云反馈
云计算
气候敏感性
环境科学
云层高度
气候模式
气候学
云分数
高度(三角形)
辐射传输
大气科学
大气(单位)
气象学
云顶
正面反馈
等温过程
云强迫
计算机科学
参数化复杂度
大气环流模式
焊剂(冶金)
反演(地质)
辐射冷却
灵敏度(控制系统)
气候变化
非正面反馈
遥感
云量
大气模式
高海拔对人类的影响
全球变暖
反馈回路
航程(航空)
大气模式
作者
Li‐Wei Chao,Mark D. Zelinka,Christopher R. Terai,Hassan Beydoun,Benjamin Hillman,Noel D. Keen,Peter Caldwell,Peter A. Bogenschutz,Stephen A. Klein
出处
期刊:Journal of Climate
[American Meteorological Society]
日期:2026-03-12
卷期号:39 (10): 2563-2578
标识
DOI:10.1175/jcli-d-25-0656.1
摘要
Abstract Cloud feedback remains the main source of uncertainty in climate sensitivity estimated by global climate models (GCMs), largely because subgrid cloud responses are parameterized in GCMs due to their coarse resolution. This study examines cloud feedback in the global 3.25-km Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM 3 km) through a pair of 1-yr atmosphere-only simulations with control and +4-K sea surface temperature perturbations. SCREAM 3 km produces a positive cloud feedback that falls within but at the upper end of the range of Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP phase 6 (CMIP6) models and expert judgment. The positive cloud feedback arises from positive contributions from both high- and low-level clouds, with increases in high-cloud altitude and decreases in low-cloud amount and optical depth playing key roles. The stronger-than-CMIP-average feedback is mainly attributable to the high-cloud altitude feedback, owing to cloud tops rising nearly isothermally in SCREAM 3 km. The positive low-cloud amount feedback is weaker in SCREAM than in GCMs because estimated inversion strength (EIS) increases more dramatically with warming. A coarser 12-km resolution version of SCREAM exhibits a weaker positive cloud feedback than SCREAM 3 km, mainly because its low-cloud-radiative flux is more sensitive to EIS, leading to a stronger negative low-cloud amount feedback. With this process-level assessment of cloud feedback, this study reveals where SCREAM aligns with and diverges from conventional GCMs and expert assessment, providing insights to inform further model improvement and future expert assessment.
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