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Future climate projections (edit)

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Replaced content with "{{Stub}} {{Disclaimer}} Placeholder text ==Background Readings== ==Conferences, Journals, and Professional Organizations== ==Libraries and Tools== ==Data== ==Future..."
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Historical record provides valuable information for evaluating the performance of climate models with respect to the observed changes. However, especially early historical observations are available only for sparse regions. ML can help with filling in the gaps in observations to provide a complete record for different climate variables, such as ocean carbon uptake<ref>{{Cite journal|last=Landschützer|first=P.|last2=Gruber|first2=N.|last3=Bakker|first3=D. C. E.|last4=Schuster|first4=U.|last5=Nakaoka|first5=S.|last6=Payne|first6=M. R.|last7=Sasse|first7=T. P.|last8=Zeng|first8=J.|date=2013-11-29|title=A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink|url=https://bg.copernicus.org/articles/10/7793/2013/|journal=Biogeosciences|language=English|volume=10|issue=11|pages=7793–7815|doi=10.5194/bg-10-7793-2013|issn=1726-4170}}</ref><ref>{{Cite journal|last=Gregor|first=Luke|last2=Lebehot|first2=Alice D.|last3=Kok|first3=Schalk|last4=Scheel Monteiro|first4=Pedro M.|date=2019-12-10|title=A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?|url=https://gmd.copernicus.org/articles/12/5113/2019/|journal=Geoscientific Model Development|language=English|volume=12|issue=12|pages=5113–5136|doi=10.5194/gmd-12-5113-2019|issn=1991-959X}}</ref> or surface air temperature using neural networks<ref>{{Cite journal|last=Kadow|first=Christopher|last2=Hall|first2=David Matthew|last3=Ulbrich|first3=Uwe|date=2020-06|title=Artificial intelligence reconstructs missing climate information|url=https://www.nature.com/articles/s41561-020-0582-5|journal=Nature Geoscience|language=en|volume=13|issue=6|pages=408–413|doi=10.1038/s41561-020-0582-5|issn=1752-0908}}</ref>, Kriging<ref>{{Cite journal|last=Cowtan|first=Kevin|last2=Way|first2=Robert G.|date=2014|title=Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends|url=https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.2297|journal=Quarterly Journal of the Royal Meteorological Society|language=en|volume=140|issue=683|pages=1935–1944|doi=10.1002/qj.2297|issn=1477-870X}}</ref><ref>{{Cite journal|last=Morice|first=C. P.|last2=Kennedy|first2=J. J.|last3=Rayner|first3=N. A.|last4=Winn|first4=J. P.|last5=Hogan|first5=E.|last6=Killick|first6=R. E.|last7=Dunn|first7=R. J. H.|last8=Osborn|first8=T. J.|last9=Jones|first9=P. D.|last10=Simpson|first10=I. R.|title=An updated assessment of near-surface temperature change from 1850: the HadCRUT5 dataset|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JD032361|journal=Journal of Geophysical Research: Atmospheres|language=en|volume=n/a|issue=n/a|pages=e2019JD032361|doi=10.1029/2019JD032361|issn=2169-8996}}</ref>, or Empirical Orthogonal Functions<ref>{{Cite journal|last=Benestad|first=R. E.|last2=Erlandsen|first2=H. B.|last3=Mezghani|first3=A.|last4=Parding|first4=K. M.|date=2019|title=Geographical Distribution of Thermometers Gives the Appearance of Lower Historical Global Warming|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL083474|journal=Geophysical Research Letters|language=en|volume=46|issue=13|pages=7654–7662|doi=10.1029/2019GL083474|issn=1944-8007}}</ref>.
==Background Readings==
 
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