, seasonal and decadal climate predictions reduce costs and hardship. For planning new reservoirs and desalination plants long-term climate projects give guidance.

Meteorologists and climatologists are building seamless prediction systems. Going from short term weather predictions and nowcasting using observations during severe weather, to long-term weather predictions to prepare for bad weather, to seasonal and decadal predictions for planning and climate projections for adaptation. In many wealthy countries governments are setting up national climate service centers to help their societies adapt. The World Meteorological Organization is building a Global Framework for Climate Services (GFCS) to coordinate such efforts and help poorer countries understand the changes their region will see. While Australia sticks its head in the sand.

We will need very good science, a very good understanding of the coming climatic changes to adapt. The Australian government destroys climatology at a moment people, communities and companies need it most to adapt to the climatic changes that we have set in motion. This is about as stupid as the US states where the civil servants are no longer allowed to talk about climate change, which will mean that these communities will suffer the consequences without being prepared for the changes.

The same is true for (nearly?) every impact of climate change. In the past we could use long-term observations to determine what kind of extremes we could expect. Now, after all the delays to solve the problem, humanity is becoming more and more dependent on climate science and climate models, the models the mitigation skeptics who campaign for more global warming claim not to trust.

If you do not know which climatic changes you need to adapt to, you need to adapt to everything. Preparing for the worst case scenario in every direction is very expensive.

Never attribute to maladaptation that which can be adequately explained by stupidity.

When Australia notices what a blunder they are making it will easily take over a decade until Australia's climate research is again where it started. It takes years until you understand a climate model or a data set well and start to be productive. Science is a social profession and once you are proficient you can start building your network. Then you notice the kinds of expertise still missing in your freshly build up institute. Unfortunately, like trust losing scientific expertise goes much faster than building it up.

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Related reading
CSIRO boss's failed logic over climate science could waste billions in taxes by Andy Pitman, Director of the Centre for Climate System Science.

The CSIRO and farming in a changing climate

'Misleading, inaccurate and in breach of Paris': CSIRO scientist criticises cuts. Stefan Rahmstorf: "Closing down climate research capacity at a time of rapid global warming is not just short-sighted, it borders on the insane."

The Sydney Morning Herald: Climate science to be gutted as CSIRO swings jobs axe

Australia's CSIRO dims the lights on climate and environment

Thomas Peterson chair of WMO Commission on Climate explains the need for climate research by example how to deal with a drought.

Top photo. Severe suburban flooding in New Orleans, USA. Aftermath of Hurricane Katrina. Photo by ark Moran, NOAA Corps, NMAO/AOC (CC BY 2.0)
Second photo. Flames burn out of control at Possum Kingdom Lake near Pickwick, TX, on April 15, 2011. Photo by Texas Military Forces, available through a CC license.
Last photo. Flash flooding stalls traffic on I-45 in Houston on May 26, 2015. Photo by Bill Shirley, available through a CC license.
Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-25483926749838324682016-01-31T18:20:00.000+00:002016-03-30T21:17:27.859+01:00The difference between Bernie Sanders and Hillary Clinton on climate change?

Just two days ago 350 Action published a comparison of the plans of the presidential candidates to combat climate change. 350 Action is the political arm of climate action group 350.org, which was founded by Bill McKibben. They tried to ask all candidates 70 questions. A summary of the differences between Sanders and Clinton can be found above. They clearly found that Bernie Sanders plans to do more.

For the non-Americans reading this blog let me add that on the Republican side they had "more luck eliciting declarations of climate denial and defenses of the fossil fuel industry than any significant evolution on the issue." The US Senate voted this week about whether human activity significantly contributes to climate change. A weird thing of itself. The more so in 2016! Of the 54 Republican senators just five accepted that statement. Relative to that extremism, the differences between the Democrats are small.

In December also Think Progress made a comparison of the 3 Democratic candidates and they similarly found Bernie Sanders to have more positions favored by the environmental movement. For the record, Martin O'Malley scored even better.

Both organizations state that it took some time of campaigning against Bernie Sanders before Hillary Clinton improved her climate change plans.

Bernie Sanders clearly came out as the favorite presidential candidate during a "Climate Emergency Caucus" of the environmental group The Climate Mobilization, which fights for zero greenhouse gas emissions by 2025. Sanders won 69 percent of votes at their mock Democratic caucus. Clinton, O'Malley and uncommitted all got about 10 percent.

If you care about climate change, Bernie Sanders is clearly your man. However even if Hillary Clinton would have had better plans, I would go for Sanders because you also have to be able to execute the plans and there are other important issues apart from climate change.

Democracy
In my assessment the main problem in America is the excessive influence of money. Everywhere rich people unfortunately have more influence, but the way corporations and billionaires determine US policies destroys the democratic heart of America. This is to a large part possible because of unlimited campaign contributions, which lead to legal bribery.

The oligarchy is first of all deeply undemocratic. Corporations have different interests than the people. It is amazing the kind of obvious highly popular policies cannot pass Congress. Renewable energy is enormously popular in the public, but does not get much political support. People who are not allowed to fly because they are on the terrorist watch list can buy an automatic weapon. The US Congress explicitly voted against a bill fixing this problem. In 2008 the population had to bail out the banks to avoid an even larger depression because they are to big to fail. That is the end of the market mechanism, when the upsides are private and the downsides get privatized. That is calling for taking too much risk, but the banks are now bigger than in 2008. Because companies legally bribed so many politicians, politics is not able to fix these obvious problems.

The money makes rational debate impossible. The politicians cannot negotiate and compromise because they have to do what their donors want them to do. That is why you get the childish debates we see in the climate "debate" because a real debate is not possible. It would be better when the donors would sit at the table. Like in the medieval times when the local war lords were "advising" the king.

"If government is to play its role in creating a successful economy, we must restore comity, compromise, openness to evidence"
Ben Bernanke

The bribed politicians also have a huge influence on the public and published opinion. By saying crazy things in the media, such as James Inhofe saying that climate change is a hoax and Cruz calling climate science dogma, these kind of statements start to sound acceptable. Most people do not take the time to carefully review the evidence; people are social animals and we normally negotiate our opinions interacting with others and the opinion of leaders is very influential. Especially authoritarians are susceptible to picking up the opinion of their leaders. If only a weather presenter from Chico, California, would blog daily about all those obvious problems with climate science that the experts do not see, the situation in the USA would be fully different. Money in politics is an important reason for the American exceptionalism in the climate "debate".

Bernie Sanders sees money in politics as the main problem that needs fixing. For that reason alone, I would vote for him if I could. Without fixing this, it is nearly impossible to fix other problems. Without fixing this, solving climate change is like running a marathon with a 50kg sack of rice on your back. First the weight needs to be removed.

Money became so dominant in large part due to disastrous Supreme court decisions that money is speech and that corporations and humans. One way to fix that is a better Supreme court. The next president will select one to three Supreme court candidates. Executive actions can make the money streams more transparent, which would likely reduce them and make them less influential. The president can press for a constitutional amendment. (Simultaneously, the people can try to get an amendment via the states.)

Winning
In national polls Clinton has more support among likely Democrat primary voters, but in this stage national polls are not very informative. Just imagine someone calling you up to ask you about something you normally do not think much about. Would you like to carpet bomb Agrabah? Polls are very different from elections and referendums. National poling results at the moment largely reflect name recognition. When an election comes up, people start paying more attention and talk to each other. Only when we get closer to elections, do polls start to have value.

National polls are especially not very informative yet because Clinton has a much higher name recognition than Sanders.

This Monday there is the first caucus in Iowa and after that in New Hampshire. In New Hampshire Sanders is well ahead by now. In Iowa Clinton and Sanders are too close together and the polls do not agree with each other. The main problem is determining who is a likely voter and especially whether young people will show up. Young people overwhelmingly support Sanders. Normally they hardly show up, except in 2008, when they thought they could get real change. I would expect this to happen again. This time it is worth it. Some polling organizations even only classify people who voted in previous caucuses as likely voters. This completely excludes young people.

[UPDATE after Iowa. The result was basically a tie Clinton and Sanders in Iowa. Clinton had 0.4% more "votes". Interestingly, it was a tie for almost all subgroups (income, education), except for young people who support Sanders more and women who support Clinton more. It was even a tie for people who voted Clinton in 2008.

You can see this as a win for Clinton because Iowa is quite white and Clinton does better among people of color. Thus you could argue that Sanders should have won. I do not see this argument as very convincing. There is nothing special about Clinton's policies when it comes to minorities compared to Sanders; that is a policy tie. This can easily change.

I find it more convincing to say that Sanders won. He came from nothing. She was the clear favorite at the beginning of the campaign. Clinton has a lot of name recognition, support of other (local) politicians, more money (from large donors) and had several year to prepared herself. It now becomes harder to ignore him in the media, where he did not get covered much up to now. And when people get to know him, they like him and his policies. So I would say: in Iowa a tie, nationally Sanders won.]

(For the same reason, polling results for Trump are unreliable because many of this supporters normally do not go to caucuses and one can only guess whether they will go the the Iowa caucus this time. [UPDATE. While wining in the polls, Trump lost, almost became third. Not good for his image.])

When Iowa and New Hampshire go to Sanders, the primaries start to get interesting. That is when the corporation will start fighting back, when people will start to inform themselves about Sanders. That is when we will learn how he handles stress and whether he will do a good job in the general presidential election.

I expect he will, but then I am biased as European. The published opinion will try to convince the public that Sander's plans are impossible. For me it is hard to imagine they can pull such nonsense off, most of what Sanders wants is completely mainstream in Europe. No matter how right-wing a European party is, I cannot imagine them accepting that people die because they waited too long with going to the doctor. That sounds as if death panels are okay as long as the hands of the panelists are invisible. It is mainstream in Europe that college is not only a personal benefit, but contributes to society and prosperity as well, that everyone who has the skills and the drive should be able to go to university.

I am afraid after the first primaries will also be when Clinton will show here inner Republican even more. The last two week she already started deceiving the electorate to attack political opponents. I have no problem with playing hard, but I do like politics to be about ideas.

Winning the primaries also depends on whether the voters believe you can win the election. That is hard to judge, but the evidence at this moment does not support Clinton's claim that she is more electable. There is also not a strong case yet to say that Sanders is more electable, but his numbers are going up as people get to know him, while Clinton's numbers are stable or go down.

In match-up polls between one Democrat and one Republican candidate Sanders on average performs better. Clinton wins over Donald Trump with a difference of +2.7%, but Sanders wins with +5.3% and the recent two polls are even above 10%.

Clinton versus Cruz would be won by Cruz by +1.3%, although the recent one Clinton wins. Sander versus Cruz would be won by Sanders by 3.3%, although the recent ones Cruz wins marginally.

Like normal polls, these match-ups do not say much. It is very hard for people to imagine the real choice they would have to make and they hardly know the candidates yet. Thus rather than look at the current polls, we have to try to understand the dynamics of the campaigns. The Daily Kos writes:

[Bernie Sanders] has the overwhelming support of independents, whereas Hillary has lukewarm support from them at best, giving him a huge general election advantage. He also has crossover appeal to Republicans, earning up to 25% of their support in his home state. Already, numerous Republicans for Bernie have been documented. But Bernie is also best positioned to win because he will bring new voters to the polls, who are then likely to vote Democrat—the young, the poor, and the disillusioned.

I would expect the real difference between a Republican and any Democrat to be large it the end. Now the Republican candidates can hide their ignorance or lack of empathy during the debates in an enormous field, in debates that cannot go into depth. In the main campaign there will be only two candidates during the debates; any Republican ignorant outsider will be destroyed there. And if a debate contrasts Cruz or Rubio to a human being, they will look even more extreme and even less sympathetic.

No sitting Republican Senator has endorses either Trump or Cruz. Their celebrity couple name is Crump (ht Stephen Colbert). The deeply conservative magazine the National Review made an entire issue Against Trump.

There are many decent Republicans who will be put in a tough spot when one of the currently leading candidates will become the official Republican candidate. I expect that easily 20% will not vote for one of these radicals. In case Hillary Clinton is the Democratic candidate they will mostly stay at home. In case Bernie Sanders is the candidate a considerable part will vote for him.

"It's like being shot or poisoned… what does it really matter?"
Sen. Lindsey Graham on Ted Cruz and Donald Trump

Climate change will become an ever large liability for the Republicans. In this primary they cannot soften on the issue, but in the general election they will look completely out of touch with reality. Even people who do not care about climate change itself, will have some doubts about giving such people the nuclear codes. That in a year that quite likely again becomes a record warm year. The third record in a row.

In Vermont Sanders got a decent amount of votes from Republicans. They hate money in politics as much as Democrats. Independents like Sanders more than Clinton. He is sympathetic and trustworthy, with a very consistent voting record. People are fed up with the establishment, which you can see by the popularity of completely incompetent outsiders in the Republican primary. Sanders not taking money from the establishment and running for real change can distance himself from that.

In a race against Trump, he can claim to be his own man, while he can claim that Clinton needs to do what her donors want. In contrast Sanders can also claim to be independent and he actually wants to stop the legal bribery. Trump does not.

In the past, a candidate in the middle would have an advantage. They take some of the voters from the other party and the wings of their party were forced to vote for them to prevent worse. Nowadays, however, with only 50 to 60% of the electorate actually votes, the most important job of a candidate is to get the supporters to actually vote. I would expect Sanders to be able to generate more enthusiasm than Clinton. He has more supporters and large rallies. Both are helped by a radicalized Republican party that makes Democrats clear that they need to vote.

Last month, I made this prediction.

