Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies

Max Callaghan (), Carl-Friedrich Schleussner, Shruti Nath, Quentin Lejeune, Thomas R. Knutson, Markus Reichstein, Gerrit Hansen, Emily Theokritoff, Marina Andrijevic, Robert J. Brecha, Michael Hegarty, Chelsea Jones, Kaylin Lee, Agathe Lucas, Nicole Maanen, Inga Menke, Peter Pfleiderer, Burcu Yesil and Jan C. Minx
Additional contact information
Max Callaghan: Mercator Research Institute on Global Commons and Climate Change
Carl-Friedrich Schleussner: Climate Analytics
Shruti Nath: Climate Analytics
Quentin Lejeune: Climate Analytics
Thomas R. Knutson: NOAA/Geophysical Fluid Dynamics Laboratory
Markus Reichstein: Max Planck Institute for Biogeochemistry
Gerrit Hansen: Robert Bosch Stiftung GmbH
Emily Theokritoff: Climate Analytics
Marina Andrijevic: Climate Analytics
Robert J. Brecha: Climate Analytics
Michael Hegarty: Climate Analytics
Chelsea Jones: Climate Analytics
Kaylin Lee: Climate Analytics
Nicole Maanen: Climate Analytics
Inga Menke: Climate Analytics
Peter Pfleiderer: Climate Analytics
Burcu Yesil: Climate Analytics
Jan C. Minx: Mercator Research Institute on Global Commons and Climate Change

Nature Climate Change, 2021, vol. 11, issue 11, 966-972

Abstract: Abstract Increasing evidence suggests that climate change impacts are already observed around the world. Global environmental assessments face challenges to appraise the growing literature. Here we use the language model BERT to identify and classify studies on observed climate impacts, producing a comprehensive machine-learning-assisted evidence map. We estimate that 102,160 (64,958–164,274) publications document a broad range of observed impacts. By combining our spatially resolved database with grid-cell-level human-attributable changes in temperature and precipitation, we infer that attributable anthropogenic impacts may be occurring across 80% of the world’s land area, where 85% of the population reside. Our results reveal a substantial ‘attribution gap’ as robust levels of evidence for potentially attributable impacts are twice as prevalent in high-income than in low-income countries. While gaps remain on confidently attributabing climate impacts at the regional and sectoral level, this database illustrates the potential current impact of anthropogenic climate change across the globe.

Date: 2021
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DOI: 10.1038/s41558-021-01168-6

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