Inertia and seasonal climate prediction as sources of skill in lake temperature, discharge and ice-off forecasting tools

Abstract

Abstract. Despite high potential benefits, the development of seasonal forecasting tools in the water sector has been slower than in other sectors. Here we assess the skill of seasonal forecasting tools for lake and reservoir set up at four sites in Australia and Europe. These tools, as previously presented, consist of coupled hydrological catchment and lake models forced with seasonal climate forecast ensembles to provide probabilistic predictions of seasonal anomalies in water discharge, temperature and ice-off. Successful implementation requires a rigorous assessment of the tools’ predictive skill and an apportionment of the predictability between legacy effects and input data. To this end, models were forced with two meteorological datasets from the European Centre for Medium Range Weather Forecasts (ECMWF), the seasonal forecasts SEAS5 and the ERA5 reanalysis. Historical skill was assessed by comparing both model outputs, i.e., seasonal lake hindcasts (forced with SEAS5) and pseudo-observations (forced with ERA5). The skill of the seasonal lake hindcasts was generally low, but higher than SEAS5 climate hindcasts. Nevertheless, lake and SEAS5 windows of opportunity were identified, although they were not always synchronous, raising questions on the source of the predictability. A set of sensitivity analyses showed that most of the forecasting skill originates from legacy effects, although during winter and spring in Norway some skill was coming from SEAS5 over the target season. When SEAS5 hindcasts were skillful, additional predictability originates from the interaction between legacy and SEAS5 skill. We conclude that a climatology driven forecast is currently likely to yield higher quality forecasts.