气候变化领域集成服务门户(CCPortal): Machine learning of cloud types in satellite observations and climate models

CCPortal

DOI	10.5194/acp-23-523-2023
	Machine learning of cloud types in satellite observations and climate models
	Kuma, Peter; Bender, Frida A. -M.; Schuddeboom, Alex; McDonald, Adrian J.; Seland, Oyvind
发表日期	2023
ISSN	1680-7316
EISSN	1680-7324
起始页码	523
结束页码	549
卷号	23 期号:1 页码:27
英文摘要	Uncertainty in cloud feedbacks in climate models is a major limitation in projections of future climate. Therefore, evaluation and improvement of cloud simulation are essential to ensure the accuracy of climate models. We analyse cloud biases and cloud change with respect to global mean near-surface temperature (GMST) in climate models relative to satellite observations and relate them to equilibrium climate sensitivity, transient climate response and cloud feedback. For this purpose, we develop a supervised deep convolutional artificial neural network for determination of cloud types from low-resolution (2.5 degrees x2.5 degrees) daily mean top-of-atmosphere shortwave and longwave radiation fields, corresponding to the World Meteorological Organization (WMO) cloud genera recorded by human observers in the Global Telecommunication System (GTS). We train this network on top-of-atmosphere radiation retrieved by the Clouds and the Earth's Radiant Energy System (CERES) and GTS and apply it to the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) model output and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalyses. We compare the cloud types between models and satellite observations. We link biases to climate sensitivity and identify a negative linear relationship between the root mean square error of cloud type occurrence derived from the neural network and model equilibrium climate sensitivity (ECS), transient climate response (TCR) and cloud feedback. This statistical relationship in the model ensemble favours models with higher ECS, TCR and cloud feedback. However, this relationship could be due to the relatively small size of the ensemble used or decoupling between present-day biases and future projected cloud change. Using the abrupt-4xCO(2) CMIP5 and CMIP6 experiments, we show that models simulating decreasing stratiform and increasing cumuliform clouds tend to have higher ECS than models simulating increasing stratiform and decreasing cumuliform clouds, and this could also partially explain the association between the model cloud type occurrence error and model ECS.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000920309500001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273315
作者单位	Stockholm University; University of Canterbury; Norwegian Meteorological Institute
推荐引用方式 GB/T 7714	Kuma, Peter,Bender, Frida A. -M.,Schuddeboom, Alex,et al. Machine learning of cloud types in satellite observations and climate models[J],2023,23(1):27.
APA	Kuma, Peter,Bender, Frida A. -M.,Schuddeboom, Alex,McDonald, Adrian J.,&Seland, Oyvind.(2023).Machine learning of cloud types in satellite observations and climate models.ATMOSPHERIC CHEMISTRY AND PHYSICS,23(1),27.
MLA	Kuma, Peter,et al."Machine learning of cloud types in satellite observations and climate models".ATMOSPHERIC CHEMISTRY AND PHYSICS 23.1(2023):27.