Predictions are more impressive when you make them early: @BernieSanders will win presidency with < 60%, majorities in House and Senate.
Climate change denial bad strategy in El Nino year, US fed up with establishment, radicalised Republicans mobilise voters, internet news.
— Victor Venema (@VariabilityBlog) December 27, 2015

I am reasonably confident Sanders will win; naturally this is not science, just my personal assessment. The Democrats also wining both chambers is a more daring prediction. On the positive side, many more Republican seats are up for election. Let's concentrate on the House, which is more difficult than the Senate. Charlie Cook and David Wasserman:

Today, the Cook Political Report counts just 33 [House] seats out of 435 as competitive, including 27 held by Republicans and six held by Democrats.
Still to win the House, the Democrats "would need to win as much as 55 percent of the popular vote, according to the Cook Political Report's David Wasserman". A ten percent difference is large, but has been done before.

Making this happen will depend on turnout and thus on enthusiasm and the hope to finally transferring the power back from the corporations to the citizens. This will not be easy, but easier with Sander.

If Congress does not change color, the climate "debate" suggests to me that reaching out, Clinton's strategy, does not help one thing. We have seen how well it worked for Obama. The only thing that helps is pressure from the electorate. Without sticks, the carrots do not work. Without disinfecting sunlight lighting the ugly spots and fear of being unseated nothing good will happen in Washington. If the Congressmen expect that their donors can no longer help (as much) them the next election, they may feel freer to actually do their job.

The problem that remains is getting money out of the media. Getting money out of politics partially solves that problem; the media gets a large part of that money from the political ads. I sometimes wonder if ads are there to influence consumers or the media. Getting all money out of the media is tough because the freedom of the press should not be endangered in the process. Any suggestions?

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Democracy is more important than climate change #WOLFPAC

National Review: Conservatives against Trump

Read more at: http://www.nationalreview.com/article/430126/donald-trump-conservatives-oppose-nomination

In 50-49 vote, US Senate says climate change not caused by humans

On Climate Questions, Only One Candidate Has All the Right Answers

Voter's Guide: How the Candidates Compare on Climate and EnergyVictor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-85664295598873052702016-01-21T17:00:00.000+00:002016-01-21T17:07:23.499+00:00Ars Technica: Thorough, not thoroughly fabricated: The truth about global temperature dataHow thermometer and satellite data is adjusted and why it must be done.
published on Ars Technica by Scott K. Johnson - Jan 21, 2016 4:30pm CET

"In June, NOAA employees altered temperature data to get politically correct results."

At least, that's what Congressman Lamar Smith (R-Tex.) alleged in a Washington Post letter to the editor last November. The op-ed was part of Smith's months-long campaign against NOAA climate scientists. Specifically, Smith was unhappy after an update to NOAA's global surface temperature dataset slightly increased the short-term warming trend since 1998. And being a man of action, Smith proceeded to give an anti-climate change stump speech at the Heartland Institute conference, request access to NOAA's data (which was already publicly available), and subpoena NOAA scientists for their e-mails.

Smith isn't the only politician who questions NOAA's results and integrity. During a recent hearing of the Senate Subcommittee on Space, Science, and Competitiveness, Senator Ted Cruz (R-Tex.) leveled similar accusations against the entire scientific endeavor of tracking Earth's temperature.

"I would note if you systematically add, adjust the numbers upwards for more recent temperatures, wouldn't that, by definition, produce a dataset that proves your global warming theory is correct? And the more you add, the more warming you can find, and you don't have to actually bother looking at what the thermometer says, you just add whatever number you want."

There are entire blogs dedicated to uncovering the conspiracy to alter the globe's temperature. The premise is as follows—through supposed "adjustments," nefarious scientists manipulate raw temperature measurements to create (or at least inflate) the warming trend. People who subscribe to such theories argue that the raw data is the true measurement; they treat the term "adjusted" like a synonym for "fudged."

Peter Thorne, a scientist at Maynooth University in Ireland who has worked with all sorts of global temperature datasets over his career, disagrees. "Find me a scientist who's involved in making measurements who says the original measurements are perfect, as are. It doesn't exist," he told Ars. "It's beyond a doubt that we have to—have to—do some analysis. We can't just take the data as a given."

Speaking of data, the latest datasets are in and 2015 is (as expected) officially the hottest year on record. It's the first year to hit 1°C above levels of the late 1800s. And to upend the inevitable backlash that news will receive (*spoiler alert*), using all the raw data without performing any analysis would actually produce the appearance of more warming since the start of records in the late 1800s.

We're just taking the temperature—how hard can it be?

So how do scientists build datasets that track the temperature of the entire globe? That story is defined by problems. On land, our data comes from weather stations, and there's a reason they are called weather stations rather than climate stations. They were built, operated, and maintained only to monitor daily weather, not to track gradual trends over decades. Lots of changes that can muck up the long-term record, like moving the weather station or swapping out its instruments, were made without hesitation in the past. Such actions simply didn't matter for weather measurements.

The impacts of those changes are mixed in with the climate signal you're after. And knowing that, it's hard to argue that you shouldn't work to remove the non-climatic factors. In fact, removing these sorts of background influences is a common task in science. As an incredibly simple example, chemists subtract the mass of the dish when measuring out material. For a more complicated one, we can look at water levels in groundwater wells. Automatic measurements are frequently collected using a pressure sensor suspended below the water level. Because the sensor feels changes in atmospheric pressure as well as water level, a second device near the top of the well just measures atmospheric pressure so daily weather changes can be subtracted out.

If you don't make these sorts of adjustments, you'd simply be stuck using a record you know is wrong.

You can continue reading at Ars Technica. The article still explains several reasons for inhomogeneities in the temperature observations, how they are removed with statistical homogenization methods, how these methods have been validated, the uncertainties in the sea surface temperature and satellite estimates of the tropospheric temperature and why it is so hard to get the right. It finishes with the harassment campaign against NOAA of Lamar Smith because of a minimal update.

Enjoy and bookmark, it is a very thorough and accessible overview.
Victor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-64549472017282176442016-01-16T07:56:00.000+00:002016-01-16T07:56:01.700+00:00The transition to automatic weather stations. We'd better study it now.This is a POST post.

The Parallel Observations Science Team (POST) is looking across the world for climate records which simultaneously measure temperature, precipitation and other climate variables with a conventional sensor (for example, a thermometer) and modern automatic equipment. You may wonder why we take the painstaking effort of locating and studying these records. The answer is easy: the transition from manual to automated records has an effect on climate series and the analysis we do over them.

In the last decades we have seen a major transition of the climate monitoring networks from conventional manual observations to automatic weather stations. It is recommended to compare these instruments before the substitution is effective with side by side measurements, which we call parallel measurements. Climatologists have also set up many longer experimental parallel measurements. They tell us that in most cases both sensors do not measure the same temperature or collect the same amount of precipitation. A different temperature is not only due to the change of the sensor itself, but automatic weather stations also often use a different, much smaller, screen to protect the sensor from the sun and the weather. Often the introduction of automatic weather stations is accompanied by a change in location and siting quality.

From studies of single temperature networks that made such a transition we know that it can cause large jumps; the observed temperatures at a station can go up or down by as much as 1°C. Thus potentially this transition can bias temperature trends considerably. We are now trying to build a global dataset with parallel measurements to be able to quantify how much the transition to automatic weather stations influences the global mean temperature estimates used to study global warming.

Temperature
This study is led by Enric Aguilar and the preliminary results below were presented at the Data Management Workshop in Saint Gallen, Switzerland last November. We are still in the process of building up our dataset. Up to now we have data from 10 countries: Argentina (9 pairs), Australia (13), Brazil (4), Israel (5), Kyrgyzstan (1), Peru (31), Slovenia (3), Spain (46), Sweden (8), USA (6); see map below.

Global map in which we only display the 10 countries for which we have data. The left map is for the maximum temperature (TX) and the right for the minimum temperature (TN). Blue dots mean that the automatic weather station (AWS) measures cooler temperatures than the conventional observation, red dots mean the AWS is warmer. The size indicates how large the difference is, open circles are for statistically not significant differences.

The impact of the automation can be better assessed in the box plots below.

The bias of the individual pairs are shown as dots and summarized per country with box plots. For countries with only a few pairs the boxplots should be taken with a grain of salt. Negative values mean that the automatic weather stations are cooler. We have data for Argentina (AR), Australia (AU), Brazil (BR), Spain (ES), Israel (IL), Kyrgyzstan (KG), Peru (PE), Sweden (SE), Slovenia (SI) and the USA (US). Panels show the maximum temperature (TX), minimum temperature (TN), mean temperature (TM) and Diurnal temperature range (DTR, TX-TN).

On average there are no real biases in this dataset. However, if you remove Peru (PE) the differences in the mean temperature are either small or negative. That one country is so important shows that our dataset is currently too small.

To interpret the results we need to look at the main causes for the differences. Important reasons are that Stevenson screens can heat up in the sun on calm days, while automatic sensors are sometimes ventilated. The automatic sensors are, furthermore, typically smaller and thus less affected by direct radiation hitting them than thermometers. On the other hand, in case of conventional observation, the maintenance of the Stevenson screens—cleaning and painting—and detection of other problems may be easier because they have to be visited daily. There are concerns that plastic screens get more grey and heat more in the sun. Stevenson screens have more thermal inertia, they smooth fast temperature fluctuations, and will thus show lower highs and higher lows.

Also the location often changes with the installation of automatic weather stations. America was one of the early adopters. The US National Weather Service installed analogue semi-automatic equipment (MMTS) that did not allow for long cables between the sensor and the display inside a building. Furthermore, the technicians only had one day per station and as a consequence many of the MMTS systems were badly sited. Nowadays technology has advanced a lot and made it easier to find good sites for weather stations. This is maybe even easier now than it used to be for manual observations because modern communication is digital and if necessary uses radio making distance much less a concern. The instruments can be powered by batteries, solar or wind, which frees them from the electricity grid. Some instruments store years of data and need just batteries.

In the analysis we thus need to consider whether the automatic sensors are placed in Stevenson screens and whether the automatic weather station is at the same location. Where the screen and the location did not change (Israel and Slovenia), the temperature jumps are small. Whether the automatic weather station reduces radiation errors by mechanical ventilation is likely also important. Because of these different categories, the number of datasets needed to get a good global estimate becomes larger. Up to now, these factors seem to be more important than the climate.

Precipitation
For most of these countries we also have parallel measurements for precipitation. The figure below was made by Petr Stepanek, who leads this part of the study.

Boxplots for the differences in monthly precipitation sums due to automation. Positive values mean that the manual observations record more precipitation. Countries are: Argentina (AG), Brazil (BR), The Check Republic (CZ), Israel (IS), Kyrgyzstan (KG), Peru (PE), Sweden (SN), Spain (SP) and the USA (US). The width of the boxplots corresponds to the size of the given dataset.

For most countries the automatic weather stations record less precipitation. This is mainly due to smaller amounts of snow during the winter. Observers often put a snow cross in the gauge in winter to make it harder for snow to blow out of it again. Observers simply melt the snow gathered in a pot to measure precipitation, while early automatic weather stations did not work well with snow and sticky snow piling up in the gauge may not be noticed. These problems can be solved by heating the gauge, but unfortunately the heater can also increase the amount of precipitation that evaporates before it could be registered. Such problems are known and more modern rain gauges use different designs and likely have a smaller bias again.

Database with parallel data
The above results are very preliminary, but we wanted to show the promise of a global dataset with parallel data to study biases in the climate record due to changes in the observing practises. To proceed we need more datasets and better information on how the measurements were performed to make this study more solid.

In future we also want to look more at how the variability around the mean is changing. We expect that changes in monitoring practices have a strong influence on the tails of the distribution and thus on estimates of changes in extreme weather. Parallel data offer a unique opportunity to study this otherwise hard problem.

Most of the current data comes from Europe and South America. If you know of any parallel datasets especially from Africa or Asia, please let us know. Up to now, the main difficulty for this study is to find the persons who know where the data is. Fortunately, data policies do not seem to be a problem. Parallel data is mostly seen as experimental data. In some cases we "only" got a few years of data from a longer dataset, which would otherwise be seen as operational data.

We would like to publish the dataset after publishing our papers about it. Again this does not seem to lead to larger problems; sometimes people prefer to first publish an article themselves, which causes some delays, and sometimes we cannot publish the daily data itself, but "only" monthly averages and extreme value indices, this makes the results less transparent, but these summary values contain most of the information.

Knowledge of the observing practices is very important in the analysis. Thus everyone who contributes data is invited to help in the analysis of the data and co-author our first paper(s). Our studies are focused on global results, but we will also provide everyone with results for their own dataset to gain a better insight into their data.

Most climate scientists would agree that it is important to understand the impact of automation on our records. So does the World Meteorological Organization. In case it helps you to convince your boss: the Parallel Observations Science Team is part of the International Surface Temperature Initiative (ISTI). It is endorsed by the Task Team on Homogenization (TT-HOM) of the World Meteorological Organization (WMO).

We expect that this endorsement and our efforts to raise awareness about our goals and their importance will help us to locate and study parallel observations from other parts of the world, especially Africa and Asia. We also expect to be able to get more data from Europe; the regional association for Europe of the WMO has designated the transition to automatic weather stations as one of its priorities and is helping us to get access to more data. We want to have datasets for all over the world to be able to assess whether the station settings (sensors, screens, data quality, etc.) have an impact, but also to understand if different climates produce different biases.

If you would like to collaborate or have information, please contact me.

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Related reading
The ISTI has made a series of brochures on POST in English, Spanish, French and German. If anyone is able to make further translations, that would be highly appreciated.

Parallel Observations Science Team of the International Surface Temperature Initiative.

