Machine learning and artificial intelligence to aid climate change research and preparedness
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The era of big data has fueled the application of artificial intelligence (AI) in many domains of Earth science.
“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
The Introduction links large Earth-system datasets to ML and AI, and ‘Existing AI applications to climate’ says many researchers use ML to study specific Earth-system components. But the paper distinguishes ML from AI and describes broader AI applications largely as potential, so it does not establish that big data has already fueled AI applications across many domains of Earth science. Page numbers are not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI has significant potential to enhance the predictive skill of climate extremes across timescales, possibly advancing our scientific understanding and societal preparedness.
“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
Concerning climate change in the urban context, some studies addressed mitigation concerning urban planning, mobility, and land use.
“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]
AI applications may 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 in real-time.
“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
Recent advancements enable scientists to incorporate AI into climate change adaptation.
“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
Models and data-driven machine learning can provide warnings and decision-making support, notably drought predictions.
“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)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The ERA‐Interim reanalysis: configuration and performance of the data assimilation system
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., et al. (2011) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.828 · Cited by 24,480 · see citing works
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384 · see citing works
Climate Change 2007: The Physical Science Basis.
Schiavon, S., & Zecchin, R. (2007) · eScholarship (California Digital Library) · Cited by 16,994 · see citing works
An Overview of CMIP5 and the Experiment Design
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052 · see citing works
(2012) · Cited by 9,062 · see citing works
(2013) · Cited by 6,752 · see citing works
Deep learning and process understanding for data-driven Earth system science
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat (2019) · Nature · doi:10.1038/s41586-019-0912-1 · Cited by 5,673 · see citing works
Tipping elements in the Earth’s climate system
Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., & Schellnhuber, H. J. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705414105 · Cited by 3,982 · see citing works
Trajectories of the Earth System in the Anthropocene
Steffen, W., Rockström, J., Richardson, K., Lenton, T. M., Folke, C., Liverman, D., Summerhayes, C. P., Barnosky, A. D., Cornell, S. E., Crucifix, M., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810141115 · Cited by 3,139 · cited 1× · see citing works
“The most commonly suggested are tipping points (Lenton et al 2008), where the Earth System changes disproportionately for small atmospheric GHG increases, even potentially triggering runaway climate change (Steffen et al 2018).”
BackgroundAn assessment of the effectiveness of a random forest classifier for land-cover classification
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J. S., Chica‐Olmo, M., & Rigol-Sánchez, J. P. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2011.11.002 · Cited by 2,963 · see citing works
Statistical Analysis in Climate Research
von STORCH, H., & Zwiers, F. W. (1999) · Cambridge University Press eBooks · doi:10.1017/cbo9780511612336 · Cited by 2,761 · see citing works
Recent decline in the global land evapotranspiration trend due to limited moisture supply
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J., Goulden, M. L., Bonan, G. B., Cescatti, A., Chen, J., de Jeu, R., et al. (2010) · Nature · doi:10.1038/nature09396 · Cited by 2,284 · see citing works
Neural ordinary differential equations
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., & Duvenaud, D. (2018) · arXiv · doi:10.48550/arxiv.1806.07366 · Cited by 2,097 · see citing works
Superintelligence: paths, dangers, strategies
(2015) · Choice Reviews Online · doi:10.5860/choice.187536 · Cited by 1,999 · see citing works
Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation
Vallis, G. K. (2017) · doi:10.1017/9781107588417 · Cited by 1,891 · see citing works
Change, I. P. O. C. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781107415324.020 · Cited by 1,836 · see citing works
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Rasmussen, C. E., & Williams, C. K. I. (2005) · The MIT Press eBooks · Cited by 1,829 · see citing works
Untangling aerosol effects on clouds and precipitation in a buffered system
Stevens, B., & Feingold, G. (2009) · Nature · doi:10.1038/nature08281 · Cited by 1,187 · see citing works
Slowing down as an early warning signal for abrupt climate change
Dakos, V., Scheffer, M., van Nes, E. H., Brovkin, V. A., Petoukhov, V. K., & Held, H. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0802430105 · Cited by 1,036 · see citing works
Vegetation demographics in Earth System Models: A review of progress and priorities
Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O., Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence, P. J., et al. (2018) · Global Change Biology · doi:10.1111/gcb.13910 · Cited by 768 · see citing works
Heavier summer downpours with climate change revealed by weather forecast resolution model
Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., & Senior, C. A. (2014) · Nature Climate Change · doi:10.1038/nclimate2258 · Cited by 761 · see citing works
Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability
Cox, P. M., Pearson, D., Booth, B. B. B., Friedlingstein, P., Huntingford, C., JONES, C. D., & Luke, C. M. (2013) · Nature · doi:10.1038/nature11882 · Cited by 742 · see citing works
Self-citationTerrestrial biogeochemical feedbacks in the climate system
Arneth, A., Harrison, S. P., Zaehle, S., Tsigaridis, K., Menon, S., Bartlein, P. J., Feichter, J., Korhola, A. A., Kulmala, M., O’Donnell, D., et al. (2010) · Nature Geoscience · doi:10.1038/ngeo905 · Cited by 586 · see citing works
A roadmap for improving the representation of photosynthesis in Earth system models
Rogers, A., Medlyn, B. E., Dukes, J. S., Bonan, G. B., von Caemmerer, S., Dietze, M. C., Kattge, J., Leakey, A. D. B., Mercado, L. M., Niinemets, Ü., et al. (2016) · New Phytologist · doi:10.1111/nph.14283 · Cited by 561 · see citing works
Do alternative energy sources displace fossil fuels?
York, R. (2012) · Nature Climate Change · doi:10.1038/nclimate1451 · Cited by 560 · see citing works
Complex networks reveal global pattern of extreme-rainfall teleconnections
Boers, N., Goswami, B., Rheinwalt, A., Bookhagen, B., Hoskins, B. J., & Kurths, J. (2019) · Nature · doi:10.1038/s41586-018-0872-x · Cited by 502 · see citing works
Could Machine Learning Break the Convection Parameterization Deadlock?
