2024/12/14 Cyclone Chido
Low confidence prevents ascribing cyclone Chido intensity to human-driven climate change
Contact Authors
Greta Cazzaniga, IPSL-CNRS, France 📨 greta.cazzaniga@lsce.ipsl.fr 🗣️ Italian, English, French
Carmen Alvarez-Castro, UPO, Spain 📨 carmen.alvarez-castro@upo.es 🗣️Spanish, English, Italian, French
Stella Bourdin, University of Oxford, UK 📨 stella.bourdin@physics.ox.ac.uk 🗣️French, English
Davide Faranda, IPSL-CNRS, France 📨 davide.faranda@lsce.ipsl.fr 🗣️French, Italian, English
Citation
Cazzaniga, G., Alvarez-Castro, M. C., Bourdin, S., & Faranda, D. (2024). Low confidence prevents ascribing cyclone Chido intensity to human-driven climate change. ClimaMeter, Institut Pierre Simon Laplace, CNRS. https://doi.org/10.5281/zenodo.14501103
Press Summary (First published 2024/01/25)
Cyclone Chido was a very exceptional event in terms of meteorological conditions.
Low-pressure systems making landfall in Comoros and Mayotte, such as Cyclone Chido, generally display precipitation and wind patterns similar to past events. However, with the influence of anthropogenic climate change, the warmer environment (+1.5°C) in which these systems develop can enhance both their intensity and rainfall potential.
Given the lack of similar events in our dataset, we cannot disentangle the influence of natural variability in driving the cyclone trajectory
Starting from December 6, 2024, our analyses integrate ERA5 data, providing coverage from 1950 with a latency of approximately 5 days and GFS forecasts, for the most recent days where ERA5 data is not yet available.
Event Description
Cyclone Chido, the fourth tropical system and third named storm of the 2024-2025 South-West Indian Ocean cyclone season, brought catastrophic damage to Mayotte and significant disruptions to the Comoros. Originating from a tropical disturbance southeast of Diego Garcia on December 5, Chido intensified rapidly, reaching intense tropical cyclone status by December 12. It made landfall near Bandraboua in Mayotte on December 14, leaving widespread destruction in its wake before continuing its trajectory across the Mozambique Channel. French authorities launched a comprehensive emergency response.
The impacts on Mayotte were unprecedented, with powerful winds exceeding 220 km/h. Much of the precarious housing was completely destroyed. Communications with the territory remained extremely challenging. The alert level, initially set to violet, was downgraded to red during the day to enable rescue operations, but the prefect of Mayotte urged the residents to stay home. Preliminary reports recorded at least 14 deaths and almost 250 injuries, though the local prefect warned that the final toll could be much higher. "Probably several hundred, maybe a thousand, even a few thousand," he stated, citing the devastation in shantytowns where structures were obliterated, and many undocumented residents lived. Access to accurate casualty numbers was further complicated by cultural burial practices and a lack of formal administrative processes in poorer areas.
The Comoros archipelago, including the islands of Anjouan, Mohéli, and Grande Comore, also experienced significant impacts as Chido traversed the region. Eleven fishermen were reported missing, and numerous buildings suffered severe damage, particularly in Anjouan, where several homes were destroyed. The region faced widespread disruptions, with recovery efforts hampered by limited resources and ongoing humanitarian needs.
The Surface Pressure Anomalies fell up to -8 hPa over Comoros and Mayotte close to landfall, while surface temperature Anomalies had an increase up +1.5°C. Precipitation Data indicate that the majority of the area experienced extreme precipitation, reaching up to 200 mm/day. Windspeed Data indicate that the cyclone generated sustained winds up to 100 km/h over the landfall surrounding area. We remind you that our analysis is based on ERA5 and GFS data. This product does integrate some station observations especially for rain data. The values reported here can be different from those observed at single weather stations.
