4A.5 Freddy: Breaking Record for Tropical Cyclone Precipitation?

Monday, 6 May 2024: 5:45 PM
Seaview Ballroom (Hyatt Regency Long Beach)
Enrico Scoccimarro, Fondazione Centro euro-Mediterraneo sui Cambiamenti Climatici, Bologna, Italy; and P. Lanteri and L. Cavicchia

Depending on the location on the Earth planet the amount of precipitation associated to Tropical Cyclones (TCs) can reach 20% of the total precipitation over land (Mexico coast, and up to 40% over some ocean regions (e.g., Eastern Pacific basin and Southeastern Indian Ocean). Moreover, focusing on a few case studies, some TCs accounted for more than 90% of the summer rainfall experienced in some regions, such as Australia. Freshwater TC induced flooding has been suggested as the largest threat to human lives due to TCs and due to their large size the associated precipitation usually affects large domains. For this reason a reliable quantification of the precipitation amount associated to each past TC is important for a better definition of the TC fingerprint on the mean climatology. The current temporal and horizontal resolution of observational dataset and atmospheric reanalysis give the possibility to quantify the past TC associated precipitation over the Earth planet following the observed TC tracks. In this work we compare results from different observational and reanalysis datasets in terms of TC associated precipitation, to verify the consistency between them. A particular focus is given to the TC Freddy (Southern Indian Ocean, 2023) with the aim to frame it in the global contest. Thanks to the support of HighResMIP general circulation models with a horizontal resolution able to represent the most intense TCs, we also investigate tendencies in TC associated precipitation in the last 40 years, highlighting inconsistencies introduced by the highest number of measurement data assimilated starting from the beginning of the current century, in ERA5 and MERRA reanalysis and MSWEP. This research was supported by the EU-funded Climate Intelligence (CLINT) project. [grant agreement ID: 101003876; DOI: 10.3030/101003876].
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