Wednesday, 9 July 2014
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, nearly global simulated cloud fraction (CF), cloud water path (CWP), 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 multi-model ensemble mean CF (57.6%) is, on average, underestimated by 7.6% (between 65° N/S) when compared to CERES-MODIS (CM) and ISCCP results with an even larger negative bias (18.5%) compared to CloudSat/CALIPSO. CWP biases are similar in comparison to CF, with negative biases of 16.1 gm-2 and 32.6 gm-2 compared to CM and CloudSat CWPs, respectively. The model simulated and CERES EBAF observed TOA reflected SW and OLR fluxes on average differ by 1.8 Wm-2 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 are only -1.3 Wm-2 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 model biases in the oceanic upwelling (15°S - 15°N) regime is much less than in the downwelling (15° - 45° N/S) regime. Sensitivity studies have shown that the magnitude of SW cooling increases significantly with increasing CF with similar tendencies (~ -1.25 Wm-2 %-1) in both regimes. Conversely, the LW CRF increases with increased CF but is regime dependent, suggested by the different slopes over the upwelling and downwelling regimes (0.81 Wm-2 %-1 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.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner