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Climate simulations for 1880-2003 with GISS modelE

We carry out climate simulations for 1880-2003 with GISS modelE driven by ten measured or estimated climate forcings. An ensemble of climate model runs is carried out for each forcing acting individually and for all forcing mechanisms acting together. We compare side-by-side simulated climate change for each forcing, all forcings, observations, unforced variability among model ensemble members, and, if available, observed variability. Discrepancies between observations and simulations with all forcings are due to model deficiencies, inaccurate or incomplete forcings, and imperfect observations. Although there are notable discrepancies between model and observations, the fidelity is sufficient to encourage use of the model for simulations of future climate change. By using a fixed well-documented model and accurately defining the 1880-2003 forcings, we aim to provide a benchmark against which the effect of improvements in the model, climate forcings, and observations can be tested. Principal model deficiencies include unrealistically weak tropical El Nino-like variability and a poor distribution of sea ice, with too much sea ice in the Northern Hemisphere and too little in the Southern Hemisphere. The greatest uncertainties in the forcings are the temporal and spatial variations of anthropogenic aerosols and their indirect effects on clouds.


Introduction
Global warming has become apparent in recent years, with the average surface temperature in 2005 about 0.8°C higher than in the late 800s (Hansen et al. 2006a). There is strong evidence that much of this warming is due to human-made climate forcing agents, especially infrared-absorbing (greenhouse) gases (IPCC 200). Concern about human-made climate alterations led to the United Nations Framework Convention on Climate Change (United Nations 992) with the agreed objective "to achieve stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system." The Earth's climate system has great thermal inertia, yielding a climate response time of at least several decades for changes of atmosphere and surface climate forcing agents (Hansen et al. 984). Thus there is a need to anticipate the nature of anthropogenic climate change and define the level of change constituting dangerous interference with nature. Simulations with global climate models on the century time scale provide a tool for addressing this need. Climate models used for simulations of future climate must be tested by means of simulations of past climate change.
Our present paper describes simulations for 880-2003 made with GISS atmospheric modelE (Schmidt et al. ), hereafter modelE (2006, specifically model III, the version of modelE "frozen" in mid-2004 for use in the 2007 IPCC assessment. This same model III version of modelE has been documented via a large set of simulations used to investigate the "efficacy" of various climate forcings ), hereafter Efficacy (2005. Efficacy (2005) and the present paper both include use of the same 0 climate forcings. In Efficacy (2005) each forcing is based on the fixed 1880-2000 change of the forcing agent, and the mean climate response for years 8-20 is examined to maximize signal/noise. The present paper uses the "transient" (timedependent) forcings for . These transient simulations are extended to 200, and in a few cases to 2300, for several GHG scenarios by Hansen et al. (2006b, hereafter Dangerous layers of geometrically increasing thickness, four of these in the top 00 m. The ocean model employs the KPP parameterization for vertical mixing (Large et al. 994) and the Gent-McWilliams parameterization for eddy-induced tracer transports (Gent et al. 995;Griffies 998). The Russell et al. (995) ocean model produces a realistic thermohaline circulation (Sun and Bleck 2006), but yields unrealistically weak El Nino-like tropical variability as a result of its coarse resolution.
(4) Ocean D, the Bleck (2002) HYCOM ocean model, which uses quasi-Lagrangian potential density as vertical coordinate. Results for this coupled model with all forcings acting at once will be presented elsewhere.
The merits and rationale for organizing the climate change investigation this way, including use of alternative ocean representations with identical atmospheric model and forcings, are discussed by Hansen et al. (997a).

Model Sensitivity
The model has sensitivity 2.7°C for doubled CO 2 when coupled to the Q-flux ocean (Efficacy 2005), but 2.9°C when coupled to the Russell et al. (995) dynamical ocean. The slightly higher sensitivity with ocean C became apparent when the model run was extended to 000 years, as the sea ice contribution to climate change became more important relative to other feedbacks as the high latitude ocean temperatures approached equilibrium. The 2.9°C sensitivity corresponds to ~0.7°C per W/m 2 . In the coupled model with the Russell et al. (995) ocean the response to a constant forcing is such that 50% of the equilibrium response is achieved in ~25 years, 75% in ~50 years, and the equilibrium response is approached only after several hundred years. Runs of 000 years and longer are available upon request. The model's climate sensitivity of 2.7-2.9°C for doubled CO 2 is well within the empirical range of 3±°C for doubled CO 2 that has been inferred from paleoclimate evidence (Hansen et al. 984,993;Hoffert and Covey 992).

Principal Model Deficiencies
ModelE (2006) compares the atmospheric model climatology with observations. Model shortcomings include ~25% regional deficiency of summer stratus cloud cover off the west coast of the continents with resulting excessive absorption of solar radiation by as much as 50 W/m 2 , deficiency in absorbed solar radiation and net radiation over other tropical regions by typically 20 W/m 2 , sea level pressure too high by 4-8 hPa in the winter in the Arctic and 2-4 hPa too low in all seasons in the tropics, ~20% deficiency of rainfall over the Amazon basin, ~25% deficiency in summer cloud cover in the western United States and central Asia with a corresponding ~5°C excessive summer warmth in these regions. In addition to the inaccuracies in the simulated climatology, another shortcoming of the atmospheric model for climate change studies is the absence of a gravity wave representation, as noted above, which may affect the nature of interactions between the troposphere and stratosphere. The stratospheric variability is less than observed, as shown by analysis of the present 20-layer 4°×5° atmospheric model by J. Perlwitz (personal communication). In a 50-year control run Perlwitz finds that the interannual variability of seasonal mean temperature in the stratosphere maximizes in the region of the subpolar jet streams at realistic values, but the model produces only six sudden stratospheric warmings (SSWs) in 50 years, compared with about one every two years in the real world.
The coarse resolution Russell ocean model has realistic overturning rates and inter-ocean transports (Sun and Bleck 2006), but tropical SST has less east-west contrast than observed and the model yields only slight El Nino-like variability (Fig. 7 of Efficacy 2005). Also the Southern Ocean is too well-mixed near Antarctica (Liu et al. 2003), while deep water production in the North Atlantic does not go deep enough, and some deep-water formation occurs in the Sea of Okhotsk region, probably because of unrealistically small freshwater input there in the model III version of modelE. Global sea ice cover is realistic, but this is achieved with too much sea ice in the Northern Hemisphere and too little sea ice in the Southern Hemisphere, and the seasonal cycle of sea ice is too damped with too much ice remaining in the Arctic summer, which may affect the nature and distribution of sea ice climate feedbacks.
Despite these model limitations, in IPCC model inter-comparisons the model used for the simulations reported here, i.e, modelE with the Russell ocean, fares about as well as the typical global model in the verisimilitude of its climatology. Comparisons so far include the ocean's thermohaline circulation (Sun and Bleck 2006), the ocean's heat uptake (Forest et al. 2006), the atmosphere's annular variability and response to forcings , and radiative forcing calculations (Collins et al. 2006). The ability of the GISS model to match climatology, compared with other models, varies from being better than average on some fields (radiation quantities, upper tropospheric temperature) to poorer than average on others (stationary wave activity, sea level pressure).

Climate Forcings
The climate forcings that drive our simulated climate change arise from changing well-mixed greenhouse gases (GHGs), ozone (O 3 ), stratospheric H 2 O from methane (CH 4 ) oxidation, tropospheric aerosols, specifically, sulfates, nitrates, black carbon (BC) and organic carbon (OC), a parameterized indirect effect of aerosols on clouds, volcanic aerosols, solar irradiance, soot effect on snow and ice albedos, and land use changes. Largest forcings on the century time scale are for GHGs and aerosols, including the aerosol indirect effect. Ozone global forcing is significant on the century time scale, and the more uncertain solar forcing may also be important. Volcanic effects are large on shorter time scales, and the clustering of volcanoes contributes to decadal climate variability. The soot effect on snow and ice albedos and land use change are small on global average, but they are large forcings on regional scales.
Global maps of the 880-2003 changes of these forcings are provided in Efficacy (2005). In this section we define the assumed atmospheric, surface or irradiance changes that give rise to the forcings, show the time dependence of global mean forcing for each mechanism, and provide partly subjective estimates of the uncertainties.
We tabulate forcings for several forcing definitions for the sake of analysis and comparison with other investigations. Fi, Fa, Fs, and Fe are, respectively, the instantaneous, adjusted, fixed SST (sea surface temperature), and effective forcings (Efficacy 2005). Fi, Fa and Fs are a priori forcings. The a posteriori forcing Fe is inferred from a long climate simulation, thus accounting in a limited way for the efficacy of each specific forcing mechanism. Fe is based on the 00-year response of global mean temperature, so of course it cannot make different forcing mechanisms equivalent in their regional climate responses. The various forcing mechanisms differ in effectiveness primarily because of their varying locations in latitude or altitude (Hansen et al. 997b,Ramaswamy et al. 200). Even the nominally "well-mixed" GHGs differ in their efficacies, because of spatial gradients in their amounts and the spectral location of absorptions (Efficacy 2005).
Fi is the easiest forcing to compute, but in some cases it provides a poor measure of the expected climate response. Fa has been used widely, e.g., by IPCC (996,200) and Hansen et al. (997b). Fa is the conventional standard forcing, in which the stratospheric temperature is allowed to adjust to the presence of the forcing agent. Fi and Fa, involving only atmospheric radiation, can be calculated rapidly with precise results for a given model, but it is not practical to compute them for some forcing mechanisms such as the indirect aerosol effect. Fs can be computed with a (fixed SST) global climate model for all forcing mechanisms, but accurate evaluation requires a long model run because of unforced atmospheric variability. Fe, dependent on the simulated response of a coupled atmosphere-ocean climate model, requires even more computer resources for accurate definition because of greater unforced variability in coupled models.
Fi, Fa, Fs and Fe form a sequence that usually should provide successively better predictions of the climate model global response to a given forcing mechanism, because each forcing incorporates further climate feedback mechanisms. For this reason, the forcings are successively more model-dependent, and tabulation of several forcings aids comparison and analysis of climate responses from different models.

Greenhouse gases
3.1.1. Well-mixed GHGs. Temporal changes of long-lived GHGs can be approximated as globally uniform. Global mean values of gas amounts, from Table of Hansen and Sato (2004) (see also data.giss.nasa.gov/modelforce/ghgases), were obtained from appropriate area weighting of in situ and ice core measurements at specific sites, as described by Hansen and Sato (2004). Trace gas measurements are from Montzka et al. (999), as updated at ftp://ftp.cmdl.noaa.gov/hats/Total_Cl_Br. Gas amounts are converted to forcings (Fa), for IPCC and alternative scenarios, using ModelE radiation code, except the minor MPTGs (Montreal Protocol Trace Gases) and OTGs (Other Trace Gases), which use conversion factors provided by IPCC (200). The gas amounts are shown in Fig. 2  Fig. summarizes climate forcings by well-mixed GHGs and the annual growth of this forcing. The growth rate declined from 5 W/m 2 per century 25 years ago to 3½ W/m 2 per century more recently as the growth of MPTGs and CH 4 declined.
3.1.2. Other greenhouse gases. The principal short-lived, and thus inhomogeneously mixed, anthropogenic greenhouse gas is ozone (O 3 ). O 3 change of the past century includes both a long-term tropospheric O 3 increase due mainly to human-made changes of CH 4 , NO X (nitrogen oxides), CO (carbon monoxide), and VOCs (volatile organic compounds), and O 3 depletion (mainly in the stratosphere) in recent decades due to humanmade Cl and Br compounds (halogens). The tropospheric historical O 3 change in our climate simulation is from a chemistry climate model  driven by prescribed changes of O 3 precursor emissions and climate conditions. Stratospheric O 3 change in recent decades is included based on observational analyses of Randel and Wu (1999). Some influence of strato-spheric O 3 depletion on tropospheric O 3 change is included by extrapolating O 3 trends in the Antarctic all the way to the surface and reducing O 3 growth rates in the Arctic troposphere. Fig. 2 shows the global mean total O 3 versus time, the O 3 change as a function of altitude and latitude for the periods 880-979 and 979-997, and the stratosphere and troposphere O 3 changes for these same periods as a function season and latitude. The resulting O 3 adjusted forcing, with global average 0.28 W/ m 2 over 880-2003, is illustrated in Fig. 0b of Efficacy (2005).
Future stratospheric O 3 may increase as halogens decline in abundance as a result of emission constraints, but the O 3 amount will also be affected by climate change. Tropospheric O 3 would increase strongly for most IPCC (200) scenarios of CH 4 and other O 3 precursors (Gauss et al. 2003). However, it is possible that efforts to control air pollution and climate change may result in tropospheric O 3 levels leveling off or even declining. Given that future O 3 changes are highly uncertain and probably not a dominant forcing, we keep O 3 in our simulations of the 2 st century fixed at the 1997 values (Hansen et al. 2002).
The other inhomogeneously mixed anthropogenic GHG included in our climate simulations is CH 4 -derived stratospheric H 2 O. Production of stratospheric H 2 O, based on the two-dimensional model of Fleming et al. (999), is proportional to tropospheric CH 4 amount with a two-year lag. As shown in Fig.  9 of Efficacy (2005)  Change in the total greenhouse gas effective climate forcing between 880 and 2003 is Fe ~ 3.0 W/m 2 (Table ). Our partly subjective estimate of uncertainty, including imprecision in gas amounts and radiative transfer is ~±5%, i.e., ±0.45 W/m 2 . Comparisons with line-by-line radiation calculations (A. Lacis and V. Oinas, personal communication) suggest that CO 2 , CH 4 and N 2 O forcings in the climate model are each accurate within several percent, but the CFC forcing may be 30-40% too large. If that correction is needed, it will reduce our estimated GHG forcing to Fe ~ 2.9 W/m 2 . The documented version of modelE, employed for simulations reported here, in modelE (2006), Efficacy (2005), andDangerous (2006) has GHG forcings as defined in Table 1

