Talks

The timing of monsoon season onset and withdrawal is of paramount importance to the population of the Indian subcontinent. Despite the rainy season occurring annually between June and September, the onset and withdrawal dates vary by up to a month from year to year, making accurate predictions a significant challenge.

However, a revolutionary approach has been developed those promises to transform our understanding of this phenomenon. By comprehending the core physical mechanisms involved in monsoon onset and withdrawal, spatial-temporal regularities have been discovered that can be used for forecasting. This approach fundamentally diverges from the traditional numerical weather and climate models by relying on the Nonlinear Dynamics and Nonlinear Phenomena in Statistical Physics.

This approach demonstrated successful results over a rigorous nine-year testing period, forecasting the onset date up to 40 days in advance and the withdrawal date up to 70 days in advance. It is also a...

The critical phenomena occur in the vicinity of the critical point. These phenomena are indicators of an impending critical transition. Earlier methods treated the critical phenomena as early warning signals. However, they do not show any example where early warning signals have been used to avert an impending transition. They have been used in models, experiments or retrospectively.

The talk will present a perspective on how to address this challenge. I will discuss important limitations that must be accepted to build the knowledge needed for better prediction. I will apply the theory of critical transitions to a prediction of the onset and withdrawal dates of the Indian summer monsoon.

The abruptness of the onset and end of the monsoon and its interannual variability within a month are key features of the phenomenon that make monsoon forecasting extremely challenging. I will describe the main principles of monsoon timing prediction and show the cases for central India. Special ...

Carl Sagan’s iconic question, "Who speaks for Earth?" from Cosmos invites us to reflect on whose voices are heard as we contemplate humanity's place in the universe. I will explore this question from the perspective of a queer mathematician, intertwining my personal journey with the broader experiences of LGBTQ+ scientists. Our diverse identities contribute to the richness of the scientific narrative, and by embracing our queerness, we ensure that the full spectrum of experiences is represented in the pursuit of knowledge. Drawing on Sagan’s legacy, I will argue that our voices, and our kindness to each other, are not just necessary but essential in shaping the future of science and our collective understanding of the world.

Over the past decade, climate networks have emerged as a powerful tool to characterise high dimensional weather and climate datasets. Climate networks are a sparse representation of the dynamical similarities between weather time series from different geographical locations. Nodes represent the locations themselves, and network edges represent high dynamical similarity between pairs of locations. The topology of the resulting complex network encodes information about how atmospheric and oceanic dynamics “connect” different locations. For instance, strong monsoon years might yield a different network structure than weak monsoon years. With the tools of graph theory and complex networks at our disposal, we can characterise climate dynamics in novel and interesting ways, which yield, in part, results that corroborate what meteorologists already know, and, in part, results that generate new hypotheses about how atmospheric and oceanic processes influence different weather patterns. In this...

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.

Oceanic flows stir and mix tracers such heat, salt, carbon, and plankton and understanding the details of the tracer dispersion is key to developing effective parameterizations for large climate-scale models. Unfortunately, the flow structure in the ocean is highly variable as a function of spatial scales. For instance O(100 km) mesoscale flows are significantly different from O(10 km) submesoscale flows. In this talk I'll use results from a recent study to explain how tracer dispersion characteristics change as we move from large mesoscales to small submesoscales in the oceans.