Can CFMIP2 models reproduce the leading modes of cloud vertical structure in the CALIPSO-GOCCP observations?

摘要

Using principal component (PC) analysis, three leading modes of cloud vertical structure (CVS) are revealed by the GCM-Oriented CALIPSO Cloud Product (GOCCP), i.e. tropical high, subtropical anticyclonic and extratropical cyclonic cloud modes (THCM, SACM and ECCM, respectively). THCM mainly reflect the contrast between tropical high clouds and clouds in middle/high latitudes. SACM is closely associated with middle-high clouds in tropical convective cores, few-cloud regimes in subtropical anticyclonic clouds and stratocumulus over subtropical eastern oceans. ECCM mainly corresponds to clouds along extratropical cyclonic regions. Models of phase 2 of Cloud Feedback Model Intercomparison Project (CFMIP2) well reproduce the THCM, but SACM and ECCM are generally poorly simulated compared to GOCCP. Standardized PCs corresponding to CVS modes are generally captured, whereas original PCs (OPCs) are consistently underestimated (overestimated) for THCM (SACM and ECCM) by CFMIP2 models. The effects of CVS modes on relative cloud radiative forcing (RSCRF/RLCRF) (RSCRF being calculated at the surface while RLCRF at the top of atmosphere) are studied in terms of principal component regression method. Results show that CFMIP2 models tend to overestimate (underestimated or simulate the opposite sign) RSCRF/RLCRF radiative effects (REs) of ECCM (THCM and SACM) in unit global mean OPC compared to observations. These RE biases may be attributed to two factors, one of which is underestimation (overestimation) of low/middle clouds (high clouds) (also known as stronger (weaker) REs in unit low/middle (high) clouds) in simulated global mean cloud profiles, the other is eigenvector biases in CVS modes (especially for SACM and ECCM). It is suggested that much more attention should be paid on improvement of CVS, especially cloud parameterization associated with particular physical processes (e.g. downwelling regimes with the Hadley circulation, extratropical storm tracks and others), which may be crucial to reduce the CRF biases in current climate models.