Subimal Ghosh, faculty member of IIT-Bombay, has been working on localised climate change models. He speaks to Dinsa Sachan about extreme rainfall events and the need to generate more localised models to cope with climate change impact
Are climate change and extreme rainfall events connected?
There's enough data now to prove there's a relationship between changing climate and extreme rainfall events. However, this is not as straightforward as we think. Climate change does not necessarily intensify and increase the frequency of extreme rainfall events everywhere. In our study, which analyses data of 50 years, we've found that there's an increased spatial variability across India, with non-uniform trend. While extreme rainfall events have increased in some areas, they have decreased in others.
Can you give an example?
In one of my studies, which I conducted in collaboration with Auroop Ganguly in North Eastern University in Boston, we found that 50 per cent of places in India show increasing trend in extremes, 30 per cent show decreasing trend and 20 per cent show no trend. This is an example of spatial variability in trends. We need to investigate the reason for this. We are planning to do some computational experiments with rainfall data from Mumbai and Alibag in Maharashtra. Alibag is a rural area, 30 km south of Mumbai. We need to compare the results from extreme value analysis for both the stations to see the impacts of urbanisation on rainfall extremes through urban heat islands (high temperature in urban areas).
How can these differences in rainfall patterns be explained?
Differences mostly occur due to local factors like urbanisation and land-use changes. For example, a city like Mumbai has a lot of buildings. Buildings are made of concrete, which absorb more heat, so less solar radiation is reflected into the atmosphere. This creates an urban heat island in the city and creates a low pressure area over it. This initiates local scale processes and alters the rate and frequency of precipitation. We need to examine data of Mumbai and Alibag for a definite conclusion. The increase of spatial variability motivates us to go for regional climate models.
What are regional climate models and what are their uses?
General Circulation Models (GCM) are large-scale climate models, with a resolution of up to 350 km/350 km. Rainfall is a very localised event, which varies every 10-100 km. So, decisions on water resource management should not be based on GCM data. We need finer-scale, regional climate models such as statistical downscaling or dynamic downscaling, through which a relationship can be established between global model outputs and regional meteorological variables. The regional projections, derived using these models, can be used in water resource management. For instance, decisions regarding constructing dams can be based on such models.
You've been on work on your localised climate model? What are its drawbacks?
While our downscaling model is good at mapping spatial variability, it has not been found to be that good with temporal variability. This is a major limitation in most of the models.
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