Machine learning techniques in a new MAPP-supported study help to understand non-linear complexities of the North Atlantic Oscillation (NAO) and its connections to the global climate system.
Can Earth System Models Capture Intricate Links between Phytoplankton, Environmental Factors, and Global Carbon?
New research on the key role of phytoplankton in the global carbon cycle was supported by a joint COM, CVP, and GOMO grant.
To improve water management in the mountainous western US, COM-supported research presents a hybrid model approach to improve simulations of western water flow using both traditional and machine learning methods.
Researchers funded by AC4 advance modeling and prediction of black carbon using machine learning techniques.
Expanded record of air-sea CO2 fluxes created using machine learning techniques improves our ability to model annual changes in the ocean carbon sink.
Recent Research Supports NOAA Artificial Intelligence Strategy and CPO Water Resources Climate Risk Area
Two recent publications demonstrate the value of machine learning techniques to characterize complex water resources in the western U.S.