4.7 Article

Evaluation of Clouds, Radiation, and Precipitation in CMIP6 Models Using Global Weather States Derived from ISCCP-H Cloud Property Data

Journal

JOURNAL OF CLIMATE
Volume 34, Issue 17, Pages 7311-7324

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-21-0076.1

Keywords

Cloud forcing; Cloud retrieval; Data mining; Satellite observations; Climate models; Clouds; Model evaluation/performance

Funding

  1. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program [17-MEASURES-0034]

Ask authors/readers for more resources

The analysis of CMIP6 models against ISCCP-H weather states shows improvements in simulating the frequency and geographical distribution of weather states compared to CMIP5 models, but biases still exist, especially in the frequency of shallow cumulus clouds and certain weather states.
A clustering methodology is applied to cloud optical depth (tau)-cloud top pressure (TAU-PC) histograms from the new 1 degrees resolution ISCCP-H dataset to derive an updated global weather state (WS) dataset. Then, TAU-PC histograms from current-climate CMIP6 model simulations are assigned to the ISCCP-H WSs along with their concurrent radiation and precipitation properties to evaluate model cloud, radiation, and precipitation properties in the context of the weather states. The new ISCCP-H analysis produces WSs that are very similar to those previously found in the lowerresolution ISCCP-D dataset. The main difference lies in the splitting of the ISCCP-D thin stratocumulus WS between the ISCCP-H shallow cumulus and stratocumulus WSs, which results in the reduction by one of the total WS number. The evaluation of the CMIP6 models against the ISCCP-H weather states shows that, in the ensemble mean, the models are producing an adequate representation of the frequency and geographical distribution of the WSs, with measurable improvements compared to the WSs derived for the CMIP5 ensemble. However, the frequency of shallow cumulus clouds continues to be underestimated, and, in some WSs the good agreement of the ensemble mean with observations comes from averaging models that significantly overpredict and underpredict the ISCCP-H WS frequency. In addition, significant biases exist in the internal cloud properties of the model WSs, such as the model underestimation of cloud fraction in middle-top clouds and secondarily in midlatitude storm and stratocumulus clouds, that result in an underestimation of cloud SW cooling in those regimes.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available