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G. (2018) · Geophysical Research Letters · doi:10.1029/2018gl078202 · Cited by 497 · see citing works
Allen, M. R. (2003) · Nature · doi:10.1038/421891a · Cited by 481 · see citing works
Progressing emergent constraints on future climate change
Hall, A. D., Cox, P. M., Huntingford, C., & Klein, S. A. (2019) · Nature Climate Change · doi:10.1038/s41558-019-0436-6 · Cited by 462 · see citing works
Self-citationReconciling controversies about the ‘global warming hiatus’
Medhaug, I., Stolpe, M. B., Fischer, E. M., & Knutti, R. (2017) · Nature · doi:10.1038/nature22315 · Cited by 456 · see citing works
Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather
McGovern, A., Elmore, K. L., Gagne, D. J., Haupt, S. E., Karstens, C. D., Lagerquist, R., Smith, T. M., & Williams, J. K. Y. (2017) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-16-0123.1 · Cited by 455 · see citing works
Zhu, C., Kobayashi, K., Loladze, I., Zhu, J., Jiang, Q., Xu, X., Liu, G., Seneweera, S. P., Ebi, K. L., Drewnowski, A., et al. (2018) · Science Advances · doi:10.1126/sciadv.aaq1012 · Cited by 449 · see citing works
Contrasting responses of water use efficiency to drought across global terrestrial ecosystems
Yang, Y., Guan, H., Batelaan, O., McVicar, T. R., Long, D., Piao, S. L., Liang, W., Liu, B., Jin, Z., & Simmons, C. T. (2016) · Scientific Reports · doi:10.1038/srep23284 · Cited by 425 · see citing works
Climatic Change: Are We on the Brink of a Pronounced Global Warming?
Broecker, W. S. (1975) · Science · doi:10.1126/science.189.4201.460 · Cited by 411 · cited 1× · see citing works
“Evidence is accumulating that fossil fuel burning is adjusting climate (IPCC 2013), as projected by many e.g. Broecker (1975), requiring accurate projections to aid adaptation.”
BackgroundEmergent constraint on equilibrium climate sensitivity from global temperature variability
Cox, P. M., Huntingford, C., & Williamson, M. S. (2018) · Nature · doi:10.1038/nature25450 · Cited by 411 · see citing works
Self-citationGRUN: an observation-based global gridded runoff dataset from 1902 to 2014
Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2019) · Earth system science data · doi:10.5194/essd-11-1655-2019 · Cited by 402 · see citing works
Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern
Kornhuber, K., Osprey, S., Coumou, D., Petri, S., Petoukhov, V. K., Rahmstorf, S., & Gray, L. J. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab13bf · Cited by 397 · see citing works
Plant secondary metabolites: a key driver of litter decomposition and soil nutrient cycling
Chomel, M., Larchevêque, M., Fernandez, C., Gallet, C., DesRochers, A., Paré, D., Jackson, B. G., & Baldy, V. (2016) · Journal of Ecology · doi:10.1111/1365-2745.12644 · Cited by 395 · see citing works
Schneider, T., Lan, S., Stuart, A., & Teixeira, J. (2017) · Geophysical Research Letters · doi:10.1002/2017gl076101 · Cited by 391 · see citing works
Satellite-based assessment of yield variation and its determinants in smallholder African systems
Burke, M., & Lobell, D. B. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1616919114 · Cited by 389 · see citing works
Tokdar, S. T., & Kass, R. E. (2009) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.56 · Cited by 381 · see citing works
Drought forecasting using feed-forward recursive neural network
Mishra, A. K., & Desai, V. R. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2006.04.017 · Cited by 374 · see citing works
Calcium Oxalate: Occurrence in Soils and Effect on Nutrient and Geochemical Cycles
Graustein, W. C., Cromack, K., & Sollins, P. (1977) · Science · doi:10.1126/science.198.4323.1252 · Cited by 365 · see citing works
Statistical downscaling of GCM simulations to streamflow using relevance vector machine
Ghosh, S., & Mujumdar, P. P. (2007) · Advances in Water Resources · doi:10.1016/j.advwatres.2007.07.005 · Cited by 354 · see citing works
Park, S., Im, J., Jang, E., & Rhee, J. (2015) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2015.10.011 · Cited by 342 · see citing works
Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models
Drijfhout, S., Bathiany, S., Beaulieu, C., Brovkin, V., Claussen, M., Huntingford, C., Scheffer, M., Sgubin, G., & Swingedouw, D. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1511451112 · Cited by 332 · see citing works
Self-citationDeo, R. C., & Şahin, M. (2015) · Atmospheric Research · doi:10.1016/j.atmosres.2014.10.016 · Cited by 317 · see citing works
Global energy growth is outpacing decarbonization
Jackson, R. B., Quéré, C. L., Andrew, R. M., Canadell, J. G., Korsbakken, J. I., Liu, Z., Peters, G. P., & Zheng, B. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aaf303 · Cited by 317 · see citing works
September sea-ice cover in the Arctic Ocean projected to vanish by 2100
Boé, J., Hall, A. D., & Qu, X. (2009) · Nature Geoscience · doi:10.1038/ngeo467 · Cited by 307 · see citing works
Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate DatasetsPreprint
Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D. A., Kunkel, K. E., Wehner, M. F., & Collins, W. D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01156 · Cited by 300 · see citing works
Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS
Goyal, M. K., Bharti, B., Quilty, J., Adamowski, J., & Pandey, A. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.02.047 · Cited by 292 · see citing works
A Common Framework for Approaches to Extreme Event Attribution
