How do polar clouds and precipitation affect global warming?

2021 ◽  
Author(s):  
Jennifer Kay
Keyword(s):  
Global Warming ◽  
Climate Model ◽  
Research Problem ◽  
Cloud Feedback ◽  
Polar Regions ◽  
Unsolved Research Problem ◽  
Cloud Feedbacks ◽  
Surface Warming ◽  
Polar Clouds ◽  
Radiative Feedbacks

<p>Understanding the influence of clouds and precipitation on global warming remains an important unsolved research problem. This talk presents an overview of this topic, with a focus on recent observations, theory, and modeling results for polar clouds. After a general introduction, experiments that disable cloud radiative feedbacks or “lock the clouds” within a state‐of‐the‐art,  well‐documented, and observationally vetted climate model will be presented. Through comparison of idealized greenhouse warming experiments with and without cloud locking, the sign and magnitude cloud feedbacks can be quantified. Global cloud feedbacks increase both global and Arctic warming by around 25%. In contrast, disabling Arctic cloud feedbacks has a negligible influence on both Arctic and global surface warming. Do observations and theory support a positive global cloud feedback and a weak Arctic cloud feedback?  How does precipitation affect polar cloud feedbacks? What are the implications especially for climate change in polar regions?  </p>

scholarly journals A cloud feedback emulator (CFE, version 1.0) for an intermediate complexity model

2017 ◽  
Vol 10 (2) ◽  
pp. 945-958 ◽  
Author(s):  
David J. Ullman ◽  
Andreas Schmittner
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Cloud Feedbacks ◽  
Intermediate Complexity ◽  
Model Cloud ◽  
Radiative Feedbacks ◽  
The University

Abstract. The dominant source of inter-model differences in comprehensive global climate models (GCMs) are cloud radiative effects on Earth's energy budget. Intermediate complexity models, while able to run more efficiently, often lack cloud feedbacks. Here, we describe and evaluate a method for applying GCM-derived shortwave and longwave cloud feedbacks from 4 × CO2 and Last Glacial Maximum experiments to the University of Victoria Earth System Climate Model. The method generally captures the spread in top-of-the-atmosphere radiative feedbacks between the original GCMs, which impacts the magnitude and spatial distribution of surface temperature changes and climate sensitivity. These results suggest that the method is suitable to incorporate multi-model cloud feedback uncertainties in ensemble simulations with a single intermediate complexity model.

scholarly journals The University of Victoria Cloud Feedback Emulator (UVic-CFE): cloud radiative feedbacks in an intermediate complexity model

2016 ◽  
Author(s):  
David Ullman ◽  
Andreas Schmittner
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Cloud Feedbacks ◽  
Intermediate Complexity ◽  
Model Cloud ◽  
Radiative Feedbacks ◽  
The University

Abstract. The dominant source of inter-model differences in comprehensive global climate models (GCMs) are cloud radiative effects on Earth's energy budget. Intermediate complexity models, while able to run more efficiently, often lack cloud feedbacks. Here, we describe and evaluate a method for applying GCM-derived shortwave and longwave cloud feedbacks from 4xCO2 and Last Glacial Maximum experiments to the University of Victoria Earth System Climate Model. The method generally captures the spread in top-of-the-atmosphere radiative feedbacks between the original GCMs, which impacts the magnitude and spatial distribution of surface temperature changes and climate sensitivity. These results suggest that the method is suitable to incorporate multi-model cloud feedback uncertainties in ensemble simulations with a single intermediate complexity model.

Why does the climate response to greenhouse warming and greenhouse cooling differ? 