Irrigation and paint as reasons for a cooling bias

Temperature trend biases due to urbanization and siting quality changes

Changes in screen design leading to temperature trend biases

Temperature bias from the village heat islandVictor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-22123749145741317832016-01-07T19:44:00.000+00:002016-01-07T19:48:33.665+00:00Interesting EGU sessions and conferences in 2016Just a quick post to advertise some interesting (new) EGU sessions and conferences this year.

At EGU there will be four interesting sessions that fit to the topic of this blog. The abstract deadline is already next Wednesday, the 13th of January at 13 CET. The conference is half April in Vienna, Austria.

Climate Data Homogenization and Climate Trend and Variability Assessment
The main session for all things homogenization.

Taking the temperature of Earth: Variability, trends and applications of observed surface temperature data across all domains of Earth's surface
On measuring temperatures: surface itself (skin temperature), surface air over land, see surface temperature, marine air temperature. With a large range of observational methods, including satellites.

Transition into the Anthropocene-causes of climate change in the 19th and 20th century
A session on climate change in the very challenging early instrumental period, where the variability of station observations has large uncertainties. This session is new, as far as I can see. But EGU is big, I hope I did not miss it last year.

Historical Climatology
Even further back in time is the session on historical climatology where people mainly look at non-instrumental evidence of climatic changes and their importance for human society.

Also this year there will be an EMS conference. Like every second year, in 2016 it will be combined with the European Conference on Applied Climatology (ECAC) and thus has more climate goodies than average. This year it will be half September in Trieste, Italy.

The main session for fans of homogenization is: Climate monitoring; data rescue, management, quality and homogenization.

Fans of variability may like the session on Spatial Climatology.

A conference I really enjoyed the last two times I was there is the International Meeting on Statistical Climatology. Its audience is half statisticians and half climatologists. Everyone loves beautiful statistical and methodological questions. Great!! This year is will be in June in Canmore, Alberta, Canada.

It also has a session on homogenization: Climate data homogenization and climate trends/variability assessment.

If I missed any interesting sessions or conferences do let us know in the comments (also if it is your own).

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Descriptions
Climate Data Homogenization and Climate Trend and Variability Assessment
Convener: Xiaolan L. Wang
Co-Conveners: Enric Aguilar, Rob Roebeling, and Petr Stepanek

The accuracy and homogeneity of climate data are indispensable for many aspects of climate research. In particular, a realistic and reliable assessment of historical climate trends and variability is hardly possible without a long-term, homogeneous time series of climate data. Accurate and homogeneous climate data are also indispensable for the calculation of related statistics that are needed and used to define the state of climate and climate extremes. Unfortunately, many kinds of changes (such as instrument and/or observer changes, and changes in station location and environment, observing practices and procedure, etc.) that took place in the period of data record could cause non-climatic changes (artificial shifts) in the data time series. Such artificial shifts could have huge impacts on the results of climate analysis, especially those of climate trend analysis. Therefore, artificial changes shall be eliminated, to the extent possible, from the time series prior to its application, especially its application in climate trends assessment.

This session calls for contributions that are related to bias correction and homogenization of climate data, including bias correction and validation of various climate data from satellite observations and from GCM and RCM simulations, as well as quality control/assurance of observations of various variables in the Earth system. It also calls for contributions that use high quality, homogeneous climate data to assess climate trends and variability and to analyze climate extremes, including the use of bias-corrected GCM or RCM simulations in statistical downscaling. This session will include studies that inter-compare different techniques and/or propose new techniques/algorithms for bias-correction and homogenization of climate data, for assessing climate trends and variability and analysis of climate extremes (including all aspects of time series analysis), as well as studies that explore the applicability of techniques/algorithms to data of different temporal resolutions (annual, monthly, daily) and of different climate elements (temperature, precipitation, pressure, wind, etc) from different observing network characteristics/densities, including various satellite observing systems.

Transition into the Anthropocene-causes of climate change in the 19th and 20th century
Convener: Gabriele Hegerl
Co-Convener: Stefan Brönnimann

This session focuses on the long view of climate variability and change as available from long records, reconstructions, reanalysis efforts and modelling, and we welcome analysis of temperature, precipitation, extreme events, sea ice, and ocean. Contributions are welcome that evaluate changes from historical data on the scale of large regions to the globe, analyse particular unusual climatic events, estimate interdecadal climate variability and climate system properties from long records, attribute causes to early observed changes and model or data assimilate this period. We anticipate that bringing observational, modelling and analysis results together will improve understanding and prediction of the interplay of climate variability and change. "

Taking the temperature of Earth: Variability, trends and applications of observed surface temperature data across all domains of Earth's surface
See also their homepage.
Convener: Darren Ghent
Co-Conveners: Nick Rayner, Stephan Matthiesen, Simon Hook, G.C. Hulley, Janette Bessembinder

Surface temperature (ST) is a critical variable for studying the energy and water balances of the Earth surface, and underpinning many aspects of climate research and services. The overarching motivation for this session is the need for better understanding of in-situ measurements and satellite observations to quantify ST. The term "surface temperature" encompasses several distinct temperatures that differently characterize even a single place and time on Earth's surface, as well as encompassing different domains of Earth's surface (surface air, sea, land, lakes and ice). Different surface temperatures play inter-connected yet distinct roles in the Earth's surface system, and are observed with different complementary techniques.

The EarthTemp network was established in 2012 to stimulate new international collaboration in measuring and better understanding ST across all domains of the Earth's surface including air, land, sea, lakes, ice. New and existing international projects and products have evolved from network collaboration (e.g. ESA Climate Change Initiative SST project, EUSTACE, FIDUCEO, International Surface Temperature Initiative, ESA GlobTemperature, HadISST, CRUTEM and HadCRUT). Knowledge gained during this EarthTemp session will be documented and published as part of the user requirements exercises for such projects and will thus benefit the wider community. A focus of this session is the use of ST's for assessing variability and long-term trends in the Earth system. In addition there will be opportunity for users of surface temperature over any surface of Earth on all space and timescales to showcase their use of the data and their results, to learn from each others' practice and to communicate their needs for improvements to developers of surface temperature products. Suggested contributions can include, but are not limited to, topics like:

* The application of ST in climate science
* How to improve remote sensing of ST in different environments
* Challenges from changes of in-situ observing networks over time
* Current understanding of how different types of ST inter-¬relate
* Nature of errors and uncertainties in ST observations
* Mutual/integrated quality control between satellite and in-situ observing systems.
* What do users of surface temperature data require in practical applications?

Historical Climatology
Convener: Stefan Grab
Co-Conveners: Rudolf Brazdil, David Nash, Georgina Endfield

Historical Climatology has gained momentum and worldwide recognition over the last couple of decades, particularly in the light of rapid global climate and environmental change. It is now well recognized that in order to better project future changes and be prepared for those changes, one should look to, and learn from, the past. To this end, historical documentary sources, in many cases spanning back several hundred years and far beyond instrumental weather records, offer detailed descriptive (qualitative) accounts on past weather and climate. Such documentary sources typically include, amongst others: weather diaries, ship log books, missionary reports and letters, historical newspapers, chronicles, accounting and government documents etc. Such proxies have particular advantages in that they in most cases offer details on the specific timing and placement of an event. In addition, valuable insights may be gained on environmental and anthropogenic consequences and responses to specific weather events and climate anomaly. Similarly, oral history records, based on people's personal accounts and experiences of past weather offer important insights on perceptions of climate change, and details on past and sometimes 'forgotten' weather events and their consequences.

This session welcomes all studies using documentary, historical instrumental and oral history based approaches to: produce historical climate chronologies (multi-decadal to centennial scale), gain insights into past climatic periods or specific weather events, detail environmental and human consequences to past climate and weather, share people's experiences and perceptions of past climate, weather events and climate change, and reflect on lessons learnt (coping and adaptation) from past climate and weather events. Whilst welcoming contributions from all global regions, we particularly appeal for contributions from Asia and the Middle East.

Climate monitoring; data rescue, management, quality and homogenization
Convener: Manola Brunet-India
Co-Conveners: Hermann Mächel, Victor Venema, Ingeborg Auer, Dan Hollis

Robust and reliable climatic studies, particularly those assessments dealing with climate variability and change, greatly depend on availability and accessibility to high-quality/high-resolution and long-term instrumental climate data. At present, a restricted availability and accessibility to long-term and high-quality climate records and datasets is still limiting our ability to better understand, detect, predict and respond to climate variability and change at lower spatial scales than global. In addition, the need for providing reliable, opportune and timely climate services deeply relies on the availability and accessibility to high-quality and high-resolution climate data, which also requires further research and innovative applications in the areas of data rescue techniques and procedures, data management systems, climate monitoring, climate time-series quality control and homogenisation.

In this session, we welcome contributions (oral and poster) in the following major topics:

• Climate monitoring , including early warning systems and improvements in the quality of the observational meteorological networks

• More efficient transfer of the data rescued into the digital format by means of improving the current state-of-the-art on image enhancement, image segmentation and post-correction techniques, innovating on adaptive Optical Character Recognition and Speech Recognition technologies and their application to transfer data, defining best practices about the operational context for digitisation, improving techniques for inventorying, organising, identifying and validating the data rescued, exploring crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records

• Climate data and metadata processing, including climate data flow management systems, from improved database models to better data extraction, development of relational metadata databases and data exchange platforms and networks interoperability

• Innovative, improved and extended climate data quality controls (QC), including both near real-time and time-series QCs: from gross-errors and tolerance checks to temporal and spatial coherence tests, statistical derivation and machine learning of QC rules, and extending tailored QC application to monthly, daily and sub-daily data and to all essential climate variables

• Improvements to the current state-of-the-art of climate data homogeneity and homogenisation methods, including methods intercomparison and evaluation, along with other topics such as climate time-series inhomogeneities detection and correction techniques/algorithms (either absolute or relative approaches), using parallel measurements to study inhomogeneities and extending approaches to detect/adjust monthly and, especially, daily and sub-daily time-series and to homogenise all essential climate variables

• Fostering evaluation of the uncertainty budget in reconstructed time-series, including the influence of the various data processes steps, and analytical work and numerical estimates using realistic benchmarking datasets

Spatial Climatology
Convener: Ole Einar Tveito
Co-Conveners: Mojca Dolinar, Christoph Frei

Gridded representation of past and future weather and climate with high spatial and temporal resolution is getting more and more important for assessing the variability of and impact of weather and climate on various environmental and social phenomena. They are also indispensable as validation and calibration input for climate models. This increased demand requires new efficient methods and approaches for estimating spatially distributed climate data as well as new efficient applications for managing and analyzing climatological and meteorological information at different temporal and spatial scales. This session addresses topics related to generation and application of gridded climate data with an emphasis on statistical methods for spatial analysis and spatial interpolation applied on observational data.

An important aspect in this respect is the creation and further use of reference climatologies. The new figures calculated for the latest normal period 1981-2010 are now recommended as reference period for assessments of regional and local climatologies. For this period new observation types (e.g. satellite and radar data) are available, and contributions taking advantage of multiple data sources are encouraged.

Spatial analysis using e.g. GIS is a very strong tool for visualizing and disseminating climate information. Examples showing developments, application and products of such analysis for climate services are particularly welcome.

The session intends to bring together experts, scientists and other interested people analyzing spatio-temporal characteristics of climatological elements, including spatial interpolation and GIS modeling within meteorology, climatology and other related environmental sciences.

Climate data homogenization and climate trends/variability assessment
Convener: Xiaolan Wang, Lucie Vincent, Markus Donat and Lisa Alexander

The accuracy and homogeneity of climate data are indispensable for many aspects of climate research. In particular, a realistic and reliable assessment of historical climate trends and variability is hardly possible without a long-term, homogeneous time series of climate data. Accurate and homogeneous climate data are also indispensable for the calculation of related statistics that are needed and used to define the state of climate and climate extremes. Unfortunately, many kinds of changes (such as instrument and/or observer changes, and changes in station location and exposure, observing practices and procedure, etc.) that took place in the period of data record could cause non-climatic sudden changes (artificial shifts) in the data time series. Such artificial shifts could have huge impacts on the results of climate analysis, especially those of climate trend analysis. Therefore, artificial changes shall be eliminated, to the extent possible, from the time series prior to its application, especially its application in climate trends assessment.

This session calls for contributions that are related to bias correction and homogenization of climate data, including bias correction and validation of various climate data from satellite observations and from GCM and RCM simulations, as well as quality control/assurance of observations of various variables in the Earth system. It also calls for contributions that use high quality, homogeneous climate data to assess climate trends and variability and to analyze climate extremes, including the use of bias-corrected GCM or RCM simulations in statistical downscaling.Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-89130960852449299722016-01-03T17:32:00.001+00:002016-01-10T18:36:28.674+00:00Harassment and that powerful male PIAfter decades of sexual harassment astronomer Geoffrey Marcy has been fired. What surprised me reading many comments was how invincible he was thought to be. I am not nearly as powerful, but can maybe still offer a view from the other side. My impression is naturally subjective and I work in a different field and in another country, but I wonder if Geoffrey Marcy was really that invincible. Unfortunately, people having that impression is partially a self-fulfilling prophesy. Making this picture more realistic may thus help reduce harassment, which is why I wanted to write about this.

Your scientific network is crucial
This impression of invincibility may partially stem from the lone genius syndrome in the media, which is not even right for Einstein. To make a story more pleasant to read journalists like to personalize everything and simplify their story enormously, which leaves little space for history and multiple contributors.

More realistic is that it is very hard to do science in isolation. Feedback is very important. If you only get feedback after publication, you will progress very slowly. Collaboration is also important to get new skills into a study. Understanding new methods and datasets takes a long time. Without collaboration you would have to invest a lot of time and still have a high risk of making rookie mistakes. Without collaboration your productivity and quality will be much lower.

