Future scenarios of albedo and radiative forcing resulting from changes in snow depth in Austria

Abstract. The presence of a seasonal snowpack decisively modulates the albedo of terrestrial land surfaces. Global warming-driven decreases in the duration of the seasonal snow cover are thus expected to lower annual albedo, result in a positive radiative forcing and thus represent a positive feedback to climate change. Here we quantify future (up to the year 2100) scenarios of albedo change and the associated radiative forcing for Austria using a machine learning approach which leverages satellite-derived albedo data and future scenarios of snow depth. Albedo was calculated from the MODIS MCD43A1 v006 BRDF/albedo product for the period 2002–2019. Snow depth was taken from a novel dataset for Austria (FuSE-AT) covering the period 1951–2100. A machine-learning model (using LightGBM) was then trained to predict albedo separately for each land cover type (MODIS MCD12Q1 v006) using snow depth, days since last snowfall, as well as several predictors related to plant canopy structure (leaf area index, canopy height) and topography (latitude, longitude, elevation above sea level, inclination, exposition). LAI turned out to be an important predictor in simulating albedo in both snow free and snow covered points in time. Time since last snowfall, as a surrogate for snow aging, was more important for short land cover types than for forests. The correlation coefficients of the trained models varied widely across the different land cover types, ranging from 0.70 to 0.94. In 5 out of the 6 scenarios used, a significant decline of albedo could be observed. The cumulative time-dependent emission equivalent resulting from the albedo changes between 2020–2100 corresponds to 0.25–1 (RCP 2.6), 0.8–2.25 (RCP 4.5) or 1–5 (RCP 8.5) times the annual CO₂-equivalent emissions of Austria for the year 2021.