Abstract
Low‐cloud based emergent constraints have the potential to substantially reduce uncertainty in Earth’s equilibrium climate sensitivity, but recent work has shown that previously developed constraints fail in the latest generation of climate models, suggesting that new approaches are needed. Here, we investigate the potential for emergent constraints to reduce uncertainty in regional cloud feedbacks, rather than the global‐mean cloud feedback. Strong relationships are found between the monthly and interannual variability of tropical clouds, and the tropical net cloud feedback. These relationships are combined with observations to substantially narrow the uncertainty in the tropical cloud feedback and demonstrate that the tropical cloud feedback is likely >0Wm−2K−1. Promising relationships are also found in the 90°–60°S and 30°–60°N regions, though these relationships are not robust across model generations and we have not identified the associated physical mechanisms.
Highlights
Emergent constraints are a promising tool for constraining uncertainty in Earth’s response to increased CO2 concentrations
The canonical example of an emergent constraint was proposed by Hall and Qu (2006), who demonstrated a strong correlation across climate models between the amplitude of the seasonal cycle in Northern Hemisphere snow cover and the reduction in Northern Hemisphere snow cover per degree of local warming
This strong correlation has proven to be robust across multiple climate model generations and, when combined with observations of the amplitude of Northern Hemisphere snow cover’s seasonal cycle, has allowed tight constraints to be placed on the sensitivity of Northern Hemisphere snow cover to warming (Qu & Hall, 2014; Thackeray et al, 2018)
Summary
Emergent constraints are a promising tool for constraining uncertainty in Earth’s response to increased CO2 concentrations. If multiple cloud-types and regions are responsible for the spread in CMIP6 models’ cloud feedback, a single metric will struggle to constrain the global-mean cloud feedback, and will struggle to constrain ECS These issues suggest that emergent constraints based on cloud variability cannot be used to narrow the spread of ECS among CMIP6 models, but emergent constraints on cloudiness may still be of use in more limited, local contexts. We demonstrate that cloud feedbacks in multiple regions contribute to the spread in CMIP6 models’ ECS, whereas tropical clouds are the primary source of spread in CMIP5 model’s ECS (Section 3) This explains the difficulty of constraining ECS in CMIP6 models using low-cloud based emergent constraints and motivates our regional approach. Previous emergent constraint studies have often used linear regression to calculate their posterior constraints; given recent concerns around the reliability of emergent constraints (e.g., Caldwell et al, 2018), we believe that having multiple, complementary approaches can build confidence in and promote adoption of the results of emergent constraints
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