Assessing Clouds Using Satellite Observations Through Three Generations of Global Atmosphere Models

Abstract

Clouds are parameterized in climate models using quantities on the model grid‐scale to approximate the cloud cover and impact on radiation. Because of the complexity of processes involved with clouds, these parameterizations are one of the key challenges in climate modeling. Differences in parameterizations of clouds are among the main contributors to the spread in climate sensitivity across models. In this work, the clouds in three generations of an atmosphere model lineage are evaluated against satellite observations. Satellite simulators are used within the model to provide an appropriate comparison with individual satellite products. In some respects, especially the top‐of‐atmosphere cloud radiative effect, the models show generational improvements. The most recent generation, represented by two distinct branches of development, exhibits some regional regressions in the cloud representation; in particular the southern ocean shows a positive bias in cloud cover. The two branches of model development show how choices during model development, both structural and parametric, lead to different cloud climatologies. Several evaluation strategies are used to quantify the spatial errors in terms of the large‐scale circulation and the cloud structure. The Earth mover's distance is proposed as a useful error metric for the passive satellite data products that provide cloud‐top pressure‐optical depth histograms. The cloud errors identified here may contribute to the high climate sensitivity in the Community Earth System Model, version 2 and in the Energy Exascale Earth System Model, version 1.

To exascale and beyond – The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), a performance portable global atmosphere model for cloud-resolving scales

“…SCREAMv1 underestimates the prevalence of mid-thickness clouds with water path of 20-600 g/m 2 and overestimates the prevalence of thick clouds with water path greater than 600 g/m 2 . This is a common bias in conventional general circulation models (Kay et al, 2012;Medeiros et al, 2023), and there is some evidence that it may be common to GSRMs as well (Kodama et al, 2012). Instrumental uncertainty prevents us from drawing conclusions regarding the prevalence of thin clouds.…”

To Exascale and Beyond—The Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM), a Performance Portable Global Atmosphere Model for Cloud‐Resolving Scales

“…SCREAMv1 underestimates the prevalence of mid-thickness clouds with water path of 20-600 g/m 2 and overestimates the prevalence of thick clouds with water path greater than 600 g/m 2 . This is a common bias in conventional GCMs (Kay et al, 2012;Klein et al, 2013;Medeiros et al, 2023), and there is some evidence that it may be common to GSRMs as well (Kodama et al, 2012). Instrumental uncertainty prevents us from drawing conclusions regarding the prevalence of thin clouds.…”