2021 ◽  
Author(s):  
Jennifer Kay ◽  
Jason Chalmers
Keyword(s):  
Carbon Dioxide ◽  
Radiative Forcing ◽  
Climate Model ◽  
Temperature Response ◽  
Greenhouse Warming ◽  
Climate Response ◽  
Cloud Feedbacks ◽  
Surface Climate ◽  
Carbon Dioxide Concentrations ◽  
Radiative Feedbacks

<p>While the long-standing quest to constrain equilibrium climate sensitivity has resulted in intense scrutiny of the processes controlling idealized greenhouse warming, the processes controlling idealized greenhouse cooling have received less attention. Here, differences in the climate response to increased and decreased carbon dioxide concentrations are assessed in state-of-the-art fully coupled climate model experiments. One hundred and fifty years after an imposed instantaneous forcing change, surface global warming from a carbon dioxide doubling (abrupt-2xCO2, 2.43 K) is larger than the surface global cooling from a carbon dioxide halving (abrupt-0p5xCO2, 1.97 K). Both forcing and feedback differences explain these climate response differences. Multiple approaches show the radiative forcing for a carbon dioxide doubling is ~10% larger than for a carbon dioxide halving. In addition, radiative feedbacks are less negative in the doubling experiments than in the halving experiments. Specifically, less negative tropical shortwave cloud feedbacks and more positive subtropical cloud feedbacks lead to more greenhouse 2xCO2 warming than 0.5xCO2 greenhouse cooling. Motivated to directly isolate the influence of cloud feedbacks on these experiments, additional abrupt-2xCO2 and abrupt-0p5xCO2 experiments with disabled cloud-climate feedbacks were run. Comparison of these “cloud-locked” simulations with the original “cloud active” simulations shows cloud feedbacks help explain the nonlinear global surface temperature response to greenhouse warming and greenhouse cooling. Overall, these results demonstrate that both radiative forcing and radiative feedbacks are needed to explain differences in the surface climate response to increased and decreased carbon dioxide concentrations.</p>

scholarly journals Probabilistic projections of future warming and climate sensitivity trajectories

2021 ◽  
Author(s):  
Philip Goodwin
Keyword(s):  
Global Warming ◽  
Climate Sensitivity ◽  
Climate Model ◽  
Heat Content ◽  
Surface Warming ◽  
Observational Uncertainty ◽  
Future Global Warming ◽  
Intercomparison Project ◽  
Future Warming ◽  
Probabilistic Projections

Abstract Projections of future global mean surface warming for a given forcing scenario remain uncertain, largely due to uncertainty in the climate sensitivity. The ensemble of Earth system models from the Climate Model Intercomparison Project phase 6 (CMIP6) represent the dominant tools for projecting future global warming. However, the distribution of climate sensitivities within the CMIP6 ensemble is not representative of recent independent probabilistic estimates, and the ensemble contains significant variation in simulated historic surface warming outside agreement with observational datasets. Here, a Bayesian approach is used to infer joint probabilistic projections of future surface warming and climate sensitivity for SSP scenarios. The projections use an efficient climate model ensemble filtered and weighted to encapsulate observational uncertainty in historic warming and ocean heat content anomalies. The probabilistic projection of climate sensitivity produces a best estimate of 2.9 °C, and 5th to 95th percentile range of 1.5 to 4.6 °C, in line with previous estimates using multiple lines of evidence. The joint projection of surface warming over the period 2030 to 2040 has a 50% or greater probability of exceeding 1.5 °C above preindustrial for all SSPs considered: 119, 126, 245, 370 and 585. Average warming by the period 2050 to 2060 has a greater than 50% chance of exceeding 2 °C for SSPs 245, 370 and 585. These results imply that global warming is no longer likely to remain under 1.5 °C, even with drastic and immediate mitigation, and highlight the importance of urgent action to avoid exceeding 2 °C warming.

scholarly journals Attributing Historical and Future Evolution of Radiative Feedbacks to Regional Warming Patterns using a Green’s Function Approach: The Preeminence of the Western Pacific

