Local Warnings, Global Lessons: South Asia's Model for Early Warning Systems for Consideration at COP30

Authors: Dr. Ajit Tyagi, Dr. Someshwar Das, Dr. M. Mohapatra, Prof. Dileep Mavalankar, Dr. Naresh Kumar, Dr. Dharam Raj Uprety

Early warnings work best when we all listen locally and act globally. South Asia teaches us that technology saves lives only when it speaks the language of affected communities, suggested the panel members on March 23, 2025, at a discussion Marking the World Meteorological Day.

Early warning systems are becoming crucial in effectively mitigating climate-related disasters. While climate change and localised extreme weather events become a quotidian reality, warning systems help reduce mortality, economic damage, and allow vulnerable communities to be better prepared. As of 2015, multi-hazard early warning systems were present in just 51 countries. As of today, 101 countries report the presence of some form of multi-hazard early warning systems^[1]. While this statistic shows the growing adoption of these systems, the world is still far from global coverage. In this regard, South Asia, with its complex climate dynamics and dense populations, sits at the heart of this challenge.

The Global Commission on Adaptation (GCA) study on the economic value of enhancing MHEWS revealed a cost-benefit ratio of 1:9. This means that for every US$ 1 invested in MHEWS, an average of US$ 9 in net economic benefits can be realised^[2]. For South Asia, these savings have a widespread positive socioeconomic impact. At the same time, closing the gap in early warning systems is more than just financial investments. It demands global cross-border collaboration along with context-driven, community-based solutions.

The first lesson in constructing efficient early warning systems stems from the idea that climate is not a local issue, but an amalgamation of large weather fluctuations beyond a country’s own borders. For instance, a cyclone that originates in the Bay of Bengal is not just a data monitoring issue for Bangladesh, but also India, Nepal and Myanmar. Therefore, it becomes essential for countries to establish a common framework to collect and share weather data. Tools like RMSC (Regional Specialized Meteorological Center), provides Cyclone forecasts to the countries of WMO RA II region that includes South Asia. Platforms like SASCOF^[3] (South Asian Seasonal Climate Outlook Forum) have fostered regional cooperation between countries like India, Bhutan, Nepal, Sri Lanka, Pakistan, Myanmar, and the Maldives. India's cyclone modelling and data-sharing capacities are now foundational for the region. Another success story is India’s fisher warning system, which provides real-time alerts across the Bay of Bengal, Arabian Sea, and Indian Ocean—supporting livelihoods and saving lives in coastal communities across the Asian and African region.

In addition to the use of an interconnected early warning network for the entire region, the assessment of risk through the correct parameters is also of equal importance. Traditional forecasts that predict just the intensity of the weather catastrophe are no longer enough. Modern systems aim for impact-based forecasting—an approach that integrates meteorological data with social, infrastructural, and geospatial insights. This means not just predicting a heatwave or cyclone but estimating its likely effects on different communities based on their vulnerabilities. For instance, while a traditional forecast would just say “Heavy rain is forecast. 100 to 150mm of rain is expected within a three-hour period” the equivalent impact-based forecast would say “Flash flooding of the County River is expected. Dwellings, farm buildings and grazing land within 30m of the river channel are expected to flood and be damaged.”^[4] Such a replacement in reporting could lead to a better implementation of an action plan set out by local governments. As a case, impact-based warning of Extremely Severe Cyclone Biparjoy in June 2022 by IMD helped in minimising the loss of lives and property.

The best example of effectively using extra information for mitigating a health calamity can be seen in Ahmedabad’s heat wave action plan. The traditional definition of a heat wave is defined with respect to a temperature deviation from the historical average. When researchers collected data from hospitals, it was clear that the reported deaths due to ‘heatstrokes’ were far lower than was anticipated. This low death rate was not a consequence of effective heat mitigation, but a reflection of the fact that there was no collection of data that holistically evaluated both weather and deaths. However, when researchers combined the total daily mortality and did a correlation study with temperature, the results were as follows^[5]

As soon as the daily temperature spiked in Ahmedabad on 5/13/10, the mortality incident witnessed a sharp peak.

