Key drivers and predictability of the unprecedented 2024 United Arab Emirates flood

Abstract In mid-April 2024, the Arabian Peninsula was struck by an extreme rainfall event, during which some areas received an entire year’s worth of precipitation within just 24 h. The United Arab Emirates was the hardest hit, experiencing widespread flooding, four fatalities, and economic damages exceeding USD 544 million. Analysis of multiple precipitation datasets confirmed that this was the most intense daily rainfall event ever recorded in the region. To investigate the system responsible, a state-of-the-art storm-tracking algorithm was applied to reconstruct the evolution of the mesoscale convective system that caused the hazard. Findings revealed that peak intensity occurred on 16 April, contributing more than 70% of the total event rainfall. Synoptic-scale diagnostics pointed to the rare convergence of two key drivers: enhanced low-level moisture transport via a strengthened Somali low-level jet (SLLJ) and an unusually strong Arabian Cold Vortex in the mid-troposphere. Long-term reanalysis data indicate that such a combination is extremely rare, observed on only 0.006% of all days in the 1940–2024 daily record, previously exceeded only during the 2019 Iran floods. However, forecasts from 11 models in the International Grand Global Ensemble (TIGGE) archive consistently underestimated rainfall (mean bias of −63.6%), due to the severe underestimation of the SLLJ and the moderate overestimation of 500 hPa geopotential height. These results underscore the growing threat of extreme precipitation in arid regions and emphasize the urgent need to improve model capabilities in forecasting moisture dynamics and upper-level disturbances under the changing climate.

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Publication Details

Journal
Environmental Research Letters
Published
2025-12-23
DOI
https://doi.org/10.1088/1748-9326/ae3096
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
1.11
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Key drivers and predictability of the unprecedented 2024 United Arab Emirates flood

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article

Key drivers and predictability of the unprecedented 2024 United Arab Emirates flood

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article en
1 citations

Abstract

Abstract In mid-April 2024, the Arabian Peninsula was struck by an extreme rainfall event, during which some areas received an entire year’s worth of precipitation within just 24 h. The United Arab Emirates was the hardest hit, experiencing widespread flooding, four fatalities, and economic damages exceeding USD 544 million. Analysis of multiple precipitation datasets confirmed that this was the most intense daily rainfall event ever recorded in the region. To investigate the system responsible, a state-of-the-art storm-tracking algorithm was applied to reconstruct the evolution of the mesoscale convective system that caused the hazard. Findings revealed that peak intensity occurred on 16 April, contributing more than 70% of the total event rainfall. Synoptic-scale diagnostics pointed to the rare convergence of two key drivers: enhanced low-level moisture transport via a strengthened Somali low-level jet (SLLJ) and an unusually strong Arabian Cold Vortex in the mid-troposphere. Long-term reanalysis data indicate that such a combination is extremely rare, observed on only 0.006% of all days in the 1940–2024 daily record, previously exceeded only during the 2019 Iran floods. However, forecasts from 11 models in the International Grand Global Ensemble (TIGGE) archive consistently underestimated rainfall (mean bias of −63.6%), due to the severe underestimation of the SLLJ and the moderate overestimation of 500 hPa geopotential height. These results underscore the growing threat of extreme precipitation in arid regions and emphasize the urgent need to improve model capabilities in forecasting moisture dynamics and upper-level disturbances under the changing climate.

Environmental Research LettersVol. 21(1)
Tongji University (CN), Nanyang Technological University (SG), Earth Observatory of Singapore (SG), Singapore Centre for Environmental Life Sciences Engineering (SG), King Abdullah University of Science and Technology (SA)
Climate action
Openalex Percentile: Top 16%
Climate variability and models
1.11
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