2019 ◽  
Vol 32 (17) ◽  
pp. 5471-5491 ◽  
Author(s):  
Yue Dong ◽  
Cristian Proistosescu ◽  
Kyle C. Armour ◽  
David S. Battisti
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Climate Sensitivity ◽  
Climate Model ◽  
Global Climate Model ◽  
Western Pacific ◽  
Surface Warming ◽  
The Future ◽  
Radiative Feedbacks ◽  
The Western Pacific

Abstract Global radiative feedbacks have been found to vary in global climate model (GCM) simulations. Atmospheric GCMs (AGCMs) driven with historical patterns of sea surface temperatures (SSTs) and sea ice concentrations produce radiative feedbacks that trend toward more negative values, implying low climate sensitivity, over recent decades. Freely evolving coupled GCMs driven by increasing CO2 produce radiative feedbacks that trend toward more positive values, implying increasing climate sensitivity, in the future. While this time variation in feedbacks has been linked to evolving SST patterns, the role of particular regions has not been quantified. Here, a Green’s function is derived from a suite of simulations within an AGCM (NCAR’s CAM4), allowing an attribution of global feedback changes to surface warming in each region. The results highlight the radiative response to surface warming in ascent regions of the western tropical Pacific as the dominant control on global radiative feedback changes. Historical warming from the 1950s to 2000s preferentially occurred in the western Pacific, yielding a strong global outgoing radiative response at the top of the atmosphere (TOA) and thus a strongly negative global feedback. Long-term warming in coupled GCMs occurs preferentially in tropical descent regions and in high latitudes, where surface warming yields small global TOA radiation change but large global surface air temperature change, and thus a less-negative global feedback. These results illuminate the importance of determining mechanisms of warm pool warming for understanding how feedbacks have varied historically and will evolve in the future.

Does Model Sensitivity to Changes in CO2 Provide a Measure of Sensitivity to Other Forcings?

2006 ◽  
Vol 19 (13) ◽  
pp. 3294-3306 ◽  
Author(s):  
Andrei P. Sokolov
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Cloud Feedback ◽  
Future Climate Change ◽  
Massachusetts Institute Of Technology ◽  
Model Sensitivity ◽  
Sea Ice Extent ◽  
Surface Warming

Abstract Simulation of both the climate of the twentieth century and a future climate change requires taking into account numerous forcings, while climate sensitivities of general circulation models are defined as the equilibrium surface warming due to a doubling of atmospheric CO2 concentration. A number of simulations with the Massachusetts Institute of Technology (MIT) climate model of intermediate complexity with different forcings have been carried out to study to what extent sensitivity to changes in CO2 concentration (SCO2) represent sensitivities to other forcings. The MIT model, similar to other models, shows a strong dependency of the simulated surface warming on the vertical structure of the imposed forcing. This dependency is a result of “semidirect” effects in the simulations with localized tropospheric heating. A method for estimating semidirect effects associated with different feedback mechanisms is presented. It is shown that forcing that includes these effects is a better measure of expected surface warming than a forcing that accounts for stratospheric adjustment only. Simulations with the versions of the MIT model with different strengths of cloud feedback show that, for the range of sensitivities produced by existing GCMs, SCO2 provides a good measure of the model sensitivity to other forcings. In the case of strong cloud feedback, sensitivity to the increase in CO2 concentration overestimates model sensitivity to both negative forcings, leading to the cooling of the surface and “black carbon”–like forcings with elevated heating. This is explained by the cloud feedback being less efficient in the case of increasing sea ice extent and snow cover or by the above-mentioned semidirect effects, which are absent in the CO2 simulations, respectively.

scholarly journals Observational evidence that cloud feedback amplifies global warming

2021 ◽  
Vol 118 (30) ◽  
pp. e2026290118
Author(s):  
Paulo Ceppi ◽  
Peer Nowack
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Atmospheric Carbon Dioxide ◽  
Climate Model ◽  
Cloud Feedback ◽  
Ecological Impacts ◽  
Two Factors ◽  
Amplifying Effect ◽  
Climate Model Simulations ◽  
Using Data

Global warming drives changes in Earth’s cloud cover, which, in turn, may amplify or dampen climate change. This “cloud feedback” is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)—the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W⋅m−2⋅K−1 (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.

scholarly journals Does disabling cloud radiative feedbacks change spatial patterns of surface greenhouse warming and cooling?