This correlation is a clear indication for the implementation of a warning system that not only includes temperature, but also does a daily mortality toll. This mortality toll’s correlation is a very helpful parameter for an impact-based forecasting system. In 2018, an estimated 1,190 heat deaths were averted in India through public awareness campaigns, improved infrastructure, and city-level action plans. Initiatives included providing free drinking water, painting slum roofs white to reflect sunlight, and keeping public parks open to offer shade. All of this was made possible only when the heatwave data was analysed in conjunction with daily mortality.

Ultimately, building climate resilience is about more than just sensors and satellites. It requires assessing the risk consumption capacity of communities—including financial, physical, social, and natural resources. Surveys that evaluate exposure and vulnerability can help tailor interventions. Zurich-based initiatives like CRMC^[6] emphasise such community-centered approaches. Experts argue for a localised heat index, similar to NOAA's system in the United States^[7], tailored to India’s diverse urban and rural environments. This would help contextualise warnings—for example, recognising that temperatures in city centres or slums may be significantly higher than official records taken at airports.

South Asia’s approach to early warning systems underscores a crucial global lesson: climate resilience is not just about technology, but about collaboration, localised action, and data-driven decision-making. The success of regional frameworks like SASCOF and RSMC, alongside impact-based forecasting and community-centred interventions, highlights the need for countries to move beyond isolated efforts and embrace transnational cooperation. From cyclone warnings to heatwave action plans, integrating meteorological data with socioeconomic insights has proven to be a game-changer in minimising disaster-related losses. As climate risks intensify, expanding and refining these systems will be critical—not just for South Asia, but for the world. By prioritising shared knowledge, proactive investment, and community-driven strategies, nations can ensure that early warnings translate into meaningful, life-saving actions. As many Least Developed Countries do not have access to Early Warning of hazardous weather, the World Meteorological Organisation and UNDRR have launched the Early Warning Initiative with the aim of ensuring that everyone on the earth is protected by a lifesaving early warning system by the end of 2027.

The strength of an early warning system lies not in how early it warns, but in how equally and for it reaches—from the coast to the classroom, from the farmer to the policymaker.

^[1] United Nations Office for Disaster Risk Reduction (UNDRR). (2023). Global status of multi-hazard early warning systems 2023. https://www.undrr.org/reports/global-status-MHEWS-2023#:~:text=The%20trend%20of%20increasing%20numbers,the%20Asia%20and%20Pacific%20region.

^[2] World Meteorological Organization (WMO). (n.d.). Weather forecasts and early warnings. https://wmo.int/site/science-action/weather-forecasts-and-early-warnings

^[3] India Meteorological Department. (2020). South Asian Climate Outlook Forum (SASCOF) – April 2020. https://mol.tropmet.res.in/sascof-april-2020/

^[4]The Future of Forecasts: Impact-based Forecasting  For Early Action https://www.climatecentre.org/downloads/files/Standalone_Impact%20based%20forecasting%20guide%202020.pdf

^[5] Hess JJ, Lm S, Knowlton K, Saha S, Dutta P, Ganguly P, Tiwari A, Jaiswal A, Sheffield P, Sarkar J, Bhan SC, Begda A, Shah T, Solanki B, Mavalankar D. Building Resilience to Climate Change: Pilot Evaluation of the Impact of India's First Heat Action Plan on All-Cause Mortality. J Environ Public Health. 2018 Nov 1;2018:7973519. doi: 10.1155/2018/7973519. PMID: 30515228; PMCID: PMC6236972.

^[6] Zurich Climate Resilience Alliance. (n.d.). Climate Risk and Resilience Management Conference (CRMC). https://zcralliance.org/crmc/

^[7] National Oceanic and Atmospheric Administration (NOAA). (n.d.). Heat waves. https://www.noaa.gov/topic-tags/heat-waves