{'fraction_non_alphanumeric': 0.030153508771929825, 'fraction_numerical': 0.006030701754385965, 'mean_word_length': 5.336805555555555, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The uncertain future of climate uncertainty

Strong scientific consensus prevails over the fact that Earth’s climate is currently warming and will be warming further over the coming decades, as a consequence of the radiative perturbation caused by anthropogenic greenhousegas (GHG) emissions. The conclusions of the IPCC’s Fourth Assessment Report (AR4: Solomon et al. [2007], [AR4] hereafter) further buttress this consensus. There is, however, substantial uncertainty regarding the extent of future warming, as pointed out in the same report and in many of its references. This uncertainty renders decision making on appropriate mitigation and adaptation steps more difficult. In addition, the uncertainty level regarding future climate evolution has not decreased significantly over the past decades, an observation that paves the way for climate-warming naysayers and is sometimes used as an argument to discredit climate science and its findings as a whole. Credence given to the naysayers tends, in turn, to slow down or even stop action on this issue; it also tends to interfere with a healthy, truly scientific debate on the real extent of and on the reasons for the uncertainty. These two reasons motivate us to (a) revisit here a key cause for the persisting uncertainties, in order to potentially reduce them; and (b) try to anticipate their future evolution as research makes further progress. Uncertainties regarding future climate warming are usually divided into three categories: (i) those regarding the GHG increase scenarios [AR4]; (ii) those arising from the climate system’s internal variability [Ghil et al., 2008]; and (iii) those due to model formulation and properties. The relative importance of either type of causes varies considerably according to lead time, spatial scale and geographic location