Guest Speaker:

Dr. Aneesh Subramanian
Assistant Professor of Atmospheric and Oceanic Sciences at CU Boulder

When and where:

February 21, 2023 4p - 5p (est) | Virutal

Abstract:

Convection and cloud processes play a key role in the dynamics of the atmosphere just as mesoscale turbulence and deep convection do in ocean dynamics and ice-shelf processes do in ice sheet dynamics. Yet, even today our shortcomings in parameterizing these subgrid scale processes in global climate models (GCMs) are limiting our ability to simulate and understand the climate and weather of the planet. Recent innovative ideas on convection parameterization such as super-parameterization (embedding cloud-resolving models within the GCM grid), stochastic-parameterization or machine learning emulation in weather and climate models have helped improve its representation of the climate and weather systems. These approaches in parameterization have emerged as new paths forward and complement the conventional approaches rather than replace them. We study the impact of these approaches on forecasts from weather to climate timescales. Results from studies using stochastic parameterization in ensemble forecasting systems as well as machine learning approaches for causal discovery, feature detection and ensemble prediction post-processing will be presented. In addition, results from using machine learning approaches for stochastic parameterization of subgrid-scale variability in an idealized system will be presented to motivate future studies in this direction with the weather and climate forecasting systems. This has implications on improving conventional parameterization using hybrid approaches as we await the exascale computing systems of the future to resolve key processes in climate models.

Learn more about Dr. Aneesh Subramanian

Dr. Aneesh Subramanian, Assistant Professor of Atmospheric and Oceanic Sciences at CU Boulder, as well as iHARP Co-PI. He is also a visiting scientist at the Center for Western Weather and Water Extremes at Scripps Institution of Oceanography, UC San Diego, and a visiting scholar in the Predictability of Weather and Climate group in the Physics Dept. at the University of Oxford. Dr. Subramanian brings expertise in climate dynamics and recent experience using machine learning techniques for varied climate science applications.