2024/05/26-29 India Heatwave

India Heatwave mostly exacerbated by human-driven Climate Change

Press Summary (First published 2024/06/07)

Event Description

From May 26th to May 29th,  large parts of northern India and southern Pakistan suffered a severe heatwave, with a provisional record temperature of 49.1°C registered in New Delhi. More than 37 cities in the country recorded temperatures over 45°C. Warnings of heat-related illnesses have been issued, with at least 56 casualties and 25000 suspected heat stroke cases. There were initial reports of a temperature recording of 53.2°C, that were later adjusted due to a faulty sensor. Yet, the heatwave in India and southern Pakistan exhibited record-high temperatures, ranging from 45.2C to 49.1C in different parts of New Delhi. The city's authorities have warned they will issue fines to those caught wasting water as the city deals with shortages, and supplies have been cut to some areas. Water minister Atishi announced that 200 teams would be deployed to crack down on people washing their cars with hose pipes and letting their tanks overflow. New Delhi power demand has soared to an all-time high, with residents turning to air conditioning, coolers, and ceiling fans to cope with the heat.

The Surface Pressure Anomalies reveal a significant negative (cyclonic) anomaly over India’s northwest regions and the southern part of Pakistan. Temperature data indicate warm anomalies reaching up to +5°C in some parts of northwest India and southern parts of Pakistan. Precipitation data show absence of precipitation in a large part of the region analyzed. Windspeed data show low to moderate winds.

Climate and Data Background for the Analysis

The IPCC AR6 report provides a clear relationship between heatwaves and climate change in India. Climate change is significantly contributing to the increase in heatwaves in India through various mechanisms: Warming resulting from climate change has led to an increased frequency, intensity, and duration of heat-related events, including heatwaves, in most land regions, with high confidence (IPCC SR OC C6 - Page 27). Climate change is projected to alter land conditions, affecting temperature and rainfall in regions, which can enhance winter warming due to decreased snow cover and albedo in boreal regions, while reducing warming during the growing season in tropical areas with increased rainfall. Global warming and urbanization can enhance warming in cities and their surroundings, especially during heatwaves, with a higher impact on night-time temperatures than daytime temperatures (IPCC AR6 WGII FR - Page 1058). Observed surface air temperature has been increasing since the 20th century in Asia, intensifying the threat of heatwaves across the region. In India specifically, the frequency and duration of heatwaves have increased, associated with Indian Ocean basin-wide warming and frequent El Ninos, leading to severe impacts on agriculture and human discomfort. The combination of global warming and population growth in already-warm cities in regions like India is a major driver for increased heat exposure, with urban heat islands elevating temperatures within cities relative to their surroundings.

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 database

ClimaMeter Analysis

We analyze here (see Methodology for more details) how events similar to the high temperature in India May heatwave 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 [68°W 85°W 20°S 35°S]. The Surface Pressure Changes show that similar events  do not display significant changes in the present climate than what they would have been in the past. The Temperature Changes show that similar events produce temperatures in the present climate that are at least 1.5°C warmer than what they would have been in the past, over a large area of the region analyzed. The Precipitation Changes do not show any significant variations. Windspeed Changes indicate up to 4 km/h windier conditions over Southern Western regions. We also note that Similar Past Events previously mainly occurred in November and December, while in the present climate they are mostly occurring in February and May. Changes in Urban Areas reveal that New Delhi,  Jalandhar and Larkana are up to 1 °C warmer in the present compared to the past. Results that are consistent with the corrected temperature readings for New Delhi (after removing the erroneous reading of 53.2°C due to a faulty sensor).

Finally, we find that sources of natural climate variability, notably the Pacific Decadal 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.

Conclusion

Based on the above, we conclude that heatwaves similar to the India May heatwave are 1.5 °C warmer than the warmest heatwaves previously observed in the country. We interpret India May heatwave as a largely unique event whose characteristics can mostly be ascribed to human driven climate change

Quotes & Contact Authors

-Gianmarco Mengaldo, NUS, Singapore 📨mpegim@nus.edu.sg  🗣️Italian, English

-Chen Chen, Centre for Climate Research Singapore chen_chen@nea.gov.sg 🗣️English

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

Additional Information : Complete Output of the Analysis

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.