Also the peer review of research proposal and scientific articles makes it important to keep friendly relationships with most colleagues. A research proposal always has weaknesses; if everything was clear already it would not be a science project. Even if not, a reviewer can always claim a proposal is too ambitious or not ambitious enough. Judging a proposal to be "very good" rather than "excellent" is always possible and reduces the funding probability a lot given the low percentage that is funded.

Negotiating conflicts
Due to the importance of collaboration and good relationships open conflicts are rare. There are open debates about specific scientific questions; questions that have an answer and enhance your scientific reputation if you are right. Vaguer questions that do not have an answer are not worth an open conflict that may damage relationships.

That normally most scientists are on talking terms with each other may give outsiders and young scientists the feeling that the seniors are one solid block. This is certainly not the case. Inside you can see the cracks. Most conflicts between groups are for long-forgotten historical reasons. Most conflicts between people because their personalities do not match.

(Hardly ever do you see conflicts for political reasons. I just send an email to Iran and got a friendly answer. My colleague from Serbia still talks to me after we bombed his country based on flimsy evidence. As far as I can judge the climate "debate" is nearly never a source of conflict; the quality of someone work naturally does influence collaboration decisions.)

Conflicts in science are not resolved by a strong man and presented at a press conference, rather it is a continual collective negotiation. That makes the conflicts and the influence of evidence of harassment less visible.

Two examples: If I had a preliminary result I was not sure about yet and I presented it at a conference to get feedback and thus had added a no-tweet sign to my talk. In this hypothetical case, our friend of the freedom to tweet, Gavin Schmidt, could naturally tweet about it anyway. I would then see that as a breach of trust and would only collaborate with Gavin if really no other scientist had the competencies I would need for a specific project. Gavin likely could not care less about a dwarf from an adjacent field. More important is how the scientific community sees the situation. If they see my request as reasonable, the tweet would hurt Gavin's reputation. For something as small as this he will not lose his post as director of GISS, but people might be a little more reluctant to collaborate and less willing to share preliminary results with him lest he may tweet it. His closest friends may communicate that to Gavin in private; most will just draw their conclusions and not say anything. That makes those close friends so important, because it is better to know.

That Gavin has tweeted that he does not agree with a conference presentation of Peter Wadhams may cool their relationship. However, there was no breach of trust because Wadhams had said similar things in public before. Thus Wadhams' unreasonable complaints about Gavin's tweet probably only lead to further reputation losses for Wadhams.

One of the famous quotes from the stolen emails of climategate is that Phil Jones writes that he wants to keep two articles out of the IPCC report even if he has to redefine what peer reviewed means. What the mitigation sceptics normally do not say, because that does not fit their narrative, is that these two articles were referenced and discussed in the IPCC report.

Given that there are no further climategate emails on this topic I assume that Phil himself thought better of it later. In this case there is no loss of reputation; scientists are also humans, he corrected his initial error and no damage was done. If not his colleagues made clear what they thought of this and Phil decided that this was not worth a loss of reputation. If the article in question was really bad, the loss of reputation would have been limited; if the article was half-way decent the loss of reputation would have been large.

Summarizing, while visually maybe nothing seems to happen and it is difficult to fire faculty members, spreading evidence of harassment does result in reputation loss, reductions in collaboration and productivity and the ability to do the research you would like to do.

Young and female scientists
Young scientists and female scientists are also more important than they may realise. When you are young, doing science is centred around writing articles and somewhat later also on getting funding for your own (small) project. The next step in a scientific career is leading larger projects. At least in Germany and the EU, it is impossible to get funding for such projects without involving female scientists. If you lose support of (nearly) all female colleagues, your career stops right there.

The final step in a scientific career is getting your people into permanent positions. This stabilises your power and somehow professors tend to think that their topic is so important that it should grow at the expense of others. For this, and for the successful completion of scientific projects, you need to be a good talent scout and find young people you can build up.

When I studied physics in Groningen, rumour had it that there were so many professors that they were fighting to be allowed to give lectures and thus get access to the students. That way you can interest the students for your topic and assess who are the good ones. Also companies often pay for professorships to be able to search for talent and lure the good students to work for them.

Binding good students to your group with interesting bachelor and master projects and with student help positions gives professors the possibility to interest the good ones to pursue a PhD in their group. The best of those will hopefully become important scientists and grow the field.

Jim C. Hines, @jimchines

Gossip and punishing bad behaviour
When I was a PhD student, two American scientists misbehaved, I made sure that everyone knew, especially all Americans I knew. When they did it to you, they likely did it to others. Multiple similar bad stories can do real damage to a scientists reputation and their work.

Even if your case is not strong or big enough to go to the police, talk to other female scientists. There are bound to be more victims, which makes the case stronger and makes it more important that the harassment stops. Talk to other male scientists, you may be surprised how many will support you. I know of one multiple harassment case. Too small to be a legal case, but once the leaders of that community heard about it, they talked to the guy, made it clear that any future complaint would have severe consequences and I did not hear about new complaints.

Fighting back is necessary; make sure people know what happened. If bad behaviour goes unpunished it will proliferate. Economic games have shown that collaborative behaviour will only prevail if cooperative people are willing to punish bad behaviour. It also showed that people from all over the world are willing to punish bad behaviour at a cost and derive pleasure from that. Gossip and punishing bad behaviour are the two main pillars of our human civilization.

Takeaways
The media likes to portray scientists as solitary geniuses. In reality scientists need to be good collaborators to become heroes in their field. Many young scientists think that senior scientists are one big pack and may feel that it would not help to talk to senior scientists about the poor behaviour of another. But this is not true either: there are conflicts, they are just not very visible.

It would be good if (female) scientists would speak about (sexual) harassment at work and this likely does more damage than they think and see. I know actually doing this is difficult and there are unfortunately likely repercussions if you do so openly. In addition much should change in the organization of science and in society to reduce harassment. I just want to say: you have more power than you may think.

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Related reading
How to end sexual harassment in astronomy on changes in the organization that are needed.

Nature: Scientific groups revisit sexual-harassment policies. Interesting: The American Geophysical Union did not receive any harassment complaints since it started tracking them 5 years ago. It could be underreporting, I hope it means that the situation is not as bad in the geosciences.

Nature: Science and sexism: In the eye of the Twitterstorm. Social media is shaking up how scientists talk about gender issues.

Astropixy: How not to "deal with it". A example of harassment by a non-famous astronomer, makes clear that unfortunately getting rid of a faculty member is hard.

Nature Blog. The faculty series: Learning to collaborate. "Collaborations are the key to success in modern scientific research, says Michelle Ma."

The ultimatum game, a key experiment showing intrinsic fairness and altruism among strangers

*PI: Principle Investigator. The leader of a scientific project and often a group.Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-19726733169547781512015-12-18T03:31:00.000+00:002015-12-26T02:02:55.368+00:00Anthony Watts at AGU2015: Comparison of Temperature Trends Using an Unperturbed Subset of The U.S. Historical Climatology Network[UPDATE. I will never understand how HotWhopper writes such understandable articles so fast, but it might be best to read the HotWhopper introduction first.]

Remember the Watts et al. manuscript in 2012? Anthony Watts putting his blog on hold to urgently finish his draft? This study is now a poster at the AGU conference and Watts promises to submit it soon to an undisclosed journal.

On first sight, the study now has a higher technical quality and some problems have been solved. The two key weakness are, however, not discussed in the press release to the poster. This is strange. I have had long discussions with second author Evan Jones about this. Scientists (real sceptics) have to be critical about their own work. You would expect a scientist to focus a large part of a study on any weaknesses, if possible try to show they probably do not matter or else at least honestly confront the weaknesses, rather than simply ignore them.

Watts et al. is about the immediate surrounding, also called micro-siting, of weather stations that measure the surface air temperature. The American weather stations have been assessed for their quality in five categories by volunteers of the blog WUWT. Watts and colleagues call the two best categories "compliant" and the three worst ones "non-compliant". For these two categories they then compare the average temperature signal for the 30-year period 1979 – 2008.

An important problem of the 2012 version of this study was that historical records typically also contain temperature changes because the method of observation has changed. An important change in the USA is the time of observation bias. In the past observations were more often made in the afternoon than in the morning. Morning measurements results in somewhat lower temperatures. This change in the time of observation creates a bias of about 0.2°C per century and was ignored in the 2012 study. Also the auditor, Steve McIntyre, who was then a co-author admitted this was an error. This problem is now fixed; stations with a change in the time of observation have been removed from the study.

A Stevenson screen.
Another important observational change in the USA is the change of the screen used to protect the thermometer from the sun. In the past, so-called Cotton Region Shelters (CRS) or Stevenson screens were used, nowadays more and more automatic weather stations (AWS) are used.

A much used type of AWS in the USA is the MMTS. America was one of the first countries to automatize its network, with then analogue equipment that did not allow for long cables between the sensor and the display, which is installed inside a building. Furthermore, the technicians only had one day per station and as a consequence many of the MMTS systems were badly sited. Although they are badly sited, these MMTS system typically measure 0.2°C 0.15°C cooler temperatures. The size of the cooling has been estimated by comparing a station with such a change with a neighbouring station where nothing happens. Because both stations experience about the same weather, the difference signal shows the jump in the mean temperature more clearly.

Two weaknesses
Weakness 1 is that the authors only know the siting quality at the end of the period. Stations in the compliant categories may have been in less well sited earlier on, while stations in the non-compliant categories may have been better sited before.

pete:
Someone has a weather station in a parking lot. Noticing their error, they move the station to a field, creating a great big cooling-bias inhomogeneity. Watts comes along, and seeing the station correctly set up says: this station is sited correctly, and therefore the raw data will provide a reliable trend estimate.
The study tries to reduce this problem by creating a subset of stations that is unperturbed by Time of Observation changes, station moves, or rating changes. At least according to the station history (metadata). The problem is that metadata is never perfect.

The scientists working on homogenization thus advise to always also detect changes in the observational methods (inhomogeneities) by comparing a station to its neighbours. I have told Evan Jones how important this is, but they refuse to use homogenization methods because they feel homogenization does not work. In a scientific paper, they will have to provide evidence to explain why they reject an established method that could ameliorate a serious problem with their study. The irony is that the MMTS adjustments, which the Watts et al. study does use, depend on the same principle.

Weakness 2 is that the result is purely statistical and that no physical explanation is provided for the result. It is clear that bad micro-siting will lead to a temperature bias, but this does not affect the trend, while the study shows a difference in trend. I would not know how bad or good constant siting quality would change a trend. The press release also does not offer an explanation.

What makes this trend difference even more mysterious, if it were real, is that it mainly happens in the 1980s and 1990s, but has stopped in the last decade. See the graph below showing the trend for compliant (blue) and non-compliant stations (orange).

[UPDATE. The beginning period in which the difference builds up and that since 1996 the trends for "compliant" and "non-compliant" stations is the same is better seen in the graph below computed from the data in the above figure digitized by George Bailley. (No idea what the unit of the y-axis is on either of these graphs. Maybe 0.001°C.)

]

That the Watts phenomenon has stopped is also suggested by a comparison of the standard USA climate network (USHCN) and a new climate-quality network with perfect siting (USCRN) shown below. The pristine network even warms a little more. (Too little to be interpreted.)

While I am unable to see a natural explanation for the trend difference, that the difference is mainly seen in the first two decades fits to the hypothesis that the siting quality of the compliant stations was worse in the past: that in the past these stations were less compliant and a little too warm. The further you go back in time, the more likely it becomes that some change has happened. And the further you go back in time, the more likely it is that this change is no longer known.

six key findings
Below I have quoted the six key findings of Watts et al. (2015) according to the press release.

1. Comprehensive and detailed evaluation of station metadata, on-site station photography, satellite and aerial imaging, street level Google Earth imagery, and curator interviews have yielded a well-distributed 410 station subset of the 1218 station USHCN network that is unperturbed by Time of Observation changes, station moves, or rating changes, and a complete or mostly complete 30-year dataset. It must be emphasized that the perturbed stations dropped from the USHCN set show significantly lower trends than those retained in the sample, both for well and poorly sited station sets.

The temperature network in the USA has on average one detectable break every 15 years (and a few more breaks that are too small to be detected, but can still influence the result). The 30-year period studied should thus contain on average 2 breaks and likely only 12.6% of the stations do not have a break (154 stations). According to Watts et al. 410 of 1218 stations have no break. 256 stations (more than half their "unperturbed" dataset) thus likely have a break that Watts et al. did not find.

That the "perturbed" stations have a smaller trend than the "unperturbed" stations confirms what we know: that in the USA the inhomogeneities have a cooling bias. In the "raw" data the "unperturbed" subset has a trend in the mean temperature of 0.204°C per decade; see table below. In the "perturbed" subset the trend is only 0.126°C per decade. That is a whooping cooling difference of 0.2°C over this period.

Table 1 of Watts et al. (2015)

2. Bias at the microsite level (the immediate environment of the sensor) in the unperturbed subset of USHCN stations has a significant effect on the mean temperature (Tmean) trend. Well sited stations show significantly less warming from 1979 – 2008. These differences are significant in Tmean, and most pronounced in the minimum temperature data (Tmin). (Figure 3 and Table 1 [shown above])

The stronger trend difference for the minimum temperature would also need an explanation.

3. Equipment bias (CRS [Cotton Region Shelter] v. MMTS [Automatic Weather station] stations) in the unperturbed subset of USHCN stations has a significant effect on the mean temperature (Tmean) trend when CRS stations are compared with MMTS stations. MMTS stations show significantly less warming than CRS stations from 1979 – 2008. (Table 1 [shown above]) These differences are significant in Tmean (even after upward adjustment for MMTS conversion) and most pronounced in the maximum temperature data (Tmax).

