معرفی
Christopher G. Fletcher is an Associate Professor cross-appointed between the Department of Geography and Environmental Management and the Department of Applied Mathematics at the University of Waterloo, where he has been faculty since 2010. He serves on the governing body for the Waterloo Climate Institute and holds affiliations with the Computational Mathematics program. Previously, he was an Assistant Professor at Waterloo (2010-2017), SHARCNET Research Chair (2010-2012), and held postdoctoral positions at the University of Toronto.
His education includes a PhD from University College London (2005), MSc from University of Reading (2001), and BSc from University of Manchester (2000). Fletcher leads a research group studying climate dynamics using global climate models to analyze variability from seasonal to centennial scales. Key research areas include:
- Arctic climate processes and snow-albedo feedbacks
- Machine learning applications to improve Earth System Models
- Extratropical teleconnections and land-ocean-atmosphere interactions
- Satellite remote sensing of snowfall and soil moisture
His publications (87+ articles) demonstrate consistent focus on Arctic climate processes, model validation, and machine learning applications in climate science. Recent work emphasizes high-resolution snow modeling, climate tipping points, and geoengineering impacts.
Honors include the SHARCNET Research Chair (2010-2012). Major grants support his research:
- NSERC Discovery Grant (2020-2024): 'Using statistical learning to build better Earth System Models'
- Microsoft AI for Social Good program funding
- Canadian Space Agency project on Arctic snowfall measurement (2018-present)
- NSERC Climate Change and Atmospheric Research networks (CanSISE, NETCARE)
Fletcher actively advises graduate students, with 4 current PhD candidates and 21+ alumni from doctoral and master's programs. His lab develops novel observational datasets to constrain climate simulations, particularly for Canada's northern regions.
