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*'''[[Detection and attribution of anthropogenic climate change]]:''' Separating forced signal (due to anthropogenic climate change) from the "noise" due to natural climate variability has been a challenging task, given only one realization of observational record<ref>{{Cite web|url=https://www.ipcc.ch/report/ar5/wg1/detection-and-attribution-of-climate-change-from-global-to-regional/|title=2013: Detection and attribution of climate change: From global to regional. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Doschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, pp. 867-952, doi:10.1017/CBO9781107415324.022.|last=Bindoff,|first=N.L., Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang|date=2013|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite journal|last=Gillett|first=Nathan P.|last2=Kirchmeier-Young|first2=Megan|last3=Ribes|first3=Aurélien|last4=Shiogama|first4=Hideo|last5=Hegerl|first5=Gabriele C.|last6=Knutti|first6=Reto|last7=Gastineau|first7=Guillaume|last8=John|first8=Jasmin G.|last9=Li|first9=Lijuan|last10=Nazarenko|first10=Larissa|last11=Rosenbloom|first11=Nan|date=2021-01-18|title=Constraining human contributions to observed warming since the pre-industrial period|url=https://www.nature.com/articles/s41558-020-00965-9|journal=Nature Climate Change|language=en|pages=1–6|doi=10.1038/s41558-020-00965-9|issn=1758-6798}}</ref>. Large ensemble simulations<ref>{{Cite web|url=https://www.cesm.ucar.edu/projects/community-projects/MMLEA/|title=Multi-Model Large Ensemble Archive|last=|first=|date=|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref>, where a given climate model is run multiple times with different initial conditions but identical radiative forcing, are one way of separating the anthropogenic signal from the total response (that is a combination of the natural and anthropogenic signals). ML methods provide another avenue for addressing this signal-to-noise problem, to aid with detecting the anthropogenic signal and attributing it to a given forcing<ref>{{Cite web|url=https://arxiv.org/pdf/1910.03346.pdf|title=A direct approach to detection and attribution of climate change|last=Szekely|first=Eniko et al.,|date=2019|website=9th International Workshop on Climate Informatics|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite journal|last=Barnes|first=Elizabeth A.|last2=Hurrell|first2=James W.|last3=Ebert‐Uphoff|first3=Imme|last4=Anderson|first4=Chuck|last5=Anderson|first5=David|date=2019|title=Viewing Forced Climate Patterns Through an AI Lens|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL084944|journal=Geophysical Research Letters|language=en|volume=46|issue=22|pages=13389–13398|doi=10.1029/2019GL084944|issn=1944-8007}}</ref>. Statistical learning also allows detecting anthropogenic climate change from a single day<ref>{{Cite journal|last=Sippel|first=Sebastian|last2=Meinshausen|first2=Nicolai|last3=Fischer|first3=Erich M.|last4=Székely|first4=Enikő|last5=Knutti|first5=Reto|date=2020|title=Climate change now detectable from any single day of weather at global scale|url=https://www.nature.com/articles/s41558-019-0666-7|journal=Nature Climate Change|language=en|volume=10|issue=1|pages=35–41|doi=10.1038/s41558-019-0666-7|issn=1758-6798|via=}}</ref>.
 
