On the Emergent Constraints of Climate Sensitivity

Journal Article · Mon Jan 01 00:00:00 EST 2018 · Journal of Climate

DOI:https://doi.org/10.1175/JCLI-D-17-0482.1· OSTI ID:1416651

Qu, Xin ^[1]; Hall, Alex ^[1]; DeAngelis, Anthony M. ^[1]; Zelinka, Mark D. ^[2]; Klein, Stephen A. ^[2]; Su, Hui ^[3]; Tian, Baijun ^[3]; Zhai, Chengxing ^[3]

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1. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
2. Cloud Processes Research Group, Lawrence Livermore National Laboratory, Livermore, California
3. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO ₂ ) 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. In addition, 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.

• Research Organization: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1416651
• Alternate ID(s): OSTI ID: 1418937
• Report Number(s): LLNL-JRNL-729272
• Journal Information: Journal of Climate, Journal Name: Journal of Climate Vol. 31 Journal Issue: 2; ISSN 0894-8755
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
Climate change
Climate sensitivity
Clouds
Feedback
Climate models