Collaborative Research: "EaSM-3": The Role of Ocean Eddies in Decadal Prediction
Collaborative Research: "EaSM-3": The Role of Ocean Eddies in Decadal Prediction
批准号:
1419569
负责人:
Benjamin Kirtman
金额:
$160.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-07-15 至 2020-06-30
中文摘要
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英文摘要
There is a growing demand for environmental predictions that include a broader range of space and time scales and that include a more complete representation of weather and climate processes. Meeting this demand necessitates a unified approach that challenges the traditional boundaries between weather and climate science, and requires a more integrated approach with much higher spatial resolution models that resolve important dynamical processes and their interactions. Estimates of predictability are likely to be sensitive to the range of processes and phenomena captured by these models. This project seeks to provide a comprehensive assessment of how ocean eddies affect decadal predictability. One of the key questions this project asks is, do prediction systems actually need to initialize individual ocean eddies or is simply including ocean eddies in the prediction system sufficient? Answering this question has profound implications in the design of decadal prediction systems. This project will contribute to workforce development through the education of a graduate student in important climate variability and change problems, and the mentoring of a post-doctoral researcher. The decadal prediction and climate services community will be advised regarding how ocean eddies impact decadal predictability and how to initialize them in eddy resolving models. The new capacity for high-resolution assimilation within the freely accessible Community Earth System Model (CESM) and Data Assimilation Research Testbed (DART) modeling frameworks will be available to the CESM climate modeling community. More broadly, any model that interfaces with the DART ensemble assimilation system can leverage the data assimilation infrastructure advances and tool development that will be developed under this project. Ocean eddy-resolving simulations, data assimilation products, predictability and experimental prediction results that are of interest to the broader climate community will be also made readily available.This research project is based on the hypothesis that the dynamic and physical process associated with ocean eddies and the accompanying interactions with the atmosphere have a large and robust impact on decadal predictability. The proposed research seeks to quantify this effect through diagnostic predictability research by analyzing long eddy resolving simulations and prognostic "perfect" model predictability experiments. Tools and capabilities that are necessary to initialize the ocean component of coupled models at eddy resolving resolutions will be developed. The new eddy resolving data assimilation system will be evaluated through experimental prediction. The project also seeks to develop a data assimilation system appropriate for initializing decadal prediction at ocean eddy-resolving scales. The current ensemble-based ocean data assimilation method available at eddy-permitting resolution will be applied to the eddy-resolving model. To support tuning the new high-resolution data assimilation system, novel tools for estimating the resolution-dependent observational error statistics used in the assimilation will be developed. The specification of resolution-dependent observational error statistics is of enormous importance for data assimilation, and tools to support this are currently unavailable. The limitations of the high-resolution ocean data assimilation system will be assessed in terms of its computational cost and its efficacy at constraining the eddy field, and the project will develop strategies for addressing these limitations that are aligned with the needs of decadal prediction that are identified as part of the decadal predictability research. Finally, the data assimilation system will be implemented at eddy-resolving scales during the data-rich years of 2006-present, and evaluated with experimental predictions.
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依托单位:
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