Shepherd, T. G. (2016) · Current Climate Change Reports · doi:10.1007/s40641-016-0033-y · Cited by 289 · see citing works
Dijkstra, H. A. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9781139034135 · Cited by 274 · see citing works
The PERSIANN family of global satellite precipitation data: a review and evaluation of products
Nguyen, P., Ombadi, M., Sorooshian, S., Hsu, K., AghaKouchak, A., Braithwaite, D. K., Ashouri, H., & Thorstensen, A. (2018) · Hydrology and earth system sciences · doi:10.5194/hess-22-5801-2018 · Cited by 264 · see citing works
Mathematical models in the applied sciences
(1998) · Choice Reviews Online · doi:10.5860/choice.36-2202b · Cited by 263 · see citing works
Helber, P., Bischke, B., Dengel, A. R., & Borth, D. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8519248 · Cited by 253 · see citing works
Energetic Limits to Economic Growth
Brown, J. H., Burnside, W. R., Davidson, A. D., DeLong, J. P., Dunn, W. C., Hamilton, M. J., Mercado‐Silva, N., Nekola, J. C., Okie, J. G., Woodruff, W. H., et al. (2011) · BioScience · doi:10.1525/bio.2011.61.1.7 · Cited by 249 · see citing works
The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei
Lee, L. A., Pringle, K. J., Reddington, C. L. S., Mann, G. W., Stier, P., Spracklen, D. V., Pierce, J. R., & Carslaw, K. S. (2013) · Atmospheric chemistry and physics · doi:10.5194/acp-13-8879-2013 · Cited by 240 · see citing works
Climate Change Effects on Secondary Compounds of Forest Trees in the Northern Hemisphere
Holopainen, J. K., Virjamo, V., Ghimire, R. P., Blande, J. D., Julkunen‐Tiitto, R., & Kivimäenpää, M. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01445 · Cited by 238 · see citing works
Azzari, G., Jain, M., & Lobell, D. B. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.04.014 · Cited by 231 · see citing works
Eiserhardt, W. L., Bjorholm, S., Svenning, J., Rangel, T. F., & Balslev, H. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027027 · Cited by 230 · see citing works
Evaluation of PERSIANN-CCS Rainfall Measurement Using the NAME Event Rain Gauge Network
Hong, Y. E., Gochis, D., Cheng, J., Hsu, K., & Sorooshian, S. (2007) · Journal of Hydrometeorology · doi:10.1175/jhm574.1 · Cited by 224 · see citing works
Krasnopolsky, V. M., Fox‐Rabinovitz, M. S., & Chalikov, D. V. (2005) · Monthly Weather Review · doi:10.1175/mwr2923.1 · Cited by 210 · see citing works
Applied Partial Differential Equations
(2007) · Springer eBooks · doi:10.1007/978-3-540-34646-3 · Cited by 207 · see citing works
Massey, N., Jones, R., Otto, F. E. L., Aina, T., Wilson, S., Murphy, J. M., Hassell, D., Yamazaki, Y. H., & Allen, M. R. (2014) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.2455 · Cited by 197 · see citing works
Potential influences on the United Kingdom's floods of winter 2013/14
Huntingford, C., Marsh, T., Scaife, A. A., Kendon, E., Hannaford, J., Kay, A. L., Lockwood, M., Prudhomme, C., Reynard, N. S., Parry, S., et al. (2014) · Nature Climate Change · doi:10.1038/nclimate2314 · Cited by 196 · see citing works
Self-citationBelayneh, A., Adamowski, J., Khalil, B., & Quilty, J. (2016) · Atmospheric Research · doi:10.1016/j.atmosres.2015.12.017 · Cited by 185 · see citing works
Fischer, R., Bohn, F. J., de Paula, M. D., Dislich, C., Groeneveld, J., Gutiérrez, Á. G., Kazmierczak, M., Knapp, N., Lehmann, S., Paulick, S., et al. (2016) · Ecological Modelling · doi:10.1016/j.ecolmodel.2015.11.018 · Cited by 184 · see citing works
Nitrogen limitation on land: how can it occur in Earth system models?
Thomas, R. Q., Brookshire, E. N. J., & Gerber, S. (2015) · Global Change Biology · doi:10.1111/gcb.12813 · Cited by 174 · see citing works
Enhanced global primary production by biogenic aerosol via diffuse radiation fertilization
Rap, A., Scott, C. E., Reddington, C. L. S., Mercado, L. M., Ellis, R. J., Garraway, S., Evans, M. J., Beerling, D. J., MacKenzie, A. R., HEWITT, C. N., et al. (2018) · Nature Geoscience · doi:10.1038/s41561-018-0208-3 · Cited by 174 · see citing works
The Retrieval of Stratocumulus Cloud Properties by Ground-Based Cloud Radar
Fox, N. I., & Illingworth, A. J. (1997) · Journal of applied meteorology · doi:10.1175/1520-0450(1997)036 · Cited by 163 · see citing works
A seasonal agricultural drought forecast system for food-insecure regions of East Africa
Shukla, S., McNally, A. L., Husak, G. J., & Funk, C. C. (2014) · Hydrology and earth system sciences · doi:10.5194/hess-18-3907-2014 · Cited by 153 · see citing works
Statistical significance of climate sensitivity predictors obtained by data mining
Caldwell, P. M., Bretherton, C. S., Zelinka, M. D., Klein, S. A., Santer, B. D., & Sanderson, B. M. (2014) · Geophysical Research Letters · doi:10.1002/2014gl059205 · Cited by 153 · see citing works
Simulating regime structures in weather and climate prediction models
Dawson, A. R., Palmer, T. N., & Corti, S. (2012) · Geophysical Research Letters · doi:10.1029/2012gl053284 · Cited by 151 · see citing works
Changes in global nitrogen cycling during the Holocene epoch
McLauchlan, K. K., Williams, J. W., Craine, J. M., & Jeffers, E. S. (2013) · Nature · doi:10.1038/nature11916 · Cited by 141 · see citing works
Self-citationControllable and fast quantum-information transfer between distant nodes in two-dimensional networks
Zhong, Z. (2016) · Scientific Reports · doi:10.1038/s41598-016-0016-1 · Cited by 140 · see citing works
Alarm Photosynthesis: Calcium Oxalate Crystals as an Internal CO 2 Source in Plants
