Estimation of aerosol and cloud radiative heating rate in tropical stratosphere using radiative kernel method

Abstract. A layer of aerosols has been identified in the upper troposphere and lower stratosphere above the Asian summer monsoon region, which is referred to as the Asian Tropopause Aerosol Layer (ATAL). This layer is fed by atmospheric pollutants over South and East Asia lifted to the upper troposphere by deep convection in summer. The radiative effects of this aerosol layer change local temperature, influence thermodynamic stability, and modulate the efficiency of air mass vertical transport near the tropopause. However, quantitative understanding of these effects is still very poor. To estimate aerosol radiative effects in the high atmosphere, a set of radiative kernels is constructed for the tropical upper troposphere and stratosphere to reduce the computational expense of decomposing the different contributions of atmospheric components to anomalies in radiative fluxes. The prototype aerosol kernels in this work are among the first to target vertically resolved heating rates, motivated by the linearity and separability of scattering and absorbing aerosol effects in ATAL. Observationally-derived lower boundary conditions and satellite observations of cloud ice within the upper troposphere and stratosphere are included and simplified in our Tropical Upper Troposphere-Stratosphere Model (TUTSM). Separate sets of kernels are derived and tested for the effects of absorbing aerosols, scattering aerosols, and cloud ice particles on both shortwave (solar) and longwave (thermal) radiative fluxes and heating rates. The results indicate that the kernels we calculated can well reproduce the aerosol radiative effects in ATAL, and these aerosol kernels are also expected to simulate radiative effects of biomass burning and volcanic eruption above troposphere. It has been proved this approach substantially reduces computational expense while achieving good consistency with direct radiative transfer model calculations. It can be applied to models that do not require high precision but have requirements for computing speed and storage space.