We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO 2 , characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6-3.9 K for our Baseline calculation and remains within 2.3-4.5 K under the robustness tests; corresponding 5-95% ranges are 2.3-4.7 K, bounded by 2.0-5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes. Plain Language Summary Earth's global "climate sensitivity" is a fundamental quantitative measure of the susceptibility of Earth's climate to human influence. A landmark report in 1979 concluded that it probably lies between 1.5°C and 4.5°C per doubling of atmospheric carbon dioxide, assuming that other influences on climate remain unchanged. In the 40 years since, it has appeared difficult to reduce this uncertainty range. In this report we thoroughly assess all lines of evidence including some new developments. We find that a large volume of consistent evidence now points to a more confident view of a climate sensitivity near the middle or upper part of this range. In particular, it now appears extremely unlikely that the climate sensitivity could be low enough to avoid substantial climate change (well in excess of 2°C warming) under a high-emission future scenario. We remain unable to rule out that the sensitivity could be above 4.5°C per doubling of carbon dioxide levels, although this is not likely. Continued ©2020. American Geophysical Union. All Rights Reserved.

Over the past decade, severe winters occurred frequently in mid-latitude Eurasia 1,2 , despite increasing global-and annual-mean surface air temperatures 3 . Observations suggest that these cold Eurasian winters could have been instigated by Arctic sea-ice decline 2,4 , through excitation of circulation anomalies similar to the Arctic Oscillation 5 . In climate simulations, however, a robust atmospheric response to sea-ice decline has not been found, perhaps owing to energetic internal fluctuations in the atmospheric circulation 6 . Here we use a 100-member ensemble of simulations with an atmospheric general circulation model driven by observation-based sea-ice concentration anomalies to show that as a result of sea-ice reduction in the Barents-Kara Sea, the probability of severe winters has more than doubled in central Eurasia. In our simulations, the atmospheric response to sea-ice decline is approximately independent of the Arctic Oscillation. Both reanalysis data and our simulations suggest that sea-ice decline leads to more frequent Eurasian blocking situations, which in turn favour cold-air advection to Eurasia and hence severe winters. Based on a further analysis of simulations from 22 climate models we conclude that the sea-ice-driven cold winters are unlikely to dominate in a warming future climate, although uncertainty remains, due in part to an insu cient ensemble size.The Siberian High, a continental surface high pressure prevailing the boreal winter Asian monsoon, causes a breakout of cold air to the mid-latitudes, the fluctuation of which greatly affects a significant part of the population of Eurasia. Northern Hemisphere winters (December-February; DJF) have frequently seen pronounced warming and cooling in recent years, in the Arctic and mid-latitudes, respectively, forming the so-called 'warm Arctic-cold continents' pattern 7 (Supplementary Fig. 1), signifying the intensification of the Siberian High over the Eurasian continent. The Arctic surface warming has been accompanied by a rapid decline of Arctic sea ice 8 , which is therefore argued to represent the Arctic amplification signature of global warming 9,10 . However, the causes of these cold winters observed over mid-latitude Eurasia, apparently counteracting the continuous rise of annual-mean surface air temperature (SAT) over land 3 , are not well understood.Observational studies show a statistically significant relationship between cold SAT anomalies over Eurasia and Arctic sea-ice decline 2,4,11,12 , suggesting that the latter forces the former. However, a robust atmospheric response to sea-ice loss has yet to be obtained by modelling studies 2,13-16 because detection of sea-ice loss impacts on the extratropical atmosphere is hampered by the large internal fluctuations of the atmospheric circulation that are prominent in winter 6,15 .Here, we successfully detected the signature of Eurasian cold winters excited by sea-ice decline in the Barents-Kara Sea (BKS), where a pronounced change has been observed during winters since 2004 ( Supplem...

