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DataWave: Collaborative Gravity Wave Research

The DataWave project is an international consortium focused on improving our modeling capability for gravity waves and large scale circulation, particularly to lead novel observationally constrained and data-driven gravity wave parameterization 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 parallelized 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 utilizes 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.


About Us

Computational modelling is key to climate science. But models are becoming increasingly complex as we seek to understand our world in more depth and model it at higher fidelity. The Institute of Computing for Climate Science studies and supports the role of software engineering, computer science, artificial intelligence, and data science within climate science.

The institute comprises a collaboration between Cambridge Zero, the Departments of Applied Mathematics and Theoretical Physics (host department of the institute), Computer Science and Technology, and University Information Services at the University of Cambridge.

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