The trend for the stations that use a Cotton Region Shelter is 0.3°C per decade. That is large and should be studied. This was the typical shelter in the past. Thus we can be quite sure that in these cases the shelter did not change, but there could naturally have been other changes.

4. The 30-year Tmean temperature trend of unperturbed, well sited stations is significantly lower than the Tmean temperature trend of NOAA/NCDC official adjusted homogenized surface temperature record for all 1218 USHCN stations.

It is natural that the trend in the raw data is smaller than the trend in the adjusted data. Mainly for the above mentioned reasons (TOBS and MMTS) the biases in the USA are large compared to the rest of the world and the trend in the USA is adjusted 0.4°C per century upwards.

5. We believe the NOAA/NCDC homogenization adjustment causes well sited stations to be adjusted upwards to match the trends of poorly sited stations.

Well, they already wrote "we believe". There is no evidence for this claim.

6. The data suggests that the divergence between well and poorly sited stations is gradual, not a result of spurious step change due to poor metadata.

The year to year variations in a single station series is about 1°C. I am not sure whether one would see whether the inhomogeneity is one or more step changes or a gradual change.

Review
If I were reviewer of this manuscript, I would ask about some choices that seem arbitrary and I would like to know whether they matter. For example using the period 1979 – 2008 and not continuing the data to 2015. It is fine to also show data until 2008 for better comparisons with earlier papers, but stopping 7 years earlier is suspicious. Also the choice to drop stations with TOBS changes, but to correct stations with MMTS changes sounds strange. It would be of interest whether any of the other 3 options show different results. Anomalies should be computed over a period, not relative to the starting year.

I hope that Anthony Watts and Evan M. Jones find the above comments useful. Jones wrote earlier this year:

Oh, a shout-out to Dr. Venema, one of the earlier critics of Watts et al. (2012) who pointed out to us things that needed to be accounted for, such as TOBS, a stricter hand on station moves, and MMTS equipment conversion.

Note to Anthony: In terms of reasonable discussion, VV is way up there. He actually has helped to point us in a better direction. I think both Victor Venema and William Connolley should get a hat-tip in the paper (if they would accept it!) because their well considered criticism was of such great help to us over the months since the 2012 release. It was just the way science is supposed to be, like you read about in books.
Watts wrote in the side notes to his press release:

Even input from openly hostile professional people, such as Victor Venema, have been highly useful, and I thank him for it.
Glad to have been of help. I do not recall having been "openly hostile" to this study. It would be hard to come to a positive judgement of the quality of the blog posts at WUWT, whether they are from the pathological misquoter Monckton or greenhouse effect denier Tim Ball.

However, it is always great when people contribute to the scientific literature. When the quality of their work meets the scientific standard, it does not matter what their motivation is, then science can learn something. The surface stations project is useful to learn more about the quality of the measurements; also for trend studies if continued over the coming decades.

Comparison of Temperature Trends Using an Unperturbed Subset of The U.S. Historical Climatology Network
Anthony Watts, Evan Jones, John Nielsen-Gammon and John Christy

Abstract. Climate observations are affected by variations in land use and land cover at all scales, including the microscale. A 410-station subset of U.S. Historical Climatology Network (version 2.5) stations is identified that experienced no changes in time of observation or station moves during the 1979-2008 period. These stations are classified based on proximity to artificial surfaces, buildings, and other such objects with unnatural thermal mass using guidelines established by Leroy (2010). The relatively few stations in the classes with minimal artificial impact are found to have raw temperature trends that are collectively about 2/3 as large as stations in the classes with greater expected artificial impact. The trend differences are largest for minimum temperatures and are statistically significant even at the regional scale and across different types of instrumentation and degrees of urbanization. The homogeneity adjustments applied by the National Centers for Environmental Information (formerly the National Climatic Data Center) greatly reduce those differences but produce trends that are more consistent with the stations with greater expected artificial impact. Trend differences between the Cooperative Observer Network and the Climate Reference Network are not found during the 2005-2014 sub-period of relatively stable temperatures, suggesting that the observed differences are caused by a physical mechanism that is directly or indirectly caused by changing temperatures.

[UPDATE. I forgot to mention the obvious: After homogenization the trend Watts et al. (2015) computed are nearly the same for all five siting categories, just like it was for Watts et al. (2012) and the published study Fall et al. Thus for the data used by climatologists, the homogenized data, the siting quality does not matter. Just like before, they did not study homogenization algorithms and thus cannot draw any conclusions about them, but unfortunately they do.]

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Related reading
Anthony Watts' #AGU15 poster on US temperature trends

Blog review of the Watts et al. (2012) manuscript on surface temperature trends

A short introduction to the time of observation bias and its correction

Comparing the United States COOP stations with the US Climate Reference Network

WUWT not interested in my slanted opinion

Some history from 2010
On Weather Stations and Climate Trends

The conservative family values of Christian man Anthony Watts

Watts not to love: New study finds the poor weather stations tend to have a slight COOL bias, not a warm one

Poorly sited U.S. temperature instruments not responsible for artificial warming
Victor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-54045417002714184662015-12-13T12:07:00.002+00:002015-12-13T20:18:30.605+00:00My theoretical #AGU15 programThis Monday the Fall Meeting of the American Geophysical Union (AGU2015) starts. An important function of conferences is knowing who is working on what. Browsing the program does a lot of that, even if you are not there. Here is an overview of where I would have had a look from my interest in climate data quality.

Links, emphasis and [explanations] are mine. The titles are linked to the AGU abstracts, where you can also mail, tweet or facebook the abstracts to others who may be interested.

Session: Taking the Temperature of the Earth: Long-Term Trends and Variability across All Domains of Earth's Surface
(Talks | Posters)

Inland Water Temperature and the recent Global Warming Hiatus
Simon J Hook, Nathan Healey, John D Lenters and Catherine O'Reilly

Extract abstract. We are using thermal infrared satellite data in conjunction with in situ measurements to produce water temperatures for all the large inland water bodies in North America and the rest of the world for potential use as climate indicator. Recent studies have revealed significant warming of inland waters throughout the world. The observed rate of warming is – in many cases – greater than that of the ambient air temperature. These rapid, unprecedented changes in inland water temperatures have profound implications for lake hydrodynamics, productivity, and biotic communities. Scientists are just beginning to understand the global extent, regional patterns, physical mechanisms, and ecological consequences of lake warming.
See also my previous post on the fast warming of rivers and lakes and the decrease in their freezing periods. Unfortunately the abstract does not say much about the "hiatus" mentioned in the title.

There is also a detailed study on the relationship between air and water temperature for Lake Tahoe.

Global near-surface temperature estimation using statistical reconstruction techniques
Colin P Morice, Nick A Rayner and John Kennedy

abstract. Incomplete and non-uniform observational coverage of the globe is a prominent source of uncertainty in instrumental records of global near-surface temperature change. In this study the capabilities of a range of statistical analysis methods are assessed in producing improved estimates of global near-surface temperature change since the mid 19th century for observational coverage in the HadCRUT4 data set. Methods used include those that interpolate according to local correlation structure (kriging) and reduced space methods that learn large-scale temperature patterns.

The performance of each method in estimating regional and global temperature changes has been benchmarked in application to a subset of CMIP5 simulations. Model fields are sub-sampled and simulated observational errors added to emulate observational data, permitting assessment of temperature field reconstruction algorithms in controlled tests in which globally complete temperature fields are known.

The reconstruction methods have also been applied to the HadCRUT4 data set, yielding a range of estimates of global near-surface temperature change since the mid 19th century. Results show relatively increased warming in the global average over the 21st century owing to reconstruction of temperatures in high northern latitudes, supporting the findings of Cowtan & Way (2014) and Karl et al. (2015). While there is broad agreement between estimates of global and hemispheric changes throughout much of the 20th and 21st century, agreement is reduced in the 19th and early 20th century. This finding is supported by the climate model trials that highlight uncertainty in reconstructing data sparse regions, most notably in the Southern Hemisphere in the 19th century. These results underline the importance of continued data rescue activities, such as those of the International Surface Temperature Initiative and ACRE.

The results of this study will form an addition to the HadCRUT4 global near-surface temperature data set.

The EUSTACE project: delivering global, daily information on surface air temperature
Nick A Rayner and Colin P Morice,
At first sight you may think my colleagues have gone crazy. A daily spatially complete global centennial high-resolution temperature dataset!

I would be so happy if we could get halfway reliable estimates of changes in weather variability and extremes from some high-quality, high-density station networks for the recent decades. It is really hard to detect and remove changes in variability due to changes in the monitoring practices and these changes are most likely huge.

However, if you read carefully, they only promise to make the dataset and do not promise that the data is fit for any specific use. One of the main ways mitigation sceptics misinform the public is by pretending that datasets provide reliable information for any application. In reality the reliability of a feature needs to be studied first. Let's be optimistic and see how far they will get; they just started, have a nice bag of tricks and mathematical prowess.

Day-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. A global daily analysis cannot be achieved with measurements made in situ alone, so incorporation of satellite retrievals is needed. To achieve this, we must develop an understanding of the relationships between traditional (land and marine) surface air temperature measurements and retrievals of surface skin temperature from satellite measurements, i.e. Land Surface Temperature, Ice Surface Temperature, Sea Surface Temperature and Lake Surface Water Temperature. These relationships can be derived either empirically or with the help of a physical model.

Here we discuss the science needed to produce a fully-global daily analysis (or ensemble of analyses) of surface air temperature on the centennial scale, integrating different ground-based and satellite-borne data types. Information contained in the satellite retrievals would be used to create globally-complete fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place. ...
A separate poster will provide more details on the satellite data.

The International Surface Temperature Initiative
Peter Thorne, Jay H Lawrimore, Kate Willett and Victor Venema

The Initiative is a multi-disciplinary effort to improve our observational understanding of all relevant aspects of land surface air temperatures from the global-mean centennial scale trends to local information relevant for climate services and climate smart decisions. The initiative was started in 2010 with a meeting that set the overall remit and direction. In the intervening 5 years much progress has been made, although much remains to be done. This talk shall highlight: the over-arching initiative framework, some of the achievements and major outcomes to date, as well as opportunities to get involved. It shall also highlight the many challenges yet to be addressed as we move from questions of global long-term trends to more local and regional data requirements to meet emerging scientific and societal needs.

Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources

Trends in atmospheric temperature and winds since 1959
Steven C Sherwood, Nidhi Nishant and Paul O'Gorman

Sherwood and colleagues have generated a new radiosonde dataset, removing artificial instrumental changes as well as they could. They find that the tropical hotspot does exist, that the models predictions of this tropic hotspot in the tropical tropospheric trends thus fit. They find that the recent tropospheric trend is not smaller than before. I hope there is no Australian Lamar Smith who needs an "hiatus" and is willing to harass scientists for political gain.

That there is not even a non-significant change from the long-term warming trend is surprising because of the recently more frequent cooling El Nino Southern Oscillation (ENSO) phases (La Nina phases). One would expect to see the influence of ENSO even stronger in the tropospheric temperatures than in the 2-m temperature. This makes it more likely that this insignificant trend change in the 2-m temperature is a measurement artefact.

Extract abstract.We present an updated version of the radiosonde dataset homogenized by Iterative Universal Kriging (IUKv2), now extended through February 2013, following the method used in the original version (Sherwood et al 2008 Robust tropospheric warming revealed by iteratively homogenized radiosonde data J. Clim. 21 5336–52). ...

Temperature trends in the updated data show three noteworthy features. First, tropical warming is equally strong over both the 1959–2012 and 1979–2012 periods, increasing smoothly and almost moist-adiabatically from the surface (where it is roughly 0.14 K/decade) to 300 hPa (where it is about 0.25 K/decade over both periods), a pattern very close to that in climate model predictions. This contradicts suggestions that atmospheric warming has slowed in recent decades or that it has not kept up with that at the surface. ...

Wind trends over the period 1979–2012 confirm a strengthening, lifting and poleward shift of both subtropical westerly jets; the Northern one shows more displacement and the southern more intensification, but these details appear sensitive to the time period analysed. Winds over the Southern Ocean have intensified with a downward extension from the stratosphere to troposphere visible from austral summer through autumn. There is also a trend toward more easterly winds in the middle and upper troposphere of the deep tropics, which may be associated with tropical expansion.

Uncertainty in Long-Term Atmospheric Data Records from MSU and AMSU
In session: Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources
Carl Mears

This talk presents an uncertainty analysis of known errors in tropospheric satellite temperature changes and an ensemble of possible estimates that makes computing uncertainties for a specific application easier.

The temperature of the Earth's atmosphere has been continuously observed by satellite-borne microwave sounders since late 1978. These measurements, made by the Microwave Sounding Units (MSUs) and the Advanced Microwave Sounding Units (AMSUs) yield one of the longest truly global records of Earth's climate. To be useful for climate studies, measurements made by different satellites and satellite systems need to be merged into a single long-term dataset. Before and during the merging process, a number of adjustments made to the satellite measurements. These adjustments are intended to account for issues such as calibration drifts or changes in local measurement time. Because the adjustments are made with imperfect knowledge, they are therefore not likely to reduce errors to zero, and thus introduce uncertainty into the resulting long-term data record. In this presentation, we will discuss a Monte-Carlo-based approach to calculating and describing the effects of these uncertainty sources on the final merged dataset. The result of our uncertainty analysis is an ensemble of possible datasets, with the applied adjustments varied within reasonable bounds, and other error sources such as sampling noise taken into account. The ensemble approach makes it easy for the user community to assess the effects of uncertainty on their work by simply repeating their analysis for each ensemble member.