Tropospheric aerosols.
Aerosol distributions in our climate model in 2000 are shown in Fig. 3a. All aerosols except sea salt and soil dust are time-variable in the current model, i.e., sulfate, black carbon (BC), organic carbon (OC), and nitrate. The changing geographical distributions of sulfate, BC and OC are from an aerosol-climate model (Koch 200) that uses estimated anthropogenic aerosol emissions based on fuel use statistics and includes temporal changes in fossil fuel use technologies , but the BC and OC amounts are normalized by time and space independent factors defined below. BC and OC sources are fossil fuels and biomass burning, including agricultural fires that occur mainly in the tropics, and forest fires that occur mainly in Asia and North America. Aerosols from biofuels are not included. OC emissions are taken as proportional to BC emissions, with the OM/BC mass ratio being 4 for fossil fuels and 7.9 for biomass burning (Liousse et al. 996), where it is assumed that the organic matter OM = .3xOC. The OC/BC ratios are reduced further, by a small amount, via separate normalization factors for OC and BC defined below. The emission ratios are intended to implicitly account for secondary OC formation (Koch 200). Global aerosol distributions are computed with the transport model for 850,875,900,925,950,960,970,980,990, interpolated linearly between these dates, and kept constant after 990.
Aerosols are approximated as externally mixed for radiative calculations. Absorption by BC is increased a factor of two over that calculated for external mixing to approximate enhancement of absorption that accompanies realistic internal mixing of BC with other aerosol compositions (Chylek et al. 995). BC and OC masses of Koch (200) were multiplied by .9 and .6, respectively, to obtain best correspondence with multispectral AERONET observations ). The GISS model includes the effect of humidity on sulfate, nitrate and OC aerosol sizes (modelE 2006), which increases aerosol optical thickness and radiative forcing. Andreae and Gelencser (2006) describe widespread occurrence of "brown carbon", produced especially by biomass burning. Brown carbon is not included as an aerosol per se in our modeling, but it is approximated by the combination of black and organic carbon. Spectral variation of absorption by organic carbon is based on measurements of Kirschstetter et al. (2004). Although more detailed treatment of carbonaceous aerosols is desirable, it is difficult to justify that with current measurement limitations (Novakov et al. 2005).
Dry nitrate in 990 is from Liao et al. (2004), with nitrate at other times proportional to global population (www.un.org/ population). Nitrate aerosol size is taken as similar to the overall aerosol size distribution (Ten Brink et al. 997), with effective radius 0.3 μm and effective variance 0.2. The nitrate aerosol refractive index at 633nm wavelength (.55 for the dry aerosol) is from Tang (996) and Tang and Munkelwitz (99), with spectral variation the same as for sulfate aerosol. Fig. 3b shows the 990 clear-sky and cloudy-sky (i.e., global mean) optical thickness of aerosols in the model. The cloudysky aerosol optical thickness, because of higher humidity in cloudy gridboxes, is twice the clear-sky case, but the clear-sky value is appropriate for comparison with observations. Fig. 3b also shows the aerosol adjusted forcing, Fa, and the reduction of downward solar radiation at the surface due to aerosols. The left side of Fig. 3b is the effect of all aerosols present in 2000, while the right side shows the change between 850 and 2000.
The aerosol effective forcing, i.e., the product Fe = EaFa, varies with aerosol type ( Table 2 in Efficacy 2005). Fe differs notably from Fa for BC aerosols, as the efficacy of BC is less than 100%. The efficacy depends on the vertical and geographical distribution of the BC, with the reduction of forcing being greater for biomass burning BC (E ~ 60%) than for fossil fuel BC (E ~ 80%) (Efficacy 2005). Fig. 3c shows the time dependence of the global mean aerosol optical thickness and effective forcing.
3.2.2. Aerosol indirect effect. We use the same aerosol indirect effect on clouds as in Efficacy (2005), i.e., a parameterization based on empirical effects of aerosols on cloud droplet number concentration (Menon and Del Genio 2006). It is argued in Efficacy (2005), based on empirical evidence, that the predominant aerosol indirect effect occurs via cloud cover change, and that the global-mean magnitude of the indirect aerosol forcing, in recent years relative to 850, is of the order of -W/ m 2 , with a largely subjective estimate of uncertainty of at least 50%.
Thus, as in Efficacy (2005), the scale factor in the indirect effect on cloud cover is chosen to yield a forcing -W/m 2 . However, this choice should be thought of as being an approximation for the entire aerosol indirect effect, as we do not explicitly include a cloud albedo effect. The indirect forcing that we employ is smaller than in most models reviewed by Lohmann and Feichter (2005), but some recent studies suggest even smaller values, e.g., -0.6 to +0. W/m 2 (Penner et al. 2006) and -0.3 to -0.4 W/m 2 for the albedo effect (Quaas and Boucher 2005).
The aerosol indirect effect, as defined by this parameterization, depends on the logarithm of the concentration of soluble aerosols and thus the effect is non-linear, with added aerosols becoming relatively less effective as their number increases. Time-dependent aerosols are anthropogenic sulfates, BC, OC and nitrates, as shown in Fig. 3. Maps of the resulting aerosol indirect forcing are provided in Efficacy (2005).
The net 880-2003 direct aerosol forcing in our transient climate simulations (Table ) is Fa = -0.38 W/m 2 and Fe = -0.60 W/m 2 . The total aerosol forcing including the indirect effect is Fe = -.37 W/m 2 . Empirical data for checking model-based temporal changes of tropospheric aerosol amount, e.g., ice core records (IPCC 200;, are meager. There is a wide spread in aerosol properties inferred from current satellite sensors, but more accurate results are anticipated from future polarization measurements designed to retrieve aerosol and cloud particle properties (Mishchenko et al. 2004(Mishchenko et al. , 2006. Our largely subjective estimate of the uncertainty in the net aerosol forcing is at least 50%. 3.2.3. Stratospheric aerosols. The history of stratospheric aerosol optical thickness that we employ is an update of the tabulation of Sato et al. (993) available at data.giss.nasa.gov/modelforce/strataer. The effective particle radius is ~0.2 μm when the optical depth is small, increasing to ~0.6 μm after the largest volcanoes, as specified on the indicated website and also illustrated by Hansen et al. (2002). Aerosols are assumed to have the optical properties of 75% sulfuric acid solution in H 2 O (Palmer and Williams 975).
The adjusted forcing by stratospheric aerosols in our model, for aerosols distributed over most of the globe, is (Efficacy 2005) where τ is the optical thickness at λ = 0.55μm. Because the efficacy of stratospheric aerosol forcing is Ea ~ 91% (Efficacy 2005), the effective forcing is Published values for the coefficient in (1) range from 20 (Tett et al. 2002) to 30 (Lacis et al. 992), with values from different GISS models ranging from 2 to 30. As discussed in Efficacy (2005), the result depends on the accuracy of spectral and angular integrations, model vertical resolution, and aerosol distribution. The present result is based on the most accurate of the GISS models, with estimated uncertainty ±15 percent (Efficacy 2005).
Satellite observations of the planetary radiation budget perturbation following the 99 Mount Pinatubo eruption (Wong et al. 2004) provide a strong constraint on the aerosol forcing for that volcano (Fig. in Efficacy 2005). That comparison suggests that the above relationship between aerosol optical thick-ness and climate forcing is accurate within about 20%.
The stratospheric aerosol forcing becomes more uncertain toward earlier times. We estimate the uncertainty as increasing from ±20% for Pinatubo to ±50% for Krakatau. At intervals between large eruptions prior to the satellite era, when small eruptions could have escaped detection, there was a minimum uncertainty ~0.5 W/m 2 in the aerosol forcing.
Stratospheric aerosol optical thickness was zero in our climate model control run. Our future control runs will include stratospheric aerosols with τ (λ = 0.55μm) = 0.0125, the mean visible optical thickness for 850-2000, with the rationale that this is a better estimate of the long-term mean stratospheric aerosol optical depth than is the use of zero aerosols. We recommend that other researchers include such a mean aerosol amount in control runs used as spin-ups for transient simulations, because the internal ocean temperature will be adjusted to a mean stratospheric aerosol amount. Because the mean aerosol amount is almost 0% of the Krakatau amount, the modeled Krakatau cooling based on a control run with mean aerosol amount is reduced almost 0%, bringing model and observations into better agreement (see Electronic Supplementary Material).

Other Forcings
3.3.1. Land use. Changes of land use, especially deforestation that has occurred at middle latitudes and in the tropics, can cause a large regional climate forcing. Hansen et al. (998a) and Betts (200) independently calculated a global forcing of -0.2 W/m 2 for replacement of today's land use pattern with natural vegetation. Much of the land use change occurred prior to 880. In Efficacy (2005) the time-dependent land use data sets of Ramankutty and Foley (999), illustrated by Foley et al. (2005), were found to yield a forcing Fe = -0.09 W/m 2 for the 880-990 change. This forcing may not fully represent land use effects, as there are other land use activities, such as irrigation, that are not included. We do not include the effect of biomass burning burn scars on surface albedo, which Myhre et al. (2005) show is a relatively small effect. Myhre and Myhre (2003) estimate an uncertainty range from -0.6 to +0.5 W/m 2 for the land use climate forcing, with positive forcings from irrigation and human plantings, but they conclude that the net land use forcing is probably negative.
We exclude land cover changes occurring as a feedback to climate change, except to the extent they are implicitly included in the Ramankutty and Foley (999) data set. Such land cover changes may have been moderate in the past century, but if the global warming trend of the past few decades continues vegetation feedbacks in the Arctic may be substantial (Chapin et al. 2005). This effect can be included in simulations via a dynamic vegetation treatment, but it is not included in our present model.
Our subjective estimate is that the global mean land use forcing for 880-2000 lies between zero and -0.2 W/m 2 . However, the global value is less relevant than the regional forcing, which can be as much as several W/m 2 , as shown in Fig. 7 Clarke and Noone (985), from measurements around the Arctic in the early 1980s, showed that soot on snow and ice significantly reduced the albedo for solar radiation. Hansen and Nazarenko (2004) estimated that spectrally-integrated albedo changes of .5% in the Arctic and 3% in snow-covered Northern Hemisphere land regions would yield a global climate forcing 0.6 W/m 2 and equilibrium global warming 0.24°C. However, Grenfell et al. (2002) and Sharma et al. (2004) found smaller soot amounts in more recent measurements, perhaps because of decreased emissions from North America, Europe and Russia, even though emissions from the Far East may have partially replaced those sources .
Climate forcing by soot in snow is difficult to simulate well, because albedo change depends sensitively on soot particle structure, how it is mixed in the snow (Warren and Wiscombe 985;Bohren 986), and how much soot is carried away in snowmelt as opposed to being retained near the snow or ice surface. We parameterize snow albedo change as proportional to local BC deposition with a scale factor yielding a conservative estimate of the soot effect, specifically a global forcing Fa ~ 0.05 W/m 2 in 990. However, Fe ~ 0.4 W/m 2 in 990, because of the high efficacy of snow albedo forcing (Efficacy 2005; see also Supplementary Material).
The soot albedo effect is imprecise because of the near absence of accurate albedo measurements and soot in snow inventories and the high efficacy of even a small snow albedo change.
Our subjective estimate is that the present soot albedo forcing is probably in the range Fa = 0-0. W/m 2 . In some of our simulations there was a programming error that caused this forcing to have an incorrect geographical distribution (see Supplementary Material). The error was corrected for 'all forcings' and snow albedo alone ensembles, but not for simulations illustrated in Figs. 6 because the effect was negligible for the comparisons illustrated in those figures.
3.3.3. Solar irradiance. The variations of total solar irradiance in our transient climate simulations submitted to IPCC, shown by the solid curve in Fig. 4, are based on Lean (2000). The irradiance changes are largest at ultraviolet wavelengths. The resulting change of climate forcing for 880-2003 (880-2000) is Fa = 0.24 W/m 2 (0.30 W/m 2 ), and Fe = 0.22 W/m 2 (0.28 W/m 2 ) based on the linear trend, as the efficacy of the solar forcing is Ea ~92% (Efficacy 2005). We do not include indirect effects of solar irradiance changes on O 3 (Haigh 994;Hansen et al. 997b;Shindell et al. 999;Tourpali et al. 2005), which may enhance the direct solar forcing, because the solar forcing itself is moderate in magnitude and uncertain. Shindell et al. (200) conclude that the solar indirect radiative forcing via ozone is small, but there may be dynamical feedbacks that are significant for regional climate change Baldwin and Dunkerton 2005;Tourpali et al. 2005). Lean et al. (2002) call into question the long-term solar irradiance changes, such as those of Lean (2000), which have been used in many climate model studies including our present simulations. The basis for questioning the previously inferred long-term changes is the realization that secular increases in cosmogenic and geomagnetic proxies of solar activity do not necessarily imply equivalent secular trends of solar irradiance. Thus, it is useful to compare the above solar irradiance forcing with a solar irradiance scenario that includes only the well-established Schwabe ~ year solar cycle, indicated by the dotted curve in Fig. 4. In this alternative solar irradiance forcing history the 880-2003 forcing based on the linear trend is Fa = 0.0 W/m 2 and Fe = 0.09 W/m 2 .
The fact that proxies of solar activity do not necessarily imply long-term irradiance change does not mean that long-term solar irradiance change did not occur. Ample evidence for long-term solar change and a link to climate has long been recognized (Eddy 976), and solar models admit the possibility of such change. Hoyt and Schatten (993), on heuristic grounds, argue for solar change at least comparable to that of Lean (2000); their inferred solar change is somewhat greater than that of Lean (2000) and their secular increase of irradiance begins earlier in the 20 th century. At least until precise measurements of irradiance extend over several decades and more comprehensive solar models are available, solar climate forcing is likely to remain highly uncertain. Fig. 5 and Table summarize  Aerosols, based on our estimates, yield a forcing Fe = -.37 W/m 2 in 2003 relative to 880. Thus the aerosol forcing in our estimate is about half of the GHG forcing, but of opposite sign. The aerosol indirect effect contributes more than half of the net aerosol forcing.
The sum of all these forcings is Fe ~ Fs ~ .90 W/m 2 in 2003. However, it is more accurate to evaluate the net forcing from the ensemble of simulations carried out with all forcings present at the same time (Efficacy 2005), thus accounting for any non-linearity in the combination of forcings and minimizing the effect of noise (unforced variability) in the climate model runs. All forcings acting together yield Fe ~ .75 W/m 2 in 2003.
Uncertainty of the net forcing is dominated by the aerosol forcing, which we suggested above to be uncertain by 50%. In that case, the net forcing is uncertain by ~ W/m 2 , implying uncertainty by about a factor of three for the net forcing. Reduction of this uncertainty requires better data on aerosol direct and indirect forcings.

