| NSF Org: |
AGS Division of Atmospheric and Geospace Sciences |
| Recipient: |
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| Initial Amendment Date: | August 11, 2022 |
| Latest Amendment Date: | August 11, 2022 |
| Award Number: | 2218829 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Eric DeWeaver
edeweave@nsf.gov (703)292-8527 AGS Division of Atmospheric and Geospace Sciences GEO Directorate for Geosciences |
| Start Date: | August 15, 2022 |
| End Date: | July 31, 2025 (Estimated) |
| Total Intended Award Amount: | $352,990.00 |
| Total Awarded Amount to Date: | $352,990.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
4333 BROOKLYN AVE NE SEATTLE WA US 98195-1016 (206)543-4043 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4333 Brooklyn Ave NE Seattle WA US 98195-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Climate & Large-Scale Dynamics, EarthCube |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050 |
ABSTRACT
Weather phenomena come in all spatial scales, from the turbulent up-and-down motions within clouds to frontal systems that span a time zone to the jet streams that circle the globe. Naturally different models are used to capture phenomena at different scales, including Large Eddy Simulation (LES) models with grid spacings of perhaps 10m used to simulate individual clouds or small cloud clusters. LES models are typically applied on a limited domain, perhaps 10km to 100km wide, and the influence of larger scales of motion on the clouds is represented by imposing domain-wide conditions, for instance a single vertical profile of temperature and moisture for the whole domain. The drawback of such simulations is that they fail to capture two-way interactions between small and large scales, for instance the effect of small clouds on the large-scale temperature and moisture profiles. Models that can capture a larger range of scales would thus be quite valuable.
One model which has proved quite useful for this purpose is the System for Atmospheric Modeling (SAM), developed by the Principal Investigator (PI) in the early 2000s. SAM has been used as an LES model, for instance in simulations of flow around a building at 1m resolution, but has also been used with grid spacings around 5km to simulate wave motions in a channel domain spanning the tropics. SAM has been a workhorse model for studies of cloud behaviors including the aggregation of convective clouds and the response of clouds to greenhouse gas-induced warming, in particular the extent to which the cloud response intensifies or counteracts the warming.
Recently the PI developed a global version of SAM called gSAM, which extends the Cartesian coordinates to spherical coordinates and makes other modifications to represent flow on a global domain. The model inherits all of the features of SAM and also adds an immersed step topography, an advance over previous versions which were more idealized and assumed a flat surface. Another way in which gSAM adds realism is the ability to run simulations starting from observational initial conditions, allowing short-term "forecasts", also called hindcasts, of real-world weather system evolution. A recent study used this feature, along with "nudging" to reanalysis data, to simulate conditions observed during the SOCRATES field campaign (see AGS-16628674). The study concluded that the formation of cloud ice particles from the shattering of earlier ice particles plays a role in determining the width of clouds, thus regulating the amount of sunlight that reaches the surface of the Southern Ocean.
The goal of this award is to further develop gSAM and make it available to the worldwide research community as a resource for weather and climate research. The work includes tasks devoted to improving model behavior near the poles, improving the accuracy and efficiency of radiative transfer calculations using machine learning techniques, improving input/ouput performance, and validating simulations against satellite data. Additional resources are developed to facilitate use and adoption of the model, including a full suite of documentation and tutorials, initial and boundary condition datasets for multiple configurations and resolutions, and model output for several six-month simulations. The model is maintained on GitHub and users can contribute to code development using GitHub repositories. The PI also maintains a model website that tracks publications using the model and provides additional information and resources. Since gSAM is an extension of SAM it is easily configured to run as a limited-domain LES model, thereby continuing to serve the SAM user community.
The work has broader impacts due to the power of gSAM as a tool for conducting basic science research on a wide range of topics. One area of particular interest is the interaction between clouds and climate change, as the sensitivies of clouds to a warming climate could affect the amount of warming that occurs. gSAM can also contribute to our understanding of how the intensity of extreme precipitation events is likely to change in a warming world. In both cases gSAM serves to lower the barriers between the research communities studying climate processes on the global scale and cloud properties on the local scale. The project also supports a graduate student, thereby building the next generation scientific workforce.
This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support Artificial Intelligence/Machine Learning and open science activities in the geosciences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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