Τοουλάκου, Γ., Giannopoulos, A., Nikolopoulos, D., Bresta, P., Ḏotsika, E., Orkoula, M. G., Kontoyannis, C. G., Fasseas, C., Λιακόπουλος, Γ., Klapa, M. I., et al. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.16.00111 · Cited by 139 · see citing works
Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions
Luo, L., Wood, E. F., & Pan, M. (2007) · Journal of Geophysical Research Atmospheres · doi:10.1029/2006jd007655 · Cited by 137 · see citing works
Otto, F. E. L., van Oldenborgh, G. J., Eden, J., Stott, P. A., Karoly, D. J., & Allen, M. R. (2016) · Nature Climate Change · doi:10.1038/nclimate3089 · Cited by 135 · see citing works
Krasnopolsky, V. M., & Schiller, H. (2003) · Neural Networks · doi:10.1016/s0893-6080(03)00027-3 · Cited by 131 · see citing works
Kratzert, F., Klotz, D., Shalev, G., et al. (2019) · doi:10.5194/hess-2019-368 · Cited by 99 · see citing works
Waters, B. M., Amundsen, K., & Graef, G. L. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.00010 · Cited by 95 · see citing works
Impact of atmosphere and sub‐surface ocean data on decadal climate prediction
Dunstone, N., & Smith, D. M. (2010) · Geophysical Research Letters · doi:10.1029/2009gl041609 · Cited by 87 · see citing works
Zhou, Y., Huang, X. H. H., Bian, Q., Griffith, S. M., Louie, P. K. K., & Yu, J. Z. (2015) · Journal of Geophysical Research Atmospheres · doi:10.1002/2015jd023531 · Cited by 84 · see citing works
Precipitation and Temperature Forecast Performance at the Weather Prediction Center
Novak, D. R., Bailey, C. M., Brill, K. F., Burke, P., Hogsett, W. A., Rausch, R., & Schichtel, M. (2013) · Weather and Forecasting · doi:10.1175/waf-d-13-00066.1 · Cited by 83 · see citing works
Centennial-scale reductions in nitrogen availability in temperate forests of the United States
McLauchlan, K. K., Gerhart, L. M., Battles, J. J., Craine, J. M., Elmore, A. J., Higuera, P. E., Mack, M. C., McNeil, B. E., Nelson, D. M., Pederson, N., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-08170-z · Cited by 83 · see citing works
Applying big data beyond small problems in climate research
Knüsel, B., Zumwald, M., Baumberger, C., Hadorn, G. H., Fischer, E. M., Bresch, D. N., & Knutti, R. (2019) · Nature Climate Change · doi:10.1038/s41558-019-0404-1 · Cited by 82 · see citing works
Shiklomanov, A., Dietze, M. C., Viskari, T., Townsend, P. A., & Serbin, S. P. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.05.023 · Cited by 80 · see citing works
Fink, S. (1991) · New Phytologist · doi:10.1111/j.1469-8137.1991.tb01006.x · Cited by 67 · see citing works
Kulikovskiy–Sveshnikova–Beghin model of powder snow avalanches: Development and application
Turnbull, B., McElwaine, J., & Ancey, C. (2007) · Journal of Geophysical Research Atmospheres · doi:10.1029/2006jf000489 · Cited by 67 · see citing works
Karstens, C. D., Stumpf, G. J., Ling, C., Hua, L., Kingfield, D. M., Smith, T. M., Correia, J., Calhoun, K. M., Ortega, K. L., Melick, C. J., et al. (2015) · Weather and Forecasting · doi:10.1175/waf-d-14-00163.1 · Cited by 66 · see citing works
Monteleoni, C., Schmidt, G. A., Saroha, S., & Asplund, E. (2011) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10126 · Cited by 65 · see citing works
MODIS Observations of Ship Tracks in Summertime Stratus off the West Coast of the United States
Segrin, M. S., Coakley, J. A., & Tahnk, W. R. (2007) · Journal of the Atmospheric Sciences · doi:10.1175/2007jas2308.1 · Cited by 59 · see citing works
Buckland, C. E., Bailey, R. M., & Thomas, D. S. G. (2019) · Scientific Reports · doi:10.1038/s41598-019-40429-5 · Cited by 54 · see citing works
Evolution of the GPU Device widely used in AI and Massive Parallel Processing
Baji, T. (2018) · IEEE Electron Devices Technology and Manufacturing Conference (EDTM) · doi:10.1109/edtm.2018.8421507 · Cited by 52 · see citing works
Brown, S. L., Warwick, N. W. M., & Prychid, C. J. (2013) · Plant Physiology and Biochemistry · doi:10.1016/j.plaphy.2013.10.006 · Cited by 47 · see citing works
Wu, C., Chen, Y., Peng, C. H., Li, Z., & Hong, X. (2019) · Journal of Environmental Management · doi:10.1016/j.jenvman.2018.12.090 · Cited by 47 · see citing works
Huntingford, C., & Mercado, L. M. (2016) · Scientific Reports · doi:10.1038/srep30294 · Cited by 42 · see citing works
Self-citationRisbey, J. S., Lewandowsky, S., Cowtan, K. D., Орескес, Н., Rahmstorf, S., Jokimäki, A., & Foster, G. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aaf342 · Cited by 35 · see citing works
Aerosol‐cloud interactions in ship tracks using Terra MODIS/MISR
Chen, Y., Christensen, M. W., Diner, D. J., & Garay, M. J. (2015) · Journal of Geophysical Research Atmospheres · doi:10.1002/2014jd022736 · Cited by 32 · see citing works
Reverse-engineering ecological theory from data
Martin, B. T., Munch, S. B., & Hein, A. M. (2018) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2018.0422 · Cited by 30 · see citing works
Jeffers, E. S., Bonsall, M. B., Froyd, C. A., Brooks, S. J., & Willis, K. J. (2014) · Journal of Ecology · doi:10.1111/1365-2745.12365 · Cited by 29 · see citing works
Self-citationStability in Ecosystem Functioning across a Climatic Threshold and Contrasting Forest Regimes
Jeffers, E. S., Bonsall, M. B., & Willis, K. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016134 · Cited by 26 · see citing works
Self-citationSalvi, K. A., Villarini, G., & Vecchi, G. A. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.07.043 · Cited by 25 · see citing works