Alternative Data Samplings and the Krakatau Problem
Comparisons of simulated climate and observations commonly involve choices that influence how well the model and data appear to agree. Choices of surface temperature data deserve scrutiny, because surface temperature provides the usual measure of long-term 'global warming' as well as a test of climate response to large volcanic eruptions. A number of researchers (e.g., Harvey and Kaufmann 2002) have noted that large volcanoes often do not produce the cooling predicted by models. In Supplementary Sect. S we examine alternative comparisons of model and observations. Here we briefly summarize principal conclusions from those comparisons.
The model and observations agree more closely when the model is sampled at the locations of observations. The main improvement occurs in the last two decades of the 9th century.
Although it may thus seem best to always appropriately subsample the model results, there would be two disadvantages to that approach. First, it makes comparison with other models difficult, because most of these are unlikely to be sampled at the same times and places defined by our specific data sets. Second, it requires additional work and introduces the possibility of error. Because we find that the differences are small in most cases, the global means in this paper are true global means, not a sample at station locations.
Sect. S also shows geographical patterns of temperature response after Krakatau and Pinatubo. The model is found to reproduce large scale summer cooling the year after both large volcanic eruptions, and winter cooling with warming in Eastern Europe. Although we cannot fully resolve the issues concerning climate response after large volcanoes, we find the model to be in reasonable accord with observations. This provides support for the model's ability to respond realistically to global forcings.

Climate Response in Historical Period
Climate model responses to the above forcing mechanisms have been reported in the literature. Nevertheless, side-by-side comparison of responses to each forcing by a single model with documented sensitivity has merit and aids interpretation of the model and real world response to all forcings acting at once. Sect. 5. sets the context by showing the response of global mean temperature, planetary radiation balance, and ocean ice cover to all forcings acting at once, and examining the contribution of each forcing to global mean surface temperature change. Sect. 5.2 illustrates the spatial and seasonal distribution of the temperature response to all forcings and individual forcings.  Table 2. Although successive versions of the Christy et al. (2000) tropospheric analysis have moved from a cooling trend to significant warming, a recent version (5.) of their analysis (vortex.nsstc.uah.edu/data.msu) has less warming than that in the analysis of Mears et al. (2003) (Table  2). Recent assessment of several data sets (Karl et al. 2006) concludes that the warming trends of Mears et al (2003) are more realistic than those in the analysis of Christy et al. (2000). Fig. 6 includes the microwave lower stratosphere (LS), troposphere/stratosphere (T/S), and middle troposphere (MT) analyses (described as MSU4, MSU3 and MSU2 in prior papers), which are based on near-nadir observations. Use of near-nadir observations yields broad weighting functions, i.e., the derived temperatures refer to thick atmospheric layers, but it avoids increased errors and uncertainty that arises in combining multiple slant-angle data to obtain sharper weighting functions. The LS, T/S and MT levels have weighting functions that peak at altitudes ~5-20 km, ~0 km and ~5 km, respectively ( Fig. 2 on Mears et al. web site given above). Note that ~5% of the MT signal comes from the stratosphere, despite its description as "middle troposphere".
The simulated global LS warming following the 99 Mt. Pinatubo eruption agrees closely with observations, which is an improvement over the model results of Hansen et al. (2002). The improved response came when the model top was raised from 0 hPa to 0. hPa with higher vertical resolution in the stratosphere. The simulated 25-year (979-2003) Table 2.
The simulated T/S temperature trend (+0.0°C/decade) is greater than in the analysis of Mears et al. (0.03°C/decade). Temperature change at this atmospheric level is very sensitive to surface temperature. If we replace the coupled model SST with observed SST (ocean A), the discrepancy with the satellite observation largely disappears (Table 2), indeed ocean A yields no warming at that level. As discussed in Sect. 5.3.3, tropical SST variability causes a large variability at the T/S level.
The simulated 25-year MT temperature trend (Fig. 6, Table  2) with all forcings is +0.4°C/decade (+0.5°C/decade for each of the altered aerosol and solar irradiance histories discussed in Sect. 5.4), which is also in good agreement with the observational analysis of Mears et al. (+0.3°C/decade) but not with the +0.05°C/decade of Christy et al. Observations have greater interannual variability than the model, which is expected as our present coupled model has only slight El Nino-like tropical variability and has unrealistically few sudden stratospheric warmings (Sect. 2.1). The large observed tropospheric fluctuation in 998, for example, is associated with an unusually strong El Nino.
A sharper lower tropospheric (LT) weighting function can be obtained from linear combination of multiple slant angle microwave observations. Analyses of such LT trends by Christy et al. (2000) led to the claim that the lower troposphere was cooling or at least warming much less than surface temperature trends reported by Jones et al. (999) and Hansen et al. (200). Fu and Johanson (2005) use linear combinations of near-nadir observations as an alternative approach to obtain tropospheric temperature trends, thus showing that the LT temperature trend of Christy et al. (2000) is inconsistent with the near-nadir (MT and LS) data of Christy et al. (2000). A recent derivation of LT temperature trends by Mears and Wentz. (2005), with an improved diurnal variation of instrumental calibration, yields an LT temperature trend (+0.9°C/decade) that is consistent with our climate simulations (+0.8°C for standard forcings and +0.20°C/decade for the alternative forcings). The recent Christy et al. LT trend, from version 5. on their web site in September 2005, with their own improved diurnal correction, is +0.2°C/ decade, which is larger than their previous results but less than our model using known forcings and less than the Mears et al. analysis of observations (Table 2).
We note that our LS, T/S, MT and LT temperature trends are all obtained using simple vertical weighting functions (Hansen et al. 998b). Resulting global temperature trends for LS, T/S and MT differ little from those obtained with elaborate radiative transfer calculations that include refraction of the microwaves and variable surface emissivity (Shah and Rind 998). However, our calculated LT temperature trend will not account for changes in atmospheric water vapor and surface emissivity, which are substantial for the (slant angle) LT data. Thus our modeled LT change, although a good measure of the model's lower tropospheric temperature change, may not be accurately comparable to the satellite-derived LT 'temperature' trends. For this reason, we emphasize LS and MT data, and, to lesser degree the shorter record of T/S data.
The simulated 880-2003 global surface temperature change (upper right of Fig. 6), agrees reasonably well with observations, although the 24-year warming based on the linear trend is slightly (~0.°C) less than observed (Table 2). Two noticeable discrepancies with the temporal variation of observed global surface temperature are the absence of strong cooling following the 883 Krakatau eruption and the lack of a warm peak at about 940. We suggested above (Sect. 4.2) that the near-absence of observed cooling after Krakatau may be, at least in part, a problem with the ocean data.
The model's fit with peak warmth near 1940 depends in part on unforced fluctuations, e.g., the runs of , with nearly identical forcings to those in this paper, appear to agree better with observations. As expected, the runs in which the solar forcing includes only the Schwabe -year solar cycle ( Fig. 4), available on the GISS web-site and included in Table  2 as AltSol, do not produce peak warmth near 940. AltSol also differs from the standard "all forcing" scenario in having the sulfate forcing reduced by 50%, thus yielding an 880-2003 global warming of 0.64°C.
It may be fruitless to search for an external forcing to produce peak warmth around 940. It is shown below that the observed maximum is due almost entirely to temporary warmth in the Arctic. Such Arctic warmth could be a natural oscillation (Johannessen et al. 2004), possibly unforced. Indeed, there are few forcings that would yield warmth largely confined to the Arctic. Candidates might be soot blown to the Arctic from industrial activity at the outset of World War II, or solar forcing of the Arctic Oscillation (Shindell et al. 999;Tourpali et al. 2005) that is not captured by our present model. Perhaps a more likely scenario is an unforced ocean dynamical fluctuation with heat transport to the Arctic and positive feedbacks from reduced sea ice. Fig. 6 also illustrates the planetary energy imbalance, which has grown in recent decades because of the rapid increase of the net climate forcing (Fig. 5) and the ocean's thermal inertia. The simulated imbalance averaged about 0.7 W/m 2 in the past decade. As discussed in Supplementary Material, our present simulated energy imbalance for the past decade is ~0.02 W/m 2 less than found by , because the stratospheric O 3 depletion in the latter paper inadvertently was only 5/9 as large as that estimated by Randel and Wu (999).
The simulated decrease of ocean ice cover over the past century, from ~4.25% of the Earth's surface area to ~4%, is only about half as large as suggested by analysis of observations . Although sea ice observations contain substantial uncertainty, we note that sea ice is more stable in the present model than in previous GISS models. The increased stability of sea ice apparently accounts for the slightly lower sensitivity, 2.7-2.9°C for doubled CO 2 , of modelE compared with ~3°C for doubled CO 2 with the prior GISS model (Hansen et al. 2002). Fig. 7 and Table 3 show global mean simulations for the same climate forcings as in Fig. 6, but with alternative oceans. Fig. 7a is for ocean A, i.e., specified SST and sea ice that follow the history of . Fig. 7b is for ocean B, i.e., the Q-flux ocean (Hansen et al. 984;Russell et al. 1985), with specified horizontal ocean heat transports inferred from the ocean A control run and diffusive uptake of heat anomalies by the deep ocean.

