Project Grant 2530609
- 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 $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),...
- 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...
- 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 $126,270 federal Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop new capabilities to monitor and understand forest carbon dynamics in the Earth system. The key products and services to be delivered include: Cross-platform and cross-region learning frameworks to enable fine-scale carbon dynamics monitoring at large geographic scales. High-fidelity fast approximations of theory-based carbon forecasting models using new meta-learning...
- This $178,059 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) will enable the University of Southern California to develop a machine learning-based table extraction tool to help streamline paleoclimate data analysis. The goal is to create an AI-powered Python toolbox that can automatically identify and extract tabular paleoclimate data from unstructured files, reducing the time scientists spend wrangling and annotating these datasets before...
- 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...
- 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 $461,045 federal Project Grant was awarded by the National Science Foundation's Geosciences Program (CFDA 47.050) to William Marsh Rice University in Houston, Texas. The grant aims to develop the Isotope-Enabled Community Earth System Model (ICESM), an innovative Earth system model that simulates stable water isotopes to provide new insights into atmospheric dynamics, plant-water interactions, ecosystem hydrology, and changes to ice sheets and glaciers. The project will leverage diverse...
- 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 $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 emulate water isotope signatures, a critical capability for improving climate model representations of the global water cycle. The project will foster interdisciplinary collaboration between climate scientists and AI experts, with the emulator and related research products made openly available to the scientific community. Outcomes are expected to enhance understanding of Earth's past and future climate through improved modeling of water isotope tracers.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $383.2k | 7/15/25 |