activity
20192023
most citedModel calibration using ESEm v1.0.0 -- an open, scalable Earth System Emulator

66 citations · 74 across the 7 of their papers we have counts for

collaborators

7 papers

stat.AP2023

Statistical constraints on climate model parameters using a scalable cloud-based inference framework

James Carzon, Bruno R. de Abreu, Leighton Regayre +5

Atmospheric aerosols influence the Earth's climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate…

cs.LG2022★ 3 cited

Physics-Informed Learning of Aerosol Microphysics

Paula Harder, Duncan Watson-Parris, Philip Stier +3

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of u…

cs.LG2022★ 1 cited

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions

Andrew Jesson, Alyson Douglas, Peter Manshausen +5

Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses…

physics.ao-ph2021★ 1 cited

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific

Andrew Jesson, Peter Manshausen, Alyson Douglas +3

Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters a cloud and acts as cloud condensation nuclei (CCN). An increase in CCN results in a decre…

cs.LG2021★ 2 cited

Emulating Aerosol Microphysics with Machine Learning

Paula Harder, Duncan Watson-Parris, Dominik Strassel +3

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of u…

physics.ao-ph2021★ 66 cited

Model calibration using ESEm v1.0.0 -- an open, scalable Earth System Emulator

Duncan Watson-Parris, Andrew Williams, Lucia Deaconu +1

Large computer models are ubiquitous in the earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core-hours to run to completio…

physics.ao-ph2019★ 1 cited

Detecting anthropogenic cloud perturbations with deep learning

Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen +3

One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud drop…