Effect of alternative oceans.
Stratospheric temperature change is similar for ocean A and ocean C, but there is greater year to year variability with ocean A because, unlike the coarse-resolution Russell ocean, the observed SSTs and sea ice capture tropospheric variability such as that due to El Ninos, which in turn affects stratospheric temperature. The resulting net radiation at the top of the atmosphere also has greater year-to-year variability with ocean A, and there is an off-set by a few tenths of W/m 2 with ocean A, reflecting the fact that the ocean A model with 880 forcings was out of radiation balance by that amount.
The simulated 880-2003 global surface temperature change is larger for ocean A than observed, even though ocean A is driven by the SSTs used to compute observed global temperature. We show in the Supplementary Material that this discrepancy seems to be due mainly to calculation of surface air temperature change over the ocean at an altitude of 0 m in model E. This difference can be reduced by using the temperature of the first ocean layer as the "surface" temperature, but that approach has not been the practice in prior climate studies.
Global mean changes obtained with the Q-flux ocean ( Fig.  7b) driven by all the climate forcings are similar to those in the coupled dynamical ocean model (Fig. 6). The change in sea ice cover is again much less than the  analysis of observations suggests. The sea ice model, calculated on the atmospheric grid, is the same for all ocean representations (mod-elE 2006). Lack of sufficient sea ice response may be related to under-prediction of seasonal sea ice change in the modelE control runs. Fig. 8 shows the global surface temperature response to individual forcings, and the response to all forcings acting at once. There are nine individual forcings, as opposed to 0 in Fig. 5a, because the reflecting and absorbing (BC) aerosols are included together in the tropospheric aerosol runs. Five 880-2003 runs were made for each forcing, with the runs started from control run conditions at intervals of 25 years. The control run was within ~0.2 W/m 2 of radiation balance at the points of experiment initiation, so model drift was small. The effect of model drift was reduced by subtracting the change of each diagnostic quantity for the corresponding year of the control run.

Response to individual forcings.
The response to well-mixed greenhouse gas (GHG) forcing is a global warming of ~.0°C over the period 880-2003. There is a slow warming of 0.25°C over the first 75 years, and then a rapid approximately linear warming of 0.75°C. The change in the warming rate reflects the jump of GHG growth rates between 950 and 975 driven about equally by Montreal Protocol Trace Gases (MPTGs) and an increase of the CO 2 growth rate ( Fig. 4 of Hansen and Sato 2004). A decline in the growth rate of the GHG forcing occurred near 990 due to halt in growth of MPTGs and slowdown of CH 4 growth (ibid.; see also Fig. above), and this is reflected in the simulated global response to the well-mixed GHG forcing.
Stratospheric (volcanic) aerosols, despite their brief lifetime (e-folding decay time ~ year), have a multi-decadal effect on simulated temperature because of clustering of volcanoes near the beginning of the 880-2003 period and from 963-99. Thus volcanoes, specifically the minimal activity during 1900-950 compared with the late 9th century and the period beginning 963, contribute to the relative global warmth at mid-century, as has been noted previously (Tett et al. 999;Harvey and Kaufmann 2002).
The global coolings due to aerosol direct and indirect forc-ings are consistent with the temporal variation of their forcings (Fig. 5). Their combined global cooling reaches ~0.55°C by 2003.
Ozone and solar irradiance changes cause global warming +0.08 and +0.07°C, respectively, over the 24-year period. Land use change causes a cooling -0.05°C.
Global mean surface temperature responses to the forcings by CH 4 -derived stratospheric H 2 O and the soot snow albedo effect are small, consistent with the small forcings. The small forcing for CH 4 -derived H 2 O occurs because, at least in our model, the large increase of middle stratospheric H 2 O caused by increasing CH 4 does not extend down to the tropopause region, where it would be effective in altering surface temperature. The soot snow albedo forcing is small by assumption in the absence of adequate measurements (Sect. 3.3.2).
Observed global warming, as well as the global warming in the model driven by all forcings, has been nearly constant at about 0.5°C/decade over the past 3-4 decades, except for temporary interruptions by large volcanoes. This high warming rate is maintained in the most recent decade despite a slowdown in the growth rate of climate forcing by well-mixed GHGs ( The assumption that global aerosol amount approximately leveled off after 990 is uncertain, because adequate aerosol observations are not available. However, there is evidence that aerosol amount declined after 980 in United States and Europe (Schwikowski et al. 999;Preunkert et al. 200;Liepert and Tegen 2002), consistent with a leveling off or decline in 'global dimming' (Wild et al. 2005). Aerosol emissions probably continued to increase in developing countries such as China and India ). An implicit conclusion is that future global warming may depend on how the global aerosol amount continues to evolve (Andreae et al. 2005), as well as on the GHG growth rate.

Spatial and Seasonal Temperature Change
We present results for each of 0 climate forcings, comparing these with appropriate standard deviations for determination of significance. This approach results in small figures, yet the figure size is sufficient for intended interpretations, which emphasize planetary scale features in the response. Indeed, the figures are a pithy alternative to detailed tables and discussion. This organization has the merit of consistency, and it provides useful information even for forcings that yield mostly 'insignificant' response. 5.2.1. Global maps of surface temperature change.  Table , which gives the difference of the 5-year running mean at the start and end of the indicated time interval. The linear trend yields a less noisy map. We provide both global mean results for a more complete description.
The lowest row in Fig. 9 shows the standard deviation (σ) in the control run, specifically σ for the temperature change in all periods of the control run of length 24, 54, 6, 40 and 25 years in the second half of the 2000-year control run, by which time the control run was near equilibrium, e.g., within 0. W/m 2 of radiation balance with space. Nominally, if the absolute value of the change simulated for a given climate forcing (the mean change for a 5-member ensemble of runs) exceeds σ, the change is significant at about the 95% level. Note that σ decreases approximately as the inverse square root of the period, consistent with expected uncertainty in estimating a trend imbedded in random noise.
There is substantial congruence in the spatial response to different global forcings, e.g., greenhouse gases and tropospheric aerosols, even though the forcing distributions differ. This canonical response is shown more clearly in Efficacy (2005) and examined quantitatively by Harvey (2004). It does not apply to highly local forcings such as land use and snow albedo.
The well-mixed GHGs by themselves cause a global mean warming of ~°C, about 50% larger than observed warming, as shown also by the line graphs (Fig. 8). Surface temperature response to GHGs varies a lot spatially. Almost all land areas warm more than °C while most ocean areas warm between 0.5 and °C. However, the Arctic warms more than 2°C, while the circum-Antarctic ocean warms only about 0.2°C. Large Arctic warming is an expected result of the positive ice/snow albedo feedback. The small response of the circum-Antarctic ocean surface is mainly a result of the inertia due to deep ocean mixing in that region (Manabe et al. 1991), although deficient sea ice in the control run may contribute. A larger response is obtained in that region in the Efficacy (2005) experiments in which the full 880-2000 forcing is maintained for 20 years. Cooling or minimal warming in the South Pacific sector of the Southern Ocean near the dateline is also obtained in many other coupled models (Kim et al. 2005).
All forcings together yield a global mean warming ~0.°C less than observed for the full period 880-2003. The primary location of deficient warming is in Europe and in Eurasia downwind of Europe. Indeed, Fig. 9 shows that the model produces cooling over Europe, where observations show substantial warming. Fig. 9 also indicates that this regional cooling is due to aerosols, for which the direct effect over Europe is about -°C and the indirect effect about -0.5°C. Land use change contributes a cooling of about -0.5°C, but in a region largely confined to the area of assumed large 20 th century agricultural development (see also Figs. 7 and 8 in Efficacy (2005)). Independent comparison of our aerosol optical depths with AERONET data (Holben et al. 200;Dubovik et al. 2002) indicate an excessive aerosol amount in Europe and downwind, as discussed in Sect. 5.4, where we carry out sensitivity experiments with aerosol amounts altered to be more consistent with regional AERONET data. Note in Fig.  9 that a large fraction of observed European warming occurred during 979-2003, when some observations suggest that aerosols had begun to decline in Europe (Schwikowski et al. 999;Preunkert et al. 200;Liepert and Tegen 2002). Our simulations had little aerosol change over Europe in 979-990 and no change after 990, except for nitrates, which increased in proportion to global population (Sect. 3.2.). 5.2.2. Zonal mean surface temperature change versus time. Fig. 0 shows the zonal mean surface air temperature ver-sus time relative to the base period 90-930. The early base period allows the total temperature change at adjacent latitudes to be compared readily. The period prior to 900 is not suitable for a base period because of limited spatial coverage of observations.
The upper two panels on the left side of Fig. 0 compare observations from only meteorological stations and the combined station plus SST observations, in both cases using the data as analyzed by Hansen et al. (200). Use of SSTs slightly reduces the analyzed temperature change over the century. Damping of the temperature change is consistent with the longer response time of the ocean, but also could be a consequence of the larger unforced variability of temperature over land. The difference in the two observational analyses in the Antarctic region is a consequence of using temperatures anomalies from the nearest latitudes with observations to define the  base period values for the Antarctic region. The early century anomalies closest to Antarctica are rather different in the two data sets, the meteorological stations showing a warming over the 900present period, while the ocean data set has negligible change.
Observations are too meager to say which data set is more accurate.
Surface air temperature in the model with observed timevarying SST and all forcings is shown in the third panel of Fig.  0. The fourth panel has observed time-varying SST, SI and forcings. Changing sea ice has a noticeable effect at high latitudes because of the large difference between surface air temperature and ocean temperature that can exist in the presence of a sea ice layer. An additional ensemble of runs driven only by changing SST and SI, with radiative forcings fixed at 1880 values, was run and is available on the GISS web site. The corresponding diagram appears very similar to the fourth diagram in Fig. 0, as forcings have little effect on surface air temperature if the ocean and sea ice are specified.
Oceans B (Q-flux) and C (coupled model, Russell's ocean) yield similar zonal mean surface temperature responses to all forcings. As expected, neither model captures substantial ENSO variability. Simulated 880-2003 warming is slightly larger than observed in the tropics, but smaller than observed at Northern Hemisphere middle and high latitudes. Cooling due to volcanoes, e.g., after 963 (Agung), 982 (El Chichon), and 99 (Pinatubo), is greater than observed, although the discrepancy is exaggerated in these plots by the deficient long-term warming trend at northern latitudes. Note that the Q-flux model has greater warming in the Southern Ocean, where deep mixing in the dynamical Russell ocean limits the surface warming.
The bottom two panels on the left side of Fig. 0 show the unforced variability in the control run, for a single 24-year period and for the mean of five 124-year periods. The latter mean is the model's noise level for a 5-member ensemble mean, which is an appropriate measure of significance of features in the ensemble responses to forcings shown in other parts of the figure. However, the real world made only a single "run" through the 880-2003 period, so the noise level in a single period of the control run is also a useful measure. As expected, the unforced variability in a single run is about twice as large as in the ensemble mean.
The well-mixed GHGs and tropospheric aerosols yield responses far larger than unforced variability, the signals being larger in the Northern Hemisphere, especially for aerosols. The multi-decadal response to stratospheric aerosols is apparent, as well as shorter-term responses. Forcings with a global magni-tude of a few tenths of a W/m 2 , such as ozone and solar irradiance, yield noticeable responses, with the response to ozone being mainly in the Northern Hemisphere and the response to solar forcing mainly in the tropics. The weak global forcings, i.e., land use and snow albedo, yield weak responses with expected signs. 5.2.3. Zonal mean temperature change versus altitude and season. Fig. a shows the zonal mean temperature change versus altitude for 880-2003 and three subperiods (880-940, 940-979, 979-2003). The bottom row is two times the standard deviation (σ) of temperature change among periods of relevant length (25, 6, 40, and 25 years). Because experiment results for each forcing are a 5-run mean, simulated temperature changes exceeding 2σ are statistically significant at >99%.
Well-mixed GHGs are a major cause of tropospheric warming and stratospheric cooling, CO 2 being the cause of stratospheric cooling (Harvey 2000). Some forcings that yield a weak response at the surface, specifically ozone change and CH 4 -derived water vapor, yield responses much larger than unforced variability at higher atmospheric levels. The assumed solar irradiance increase yields significant warming in the upper atmosphere due to the large irradiance change at ultraviolet wavelengths (Lean et al. 2002) that are absorbed at high levels.
Note that substantial temperature changes in the troposphere are often accompanied by temperature changes of the opposite sign in the stratosphere. A primary mechanism for the stratospheric temperature change is the change in stratospheric water vapor, as illustrated for many forcing mechanisms in Fig.  20 of Efficacy (2005). Generally the forcings that warm the troposphere inject more water vapor into the stratosphere, which allows the (optically thin) stratosphere to cool more effectively. Of course, those forcings that alter a direct source of stratospheric heating, such as volcanic aerosols, stratospheric ozone, and solar irradiance, can override this stratospheric effect of the tropospheric climate change. In addition, as the infrared opacity of the atmosphere increases (decreases) the radiative lapse rate increases (decreases), thus tending to increase (decrease) the temperature at levels below (above) the mean level of radiation to space. The mean level of emission to space is at altitude about 6 km, but tropospheric convection tends to spread the temperature anomaly of the lower troposphere throughout the troposphere. Thus there is a tendency for temperature changes to be of opposite sign in the troposphere and stratosphere, for forcings that do not provide a direct heating source within the stratosphere. Fig. b shows the zonal mean surface and lower stratosphere (MSU LS channel) temperature changes as a function of month and latitude. Stratospheric cooling, with maximum at the poles, is caused by well-mixed GHGs (mainly CO 2 ), O 3 and H 2 O. The surface temperature change is dominated by the warming effect of well-mixed GHGs, which is partly balanced by the cooling effect of tropospheric aerosols. The net effect of all forcings is compared with observations in the next subsection. Fig. 2 compares observed temperature change to simulations using the coupled model driven by all forcings of Fig. 5. Fig. 2a is the latitude-altitude change of zonal-mean atmospheric temperature for several periods with readily available observational data: 958-998, 1979-1998, and 1987-2003. For the first two periods the data are as graphed by Hansen et al. (2002) using radiosonde analyses of Parker et al. (997). For the third period the observational data are for four levels: surface data from the analysis of Hansen et al. (200) and satellite MT, T/S and LS levels from the analysis of Mears et al. (2003; see also www.ssmi.com/msu/msu_data_ description.html).

