期刊名称：Journal of Advances in Modeling Earth Systems
电子版ISSN：1942-2466
出版年度：2021
卷号：13
期号：2
页码：e2020MS002101
DOI：10.1029/2020MS002101
出版社：John Wiley & Sons, Ltd.
摘要：Seasonal prediction is one important element in a seamless prediction chain between weather forecasts and climate projections. After several years of development in collaboration with Universität Hamburg and Max Planck Institute for Meteorology, the Deutscher Wetterdienst performs operational seasonal forecasts since 2016 with the German Climate Forecast System, now in Version 2 (GCFS2.0). Here, the configuration of the previous system GCFS1.0 and the current GCFS2.0 are described and the performance of the two systems is compared over the common hindcast period of 1990–2014. In GCFS2.0, the forecast skill is improved compared to GCFS1.0 during boreal winter, especially for the Northern Hemisphere where the Pearson correlation has increased for the North Atlantic Oscillation index. Overall, a similar performance of GCFS2.0 in comparison to GCFS1.0 is assessed during the boreal summer. Future developments for climate forecasts need a stronger focus on the performance of interannual variability in a model system. Plain Language Abstract Information about the expected departure from the “normal” climatic conditions of an upcoming season would be tremendously valuable for many sectors of society. In Germany, three institutes join their expertise to build a climate forecast system using the Earth system model of the Max Planck Institute for Meteorology. This model describes the atmosphere, land and rivers as well as the ocean and sea ice. The model describes their interactions and is well designed for climate studies on a much longer timescale than a season. Max Planck Institute for Meteorology, Universität Hamburg and the German Meteorological Service Deutscher Wetterdienst have developed the methods those are necessary for such a forecast system and operationally perform the seasonal predictions. This paper compares two versions of our forecast system. The forecast quality during different seasons is particularly investigated. The expectation that the second model system is much better than the first system is not entirely fulfilled. We discuss possible reasons and suggest a stronger focus on the model quality for interannual variability for future model development.