*'''[[Detection and attribution of anthropogenic climate change]]:''' Separating forced signal (due to anthropogenic climate change) from the "noise" due to natural climate variability has been a challenging task, given only one realization of observational record<ref>{{Cite web|url=https://www.ipcc.ch/report/ar5/wg1/detection-and-attribution-of-climate-change-from-global-to-regional/|title=2013: Detection and attribution of climate change: From global to regional. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Doschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, pp. 867-952, doi:10.1017/CBO9781107415324.022.|last=Bindoff,|first=N.L., Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang|date=2013|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite journal|last=Gillett|first=Nathan P.|last2=Kirchmeier-Young|first2=Megan|last3=Ribes|first3=Aurélien|last4=Shiogama|first4=Hideo|last5=Hegerl|first5=Gabriele C.|last6=Knutti|first6=Reto|last7=Gastineau|first7=Guillaume|last8=John|first8=Jasmin G.|last9=Li|first9=Lijuan|last10=Nazarenko|first10=Larissa|last11=Rosenbloom|first11=Nan|date=2021-01-18|title=Constraining human contributions to observed warming since the pre-industrial period|url=https://www.nature.com/articles/s41558-020-00965-9|journal=Nature Climate Change|language=en|pages=1–6|doi=10.1038/s41558-020-00965-9|issn=1758-6798}}</ref>. Large ensemble simulations<ref>{{Cite web|url=https://www.cesm.ucar.edu/projects/community-projects/MMLEA/|title=Multi-Model Large Ensemble Archive|last=|first=|date=|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref>, where a given climate model is run multiple times with different initial conditions but identical radiative forcing, are one way of separating the anthropogenic signal from the total response (that is a combination of the natural and anthropogenic signals). ML methods provide another avenue for addressing this signal-to-noise problem, to aid with detecting the anthropogenic signal and attributing it to a given forcing<ref>{{Cite web|url=https://arxiv.org/pdf/1910.03346.pdf|title=A direct approach to detection and attribution of climate change|last=Szekely|first=Eniko et al.,|date=2019|website=9th International Workshop on Climate Informatics|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite journal|last=Barnes|first=Elizabeth A.|last2=Hurrell|first2=James W.|last3=Ebert‐Uphoff|first3=Imme|last4=Anderson|first4=Chuck|last5=Anderson|first5=David|date=2019|title=Viewing Forced Climate Patterns Through an AI Lens|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL084944|journal=Geophysical Research Letters|language=en|volume=46|issue=22|pages=13389–13398|doi=10.1029/2019GL084944|issn=1944-8007}}</ref>. Statistical learning also allows detecting anthropogenic climate change from a single day<ref>{{Cite journal|last=Sippel|first=Sebastian|last2=Meinshausen|first2=Nicolai|last3=Fischer|first3=Erich M.|last4=Székely|first4=Enikő|last5=Knutti|first5=Reto|date=2020|title=Climate change now detectable from any single day of weather at global scale|url=https://www.nature.com/articles/s41558-019-0666-7|journal=Nature Climate Change|language=en|volume=10|issue=1|pages=35–41|doi=10.1038/s41558-019-0666-7|issn=1758-6798|via=}}</ref>.
   
=== Forecasting of seasonal variations and extreme events ===
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=== Forecasting ===
   
 
*'''[[Seasonal forecasting]]''': Seasonal variations, such as those due to [https://en.wikipedia.org/wiki/El_Ni%C3%B1o%E2%80%93Southern_Oscillation El Niño/Southern Oscillation (ENSO)] are difficult to predict using traditional methods. ML and deep learning can be useful for multi-year ENSO forecasting<ref>{{Cite journal|last=Ham|first=Yoo-Geun|last2=Kim|first2=Jeong-Hwan|last3=Luo|first3=Jing-Jia|date=2019|title=Deep learning for multi-year ENSO forecasts|url=https://www.nature.com/articles/s41586-019-1559-7|journal=Nature|language=en|volume=573|issue=7775|pages=568–572|doi=10.1038/s41586-019-1559-7|issn=1476-4687|via=}}</ref><ref>{{Cite journal|last=Toms|first=Benjamin A.|last2=Barnes|first2=Elizabeth A.|last3=Ebert‐Uphoff|first3=Imme|date=2020|title=Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS002002|journal=Journal of Advances in Modeling Earth Systems|language=en|volume=12|issue=9|pages=e2019MS002002|doi=10.1029/2019MS002002|issn=1942-2466}}</ref><ref>{{Cite web|url=https://www.climatechange.ai/papers/neurips2019/40/paper.pdf|title=Forecasting El Niño with Convolutional andRecurrent Neural Networks|last=Mahesh|first=A., et al.,|date=2019|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite web|url=https://arxiv.org/pdf/2012.01598.pdf|title=Graph Neural Networks for Improved El NiñoForecasting|last=Cachay|first=S. R. et al.,|date=|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite journal|last=Guo|first=Yanan|last2=Cao|first2=Xiaoqun|last3=Liu|first3=Bainian|last4=Peng|first4=Kecheng|date=2020|title=El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition|url=https://www.mdpi.com/2073-8994/12/6/893|journal=Symmetry|language=en|volume=12|issue=6|pages=893|doi=10.3390/sym12060893|via=}}</ref>.
 
