ITR/AP+IM (GEO) Reanalysis of the Climate of the Global Ocean

0113148 Carton本项目将通过应用顺序估计技术，利用直接和间接观测来约束全球海洋环流、温度和盐度的数值模型，从而改进对海洋气候历史变化的估计。序贯估计在海洋再分析问题中的应用面临许多问题。在目前的计算机上完全实施是不切实际的。进一步的问题是，观测中存在较长的空间和时间相关尺度减去预测模型的差异。此外，地下数据集是有限的和非平稳的。该项目将利用计算机科学和气象学的现代发展来应对这些挑战。具体重点是1940-2000年这60年期间全球海洋的变化情况。利用数据同化对海洋的月温度、盐度和速度场进行了一系列初步的再分析，这有助于确定一些必须克服的问题。与此同时，历史观察数据库也有了很大的改进。该项目将支持马里兰大学的一名海洋学家和气象学家(Jim Carton和Eugenia Kalnay)与NASA/GSFC的一名应用数学家(Dick Dee)之间的跨学科协作，以继续探索为大气数据同化问题开发的新的计算方法和算法，以探索海洋的季节和年代际变化。科学目标还涉及气候可变性和可预报性(CLIVAR)计划的第一个科学目标的一部分：通过收集和分析观测数据，并与其他相关的气候研究和观测计划合作，开发和应用耦合气候系统模型，描述和理解在季节、年际、十年和百年时间尺度上导致气候可变性和可预测性的物理过程(CLIVAR，1998)。资金通过信息技术研究倡议和CLIVAR方案提供。

0113148 Carton This project will provide improved estimates of the historical changes in the climate of the oceans by using direct and indirect observations to constrain numerical models of the global ocean circulation, temperature, and salinity through application of sequential estimation techniques. Application of sequential estimation to the problem of ocean reanalysis faces a number of problems. Full implementation is impractical on current computers. Further problems result from the presence of long spatial and temporal correlation scales in the observation minus forecast model differences. In addition, the subsurface data sets are limited and non-stationary. This project will exploit modern developments in computer science and meteorology to address these challenges. The specific focus is on variability of the global ocean during the 60-year period 1940-2000. A suite of preliminary reanalyses of the monthly temperature, salinity, and velocity fields of the ocean has been conducted using data assimilation, which have helped to identify a number of problems that must be surmounted. At the same time, there have been substantial improvements in the database of historical observations. This project will support an interdisciplinary collaboration between an oceanographer and meteorologist at University of Maryland (Jim Carton and Eugenia Kalnay) and an applied mathematician at NASA/GSFC (Dick Dee) to continue to explore new computational methods and algorithms developed for the problem of atmospheric data assimilation to explore seasonal to decadal variability of the oceans. The scientific objectives also address parts of the first of the scientific objectives of the CLImate Variability and predictability (CLIVAR) program: to describe and understand the physical processes responsible for climate variability and predictability on seasonal, interannual, decadal and centennial time scales, through the collection and analysis of observations and the development and application of models of the coupled climate system, in cooperation with other relevant climate research and observing programs (CLIVAR, 1998). Funding is provided through the Information Technology Research Initiative and the CLIVAR program.0113148