The impacts of precipitating hydrometeors radiative effects on land surface temperature in contemporary GCMs using satellite observations

An accurate representation of the land surface temperature (LST) climatology of the coupled land-atmosphere system has strong implications for the reliability of projected land surface processes and their variability inferred by the global climate models (GCMs) contributed to the Intergovernmental Panel on Climate Change CMIP5. We have identified a substantial underestimation of the total ice water path and biases of surface radiation budget commonly seen in the CMIP models which are highly correlated to the biases of LST over land. One of the potential causes of the CMIP model biases is the missing representation of large frozen precipitating hydrometeors and their radiative effects (i.e., snow) in all CMIP3 and most CMIP5 models. We examine the impacts of snow on the radiation, all-sky and clear-sky LST, and air-land heat fluxes to explore the implications to the common biases in CMIP models by performing sensitivity experiments with and without snow radiation effects using the National Center for Atmospheric Research Community Earth System Model version 1. It is found that an exclusion of the snow radiative effects the CESM1 generates the LST biases (up to 2–3 K) in the midlatitude and high latitude, in particular, in December, January, and February (DJF). All-sky and clear-sky LST in model simulations are found to be too cold and are mainly due to underestimated downward surface (longwave) LW radiation in DJF, which is consistent with those in CMIP models. The correlation between the changes of the LST and downward surface LW radiation is very high both in summer and winter seasons.