A new research study focuses on understanding how interactions between the land and the atmosphere impact various climate phenomena like droughts, heatwaves, and rainfall patterns. With support from the Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) Program in collaboration with the Department of Energy (DOE), this project contributes to the broader US-CLIVAR Climate Process Team concept to improve parameterizations of land processes in models. MAPP-funded scientists Kirsten Findell of NOAA GFDL, Paul Dirmeyer of George Mason University, and Nathaniel Chaney of Duke University worked on this with a team of US and international researchers. The results, published in Geoscientific Model Development, show that increasing the time resolution of model output and collecting high-frequency data from climate models is essential for capturing land-atmosphere interactions, which are critical for predicting climate phenomena and improving resilience.
In this study, the researchers propose a method to collect and analyze high-frequency data from climate models, which would enable a more detailed understanding of these interactions at different timescales. By comparing model simulations with observational data, they aim to improve the accuracy of climate models in representing land-atmosphere interactions, with the ultimate goal of predicting and adapting to climate change and extreme weather events. This research not only enhances our understanding of Earth’s climate system but also helps in developing strategies to mitigate the effects of climate change on society and ecosystems.
For more information, contact Clara Deck.
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