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.

This study links a framework for global and local sea-level rise projections with simulations of two major mechanisms by which climate change can affect the vast Antarctic ice sheet. It shows that future sea level rise may be more than previously thought, but just how much won’t become evident until after mid-century.

Lab co-director Robert Kopp helped write the “Climate Science Special Report: Fourth National Climate Assessment, Volume I,” released by the United States Global Change Research Program. This comprehensive report provides the most up-to-date climate science assessment in the world. It was compiled by 51 authors, the majority of whom were drawn from federal agencies.

The impacts of heat waves in a warming climate depend not only on changing temperatures but also on changing humidity. This study investigates the long‐term evolution of summertime humidity and daily maximum temperature near four U.S. cities, New York City, Chicago, Phoenix, and New Orleans, under a high‐emissions pathway. The results suggest that, despite a modest decrease in median relative humidity, heat stress--a measure of both humidity and temperature--will increase faster than temperature projections alone would indicate.