How Observation-Based Data Influence Uncertainty in Local Climate Projections
This study investigates how uncertainties in high-resolution observation-based gridded datasets (OBGDs) influence downscaled climate projections in the Puget Sound region of the Pacific Northwest, U.S. We compare four OBGDs (gridMET, nClimGrid, Livneh, and GMFD) with station observations and identify significant disagreement in annual Frost Days. These biases influence uncertainty in three widely used bias-corrected and statistically downscaled (BSD) products (STAR-ESDM, LOCA2, NEX-GDDP-CMIP6), resulting in mid- and late-century projections that differ by up to 100% in comparisons based on the same sixteen CMIP6 models. Differences among BSD products also exceed 1°C in winter minimum temperature warming, 50 Frost Days and 30 Summer Days in areas with complex terrain. These findings emphasize that high spatial resolution does not ensure local accuracy, and reliance on a single dataset can obscure critical uncertainties. This has important implications for infrastructure and ecosystem planning, where decisions are often based on temperature thresholds. We recommend users consider multiple OBGDs and BSD products and account for known biases when using climate data for decision-making and probabilistic projections.