A role for analogue memory in AI hardware
Burr, G. W. (2018) · Nature Machine Intelligence · doi:10.1038/s42256-018-0007-y · Cited by 23 · see citing works
A Teleconnection between the West Siberian Plain and the ENSO Region
Liess, S., Agrawal, S., Chatterjee, S., & Kumar, V. (2016) · Journal of Climate · doi:10.1175/jcli-d-15-0884.1 · Cited by 22 · see citing works
A Description of the Madden–Julian Oscillation Based on a Self-Organizing Map
Chattopadhyay, R., Vintzileos, A., & Zhang, C. (2012) · Journal of Climate · doi:10.1175/jcli-d-12-00123.1 · Cited by 20 · see citing works
GRUN: An observations-based global gridded runoff dataset from 1902 to 2014Preprint
Ghiggi, G., Humphrey, V., Seneviratne, S. I., et al. (2019) · doi:10.5194/essd-2019-32 · Cited by 20 · see citing works
A Student's Guide to Dimensional Analysis
Lemons, D. S. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316676165 · Cited by 18 · see citing works
Strong but Intermittent Spatial Covariations in Tropical Land Temperature
Yang, H., Piao, S. L., Huntingford, C., Peng, S., Ciais, P., Chen, A., Zhou, G., Wang, X., Gao, M., & Jakob Zscheischler, A. (2018) · Geophysical Research Letters · doi:10.1029/2018gl080463 · Cited by 13 · see citing works
Self-citationHuntingford, C. (2017) · Nature Climate Change · doi:10.1038/nclimate3391 · Cited by 7 · see citing works
Self-citationA student’s guide to dimensional analysis
Ashworth, S. H. (2018) · Contemporary Physics · doi:10.1080/00107514.2018.1515255 · Cited by 7 · see citing works
Rischard, M., Pillai, N. S., & McKinnon, K. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10214 · Cited by 6 · see citing works
More frequent moments in the climate change debate as emissions continue
Huntingford, C., & Friedlingstein, P. (2015) · Environmental Research Letters · doi:10.1088/1748-9326/10/12/121001 · Cited by 3 · see citing works
Self-citationMatuk-Villazon, O., & Rowland, K. (2020) · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Barenblatt G I 2003 Scaling. Cambridge Texts in Applied Mathematics. (Cambridge: Cambridge University Press) p 171
- Drummond C 2009 Replicability is not Reproducibility Proc. Evaluation Methods for Machine Learning Workshop at the 26th ICML (Montreal, Canada)
- Vallis G K 2006 Atmospheric and Oceanic Fluid Dynamics (Cambridge: Cambridge University Press) p 745
Cited by
Sorted by citations
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01294-x · Cited by 552 · see citing works
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021) · doi:10.2139/ssrn.3804983 · Cited by 552 · see citing works
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2023.100330 · Cited by 536 · see citing works
Mitigation of emerging implications of climate change on food production systems
Gómez‐Zavaglia, A., Mejuto, J. C., & Simal‐Gándara, J. (2020) · Food Research International · doi:10.1016/j.foodres.2020.109256 · Cited by 383 · see citing works
Yigitcanlar, T., & Cugurullo, F. (2020) · Sustainability · doi:10.3390/su12208548 · Cited by 337 · see citing works
Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives
Bochenek, B., & Ustrnul, Z. (2022) · Atmosphere · doi:10.3390/atmos13020180 · Cited by 331 · see citing works
Yigitcanlar, T., Mehmood, R., & Corchado, J. M. (2021) · Sustainability · doi:10.3390/su13168952 · Cited by 319 · see citing works
Mannuru, N. R., Shahriar, S., Teel, Z. A., Wang, T., Lund, B. D., Tijani, S., Pohboon, C. O., Agbaji, D. A., Alhassan, J., Galley, J., et al. (2023) · Information Development · doi:10.1177/02666669231200628 · Cited by 392 · see citing works
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., & Saynisch-Wagner, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00374-3 · Cited by 308 · see citing works
Potential Use of Chat GPT in Global Warming
Biswas, S. S. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03171-8 · Cited by 291 · see citing works
Lees, T., Buechel, M., Anderson, B. J., Slater, L. J., Reece, S., Coxon, G., & Dadson, S. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-5517-2021 · Cited by 280 · see citing works
Self-citationPalomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., Alonso, S., Melero, F. J., Molina, D., Fernández, B., et al. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02264-y · Cited by 276 · see citing works
Deploying artificial intelligence for climate change adaptation
Leal Filho, W., Wall, T., Rui Mucova, S. A., Nagy, G. J., Balogun, A.-L., Luetz, J. M., Ng, A. W., Kovaleva, M., Safiul Azam, F. M., Alves, F., et al. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121662 · Cited by 266 · see citing works
Abou-Foul, M., Ruiz-Alba, J. L., & López-Tenorio, P. J. (2023) · Journal of business research · doi:10.1016/j.jbusres.2022.113609 · Cited by 234 · see citing works
Mersha, M. A., Lâm, K. N., Wood, J., AlShami, A. K., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128111 · Cited by 302 · see citing works
Yiğitcanlar, T., Butler, L., Windle, E., Desouza, K. C., Mehmood, R., & Corchado, J. M. (2020) · Sensors · doi:10.3390/s20102988 · Cited by 185 · see citing works
Carbon Emissions and TCFD Aligned Climate-Related Information Disclosures
Ding, D., Liu, B., & Chang, M. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05292-x · Cited by 190 · see citing works
Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding
