The objective of the present study is to construct data based models of climate networks, and analyze them using tools of statistical physics. We note that extensive data on climate variables and on parameters which affect the climate has been collected almost all over the earth, over a large number of years. Data is available for numerous parameters such as air and surface temperatures, annual and daily precipitation, snowfall and wind velocities etc. The data span huge geographic regions, and are also available for many decades.
The organization and analysis of this data and to use it for the purposes of prediction is an important problem, and also a herculean task which has been occupying climate researchers for years. The tools of complex systems theory have assumed great importance for this analysis. In this project, we plan to use network theory for the construction of climate networks based on actual climate data, and use the techniques of statistical physics for their analysis. As the German side is experienced in handling climate data, they would share their knowledge of it with the Indian partners to ensure that the network construction is performed appropriately and provide geophysical features of the results on the network measures. On the other hand, the Indian side would construct order parameters and investigate the occurrences of microtransitions on the constructed networks to identify phase transitions which occur in the system, which is beneficial to the German side to investigate the variability in the monsoon and El Nino timings.
The data collection, analysis and development of the methodologies for the prediction of extreme events in climate dynamics would be performed by early stage researchers, in particular, PhD students and Post Docs. This research would train them in dealing with a challenging problem of prediction of these extreme climate events using complex network tools analysis using statistical physics. This international exchange within the research would benefit the young academics to explore avenues of the implementing the novel methodologies developed in this project in various areas of their future research. The research stays are also arranged such that they benefit the most from this collaborative project.
Successful prediction of extreme climate events using the novel methodology would result in further strengthening of the ties between the two research groups with respect to identification of other fields of science and industry where similar transitions are observed. Subsequently, this would lead to further collaborations between the partner countries at the academic level.
