CYCLOCIM: A 4-D variational assimilation system for the climatological mean seasonal cycle of the ocean circulation

CYCLOCIM: A 4-D variational assimilation system for the climatological mean seasonal cycle of the ocean circulation
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CYCLOCIM:海洋环流气候平均季节循环的 4-D 变分同化系统

DOI:
10.1016/j.ocemod.2021.101762
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发表时间:
2021
期刊:
影响因子:
3.2
通讯作者:
DeVries, Tim
DeVries, Tim
中科院分区:
地球科学3区
文献类型:
--
作者:
Huang, Qian;Primeau, François;DeVries, Tim

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我们描述了一种新的 4-D 变分同化系统,称为 CYCLOCIM,用于估计残余平均海洋环流的气候季节循环。 CYCLOCIM 同化来自世界海洋地图集的月平均位温和盐度数据,以及来自全球数据分析项目第 2 版的 CFC-11、CFC-12 和深海天然放射性碳测量值。CYCLOCIM 的控制参数包括:(i) 出现在水平动量方程中的未解决涡流应力散度的季节性变化 3-D 场,(ii) 季节性变化 地表热量和淡水通量的二维校正场,以及 (iii) CFC 的海气通量的恒定比例因子。初始条件对完全旋转的海洋模型的影响消失了。因此,与其他 4-D 变分同化系统不同,CYCLOCIM 不包括初始条件作为控制参数。贝叶斯过程用于制定反问题,通过找到后验概率分布的最大值来解决。用于找到最大值的优化过程包括用于计算流速和示踪剂分布的前向模拟,然后是用于计算后验梯度的后向(“伴随”)模拟。使用拟牛顿搜索算法来查找参数集以最大化后验概率。我们发现,与之前的稳态版本模型相比,通过解决季节周期问题,该模型能够更好地拟合上层海洋的观测结果。 CYCLOCIM 的主要输出是一组 12 个数据受限的每月示踪剂传输矩阵,将为全球海洋生物地球化学循环研究提供有用的循环模型。
We describe a new 4-D variational assimilation system, called CYCLOCIM, to estimate the climatological seasonal cycle of the residual mean ocean circulation. CYCLOCIM assimilates monthly mean potential temperature and salinity data from the World Ocean Atlas, and CFC-11, CFC-12 and natural radiocarbon measurements for the deep ocean from the Global Data Analysis Project, Version 2. CYCLOCIM’s control parameters include: (i) a seasonally varying 3-D field of unresolved eddy-stress divergences that appear in the horizontal momentum equations, (ii) seasonally varying 2-D correction fields for the surface heat and freshwater fluxes, and (iii) a constant scaling factor for the air–sea flux of CFCs. The influence of initial conditions on a fully spun-up ocean model vanishes. Thus, unlike other 4-D variational assimilation systems, CYCLOCIM does not include initial conditions as control parameters. A Bayesian procedure is used to formulate the inverse problem, which is solved by finding the maximum of the posterior probability distribution. The optimization process used to find the maximum includes a forward simulation to calculate the flow velocities and tracer distributions followed by a backward (“adjoint” ) simulation to compute the gradient of the posterior. A quasi-Newton search algorithm is used to find the set of parameters to maximize the posterior probability. We find that by resolving the seasonal cycle the model is able to better fit the observations in the upper ocean compared to a previous steady-state version of the model. The main output from CYCLOCIM is a set of 12 data-constrained monthly tracer transport matrices that will provide a useful circulation model for global marine biogeochemical cycle studies.
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DOI: --
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