SGER: Evaluation of Community Climate System Model (CCSM) Constituent Transport Variability
SGER:社区气候系统模型 (CCSM) 成分传输变异性的评估
基本信息
- 批准号:0405451
- 负责人:
- 金额:$ 2.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2004
- 资助国家:美国
- 起止时间:2004-01-15 至 2004-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This research uses statistical tools to evaluate the simulation of chemical constituent transport in the Community Climate System Model (CCSM). Traditional methods for evaluating the ability of climate models to represent the transport of constituent gases rests on simple correlation and mean quantity analyses of simulated transports and observed fields. The new approach of the PIs is to use an off-line chemical model that includes the same representations (physics) for advection, convective transfer and boundary layer mixing as used in the CCSM atmospheric model (CAM). The off-line chemical transport model is very efficient, enabling long simulations in relatively short computer wall-clock time. The diagnostics tools to be developed will enable improved analyses of the spatial and temporal variability of the simulated constituent transports, as measured against decades of reanalysis data, including better evaluations of the statistical significance of the model results. Broader Impacts:Improvements in the ability of climate models to simulate the atmospheric transport of radiatively active trace gases is important for future climate projections; a benefit for environmental managers and policy makers in their efforts to understand and manage climate change. The CCSM is used by many university climate scientists who should benefit through the results of this research.
本研究使用统计工具来评估社区气候系统模式(CCSM)中化学成分输送的模拟。 评价气候模式代表组分气体输送能力的传统方法依赖于模拟输送和观测场的简单相关性和平均量分析。 PI的新方法是使用离线化学模型,该模型包括与CCSM大气模型(CAM)中使用的平流、对流传输和边界层混合相同的表示(物理)。 离线化学传输模型是非常有效的,使长时间的模拟在相对较短的计算机挂钟时间。 将开发的诊断工具将能够改进对模拟的成分输送的空间和时间变异性的分析,如对几十年的再分析数据的测量,包括更好地评价模型结果的统计意义。 更广泛的影响:提高气候模型模拟辐射活性痕量气体大气传输的能力对于未来的气候预测很重要;有利于环境管理人员和决策者努力了解和管理气候变化。 CCSM被许多大学气候科学家使用,他们应该从这项研究的结果中受益。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Bruno Sanso其他文献
Bruno Sanso的其他文献
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{{ truncateString('Bruno Sanso', 18)}}的其他基金
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大型数据集的空间和时空模型
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$ 2.5万 - 项目类别:
Standard Grant
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