Comparisons with observations.
The main difference between simulations and radiosonde data is that the switchover from warming to cooling occurs at a lower altitude in the radiosonde data and stratospheric cooling is greater. This difference is of the nature identified by Sherwood et al. (2005) as spurious cooling in the radiosonde data that increases with altitude, which can at least partly account for the discrepancy. Agreement is better in the period 987-2003, which employs satellite and surface data, but the model has 0.-0.2°C too much tropical upper tropospheric warming, consistent with the excessive warming at the surface in the tropics (Figs. 9 and 0). Interpretation of this apparently excessive tropical warming is provided in Sect. 6.. Near the North Pole observed warming exceeds that modeled, but the discrepancy there is less than the unforced variability at those latitudes (bottom row of Fig. b). In spite of these differences, it seems clear that, in both model and observations, there has been slight cooling at the tropopause level (defined in Fig. 3 of Efficacy [2005]) in recent decades, as has been discussed (Zhou et al. 200;Efficacy 2005) because of its relevance to stratospheric water vapor trends.
Tropopause height must change in response to tropospheric warming and cooling near and above the tropopause. Fig. 2a shows that the level of zero temperature change occurs beneath the tropopause and the degree of cooling increases with altitude at the tropopause level in observations and model. This implies that the tropopause height increased over these periods. Santer et al. (2003Santer et al. ( , 2005a examined temperature profile and tropopause height changes in climate models and reanalysis data, showing that the tropopause height provides a useful verification that the atmosphere is responding as expected to climate forcings. Fig. a shows that several forcing mechanisms contribute to tropospheric warming with temperature change decreasing with altitude at the tropopause: well-mixed GHGs, O 3 , and CH 4derived H 2 O, while tropospheric aerosols and stratospheric aerosols have an opposite response that would tend to decrease tropopause height, although the response in polar regions is more complex.  Fig. 2c compares observed and simulated month-latitude temperature change at three levels observed by satellite. The model cools at the lower stratosphere (LS) level. Month-latitude features are less prominent in the model than in observations, which is expected because () a 5-run mean is compared with a single realization, and (2) the model's variability is known to be deficient (see Sect. 2.4). Observed October-January cooling at the South Pole is captured by the model, in which the cooling is a consequence of specified O 3 depletion there (Fig. 2c). Observed temperature changes at the North Pole do not exceed the variability among individual runs (Fig. , bottom row), suggesting that they could be unforced variability. Observed equa-torial warming in February-April, which is clearer at the T/S level, seems to be weakly simulated by the model.
The T/S level, with weighting function peaking at 250 hPa, is within the troposphere at low latitudes, and thus shows warming at low latitudes in observations and model (Fig. 2c). At the MT level, with weighting function peaking at 500 hPa, warming extends to all latitudes except near the South Pole. Cooling near the South Pole is a consequence of O 3 and GHG changes Solomon 2002, 2005;. Seasonal variation of the cooling at the South Pole at the LS, T/S and MT levels is captured by the model as well as could be expected given the unforced variability there (Fig. b). Associated sea level pressure and wind changes are illustrated below.

Other Climate Variables
Many climate variables from all of the simulations described in this paper are available at data.giss.nasa.gov/modelE/ transient. The runs are organized as summarized in the tables of this paper. Diagnostics can be viewed conveniently via the web site as color maps and graphs, and the data can be downloaded. Figs. 3-5 provide examples for several climate variables for 5-member ensembles of simulations with the coupled atmosphere-ocean model driven by all forcings of Fig. 5 and driven by individual forcings. All maps shown here are for the same period (900-2003) to allow appropriate intercomparison among different variables. Comparison with observations that are available only for shorter periods is meaningful in many cases, as most aerosol forcing was added after 950 and most greenhouse forcing after 970 (Fig. 5). All parameters can be viewed and downloaded for arbitrary periods from our web site, including results for the forcings not included in Figs. 3 and 4 (CH 4derived H 2 O and volcanic aerosols) that produced the smallest trends for the 900-2003 period. We excluded these two forcings from Figs. 3-4 to allow space for maps of two standard deviations.
The bottom row in Figs. 3-4 is the interannual standard deviation of the annual mean in years 30-675 of the control run. A forced change exceeding the interannual σ should be apparent to casual observers. The next to bottom row of maps is the standard deviation among 04-year changes in the control run. A simulated change of the 5-run ensemble-mean for a given forcing that exceeds σ is significant at ~95% level, while a change exceeding 2σ is significant at > 99%. Of course the significance of global mean changes far exceeds the significance of local changes. The number on the upper right of these maps is the global mean of the local standard deviation. Fig. 3 is the simulated 900-2003 change of net radiation at the top of the atmosphere based on the local linear trend. Positive radiation is downward. The 900-2003 linear trend for all forcings operating at once is an increase of ~0.5 W/m 2 of radiation into the planet. Greenhouse gases cause an increase of ~ W/m 2 , but the aerosol direct and indirect forcings reduce the planetary radiation imbalance by almost half, as the dominant effect of increasing aerosols and clouds is to increase reflection of solar radiation to space. Incoming net radiation increases in the tropics and Southern Ocean (mainly due to well-mixed GHGs), while it decreases at middle latitudes in the Northern Hemisphere (due to aerosol direct and indirect forcings). The change due to GHGs is an amplification of their normal greenhouse effect, which decreases outgoing radiation in the tropics and increases it at the poles. We note that tropical (20N-20S) net radiation imbalance +.4 W/m 2 for the past two decades (a period including about half of the increase in climate forcing for the century) measured by satellite (Wong et al. 2006) is consistent with the sign and approximate magnitude of the "all forcing" net radiation change in Fig. 3.

Radiation-related quantities. The first column of
Cloud cover increases in most of the Northern Hemisphere in the climate simulations, on average by more than % of the sky cover (Fig. 3 column 2). Cloud cover increase is due mainly to the aerosol indirect effect, which primarily increases low clouds and thus causes surface cooling. Greenhouse gases and resulting global warming, by themselves, lead to a small overall decrease of cloud cover in the GISS model (Del Genio et al. 2005). The cloud changes occurring as a climate feedback are distributed in all layers, and their net effect on global surface temperature is near neutral (Del Genio et al. 2005). Modeled cloud changes as a function of altitude are available on the GISS web site.
Local modeled cloud cover changes at some locations exceed the local standard deviation (second row from bottom of Fig. 3) for unforced variability of the simulated 04-year change. However, note that σ increases for the shorter period of most observations, e.g., σ is about twice as large for a 25year period compared to a 00-year period (Fig. 9, bottom row). In addition, changes in cloud observing procedures with time make it difficult to detect reliably cloud changes of the expected magnitudes (Warren and Hahn 2003), and the cloud 'observations' in Fig. 3 (Mitchell et al. 2004) are in part proxy estimates inferred from observed changes in the amplitude of the diurnal cycle of surface air temperature. Thus little trust can be placed in these 'observed' cloud changes.
Modeled solar radiation (SW, for shortwave) incident on the ground (Fig. 3 column 3) decreases substantially, especially in the Northern Hemisphere. Despite increased cloud cover, reduced SW at the surface is primarily the direct effect of anthropogenic aerosols (compare 2 nd and 8 th rows in Fig. 3). Reduction is due mainly to strongly absorbing BC aerosols, as the main effect of non-absorbing aerosols is to convert direct downward radiation to diffuse downward radiation. Breakdown by aerosol composition can be seen on the GISS web site for the Efficacy (2005) paper (data.giss.nasa.gov/efficacy), which includes 120year simulations for individual aerosol types. Observational SW data in Fig. 3 (Gilgen et al. 998) are for 950-990, which includes more than half of the anthropogenic aerosol increase in our model (Fig. 3c). Observed SW decreases exceed modeled values on average, perhaps in part because observing sites tend to be located near aerosol sources. Observed decrease of SW in the United States is larger than in the simulations, however, analyses based on observed sunshine duration (Stanhill and Cohen 200) for 900-985 suggest that longer period changes in the United States were smaller. The large simulated decrease in SW in China is in good agreement with observations for 96-2000 (Che et al. 2005).
Modeled snow and ice cover (Fig. 3 column 4) decreases for all forcings together, with GHGs being the most effective forcing mechanism. Snow increases in regions of deforestation, if land use change is the only forcing, but with 'all forcings' this effect is overridden by global warming. The 'observed' change includes only the sea ice change of , which has about a factor of two greater sea ice loss than simulated by our model. If the  data are approximately correct, the sea ice formulation in the GISS model may be too stable, implying that climate sensitivity in the model III version of modelE may be too small. However, we suggest in the Supplementary Material that much of the large sea ice change in the  data set (Fig. 7a) could be spurious. The large rapid sea ice decrease between 940 and 945, e.g., occurs in the Southern Hemisphere, where no warming is evident at that time. Available data on sea ice are inhomogeneous in time, and it is possible, for example, that regions of open water behind the ice edges may be unaccounted for in early sparse observations.
Column 5 in Fig. 3 is the change of the amplitude of the diurnal cycle of surface air temperature. With all climate forcings applied, almost all land regions on Earth are calculated to have a decrease in the amplitude of Ts, by a few tenths of a degree Celsius up to a maximum of about °C. GHGs and aerosols (both direct and indirect effects) are principal contributors to damping of the diurnal cycle. The indicated impact of each forcing on the diurnal cycle includes the effect of feedbacks, e.g., increased water vapor and cloud changes. In the case of the well-mixed GHGs the primary mechanism reducing the diurnal cycle is increased water vapor, and thus, since the GHG global warming exceeds actual warming, in a sense the contribution of the GHGs is exaggerated. To the contrary, the impact of aerosols and clouds on the diurnal cycle in Fig. 3 is an understatement of their direct effect, as it includes a negative water vapor contribution. Observed diurnal changes in Fig. 3 are an inhomogeneous data collection (Mitchell et al. 2004), but it is clear (Karl et al. 993) that there has been a decrease of the amplitude of the diurnal cycle over most land areas of the globe by several tenths of a degree Celsius, comparable in magnitude to the simulated change.
Pan evaporation is closely associated with radiation variables. Observed pan evaporation decreased in most land areas in the last few decades of the 20th century (Stanhill and Cohen 200;Roderick and Farquhar 2002;Ohmura and Wild 2002). Fig. 4 shows Penman potential evaporation (Rind et al. 997) over land and actual evaporation over ocean. Pan evaporation can be approximated as 0.7 times potential evaporation (Roderick and Farquhar 2002). Simulated reductions of evaporation over most land areas are comparable to observations reported in the above references for the latter half of the 20 th century. At almost any location twice the standard deviation (2 nd row from bottom of Fig. 4) exceeds the locally simulated change of evaporation, the large variability being associated with large unforced cloud variability. Nevertheless, there is a clear decrease of pan evaporation over land, which is consistent with observations. Fig. 4 indicates that the aerosol direct effect is primarily responsible for reduced potential evaporation over land, but the aerosol indirect effect and land use (in areas of large change, Fig. 7 in Efficacy 2005) also reduce potential evaporation over land.
Overall, we conclude that there is good qualitative and semi-quantitative agreement between simulated radiation-related quantities and observations, e.g., with the phenomenon of "global dimming" (Stanhill and Cohen 200;Liepert 2002;Cohen et al. 2004), the reduction of solar radiation incident on the surface. Our model results indicate that dimming is due primarily to absorbing aerosols and secondarily to the aerosol indirect effect on clouds. The same forcings are principal causes of a decrease in the amplitude of the diurnal cycle of surface air temperature, such a decrease being in accord with observational evidence (Karl et al. 1993). The model yields a significant increase of global mean cloud cover, largely due to the aerosol indirect effect, but the local effect does not generally exceed local unforced variability in the model. Large positive global net radiation imbalance simulated for the past decade, due to increasing GHGs, is confirmed by comparison of model results Delworth et al. 2005) with observations of ocean heat storage (Willis et al. 2004;Levitus et al. 2005;Lyman et al. 2006). A positive energy imbalance in the tropics is consistent with satellite observations (Wong et al. 2006), although it is a challenge to obtain an accuracy in satellite observations that is sufficient to measure the small simulated radiation imbalance.