Other sessions

The statistical inhomogeneity of surface air temperature in global atmospheric reanalyses
In session: Evaluating Reanalysis: What Can We Learn about Past Weather and Climate? (Talks I | Talks II | posters)
Craig R Ferguson and Min-Hee Lee

Recently, a new generation of so-called climate reanalyses has emerged, including the 161-year NOAA—Cooperative Institute for Research in Environmental Sciences (NOAA-CIRES) Twentieth Century Reanalysis Version 2c (20CR V2c), the 111-year ECMWF pilot reanalysis of the twentieth century (ERA-20C), and the 55-year JMA conventional reanalysis (JRA-55C). These reanalyses were explicitly designed to achieve improved homogeneity through assimilation of a fixed subset of (mostly surface) observations. We apply structural breakpoint analysis to evaluate inhomogeneity of the surface air temperature in these reanalyses (1851-2011). For the modern satellite era (1979-2013), we intercompare their inhomogeneity to that of all eleven available satellite reanalyses. Where possible, we distinguish between breakpoints that are likely linked to climate variability and those that are likely due to an artificial observational network shift. ERA-20C is found to be the most homogeneous reanalysis, with 40% fewer artificial breaks than 20CR V2c. Despite its gains in homogeneity, continued improvements to ERA-20C are needed. In this presentation, we highlight the most spatially extensive artificial break events in ERA-20C.
There is also a more detailed talk about the quality of humidity in reanalysis over China.

Assessment of Precipitation Trends over Europe by Comparing ERA-20C with a New Homogenized Observational GPCC Dataset
In session: Evaluating Reanalysis: What Can We Learn about Past Weather and Climate?
Elke Rustemeier, Markus Ziese, Anja Meyer-Christoffer, Peter Finger, Udo Schneider and Andreas Becker

...The monthly totals of the ERA-20C reanalysis are compared to two corresponding Global Precipitation Climatology Centre (GPCC) products; the Full Data Reanalysis Version 7 and the new HOMogenized PRecipitation Analysis of European in-situ data (HOMPRA Europe).
ERA-20C...covers the time period 1900 to 2010. Only surface observations are assimilated namely marine winds and pressure. This allows the comparison with independent, not assimilated data.
Sounds interesting, the abstract unfortunately does not give much results yet.

Cyclone Center: Insights on Historical Tropical Cyclones from Citizen Volunteers
In session: Era of Citizen Science and Big Data: Intersection of Outreach, Crowd-Sourced Data, and Scientific Research
Peter Thorne, Christopher Hennon, Kenneth Knapp, Carl Schreck, Scott Stevens, James Kossin, Jared Rennie, Paula Hennon, Michael Kruk

The cyclonecenter.org project started in fall 2012 and has been collecting citizen scientist volunteer tropical cyclone intensity estimates ever since. The project is hosted by the Citizen Science Alliance (zooniverse) and the platform is supported by a range of scientists. We have over 30 years of satellite imagery of tropical cyclones but the analysis to date has been done on an ocean-basin by ocean-basin basis and worse still practices have changed over time. We therefore do not, presently, have a homogeneous record relevant for discerning climatic changes. Automated techniques can classify many of the images but have a propensity to be challenged during storm transitions. The problem is fundamentally one where many pairs of eyes are invaluable as there is no substitute for human eyes in discerning patterns. Each image is classified by ten unique users before it is retired. This provides a unique insight into the uncertainty inherent in classification. In the three years of the project much useful data has accrued. This presentation shall highlight some of the results and analyses to date and touch on insights as to what has worked and what perhaps has not worked so well. There are still many images left to complete so its far from too late to jump over to www.cyclonecenter.org and help out.

Synergetic Use of Crowdsourcing for Environmental Science Research, Applications and Education
In session: Era of Citizen Science and Big Data: Intersection of Outreach, Crowd-Sourced Data, and Scientific Research
Udaysankar Nair

...Contextual information needed to effectively utilize the data is sparse. Examples of such contextual information include ground truth data for land cover classification, presence/absence of species, prevalence of mosquito breeding sites and characteristics of urban land cover. Often, there are no agencies tasked with routine collection of such contextual information, which could be effectively collected through crowdsourcing.

Crowdsourcing of such information, that is useful for environmental science research and applications, also provide opportunities for experiential learning at all levels of education. Appropriately designed crowdsourcing activity can be transform students from passive recipients of information to generators of knowledge. ... One example is crowdsourcing of land use and land cover (LULC) data using Open Data Kit (ODK) and associated analysis of satellite imagery using Google Earth Engine (GEE). Implementation of this activity as inquiry based learning exercise, for both middle school and for pre-service teachers will be discussed. Another example will detail the synergy between crowdsourcing for biodiversity mapping in southern India and environmental education...
There is also a crowd sourced project for Land use and land cover for (urban) climatology: the World Urban Database. A great initiative.

Steps Towards a Homogenized Sub-Monthly Temperature Monitoring Tool
In session: Characterizing and Interpreting Changes in Temperature and Precipitation Extremes
Jared Rennie and Kenneth Kunkel

Land surface air temperature products have been essential for monitoring the evolution of the climate system. Before a temperature dataset is included in such reports, it is important that non-climatic influences be removed or changed so the dataset is considered homogenous. These inhomogeneities include changes in station location, instrumentation and observing practices. Very few datasets are free of these influences and therefore require homogenization schemes. While many homogenized products exist on the monthly time scale, few daily products exist... Using these datasets already in existence, monthly adjustments are applied to daily data [of NOAA's Global Historical Climatology Network – Daily (GHCN-D) dataset]...
Great to see NOAA make first steps towards homogenization of their large daily data collection, a huge and important task.

Note that the daily data is only adjusted for changes in the monthly means. This is an improvement, but for the weather extremes, the topic of this session, also the rest of the marginal distribution needs to be homogenized.

Temperature Trends over Germany from Homogenized Radiosonde Data
In session: Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources
Wolfgang Steinbrecht and Margit Pattantyús Ábráham

We present homogenization procedure and results for Germany's historical radiosonde [(RS)] records, dating back to the 1950s. Our manual homogenization makes use of the different RS networks existing in East and West-Germany from the 1950s until 1990.

The largest temperature adjustments, up to 2.5K, are applied to Freiberg sondes used in the East in the 1950s and 1960s. Adjustments for Graw H50 and M60 sondes, used in the West from the 1950s to the late 1980s, and for RKZ sondes, used in the East in the 1970s and 1980s, are also significant, 0.3 to 0.5K. Small differences between Vaisala RS80 and RS92 sondes used throughout Germany since 1990 and 2005, respectively, were not corrected for at levels from the ground to 300 hPa.

Comparison of the homogenized data with other radiosonde datasets, RICH (Haimberger et al., 2012) and HadAT2 (McCarthy et al., 2008), and with Microwave Sounding Unit satellite data (Mears and Wentz, 2009), shows generally good agreement. HadAT2 data exhibit a few suspicious spikes in the 1970s and 1980s, and some suspicious offsets up to 1K after 1995. Compared to RICH, our homogenized data show slightly different temperatures in the 1960s and 1970s. We find that the troposphere over Germany has been warming by 0.25 ± 0.1K per decade since the early 1960s, slightly more than reported in other studies (Hartmann et al., 2013). The stratosphere has been cooling, with the trend increasing from almost no change near 230hPa (the tropopause) to -0.5 ± 0.2K per decade near 50hPa. Trends from the homogenized data are more positive by about 0.1K per decade compared to the original data, both in troposphere and stratosphere.
Statistical relative homogenization can only partially remove trend biases. Given that the trend needed to be corrected upwards, the real temperature trend may thus be larger.

Observed Decrease of North American Winter Temperature Variability
In session: Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources
Andrew Rhines, Martin Tingley, Karen McKinnon, Peter Huybers

There is considerable interest in determining whether temperature variability has changed in recent decades. Model ensembles project that extratropical land temperature variance will detectably decrease by 2070. We use quantile regression of station observations to show that decreasing variability is already robustly detectable for North American winter during 1979--2014. ...

We find that variability of daily temperatures, as measured by the difference between the 95th and 5th percentiles, has decreased markedly in winter for both daily minima and maxima. ... The reduced spread of winter temperatures primarily results from Arctic amplification decreasing the meridional temperature gradient. Greater observed warming in the 5th relative to the 95th percentile stems from asymmetric effects of advection [air movements] during cold versus warm days; cold air advection is generally from northerly regions that have experienced greater warming than western or southwestern regions that are generally sourced during warm days.
Studies on changes in variability are the best. Guest appearances of the Arctic polar vortex in the eastern USA had given me the impression that the variability had increased, not decreased. Interesting.

Century-Scale of Standard Deviation in Europe Historical Temperature Records
In session: Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources
Fenghua Xie

The standard deviation (STD) variability in long historical temperature records in Europe is analyzed. It is found that STD is changeable with time, and a century-scale variation is revealed, which further indicates a century-scale intensity modulation of the large-scale temperature variability.

The Atlantic multidecadal oscillation (AMO) can cause significant impacts in standard deviation. During the periods of 1870–1910 and 1950–80, increasing standard deviation corresponds to increasing AMO index, while the periods of 1920-50 and 1980-2000 decreasing standard deviation corresponds to decreasing AMO index. The findings herein suggest a new perspective on the understanding of climatic change
Studies on changes in variability are the best. This intriguing osculation in the standard deviation of temperature was found before for the Greater Alpine Region by Reinhard Böhm. He also found it in precipitation and pressure (with a little fantasy). One be careful with such studies, changes in the standard deviation due to changes in monitoring practises (inhomogeneities) are mostly not detected, nor corrected. Only a few national and regional daily datasets have been (partially) homogenized in this respect.

Could scientists 'peer-review' the web?
A Town Hall presentation by Emmanuel Vincent of Climate Feedback and Dan Whaley, the founder of Hypothesis, which is the software basis of Climate Feedback. On Tuesday Dec.15, 12:30 - 13:30 pm

It is quite sure that I missed relevant presentations. Please add them in the comments.

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Why raw temperatures show too little global warming

Lakes are warming at a surprisingly fast rateVictor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-24870499816647157662015-12-07T22:50:00.001+00:002015-12-07T22:51:50.989+00:00Fans of Judith Curry: the uncertainty monster is not your friend

I think that uncertainties in global surface temperature anomalies is substantially understated.

Judith Curry

People often see uncertainty as a failing of science. It's the opposite: uncertainty is what drives science forward.

Dallas Campbell

Imagine you are driving on a curvy forest road and it gets more foggy. Do you slow down or do you keep your foot on the pedal? More fog means more uncertainty, means less predictability, means that you see the deer in your headlights later. Climate change mitigations sceptics like talking about uncertainty. They seem to see this as a reason to keep the foot op the pedal.

While this is madness, psychology suggests that this is an effective political strategy. When you talk about uncertainty people have a tendency to become less decisive. Maybe people want to wait deciding until the situation is clearer? That is exactly what the advocates for inaction want and neglects that deciding not to start solving the problem is also a decision. For someone who would like to see all of humanity prosper deciding not to act is a bad counter-productive decision.

Focusing on uncertainty is so common in politics that this strategy got its own name:
Appeals to uncertainty to preclude or delay political action are so pervasive in political and lobbying circles that they have attracted scholarly attention under the name  "Scientific Certainty Argumentation Methods", or "SCAMs" for short. SCAMs are politically effective because they equate uncertainty with the possibility that a problem may be less serious than anticipated, while ignoring the often greater likelihood that the problem may be more deleterious.

Meaning of uncertainty
Maybe people do not realise that uncertainty can have multiple meanings. In case of climate change "uncertainty" does not mean that scientists are not sure. Science is very sure it is warming, that this is due to us and that it will continue if we do not do anything.

When we talk about climate change, "uncertainty" means that we do not know exactly how much it has warmed. It means that the best estimate of the man made contribution is "basically all", but that it is possible that it is more or that it is less. It means that we know it will warm a lot the coming century, but not exactly how much, if only because no one knows whether humanity gets it act together. It means that the seas will rise more than one meter, but that we can only give a period in which this threshold will be crossed.

Rather than talking about "uncertainty" I try to talk about "confidence intervals" nowadays, that conveys the idea of this latter kind of uncertainty much better. Science may not know the exact value, but the value will most likely be in the confidence interval.

That the term uncertainty can be misunderstood is especially a problem because scientists love to talk about uncertainties. A value is worth nothing if you do not have an idea how accurate it is. Thus most of the time scientists work on making sure that the uncertainties are accurate and as small as possible.

More uncertainty means higher risks
The damages of climate change rise with its magnitude (let's call this "temperature increase" for simplicity). I will argue in the next section that these damages rise faster than linear. If the relationship were linear, twice as much temperature increase would mean twice as much damages. Super-linear means that damages rise faster than that. Let us for this post assume that the damages are proportional to the square of the temperature increase. Any other super-linear relationship would show the same effect: that more uncertainty means higher risks.

In this case, if there were no uncertainty and the temperature in 2100 will increase by 4 degrees Celsius. For comparison, the temperature increase in 2100 is projected to be between 3 and 5.5°C for the worst scenario considered by the IPCC; RCP8.5. With 4 degrees warming the damages would be 16*D (42*D) dollar or 16*H human lives.

In the case with uncertainty, the temperature in 2100 would still increase by 4 degrees on average, but it could also be 3°C or 5°C. The damages for 4 degrees are still 16*D dollar. At 3 degrees the damage would be 9*D and at 5 degrees 25*D dollar, which is on average 17*D. The total damages will thus be higher than the 16*D dollar we had for the case without uncertainty.

