Streamflow observations contain information about snow, but their potential to constrain seasonal snow mass reconstructions remains underexplored. Using inverse hydrological modeling, we show that streamflow is particularly effective at constraining catchment-aggregated melt rates, but that non-uniqueness in the snow–streamflow relationship and uncertainties in the inverse modeling chain can easily limit inversion performance.

Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct air–sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.

Climate and weather in spring are important because they can have far-reaching impacts, e.g. on plant growth, due to cold spells. Here, we study changes in climate and phenological indices for the period from 1763 to 2020 based on newly published reconstructed fields of daily temperature and precipitation for Switzerland. We look at three cases of extreme spring conditions, namely a warm spring in 1862, two frost events in 1873 and 1957, and three cold springs in 1785, 1837, and 1852.