Precipitation and run-off.
Well-mixed GHGs by themselves yield large increases of simulated global precipitation and run-off (Fig. 4 columns 2 and 3), with precipitation generally increasing in the tropics and at high latitudes while decreasing in the subtropics, as found in many prior modeling studies (IPCC 200). A tendency for these changes remains when all forcings are applied, but the additional forcings make the hydrologic changes weaker and more variable at middle and high latitudes in the Northern Hemisphere, as direct and indirect aerosol effects tend to reduce precipitation and run-off.
Simulated changes of precipitation and run-off at a given location are usually smaller than unforced variability of the trends among individual model runs (Fig. 4, 2 nd row from bottom) and much less than interannual variability (bottom row). This large unforced variability makes it difficult to predict local or regional hydrologic changes. Nevertheless, we notice some consistent tendencies among the present runs and a large number of additional runs with slightly altered forcings (Sect. 6 below and Efficacy [2005]) including decreased precipitation in the Southwest United States, the Mediterranean region, and the Sahel, but increased precipitation in the Eastern United States. Precipitation observations compiled by Mitchell et al. (2004) have increased precipitation in most areas, although decreases in the Sahel and Mediterranean, with little change in the southwest United States. Note that the Mitchell et al. (2004) data, graphically available for arbitrary periods on the GISS web site, have more moderate changes over the past half century (when the data are probably more reliable) and better agreement with the model results.

Sea level pressure and surface wind.
GHGs have the largest effect on annual-mean sea level pressure and surface zonal wind around Antarctica in our simulations (Fig. 4), exceeding the noise in long-term trends and rivaling interannual variations (bottom two rows, Fig. 4). Ozone and solar irradiance changes reinforce the effect of GHGs, but aerosols work in the opposite sense by cooling low latitudes. Thus surface pressure and zonal wind changes for all forcings are less than for GHGs alone. Observations concur with the net effects, as pressure has decreased over the Antarctic continent and wind speed has increased at most coastal stations (Turner et al. 2005), which is a trend toward the positive phase of the Southern Annular Mode (SAM) Hartmann et al. 2000). Kindem and Christiansen (200) and Sexton (200) used observed stratospheric O 3 depletion scenarios in global climate models to show that O 3 change yielded stratospheric cooling and a trend toward the positive phase of SAM. Thompson and Solomon (2002) showed that O 3 changes were consistent with trends of temperature and zonal wind as a function of season and altitude, and that O 3 depletion was probably responsible for a slight surface cooling trend in East Antarctica, conclusions supported by climate simulations of Gillett and Thompson (2003). Fyfe et al. (999) and Kushner et al. (200) found that wellmixed GHGs also induce a positive response of the SAM in their models, as upper tropospheric warming and lower stratospheric cooling strengthen the polar vortex in all seasons. This effect is enhanced by minimal warming in the deep-mixing circum-Antarctic Southern Ocean during the transient climate phase. Cai et al. (2003) find that stabilizing the GHG amount would weaken the SAM, as the high latitude surface warms and reduces the zonal temperature gradient that drives the subpolar jet stream. , with a version of the GISS model that includes gravity wave drag and a higher model top, find comparable contributions of O 3 and GHGs to surface change, with O 3 dominating above the middle troposphere. The larger response to GHGs in our present model is likely a tropospheric effect, as we obtain large low-latitude upper-tropospheric warming (Figs. a and 2), consistent with larger-than-observed surface warming in the tropical Pacific Ocean (over most periods, albeit not over 950-2003, Fig. 9). The larger-than-observed tropical Pacific warming and upper tropospheric warming in the satellite era disappear when we use observed SSTs, as noted in Sect. 5.1.1 and Table 2. Given the large fluctuations of observed tropical Pacific warming (Fig. 9), we suggest that the discrepancy between our ensemble mean result and the real world's single realization in the satellite period , including surface cooling of the Antarctic continent, could be a reflection of real world variability rather than a model flaw. Indeed, despite the lack of ENSO-like variability in the present model, some ensemble members have cooling over East Antarctica in 979-2003. Quantitative analysis requires a version of the model with more realistic tropical variability.
Observed warming in recent decades (Fig. 9) and warming simulated by a large IPCC ensemble of many climate models in response to increasing GHGs (Carril et al. 2005) is concentrated in the Amundson Sea-Antarctic Peninsula-Western Weddell Sea region, where recent acceleration of outlet glaciers has been noted (Thomas et al. 2004;Cook et al. 2004). Despite the coarse resolution of our model, the warming pattern that we obtain with "all forcings" is consistent with results of the ensemble of IPCC models (Carril et al. 2005). Fig. 5 shows 5-run ensemble mean changes of zonal mean temperature, water vapor, zonal wind, and stream function produced by each forcing for 950-2003 and 979-2003 as a function of altitude and latitude. Control run values of these quantities are shown in Efficacy (2005). The standard deviation for each quantity among changes in 54year and 25-year segments of the control run are shown in the bottom row of Fig. 5.

Zonal mean quantities.
Conspicuous features in the zonal temperature response were discussed in connection with Figs. -2. Water vapor changes, including stratospheric H 2 O, mainly reflect tropospheric temperature change, except for forcings that directly alter stratospheric temperature. The large stratospheric H 2 O change due to CH 4 -oxidation warms the upper troposphere but barely affects the surface temperature.
Note the large unforced variability of zonal winds at high latitudes. In the Northern Hemisphere only GHGs produce a response exceeding the variability among runs, and only on the 54-year period, not 25 years. A greater zonal-wind response is obtained in the Southern Hemisphere for GHG and O 3 forcings, but the effect at the surface exceeds unforced variability only for GHGs on the longer time scale.
The salient change of the meridional stream function caused by "all forcings" is a strengthening of the overturning Brewer-Dobson circulation in the stratosphere. However, response of the Hadley circulation in the lower troposphere is the opposite, i.e., a weakening. These changes are due mainly to the well-mixed GHGs. Knutson and Manabe (995) and Held and Soden (2006) show that weakening of mean overturning in the lower troposphere, including the Walker circulation, is due to the increasing proportion of vertical energy flux carried by moist convection as global warming increases. Changes of the stream function for different forcings are more systematic and apparent when viewed for individual seasons as in Efficacy (2005), where it is shown that stream function changes are approximately proportional to the effective forcing Fe and in the opposite sense for positive and negative Fe.

Alternative Aerosol Forcings
A primary discrepancy between our simulated global temperature change and observations is the small modeled warming at middle latitudes in the Northern Hemisphere. A likely cause of this deficient warming is the large increase of aerosol optical depth in our model between 880 and 2003. Causes of large aerosol optical depth in our model include (see Sect. 3.2.): () Sulfate optical depths are from results of Koch (200) available at the time our "IPCC" runs were initiated. More recent results of Koch (personal communication), based on new emission data and more efficient removal including the effect of heterogeneous chemical reactions of sulfate and mineral dust (Bauer and Koch 2005), reduce the sulfate optical depth about 50%.
(2) Our aerosol distributions were calculated at coarse temporal intervals (Sect. 3.2), the most recent date being 990, after which aerosol amount is constant (except nitrate, which increases in proportion to global population). Thus we do not capture post-990 aerosol decreases in Europe and the United States (see Sect. 5.), nor aerosol increases in developing countries.
It would be useful to rerun some of our experiments with the best current estimates of global aerosol distributions. However, several significant improvements of our ocean and atmosphere models are also now practical. Thus we plan to repeat the transient climate change experiments with the next documented version of our climate model using the best data then available for aerosols and other transient climate forcings. Here we only make two aerosol sensitivity runs with the present climate model, to provide an indication of how changes in the assumed aerosols affect the simulated climate.
In the aerosol "½ Sulfates" experiment the sulfate changes were reduced by 50%, which also reduced the (negative) AIE by about 8%. In the "½ Sulfates + 2x[Biomass Burning]" experiment the temporal change of BC and OC aerosols from biomass burning was doubled, in addition to the reduction of fossil fuel sulfates. Fig. 6 summarizes the impact of these alternative aerosol histories on the simulated temperature change, comparing the results with observations and the standard "all forcing" model runs.
The reduction of sulfates, which changes the net aerosol 880-2003 global forcing from -.37 W/m 2 to -0.9 W/m 2 , increases global warming from 0.53 to 0.75°C for 880-2003 and from 0.40 to 0.53°C for 950-2003. Doubling of the biomass burning aerosol change has little further effect on global temperature (Fig. 6a) as the warming from added BC approximately cancels cooling from added OC.
Both alternative aerosol distributions increase the warm-ing at middle latitudes in the Northern Hemisphere to a realistic level as a result of reduced reflective (sulfate) aerosol optical depth. Given the evidence that our standard aerosol amount was excessive, and does not include post-990 changes, it is likely that aerosols are a principal factor that needs to be improved in the standard forcings. However, the present alternative distributions should only be viewed as sensitivity studies, as acceptable alternatives need to be obtained from first principles using improved aerosol observations and modeling.
There is notable polar amplification of simulated surface warming in the Northern Hemisphere over the past half century and longer periods, in the cases with aerosol amount reduced to a level that yields realistic mid-latitude warming (Fig. 6c). However, observed polar amplification fluctuates greatly depending on the period considered, which is a reflection of how 'noisy' high latitude temperature is (Sorteberg et al. 2005;Bitz and Goosse 2006. The gray band in Fig. 16c is defined by the 1σ variation in the 5-member ensemble of runs for our standard case with all forcings. As would be expected, for some periods and latitudes the observations fall outside the 1σ range, although almost all cases are within 2σ. Note the great variability of observed high latitude temperature change depending on the period considered and the large unforced variability of the polar temperatures among model runs. Given that the real world represents a single realization, the large unforced variability at high latitudes indicates that observed climate changes at high latitudes should be interpreted with caution.

Summary: Model and Data Limitations and Potential
We summarize model limitations and capabilities (Sect. 6.) and briefly discuss (Sect. 6.2) why, despite the model limitations, extension of model runs into the future can provide meaningful results. In Sect. 6.3 we summarize lessons learned that may be helpful for future climate simulations.

Global Climate Change: 1880-2003
Our climate model, driven by all of the estimated forcings, simulates observed global mean temperature change over the period 1880-2003 reasonably well. The results fit observations better if tropospheric aerosol change is smaller over Europe than it is in our standard 'all forcing' run. There are independent reasons to believe that a reduced aerosol change there is more realistic, as discussed in Sect. 5.4.
The match of simulated and observed global temperature curves is not an indication that knowledge of climate sensitivity and the mechanisms causing climate change is as accurate as suggested by that fit. An equally good match to observations probably could be obtained from a model with larger sensitivity (than 2.9°C for doubled CO 2 ) and smaller net forcing, or a model with smaller sensitivity and larger forcing. Indeed, although detailed temporal and spatial patterns of climate change, in principle, may allow extraction of inferences about both climate sensitivity and forcings, in practice the large unforced variability of climate, uncertainties in observed climate change, poor knowledge of aerosol forcings, and imprecision in the modelgenerated response to forcings make it difficult to place useful quantitative limits on climate sensitivity from observed climate change of the past century.
On the other hand, paleoclimate evidence of climate change between periods with well-known boundary conditions (forcings) yields a climate sensitivity 3±°C for doubled CO 2 (Hansen et al. 993), so our model sensitivity of 2.9°C for doubled CO 2 is reasonable. The realistic response of this model to the accurately known short-term forcing by Pinatubo volcanic aerosols and the realistic rate of simulated global warming in the past 3-4 decades, when greenhouse gases increased so rapidly that they dominated over other estimated forcings (Fig. 5), suggest that approximate agreement between observed and simulated global temperature is not fortuitous.
The largest discrepancy in simulated 880-2003 surface temperature change, other than deficient warming in Eurasia, is warming of the tropical Pacific Ocean that exceeds observations (Fig. 9). Cane et al. (997) suggest that reduced warming in that region may occur with global warming due to increased frequency or intensity of La Ninas, but among all models there is a slight tendency toward increased El Ninos with global warming (Collins et al. 2005). Our present model would not be able to capture a change in ENSO variability, whether forced or unforced. However, note that the existence of a discrepancy in Pacific Ocean warming depends on the period considered. For example, our modeled tropical Pacific warming is larger than observed for 880-2003, but not for 950-2003 (Figs. 9 and 6c). We conclude that observed variability in tropical surface temperature trends is a strong function of ENSO variability, and the differences between observations and simulations in this region may be due simply to unforced variability.
The greatest apparent discrepancy with observations of temperature change versus altitude is the large simulated warming in the tropical upper troposphere. On the 50-year time scale there is no clear evidence that modeled upper tropospheric warming exceeds observations, given increasing evidence that deficient warming there in radiosonde records may be a figment of inhomogeneities in the radiosonde data sources (Sherwood et al. 2005;Free and Seidel 2005;Santer et al. 2005b;Karl et al. 2006). However, on shorter time scales our modeled upper tropospheric warming trend is excessive, as shown most convincingly by MSU T/S data for 987-2003. But this excessive upper tropospheric warming disappears when we use ocean A (observed SSTs) instead of the fully coupled atmosphere-ocean model (complete ocean A results are available at data.giss.nasa. gov/modelE/transient). Thus the excessive upper tropospheric warming is a reflection of larger than observed warming of the tropical Pacific Ocean in the coupled model. Real world trends toward La Nina conditions for 987-2003, toward El Nino conditions for 950-2003, and slightly toward La Nina conditions for 880-2003 ( Fig. 9) are within the range of Southern Oscillation variability. There is no reason to expect our model to capture this specific ENSO variability, and there is no inconsistency between upper tropospheric and surface temperature changes. Thus we anticipate that magnified warming in the tropical upper troposphere will become apparent as the tropical ocean continues to warm in response to increasing GHGs.
In summary, simulated climate change for the past century does not agree in detail with observations, nor would it be expected to agree, given unforced climate variability, uncertainty in climate forcings, and current model limitations. But in a broad sense our climate model does a credible job of simulating observed global temperature change in response to short timescale (volcanic aerosol) as well as century time-scale forcings. This model capability provides sufficient reason to examine the model for information about large-scale regional climate effects of practical importance and to extend the climate simulations to investigate potential global consequences of alternative climate forcing scenarios.

