2024 Spring Lectures in Climate Data Science

Biweekly on Thursdays || Jan. 18, 2024 - May 23, 2024

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

THURSDAY || JAN. 18, 2024

MOHAMED AZIZ BHOURI (Columbia University)
KAITLYN LOFTUS (Columbia University)

LEAP Research Updates
Bhouri: “Multi-fidelity climate model parameterization for extrapolation beyond training data”
Loftus: “Parameterizing cloud microphysics with machine learning-enabled Bayesian parameter inference”

Carl Vondrick
THURSDAY || FEB. 1, 2024

CARL VONDRICK
Columbia University

Multimodal Learning from Pixels to People
People experience the world through modalities of sight, sound, words, touch, and more. By leveraging their natural relationships and developing multimodal learning methods, my research creates artificial perception systems with diverse skills, including spatial, physical, logical, and cognitive abilities, for flexibly analyzing visual data. This multimodal approach provides versatile representations for tasks like 3D reconstruction, visual question answering, and object recognition, while offering inherent explainability and excellent zero-shot generalization across tasks. By closely integrating diverse modalities, we can overcome key challenges in machine learning and enable new capabilities for computer vision, especially for the many upcoming applications where trust is required.

THURSDAY || FEB. 15, 2024

SUNGDUK YU (UC Irvine)
QINGYUAN YANG (Columbia University)

LEAP Research Updates
Yu: “ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators”
Yang: “Flexible use of additive Gaussian Processes as a powerful tool for more interpretable analysis and emulation of climate model PPEs”

Ryan Abernathey
THURSDAY || FEB. 29, 2024

RYAN ABERNATHEY
Columbia University / Earthmover

The Future of Earth-System Data Infrastructure
Responding to the climate crisis requires a coordinated mobilization of academic research, government agencies, and a rapidly growing suite of private companies (broadly described as “climate tech”) aimed at addressing climate change adaptation and mitigation through commercial products and services. At the heart of this work is data: exabytes of data about the earth system, originating from satellites, sensors, and simulations and passing through many stages of processing and refinement as they reach end-user applications.

The growth of AI is fueling an insatiable demand for data across the board while also producing new sources of data, as AI-driven forecasts begin to emerge. Earth system data has many more users and use cases than it did a decade ago.

These trends require us to rethink our approach to data infrastructure, which has traditionally emphasized a one-way exchange of data files from a few large data providers to data consumers. My talk will review exciting recent progress in moving towards a cloud-native earth-system-data ecosystem, incorporating lessons from my work on open-source software such as Xarray, Zarr, and Pangeo. I’ll conclude with a vision for how a truly frictionless global data infrastructure can enable a radically more effective response to the climate crisis while also empowering those most impacted by climate change to play a greater role in solutions.

THURSDAY || MAR. 14, 2024

FLORENCIO PORTOCARRERO (Columbia Business School)
JAEYOUNG JUNG (Columbia University)

LEAP Research Updates
Portocarrero: “Corporate responses to climate change and adaptation information: a field experimental study”
Jung: TBA

THURSDAY || MAR. 28, 2024

PATRICK HEIMBACH
UT Austin

Title + Abstract TBA

THURSDAY || APR. 11, 2024

AYA LAHLOU (Columbia University)
YONGQUAN QU (Columbia University)

LEAP Research Updates
Lahlou: “Modeling phenology under climate change using deep learning”
Qu: “Machine learning for low cloud turbulence”

THURSDAY || APR. 25, 2024

CHAD SMALL
Univ. of Washington

Title + Abstract TBA

THURSDAY || MAY 9, 2024

DION HO (Columbia University)
JIARONG WU (NYU)

LEAP Research Updates
Ho: “Data science and physical modeling of energy flows in the atmosphere”
Wu: TBA

THURSDAY || MAY 23, 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.