Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations

A large degree of uncertainty in global climate models (GCMs) can be attributed to the representation of clouds and how they interact with incoming solar and outgoing longwave radiation. In this study, the simulated total cloud fraction (CF), cloud water path (CWP), top of the atmosphere (TOA) radiation budgets and cloud radiative forcings (CRFs) from 28 CMIP5 AMIP models are evaluated and compared with multiple satellite observations from CERES, MODIS, ISCCP, CloudSat, and CALIPSO. The multimodel ensemble mean CF (57.6 %) is, on average, underestimated by nearly 8 % (between 65°N/S) when compared to CERES–MODIS (CM) and ISCCP results while an even larger negative bias (17.1 %) exists compared to the CloudSat/CALIPSO results. CWP bias is similar in comparison to the CF results, with a negative bias of 16.1 gm-2 compared to CM. The model simulated and CERES EBAF observed TOA reflected SW and OLR fluxes on average differ by 1.8 and -0.9 Wm-2, respectively. The averaged SW, LW, and net CRFs from CERES EBAF are -50.1, 27.6, and -22.5 Wm-2, respectively, indicating a net cooling effect of clouds on the TOA radiation budget. The differences in SW and LW CRFs between observations and the multimodel ensemble means are only -1.3 and -1.6 Wm-2, respectively, resulting in a larger net cooling effect of 2.9 Wm-2 in the model simulations. A further investigation of cloud properties and CRFs reveals that the GCM biases in atmospheric upwelling (15°S–15°N) regimes are much less than in their downwelling (15°–45°N/S) counterparts over the oceans. Sensitivity studies have shown that the magnitude of SW cloud radiative cooling increases significantly with increasing CF at similar rates (*-1.25 Wm-2 %-1) in both regimes. The LW cloud radiative warming increases with increasing CF but is regime dependent, suggested by the different slopes over the upwelling and downwelling regimes (0.81 and 0.22 Wm-2 %-1, respectively). Through a comprehensive error analysis, we found that CF is a primary modulator of warming (or cooling) in the atmosphere. The comparisons and statistical results from this study may provide helpful insight for improving GCM simulations of clouds and TOA radiation budgets in future versions of CMIP.