Preprints
https://doi.org/10.5194/hess-2022-312
https://doi.org/10.5194/hess-2022-312
 
08 Sep 2022
08 Sep 2022
Status: this preprint is currently under review for the journal HESS.

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

François Clayer1, Leah Jackson-Blake1, Daniel Mercado2,3, Muhammed Shikhani4, Andrew French5, Tadhg Moore6,a, James Sample1, Magnus Norling1, Maria-Dolores Frías7, Sixto Herrera7, Elvira de Eyto5, Eleanor Jennings6, Karsten Rinke4, Leon van der Linden8, and Rafael Marcé2,3 François Clayer et al.
  • 1Norwegian Institute for Water Research (NIVA), Oslo, Norway
  • 2Catalan Institute for Water Research (ICRA), Girona, Spain
  • 3Universitat de Girona, Girona, Spain
  • 4Department of Lake Research, Helmholtz Centre for Environmental Research, Magdeburg, Germany
  • 5Foras na Mara - Marine Institute, Furnace, Newport, Co. Mayo, Ireland
  • 6Dundalk Institute of Technology, Dundalk, Co. Louth, Ireland
  • 7Grupo de Meteorología. Dpto. de Matemática Aplicada y Ciencias de la Computación. Universidad de Cantabria, Santander, Spain
  • 8SA Water, Adelaide SA 5000, Australia
  • apresent address: Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA

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.

François Clayer et al.

Status: open (until 03 Nov 2022)

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François Clayer et al.

Data sets

WATExR dataset Leah Jackson-Blake, Daniel Mercado Bettin, Francois Clayer https://github.com/NIVANorge/seasonal_forecasting_watexr/tree/main/paper2_Clayer_etal/datasets

François Clayer et al.

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Short summary
We assessed the predictive skill of forecasting tools over the next season for water discharge and lake temperature. Tools were forced with seasonal climate predictions, however, most of the prediction skill originates from legacy effects and not from seasonal climate predictions. Yet, when skills from seasonal climate predictions are present, additional skill comes from interaction effects. Skillful lake seasonal predictions require better climate predictions and realistic antecedent conditions.