Search Talks

I will discuss two fundamental systems in moist geophysical fluid dynamics: (1) The moist shallow water equations, giving rise to a theory of the MJO, and (2) A idealised system of moist convection, the 'Rainy-Benard Model'.

The tropical climate varies in a wide range of spatial and temporal scales. The tropical climate variability is more complex than the climate of the mid-latitudes because of the important role played by latent heat of condensation. The land-sea contrast theory that was proposed more than 300 years ago has been shown to be inadequate. A new paradigm based on the moist static energy budget has gained currency. I will discuss how this new paradigm provides new insight into the variability of the monsoon and the role land and oceans play in the evolution of tropical climate variability

The main focus of these pedagogical talks will be on discussing the interplay between statistics and climate science as a two-way street. On one hand, thinking about the climate helps us understand many aspects of statistics, from the fundamental to conceptual to practical. On the other, statistical thinking is crucial and indispensable in studying climate. I will also emphasize that statistics plays an important role not just in climate studies, but more generally in understanding any complex system such as those from biological and social sciences as well. Another thread will be the discussion of interplay between uncertainty and dynamics, with an emphasis on the role of dynamical instabilities.

A brief tour of Chaos in 1-dimensional maps followed by a quick primer on Machine Learning. This will help researchers in Climate Science as there is an increasing use of AI/ML methods in this domain.

Climate networks can be used to forecast some important climate phenomena, such as the monsoon, the North Atlantic Oscillation, El Niño events and cyclones. A percolation framework is used to study the cluster structure properties which brings out the global structural changes in the climate network.

I present the recurrence analysis of temperature and relative humidity data from various locations spread over India, including the mountainous region, coastal region, and central and north eastern parts of India. This study reveals the spatiotemporal pattern underlying the climate dynamics and captures the variations in the complexity of the dynamics over the period 1948 to 2022. By reconstructing the dynamics from data, the recurrence pattern is studied using recurrence networks and the measures of the networks computed using a sliding window analysis on the data sets. This brings out the climate variability in different spatial locations and the heterogeneity across the locations chosen. The variations observed in dynamics can be correlated with reported shifts in the climate related to strong and moderate El Niño–Southern Oscillation events.

Climate change is often related to the temporal variation of external driving forces, following a certain arbitrary trend. This poses difficulties to the analysis of complex dynamical systems under the impact of climate change, since all the analysis tools of nonlinear dynamics work only for autonomous systems or systems with periodic driving. To study the impact of climate change characterized by arbitrary time- dependence requires new methods to still use ideas of attractors, basins of attraction and bifurcations in the non-autonomous case. We discuss approaches which allow to study non-autonomous systems in the spirit of nonlinear dynamics: snapshot/pullback attractors, non-autonomous basins of attractions and bifurcations in non-autonomous systems like rate-induced transitions and basin boundary crossings. We use simple conceptual models of climate and ecosystem dynamics to illustrate these concepts.

The main focus of these pedagogical talks will be on discussing the interplay between statistics and climate science as a two-way street. On one hand, thinking about the climate helps us understand many aspects of statistics, from the fundamental to conceptual to practical. On the other, statistical thinking is crucial and indispensable in studying climate. I will also emphasize that statistics plays an important role not just in climate studies, but more generally in understanding any complex system such as those from biological and social sciences as well. Another thread will be the discussion of interplay between uncertainty and dynamics, with an emphasis on the role of dynamical instabilities.