If the uncertainty becomes larger, and we also have to take 2 and 6 degrees into account we get 4*D (for 2°C) and 36*D (for 6°C), which averages to 20*D dollar. When we are less certain that the temperature increase is near 4°C and uncertainty forces us to take 2°C and 6°C into account, the average expected damages become higher.

Judith Curry thinks that we should take even more uncertainty into account: "I think we can bound [equilibrium climate sensitivity] between 1 and 6°C at a likely level, I don't think we can justify narrowing this further. ... [T]here is a 33% probability that that actual [climate] sensitivity could be higher or lower than my bounds. To bound at a 90% level, I would say the bounds need to be 0-10°C."

If the climate sensitivity were zero, the damages in 2100 would be zero. Estimating the temperature increase for a climate sensitivity of 10°C is more challenging. If we would still follow the carbon-is-life scenario mitigation skeptics prefer (RCP8.5), we would get a temperature increase of around 13°C in 2100**. It seems more likely that civilization will collapse before, but 13°C would give climate change damages of 132*D, which equals 169*D. The average damages for Curry's limiting case are thus 85*D, a lot more than the 16*D for the case were we are certain. If the uncertainty monster were this big, that would make the risk of climate change a lot higher.

Uncertainty is not the friend of people arguing against mitigation. The same thinking error is also made by climate change activists that sometimes ask scientists to emphasis uncertainty less.

Super-linear damages

Accumulated loss of regional species richness of macro-invertebrates as a function of glacial cover in catchment. They begin to disappear from assemblages when glacial cover in the catchment drops below approximately 50%, and 9 to 14 species are predicted to be lost with the complete disappearance of glaciers in each region, corresponding to 11, 16, and 38% of the total species richness in the three study regions in Ecuador, Europe, and Alaska. Figure RF-2 from IPCC AR5 report.
The above argument rests on the assumption that climate change damages rise super-linearly. If damages would rise linearly, uncertainty would not matter for the risk. In theory, if damages would rise less fast than linear (sub-linear), the risk would become lower with more uncertainty.

I am not aware of anyone doubting that the damages are super-linear. Weather and climate are variable. Every system will thus be adjusted to a small temperature increase. On the other hand, a 10°C temperature increase will affect, if not destroy, nearly everything. Once a large part of civilization is destroyed, the damages function may become sub-linear again. Whether the temperature increase is 10 or 11 degrees Celsius likely does not matter much any more.

What is a small temperature increase depends on the system. For sea level rise, the global mean temperature is important and the average temperature over centuries to millennia. This did not vary much, thus climate change quickly shows an influence. For the disappearance of permafrost, the long-term temperature is also important, but the damage to the infrastructure build on them depend on the local temperature, which varies more than the global temperature. On the local annual scale the variability is about 1°C, which is the global warming we have seen up to now and where new effects are now seen, for example nature moving poleward and up mountains (if it can). In summary, the more temperature increases, the more systems notice the change and naturally the more they are affected.

Damages can be avoided by adaptation. Both natural adaptation to the vagaries of weather and climate, as well as man-made adaptation in anticipation of the mess we are getting into. In the beginning there will still be low-hanging fruit, but the larger the changes will become, the more expensive adaptation becomes. More uncertainty also makes man-made adaptation more costly. If you do not know accurately how much bigger a centennial flood is, that is costly. To build bigger flood protections you need to have a best estimate, but also need to add a few times the uncertainty to this estimate, otherwise you would still be flooded half the time such a flood comes by.

An example of super-linear impacts is species loss for glacier catchments when the glaciers disappear. The figure above shows that initial small reductions in the glaciers did not impact nature much yet, but that it rises fast near the time the glacier is lost.

The costs of sea level rise rise super-linearly as a function of the amount of sea level rise. Data from Brown et al. (2011).
Another example are the super-linear costs of sea level rise. This plot to the right was generated by sea level rise expert Aslak Grinsted from data of a large European scientific project on the costs of climate change. He added that this shape is consistent across many studies on the costs of sea level rise.

Those were two examples for specific systems, which I trust most as natural scientist. Economists have also tried to estimate the costs of climate change. This is hard because many damages cannot really be expressed in money. Climate change is one stressor and in many cases damages also depend on many other stressors. Furthermore, solutions can often solve multiple problems. As economist Martin Weitzman writes: "The "damages function" is a notoriously weak link in the economics of climate change

The climate change damages functions of Weitzman (2010b) and Nordhaus (2008). For a zero temperature change Ω(t)=1Ω(t)=1 (no damage) and for very large temperature changes it approaches 0 (maximum damage).
Consequently, the two damages functions shown to the right differ enormously. Important for this post: they are both super-linear.

Unknown unknowns
If there is one thing I fear about climate change, then it is uncertainty. There will be many surprises. We are taking the climate system out of the state we know well and are perform a massive experiment with it. Things are bound to happen, we did not think of. Some might be nice, more likely the surprises will not be nice. As Judith Curry calls it: climate change is a wicked problem.

Medical researchers like to study rare deceases. They do so much more than the number of patients would justify. But seeing things go wrong helps you understand how things work. The other way around this means that until things go wrong, we will often not even know we should have studied it. Some surprises will be that an impact that science did study turns out to be better or worse; the known unknowns. The most dangerous surprises are bound to be the unknown unknowns, which we never realised we would have had to study.

The uncertainty monster is my main reason as citizen to want to solve this problem. Call me a conservative. The climate system is one of the traditional pillars of our society. Something you do not change without considering long and hard what the consequences will be. The climate is something you want to understand very well before you change it. If we had perfect climate and weather predictions, climate change would be a much smaller problem.

Gavin Schmidt

I think the first thing to acknowledge is that there will be surprises. We're moving into a regime of climate that we have not experienced as humans, most ecosystems have not experienced since the beginning of the ice age cycle some three million years ago. We don't know very well what exactly was happening then. We know some big things, like how much the sea level rose and what the temperatures were like, but there's a lot of things that we don't know. And so we are anticipating "unknown unknowns". But, of course, they're unknown, so you don't know what they're going to be.

One irony is that climate models reduce the uncertainty by improving the scientific understanding of the climate system. Without climate models we would still know that the Earth warms when we increase greenhouse gasses. The greenhouse effect can be directly measured by looking at the infra red radiation from the sky. When you make that bigger, it gets warmer. When you put on a second sweater, you get warmer. We know it gets warmer from simple radiative transfer computations. We know that CO2 influences temperature by studying past climates.

The climate models have about the same climate sensitivity as those simple radiative transfer computations and the estimates from past climates. It could have been different because the climate is more complicated than the simple radiative transfer computation. It could have been different because the increase in greenhouse gasses goes so fast this time. That all those climate sensitivity estimates fit together reduces the uncertainty, especially the uncertainty from the unknown unknowns.

Without climate models it would be very hard to estimate all the other changes in the climate system. The increases in precipitation and especially increases in severe precipitation, in floods, in droughts, in the circulation patterns, how fast sea level rise will go. Without climate models these risks would be much higher, without climate models we would have to adapt to a much wider range of changes in weather and climate. This would make adaptation a lot more intrusive and expensive.

Without climate models and climate research in general the risks of changing our climate would be larger. We would need to be more careful, the case for reductions of greenhouse gas emissions would have been stronger.

Especially for mitigation sceptics advocating adaptation-only policies, climate research should be important. Adaptation needs high-quality local information. For a 1000-year event such as the downpour in South Carolina earlier this year or the record precipitation this week in the UK, we may have to life with the damages. If this will happen much more often under climate change, we will have to change our infrastructure. If we do not know what is coming, we will have to prepare for everything. That is expensive. Reductions in this uncertainty save money by reducing unnecessary adaptation measures and by reducing damages due to effective adaptation.

People who are against mitigation policies should be cheering for climate research, rather than try to defund it or harass scientists for their politically inconvenient message. Let's be generous and assume that they do not know what they are doing.

In summary. Uncertainty makes the risk of climate change larger. Uncertainty makes adaptation a less attractive option relative to solving the problem (mitigation). The more we take the climate system out of known territories the more surprises (unknown unknowns) we can expect. In a logical world uncertainty would be the message of the environmental movement. In the real world uncertainty is one of the main fallacies of the mitigation skeptics and their "think" tanks.

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Related reading
Tail Risk vs. Alarmism by Kerry Emanuel

Stephan Lewandowsky, Richard Pancost and Timothy Ballard making the same case and adding psychological and sociological explanations why SCAMs still work: Uncertainty is Exxon's friend, but it's not ours

Talking climate: Communicating uncertainty in climate science

European Climate Adaptation Platform: How to communicate uncertainty?

Dana Nuccitelli: New research: climate change uncertainty translates into a stronger case for tackling global warming

Uncertainty isn't cause for climate complacency – quite the opposite

Michael Tobis in 1999 about uncertainty and risk: Wisdom from USENET

References
Botzen, W.J.W. and J.C. van den Bergh, 2012: How sensitive is Nordhaus to Weitzman? climate policy in DICE with an alternative damage function. Economics Letters, 117, pp. 372–374, doi: 10.1016/j.econlet.2012.05.032.

Nordhaus, W.D., 2008: A Question of Balance: Weighing the Options on Global Warming Policies. Yale University Press, New Haven.

Brown, Sally, Robert Nicholls, Athanasios Vafeidis, Jochen Hinkel and Paul Watkiss, 2011: The Impacts and Economic Costs of Sea-Level Rise in Europe and the Costs and Benefits of Adaptation. Summary of Results from the EC RTD ClimateCost Project. In Watkiss, P. (Editor), 2011. The ClimateCost Project. Final Report. Volume 1: Europe. Published by the Stockholm Environment Institute, Sweden, 2011. ISBN 978-91-86125-35-6.

Lewandowsky, Stephan , James S. Risbey, Michael Smithson, Ben R. Newell, John Hunter, 2014: Scientific uncertainty and climate change: Part I. Uncertainty and unabated emissions. Climatic Change, 124, pp. 21–37, doi: 10.1007/s10584-014-1082-7.

Tomassini, L., R. Knutti, G.-K. Plattner, D.P. van Vuuren, T.F. Stocker, R.B. Howarth and M.E. Borsuk, 2010: Uncertainty and risk in climate projections for the 21st century: comparing mitigation to non-intervention scenarios. Climate Change, 103, pp. 399–422. doi: .

Weitzman M.L., 2010a: What is the damages function for global warming and what difference might it make?
Climate Change Economics, 1, pp. 57–69, doi: 10.1142/S2010007810000042.

Weitzman, M.L., 2010b: GHG Targets as Insurance Against Catastrophic Climate Damages. Mimeo, Department of Economics, Harvard University.

Acknowledgements.
* Thanks to Michael Tobis for the foggy road analogy, which he seems to have gotten from [[Stephen Schneider]].

** Judith Curry speaks of the equilibrium climate sensitivity begin between 0 and 10°C per doubling of CO2. The TCR to ECS ratio peaks at around 0.6, so an ECS of 10°C could be a TCR of 6°C. Since doubling of CO2 is a forcing of 3.7 W/m2 and RCP8.5 is defined as 8.5 W/m2 in 2100, that would mean a warming of 8.5/3.7 x 6 = 13°C (Thank you ATTP).

For comparison, the IPCC estimates the climate sensitivity to between 1.5 and 4.5°C. Last IPCC report: "Equilibrium climate sensitivity is likely in the range 1.5°C to 4.5°C (high confidence), extremely unlikely less than 1°C (high confidence), and very unlikely greater than 6°C (medium confidence)"

*** Top photo by Paige Jarreau.Victor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-36394459479079130502015-11-20T00:14:00.002+00:002015-11-28T19:23:37.419+00:00Sad that for Lamar Smith "hiatus" has far-reaching policy implications

Earlier this year, NOAA made a new assessment of the surface temperature increase since 1880. Republican Congressman Lamar Smith, Chair of the House science committee did not like the adjustments NOAA made and started a harassment campaign. In the Washington Post he wrote about his conspiracy theory (my emphasis):
In June, NOAA employees altered temperature data to get politically correct results and then widely publicized their conclusions as refuting the nearly two-decade pause in climate change we have experienced. The agency refuses to reveal how those decisions were made. Congress has a constitutional responsibility to review actions by the executive branch that have far-reaching policy implications.
I guess everyone reading this blog knows that all the data and code are available online.

The debate is about this minor difference you see at the top right. Take your time. Look carefully. See it? The US mitigation sceptical movement has made the trend since the super El Nino year 1998 a major part of their argumentation that climate change is no problem. When for Lamar Smith such minute changes have "far-reaching policy implications", then maybe he is not a particularly good policy maker. The people he represents in the Texas TX-21 district deserve better.

I have explained the mitigation sceptics so many times that they should drop their "hiatus" fetish. That that would come back to hound them. That such extremely short term trends have huge uncertainties and that interpreting such changes as climatic changes assumes a data quality that I see as unrealistic. With their constant wailing about the quality data, they should theoretically certainly see it that way. But well, they did not listen.

Some political activists like to claim that the "hiatus" means that global warming has stopped. It seems like Lamar Smith is in this group, at least I see no other reason why he would think that it is policy relevant. But only 2 percent of global warming warms the atmosphere (most warms the oceans) and this "hiatus" is about 2% of the warming we have seen since 1880. It is thus a peculiar debate about 2% of %2 of the warming and not about global warming.

This peculiar political debate is the reason this NOAA study became a Science paper (Science magazine prefers article of general interest) and why NOAA's Karl et al. (2015) paper was heavily attacked by the mitigation sceptical movement.