*'''[[Seasonal forecasting]]''': Seasonal variations, such as those due to [https://en.wikipedia.org/wiki/El_Ni%C3%B1o%E2%80%93Southern_Oscillation El Niño/Southern Oscillation (ENSO)] are difficult to predict using traditional methods. ML and deep learning can be useful for multi-year ENSO forecasting<ref>{{Cite journal|last=Ham|first=Yoo-Geun|last2=Kim|first2=Jeong-Hwan|last3=Luo|first3=Jing-Jia|date=2019|title=Deep learning for multi-year ENSO forecasts|url=https://www.nature.com/articles/s41586-019-1559-7|journal=Nature|language=en|volume=573|issue=7775|pages=568–572|doi=10.1038/s41586-019-1559-7|issn=1476-4687|via=}}</ref><ref>{{Cite journal|last=Toms|first=Benjamin A.|last2=Barnes|first2=Elizabeth A.|last3=Ebert‐Uphoff|first3=Imme|date=2020|title=Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS002002|journal=Journal of Advances in Modeling Earth Systems|language=en|volume=12|issue=9|pages=e2019MS002002|doi=10.1029/2019MS002002|issn=1942-2466}}</ref><ref>{{Cite web|url=https://www.climatechange.ai/papers/neurips2019/40/paper.pdf|title=Forecasting El Niño with Convolutional andRecurrent Neural Networks|last=Mahesh|first=A., et al.,|date=2019|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite web|url=https://arxiv.org/pdf/2012.01598.pdf|title=Graph Neural Networks for Improved El NiñoForecasting|last=Cachay|first=S. R. et al.,|date=|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite journal|last=Guo|first=Yanan|last2=Cao|first2=Xiaoqun|last3=Liu|first3=Bainian|last4=Peng|first4=Kecheng|date=2020|title=El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition|url=https://www.mdpi.com/2073-8994/12/6/893|journal=Symmetry|language=en|volume=12|issue=6|pages=893|doi=10.3390/sym12060893|via=}}</ref>.
*'''[[Localized Extreme Event Forecasting|Extreme events predictions]]''': Storms, droughts, fires, floods, and other extreme events are expected to become stronger and more frequent as climate change progresses. Machine learning can be used to refine what are otherwise coarse-grained forecasts (e.g., generated from climate or weather prediction models) of these extreme weather events. These high-resolution forecasts can guide improvements in system robustness and resilience.
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*'''[[Localized Extreme Event Forecasting|Local forecasts]] of extreme events''': Storms, droughts, fires, floods, and other extreme events are expected to become stronger and more frequent as climate change progresses. Machine learning can be used to refine what are otherwise coarse-grained forecasts (e.g., generated from climate or weather prediction models) of these extreme weather events. These high-resolution forecasts can guide improvements in system robustness and resilience.
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*'''[[Storm Tracking|Storm tracking]]''': While climate models can forecast long-term changes in the climate system, separate systems are required to detect specific extreme weather phenomena, like cyclones, atmospheric rivers, and tornadoes. Identifying extreme events in climate model outputs can inform scientific understanding of where and when these events may occur. ML can help classify, detect, and track climate-related extreme events such as hurricanes in climate model outputs.
For use of ML in weather forecasting, see [[weather prediction]].
 
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*'''[[Weather prediction]]''': ML is a suitable tool for making short-term weather predictions<ref>{{Cite web|url=https://www.ecmwf.int/en/elibrary/19877-machine-learning-ecmwf-roadmap-next-10-years|title=Machine learning at ECMWF: A roadmap for the next 10 years|last=Düben|first=Peter|last2=Modigliani|first2=Umberto|date=2021|website=www.ecmwf.int|access-date=2021-01-22|last3=Geer|first3=Alan|last4=Siemen|first4=Stephan|last5=Pappenberger|first5=Florian|last6=Bauer|first6=Peter|last7=Brown|first7=Andy|last8=Palkovic|first8=Martin|last9=Raoult|first9=Baudouin}}</ref> based on the observed initial conditions, and post-processing the output from weather models<ref>{{Cite journal|last=Rasp|first=Stephan|last2=Lerch|first2=Sebastian|date=2018-11-01|title=Neural Networks for Postprocessing Ensemble Weather Forecasts|url=https://journals.ametsoc.org/view/journals/mwre/146/11/mwr-d-18-0187.1.xml|journal=Monthly Weather Review|language=EN|volume=146|issue=11|pages=3885–3900|doi=10.1175/MWR-D-18-0187.1|issn=1520-0493}}</ref>.
   
 
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