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Title: On the Emergent Constraints of Climate Sensitivity [On proposed emergent constraints of climate sensitivity]

Abstract

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical linksmore » with that feedback should be investigated to verify that the constraint is real. Additionally, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.« less

Authors:
 [1];  [1];  [1];  [2];  [2];  [3];  [3];  [3]
  1. Univ. of California, Los Angeles, CA (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. California Inst. of Technology (CalTech), Pasadena, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1418937
Alternate Identifier(s):
OSTI ID: 1416651
Report Number(s):
LLNL-JRNL-729272
Journal ID: ISSN 0894-8755
Grant/Contract Number:
AC52-07NA27344
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 31; Journal Issue: 2; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Climate change; Climate sensitivity; Clouds; Feedback; Climate models

Citation Formats

Qu, Xin, Hall, Alex, DeAngelis, Anthony M., Zelinka, Mark D., Klein, Stephen A., Su, Hui, Tian, Baijun, and Zhai, Chengxing. On the Emergent Constraints of Climate Sensitivity [On proposed emergent constraints of climate sensitivity]. United States: N. p., 2018. Web. doi:10.1175/JCLI-D-17-0482.1.
Qu, Xin, Hall, Alex, DeAngelis, Anthony M., Zelinka, Mark D., Klein, Stephen A., Su, Hui, Tian, Baijun, & Zhai, Chengxing. On the Emergent Constraints of Climate Sensitivity [On proposed emergent constraints of climate sensitivity]. United States. doi:10.1175/JCLI-D-17-0482.1.
Qu, Xin, Hall, Alex, DeAngelis, Anthony M., Zelinka, Mark D., Klein, Stephen A., Su, Hui, Tian, Baijun, and Zhai, Chengxing. Thu . "On the Emergent Constraints of Climate Sensitivity [On proposed emergent constraints of climate sensitivity]". United States. doi:10.1175/JCLI-D-17-0482.1.
@article{osti_1418937,
title = {On the Emergent Constraints of Climate Sensitivity [On proposed emergent constraints of climate sensitivity]},
author = {Qu, Xin and Hall, Alex and DeAngelis, Anthony M. and Zelinka, Mark D. and Klein, Stephen A. and Su, Hui and Tian, Baijun and Zhai, Chengxing},
abstractNote = {Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. Additionally, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.},
doi = {10.1175/JCLI-D-17-0482.1},
journal = {Journal of Climate},
number = 2,
volume = 31,
place = {United States},
year = {Thu Jan 11 00:00:00 EST 2018},
month = {Thu Jan 11 00:00:00 EST 2018}
}

Journal Article:
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  • Emergent constraints are physically explainable empirical relationships between characteristics of the current climate and long-term climate prediction that emerge in collections of climate model simulations. With the prospect of constraining long-term climate prediction, scientists have recently uncovered several emergent constraints related to long-term cloud feedbacks. We review these proposed emergent constraints, many of which involve the behavior of low-level clouds, and discuss criteria to assess their credibility. With further research, some of the cases we review may eventually become confirmed emergent constraints, provided they are accompanied by credible physical explanations. Because confirmed emergent constraints identify a source of model errormore » that projects onto climate predictions, they deserve extra attention from those developing climate models and climate observations. While a systematic bias cannot be ruled out, it is noteworthy that the promising emergent constraints suggest larger cloud feedback and hence climate sensitivity.« less
  • Emergent constraints are quantities that are observable from current measurements and have skill predicting future climate. Here, this study explores 19 previously proposed emergent constraints related to equilibrium climate sensitivity (ECS; the global-average equilibrium surface temperature response to CO 2 doubling). Several constraints are shown to be closely related, emphasizing the importance for careful understanding of proposed constraints. A new method is presented for decomposing correlation between an emergent constraint and ECS into terms related to physical processes and geographical regions. Using this decomposition, one can determine whether the processes and regions explaining correlation with ECS correspond to the physicalmore » explanation offered for the constraint. Shortwave cloud feedback is generally found to be the dominant contributor to correlations with ECS because it is the largest source of intermodel spread in ECS. In all cases, correlation results from interaction between a variety of terms, reflecting the complex nature of ECS and the fact that feedback terms and forcing are themselves correlated with each other. For 4 of the 19 constraints, the originally proposed explanation for correlation is borne out by our analysis. These four constraints all predict relatively high climate sensitivity. The credibility of six other constraints is called into question owing to correlation with ECS coming mainly from unexpected sources and/or lack of robustness to changes in ensembles. Another six constraints lack a testable explanation and hence cannot be confirmed. Lastly, the fact that this study casts doubt upon more constraints than it confirms highlights the need for caution when identifying emergent constraints from small ensembles.« less
  • Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO 2, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C. Clouds are among the leading causes of this uncertainty. Here, in this paper, we show that the ECS can be up to 1.3°C higher in simulations where mixed-phase clouds consisting of ice crystals and supercooled liquid droplets are constrained by global satellite observations. The higher ECS estimates are directly linked to a weakened cloud-phase feedback arising from a decreased cloud glaciation rate in a warmer climate. Finally, wemore » point out the need for realistic representations of the supercooled liquid fraction in mixed-phase clouds in GCMs, given the sensitivity of the ECS to the cloud-phase feedback.« less
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