[1] On the basis of 3-D Monte Carlo photon tracing simulations, we have developed a parameterization of solar fluxes over mountain surfaces by means of the multiple linear regression analysis associated with topographic information, including elevation, solar incident angle, sky view factor, and terrain configuration factor. For clear skies without aerosols and clouds, the regression equation for the direct flux can explain more than 98% of the variation in which the solar incident angle is the dominant factor, except when the Sun is very low or at zenith. About 60% of the variation in the diffuse flux is predicted by the regression equation in which the mean elevation, sky view factor, and solar incident angle are key factors. The terrain-reflected fluxes, proportional to the surface albedo, are well correlated with the terrain configuration factor with more than 80% of the variation that can be explained. The coupled fluxes involve intricate interactions, and the regression analysis is less satisfactory in cases of low albedo values. However, over high-albedo surfaces, the terrain configuration factor becomes most dominant, leading to a significant improvement in regression performance. In these analyses, a surface albedo invariant with wavelength has been used. Using a region over the Sierra Nevada as a testbed, the preceding regression parameterizations have been specifically developed so that the fluxes evaluated from the 3-D Monte Carlo model over intense topography can be used as a perturbation term to correct those computed from the plane-parallel counterpart, commonly used in regional climate models and GCMs.

This study characterizes biases in water vapor, dynamics, shortwave (SW) and longwave (LW) radiative properties in contemporary global climate models (GCMs) against observations over tropical Pacific Ocean. The observations are based on Atmospheric Infrared Sounder for water vapor, CloudSat 2B‐FLXHR‐LIDAR for LW and SW radiative heating profiles, and radiative flux from Clouds and the Earth's Radiant Energy System products. The model radiative heating profiles are adopted from the coupled and uncoupled National Center for Atmospheric Research (NCAR) Community Earth System Model version 1 (CESM1) and joint Year of Tropical Convection (YOTC)/Madden Julian Oscillation (MJO) Task Force‐Global Energy and Water Cycle Experiment Atmospheric System Studies (GASS) Multi‐Model Physical Processes Experiment (YOTC‐GASS). The results from the model evaluation for YOTC‐GASS and NCAR CESM1 demonstrate a number of systematic radiative biases. These biases include excessive outgoing LW radiation and excessive SW surface radiative fluxes, in conjunction with a radiatively unstable atmosphere with excessive LW cooling in the upper troposphere over convectively active areas, such as the Intertropical Convergence Zone/South Pacific Convergence Zone (ITCZ/SPCZ) and warm pool. Using sensitivity experiments with the NCAR‐uncoupled/NCAR‐coupled CESM1, we infer that these biases partly result from the interactions between falling snow and radiation that are missing in most contemporary GCMs (e.g., YOTC‐GASS, Coupled Model Intercomparison Project 3 (CMIP)3, and Atmospheric Model Intercomparison Project 5 (AMIP5)/CMIP5). A number of biases in the YOTC‐GASS model simulations are consistent with model biases in CMIP3, AMIP5/CMIP5, and NCAR‐uncoupled/NCAR‐coupled model simulation without snow‐radiation interactions. These include excessive upper level convection and low level downward motion with outflow from ITCZ/SPCZ. This generates weaker low‐level trade winds and excessive precipitation in the Central Pacific Trade wind regions. The excessive LW radiative cooling in NCAR‐coupled/NCAR‐uncoupled GCM simulations is reduced by 10–20% with snow‐radiative effects considered.

Aerosol-cloud interactions (aerosol indirect effects) play an important role in regional meteorological variations, which could further induce feedback on regional air quality. While the impact of aerosol-cloud interactions on meteorology and climate has been extensively studied, their feedback on air quality remains unclear. Using a fully coupled meteorology-chemistry model, we find that increased aerosol loading due to anthropogenic activities in China substantially increases column cloud droplet number concentration and liquid water path (LWP), which further leads to a reduction in the downward shortwave radiation at surface, surface air temperature and planetary boundary layer (PBL) height. The shallower PBL and accelerated cloud chemistry due to larger LWP in turn enhance the concentrations of particulate matter with diameter less than 2.5 μm (PM2.5) by up to 33.2 μg m−3 (25.1%) and 11.0 μg m−3 (12.5%) in January and July, respectively. Such a positive feedback amplifies the changes in PM2.5 concentrations, indicating an additional air quality benefit under effective pollution control policies but a penalty for a region with a deterioration in PM2.5 pollution. Additionally, we show that the cloud processing of aerosols, including wet scavenging and cloud chemistry, could also have substantial effects on PM2.5 concentrations.

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