When we hear of climate change and global warming, images of ice cracking in the Arctic come to our minds and, with them, the possible catastrophes that can occur in coastal areas when the ice melts and sea levels rise. This is the classic image of a disaster caused by climate change, but there are more.
From the meteorological point of view, the relationship between climate change and natural disasters is full of examples. Because not only the ice melts, but also the oceans water heats up. An increase of ocean heat in some tropical regions can cause more convection activity producing extreme hurricanes and severe floods. Likewise, an increase of temperatures or a change in rainfall in some arid regions can cause severe droughts never experienced before. What is worse and concerning, if these two last events occur consecutively in the same region, flooding in an arid zone without vegetation -that normally holds the soil together- will lead to large landslides and, with them, great economic and natural losses.
This occurs globally, but nowadays it has local impact in areas of the planet that had never suffered catastrophes associated with this kind of extreme weather events. And many of them are not prepared. Our climate is changing and, particularly, extreme events can vary in their location and their frequency. Then, we must work to anticipate the future local impacts of this progressive global change. But, if predicting the short-term weather is sometimes tricky, how can we guess what will happen in 20, 50 or 100 years?
For this challenging task, we need the so-called Global Climate Models. These are mathematical models that take advantage of the power of computing and specialized software designed to solve the equations that describe meteorological phenomena. These models simulate what has happened since 1850 -just before the start of the Industrial Revolution- and what will happen until 2100. The outcome of Global Climate Models are simulations, analysed as with a range of different scenarios and probabilities, of climate change that help us understand the uncertainty of this phenomenon. The models take into account the initial conditions (also called internal variability), the errors due to the lack of knowledge in some physical processes and also the unknown influence of other external perturbations in the future (such as the increase of atmospheric CO2, which is the main culprit for global warming).
The results of Global Climate Models have a very low spatial resolution, since these models study the Earth in grids of about 300km2 describing global scale phenomena, reason why we need to downscale them to obtain a much larger resolution and local impact of climate change. Our partner METEOSIM oversees developing downscaling techniques and adapting this valuable information to our I-REACTOR system. They are responsible for generating, calibrating and validating models that will tell us how likely certain European regions will suffer from these extreme phenomena due to climate change.
This information will be linked to that obtained with the technologies we both exploit and develop in the project and will enable us to implement emergency prevention throughout Europe with unparalleled accuracy.
Having this data under our control is a tremendous advantage but is not the only prevention action we can take. On an individual level, we can try to reverse the road to 1850, to see if luckily, we can reduce day by day our carbon footprint and the disasters it entails.