| NSF Org: |
AGS Division of Atmospheric and Geospace Sciences |
| Recipient: |
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| Initial Amendment Date: | May 3, 2011 |
| Latest Amendment Date: | May 3, 2011 |
| Award Number: | 1048834 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Anjuli Bamzai
AGS Division of Atmospheric and Geospace Sciences GEO Directorate for Geosciences |
| Start Date: | May 15, 2011 |
| End Date: | April 30, 2016 (Estimated) |
| Total Intended Award Amount: | $381,000.00 |
| Total Awarded Amount to Date: | $381,000.00 |
| 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 98195-1016 |
| 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): | CR, Earth System Models |
| 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
The need for more accurate and detailed regional climate predictions is widely recognized; industry, local government and society increasingly require sufficient understanding and warning to enable proper planning and adaptation to mitigate future costs and disruptions arising from climate variability and change. This project is addressing critical research and model development issues aimed at improving such regional predictions in a three-phase approach. The Nested Regional Climate Model from the National Center for Atmospheric Research is being further developed with the inclusion of ocean and land coupling, atmospheric chemistry, and decision support tools for societal and industry users. This model also forms a test bed for the next generation Model for Prediction Across Scales (MPAS), a next-generation regional weather-climate-ocean prediction system with sophisticated atmospheric chemistry components. A number of statistical and related downscaling techniques will also be developed to enable improved assessment of extreme weather systems and their impacts.
Broader Impacts. A feature of the program is the wide, cross-disciplinary community approach and novel collaboration amongst experts in regional climate, severe weather, numerical modeling, data assimilation, atmospheric chemistry, industry, and societal applications. Coordinating the development of the new prediction systems and downscaling approaches directly with societal applications and development of decision tools will enable each to influence the other to their mutual benefit. Thus, the program entails both substantial intellectual merit and societal and scientific benefit. For example, it will enable earlier societal and industry use of the developing predictive capacity and lead to an improved, tightly-coupled predictive and decision system. This will feature novel societal approaches, such as the role that long-term contracts like multi-year insurance policies coupled with long-term loans can play in encouraging investments in cost-effective adaptation measures in the presence of climate change.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Overview
In order to address societal needs for climate projections over the next 10-30 years, model forecasts require proper initialization of the full climate system. Initialization of these model forecasts requires using observations of the climate system to determine the starting point for the forecast (the "initial condition"). Data assimilation is a well developed field used for the initialization problem in weather forecasting, but remains in infancy for climate prediction. Here we take on this challenge, working from fundamental aspects of data assimilation for the coupled climate system with interacting fast (atmospheric weather) and slow components (deep ocean circulation), toward an operational system for use in decadal climate prediction. That is, our main goals is to develop a basic understanding of how to optimally perform data assimilation for the coupled climate system by considering a wide range of approaches. This goal is pursued by a set of numerical experiments in a simplified model to identify a useful strategy that is then tested using data from version 4 of the Community Climate System Model (CCSM4).
Findings
Our findings organize on three fundamental questions.
1. Is data assimilation (DA) needed for the coupled atmosphere--ocean problem?
Yes, convergence of the ocean circulation to a known atmosphere is very slow (>1000 years).
2. Is fully coupled atmosphere--ocean DA needed?
We find that the answer to this question falls on a continuum, depending on the density of ocean observations. In the limit of a densely observed ocean, coupled DA is not important, and the problem reduces to ocean DA. In the limit of no ocean observations, coupling is fundamental, since the only constraints on ocean state derive from observations of the atmosphere. At present, we suspect that the real problem is closer to the poorly observed limit, and therefore that coupled atmosphere--ocean DA is important.
3. What are the essential elements of an efficient approach for initializing the slow ocean?
We cast the answer to this question in terms of the time required to track the true state for a given set of observations in idealized experiments. We find that for a densely observed ocean it takes about 30 days to track the true state. For assimilation of annually averaged observations, it takes about 5--10 years for the atmosphere and upper-ocean temperature to track the true state. For the situation where only annually averaged observations of the atmosphere are available, it takes about 15--30 years to track the true system, provided that a useful measure of large-scale atmospheric circulation is available.
Last Modified: 08/29/2016
Modified by: Gregory J Hakim
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