Applications of large deviation theory in geophysical fluid dynamics and climate science

Gálfi, Vera Melinda; Lucarini, Valerio; Ragone, Francesco; Wouters, Jeroen

doi:10.1007/s40766-021-00020-z

Cited by 24 publications

(32 citation statements)

References 287 publications

(527 reference statements)

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“…The emergence of dominant zonal wavenumbers associated with extreme meridional energy transports also has implications for teleconnections. Investigations based on large deviation theory have shown that persistent weather extremes are associated with very large scale atmospheric features Gálfi et al (2019); Galfi and Lucarini (2021); Gálfi et al (2021). We emphasized here the similarity between our results and the concurrent emergence of heat waves across the Northern Hemisphere associated with a persistent k = 5 − −7 Rossby wave pattern, as found by Kornhuber et al (2020).…”

Section: Discussionsupporting

confidence: 85%

Meridional energy transport extremes and the general circulation of NH mid-latitudes: dominant weather regimes and preferred zonal wavenumbers

Lembo

Fabiano

Gálfi

et al. 2022

Preprint

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Abstract. The extratropical meridional energy transport in the atmosphere is fundamentally intermittent in nature, having extremes large enough to affect the net seasonal transport. Here, we investigate how these extreme transports are associated with the dynamics of the atmosphere at multiple scales, from planetary to synoptic. We use ERA5 reanalysis data to perform a wavenumber decomposition of meridional energy transport in the Northern Hemisphere mid-latitudes during winter and summer. We then relate extreme transport events to atmospheric circulation anomalies and dominant weather regimes, identified by clustering 500 hPa geopotential height fields. In general, planetary-scale waves determine the strength and meridional position of the synoptic-scale baroclinic activity with their phase and amplitude, but important differences emerge between seasons. During winter, large wavenumbers (k = 2 − 3) are key drivers of the meridional energy transport extremes, and planetary and synoptic-scale transport extremes virtually never co-occur. In summer, extremes are associated with higher wavenumbers (k = 4 − 6), identified as synoptic-scale motions. We link these waves and the transport extremes to recent results on exceptionally strong and persistent co-occurring summertime heat waves across the Northern Hemisphere mid-latitudes. We show that these events are typical, in terms of dominant regime patterns associated with extremely strong meridional energy transports.

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Section: Discussionsupporting

confidence: 85%

Meridional energy transport extremes and the general circulation of NH mid-latitudes: dominant weather regimes and preferred zonal wavenumbers

Lembo

Fabiano

Gálfi

et al. 2022

Preprint

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“…Heatwave definition. Let us precisely define heatwaves, as proposed for the present work and following recent studies (Ragone et al, 2018;Gálfi et al, 2019;Gálfi et al, 2021;Bouchet, 2019, 2021;Galfi and Lucarini, 2021). For that, it is first needed to define the fluctuations in temperature and geopotential height, which are called anomalies when the fluctuations are large.…”

Section: Climate Data and Heatwavesmentioning

confidence: 99%

“…As explained in the introduction, this definition of heat-waves follows seminal studies (Schär et al, 2004;Barriopedro et al, 2011;Coumou and Rahmstorf, 2012) of the 2003 and 2010 heatwaves; it is specifically suited for the study of high impact events, and has been adopted in a set of recent studies (Ragone et al, 2018;Gálfi et al, 2019;Gálfi et al, 2021;Bouchet, 2019, 2021;Galfi and Lucarini, 2021). Heatwave prediction dataset.…”

Section: Climate Data and Heatwavesmentioning

confidence: 99%

“…Seminal studies (Schär et al, 2004;Barriopedro et al, 2011;Coumou and Rahmstorf, 2012) of the 2003 and 2010 heatwaves already considered the averaged temperature over variable long time periods (7 days, 15 days, 1 month, and 3 months). To deal with the goal of quantifying heat wave amplitudes for several independent duration, heatwave indices based on the combined temporal and spatial averages of the surface or 2-meter temperature has been adopted in a set of recent studies (Ragone et al, 2018;Gálfi et al, 2019;Gálfi et al, 2021;Bouchet, 2019, 2021;Galfi and Lucarini, 2021). This viewpoint is expected to be complementary with the classical definitions (Perkins, 2015), and extremely relevant to events with the most severe impacts.…”

Section: Introductionmentioning

confidence: 99%

“…Defining long-lasting heat waves as temporal and spatial averages, the first works (Ragone et al, 2018;Gálfi et al, 2019;Gálfi et al, 2021;Bouchet, 2019, 2021;Galfi and Lucarini, 2021) focused, on the one hand, on discussing their statistics and probability, and on the other hand, on improving the statistics of extremely rare events using rare event algorithms (Ragone et al, 2018;Bouchet, 2019, 2021). The second goal of this work will be to assess the predictability and forecast potential of these extreme heat waves.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Extreme Heatwave Forecast

et al. 2022

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Because of the impact of extreme heat waves and heat domes on society and biodiversity, their study is a key challenge. We specifically study long-lasting extreme heat waves, which are among the most important for climate impacts. Physics driven weather forecast systems or climate models can be used to forecast their occurrence or predict their probability. The present work explores the use of deep learning architectures, trained using outputs of a climate model, as an alternative strategy to forecast the occurrence of extreme long-lasting heatwave. This new approach will be useful for several key scientific goals which include the study of climate model statistics, building a quantitative proxy for resampling rare events in climate models, study the impact of climate change, and should eventually be useful for forecasting. Fulfilling these important goals implies addressing issues such as class-size imbalance that is intrinsically associated with rare event prediction, assessing the potential benefits of transfer learning to address the nested nature of extreme events (naturally included in less extreme ones). We train a Convolutional Neural Network, using 1,000 years of climate model outputs, with large-class undersampling and transfer learning. From the observed snapshots of the surface temperature and the 500 hPa geopotential height fields, the trained network achieves significant performance in forecasting the occurrence of long-lasting extreme heatwaves. We are able to predict them at three different levels of intensity, and as early as 15 days ahead of the start of the event (30 days ahead of the end of the event).

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Investigating the typicality of the dynamics leading to extreme temperatures in the IPSL-CM6A-LR model

Noyelle,

Yiou,

Faranda

2023

Clim Dyn

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Determining the underlying mechanisms leading to extreme events in dynamical systems is a challenging task. Under mild hypotheses, large deviations theory predicts that as one increases the threshold defining an extreme, dynamical trajectories which reach the extreme will look more and more like one another: they converge towards a typical, i.e. most probable, one called the instanton. In this paper, we use a 2000-year simulation of the IPSL-CM6A-LR model under a stationary pre-industrial climate to test this prediction on the case of hot extremes. We investigate whether the physical mechanisms leading to extreme temperatures at four locations in Europe are more similar with increasing extreme temperatures. Our results show that most physical variables exhibit the expected convergence towards a most probable trajectory, with some geographical and temporal variations. In particular, we observe the presence of a cut-off low in some trajectories, which suggests the existence of multiple pathways leading to extreme temperatures. These findings confirm the relevance of instanton dynamics in understanding the physical mechanisms driving extreme events in climate models.

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Applications of large deviation theory in geophysical fluid dynamics and climate science

Cited by 24 publications

References 287 publications

Meridional energy transport extremes and the general circulation of NH mid-latitudes: dominant weather regimes and preferred zonal wavenumbers

Meridional energy transport extremes and the general circulation of NH mid-latitudes: dominant weather regimes and preferred zonal wavenumbers

Deep Learning-Based Extreme Heatwave Forecast

Investigating the typicality of the dynamics leading to extreme temperatures in the IPSL-CM6A-LR model

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