Machine learning and artificial intelligence to aid climate change research and preparedness

Chris Huntingford, Elizabeth S. Jeffers, Michael B. Bonsall, Hannah M. Christensen, Thomas Lees, Hui Yang

Environmental Research Letters · 22 November 2019 · University of Oxford

In one sentence. Perspective arguing that machine learning should mine existing climate data and simulations for teleconnections and warnings, not only improve Earth system models.

Abstract

Abstract Climate change challenges societal functioning, likely requiring considerable adaptation to cope with future altered weather patterns. Machine learning (ML) algorithms have advanced dramatically, triggering breakthroughs in other research sectors, and recently suggested as aiding climate analysis (Reichstein et al 2019 Nature 566 195–204, Schneider et al 2017 Geophys. Res. Lett. 44 12396–417). Although a considerable number of isolated Earth System features have been analysed with ML techniques, more generic application to understand better the full climate system has not occurred. For instance, ML may aid teleconnection identification, where complex feedbacks make characterisation difficult from direct equation analysis or visualisation of measurements and Earth System model (ESM) diagnostics. Artificial intelligence (AI) can then build on discovered climate connections to provide enhanced warnings of approaching weather features, including extreme events. While ESM development is of paramount importance, we suggest a parallel emphasis on utilising ML and AI to understand and capitalise far more on existing data and simulations.

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