This program aims to develop global process-resolving models to help quantify the roles of climate feedbacks in anthropogenic climate change.
Advancing fundamental understanding of atmospheric radiation, clouds, and precipitation, and their interactions with Earth’s surface and climate.
Advancing the nation’s ability to identify and project climate extremes and how they are affected by environmental drivers.
A network of principal investigator-managed sites measuring ecosystem carbon dioxide, water, and energy fluxes in North, Central, and South America.
Advancing model predictions of tropical forest carbon cycle responses to a changing climate over the 21st century.
Advancing scientific understanding of the major atmospheric physical processes and land-atmosphere interactions affecting how mountainous watersheds in the Rocky Mountains deliver water.
Advancing understanding of terrestrial biogeochemistry, with a focus on belowground soil carbon cycling.
A next-generation numerical terrestrial ecosystem model that simulates and predicts growth, death, and regeneration of plants and subsequent tree size distributions.
Aims to improve our ability to predict exchanges of carbon, water, and energy at the landscape scale.
A model with weather-scale resolution to simulate aspects of Earth’s variability and decadal changes expected to affect the U.S. energy sector in coming years.
Identifying and quantifying interactions between biogeochemical and hydrological cycles and the Earth system.
A data repository that collects, stores, manages, and shares earth and environmental systems data created through research sponsored by the U.S. Department of Energy.
Daniel Feldman is a staff scientist in the Climate and Ecosystems Sciences Division and Principal Investigator of both the Surface Atmosphere Integrated Field Laboratory (SAIL) Campaign and the climate model downscaling that informed the 5th National Climate Assessment. His research focuses on the nexus of climate modeling and remote sensing.
Jennifer Holm is a research scientist in the Climate and Ecosystem Science Division and a collaborator in the NGEE-Tropics project. Her research focuses on modeling terrestrial ecosystems, with an emphasis on tropical forests.
Michael F. Wehner is a senior scientist in the Computational Research Division researching the behavior of extreme weather events in a changing climate, especially heat waves, intense precipitation, drought, and tropical cyclones.
In California’s 2022-2023 winter season, the state faced nine atmospheric rivers (ARs) that led to extreme flooding, landslides, and power outages – the longest duration of continuous AR conditions in the past 70 years. Scientists recently conducted a study using machine learning to better understand these complex weather systems, finding that more intense atmospheric rivers are more likely to occur in succession within a short period of time.
We hear about climate models all the time, but how many of us know how they actually work? In this episode, we peel back the curtain, discussing where these models came from, what they can do amazingly well, and their current limitations. And our guests talk about what it’s like for them, personally, when their work is doubted, minimized, or politicized. After all, climate scientists find themselves in the hot seat a lot more often than other scientists. Today’s guests are experts not only in the science itself, but with staying cool under pressure, communicating their science with the public, and laughing off the negativity.