Khan, M. H. U., Wang, S., Wang, J., Ahmar, S., Saeed, S., Khan, S. U., Xu, X., Chen, H., Bhat, J. A., & Feng, X. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231911156 · Cited by 161 · see citing works
Artificial intelligence potential for net zero sustainability: Current evidence and prospects
Olawade, D. B., Wada, O. Z., David-Olawade, A. C., Fapohunda, O., Ige, A. O., & Ling, J. (2024) · Next Sustainability · doi:10.1016/j.nxsust.2024.100041 · Cited by 155 · see citing works
Advances and challenges in climate modeling
Alizadeh, O. (2022) · Climatic Change · doi:10.1007/s10584-021-03298-4 · Cited by 155 · see citing works
Interpretable deep-learning models to help achieve the Sustainable Development Goals
Vinuesa, R., & Sırmaçek, B. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00414-y · Cited by 140 · see citing works
Scheelbeek, P., Dangour, A. D., Jarmul, S., Turner, G., Sietsma, A. J., Minx, J. C., Callaghan, M., Ajibade, I. M., Austin, S. E., Biesbroek, R. G., et al. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac092c · Cited by 129 · see citing works
Applying machine learning approach in recycling
Özdemir, M. E., Ali, Z., Subeshan, B., & Asmatulu, E. (2021) · Journal of Material Cycles and Waste Management · doi:10.1007/s10163-021-01182-y · Cited by 132 · see citing works
Bai, P., Liu, X., & Xie, J. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125779 · Cited by 128 · see citing works
Deploying digitalisation and artificial intelligence in sustainable development research
Filho, W. L., Yang, P. J., Eustachio, J. H. P. P., Azul, A. M., Gellers, J. C., Giełczyk, A., Dinis, M. A. P., & Kozlova, V. (2022) · Environment Development and Sustainability · doi:10.1007/s10668-022-02252-3 · Cited by 125 · see citing works
Amiri, Z., Heidari, A., & Navimipour, N. J. (2024) · Energy · doi:10.1016/j.energy.2024.132827 · Cited by 123 · see citing works
Materia, S., García, L. P., van Straaten, C., O, S., Mamalakis, A., Cavicchia, L., Coumou, D., de Luca, P., Kretschmer, M., & Donat, M. (2024) · WIREs Climate Change · doi:10.1002/wcc.914 · Cited by 130 · see citing works
“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).”
Two Dimensions of Opacity and the Deep Learning Predicament
Boge, F. J. (2021) · Minds and Machines · doi:10.1007/s11023-021-09569-4 · Cited by 116 · see citing works
Reworking the political in digital forests: The cosmopolitics of socio-technical worlds
Gabrys, J., Westerlaken, M., Urzedo, D., Ritts, M., & Simlai, T. (2022) · Progress in Environmental Geography · doi:10.1177/27539687221117836 · Cited by 104 · see citing works
Artificial intelligence and numerical weather prediction models: A technical survey
Waqas, M., Humphries, U. W., Chueasa, B., & Wangwongchai, A. (2024) · Natural Hazards Research · doi:10.1016/j.nhres.2024.11.004 · Cited by 120 · see citing works
Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes
Seifert, A., & Rasp, S. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002301 · Cited by 101 · see citing works
Personal assessment of urban heat exposure: a systematic review
Nazarian, N., & LEE, J. K. W. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/abd350 · Cited by 96 · see citing works
Regionalization of hydrological model parameters using gradient boosting machine
Song, Z., Xia, J., Wang, G., She, D., Hu, C., & Hong, S. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-26-505-2022 · Cited by 100 · see citing works
Learning to Approximate Density Functionals
Kalita, B., Li, L., McCarty, R. J., & Burke, K. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00742 · Cited by 98 · see citing works
AI for climate impacts: applications in flood risk
Jones, A., Kuehnert, J., Fraccaro, P., Meuriot, O., Ishikawa, T., Edwards, B., Stoyanov, N., Remy, S. L., Weldemariam, K., & Assefa, S. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00388-1 · Cited by 100 · see citing works
The current and future uses of machine learning in ecosystem service research
Scowen, M., Athanasiadis, I. N., Bullock, J. M., Eigenbrod, F., & Willcock, S. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.149263 · Cited by 96 · see citing works
Tyagi, S., Zhang, X., Saraswat, D., Sahany, S., Mishra, S. K., & Niyogi, D. (2022) · Earth s Future · doi:10.1029/2022ef002723 · Cited by 98 · see citing works
Impact of nanomaterials on human health: a review
Asmatulu, E., Andalib, M. N., Subeshan, B., & Abedin, F. (2022) · Environmental Chemistry Letters · doi:10.1007/s10311-022-01430-z · Cited by 85 · see citing works
Climate services for managing societal risks and opportunities
Hewitt, C. D., & Stone, R. C. (2021) · Climate Services · doi:10.1016/j.cliser.2021.100240 · Cited by 80 · see citing works
Decision-Making Options for Managing Risk
(IPCC), I. P. O. C. C. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009325844.026 · Cited by 83 · see citing works
Advancements and future outlook of Artificial Intelligence in energy and climate change modeling
Shobanke, M., Bhatt, M., & Shittu, E. (2025) · Advances in Applied Energy · doi:10.1016/j.adapen.2025.100211 · Cited by 79 · see citing works
Detecting Climate Signals Using Explainable AI With Single‐Forcing Large Ensembles
Labe, Z. M., & Barnes, E. A. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002464 · Cited by 75 · see citing works
D’Amico, G., Arbolino, R., Ши, Л., Yiğitcanlar, T., & Ioppolo, G. (2021) · Sustainability · doi:10.3390/su13116043 · Cited by 78 · see citing works
Using Artificial Intelligence for Space Challenges: A Survey
