Constraining low-cloud climate feedbacks

Contraining the equilibrium climate sensitivity

Low cloud feedbacks contribute some of the largest uncertainties to projections of future climate. A key physical process modulating these feedbacks is shallow convective mixing between the boundary layer and the free troposphere, which explains up to half of the variance in climate sensitivity estimates. Shallow convective mixing transports moisture upward, moistening the lower troposphere and drying the boundary layer; this suppresses further developments of low-level clouds and warms the Earth’s surface. While many climate models simulate this process, evidence from Large Eddy Simulations suggests that these models may erroneously amplify the positive feedback. This discrepancy necessitates additional observational constraints on shallow convective mixing processes over broad regions and across a wide range of environmental conditions. To this end, we here employ stable water isotope profiles from satellite retrievals (TES and AIRS) with global coverage to augment existing constraints on mixing. We also perform a proof of concept, demonstrating that isotopic lapse rates derived from satellite retrievals can be used to track shallow convective mixing. Data-model comparison suggests the atmospheric general circulation model iCAM5 overestimates shallow convective mixing over the Warm Pool. Further evaluations of climate models with observational shallow convective mixing will provide guidance for tuning cloud and convection parameterizations and thus refine estimates of climate sensitivity.

Jun Hu
Associate Professor

My research focuses on paleoclimate to better understand large-scale climate dynamics via modeling and statistical tools.