Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm
<p>Initialized ensemble simulations can help identify the physical drivers and assess the probabilities of weather and climate extremes based on a given initial state. However, the significant computational burden of complex climate models makes it challenging to quantitatively investigate ext...
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Copernicus Publications
2025-05-01
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| Series: | Earth System Dynamics |
| Online Access: | https://esd.copernicus.org/articles/16/683/2025/esd-16-683-2025.pdf |
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| author | J. Sauer F. Massonnet G. Zappa F. Ragone |
| author_facet | J. Sauer F. Massonnet G. Zappa F. Ragone |
| author_sort | J. Sauer |
| collection | DOAJ |
| description | <p>Initialized ensemble simulations can help identify the physical drivers and assess the probabilities of weather and climate extremes based on a given initial state. However, the significant computational burden of complex climate models makes it challenging to quantitatively investigate extreme events with probabilities below a few percent. A possible solution to overcome this problem is to use rare event algorithms, i.e. computational techniques originally developed in statistical physics that increase the sampling efficiency of rare events in numerical simulations. Here, we apply a rare event algorithm to ensemble simulations with the intermediate-complexity coupled climate model PlaSim-LSG to study extremes of pan-Arctic sea ice area reduction under pre-industrial greenhouse gas conditions. We construct four pairs of control and rare event algorithm ensemble simulations, each starting from four different initial winter sea ice states. The rare event simulations produce sea ice lows with probabilities of 2 orders of magnitude smaller than feasible with the control ensembles and drastically increase the number of extremes compared to direct sampling. We find that for a given probability level, the amplitude of negative late-summer sea ice area anomalies strongly depends on the baseline winter sea ice thickness but hardly on the baseline winter sea ice area. Finally, we investigate the physical processes in two trajectories leading to sea ice lows with conditional probabilities of less than 0.001 <span class="inline-formula">%</span>. In both cases, negative late-summer pan-Arctic sea ice area anomalies are preceded by negative spring sea ice thickness anomalies. These are related to enhanced surface downward longwave radiative and sensible heat fluxes in an anomalously moist, cloudy and warm atmosphere. During summer, extreme sea ice area reduction is favoured by enhanced open-water-formation efficiency, anomalously strong downward solar radiation and the sea ice–albedo feedback. This work highlights that the most extreme summer sea ice conditions result from the combined effects of preconditioning and weather variability, emphasizing the need for thoughtful ensemble design when turning to real applications.</p> |
| format | Article |
| id | doaj-art-b0d9c262835d4705b0dc94a7cdf492bf |
| institution | Kabale University |
| issn | 2190-4979 2190-4987 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Dynamics |
| spelling | doaj-art-b0d9c262835d4705b0dc94a7cdf492bf2025-08-20T03:52:11ZengCopernicus PublicationsEarth System Dynamics2190-49792190-49872025-05-011668370210.5194/esd-16-683-2025Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithmJ. Sauer0F. Massonnet1G. Zappa2F. Ragone3Earth and Climate Research Center, Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, BelgiumEarth and Climate Research Center, Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, BelgiumNational Research Council of Italy, Institute of Atmospheric Sciences and Climate, Bologna, ItalySchool of Computing and Mathematical Sciences, University of Leicester, Leicester, UK<p>Initialized ensemble simulations can help identify the physical drivers and assess the probabilities of weather and climate extremes based on a given initial state. However, the significant computational burden of complex climate models makes it challenging to quantitatively investigate extreme events with probabilities below a few percent. A possible solution to overcome this problem is to use rare event algorithms, i.e. computational techniques originally developed in statistical physics that increase the sampling efficiency of rare events in numerical simulations. Here, we apply a rare event algorithm to ensemble simulations with the intermediate-complexity coupled climate model PlaSim-LSG to study extremes of pan-Arctic sea ice area reduction under pre-industrial greenhouse gas conditions. We construct four pairs of control and rare event algorithm ensemble simulations, each starting from four different initial winter sea ice states. The rare event simulations produce sea ice lows with probabilities of 2 orders of magnitude smaller than feasible with the control ensembles and drastically increase the number of extremes compared to direct sampling. We find that for a given probability level, the amplitude of negative late-summer sea ice area anomalies strongly depends on the baseline winter sea ice thickness but hardly on the baseline winter sea ice area. Finally, we investigate the physical processes in two trajectories leading to sea ice lows with conditional probabilities of less than 0.001 <span class="inline-formula">%</span>. In both cases, negative late-summer pan-Arctic sea ice area anomalies are preceded by negative spring sea ice thickness anomalies. These are related to enhanced surface downward longwave radiative and sensible heat fluxes in an anomalously moist, cloudy and warm atmosphere. During summer, extreme sea ice area reduction is favoured by enhanced open-water-formation efficiency, anomalously strong downward solar radiation and the sea ice–albedo feedback. This work highlights that the most extreme summer sea ice conditions result from the combined effects of preconditioning and weather variability, emphasizing the need for thoughtful ensemble design when turning to real applications.</p>https://esd.copernicus.org/articles/16/683/2025/esd-16-683-2025.pdf |
| spellingShingle | J. Sauer F. Massonnet G. Zappa F. Ragone Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm Earth System Dynamics |
| title | Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm |
| title_full | Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm |
| title_fullStr | Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm |
| title_full_unstemmed | Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm |
| title_short | Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm |
| title_sort | ensemble design for seasonal climate predictions studying extreme arctic sea ice lows with a rare event algorithm |
| url | https://esd.copernicus.org/articles/16/683/2025/esd-16-683-2025.pdf |
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