Russo, A., & Lax, G. (2022) · Applied Sciences · doi:10.3390/app12105106 · Cited by 77 · see citing works
D’Amico, G., Arbolino, R., Ши, Л., Yiğitcanlar, T., & Ioppolo, G. (2021) · Land Use Policy · doi:10.1016/j.landusepol.2021.105819 · Cited by 73 · see citing works
Kisekka, I., Peddinti, S. R., Kustas, W. P., McElrone, A. J., Bambach-Ortiz, N. E., McKee, L. G., & Bastiaanssen, W. G. M. (2022) · Irrigation Science · doi:10.1007/s00271-022-00775-1 · Cited by 72 · see citing works
Fast Stencil-Code Computation on a Wafer-Scale Processor
Rocki, K., Essendelft, D. T. V., Sharapov, I., Schreiber, R., Morrison, M., Kibardin, V., Portnoy, A., Dietiker, J.-F., Syamlal, M., & James, M. (2020) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41405.2020.00062 · Cited by 70 · see citing works
Artificial intelligence for climate change adaptation
Cheong, S., Sankaran, K., & Bastani, H. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1459 · Cited by 65 · see citing works
Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management
Chakraborty, D., Başağaoğlu, H., Gutierrez, L., & Mirchi, A. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac2fde · Cited by 64 · see citing works
Barriers Related to AI Implementation in Supply Chain Management
Shrivastav, M. (2022) · Journal of Global Information Management · doi:10.4018/jgim.296725 · Cited by 64 · see citing works
Machine learning for weather and climate are worlds apart
Watson‐Parris, D. (2021) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2020.0098 · Cited by 62 · see citing works
Using Machine Learning to Cut the Cost of Dynamical Downscaling
Hobeichi, S., Nishant, N., Shao, Y., Abramowitz, G., Pitman, A. J., Sherwood, S. C., Bishop, C. H., & Green, S. (2023) · Earth s Future · doi:10.1029/2022ef003291 · Cited by 59 · see citing works
Gupta, S., Zeballos, J. C., Castro, G. D. R., Tomičić, A., Morales, S. A., Mahfouz, M., Osemwegie, I., Sessi, V. P. C., Schmitz, M. A., Mahmoud, N., et al. (2023) · Sustainability · doi:10.3390/su15086844 · Cited by 66 · see citing works
Harper, A., Williams, K., McGuire, P. C., Rojas, M. C. D., Hemming, D., Verhoef, A., Huntingford, C., Rowland, L. M., Marthews, T. R., Eller, C. B., et al. (2021) · Geoscientific model development · doi:10.5194/gmd-14-3269-2021 · Cited by 56 · see citing works
Self-citationClare, M., Sonnewald, M., Lguensat, R., Deshayes, J., & Balaji, V. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003162 · Cited by 56 · see citing works
Aarts, E., Fleuren, H. A., Sitskoorn, M. M., & Wilthagen, T. (2021) · Springer eBooks · doi:10.1007/978-3-030-65355-2 · Cited by 51 · see citing works
Kumar, S. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102253 · Cited by 51 · see citing works
Mondal, S. R., Das, S., & Vrana, V. G. (2024) · Sustainability · doi:10.3390/su162310299 · Cited by 58 · see citing works
Greening the Artificial Intelligence for a Sustainable Planet: An Editorial Commentary
Yiğitcanlar, T. (2021) · Sustainability · doi:10.3390/su132413508 · Cited by 49 · see citing works
Recording provenance of workflow runs with RO-Crate
Leo, S., Crusoe, M. R., Rodríguez‐Navas, L., Sirvent, R., Kanitz, A., Geest, P. F. G. D., Wittner, R., Pireddu, L., Garijo, D., Fernández, J. M., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309210 · Cited by 55 · see citing works
Urban Climate Informatics: An Emerging Research Field
Middel, A., Nazarian, N., Demuzere, M., & Bechtel, B. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.867434 · Cited by 48 · see citing works
Artificial Intelligence for the Management of Servitization 5.0
Nicoletti, B., & Appolloni, A. (2023) · Sustainability · doi:10.3390/su151411113 · Cited by 50 · see citing works
Ajibade, S.-S. M., Zaidi, A. T., Bekun, F. V., Adediran, A. O., & Bassey, M. A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20297 · Cited by 45 · see citing works
Unleashing the power of artificial intelligence for climate action in industrial markets
Akter, S., Babu, M. M., Hani, U., Sultana, S., Bandara, R. J., & Grant, D. (2024) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.12.011 · Cited by 52 · see citing works
Climate change impacts on water resources: An overview
Sukanya, S., & Joseph, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99714-0.00008-x · Cited by 44 · see citing works
Use and Impact of Artificial Intelligence on Climate Change Adaptation in Africa
Rutenberg, I., Gwagwa, A. E., & Omino, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-45106-6_80 · Cited by 43 · see citing works
Selvam, A. P., & Saud, S. N. (2023) · Research Square · doi:10.21203/rs.3.rs-3579172/v1 · Cited by 49 · see citing works
Feldmeyer, D., Nowak, W., Jamshed, A., & Birkmann, J. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.145734 · Cited by 41 · see citing works
Qerimi, Q. R., & Sergi, B. S. (2022) · International journal of greenhouse gas control · doi:10.1016/j.ijggc.2022.103757 · Cited by 41 · see citing works
Hafyani, M. E., Himdi, K. E., & Adlouni, S. E. (2024) · Frontiers in Water · doi:10.3389/frwa.2024.1378598 · Cited by 39 · see citing works
Disaster and risk management in outdoor recreation and tourism in the context of climate change
Hsu, J. L., & Sharma, P. K. (2023) · International Journal of Climate Change Strategies and Management · doi:10.1108/ijccsm-10-2021-0118 · Cited by 38 · see citing works
Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
Olayiwola, O., Cali, U., Elsden, M., & Yadav, P. (2025) · Solar · doi:10.3390/solar5010007 · Cited by 45 · see citing works
