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Machine learning and artificial intelligence to aid climate change research and preparedness
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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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“This era of”
From Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives (2024) · cites this paper as Huntingford et al., 2019
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“AI has significant potential to enhance the predictive skill of climate extremes across timescales, possibly advancing our scientific understanding and societal preparedness (Huntingford et al., 2019).”
From Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives (2024) · cites this paper as Huntingford et al., 2019
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“Concerning climate change in the urban context, some studies addressed mitigation concerning urban planning, mobility, and land use (e.g., Ref. [211,212]).”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [212]
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“AI applications may also upscale climate engagement of the public and stimulate collective action by predicting or visualising climate change risks and aid decision-support efforts by monitoring extreme weather disasters (remotely and) in real-time (Alemany et al., 2019; Huntingford et al., 2019; Walsh et al., 2020).”
From Deploying artificial intelligence for climate change adaptation (2022) · cites this paper as Huntingford et al., 2019
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“However, recent advancements enable scientists to incorporate AI into climate change adaptation (Huntingford et al., 2019).”
From Deploying artificial intelligence for climate change adaptation (2022) · cites this paper as Huntingford et al., 2019
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“Huntingford et al. (2019), who encouraged the use of models and data-driven machine learning to provide warnings and decision-making support, notably drought predictions.”
From Deploying artificial intelligence for climate change adaptation (2022) · cites this paper as Huntingford et al. (2019)
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Metadata
- Authors
- Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., & Yang, H.
- Affiliations
- UK Centre for Ecology & Hydrology (GB)
- University of Oxford (GB)
- Peking University (CN)
- ORCID
- Venue
- Environmental Research Letters
- Publisher
- IOP Publishing
- Published
- 2019-11-22
- Volume
- 14
- Issue
- 12
- Pages
- 124007
- ISSN
- 1748-9326 (ISSN-L)
- OpenAlex
- W2990621239
- Cited by
- 469
- References
- 116
- Citations by year
- 2026: 65 · 2025: 84 · 2024: 90 · 2023: 66 · 2022: 55 · 2021: 89 · 2020: 20
- FWCI
- 21.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Environmental Research Letters (publishedVersion, cc-by)
- NERC Open Research Archive (Natural Environment Research Council) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Greater South Information System
- Funders
- Natural Environment Research Council (CEH National Capability Fund)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- machine learning, artificial intelligence, climate change, climate change adaptation, climate feedbacks, Earth system models, extreme weather events, early warning systems, climate data analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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