Assessing Satellite‐Derived Radiative Forcing From Snow Impurities Through Inverse Hydrologic Modeling

Abstract

AbstractLight‐absorbing impurities in snow and ice (LAISI) lower the snow albedo and cause accelerated snowmelt. The radiative forcing caused by LAISI is in this connection the key variable in understanding LAISI‐snowpack dynamics. Here we present an approach combining distributed hydrologic model simulations and remotely sensed radiative forcing from LAISI in order to improve model predictions of radiative forcing impacts. In a case study, we assess the seasonal cycle of instantaneous at‐surface clear‐sky radiative forcing from LAISI as predicted by model and satellite observations for a river basin located at the southern slope of the Himalayas. By scaling dust depositions, we optimize simulated radiative forcing conditioned on satellite observations. The optimized model predicts that LAISI‐induced radiative forcing in snow contributes to 4.1% to 5.8% of the annual discharge. The presented approach has a wide range of applications as it provides a novel method to constrain and evaluate measures of LAISI‐induced radiative forcing.