Filling in gaps in the observations

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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[1][2] or surface air temperature using neural networks[3], Kriging[4][5], or Empirical Orthogonal Functions[6].

Background Readings

Conferences, Journals, and Professional Organizations

Libraries and Tools

Data

Future Directions

Relevant Groups and Organizations

References

  1. ↑ Landschützer, P.; Gruber, N.; Bakker, D. C. E.; Schuster, U.; Nakaoka, S.; Payne, M. R.; Sasse, T. P.; Zeng, J. (2013-11-29). "A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink". Biogeosciences. 10 (11): 7793–7815. doi:10.5194/bg-10-7793-2013. ISSN 1726-4170.
  2. ↑ Gregor, Luke; Lebehot, Alice D.; Kok, Schalk; Scheel Monteiro, Pedro M. (2019-12-10). "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?". Geoscientific Model Development. 12 (12): 5113–5136. doi:10.5194/gmd-12-5113-2019. ISSN 1991-959X.
  3. ↑ Kadow, Christopher; Hall, David Matthew; Ulbrich, Uwe (2020-06). "Artificial intelligence reconstructs missing climate information". Nature Geoscience. 13 (6): 408–413. doi:10.1038/s41561-020-0582-5. ISSN 1752-0908. Check date values in: |date= (help)
  4. ↑ Cowtan, Kevin; Way, Robert G. (2014). "Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends". Quarterly Journal of the Royal Meteorological Society. 140 (683): 1935–1944. doi:10.1002/qj.2297. ISSN 1477-870X.
  5. ↑ Morice, C. P.; Kennedy, J. J.; Rayner, N. A.; Winn, J. P.; Hogan, E.; Killick, R. E.; Dunn, R. J. H.; Osborn, T. J.; Jones, P. D.; Simpson, I. R. "An updated assessment of near-surface temperature change from 1850: the HadCRUT5 dataset". Journal of Geophysical Research: Atmospheres. n/a (n/a): e2019JD032361. doi:10.1029/2019JD032361. ISSN 2169-8996.
  6. ↑ Benestad, R. E.; Erlandsen, H. B.; Mezghani, A.; Parding, K. M. (2019). "Geographical Distribution of Thermometers Gives the Appearance of Lower Historical Global Warming". Geophysical Research Letters. 46 (13): 7654–7662. doi:10.1029/2019GL083474. ISSN 1944-8007.
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