Diagnosing the factors that contribute to the intermodel spread of climate feedback in CMIP6

Abstract

Abstract The global surface temperature climate feedback parameter (λ) varies significantly across climate models, and its real-world value remains uncertain. Studies have found that sea surface temperature (SST) response pattern and atmospheric model physics can each affect the climate feedback parameter in historical and idealized warming simulations. In this study, we design and analyze a series of targeted atmospheric global climate model (AGCM) experiments to quantify how much the SST (both the warming pattern and the base climatology) and atmospheric model physics contributes to the intermodel spread of the climate feedback parameter and cloud feedback in in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We use three AGCMs, HiRAM, AM2.5, and AM4, developed in the Geophysical Fluid Dynamics Laboratory (GFDL), which span a wide range of feedbacks in response to uniform surface warming. The three GFDL models indicate that historical patterns of SST change systematically alter the λ, supporting the hypothesized role for a “pattern effect” in historical climate sensitivity. However, we found that forcing one AGCM with the different CO2-induced SST warming patterns or model SST climatology from a suite of climate models from CMIP6 can only reproduce ~10% the intermodel spread of CO2-induced λ, while the atmospheric model used determines the magnitude of λ (~45%). This underscores the role of atmospheric model physics in altering λ, particularly the cloud-related schemes. In addition, we demonstrate that the nonlinear interaction between SST and AGCM has a non-negligible role in affecting λ.