Collaborative Research: Using ocean data assimilation to explore Arctic/subarctic climate variability

智力优势：近几十年来，高纬度气候发生了深刻变化。该项目针对这些变化提出了两个目标。第一个目标是通过将数据同化应用于气象和海洋测量的稀少历史记录，改进对过去一个世纪高纬度地区发生的海洋变化的了解。与这一目标有关的第一项任务将是改进作为高纬度研究工具的简单海洋数据同化(SODA)数据同化海洋再分析。这项任务直接建立在这笔赠款的前身资助的工作之上。由于该地区复杂的地形、水团和水流、稀疏、不均匀的海洋和气象观测网络以及海冰和盐碱过程的重要作用，需要进行这种特殊的高纬度努力。第二个任务是通过实验测试长期海洋再分析的可信度，探索历史数据采样和地表气象学对过去气候可变性所能了解到的限制。第二个目标是检查这些结果，以探索支配高纬度海洋、低纬度海洋和上覆大气之间相互作用的过程，以及这些相互作用可能如何促进气候可变性。北欧海洋和北极在过去十年中经历的显著变暖与20世纪20年代至30年代的暖期相似。这两个时期在规模和对亚极地北大西洋和上面的大气的影响上有多相似？在较短的年代际时间尺度上，流入北欧海域的大西洋水表现出很大的变异性，部分与气象的大尺度模式有关。与此相关的是地表盐度异常，如大盐度异常。这种变化的来源是什么，它与大气的关系，它的气候意义是什么？要实现这第二个目标，将需要以同化研究的结果为指导，在大气/海洋/冰的耦合模式中探索这些机制，特别是新的气专委类模式的运行。耦合气候模型具有完整的物理特性，允许跟踪淡水和热量等特性在气候系统中的移动，而与苏打水和历史观测的比较提供了关于模型真实性的信息。更广泛的影响：该项目将通过开发和传播改进的苏打水海洋再分析和对其不确定性的更好理解，为科学界提供对过去高纬度海洋温度、盐度和环流变化的更好分析。该项目-S参与北冰洋模式对比项目和其他场所将有助于促进对海洋再分析的更多了解和使用，特别是苏打水。这项研究项目的第二部分，即加强对洋盆交换作用的理解是一个需要理解的过程，以便开发预测模型。该项目将通过将作为这项工作的一部分而开发的数据集、分析和计算机资源免费提供给系的研究生，从而具有实质性的教育成分。

Intellectual merit: Recent decades have seen profound changes in higher latitude climate. This project addresses two goals responding to these changes. The first goal is to develop improved understanding of what past oceanic changes have occurred at high latitude during the past century through application of data assimilation to the sparse historical record of meteorological and oceanic measurements. The first task associated with this goal will be to improve the Simple Ocean Data Assimilation (SODA) data assimilation ocean reanalysis as a tool for high latitude research. This task builds directly on work funded by the predecessor to this grant. This special high latitude effort is required because of the complexities of the region?s topography, water masses and currents, the sparse, inhomogeneous oceanic and meteorological observational network, and the important role of sea ice and haline processes. The second task will be to explore the limits the historical data sampling and surface meteorology place on what can be learned about past climate variability through experiments testing the credibility of long ocean reanalyses.The second goal is to examine these results to explore the processes governing interaction between the high latitude ocean, the lower latitude ocean, and the overlying atmosphere, and how these interactions may contribute to climate variability. The remarkable warming the Nordic Seas and Arctic has experienced in the last decade bears similarity to a warm period in the 1920s-1930s. How similar are these two periods, both in magnitude and in impact on the subpolar North Atlantic and on the overlying atmosphere? On shorter decadal timescales the Atlantic Water flowing into the Nordic Seas shows substantial variability partially related to large-scale patterns in meteorology. Related to this are the anomalies of surface salinity such as the Great Salinity Anomaly. What is the origin of this variability, its relationship to the atmosphere, and what is its climate significance? Addressing this second goal will require exploring these mechanisms in coupled atmosphere/ocean/ice models, and in particular the new IPCC-class model runs, using the results of the assimilation studies as guidance. Coupled climate models have complete physics that allow the tracking of the movement of properties such as freshwater and heat through the climate system, while the comparisons to SODA and the historical observations provides information about the realism of the models.Broader impacts: This project will provide the scientific community with an improved analysis of past changes in the upper ocean temperature, salinity, and circulation at high latitude through development and dissemination of an improved SODA ocean reanalysis and improved understanding of its uncertainties. The project?s involvement in the Arctic Ocean Model Intercomparison Project and other venues will help promote the greater knowledge and use of ocean reanalyses generally, and SODA in particular. The second part of this research project, improving understanding of the role of oceanic basin exchanges is a process that needs to be understood in order to develop predictive models. The project will have a substantial educational component by making the data sets, analyses, and computer resources developed as part of this work freely available to the graduate students in the department.