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Global downscaled projections for climate impacts research (GDPCIR): Preserving extremes for modeling future climate impacts
Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023.
Global climate models are important tools for understanding the climate system and how it is projected to evolve under scenario-driven emissions pathways. Their output is widely used in climate impacts research for modeling the current and future effects of climate change. However, climate model output remains coarse in relation to the high-resolution climate data needed for climate impacts studies, and it also exhibits biases relative to observational data. Treatment of the distribution tails is a key challenge in existing downscaled climate datasets available at a global scale; many of these datasets used quantile mapping techniques that were known to dampen or amplify trends in the tails. In this study, we apply the trend-preserving Quantile Delta Mapping (QDM) bias-adjustment method (Cannon et al., 2015) and develop a new downscaling method called the Quantile-Preserving Localized-Analog Downscaling (QPLAD) method that also preserves trends in the distribution tails. The output dataset of this study is the Global Downscaled Projections for Climate Impacts Research (GDPCIR), a global, daily, 0.25° horizontal-resolution product which is publicly hosted on Microsoft AI for Earth’s Planetary Computer.
Published January 16, 2023Read