Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

Abstract

Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snow pit measurements, and a surface-based passive microwave radiometer. The validation results indicate the algorithm has a high success rate for melt durations lasting multiple hours/days and where the melt event is preceded by warm air temperatures. The algorithm does not reliably identify short-duration events or events that occur immediately after or before periods with extremely cold air temperatures due to the thermal inertia of the snowpack and/or overpass and resolution limitations of the satellite data. The results of running the algorithm over the pan-Arctic region (north of 50° N) for the 1988–2013 period show that winter melt events are relatively rare, totaling less than 1 week per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g., central Québec and Labrador, southern Alaska and Scandinavia). The observed spatial pattern is similar to winter melt events inferred with surface air temperatures from the ERA-Interim (ERA-I) and Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis datasets. There was little evidence of trends in winter melt event frequency over 1988–2013 with the exception of negative trends over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false positive trends from shifts in the timing of the snow cover season.