Dynamical seasonal prediction, Land Effects, and Modern Reanalysis

Host: CIRA

Presenter: Prof. Jagadish Shukla

Location: ATS 101

This seminar has three parts. The first part is a brief scientific and biographical overview from
my memoir – “A Billion Butterflies: A Life in Climate and Chaos Theory” that was published
in April, and to thank my students, professors and research colleagues at MIT, GSFC/NASA,
UMD, COLA, GMU, and other research centers in the world with whom I had the privilege of
working during the past five decades.
The second part of the seminar was inspired by a question frequently asked by students, “How
does one get research ideas?” The seminar will give a brief personal retrospective of the origins
of ideas for modern reanalysis, dynamical seasonal prediction (DSP), and the importance of
land surface processes for modelling and prediction of weather and climate.
During the mid-20th Century, the butterfly effect and the limits of weather predictability were the
dominant paradigms, indicating that dynamical seasonal prediction would not be possible. Yet
the demonstration of significant impacts of slowly varying boundary conditions of sea surface
temperature and soil wetness using fledgling climate models of early 1980s provided a scientific
basis for research on dynamical seasonal prediction (DSP). Within a decade, global coupled
Ocean-Atmosphere models succeeded in simulation and prediction of sea surface temperature for
1-2 seasons, and DSP became operational like NWP.
There are several national and international research programs and field experiments for
measurements and parametrizations of land surface processes demonstrating the importance of
land surface processes in variability and predictability of sub-seasonal and seasonal variations of
global weather and climate. What were the origins of the ideas and model experiments that led to
this recognition.
Reanalysis products have now become an indispensable data source for weather and climate
research and have recently been important for training Artificial Intelligence (AI)/Machine
Learning (ML) models. How reanalysis got started? It was not easy!
The third part of the seminar will present some recent results on the variability and predictability
of Indian summer monsoon rainfall.