NATURE GEOSCIENCE | VOL 8 | APRIL 2015 | www.nature.com/naturegeoscience 261 C louds stimulate the human spirit. Although they have been recognized for centuries as harbingers of weather, only in recent decades have scientists begun to appreciate the role of clouds in determining the general circulation of the atmosphere and its susceptibility to change.Forming mostly in the updrafts of the turbulent and chaotic airflow, clouds embody the complex and multiscale organization of the atmosphere into dynamical entities, or storms. These entities mediate the radiative transfer of energy, distribute precipitation and are often associated with extreme winds. It has long been recognized that the water and heat transfer that clouds mediate plays a fundamental role in tropical circulations, and there is increasing evidence that they also influence extratropical circulations 1 . Globally, the impact of clouds on Earth's radiation budget -and hence surface temperatures -also depends critically on how clouds interact with one another and with larger-scale circulations 2 . Far from being passive tracers of a turbulent atmosphere, clouds thus embody processes that can actively control circulation and climate (Box 1).For practical reasons, early endeavours to understand climate deployed a 'divide and conquer' strategy in which efforts to understand clouds and convective processes developed separately from efforts to understand larger-scale circulations. Over time, a gap developed between the subdisciplines. But technological progress and conceptual advances have tremendously increased our capacity to observe and simulate the climate system, such that it is now possible to study more readily how small-scale convective processes -that is, clouds -couple to large-scale circulations (Box 2). Much as a new accelerator allows physicists to explore the implication of the interactions among forces acting over different length scales, these new capabilities are transforming how atmospheric scientists think about the interplay of clouds and climate. This offers a great opportunity not only to close the gap between scientific communities, but Fundamental puzzles of climate science remain unsolved because of our limited understanding of how clouds, circulation and climate interact. One example is our inability to provide robust assessments of future global and regional climate changes. However, ongoing advances in our capacity to observe, simulate and conceptualize the climate system now make it possible to fill gaps in our knowledge. We argue that progress can be accelerated by focusing research on a handful of important scientific questions that have become tractable as a result of recent advances. We propose four such questions below; they involve understanding the role of cloud feedbacks and convective organization in climate, and the factors that control the position, the strength and the variability of the tropical rain belts and the extratropical storm tracks.also to answer some of the most pressing questions about the fate of our pl...

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A new version of the atmosphere-ocean general circulation model cooperatively produced by the Japanese research community, known as the Model for Interdisciplinary Research on Climate (MIROC), has recently been developed. A century-long control experiment was performed using the new version (MIROC5) with the standard resolution of the T85 atmosphere and 18 ocean models. The climatological mean state and variability are then compared with observations and those in a previous version (MIROC3.2) with two different resolutions (medres, hires), coarser and finer than the resolution of MIROC5.A few aspects of the mean fields in MIROC5 are similar to or slightly worse than MIROC3.2, but otherwise the climatological features are considerably better. In particular, improvements are found in precipitation, zonal mean atmospheric fields, equatorial ocean subsurface fields, and the simulation of El Niñ o-Southern Oscillation. The difference between MIROC5 and the previous model is larger than that between the two MIROC3.2 versions, indicating a greater effect of updating parameterization schemes on the model climate than increasing the model resolution. The mean cloud property obtained from the sophisticated prognostic schemes in MIROC5 shows good agreement with satellite measurements. MIROC5 reveals an equilibrium climate sensitivity of 2.6 K, which is lower than that in MIROC3.2 by 1 K. This is probably due to the negative feedback of low clouds to the increasing concentration of CO 2 , which is opposite to that in MIROC3.2.

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

[1] Using NASA's A-Train satellite measurements, we evaluate the accuracy of cloud water content (CWC) and water vapor mixing ratio (H 2 O) outputs from 19 climate models submitted to the Phase 5 of Coupled Model Intercomparison Project (CMIP5), and assess improvements relative to their counterparts for the earlier CMIP3. We find more than half of the models show improvements from CMIP3 to CMIP5 in simulating column-integrated cloud amount, while changes in water vapor simulation are insignificant. For the 19 CMIP5 models, the model spreads and their differences from the observations are larger in the upper troposphere (UT) than in the lower or middle troposphere (L/MT). The modeled mean CWCs over tropical oceans range from $3% to $15Â of the observations in the UT and 40% to 2Â of the observations in the L/MT. For modeled H 2 Os, the mean values over tropical oceans range from $1% to 2Â of the observations in the UT and within 10% of the observations in the L/MT. The spatial distributions of clouds at 215 hPa are relatively well-correlated with observations, noticeably better than those for the L/MT clouds. Although both water vapor and clouds are better simulated in the L/MT than in the UT, there is no apparent correlation between the model biases in clouds and water vapor. Numerical scores are used to compare different model performances in regards to spatial mean, variance and distribution of CWC and H 2 O over tropical oceans. Model performances at each pressure level are ranked according to the average of all the relevant scores for that level.Citation: Jiang, J. H., et al. (2012), Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA "A-Train" satellite observations,

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