Review demonstrates statistical methods for comparing and combining climate datasets, highlighting improved estimates of past, present, and future climate conditions.
Key Points
To review modern statistical methodologies for climate data differentiation and integration across diverse data sources, including observations, proxies, and model simulations.
Surveyed statistical frameworks designed to quantify discrepancies in means, distributions, dependence structures, extremes, spatial signals, and geometric shapes.
Reviewed data synthesis approaches that merge climate models and observational records via ensemble methods, bias correction, hierarchical modeling, and machine learning.
Identified robust statistical solutions for overcoming widespread analytical hurdles, including high dimensionality, spatial dependence, observational sparsity, and structural model uncertainty.
Demonstrated that pairing differentiation metrics with integration models provides complementary perspectives that enhance the precision of historical reconstructions and future climate projections.