Genesis Potential Indices (GPIs) link the occurrence of Tropical Cyclones (TCs) to large-scale environmental conditions favorable for TC development. In the last few decades, they have been routinely used as a way to overcome the limitations of climate models (GCM), whose resolution is too coarse to produce realistic TCs. Recently, the first GCM ensemble with high enough horizontal resolution to realistically reproduce TCs was made available. Here, we address the questions of whether GPIs are still relevant in the era of TC-permitting climate model ensembles, and whether they have sufficient predictive skills. The predictive skills of GPIs are assessed against the TCs directly simulated in a climate model ensemble. We found that GPIs have poor skill in two key metrics: inter-annual variability and multi-decadal trends. We discuss possible ways to improve the understanding of the predictive skill of GPIs and therefore enhance their applicability in the era of TC-permitting GCMs.
Tropical Cyclone Genesis Potential Indices in a New High‐Resolution Climate Models Ensemble: Limitations and Way Forward
Ascenso, G.;Castelletti, A.;Giuliani, M.;
2023-01-01
Abstract
Genesis Potential Indices (GPIs) link the occurrence of Tropical Cyclones (TCs) to large-scale environmental conditions favorable for TC development. In the last few decades, they have been routinely used as a way to overcome the limitations of climate models (GCM), whose resolution is too coarse to produce realistic TCs. Recently, the first GCM ensemble with high enough horizontal resolution to realistically reproduce TCs was made available. Here, we address the questions of whether GPIs are still relevant in the era of TC-permitting climate model ensembles, and whether they have sufficient predictive skills. The predictive skills of GPIs are assessed against the TCs directly simulated in a climate model ensemble. We found that GPIs have poor skill in two key metrics: inter-annual variability and multi-decadal trends. We discuss possible ways to improve the understanding of the predictive skill of GPIs and therefore enhance their applicability in the era of TC-permitting GCMs.File | Dimensione | Formato | |
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