Extended Simulations
Given the large uncertainty in the net climate forcing during the past century (Sect. 3.4), due mainly to aerosols, it may be questioned whether useful accuracy can be achieved in simulations of the future. However, GHG climate forcing is expected to be dominant over aerosol forcing in the future; indeed, it seems likely that at least a moderate decrease of aerosol amount will occur (Andreae et al. 2005;Smith et al. 2005). Therefore, given a specified GHG scenario, with large increases of GHGs as projected by IPCC (200), the relative uncertainty in the net forcing is reasonably small.
Thus, continuations of some of our present 880-2003 simulations into the future seem justified. We have extended the 880-2003 "all forcing" simulations of this paper to 200, and in a few cases to 2300 using fixed 2003 forcings, several IPCC GHG scenarios, and the "alternative" and "2°C" scenarios of Hansen and Sato (2004). These extended simulations are described in Dangerous (2006).

Lessons Learned
Our control run had negligible stratospheric aerosols, representing only "background" conditions far removed from the injection of aerosols by large volcanoes. If volcanic aerosols are to be included as a forcing in experiment runs, the use of ocean initial conditions from a control run without volcanic aerosols yields misleading results, especially for ocean heat storage.
In Supplement Sect. S2 we suggest that inclusion of a mean aerosol amount in the control run significantly improves realism of results. We now include in our control runs a mean stratospheric aerosol optical thickness τ = 0.0125 at 0.55 μm wavelength, which is the 850-2000 mean value of the Sato et al. (993) aerosol climatology. Other researchers may wish to consider including this mean stratospheric aerosol amount in their control runs.
Other "lessons learned" concern methods to minimize the effect of model drift (S3) and the effect of mixed definitions of surface air temperature (S3). We also describe minor errors in ozone forcing (S4) and the forcing due to black carbon deposition on snow (S5). The efficacy of the BC snow albedo effect is found to be 2.7 after the programming error is corrected, rather than .7 that was reported in Efficacy (2005), where the same programming error was included.    Fig. 1. Climate forcing (W/m 2 ) and its annual changes (W/m 2 per year) for observed 880-2003 greenhouse gas changes as tabulated by Hansen and Sato (2004). MPTGs and OTGs are Montreal Protocol trace gases and other trace gases (Hansen and Sato 2004 Fig. 4. The standard solar variability for most of our present climate simulations is from Lean (2000), which is based on irradiance observations since 979 and solar proxy variables in prior years. The alternative solar forcing using only the Schwabe -year variability is from Lean et al. (2002).  . 6. Global mean climate change of GISS model III version of modelE atmosphere coupled to Russell ocean model and driven by the climate forcings of Fig. 5. Left column shows temperature change at three levels using vertical weighting functions of the MSU satellite instrument (Mears et al. 2003; see also www.ssmi.com/msu/msu_data_description.html). Observed surface temperature, in the upper right, uses meteorological station data over land and SSTs over the ocean. "Observed" ocean ice cover is the analysis of . A small radiation imbalance in the control run is subtracted from planetary net radiation. 1880 1900 1920 1940 1960 1980 2000 -.5 .0 .5  : 1979-2003 1880 1900 1920 1940 1960 1980 2000 -.5 .0 .5 Surface Temperature Anomaly ( C) Base Period: 1951Period: -1980Period: 1880Period: 1900Period: 1920Period: 1940Period: 1960Period: 1980Period: 2000 .0 Troposphere/Stratosphere Temperature Anomaly ( C) Ocean Ice Cover (%) Fig. 7. Four of the six quantities in Fig. 6, but for alternative representations of the ocean: (a) ocean A, specified SST and sea ice of , and (b) ocean B, q-flux ocean (Hansen et al. 1984), with ocean horizontal heat flux inferred from control run of ocean A and locally varying diffusion of heat into 4 km deep ocean. As discussed in Sect. 5.3 and Supplementary Sect. S2, the sea ice change In observations between 940 and 945 is likely a spurious effect of data inhomogeneities. 1880 1900 1920 1940 1960 1980 2000 -.5 .0 . Ocean Ice Cover (%) Fig. 8. Simulated global temperature response to all climate forcings of Fig. 5 operating at once, and the response to individual forcings. Five runs for each forcing were initiated at 25-year or 30-year intervals of the control run. Small model drift was removed by subtracting the control run year-by-year. Response to the aerosol indirect forcing is obtained as the difference between runs with direct and indirect forcings and runs with only the direct aerosol forcing. Climate model is the model III version of GISS modelE with the Russell ocean model. Fig. 9. Global maps of temperature change in observations (top row) and in the model runs of Fig. 8, for 880-2003 and several subperiods. Observations are based on analysis of Hansen et al. (200), which uses urban-adjusted meteorological station measurements of surface air over land and SST data of  over the ocean.  Fig. 11. (a) Ensemble-mean zonal-mean temperature change versus latitude and altitude (in hPa of pressure) for 880-2003 period and three subperiods. Ensemble-mean changes > 2σ (lowest panel) are significant at > 99%. (b) Ensemble-mean zonal-mean temperature change versus month and latitude for the LS (lower stratosphere) level of microwave satellite observations (left two columns) and for surface air (right two columns). Changes > 2σ (lowest panel) are significant at > 99%. Fig. 12. Observed temperature change compared with simulations for the coupled climate model (ocean C) driven by all forcings of Fig. 5. (a) altitude-latitude temperature changes in three periods, for radiosonde data as graphed by Hansen et al. (2002) from analysis of Parker et al. (997), and using satellite observations at three atmospheric levels (Mears et al. 2003; see also www.ssmi. com/msu/msu_data_description.html) and surface temperature (Hansen et al. 200), (b) modeled tropopause height changes for all forcings and several individual forcings, (c, d) latitude-month temperature changes for levels observed by satellite and for the surface. Fig. 13. Changes of indicated quantities simulated with ocean C for 900-2003 based on trend of annual mean. Observed changes of several quantities are shown in the top row for specified periods. Numbers on upper right corners are global means; number on upper left corner of downward shortwave radiation is the area-weighted mean for gridboxes with observations. CRU: Climate Research Unit (Mitchell et al. 2004); GEBA: Global Energy Balance Archive (Gilgen et al. 998).  Fig. 15. Changes of annual-mean zonal-mean atmospheric temperature, water vapor, zonal wind, and stream function versus latitude and altitude (in hPa of pressure) for ocean C simulations based on linear trends over periods (a) 950-2003 and(b) 979-2003. Fig. 16. Surface temperature change in observations and simulations for standard 'all forcings' scenario, two aerosol sensitivity runs [½ ΔSulfate and ½ ΔSulfate + 2 × Δ (biomass burning BC and OC)], and the alternative solar forcing, AltSol. AltSol includes only the Schwabe -year solar variability of Lean et al. (2002). Gray area is region within one standard deviation for 5-run ensemble with standard "all forcings".

S1. Alternative Data Samplings and the Krakatau Problem
Comparisons of simulated climate and observations commonly involve choices that inflluence how well the model and data appear to agree. Choices of surface temperature data deserve scrutiny, because surface temperature provides the usual measure of long-term 'global warming' as well as a test of possible global cooling after large volcanic eruptions. We illustrate here alternative comparisons of model and observations, with model results being those of the coupled model (ocean C, the Russell et al. [995] model) driven by all climate forcings of Fig. 5. This model run is discussed in later sections.

S1.1. Century time-scale
Temperature measurements at meteorological stations provide a reasonably consistent data set for continental and island locations (Jones et al. 986;Hansen and Lebedeff 987), albeit one in which the station records are spatially inhomogeneous, often broken temporally, and subject to non-climatic effects. The meteorological station records that we employ have been adjusted for urban effects using neighboring rural stations (Hansen et al. 200). Such adjustments are imperfect, but the impact on global mean 00-year temperature change of uncertainties in urban adjustments is not larger than about 0.°C (IPCC 200). For the short interval after a volcano considered here, urban adjustments are negligible.
The GISS analysis of station data (Hansen et al. 98; Hansen and Lebedeff 987) combines stations with overlapping periods of record so as to preserve information on temporal variability while allowing individual stations to affect estimated temperature change at distances up to 200 km. It has been shown, by sampling a global model with realistic temperature variability at the station locations, that after about 880 the station network is capable of yielding a good estimate of global temperature change despite poor coverage in the Southern Hemisphere (Hansen and Lebedeff 987). However, island and coastal stations fail to sample part of the ocean, and both observations and models indicate that the long-term temperature response tends to be less over the ocean than over continental areas. Thus we expect the long-term "global" temperature change estimated from the meteorological station network alone to slightly overestimate true global mean temperature change.
Improved global coverage is obtained by combining meteorological station data with sea surface temperature (SST) measurements of ocean areas (Jones et al. 999;Hansen et al. 200). However, ocean data have their own problems, including changes of measurement methods and infrequent sampling of large regions (Parker et al. 994). Sampling is especially poor in the 1800s, and spatial-temporal data-fill procedures risk smoothing real variations. In addition, the ocean area with the largest response to climate forcings in climate models, regions of sea ice, are practically unobserved. Fig. Sa shows an estimate of global temperature using only meteorological station measurements (Hansen et al. 200). The observed 880-2003 temperature change, based on the lin-ear trend, is 0.69°C in this case. The model 5-run "all forcings" ensemble mean yields 0.56°C, with the model result being a true global mean.
Fig. Sb uses the same land temperatures as in Fig. Sa, but it adds SST data for the oceans, using ship data of  for 880-98 and subsequently satellite data (Reynolds and Smith 994;Smith and Reynolds 2004). Inclusion of ocean data reduces the observed global temperature change to 0.59°C. It also practically eliminates evidence for cooling after the 883 Krakatau eruption. Fig. Sc is a third alternative, comparing observed temperature from meteorological stations with the model sampled at the places and times of the observations. This sampled model data is run through the same temperature analysis program as observations to produce the global mean. This third procedure is optimum in the sense of having the most consistent treatment of model and data, as well as preserving a best estimate for high frequency temperature change in the period of sparse observations in the 800s. The model sampled at observing stations yields a global warming of 0.59°C based on the linear trend, which is less than the observed 0.69°C. This discrepancy occurs because the model warms less over land areas than observed, a result that we identify with excessive anthropogenic tropospheric aerosols over Eurasia in our standard "all forcings", as discussed in Sect. 5. This third procedure provides a clean comparison of model and observations, but the integration over the globe is not a true global mean. In addition, it is unlikely that most modelers will sample their model results at the times and places of meteorological station measurements and run the results through the GISS temperature analysis program, thus making it difficult to compare GISS model results with other models. We present all four alternatives to help readers make their own assessment. For simplicity we use the procedure of Fig. Sb in following sections, i.e., we use the true global mean for the model and the land + ocean data for observations. However, it should be born in mind that these observations probably miss some actual cooling after Krakatau.
We examine the Krakatau period in more detail, because it has an effect on how well the model and observations appear to agree over the 120-year temperature record. We find it useful to compare the 883 Krakatau and 99 Pinatubo eruptions, the two largest volcanic aerosol climate forcings in the period of instrumental climate data (Sato et al. 993). These volcanoes have the best chance of producing signals above the climate noise level and the Pinatubo period has extensive climate observations. S1.

