Project Grant 2530608
- This National Science Foundation (NSF) Geosciences Program (CFDA 47.050) Project Grant award of $319,907 to the University of Maryland, College Park aims to build a machine-learning-based "emulator" that can efficiently predict water isotope patterns in climate models. The three-year project, starting November 1, 2025, seeks to develop a smart shortcut to incorporate water isotopes, which act as natural recorders of climate history, into complex climate simulations. This...
- This $383,203 federal Project Grant awarded by the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop a machine learning-based "emulator" that can efficiently predict water isotope patterns from climate model variables. The University of Pittsburgh is the primary awardee on this 3-year collaborative research project, working with partners to leverage isotope-enabled global climate model simulations. The goal is to create a smart shortcut that can rapidly...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $1,276,110 to the University Corporation for Atmospheric Research (UCAR) to develop the Isotope-Enabled Community Earth System Model (ICESM). The project aims to improve modeling of the water cycle and enable new scientific insights across atmospheric dynamics, hydrology, and climate change research. Key objectives include...
- This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) provides $410,837 to Boston University to conduct collaborative research aimed at improving the performance of climate models used to simulate weather and climate. The research addresses two key shortcomings of current climate models: their computational intensity and lack of incorporation of observational data. The project will develop climate emulators that use machine learning to extract...
- The National Science Foundation (NSF) awarded a $783,920 project grant under its Geosciences Program (CFDA 47.050) to Colorado State University (CSU) for the project "COLLABORATIVE RESEARCH: CAIG: UNDERSTANDING RADIATIVE FEEDBACKS, OCEAN HEAT UPTAKE, AND ENERGY CONSERVATION TO IMPROVE ML-CLIMATE EMULATIONS." The project aims to develop climate emulators using machine learning to extract statistical relationships from observational data and physics-based climate simulations, with the...
- This $399,162 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop interpretable, stable, and mass-conserving artificial intelligence (AI) models to improve the computational speed and efficiency of geoscientific models, such as those used for air pollution and climate research. The project will create simpler "surrogate" machine learning models for key components like atmospheric chemistry and wildfire plume rise, allowing for...
- This three-year, $511,918 project grant from the National Science Foundation's Geosciences program (CFDA 47.050) funds research to develop a new framework for advancing climate model representations of cloud and convective processes. The grantee, Columbia University, will conduct fully-coupled, isotope-enabled climate simulations for the Last Glacial Maximum, Mid-Holocene, and Pre-Industrial periods using an ensemble of cloud and convective parameter sets. Paleoclimate data assimilation will...
- This $256,252 National Science Foundation award under the Geosciences program (CFDA 47.050) will fund research by the Aspen Global Change Institute to estimate the emergence of anthropogenic warming signals in snow water resource metrics in mountainous regions. The three-year project grant will use an innovative modeling framework including a large ensemble climate model, statistical learning techniques, and process-based hydrological modeling to analyze uncertainty and attribute changes in...
- This $599,998 National Science Foundation award under the Geosciences program (CFDA 47.050) will fund research to estimate the emergence of anthropogenic climate change signals in snow water resource metrics in mountainous regions. Led by researchers at the University of Colorado Boulder, the project will use an innovative modeling framework including a large ensemble climate model and process-based hydrological modeling. It aims to strengthen understanding of when and how much climate change...
- This Project Grant award of $199,316.00 from the National Science Foundation's (NSF) Geosciences Program (CFDA 47.050) aims to advance the understanding of how extreme weather events, such as heavy rainfall and flooding, may change in response to future climate scenarios. The project, titled "EMBRACE-AGS-SEED: Harnessing the Power of Machine Learning to Generate Ensembles of Regional Climate Projections," will evaluate whether artificial intelligence and machine learning can provide...
This $252,059 Project Grant award from the National Science Foundation's Geosciences program (CFDA 47.050) aims to develop a machine learning-based "emulator" that can efficiently predict water isotope patterns in fully-coupled global climate models. The goal is to build a powerful tool that can enhance climate science, hydrology, and understanding of the Earth's past and future climate by leveraging water isotope data. The University Corporation for Atmospheric Research (UCAR), which manages the National Center for Atmospheric Research (NCAR), will lead this collaborative research project from November 2025 to October 2028. The emulator developed under this award is expected to offer new insights into the Earth's water cycle and climate, while the project's open-source products and educational outreach will benefit the broader scientific community.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 8/25/25 | ||
| Not listed | $252.1k | 7/15/25 |