Cloud Impact Parameters Derived from A-Train Satellite, ERA-Interim, MERRA-2 and Their Relationship to the Environment
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Cloud feedback remains one of the largest sources of uncertainty in model climate sensitivity estimates, partly because of the complicated interactions between convective processes, radiative effects, and the large-scale circulation. Cloud radiative effects and precipitation processes have been linked in both deep convective clouds (DC) and low cloud regimes, which points to the importance of understanding the connections between the latent heating from precipitation and surface and atmospheric cloud radiative effects. In this paper, cloud impact parameters (CIPs), including Gvc, Avc and Nvc and energy and water coupling parameters (EWCPs) are examined. The two EWCPs, the surface radiative cooling efficiency, Rvc and the atmospheric heating efficiency, Rvh are used to characterize how efficiently a cloud can heat the atmosphere or cool the surface per unit rain. EWCPs link both cloud radiative properties and precipitation properties together to demonstrate the synergistic effects of the cloud-precipitation-radiation interaction (CPRI). Global distributions of CIPs and EWCPs are highly dependent on cloud regimes and reanalyses fail to simulate strong Rvc and Rvh over deep convection regions in the Indo-Pacific warm pool region, but produce stronger Rvc and Rvh over marine stratocumulus regions. Together, these indicate the possibility that the variability of the Walker circulation simulated by reanalysis is underestimated. To understand how the environment modulates the EWCPs, the EWCPs from A-Train observations, ERA-Interim and MERRA-2 datasets are conditionally sampled by dynamic and thermodynamic variables including vertical pressure velocity (w), sea surface temperature (SST), and column water vapor (CWV). The dynamic regime controls the sign of Rvh, while the CWV appears to be the larger control on the magnitude. The magnitude of Rvc is highly coupled to the dynamic regime. Observations also show two thermodynamic regions of strong Rvc, at low SST and CWV and at high SST and CWV, only the former of which is captured by the reanalyses. The results in this paper can be a reference for improving parameterizations important for coupling the energy and water cycles in global climate models.
Sun, Lu (2019). Cloud Impact Parameters Derived from A-Train Satellite, ERA-Interim, MERRA-2 and Their Relationship to the Environment. Master's thesis, Texas A & M University. Available electronically from