Temperature change after Krakatau and Pinatubo
Estimated aerosol optical depths after Krakatau and Pintubo are shown in Fig. S2a. The shape of the Krakatau curve is assumed to be similar to that after Pinatubo, as they were both low latitude injections to high altitudes at similar times of year. Measurements of decreased solar irradiance integrated over three years after Krakatau were used to set the aerosol optical depth (Sato et al. 993). Effective forcings are shown in Fig.  S2b. Resulting temperature anomalies, relative to the three-year mean preceding the eruption, are shown in Fig. S2c. The simulated cooling after Krakatau exceeds that after Pinatubo by more than the assumed 0% difference in their forcings. This must be at least in part because of planetary radiation imbalance of about +0.5 W/m 2 that existed just prior to the Pinatubo eruption (Hansen et al. 993) but not at the time of the Krakatau eruption. Further, as mentioned in Sect. 3.2., the response to the Krakatau aerosols would have been reduced about 0 percent if the control run ocean temperatures had included the effect of prior volcanic eruptions via a mean stratospheric aerosol optical depth. Fig. S2c shows that the global mean temperature based on meteorological station data after Krakatau is consistent with the climate simulations. The seasonal mean 1σ error bar for global temperature estimated from the meteorological station network in the 880s is 0.5°C (Hansen and Lebedeff 987). Thus the cooling observed by the station network after Krakatau for a given season could be a sampling error, but not the nearly continuous cool period for several years after the eruption. Furthermore, comparison of the global temperature curve estimated from meteorological stations in the Pinatubo era (right side of Fig. S2c) with the global temperature curve that has complete ocean coverage from satellite data shows that the station network tracks the complete global data within the expected error for the station network (1σ sampling error being 0.09°C for the station distribution in the 990s). We conclude that there was global cooling after Krakatau. Fig. S2d shows the observed and simulated surface temperature anomalies in the northern winter (DJF) following the eruptions and the northern summer (JJA) about one year after the eruptions. As expected, the model and observations show strong cooling in the summer after the eruption, especially over the continents. Also, the model and observations show global cooling in DJF, with evidence for regional Eurasian "winter warming", an expected dynamical response (Groisman 992;Perlwitz and Graf 995;Robock 2000;, which has previously been reported to occur in current GISS models . The model, using the coarse-resolution Russell et al. (995) ocean, is not able to produce El Ninos, which have accompanied several large volcanoes in the past century (Handler 984;Robock 2000;Mann et al. 2005) and may be responsible for warming in the region of Alaska. Temperature anomalies are muted in the 5-run model mean in Fig. S2d, but the magnitude of anomalies is more realistic in the individual runs, which are available on the GISS web-site.

S2. Mean Stratospheric Aerosols in Control Run
Our control run had no stratospheric aerosols. Aerosols from the 883 Krakatau eruption caused ocean heat content in the experiment runs to fall below that in the control run, as expected. However, despite steadily increasing greenhouse gases, the ocean heat content did not recover to that of the control run until about 2000. In reality, ocean temperature is also influenced by volcanoes that erupted prior to 880. Ideally, ocean initial conditions in 880 would be obtained from a spin-up run that had time-dependent forcings, including volcanoes, for several centuries prior to 880. That is not usually practical, if for no other reason than the absence of information on earlier volcanic eruptions. However, it is easy to include a mean stratospheric aerosol amount in the control run.
Current control runs with our model include a mean stratospheric aerosol optical thickness τ = 0.0125 at 0.55 μm wavelength, which is the 850-2000 mean value of the Sato et al. (993) aerosol climatology. The equilibrium global (surface) cooling for τ = 0.0125 (10% of the maximum τ for Pinatubo) is ~0.2°C, and the effect on deeper ocean temperatures is sufficient to alter the rate of ocean heat storage in transient climate simulations. Using a control run in which the ocean temperature had equilibrated with an atmosphere including this mean aerosol amount, we carried out an ensemble of runs for 850-2003. The concentration of volcanoes near the end of the 9 th century caused the ocean heat content anomaly to be negative for several decades, but it recovered to the control run value by the mid 20 th century and it subsequently increased at a rate comparable to that reported by Levitus et al. (2000).

S3. Control Run Disequilibrium and Drift.
Our coupled atmosphere-ocean (ocean C) simulations, to meet the deadline for submission to IPCC, were initiated before the control run (which provides initial conditions for the experiments) had reached equilibrium, i.e., while there was still an imbalance between the amounts of energy absorbed and emitted by the planet. As a result, the model response to any forcing included a small drift.
We minimize drift effects by subtracting, year-by-year, the same quantities from the same period of the control run. This procedure yields diagnostics with 'double noise', i.e., it contains unforced variability of both the control and experiment runs, while the real world has only a single source of unforced variability. Double noise can be minimized by initiating additional control runs at the same points at which experiments are initiated.
An alternative way to remove drift is to calculate and subtract from the experiment result the mean drift in the control over the period of the experiment. For example, for a 24-year 880-2003 experiment initiated at year X of the control run, we could calculate the linear trends of control run diagnostics over the period X to X + 23 and subtract the control run diagnostics based on their linear trends from the corresponding quantities in the experiment run. This alternative procedure avoids year-toyear double noise, but it does not eliminate drift effects entirely because variability occurs on all time scales.
Noise effects were exacerbated by the fact that most of our experiments, with individual forcings and with multiple forcings, were initiated at the same points of the control run. The control run has unforced variability not only interannually, but on 24-year and all other time scales. Thus when we add up responses to individual forcings, with drift subtracted, we are including the same unforced 124 year fluctuation for each forcing. Therefore we cannot expect the sum of the responses to individual forcings to equal the response to the sum of the forcings, even if there is no non-linearity in the climate response.
An improved procedure would be to initiate experiments for different forcings at different points on the control run, in addition to spacing ensemble members. It would perhaps be still better to carry out a long control run that reaches equilibrium before experiments are initiated, so there would be no need to subtract a control run. However, the merits of waiting until the control run equilibrates before initiating experiments may be reduced if the equilibrium climate drifts too far from the real world.

S4. Surface Temperature Definition.
Surface air temperature (Ts) in modelE is calculated at 0 m height. The land-ocean temperature index (Tx) (Hansen et al. 200) is from observations at 2 m height at meteorological stations and SST data of  and Reynolds and Smith (994) over the ocean. Temperature changes of model and observations are compared, which minimizes, but does not eliminate, the effect of these height differences. Fig. S3 shows the modeled 880-2003 temperature change for () ocean A driven by no forcings except SST and sea ice change, (2) the same as () but including "all forcings" (GHGs, aerosols, etc.), (3) the same as (2), but for the coupled atmosphere-ocean climate model. For each of these three models we show the global surface air temperature (Ts), the temperature index (Tx), which uses the ocean temperature instead of Ts for ocean areas, and their difference.
In the case of ocean A with no forcings, Ts and Tx are practically the same on global average, even though there are regions where they differ by a few tenths of a degree. In the case of ocean A with radiative forcings, the forcings are able to change atmospheric temperature slightly even though SST is fixed. Global mean Ts increases 0.03ºC more than Tx increases over the period 880-2003. In the case of ocean C the ocean temperature is able to respond to the change of near surface temperature gradient, and Ts increases 0.05ºC more than Tx increases over the 880-2003 period.
These comparisons indicate that our use of global Ts at 0 m height overstates global mean ∆T by several hundredths of a degree, if our aim is comparison with a temperature index that uses SSTs. We could employ ∆Tx from the model based on the first layer ocean temperature, but that would be inconsistent with the procedure used in previous studies with the GISS model and other models, and thus we used ∆Ts in this paper. This issue may be noticeable only in the GISS model, which calculates Ts in an iterative fashion (Hansen et al. 983;modelE 2006). In the future the issue might be practically eliminated by calculating Ts at 2 m height, rather than 0 m. These small changes in ∆T do not alter the geographical pattern of the discrepancy between model and observations. The main implication is that the 24-year warming in our model with "all forcings" is ~0.0ºC less than observed, rather than 0.05ºC less. Thus the need for less tropospheric aerosol amount becomes clearer in the global mean temperature, as well as from unrealistic cooling over Europe.
As future models are better able to simulate observed climate change, it will be worth removing any such discrepancy in comparison with observed surface temperature. We are uncertain whether this comparison issue exists for other climate models. S5. Ozone Scenario.
The first set of runs that we provided to IPCC inadvertently used the Randel and Wu (999) decadal rate of stratospheric O 3 depletion as the 8-year change, thus understating stratospheric O 3 depletion by the factor 0/8. Corrected runs were submitted several months later, and both sets of runs remain available at www-pcmdi.llnl.gov/ipcc/about_ipcc.php. The correction reduced the 880-2003 global forcing Fa by 0.03 W/m 2 . The main impact of the correction was on stratospheric cooling in the Antarctic region during the time of O 3 depletion, with the corrected results providing better agreement with observations. The present paper and Efficacy (2005) use the corrected O 3 change.
A second issue with the O 3 scenario concerns O 3 forcing due to tropospheric pollution. The O 3 scenario was derived from an off-line simulation of a tropospheric chemistry model , which yielded an 880-2000 O 3 change from the surface to the 50 hPa level at all latitudes. Global forcings for this O 3 change were Fi = 0.44, Fa = 0.38 W/m 2 . However, tropospheric O 3 forcing implemented in our transient simulations was less, as high-latitude O 3 increases above the model's tropopause (Fig. 3 of Efficacy [2005]) were excluded, reducing O 3 forcing by 0.05 W/m 2 . As the pollution effect on O 3 at low latitudes was only allowed to reach the 50 hPa level, we suspect that our total O 3 forcing (Fa = 0.28, Fs = 0.26, Fe = 0.23 W/m 2 , including tropospheric pollution and stratospheric depletion, from Table  ) underestimates actual O 3 forcing. Future O 3 scenarios should be generated by models with improved vertical resolution and higher model top, preferably integrating effects of tropospheric pollution and stratospheric change.

S6. Snow Albedo.
A computer programming error was present in the calculation of snow albedo in several of our climate simulations. Some of these runs were repeated with the error corrected, as delineated below. Our intention was for snow albedo change to be proportional to BC deposition as calculated by the aerosol transport model of Koch (200). The error caused albedo change to be exaggerated in partially snow-covered land gridboxes and understated over sea ice, because total albedo change was fixed.
Our initial 'all forcing' run provided to IPCC contained both the ozone error (A.4) and snow albedo error (A.5). We also provided to IPCC 'all forcing' runs with the ozone error corrected and later runs with both errors corrected. Because of space limitations, the DOE web site includes only the original 'all forcing' ensemble and the ensemble with the ozone forcing corrected. All three ensembles are available on the GISS web site.
The 'all forcing' and snow albedo alone ensembles were rerun with the snow albedo error corrected. The corrected program was also used in 'Arctic pollution' runs (Fig. 5 in Dangerous [2006]). However, the AltAer, AltAer3, and AltSol runs contain the snow albedo error, but not the ozone error. These ensembles were not rerun with corrected snow albedo because of the small magnitude of the error and the fact that it would not alter conclusions from those runs. To allow precise comparison with AltAer, AltAer2, and AltSol, the standard model results in Fig. 6 of this paper and Fig. 6 in Dangerous (2006) are the 'all forcing' results that include the snow albedo error.
The simulations employed in the energy imbalance study of  contained both errors. The errors in global forcing, +0.03 W/m 2 and -0.02 W/m 2 , opposed each other, but regional and temporal effects would not cancel, e.g., stratospheric cooling over Antarctica was underestimated. However, the magnitude of these errors is too small to affect conclusions of that paper.
Efficacy (2005) simulations included the snow albedo error but not the ozone error. In Table 4 and Fig. 6 of Efficacy (2005) the snow albedo forcing was calculated with the incorrect program. Fa was actually 0.05 W/m 2 , not 0.08 W/m 2 , and the correct efficacy for the snow albedo effect was Ea ~ 2.7, not Ea ~ 1.7.   Fig. S2. (a) Stratospheric aerosol optical thickness and (b) effective forcing for the assumed aerosol scenario, based on update of Sato et al. (993). (c, d) Temperatures simulated by the climate model normalized to the mean for the 36 months before the eruption, with the circles and asterisks in (c) being the Jun-Jul-Aug and Dec-Jan-Feb means, respectively. Observed 'station' data and 'land + ocean' are based on analyses of Hansen et al. (200), using, respectively, meteorological stations alone and those same stations plus ocean data of . Fig. S3. Simulated surface temperature change for 880-2003 based on local linear trends. Ts is the surface air temperature at 0 m altitude, Tx substitutes SST for Ts over the ocean. Ocean A uses the SST and sea ice history of  coupled to atmospheric modelE, while ocean C couples modelE with the Russell et al. (995) ocean model.