Evaluation of Stratocumulus Evolution Under Contrasting Temperature Advections in CESM2 Through a Lagrangian Framework

This study leveraged a Lagrangian framework to examine the evolution of stratocumulus clouds under cold and warm advections (CADV and WADV) in the Community Earth System Model 2 (CESM2) against observations. We found that CESM2 simulates a too rapid decline in low-cloud fraction (LCF) and cloud liquid water path (CLWP) under CADV conditions, while it better aligns closely with observed LCF under WADV conditions but overestimates the increase in CLWP. Employing an explainable machine learning approach, we found that too rapid decreases in LCF and CLWP under CADV conditions are related to overestimated drying effects induced by sea surface temperature, whereas the substantial increase in CLWP under WADV conditions is associated with the overestimated moistening effects due to free-tropospheric moisture and surface winds. Our findings suggest that overestimated drying effects of sea surface temperature on cloud properties might be one of crucial causes for the high equilibrium climate sensitivity in CESM2. Stratocumulus clouds have extensive coverage over the oceans and modulate the climate system by efficiently reflecting incoming solar radiation back to space. However, their simulations in climate models are challenging due to complex meteorological controls, in which temperature advection is one of the most uncertain controlling factors. To enhance our understanding, we examine the stratocumulus evolution influenced by cold-advection (CADV) and warm-advection (WADV) in the midlatitudes in a climate model, CESM2. A too rapid decrease in low-cloud fraction (LCF) and cloud liquid water path (CLWP) is erroneously simulated under CADV conditions, while an increase in CLWP is substantially overestimated under WADV conditions. Using an explainable machine learning approach, these errors are found to be caused by the amplified drying or moistening effects due to improper treatments of meteorological controls on clouds in CESM2. This study suggests that these misrepresentations of cloud physics in the midlatitudes should be imperatively improved to reduce climate prediction uncertainties. Community Earth System Model 2 simulates a too rapid decline in low-cloud fraction and cloud liquid water path when clouds experience cold-air advection It substantially overestimates increases in cloud liquid water path when clouds experience warm-air advection Utilizing an explainable machine learning methodology, the effects of meteorological factors on cloud evolution are assessed