Authentic Integration of Ethics and AI through Sociotechnical, Problem-Based Learning
Krakowski, A., Greenwald, E., Hurt, T., Nonnecke, B. M., & Cannady, M. A. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21556 · Cited by 42 · see citing works
Algorithmic conservation in a changing climate
Scoville, C., Chapman, M., Amironesei, R., & Boettiger, C. (2021) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2021.01.009 · Cited by 37 · see citing works
Copula-based synthetic data augmentation for machine-learning emulators
Meyer, D., Nagler, T., & Hogan, R. J. (2021) · Geoscientific model development · doi:10.5194/gmd-14-5205-2021 · Cited by 40 · see citing works
Forecasting drought using machine learning: a systematic literature review
Oyarzabal, R. S., Santos, L. B. L., Cunningham, C., Broedel, E., de Lima, G. R. T., Cunha‐Zeri, G., Peixoto, J. D. S., Anochi, J. A., Garcia, K., Costa, L. C. O., et al. (2025) · Natural Hazards · doi:10.1007/s11069-025-07195-2 · Cited by 36 · see citing works
A Review of Machine Learning Applications in Land Surface Modeling
Pal, S., & Sharma, P. (2021) · Earth · doi:10.3390/earth2010011 · Cited by 38 · see citing works
Ikegwu, A. C., Nweke, H. F., Mkpojiogu, E., Anikwe, C. V., Igwe, S. A., & Alo, U. R. (2024) · Energy Informatics · doi:10.1186/s42162-024-00307-5 · Cited by 35 · see citing works
Towards Sustainable Color Cosmetics Packaging
Dube, M., & Dube, S. (2023) · Cosmetics · doi:10.3390/cosmetics10050139 · Cited by 44 · see citing works
Naidu, D. G. T., & Bagchi, S. (2021) · Global Change Biology · doi:10.1111/gcb.15531 · Cited by 35 · see citing works
Jiménez‐Esteve, B., Kornhuber, K., & Domeisen, D. I. V. (2022) · Geophysical Research Letters · doi:10.1029/2021gl096337 · Cited by 39 · see citing works
Ladi, T., Jabalameli, S., & Sharifi, A. (2022) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083221085281 · Cited by 38 · see citing works
Shankar, R., & Gupta, L. (2024) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.12.019 · Cited by 34 · see citing works
Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning
Lumiaro, E., Todorović, M., Kurtén, T., Vehkamäki, H., & Rinke, P. (2021) · Atmospheric chemistry and physics · doi:10.5194/acp-21-13227-2021 · Cited by 37 · see citing works
Ugwu, O. P.-C., Ogenyi, F. C., Alum, E. U., Eze, V. H. U., Basajja, M., Ugwu, J. N., Ugwu, C. N., Ejemot-Nwadiaro, R. I., Okon, M. B., Egba, S. I., et al. (2025) · Cogent Food & Agriculture · doi:10.1080/23311932.2025.2569982 · Cited by 33 · see citing works
Lu, Y., Chen, Q., Yu, M., Wu, Z., Huang, C., Fu, J., Yu, Z., & Yao, J. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104586 · Cited by 33 · see citing works
Labe, Z. M., & Barnes, E. A. (2022) · Earth and Space Science · doi:10.1029/2022ea002348 · Cited by 31 · see citing works
Nishant, N., Hobeichi, S., Sherwood, S. C., Abramowitz, G., Shao, Y., Bishop, C. H., & Pitman, A. J. (2023) · Environmental Research Letters · doi:10.1088/1748-9326/ace463 · Cited by 30 · see citing works
Álvarez, M. R., Gomariz‐Castillo, F., & Alonso‐Sarría, F. (2021) · Water · doi:10.3390/w13020222 · Cited by 31 · see citing works
Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels
Hamzi, B., Maulik, R., & Owhadi, H. (2021) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2021.0326 · Cited by 29 · see citing works
García-Rodríguez, A., Granados-López, D., García-Rodríguez, S., Díez-Mediavilla, M., & Alonso‐Tristán, C. (2021) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2021.108627 · Cited by 29 · see citing works
Ecological restoration and artificial intelligence: whose values inform a project?
Woodrich, S. T., & Pape, T. (2024) · Restoration Ecology · doi:10.1111/rec.14128 · Cited by 28 · see citing works
Artificial Intelligence in Ecology: A Commentary on a Chatbot's Perspective
Haghighi, S. R., Saqalaksari, M. P., & Johnson, S. N. (2023) · Bulletin of the Ecological Society of America · doi:10.1002/bes2.2097 · Cited by 35 · see citing works
Suid, M. H., Ahmad, M. A., Nasir, A. N. K., Ghazali, M. R., & Jui, J. J. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102506 · Cited by 27 · see citing works
Accountability, Trust, and Transparency in AI Systems From the Perspective of Public Policy
Agrawal, G. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-7452-8.ch009 · Cited by 27 · see citing works
AI for development: implications for theory and practice
Bjola, C. (2021) · Oxford Development Studies · doi:10.1080/13600818.2021.1960960 · Cited by 34 · see citing works
Quantum Artificial Intelligence for the Science of Climate Change
Singh, M., Dhara, C., Kumar, A., Gill, S. S., & Uhlig, S. (2022) · CRC Press eBooks · doi:10.1201/9781003250357-13 · Cited by 27 · see citing works
Interpretable Deep Learning for Probabilistic MJO Prediction
Delaunay, A., & Christensen, H. M. (2022) · Geophysical Research Letters · doi:10.1029/2022gl098566 · Cited by 27 · see citing works
Self-citationIntegration of Artificial Intelligence and Life Cycle Assessment Methods
de Jesus, J. O. N., Esquerre, K., & Medeiros, D. L. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1196/1/012028 · Cited by 26 · see citing works
Cross-jurisdictional disaster preparedness: A Nigeria-USA data-analytical approach
Eneh, N. E., Bakare, S. S., Akpuokwe, C. U., & Adeniyi, A. O. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.2.0638 · Cited by 23 · see citing works
- 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
