资源环境科技发展态势分析平台

Representing large-scale co-variability between variables related to aerosols, clouds and radiation is one of many aspects of agreement with observations desirable for a climate model. In this study such relations are investigated in terms of temporal correlations on monthly mean scale, to identify points of agreement and disagreement with observations. Ten regions with different meteorological characteristics and aerosol signatures are studied and correlation matrices for the selected regions offer an overview of model ability to represent present day climate variability. Global climate models with different levels of detail and sophistication in their representation of aerosols and clouds are compared with satellite observations and reanalysis assimilating meteorological fields as well as aerosol optical depth from observations. One example of how the correlation comparison can guide model evaluation and development is the often studied relation between cloud droplet number and water content. Reanalysis, with no parameterized aerosol-cloud coupling, shows weaker correlations than observations, indicating that microphysical couplings between cloud droplet number and water content are not negligible for the co-variations emerging on larger scale. These observed correlations are, however, not in agreement with those expected from dominance of the underlying microphysical aerosol-cloud couplings. For instance, negative correlations in subtropical stratocumulus regions show that suppression of precipitation and subsequent increase in water content due to aerosol is not a dominating process on this scale. Only in one of the studied models are cloud dynamics able to overcome the parameterized dependence of rain formation on droplet number concentration, and negative correlations in the stratocumulus regions are reproduced.