Forecasting atmospheric persistence and implications for the predictability of temperature and temperature extremes

Abstract Extreme temperatures can cause severe disruptions to society, from negative health consequences to infrastructure damage. Accurate and timely weather forecasts contribute to minimising these detrimental effects, by supporting early‐warning systems. In this context, information on the expected performance of the forecasts is valuable. Here, we investigate whether there is a relationship between the persistence of atmospheric circulation patterns in the Euro‐Atlantic sector and forecast skill for temperatures and temperature extremes in Europe. We first apply an objective method to compute the persistence of large‐scale atmospheric patterns in European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal retrospective forecasts. We find that the forecasts successfully predict atmospheric persistence up to time‐scales of approximately two weeks. We next investigate the relationship between the persistence of an atmospheric state and the practical predictability of temperature in terms of the error in surface temperature forecasts. The relationship between the two varies depending on season and location. Nonetheless, in a number of cases atmospheric persistence provides potentially valuable information on the practical predictability of temperature. We specifically highlight the cases of wintertime temperature forecasts up to three weeks lead time and wintertime cold spells up to roughly two weeks lead time.

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Publication Details

Journal
Quarterly Journal of the Royal Meteorological Society
Published
2024-10-01
DOI
https://doi.org/10.1002/qj.4885
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.28

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article

Forecasting atmospheric persistence and implications for the predictability of temperature and temperature extremes

Gabriele Messori, Steffen Tietsche, Emma Holmberg
1 citations
Quarterly Journal of the Royal Meteorological Society
Climate variability and models
0.28
article

Forecasting atmospheric persistence and implications for the predictability of temperature and temperature extremes

Gabriele Messori, Steffen Tietsche, Emma Holmberg
article en
1 citations

Abstract

Abstract Extreme temperatures can cause severe disruptions to society, from negative health consequences to infrastructure damage. Accurate and timely weather forecasts contribute to minimising these detrimental effects, by supporting early‐warning systems. In this context, information on the expected performance of the forecasts is valuable. Here, we investigate whether there is a relationship between the persistence of atmospheric circulation patterns in the Euro‐Atlantic sector and forecast skill for temperatures and temperature extremes in Europe. We first apply an objective method to compute the persistence of large‐scale atmospheric patterns in European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal retrospective forecasts. We find that the forecasts successfully predict atmospheric persistence up to time‐scales of approximately two weeks. We next investigate the relationship between the persistence of an atmospheric state and the practical predictability of temperature in terms of the error in surface temperature forecasts. The relationship between the two varies depending on season and location. Nonetheless, in a number of cases atmospheric persistence provides potentially valuable information on the practical predictability of temperature. We specifically highlight the cases of wintertime temperature forecasts up to three weeks lead time and wintertime cold spells up to roughly two weeks lead time.

Quarterly Journal of the Royal Meteorological SocietyVol. 150(765)
Uppsala University (SE), Stockholm University (SE), Bolin Centre for Climate Research (SE), Centre of Natural Hazards and Disaster Science (SE)
European Commission, Vetenskapsrådet, Horizon 2020 Framework Programme, H2020 Marie Skłodowska-Curie Actions
Industry, innovation and infrastructure
Openalex Percentile: Top 41%
Climate variability and models
0.28
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