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Extratropical storms are one of the major coastal hazards along the coastline of the German Bight, the southeastern part of the North Sea, and a major driver of coastal protection efforts. However, the predictability of these regional extreme events on a seasonal scale is still limited. We therefore improve the seasonal prediction skill of the Max Planck Institute Earth System Model (MPI-ESM) large-ensemble decadal hindcast system for German Bight storm activity (GBSA) in winter. We define GBSA as the 95th percentiles of three-hourly geostrophic wind speeds in winter, which we derive from mean sea-level pressure (MSLP) data. The hindcast system consists of an ensemble of 64 members, which are initialized annually in November and cover the winters of 1960/61–2017/18. We consider both deterministic and probabilistic predictions of GBSA, for both of which the full ensemble produces poor predictions in the first winter. To improve the skill, we observe the state of two physical predictors of GBSA, namely 70 hPa temperature anomalies in September, as well as 500 hPa geopotential height anomalies in November, in areas where these two predictors are correlated with winter GBSA. We translate the state of these predictors into a first guess of GBSA and remove ensemble members with a GBSA prediction too far away from this first guess. The resulting subselected ensemble exhibits a significantly improved skill in both deterministic and probabilistic predictions of winter GBSA. We also show how this skill increase is associated with better predictability of large-scale atmospheric patterns. [ABSTRACT FROM AUTHOR]

Extratropical storms are one of the major coastal hazards along the coastline of the German Bight, the southeastern part of the North Sea, and a major driver of coastal protection efforts. However, the predictability of these regional extreme events on a seasonal scale is still limited. We therefore improve the seasonal prediction skill of the Max-Planck-Institute Earth System Model (MPI-ESM) large-ensemble decadal hindcast system for German Bight storm activity (GBSA) in winter. We define GBSA as the 95th percentiles of three-hourly geostrophic wind speeds in winter, which we derive from mean sea-level pressure (MSLP) data. The hindcast system consists of an ensemble of 64 members, which are initialized annually in November and cover the winters of 1960/61–2017/18. We consider both deterministic and probabilistic predictions of GBSA, for both of which the full ensemble produces poor predictions in the first winter. To improve the skill, we observe the state of two physical predictors of GBSA, namely 70 hPa temperature anomalies in September, as well as 500 hPa geopotential height anomalies in November, in areas where these two predictors are correlated with winter GBSA. We translate the state of these predictors into a first guess of GBSA and remove ensemble members with a GBSA prediction too far away from this first guess. The resulting subselected ensemble exhibits a significantly improved skill in both deterministic and probabilistic predictions of winter GBSA. We also show how this skill increase is associated with better predictability of large-scale atmospheric patterns. [ABSTRACT FROM AUTHOR]

We evaluate the prediction skill of the Max-Planck-Institute Earth System Model (MPI-ESM) decadal hindcast system for German Bight storm activity (GBSA) on a multiannual to decadal scale. We define GBSA every year via the most extreme three-hourly geostrophic wind speeds, which are derived from mean sea-level pressure (MSLP) data. Our 64-member ensemble of annually initialized hindcast simulations spans the time period 1960-2018. For this period, we compare deterministically and probabilistically predicted winter MSLP anomalies and annual GBSA with a lead time of up to ten years against observations. The model shows limited deterministic skill for single prediction years, but significant positive deterministic skill for long averaging periods. For probabilistic predictions of high and low storm activity, the model is skillful at both short and long averaging periods, and outperforms persistence-based predictions. For short lead years, the skill of the probabilistic prediction for high and low storm activity notably exceeds the deterministic skill. We therefore conclude that, for the German Bight, skillful decadal predictions of regional storm activity can be viable with a large ensemble and a carefully designed approach. [ABSTRACT FROM AUTHOR]

This study investigates the evolution of German Bight (southeastern North Sea) storminess from 1897 to 2018 through analysing upper quantiles of geostrophic wind speeds, which act as a proxy for past storm activity. Here, geostrophic wind speeds are calculated from triplets of mean sea level pressure observations that form triangles over the German Bight. The data used in the manuscript are provided by the International Surface Pressure Databank and the national meteorological services of Denmark, Germany, and the Netherlands. The derivation of storm activity is achieved by enhancing the established triangle proxy method via combining and merging storminess time series from numerous partially overlapping triangles in an ensemble‐like manner. The utilized approach allows for the construction of robust, long‐term and subdaily German Bight storminess time series. Further, the method provides insights into the underlying uncertainty of the time series. The results show that storm activity over the German Bight is subject to multidecadal variability. The latest decades are characterized by an increase in activity from the 1960s to the 1990s, followed by a decline lasting into the 2000s and below‐average activity up until present. The results are backed through a comparison with reanalysis products from four datasets, which provide high‐resolution wind and pressure data starting in 1979 and offshore wind speed measurements taken from the FINO‐WIND project. This study also finds that German Bight storminess positively correlates with storminess in the NE Atlantic in general. In certain years, however, notably different levels of storm activity in the two regions can be found, which likely result from shifted large‐scale circulation patterns. [ABSTRACT FROM AUTHOR]