2023/10/23 Cyclone Tej Landfall in Yemen
High Rainfall in Cyclone Tej mostly strengthened by human-driven climate change
Cyclones landfalling in Yemen and Oman are 3 to 20 mm wetter in the present than they have been in the past in Al Ghayzah.
On October 23, 2023, tropical Cyclone Tej struck south-eastern Yemen and Oman, resulting in severe flooding and displacing thousands of people. In Yemen, Al Ghaydah Airport recorded a staggering 406 mm of rain in just a few hours. The destruction was particularly acute in Al Mahrah Governorate, impacting districts such as Al Ghaydah and Haswayn. Tragically, one person lost their life, and six were reported missing in Haswayn District. The Yemen Red Crescent Society reported that around 1,800 families were displaced in the wake of Cyclone Tej, necessitating immediate assistance for their recovery efforts. Despite the dedicated efforts of aid teams and ambulances, severe weather conditions hampered their access to the affected areas, prompting the need for aircraft to rescue stranded individuals and protect lives.
In addition to Yemen, some areas of Oman, particularly Dhofar and Al Wusta Governorates, experienced heavy rain and localized flooding, causing damage to roads, power networks, and communication infrastructure. Rainfall figures from October 22 to 24 showed Rakhyut receiving 232 mm of rain, Dhalkut 203 mm, and Salalah 56 mm. The impacts of Cyclone Tej reverberated across the region, underscoring the vulnerability of coastal areas to extreme weather events and highlighting the need for robust disaster response and climate resilience measures.
The Surface Pressure Anomalies reveal a narrow band of low pressure which reflect the track of hurricane Tej during the landfall day. Precipitation Data indicate that most of the area covered by the analysis experienced extreme precipitation reaching up to 400 mm per day over the area affected by the landfall.
The IPCC AR6 WG1 report states that it is likely that the global proportion of Category 3–5 tropical cyclones has increased over the past four decades, and the global frequency of TC rapid intensification events has likely increased over the past four decades. None of these changes can be explained by natural variability alone (medium confidence). Moreover, the proportion of intense TCs, average peak TC wind speeds, and peak wind speeds of the most intense TCs will increase on the global scale with increasing global warming (high confidence). At the regional scale the frequency and duration of tropical cyclones has increased over time over the Arabian Sea (low confidence) and low confidence in the direction of change for future projection.
Our analysis approach rests on looking for weather situations similar to those of the event of interest having been observed in the past. For cyclone Tej, 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.
We analyse here (see Methodology for more details) how events similar to the low pressure system leading to the lanfdfall of tropical cyclone Tej between Yemen and Oman have changed in the present (2001–2022) compared to what they would have looked like if they had occurred in the past (1979–2000) in the region [49°E -55°E 10°-19°N]. The Surface Pressure Changes show that cyclones have not significantly changed their intensity compared to the past. Precipitation Changes show that similar events produce no more precipitation in the present than in the past. Considering the affected urban areas, only Al Ghayzah sees an increase in precipitation (3-20 mm/day) in the present. We also find that Similar Past Events have the same frequency in the present and in the past.
Finally, we find that sources of natural climate variability, notably the Atlantic Multidecadal Oscillation, may have influenced the event. This means that the changes we see in the event compared to the past may be mostly due to human driven climate change.
Based on the above, we conclude that cyclones following similar tracks as cyclone Tej during the landfall have become 6-12mm wetter in the present than in the past. We interpret cyclone Tej landfall as a unique event whose characteristics can mostly be ascribed to human driven climate change.
Davide Faranda, IPSL-CNRS, France 📨firstname.lastname@example.org 🗣️French, Italian, English
Erika Coppola, ICTP, Italy 📨email@example.com 🗣️Italian, English
Flavio Pons, IPSL-CNRS, France 📨firstname.lastname@example.org 🗣️French, Italian, English
Additional Information : Complete Output of the Analysis
NB1: The following output is specifically intended for researchers and contain details that are fully understandable only by reading the methodology described in Faranda, D., Bourdin, S., Ginesta, M., Krouma, M., Noyelle, R., Pons, F., Yiou, P., and Messori, G.: A climate-change attribution retrospective of some impactful weather extremes of 2021, Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, 2022.
NB2: Colorscales may vary from the ClimaMeter figure presented above.
The figure shows the average of surface pressure anomaly (msl) (a), average 2-meter temperatures anomalies (t2m) (e), cumulated total precipitation (tp) (i), and average wind-speed (wspd) in the period of the event. Average of the surface pressure analogs found in the counterfactual [1979-2000] (b) and factual periods [2001-2022] (c), along with corresponding 2-meter temperatures (f, g), cumulated precipitation (j, k), and wind speed (n, o). Changes between present and past analogues are presented for surface pressure ∆slp (d), 2 meter temperatures ∆t2m (h), total precipitation ∆tp (i), and windspeed ∆wspd (p): color-filled areas indicate significant anomalies with respect to the bootstrap procedure. Violin plots for past (blue) and present (orange) periods for Quality Q analogs (q), Predictability Index D (r), Persistence Index Θ (s), and distribution of analogs in each month (t). Violin plots for past (blue) and present (orange) periods for ENSO (u), AMO (v) and PDO (w). Number of the Analogues occurring in each subperiod (blue) and linear trend (black). Values for the peak day of the extreme event are marked by a blue dot. Horizontal bars in panels (q,r,s,u,v,w) correspond to the mean (black) and median (red) of the distributions.