2024 Summer Lectures in Climate Data Science

May 30, 2024 - July 18, 2024

Click to see past Lectures in Climate Data Science from Spring 2024, Fall 2023, Spring 2023, and Fall 2022.

THURSDAY || MAY 30, 2024

JULIUS BUSECKE
Columbia University

TIM HERMANS 
Utrecht

Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models
When different flooding drivers co-occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections are based on only 5 to 6 climate model simulations because flooding drivers such as storm surges and river run-off need to be simulated offline using computationally expensive hydrodynamic and hydrological models. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation in Europe, enabled by data-proximate cloud computing on the LEAP-Pangeo JupyterHub. To compute storm surges for so many simulations, we apply a statistical storm surge model trained with tide gauge observations and atmospheric forcing from the ERA5 reanalysis. In this seminar, Tim Hermans (Utrecht University) & Julius Busecke (Columbia University) will present these projections, including an in-depth discussion of the statistical methods and full-cloud CMIP6 workflow that were used to develop them.

THURSDAY || JUNE 6, 2024

VERONIKA EYRING
DLR / Univ. of Bremen

Understanding and Modelling the Earth System with Machine Learning
Earth System Models (ESMs) are fundamental to understanding and projecting climate change. The models have continued to improve over the years, but systematic errors and large uncertainties in their projections remain. A large contribution to this uncertainty stems from the representation of processes such as clouds and convection that occur at scales smaller than the resolved model grid. This impacts the models’ ability to accurately project global and regional climate change, climate variability, and extremes. New approaches are required with breakthroughs expected in particular from the combination of high-resolution simulations that can resolve small-scale and fast processes, the wealth of Earth observations, and machine learning (ML) techniques. High-resolution, cloud resolving models with horizontal grid resolution of a few kilometers alleviate many biases of coarse-resolution models for deep clouds and convection, wave propagation and precipitation, but they cannot be run at climate timescales for multiple decades or longer due to high computational costs. Yet short simulations from high-resolution models can serve as information to develop ML-based parametrisations that are then incorporated into hybrid ESMs that promise to have significantly reduced systematic errors and enhanced projection capability compared to current ESMs. In contrast to km-scale climate models, ESMs incorporate important Earth system processes and feedbacks while still being fast enough to provide large ensembles important to simulate internal variability and extremes and to improve attribution and understanding. This combination can drive a paradigm shift in current Earth system modelling and analyses towards a new data-driven, yet still physics-aware, science. The key goal is a hybrid modelling approach that maintains physical consistency and realistically extrapolates to unseen climate regimes while reducing climate projection uncertainties and improving Earth system understanding. The talk presents progress in hybrid modelling work with the ICOsahedral Non-hydrostatic (ICON) atmospheric model from the European Research Council (ERC) Synergy Grant on “Understanding and Modelling the Earth System with Machine Learning (USMILE)” as well as key challenges and visions on AI-empowered next-generation multiscale climate modeling for mitigation and adaptation.

Courtney Cogburn
THURSDAY || JUNE 13, 2024

Registration link forthcoming

COURTNEY COGBURN
Columbia

Title + Abstract TBA

DATE TBD

Registration link forthcoming

{ A Professional Development Session with: }

KAITLYN LOFTUS
Columbia

THEA HEIMDAL
Columbia

THURSDAY || JUNE 27, 2024

Registration link forthcoming

MARCUS van LIER-WALQUI
Columbia

Title + Abstract TBA

THURSDAY || JULY 11, 2024

Registration link forthcoming

JOSH DeVINCENZO
NCDP / Columbia

Title + Abstract TBA

THURSDAY || JULY 18, 2024

Registration link forthcoming

CHAD SMALL
Univ. of Washington

Title + Abstract TBA