Preprints
https://doi.org/10.5194/egusphere-2024-3082
https://doi.org/10.5194/egusphere-2024-3082
10 Oct 2024
 | 10 Oct 2024
Status: this preprint is open for discussion.

Ensemble design for seasonal climate predictions: Studying extreme Arctic sea ice lows with a rare event algorithm

Jerome Sauer, Francesco Ragone, François Massonnet, and Giuseppe Zappa

Abstract. 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 seven pairs of control and rare event algorithm ensemble simulations each starting from seven different initial winter sea ice states. The rare event simulations produce sea ice lows with probabilities of at least two 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. The experiments furthermore indicate a quasi-zero probability to internally generate a seasonally sea ice-free Arctic in this set-up. Finally, we investigate the physical processes in two trajectories leading to sea ice lows with conditional probabilities of less than 0.001 %. 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.

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Jerome Sauer, Francesco Ragone, François Massonnet, and Giuseppe Zappa

Status: open (until 28 Nov 2024)

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  • RC1: 'Comment on egusphere-2024-3082', Anonymous Referee #1, 31 Oct 2024 reply
Jerome Sauer, Francesco Ragone, François Massonnet, and Giuseppe Zappa
Jerome Sauer, Francesco Ragone, François Massonnet, and Giuseppe Zappa

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Short summary
An obstacle in studying climate extremes is the lack of robust statistics. We use a rare event algorithm to gather robust statistics on extreme Arctic sea ice lows with probabilities below 0.1 % and to study drivers of events with amplitudes larger than observed in 2012. The work highlights that the most extreme sea ice reductions result from the combined effects of preconditioning and weather variability, emphasizing the need for thoughtful ensemble design when turning to real applications.