The Climate Analytics Lab aims to reduce uncertainty in the role of anthropogenic aerosol on the climate by exploiting recent advances in machine learning to enable improved projections and gain new insights from observations.
Project Highlights
Detecting ship tracks
Using machine learning to automatically detect the brightening effect that shipping can have on clouds.
Model Emulation for Calibration
Developing climate model emulators for better parameter estimation and calibration.
News
- 24/11 - New research in Science which uncoverd a new mechanism of cloud reduction through aerosol pollution was highlighted in UCSD Today!
- 24/10 - Our research on the climate impacts of recent shipping emissions changes on the record breaking temperatures of 2023 was discussed in Science.
- 24/10 - LaKeta Kemp joined CAL this year as a Data Science PhD student with a GEM Fellowship. Welcome LaKeta!
- 24/03 - “Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling” has been accepted to ICML 2024! It introduces a novel multi-fidelity neural process and apply it to climate projections - generating a temperature projection of ERA5 out to 2100. The paper is available on arXiv.
- 24/03 - Prof. Watson-Parris gave a talk at the 2024 American Association for the Advancement of Science (AAAS) Annual Meeting in Denver on the use of Generative AI for Climate Science. It was covered by ACM.