Living with extreme climate events

India needs early warning systems at the neighbourhood scale to plan for the alleviation of the impact of extreme climate events on the economy

climate change, cyclone
India has invested over Rs 1,000 crore since 2009 on establishing and improving weather and climate models, which have yielded impressive results such as improved forecasts of weather, cyclones, and the monsoon. (IE)

By Raghu Murtugudde

Headlines tend to focus on how extreme events such as heatwaves have been made worse by global warming and how much worse they will get in the future. It is clear that the future is already here and heatwaves are becoming worse year after year, especially over North and North West India. There has to be greater attention on protecting people, crops, and forests, and other living species from heatwaves. The most effective tool for managing heatwaves and other extreme events is the Early Warning System, at decision-timescales and at impact spatial-scales. The timescales are days to weeks and spatial scales are at the kilometre level.

The necessary scientific understanding exists for delivering early warning systems at critical timescales, and models are already delivering some of this. Even as IMD’s skill on heatwave early warnings continues to improve, the last-mile gaps are still too large in making this information usable at the requisite temporal and spatial scales. Some background on the anatomy of heatwaves in the Indian subcontinent can highlight the challenges in improving the forecasts further.

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Weather and climate models have made amazing advances in the last few decades. With the only ‘given’ being the energy from the sun at the top of the atmosphere, these models can predict all features of weather and climate, from the monsoon to active and break periods, El Niños and La Niñas, heatwaves, heavy rains, etc. Even as the models continue to get better, there are some shortcomings that are inevitable. Models have a high accuracy in predicting temperatures at continental scales but are not as sharp at the local levels. They are much less sharper at predicting rainfall at any scale.

India has invested over Rs 1,000 crore since 2009 on establishing and improving weather and climate models, which have yielded impressive results such as improved forecasts of weather, cyclones, and the monsoon. Cyclone forecasts save lives and property and greatly alleviate the worst of impacts. Far too many lives and properties are still lost each year, requiring that the forecasts system be made end-to-end to ensure that the improved forecasts reduce exposure, vulnerabilities, and risk for all.

When it comes to extreme events, two issues need urgent attention. The first is skillful forecasting over a continuum of timescales from short (1-3 days) and medium (3-10 days) to extended (2-4 weeks) ranges. The second is the downscaling of the forecast—typically at the order of 10 km—to neighbourhood scales. The three timescales together are generally referred to as subseasonal-to-seasonal or S2S forecasts. Subseasonal refers to timescales shorter than a season and is best thought of as the active/break periods of the monsoon. Other seasons over India are also dominated by this intrinsic subseasonal timescale.

India’s forecast system continues to stay integrated with a unified system to deliver the forecasts at S2S timescales . The use of the S2S forecasts on the ground requires a so-called Ready-Set-Go approach developed under the auspices of the World Climate Research Program under the World Meteorological Organization. India is a member of the international S2S project. Ground-level adaptation of the Ready-Set-Go framework for managing heatwaves and other extremes is an urgent need for the country.

The extended-range forecasts serve the Ready step where resources are prepared in response to the longer-lead forecasts—the cooling centres, hydration packs, the hospitals, etc. The medium-range forecasts bring more specific information on where heatwaves are most likely and serve the Set step—preparing the workforce to handle the disaster and run a systems-check. The Go step relies on the skillful short-range forecasts for action on prevention, rescue and recovery.

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The key step to sharpen the forecasts of the models, from scales of ~10 km to the village level of ~10 metres, relies on big data approaches using AI and machine learning. Many examples exist in India on such scaling; for example, the IMD forecasts have been rendered at farm-scales to issue advisories for irrigation and shown to save water without reducing crop yields. Such early warnings at scale have to be produced for water, agriculture, energy, transportation and health to manage the extreme events in temperature, rainfall, cyclones, and so on.

Once the Ready-Set-Go system is fully in place, all citizens need to be trained to ingest the information and reduce their own exposure and vulnerabilities to hydroclimatic hazards. Morbidities, mortalities, and losses in productivity and crop yield cost India significantly each year. Indian scientists, private sector, NGOs, and the government must work on advancing forecasts skills, focussed forecasts, and bringing science and technology to serve the public . India’s sustained economic growth will be a great challenge without such early warning systems.

The writer is Emeritus professor, University of Maryland, and visiting professor, IIT-Bombay

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First published on: 09-03-2023 at 04:15 IST