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Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics

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Abstract

Although the life-cycle of tropical cyclones is relatively well understood, many of the underlying physical processes occur at scales below those resolved by global climate models (GCMs). Projecting future changes in tropical cyclone characteristics thus remains challenging. We propose a methodology, based on dynamical system metrics, to reconstruct the statistics of cyclone intensities in coarse-resolution datasets, where maximum wind speed and minimum sea-level pressure may not be accurately represented. We base our analysis on 411 tropical cyclones occurring between 2010 and 2020, using both ERA5 reanalysis data and observations from the HURDAT2 database, as well as a control simulation of the IPSL-CM6A-ATM-ICO-HR model. For both ERA5 and model data, we compute two dynamical system metrics related to the number of degrees of freedom of the atmospheric flow and to the coupling between different atmospheric variables, namely the local dimension and the co-recurrence ratio. We then use HURDAT2 data to develop three bias-correction approaches for SLP minima: a univariate, unconditional  quantile–quantile bias correction, a quantile–quantile bias correction conditioned on the two dynamical systems metrics, and a multivariate correction method. The conditional approach generally outperforms the unconditional approach for ERA5, pointing to the usefulness of the dynamical systems metrics in this context. We then show that the multivariate approach can be used to recover a realistic distribution of cyclone intensities from comparatively coarse-resolution model data.

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Data availability

ERA5 is the latest climate reanalysis being produced by ECMWF as part of implementing the EU-funded Copernicus Climate Change Service (C3S), providing hourly data on atmospheric, land-surface and sea-state parameters together with estimates of uncertainty from 1979 to present day. ERA5 data are available on the C3S Climate Data Store on regular latitude-longitude grids at 0.25\(^\circ\) x 0.25\(^\circ\) resolution at https://cds.climate.copernicus.eu, accessed on 2022-04-11. The Atlantic hurricane database (HURDAT2) 1851-2020 is publicly available at https://www.nhc.noaa.gov/data/hurdat/hurdat2-1851-2020-020922.txt, accessed on 2022-04-11. The IPSL simulations are available upon request.

Code Availability

The main results of this work were obtained using Matlab. The scripts for computing the dimension and the co-recurrence ratio are available by downloading the package https://fr.mathworks.com/matlabcentral/fileexchange/95768-attractor-local-dimension-and-local-persistence-computation

Notes

  1. We follow here ECMWF’s terminology, see: https://confluence.ecmwf.int.

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Funding

The authors acknowledge the support of the INSU-CNRS-LEFE-MANU grant (project CROIRE), as well as the grant ANR-19-ERC7-0003 (BOREAS). This work has received support from the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 101003469, XAIDA) and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 948309, CENÆ ). MV was partly supported by the “COESION” project funded by the French National program LEFE (Les Enveloppes Fluides et l’Environnement), as well as the French National “Explore2” project funded by the French Ministry of Ecological Transition (MTE) and the French Office for Biodiversity (OFB).

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DF performed the analysis. DF and GM co-designed the analyses. ST and SB prepared the datasets. All authors participated to the manuscript preparation and contributed to the interpretation of the results.

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Correspondence to Davide Faranda.

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Faranda, D., Messori, G., Bourdin, S. et al. Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics. Clim Dyn 61, 4393–4409 (2023). https://doi.org/10.1007/s00382-023-06794-8

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