SST Dataset Choice Affects Estimates of Historical Climate Variability

Understanding the origins of climate variability (e.g., ENSO and AMV) and their interactions across timescales, as well as assessing model performance in simulating them, relies on robust sea-surface temperature (SST) datasets. Yet, there are numerous instrumental SST products that differ in their bias adjustments and gridding/infilling strategies. These structural choices propagate to key inferences about the climate system such as climate variability indices, the separation of internal and forced components, and teleconnection magnitudes and spatial patterns. Here, we explain why instrumental SST products differ, what these differences imply for climate variability and teleconnection analyses, and which products are best suited for specific applications. We review recent advances in bias adjustment and gridding/infilling of in situ data and assess the implications of these methods for global mean SST evolution and regional variability indices. We find substantial discrepancies in trends during the satellite era among older products, whereas state-of-the-art datasets are much more consistent. State-of-the-art SST datasets are also more consistent with signals from CMIP-class climate models in global mean SST during World War II, Atlantic multidecadal variability indices, and trends in the tropical Pacific zonal gradient — demonstrating the need to carefully choose SST datasets when investigating climate variability. Disagreements persist, however, for early-20th-century warming, which has implications for separating forced response from internal variability, and in data-sparse regions such as the Southern Ocean and Arctic. To support robust, physically interpretable teleconnection diagnostics, we articulate practical principles for dataset selection and highlight the NCAR Climate Data Guide as an evolving resource for updated SST benchmarking.