Publication | Open Access
Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A‐Train” satellite observations
415
Citations
102
References
2012
Year
EngineeringClimate ModelingAtmospheric ModelEarth System ScienceWater Vapor SimulationsEarth ScienceEarlier Cmip3Climate PhysicsAtmospheric ScienceClimate ProjectionH 2Atmospheric ModelingClimate ChangeHydrometeorologyMeteorologyAtmospheric InteractionCloud DynamicCmip5 Climate ModelsRadiation MeasurementCloud PhysicClimate DynamicsClimatologyCloud Water ContentClimate Modelling
Using NASA's A‐Train satellite measurements, we evaluate the accuracy of cloud water content (CWC) and water vapor mixing ratio (H 2 O) outputs from 19 climate models submitted to the Phase 5 of Coupled Model Intercomparison Project (CMIP5), and assess improvements relative to their counterparts for the earlier CMIP3. We find more than half of the models show improvements from CMIP3 to CMIP5 in simulating column‐integrated cloud amount, while changes in water vapor simulation are insignificant. For the 19 CMIP5 models, the model spreads and their differences from the observations are larger in the upper troposphere (UT) than in the lower or middle troposphere (L/MT). The modeled mean CWCs over tropical oceans range from ∼3% to ∼15× of the observations in the UT and 40% to 2× of the observations in the L/MT. For modeled H 2 Os, the mean values over tropical oceans range from ∼1% to 2× of the observations in the UT and within 10% of the observations in the L/MT. The spatial distributions of clouds at 215 hPa are relatively well‐correlated with observations, noticeably better than those for the L/MT clouds. Although both water vapor and clouds are better simulated in the L/MT than in the UT, there is no apparent correlation between the model biases in clouds and water vapor. Numerical scores are used to compare different model performances in regards to spatial mean, variance and distribution of CWC and H 2 O over tropical oceans. Model performances at each pressure level are ranked according to the average of all the relevant scores for that level.
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