2022 ◽  
pp. 1-60
Keyword(s):  
Global Warming ◽  
Surface Temperature ◽  
High Latitude ◽  
Radiative Forcing ◽  
Tropical Pacific ◽  
Lapse Rate ◽  
Cloud Feedbacks ◽  
Global Cooling ◽  
Effective Radiative Forcing ◽  
Radiative Feedbacks

Abstract The processes controlling idealized warming and cooling patterns are examined in 150 year-long fully coupled Community Earth System Model version 1 (CESM1) experiments under abrupt CO2 forcing. By simulation end, 2xCO2 global warming was 20% larger than 0.5xCO2 global cooling. Not only was the absolute global effective radiative forcing ∼10% larger for 2xCO2 than for 0.5xCO2, global feedbacks were also less negative for 2xCO2 than for 0.5xCO2. Specifically, more positive shortwave cloud feedbacks led to more 2xCO2 global warming than 0.5xCO2 global cooling. Over high latitude oceans, differences between 2xCO2 warming and 0.5xCO2 cooling were amplified by familiar linked positive surface albedo and lapse rate feedbacks associated with sea ice change. At low latitudes, 2xCO2 warming exceeded 0.5xCO2 cooling almost everywhere. Tropical Pacific cloud feedbacks amplified: 1) more fast warming than fast cooling in the west, 2) slow pattern differences between 2xCO2 warming and 0.5xCO2 cooling in the east. Motivated to quantify cloud influence, a companion suite of experiments were run without cloud radiative feedbacks. Disabling cloud radiative feedbacks reduced the effective radiative forcing and surface temperature responses for both 2xCO2 and 0.5xCO2. Notably, 20% more global warming than global cooling occurred regardless of whether cloud feedbacks were enabled or disabled. This surprising consistency resulted from the cloud influence on non-cloud feedbacks and circulation. With the exception of the Tropical Pacific, disabling cloud feedbacks did little to change surface temperature response patterns including the large high-latitude responses driven by non-cloud feedbacks. The findings provide new insights into the regional processes controlling the response to greenhouse gas forcing, especially for clouds.

scholarly journals Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5*

2013 ◽  
Vol 26 (14) ◽  
pp. 5007-5027 ◽  
Author(s):  
Mark D. Zelinka ◽  
Stephen A. Klein ◽  
Karl E. Taylor ◽  
Timothy Andrews ◽  
Mark J. Webb ◽  
...  
Keyword(s):  
Optical Depth ◽  
Climate Model ◽  
Cloud Amount ◽  
Longwave Radiation ◽  
Cloud Feedback ◽  
Shortwave Radiation ◽  
Rapid Reduction ◽  
Cloud Feedbacks ◽  
Cloud Properties ◽  
Climate Model Simulations

Abstract Using five climate model simulations of the response to an abrupt quadrupling of CO2, the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. Upon CO2 quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m−2 net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low cloud changes are the largest contributor to the mean and spread in net cloud feedback. The importance of the negative optical depth feedback relative to the amount feedback at high latitudes is even more marked than in earlier models. The authors show that the negative longwave cloud adjustment inferred in previous studies is primarily caused by a 1.3 W m−2 cloud masking of CO2 forcing. Properly accounting for cloud masking increases net cloud feedback by 0.3 W m−2 K−1, whereas accounting for rapid adjustments reduces by 0.14 W m−2 K−1 the ensemble mean net cloud feedback through a combination of smaller positive cloud amount and altitude feedbacks and larger negative optical depth feedbacks.

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