Measuring surface mass-balance in the accumulation areas of glaciers is challenging because of the high spatial variability of snow accumulation and the difficulty of conducting annual field glaciological measurements. Here, we propose a method that can solve both these problems for many locations. Ground-penetrating radar measurements and firn cores extracted from a site in the French Alps were first used to reconstruct the topography of a buried end-of-summer snow horizon from a past year. Using these data and surface elevation observations from LiDAR and Global Navigation Satellite System instruments, we calculated the submergence velocities over the period between the buried horizon and more recent surface elevation observations. The differences between the changes in surface elevation and the submergence velocities were then used to calculate the annual surface mass-balances with an accuracy of ±0.34 m w.e. Assuming that the submergence velocities remain stable over several years, the surface mass-balance can be reconstructed for subsequent years from the differences in surface elevation alone. As opposed to the glaciological method that requires substantial fieldwork year after year to provide only point observations, this method, once submergence velocities have been calculated, requires only remote-sensing data to provide spatially distributed annual mass-balances in accumulation areas.

We investigate relationships between synoptic-scale atmospheric variability and the mass-balance of 13 Andean glaciers (located 16–55° S) using Pearson correlation coefficients (PCCs) and multiple regressions. We then train empirical glacier mass-balance models (EGMs) in a cross-validated multiple regression procedure for each glacier. We find four distinct glaciological zones with regard to their climatic controls: (1) The mass-balance of the Outer Tropics glaciers is linked to temperature and the El Niño-Southern Oscillation (PCC ⩽ 0.6), (2) glaciers of the Desert Andes are mainly controlled by zonal wind intensity (PCC ⩽ 0.9) and the Antarctic Oscillation (PCC ⩽0.6), (3) the mass-balance of the Central Andes glaciers is primarily correlated with precipitation anomalies (PCC ⩽ 0.8), and (4) the glacier of the Fuegian Andes is controlled by winter precipitation (PCC ≈ 0.7) and summer temperature (PCC ≈ −0.9). Mass-balance data in the Lakes District and Patagonian Andes zones, where most glaciers are located, are too sparse for a robust detection of synoptic-scale climatic controls. The EGMs yield R2 values of ~ 0.45 on average and ⩽ 0.74 for the glaciers of the Desert Andes. The EGMs presented here do not consider glacier dynamics or geometry and are therefore only suitable for short-term predictions.