Climate and Data Background for the Analysis
The southwestern Indian Ocean (SWIO) is a common place for cyclones to form, but landfalls in Comore islands. Although the Indian Ocean is an active cyclogenesis zone, cyclones reaching the Comoros are relatively rare due to Madagascar's shielding effect, which disrupts many cyclone trajectories. However, this geographic barrier is not absolute, as historical data reveal significant events. Between 1910 and 1990, the Comoros experienced about 40 meteorological events, ranging from tropical disturbances to full cyclones. Cyclones such as Disseli (1934) and Kamisy (1984) were particularly destructive. Disseli caused widespread devastation in Mayotte, erasing several villages, while Kamisy destroyed 90% of the island’s traditional homes, displaced 25,000 people, and caused €26 million in damages. In 2004, Cyclone Gafilo passed near the Comoros, causing agricultural losses in Mayotte and severe infrastructure damage in Anjouan, including the destruction of Mirontsy's coastal facilities. Despite limited exposure compared to other Indian Ocean islands, the increasing intensity of cyclones, driven by warming seas, poses significant risks to the region’s already vulnerable coastal communities.
According to the IPCC report (IPCC AR6 WGI FR - Page 205), anthropogenic climate change has increased observed precipitation, winds, and extreme sea level associated with some tropical cyclones, and there is evidence for an increase in the annual global proportion of Category 4 or 5 tropical cyclones in recent decades. However, the confidence level for these findings varies from medium to low. Regarding the economic damages caused by individual extreme events, the report (IPCC AR6 WGII FR - Page 1990) states that formal attribution to anthropogenic climate change has been limited, but climate change could account for a substantial fraction of the damages. In summary, the IPCC report suggests that climate change could have an impact on cyclone intensity and associated hazards, but the confidence level for these findings varies. The IPCC report also highlights the potential economic damages caused by individual extreme events, and recent studies have used a variety of approaches for attributing these damages to climate change.
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 there are few cyclones making landfall in Comoros in the period considered.
ClimaMeter Analysis
We analyze here (see Methodology for more details) how events similar to the low pressure system leading to Cyclone Chido have changed in the present (1987–2023) compared to what they would have looked like if they had occurred in the past (1950–1986) in the region [43°W 46°W 11.4°S 14°S]. In this particular case, considering the limited number of low pressure systems impacting Comoros and Mayotte, namely we narrow down the search to the top 12 low pressure systems landfalling in Comoros and Mayotte for the two periods. The Surface Pressure Changes show minimal changes (<1 hPa) in the present compared to the past. There are positive Temperature Changes but they are modest in magnitude (<1°C). Precipitation Changes show that similar events produce similar precipitation in the present than in the past. Windspeed Changes show that similar events produce modestly weaker (<4 km/h) winds in the Indian Ocean to the South-East of Comoros and Mayotte and Mauritius islands. We also observe that Similar Past Events occur more frequently in November in the present, whereas in the past, they were more prevalent in December. Regarding the affected urban areas, Mamoudzou in Mayotte shows no significant changes in precipitation and winds, Domoni and Fomboni in Comoros experience a decrease in precipitation of up to 5 mm/day (0% to 10%) during present low pressure systems than in the past. All urban areas exhibit a modest decrease in wind speed, up to 5 km/h (0% to 5%) in present low pressure systems than in the past.
Finally, given the exceptionality of the cyclone and the lack of similar events in our dataset, we cannot disentangle the influence of natural variability in driving the cyclone trajectory
Conclusion
Based on the above, we conclude that low-pressure systems making landfall in Comoros and Mayotte, such as Cyclone Chido, generally display precipitation and wind patterns similar to past events. However, with the influence of anthropogenic climate change, the warmer environment (+1.5°C) in which these systems develop can enhance both their intensity and rainfall potential. Given the exceptionality of the cyclone and the lack of similar events in our dataset, we cannot disentangle the influence of natural variability in driving the cyclone trajectory.
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.