Attractor dimension of time-averaged climate observables: insights from a low-order ocean-atmosphere model

Faranda, Davide; Messori, Gabriele; Vannitsem, Stéphane

doi:10.1080/16000870.2018.1554413

Cited by 38 publications

(36 citation statements)

References 41 publications

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“…Additional reasons motivating the use of the co‐recurrence metric we propose here over a CCA approach are that α is linked to other dynamical properties of the phase space underlying the atmospheric motions, such as local dimension and persistence. It therefore indirectly provides a rich set of information on the system, including on its intrinsic predictability (Messori et al ., ; Scher and Messori, ; Faranda et al ., ). One may, for example, further investigate the predictability implications of the fact that concurrent precipitation–wind extremes over Europe display a low d and high θ −1 , and compare this to the empirical results from ensemble reforecast data.…”

Section: Discussionmentioning

confidence: 97%

Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North America

Luca

Messori

Pons

et al. 2020

Quart J Royal Meteoro Soc

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We propose a novel approach to the study of compound extremes, grounded in dynamical systems theory. Specifically, we present the co‐recurrence ratio (α), which elucidates the dependence structure between variables by quantifying their joint recurrences. This approach is applied to daily climate extremes, derived from the ERA‐Interim reanalysis over the 1979–2018 period. The analysis focuses on concurrent (i.e., same‐day) wet (total precipitation) and windy (10 m wind gusts) extremes in Europe and concurrent cold (2 m temperature) extremes in Eastern North America and wet extremes in Europe. Results for wet and windy extremes in Europe, which we use as a test‐bed for our methodology, show that α peaks during boreal winter. High α values correspond to wet and windy extremes in northwestern Europe, and to large‐scale conditions resembling the positive phase of the North Atlantic Oscillation (NAO). This confirms earlier findings which link the positive NAO to a heightened frequency of extratropical cyclones impacting northwestern Europe. For the Eastern North America–Europe case, α extremes once again reflect concurrent climate extremes – in this case cold extremes over North America and wet extremes over Europe. Our analysis provides detailed spatial information on regional hotspots for these compound extreme occurrences, and encapsulates information on their spatial footprint which is typically not included in a conventional co‐occurrence analysis. We conclude that α successfully characterises compound extremes by reflecting the evolution of the associated meteorological maps. This approach is entirely general, and may be applied to different types of compound extremes and geographical regions.

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Section: Discussionmentioning

confidence: 97%

Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North America

Luca

Messori

Pons

et al. 2020

Quart J Royal Meteoro Soc

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“…To create long climate model runs of different complexity, we used the Planet Simulator (PLASIM) intermediatecomplexity GCM, and its dry dynamical core: the Portable University Model of the Atmosphere (PUMA) (Fraedrich et al, 2005). Each model was run for 830 years with two different horizontal resolutions (T21 and T42, corresponding to ∼ 5.65 and ∼ 2.8 • latitude, respectively) and 10 vertical levels.…”

Section: Climate Modelsmentioning

confidence: 99%

Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground

2019

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Abstract. Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. We use a bottom–up approach for assessing whether it should, in principle, be possible to do this. We use the relatively simple general circulation models (GCMs) PUMA and PLASIM as a simplified reality on which we train deep neural networks, which we then use for predicting the model weather at lead times of a few days. We specifically assess how the complexity of the climate model affects the neural network's forecast skill and how dependent the skill is on the length of the provided training period. Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging – in contrast to earlier promising results on a model without seasonal cycle.

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“…Additional reasons motivating the use of the co-recurrence metric we propose here over a CCA approach are that α is linked to other dynamical properties of the phase space underlying the atmospheric motions, such as local dimension and persistence. It therefore indirectly provides a rich set of information on the system, including on its intrinsic predictability (Faranda et al, 2019b;Messori et al, 2017;Scher and Messori, 2018). One may, for example, further investigate the predictability implications of the fact that concurrent precipitation-wind extremes over Europe display a low d and high θ −1 and compare this to the empirical results from ensemble reforecast data.…”

Section: Discussionmentioning

confidence: 99%

Dynamical Systems Theory Sheds New Light on Compound Climate Extremes in Europe and Eastern North America

Luca¹,

Messori²,

Pons³

et al. 2019

Preprint

Self Cite

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We propose a novel approach to the study of compound extremes, grounded in dynamical systems theory. Specifically, we present the co-recurrence ratio (α), which elucidates the dependence structure between variables by quantifying their joint recurrences. This approach is applied to daily climate extremes, derived from the ERA-Interim reanalysis over the 1979-2018 period. The analysis focuses on concurrent (i.e. same-day) wet (total precipitation) and windy (10m wind gusts) extremes in Europe and concurrent cold (2m temperature) extremes in Eastern North America and wet extremes in Europe. Results for wet and windy extremes in Europe, which we use as a test-bed for our methodology, show that α peaks during boreal winter. High α values correspond to wet and windy extremes in north-western Europe, and to large-scale conditions resembling the positive phase of the North Atlantic Oscillation (NAO). This confirms earlier findings which link the positive NAO to a heightened frequency of extra-tropical cyclones impacting north-western Europe. For the Eastern North America-Europe case, α extremes once again reflect concurrent climate extremes - in this case cold extremes over North America and wet extremes over Europe. Our analysis provides detailed spatial information on regional hotspots for these compound extreme occurrences, and encapsulates information on their spatial footprint which is typically not included in a conventional co-occurrence analysis. We conclude that α successfully characterises compound extremes by reflecting the evolution of the associated meteorological maps. This approach is entirely general, and may be applied to different types of compound extremes and geographical regions.

show abstract

Attractor dimension of time-averaged climate observables: insights from a low-order ocean-atmosphere model

Cited by 38 publications

References 41 publications

Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North America

Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North America

Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground

Dynamical Systems Theory Sheds New Light on Compound Climate Extremes in Europe and Eastern North America

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