Low confidence prevents ascribing Dubai Floods changes in intensity to human-driven climate change

Contact Authors

-Davide Faranda, IPSL-CNRS, France 📨davide.faranda@lsce.ipsl.fr 🗣️French, Italian, English

-Flavio Pons, IPSL, Italy 📨flavio.pons@lsce.ipsl.fr 🗣️Italian, English, French

-Tommaso Alberti, INGV, Italy 📨tommaso.alberti@ingv.it 🗣️Italian, English

-Erika Coppola, ICTP, Italy 📨coppolae@ictp.it 🗣️Italian, English

Press Summary (First published 2024/04/18)

• Depressions similar to those producing Dubai Floods are 0-3 mm/day drier (0-1% of the event rain amount) over the UAE in the present than they were in the past. 

• This was a largely unique event.

• Low confidence prevents ascribing the changes in intensity in Dubai Floods to human-driven climate change 

Event Description

On April 16, 2024, countries around the Persian Gulf were affected by an unprecedented outbreak of thunderstorms. A low pressure area developed and deepened over central Saudi Arabia on April 15, slowly moving to the East the following day.  As a result, a very large storm complex formed over these countries, causing an impressive series of severe impacts. The UAE received the largest amount of rain ever recorded since measurement started in 1949, with up to 254 mm in less than 24 hours in Al Ain, corresponding to roughly two years worth of precipitation for the region. Many reports and viral videos came from Dubai, where both the city and the international airport were completely disrupted by the storm, which was also accompanied by severe straight line winds. Numerous areas experience flash flooding, causing several stranded people to be rescued. Unfortunately, despite the storm had been forecasted and several precautionary measures had been taken in UAE and Oman, the death toll of the storm is of at least 19 victims.

The Surface Pressure Anomalies show a large negative (cyclonic) anomaly over Saudi Arabia. The development of this depression was associated with the presence of a core of the tropical jet, a high-altitude wind current, which enhances vertical motion, encouraging thunderstorms formation. Temperature anomalies show negative values over a large part of the analysed domain. Due to these conditions, the area including Bahrain, Qatar, UAE, and Oman saw the advection of large quantities of precipitable water from the Arabian Sea, and desert dust from the Arabian Peninsula, a source of condensation nuclei for raindrops. As a consequence, Precipitation data show high amounts exceeding 200 mm/day over Dubai and the surrounding area. Wind speed data show large areas of the UAE affected by moderate winds, with few areas exceeding 50 km/h in Oman.

Climate and Data Background for the Analysis

In the IPCC AR6 report, Chapter 12, states that heavy precipitation events in Asia will become more intense and frequent for a 2°C global warming or higher. This trend is given high confidence in all areas of Asia, except for the Arabian Peninsula, where medium confidence is assigned. 

The area including Bahrain, Qatar, UAE and Oman is characterised by a very arid climate, with most areas experiencing 150-250 mm of rain per year. Dubai, completely flooded by this depression, normally receives only around 79 mm of rain yearly, and the average precipitation for April is only 8 mm. The development of this depression was preceded by the build up of a persistent positive anomaly of precipitable water between the southern Arabian Peninsula and the Arabian Sea. This pool of potential rain has been advected over the affected area by the southerly flow in the forward sector of the low pressure system, and entered an environment characterised by strong wind shear and lifting motion, triggering the formation of the anomalous depression system.

Our analysis approach rests on looking for weather situations similar to those of the event of interest having been observed in the past. For this event we have low confidence in the robustness of our approach given the available climate data, as the event is largely unique in the data record.

ClimaMeter Analysis

We analyze here (see Methodology for more details) how events similar to the pressure system leading to Dubai Floods changed in the present (2001–2023) compared to what they would have looked like if they had occurred in the past (1979–2001) in the region [50°E 60°E 20°N 30°N]. The Surface Pressure Changes show that similar events are slightly deeper in the present climate than what they would have been in the past over the region including Dubai. The Temperature Changes show that similar events produce temperatures in the present climate that are up to 1 ºC warmer than what they would have been in the past, over a large area of the region analysed. The Precipitation Changes show drier conditions (up to 3 mm/day) over the  UAE. Windspeed Changes indicate up to 4 km/h windier conditions over the Southern-Eastern coasts of Oman compared to the past. We also note that Similar Past Events previously mainly occurred from January to March, while in the present climate they are mostly occurring in January and April. Changes in Urban Areas reveal that Dubai, Adu Dhabi, Sharjah are up to 3 mm/day wetter in the present compared to the past. Additionally, all cities experience up to 1 °C higher temperatures in the present than in the past.

Finally, we find that sources of natural climate variability, notably the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation may have influenced the event. This suggests that the changes we see in the event compared to the past may be partly due to human driven climate change, with a contribution from natural variability.

Conclusion

Based on the above, we conclude that depressions similar to those producing Dubai Floods are slightly more intense, up to 1 °C warmer, and 3 mm/day drier in the present than they would have been in the past. We interpret Dubai Floods as a largely unique event for which both human driven climate change and natural climate variability played a role. We remark that the confidence on these detected changes is low because of the exceptionality of the analysed event.