Talks

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.

The failure to calculate the vacuum energy remains a central problem in theoretical physics. In my talk I present a new understanding of the cosmological constant problem, grounded in the insight that vacuum energy density can be expressed in terms of phase space volume. Introduction of a UV-IR regularization implies a relationship between the vacuum energy and entropy. Combining this insight with the holographic bound on entropy then yields a bound on the cosmological constant consistent with observations. It follows that the universe is large, and the cosmological constant is naturally small, because the universe is filled with a large number of degrees of freedom. The talk is based on our papers Phys.Rev.D 107 (2023) 12, 126016; e-Print: 2212.00901 [hep-th] and Int.J.Mod.Phys.D 32 (2023) 14, 2342004; e-Print: 2303.17495 [hep-th].