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

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.