Before this reassessment NOAA's trend since 1998 was rather low compared to the other datasets. The right panel of the figure below made by Zeke Hausfather shows the trends since 1998. In the figure the old NOAA assessment are the green dots, the new assessment the black dots.

The new assessment solved problems in the NOAA dataset that were already solved in the HadCRUTT4 dataset from the UK (red dots). The trends in HadCRUT4 are somewhat lower because it does not take the Arctic fully into account, where a lot of the warming in the last decade happened to have occurred. The version of HadCRUT4 were this problem is fixed is indicated as "Cowtan & Way" (brownish dots).

The privately funded Berkeley Earth) also takes the Arctic into account and already had somewhat larger recent trends.

Thus the new assessment of NOAA is in line with our current understanding. Given how minute this feature is, it is actually pretty amazing how similar the various assessments are.

"Karl raw" (open black circle) is the raw data of NOAA before any adjustments, the green curve in the graph at the top of this post. "Karl adj" (black dot) is the new assessment, the thick black line in the graph at the top. The previous assessment is "NCDA old" (green dot). The other dots, four well-known global temperature datasets.

Whether new assessments are seen as having "far-reaching policy implications" by Lamar Smith may also depend on the direction in which the trends change. Around the same time as the NOAA article, the Roy Spencer and Chris Christy published a new dataset with satellite estimates of the tropospheric temperatures. As David Appell reports, they make considerable changes to their dataset. Somehow I did not hear anything about a subpoena against them yet.

More important adjustments to the surface temperatures are made for data before 1940. Looking at the figure below, most would probably guess that Lamar Smith did not like the strong adjustments that made global warming a lot small. May he liked the direction better.

The adjustments before 1940 are necessary because in that period the dominant way to measure sea surface temperature was by taking a bucket of water out of the sea. During the measurement the water would cool due to evaporation. How large this adjustment should be is uncertain, anything between 0 and 0.4°C is possible. That makes a huge difference for the scientific assessment of how much warming we have seen up to now.

Also the size of the peak in the second world war is highly uncertain; the merchant ships were replaced by war ships, making the measurements differently.

This is outside of my current expertise, but the first article I read about this, a small study for the Baltic see, suggested that the cooling bias due to evaporation is small, but that there is a warming bias of 0.5°C because the thermometer is stored in the warm cabin and the sailors did not wait long enough until the thermometer equilibrates. Such uncertainties are important and a hand full of scientists are working on sea surface temperature. And now a political witch hunt keeps some of them from their work.

Whether the adjustments for buckets are 0.4 or 0°C that may be policy relevant. At least if we were already close to an optimal policy response. This adjustment affects the data over a long period and can thus influence estimates of climate sensitivity. What counts for the climate sensitivity is basically the area under the temperature graph. A change of 0.4°C over 60 years is a lot more than 0.2° over 15 years. Nic Lewis and Judith Curry (2014), who I hope Lamar Smith will trust, also do not see the "hiatus" as important for the climate sensitivity.

For those who still think that global warming has stopped, climatologist John Nielsen-Gammon (and friend of Anthony Watts of WUWT) made the wonderful plot below, that immediately helps you see, that most of the deviations from the trend line can be explained by variations in El Nino.

It is somewhat ironic that Lamar Smith claims that NOAA rushed the publication of their dataset. It would be more logical if he hastened his campaign. It is now shortly before the Paris climate conference and the strong El Nino does not bode well for his favourite policy justification as the plot below shows. You do not need statistics any more to be completely sure that there was no change in the trend in 1998.

RT to show @LamarSmithTX21 that bullying @NOAA climate scientists is unacceptable and dangerous: pic.twitter.com/my1h0WP1Qi #ISupportNOAA
— Concerned Scientists (@UCSUSA) November 19, 2015

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Related reading
WOLF-PAC has a good plan to get money out of US politics. Let's first get rid of this weight vest before we run the century long climate change marathon.

Margaret Leinen, president of the American Geophysical Union (AGU): A Growing Threat to Academic Freedom

Keith Seitter, Executive Director of the American Meteorological Society (AMS): "The advancement of science depend
s on investigators having the freedom to carry out research objectively and without the fear of threats or intimidation whether or not their results are expedient or popular."

The article of Chris Mooney in the Washington Post is very similar to mine, but naturally better written and with more quotes: Even as Congress investigates the global warming 'pause,' actual temperatures are surging

Letters to the Editor of the Washington Post: Eroding trust in scientific research. The writer, a Republican, is chairman of the House Committee on Science, Space and Technology and represents Texas's 21st District in the House.

House science panel demands more NOAA documents on climate paper

Michael Halpern of the Union of Concerned Scientists in The Guardian: The House Science Committee Chair is harassing US climate scientists

And Then There's Physics on the hypocrisy of Judith Curry: NOAA vs Lamar Smith.

Michael Tobis: House Science, Space, and Technology Committee vs National Oceanic and Atmospheric Administration

Ars Technica: US Congressman subpoenas NOAA climate scientists over study. Unhappy with temperature data, he wants to see the e-mails of those who analyze it.

Ars Technica: Congressman continues pressuring NOAA for scientists' e-mails. Rep. Lamar Smith seeks closed-door interviews, in the meantime.

Guardian: Lamar Smith, climate scientist witch hunter. Smith got more money from fossil fuels than he did from any other industry.

Wired: Congress' Chief Climate Denier Lamar Smith and NOAA Are at War. It's Benghazi, but for nerds. I hope the the importance of independent science is also clear to people who do not consume it on a daily basis.

Mother Jones: The Disgrace of Lamar Smith and the House Science Committee.

Eddie Bernice Johnson, Democrat member of the Committee on Science from Texas reveals temporal inconsistencies in the explanations offered by Lamar Smith for his harassment campaign.

Raymond S. Bradley in Huffington Post: Tweet This and Risk a Subpoena. "OMG! [NOAA] tweeted the results! They actually tried to communicate with the taxpayers who funded the research!"

David Roberts at Vox: The House science committee is worse than the Benghazi committee

Union of Concerned Scientists: The House Science Committee's Witch Hunt Against NOAA Scientists

Reference

Karl, T.R., A. Arguez, B. Huang, J.H. Lawrimore, J.R. McMahon, M.J. Menne, T.C. Peterson, R.S. Vose, and H. Zhang, "Possible artifacts of data biases in the recent global surface warming hiatus", Science, vol. 348, pp. 1469–1472, 2015. doi: 10.1126/science.aaa5632Victor Venemahttp://firstname.lastname@example.com:blogger.com,1999:blog-9093436161326155359.post-36487234570439407682015-10-15T08:28:00.000+01:002015-10-15T08:28:00.446+01:00Invitation to participate in a PhD research project on climate bloggingMy name is Giorgos Zoukas and I am a second-year PhD student in Science, Technology and Innovation Studies (STIS) at the University of Edinburgh. This guest post is an invitation to the readers and commenters of this blog to participate in my project.

This is a self-funded PhD research project that focuses on a small selection of scientist-produced climate blogs, exploring the way these blogs connect into, and form part of, broader climate science communication. The research method involves analysis of the blogs' content, as well as semi-structured in-depth interviewing of both bloggers and readers/commenters.

Anyone who comments on this blog, on a regular basis or occasionally, or anyone who just reads this blog without posting any comments, is invited to participate as an interviewee. The interview will focus on the person's experience as a climate blog reader/commenter.*

The participation of readers/commenters is very important to this study, one of the main purposes of which is to increase our understanding of climate blogs as online spaces of climate science communication.

If you are interested in getting involved, or if you have any questions, please contact me at: G.Zoukas -at- sms.ed.ac.uk (Replace the -at- with the @ sign)

(Those who have already participated through my invitation on another climate blog do not need to contact me again.)

*The research complies with the University of Edinburgh's School of Social and Political Sciences Ethics Policy and Procedures, and an informed consent form will have to be signed by both the potential participants (interviewees) and me.

VV: I have participated as blogger. For science.

I was a little sceptical at first, with all the bad experiences with the everything-is-a-social-construct fundamentalists in the climate "debate". But Giorgos Zoukas seems to be a good guy and gets science.

I even had to try to convince him that science is very social; science is hard to do on your own.

A good social surrounding, a working scientific community, increases speed of scientific progress. That science is social does not mean that imperfections lead to completely wrong results for social reasons, that the results are just a social construct.Victor Venemahttp://email@example.com:blogger.com,1999:blog-9093436161326155359.post-13536035909484383862015-10-04T16:32:00.001+01:002015-10-04T23:04:11.840+01:00Measuring extreme temperatures in Uccle, Belgium
Open thermometer shelter with a single set of louvres.

That changes in the measurement conditions can lead to changes in the mean temperature is hopefully known by most people interested in climate change by now. That such changes are likely even more important when it comes to weather variability and extremes is unfortunately less known. The topic is studied much too little given its importance for the study of climatic changes in extremes, which are expected to be responsible for a large part of the impacts from climate change.

Thus I was enthusiastic when a Dutch colleague send me a news article on the topic from the homepage of the Belgium weather service, Koninklijk Meteorologisch Instituut (KMI). It describes a comparison of two different measurement set-ups, old and new, made side by side in [[Uccle]], the main office of the KMI. The main difference is the screen used to protect the thermometer from the sun. In the past these were often more open, that makes ventilation better, nowadays they are more closed to reduce (solar and infra red) radiation errors.

The more closed screen is a [[Stevenson screen]], invented in the last decades of the 19th century. I had assumed that most countries had switched to Stevenson screens before the 1920s. But I recently learned that Switzerland changed in the 1960s and in Uccle they changed in 1983. Making any change to the measurements is a difficult trade off between improving the system and breaking the homogeneity of the climate record. It would be great to have a historical overview of such historical transitions in the way climate is measured for all countries.

I am grateful to the KMI for their permission to republish the story here. The translation, clarifications between square brackets and the related reading section are mine.

Closed thermometer screen with a double-louvred walls [Stevenson screen].

In the [Belgian] media one reads regularly that the highest temperature in Belgium is 38.8°C and that it was recorded in Uccle on June 27, 1947. Sometimes, one also mentions that the measurement was conducted in an "open" thermometer screen. On warm days the question typically arises whether this record could be broken. In order to be able to respond to this, it is necessary to take some facts into account that we will summarize below.

It is important to know that temperature measurements are affected by various factors, the most important one is the type of the thermometer screen in which the observations are carried out. One wants to measure the air temperature and therefore prevent a warming of the measuring equipment by protecting the instruments from the distorting effects of solar radiation. The type of thermometer screen is particularly important on sunny days and this is reflected in the observations.

Since 1983, the reference measurements of the weather station Uccle are made in a completely "closed" thermometer screen [a Stevenson screen] with double-louvred walls. Until May 2006, the reference thermometers were mercury thermometers for daily maximums and alcohol thermometers for daily minimums. [A typical combination nowadays because mercury freezes at -38.8°C.] Since June 2006, the temperature measurements are carried out continuously by means of an automatic sensor in the same type of closed cabin.

Before 1983, the measurements were carried out in an "open" thermometer screen with only a single set of louvres, which on top of that offered no protection on the north side. Because of the reasons mentioned above, the maximum temperature in this type of shelter were too high, especially during the summer period with intense sunshine. On July 19, 2006, one of the hottest days in Uccle, for example, the reference [Stevenson] screen measured a maximum temperature of 36.2°C compared to 38.2°C in the "open" shelter on the same day.

As the air temperature measurements in the closed screen are more relevant, it is advisable to study the temperature records that would be or have been measured in this type of reference screen. Recently we have therefore adjusted the temperature measurements of the open shelter from before 1983, to make them comparable with the values from the closed screen. These adjustments were derived from the comparison between the simultaneous [parallel] observations measured in the two types of screens during a period of 20 years (1986-2005). Today we therefore have two long series of daily temperature extremes (minimum and maximum), beginning in 1901, corresponding to measurements from a closed screen.

When one uses the alignment method described above, the estimated value of the maximum temperature in a closed screen on June 27, 1947, is 36.6°C (while a maximum value of 38.8°C was measured in an open screen, as mentioned in the introduction). This value of 36.6°C should therefore be recognized as the record value for Uccle, in accordance with the current measurement procedures. [For comparison, David Parker (1994) estimated that the cooling from the introduction of Stevenson screens was less than 0.2°C in the annual means in North-West Europe.]

For the specialists, we note that the daily maximum temperature shown in the synoptic reports of Uccle, usually are up to a few tenths of a degree higher compared with the reference climatological observations that were mentioned previously. This difference can be explained by the time intervals over which the temperature is averaged in order to reduce the influence of atmospheric turbulence. The climatic extremes are calculated over a period of ten minutes, while the synoptic extremes are calculated from values that were averaged over a time span of a minute. In the future, will make these calculation methods the same by applying the climatic procedures always.

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KMI: Het meten van de extreme temperaturen te Ukkel

To study the influence of such transitions in the way the climate is measured using parallel data we have started the Parallel Observations Science Team (ISTI-POST). One of the POST studies is on the transition to Stevenson screens, which is headed by Theo Brandsma. If you have such data please contact us. If you know someone who might, please tell them about POST.

Another parallel measurement showing huge changes in the extremes is discussed in my post: Be careful with the new daily temperature dataset from Berkeley

More on POST: A database with daily climate data for more reliable studies of changes in extreme weather

Introduction to series on weather variability and extreme events

On the importance of changes in weather variability for changes in extremes

A research program on daily data: HUME: Homogenisation, Uncertainty Measures and Extreme weather

Reference
Parker, David E., 1994: Effect of changing exposure of thermometers at land stations. International journal of climatology, 14, pp. 1-31, doi: 10.1002/joc.3370140102. Victor Venemahttp://email@example.com