Filling in gaps in the observations

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 or surface air temperature using neural networks, Kriging  , or Empirical Orthogonal Functions.