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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:
Free Publicly Available Full Text
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