Along-isopycnal transport in the Southern Ocean: Assessing DIMES results with an assimilating model

智力优势：南大洋的深流和等流混合实验（DIMES）旨在评估沿南大洋陡峭倾斜的密度表面的等流（主要是水平）混合的作用，同时确定深流（主要是垂直）混合过程的强度，这些过程有可能改变中深度的水性质。这两个过程共同是全球经向翻转环流的关键因素。DIMES进行了广泛的采样计划，包括声学跟踪水下浮子，中深度示踪剂释放，系泊阵列和水性质调查。现在DIMES数据的大部分收集已经完成，本项目的目标是通过将DIMES观测数据作为海洋环流模式——南大洋状态估计（SOSE）的约束条件，从而优化利用DIMES观测数据，该模式使用4维变分数据同化技术运行。SOSE将提供DIMES研究领域背景条件的最佳估计，这对DIMES浮子和示踪剂分析至关重要。将把SOSE场与DIMES和其他独立观测进行密切比较，以评估SOSE的不确定性，以及DIMES观测及其推导的拉格朗日混合估计的不确定性。SOSE场将揭示DIMES域和整个南大洋等压混合和海洋热输运的细节。DIMES领域关于等环流混合的经验教训将扩展到整个南大洋，以评估等环流混合的空间变化及其对当地测深、风和浮力强迫的敏感性。还将分析sse场，以评估经向翻转环流的强度，并评估横跨ACC和朝向南极洲周围气候敏感的冰架的极向热输送。更广泛的影响：本项目使用SOSE进行的研究将有助于更广泛的多机构、多pi的努力，以解释作为DIMES的一部分收集的数据，最终目标是更好地了解海洋中的沿等环流和准环流混合过程。SOSE的产出将公开提供给dimes附属研究小组和其他感兴趣的研究人员进行分析。状态估计设置（正向和伴随模型）将由DIMES研究小组处理，用于支持他们的科学目标的额外运行。DIMES的结果将最终导致对这些混合过程更好的物理理解，并建议改进用于未来气候预估的数值模式的混合参数化。此外，该项目将有助于培训一名目前持有NASA地球系统科学研究生奖学金的研究生，并将为一名博士后研究员提供支持，该博士后研究员将发展他在同化和模型分析方面的技能。该项目是对美国CLIVAR（气候变率和可预测性）计划的贡献。

Intellectual Merit:The Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean (DIMES) is designed to evaluate the role of isopycnal (mostly horizontal) mixing along the steeply tilted density surfaces of the Southern Ocean, and at the same time to determine the magnitude of diapycnal (mostly vertical) mixing processes that have the potential to transform water properties at mid-depth. Together these two processes are key contributors to the global meridional overturning circulation. DIMES carried out an extensive sampling program including acoustically-tracked sub-surface floats, a mid-depth tracer release, a mooring array and water property surveys. Now that the bulk of DIMES data collection has taken place, the objective of this project is to make optimal use of the DIMES observations by employing them as constraints for an ocean general circulation model, the Southern Ocean State Estimate (SOSE), which is run using a 4-dimendional variational data assimilation technique. SOSE will provide best estimates of the background conditions in the DIMES research domain, which is vital for the DIMES float and tracer analysis. SOSE fields will be closely compared with DIMES and other independent observations to provide an assessment of uncertainties in SOSE, as well as uncertainties in the DIMES observations and in its derived estimates of Lagrangian mixing. SOSE fields will reveal the details of isopycnal mixing and oceanic heat transport in the DIMES domain and throughout the Southern Ocean. Lessons learned in the DIMES domain about isopycnal mixing will be extended to the entire Southern Ocean to evaluate spatial variations in isopycnal mixing and its sensitivity to local bathymetry, wind, and buoyancy forcing. SOSE fields will also be analyzed to evaluate the strength of meridional overturning circulation, and to assess poleward heat transport across the ACC and towards the climatically sensitive ice shelves surrounding Antarctica.Broader Impacts:Research conducted for this project using SOSE will contribute to the broader multi-institution, multi-PI efforts to interpret data collected as part of DIMES, with the ultimate goal of developing a better understanding of along-isopycnal and diapycnal mixing processes in the ocean. Output from SOSE will to be made publicly available to be used for analysis by DIMES-affiliated research groups and by other interested researchers. The state estimate setup (forward and adjoint model) will be at the DIMES research groups' disposal for additional runs in support of their science goals. Results from DIMES will ultimately lead to better physical understanding of these mixing processes and suggest improvements in the mixing parameterizations of numerical models used for future climate projections. In addition, the project will contribute to the training of one graduate student who currently holds a NASA Earth System Science graduate fellowship and will provide support for a post-doctoral researcher, who will develop his skills in assimilation and model analysis.This project is a contribution to the U.S. CLIVAR (Climate Variability and Predictability) Program.