About the Focus Area

Data science has a key role to play in climate change.

The science, policy, and communication practices around data science, machine learning, and artificial intelligence have important implications for the climate crisis and the solutions society will utilize in the future.

From machine learning to data visualization, data science techniques are used to study the effects of climate change on marine biology, land use and restoration, food systems, patterns of change in vector borne diseases, and other climate-related issues. Data science is a powerful tool to help researchers understand the uncertainties and ambiguities inherent in data, to identify interventions, strategies, and solutions that realize co-benefits for humanity and the environment, and to evaluate the multiple–and sometimes conflicting–goals of decision-makers.

DSI researchers use the methods and tools of the growing field of data science and apply them to issues relevant to climate change and the environment.


Our researchers combine techniques from data science and environmental science to understand patterns in the global food system and develop strategies that make food-supply chains more nutritious and sustainable. They look at how machine learning can reduce the uncertainty of climate models, use deep learning for climate model superresolution, and help visualize carbon emissions based on raw data. Some combine machine learning with simulations of atmospheric turbulence to develop new models that can track air pollutants and reconstruct 3D scalar fields from 2D satellite images. Others provide innovative training in environmental health sciences, including climate and health. Researchers developed a data collaborative to harness geospatial data to help characterize populations displaced by disaster. This kind of information may help with planning for and responding to large scale natural disasters associated with climate change.

DSI students complete capstone projects to apply data science techniques to real world problems. One recent project used climate data to predict heavy snowfall. Using a data set of regional climate simulations, the student team calculated the frequency of large snow storms and tracked how the storm statistics will change in the future. Another project used machine-learning methods to develop mapped estimates of surface ocean CO2 concentrations from the limited ocean data available to monitor carbon sink to predict climate change. We help our students interpret environmental data by teaching how data are managed, stored, and disseminated, as well as how they are enrolled into various narratives and models of climate change.


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