Skillful decadal prediction of German Bight storm activity

Krieger, Daniel; Brune, Sebastian; Pieper, Patrick; Weisse, Ralf; Baehr, Johanna

doi:10.5194/nhess-22-3993-2022

Cited by 3 publications

(9 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we employ the extended large-ensemble decadal hindcast system based on the Max Planck Institute Earth System Model (MPI-ESM) in low-resolution (LR) mode (Mauritsen et al, 2019;Hövel et al, 2022;Krieger et al, 2022). Even though this study focuses on the seasonal timescale, we choose decadal hindcasts over any seasonal prediction systems, as the already available MPI-ESM decadal hindcast system provides us with a large 64-member ensemble.…”

Section: Mpi-esm-lr Decadal Hindcastsmentioning

confidence: 99%

“…We calculate winter GBSA as the standardized seasonal (December-February) 95th percentiles of three-hourly geostrophic winds over the German Bight. The calculation follows the methodology of Krieger et al (2022), however it uses seasonal instead of annual 95th percentiles. Doing so, we ensure that the calculation of GBSA in the hindcast is consistent with the derivation of observed GBSA in Krieger et al (2021).…”

Section: Mpi-esm-lr Decadal Hindcastsmentioning

confidence: 99%

“…Still, skillful predictions of storm activity on a regional scale are a challenging task, even with today's state-of-the-art modeling capabilities. A recent study on the decadal predictability of German Bight storm activity (GBSA) has indicated that, with a carefully chosen approach, a large model ensemble, and an evaluation of different forecast categories, probabilistic predictions of high storm activity can be skillful for averaging periods longer than 5 years (Krieger et al, 2022). Krieger et al (2022)…”

Section: Introductionmentioning

confidence: 99%

“…These extreme events repeatedly issue challenges to coastal protection agencies, emergency management, and other interests in the region.Therefore, local actors and stakeholders may benefit from skillful predictions of these events on a seasonal-to-decadal scale.Still, skillful predictions of storm activity on a regional scale are a challenging task, even with today's state-of-the-art modeling capabilities. A recent study on the decadal predictability of German Bight storm activity (GBSA) has indicated that, with a carefully chosen approach, a large model ensemble, and an evaluation of different forecast categories, probabilistic predictions of high storm activity can be skillful for averaging periods longer than 5 years (Krieger et al, 2022). Krieger et al (2022)…”

mentioning

confidence: 99%

“…also showed, however, that the predictive skill for single lead years in general and the next year in particular is often low and barely statistically significant, even when using a large-ensemble decadal prediction system. While Krieger et al (2022) did not explicitly investigate the predictability of GBSA on a seasonal scale, the low skill for lead year 1 warrants an investigation into the seasonal predictability and its potential for improvement.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Improving seasonal predictions of German Bight storm activity

Krieger,

Brune,

Baehr

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. 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.

show abstract

Section: Mpi-esm-lr Decadal Hindcastsmentioning

confidence: 99%

Section: Mpi-esm-lr Decadal Hindcastsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Improving seasonal predictions of German Bight storm activity

Krieger,

Brune,

Baehr

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Decadal Predictability of Seasonal Temperature Distributions

Düsterhus,

Brune

2024

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Decadal predictions focus regularly on the predictability of single values, like means or extremes. In this study we investigate the prediction skill of the full underlying surface temperature distributions on global and European scales. We investigate initialized hindcast simulations of the Max Planck Institute Earth system model decadal prediction system and compare the distribution of seasonal daily temperatures with estimates of the climatology and uninitialized historical simulations. In the analysis we show that the initialized prediction system has advantages in particular in the North Atlantic area and allow so to make reliable predictions for the whole temperature spectrum for two to 10 years ahead. We also demonstrate that the capability of initialized climate predictions to predict the temperature distribution depends on the season.

show abstract

Improving seasonal predictions of German Bight storm activity

Krieger,

Brune,

Baehr

et al. 2024

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. 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.

show abstract

Skillful decadal prediction of German Bight storm activity

Cited by 3 publications

References 47 publications

Improving seasonal predictions of German Bight storm activity

Improving seasonal predictions of German Bight storm activity

Decadal Predictability of Seasonal Temperature Distributions

Improving seasonal predictions of German Bight storm activity

Contact Info

Product

Resources

About