The impacts of cloud snow radiative effects on Pacific Ocean surface heat fluxes, surface wind stress, and ocean temperatures in coupled GCM simulations

Abstract

An accurate representation of the climatology of the coupled ocean-atmosphere system in global climate models has strong implications for the reliability of projected climate change inferred by these models. Our previous efforts have identified substantial biases of ocean surface wind stress that are fairly common in two generations of the Coupled Model Intercomparison Project (CMIP) models, relative to QuikSCAT climatology. One of the potential causes of the CMIP model biases is the missing representation of large frozen precipitating hydrometeors (i.e., cloud snow) in all CMIP3 and most CMIP5 models, which has not been investigated previously. We examine the impacts of cloud snow on the radiation and atmospheric circulation, air-sea fluxes, and explore the implications to common biases in CMIP models using the National Center for Atmospheric Research coupled Community Earth System Model (CESM) to perform sensitivity experiments with and without cloud snow radiative effects. This study focuses on the impacts of cloud snow in CESM on ocean surface wind stress and air-sea heat fluxes, as well as their relationship with sea surface temperature (SST) and subsurface ocean temperatures in the Pacific sector. It is found that inclusion of the cloud snow parameterization in CESM reduces the surface wind stress and upper ocean temperature (including SST) biases in the tropical and midlatitude Pacific. The differences in the upper ocean temperature with and without the cloud snow parameterization are consistent with the effect of different strength of vertical mixing due to ocean surface wind stress differences but cannot be explained by the differences in net air-sea heat fluxes.

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

“…Following Li et al . [], we continue to examine the sensitivity of the snow radiation effects using CESM1 to investigate the extent to which the snow radiation effect affects the biases in the surface heat fluxes and LST.…”

“…Snow in the model represents falling large ice crystals with appreciable falling velocities that are diagnosed from falling ice mass flux profiles at each model level and every model physical time step. Because CAM5 incorporates the impact of snow on radiative fluxes, it is suitable for the objectives of this study [ Li et al ., , , , , ].…”

“…The latter, including those particles associated with convective clouds, are generally not included as prognostic variables in most GCMs as indicated in Figure [e.g., Li et al ., , ; Waliser et al ., , ]. Therefore, their ice water content (IWC) and the associated derived CERES radiative fluxes contribution from the convective core are not considered in both the cloud mass budget and its radiative effect in conventional GCMs [ Li et al ., ].…”

Considering the radiative effects of snow on tropical Pacific Ocean radiative heating profiles in contemporary GCMs using A‐Train observations

“…[]) against the Clouds and the Earth's Radiant Energy System‐Energy Balanced and Filled (CERES‐EBAF) [ Loeb et al , , ] observations. These radiation biases are found to be closely linked to the biases in ocean surface such as the excessive rainfall within the tropical Pacific trade wind regions [ Li et al , , ]. All of these radiation biases are fairly common in terms of pattern and amplitude; similar to each other in the state‐of‐the‐art CGCMs, such as those in CMIP3/CMIP5 models; and are partially attributable to the missing (or improper) representation of precipitating hydrometeors (i.e., snow) and their radiative effects [e.g., Li et al , , , ].…”

An observationally based evaluation of WRF seasonal simulations over the Central and Eastern Pacific

“…The uncounted snow, rain, and convective core of mass result in underestimates of total cloud water paths contribute to model biases of radiation budget. These biases produce persistent bias of modeled sea surface temperatures (SST) [ Li et al ., ]. Niznik et al .…”