Our collaborators

Members of the Virtual Earth Systems Research Institute

ICCS supports members of the Virtual Earth Systems Research Institute (VESRI). We apply our expertise in climate sciences and artificial intelligence with the VESRI teams to address their specific computation and research software needs in the area of climate modelling.

DataWave: Collaborative Gravity Wave Research

The DataWave project is an international consortium focused on improving our modelling capability for gravity waves and large scale circulation, particularly to lead novel observationally constrained and data-driven gravity wave parameterisation schemes. Results from this project will enable better predictions of how atmospheric circulation responds to global warming and impacts subseasonal-to-seasonal forecasts.

LEMONTREE: Land Ecosystem Models based On New Theory, obseRvations, and ExperimEnts

LEMONTREE is an international consortium developing a next-generation model of the terrestrial biosphere and its interactions with the carbon cycle, water cycle and climate. Their approach is to create ecosystem models that rest on firm theoretical and empirical foundations, and eventually, more reliable projections of future climates and a newfound ability to address issues in sustainability.

M²LInES: Multiscale Machine Learning In Coupled Earth System Modeling

M²LInES is a large international collaborative project with the goal of improving climate projections, using scientific and interpretable Machine Learning (ML) to capture unaccounted physical processes at the air-sea-ice interface. ML will guide the development of innovative, physics-guided, and interpretable representations of these complex processes directly from data for use in global climate simulations.

SASIP: The Scale-Aware Sea Ice Project

SASIP is an international consortium that is developing a scale-aware continuum sea ice model for climate research; one that faithfully represents sea ice dynamics and thermodynamics and that is physically sound, data-adaptive, highly parallelised and computationally efficient. SASIP is using machine learning and data assimilation to exploit large datasets obtained from both simulations and remote sensing.

Carbon Loss in Plants, Soils, and Oceans (CALIPSO)

Led by research teams at the Université de Versailles Saint-Quentin-en-Yvelines, University of East Anglia, and University of Exeter, CALIPSO quantifies vulnerabilities of terrestrial and oceanic carbon stocks under climate change. By integrating novel observations, theoretical understanding, and machine learning tools, the project assesses risks associated with carbon cycle tipping points and provides refined emissions reduction strategies.

Fate, Emissions, and Transport of CH₄ (FETCH₄)

Led by the University of Washington and the University of Rochester, FETCH₄ aims to improve understanding of the historic and modern methane cycle. The consortium utilises unique chemical fingerprints, satellite observations, and machine learning models to enhance data representation of methane in global climate models. Measuring methane is important to mitigating and understanding climate change, as it traps heat in the atmosphere, exacerbating warming and contributing to air pollution.

CliMA: Climate Modeling Alliance

CliMA is building a new Earth system model that leverages recent advances in the computational and data sciences to learn directly from a wealth of Earth observations from space, from the ground, as well as high-resolution simulations spun off on the fly.

Multi-institutional collaborations

Forecasting Tipping points In Marine Biogeochemistry and Ecosystem Responses (TiMBER)

This is a collaboration between ICCS, the University of East Anglia, Cefas, the National Oceanography Centre and the Scottish Association for Marine Science. It aims to understand and predict the little-known ‘tipping points’ in marine ecosystems due to climate change, and their consequences and opportunities for the UK.

Industry collaborations

Inigo Storm Prediction and Impact Research (InSPIRe)

Inigo is funding a three-year research programme at ICCS. It will apply the latest advances in computing and artificial intelligence to deepen understanding of how climate change has affected the risk of hurricanes. These kinds of extreme weather events have a devastating impact on communities around the world and are a significant driver of insurance claims.