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Modeling Global Ocean Biogeochemistry With Physical Data Assimilation: A Pragmatic Solution to the Equatorial Instability
Journal of Advances in Modeling Earth Systems ( IF 6.8 ) Pub Date : 2018-03-30 , DOI: 10.1002/2017ms001223
Jong-Yeon Park 1, 2 , Charles A. Stock 2 , Xiaosong Yang 2 , John P. Dunne 2 , Anthony Rosati 2 , Jasmin John 2 , Shaoqing Zhang 3
Affiliation  

Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation. Most notably, the breakdown of geostrophic constraints on data assimilation in equatorial regions can lead to spurious upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to understand and predict the biogeochemical consequences of El Niño and La Niña. We develop a strategy to robustly integrate an ocean biogeochemical model with an ensemble coupled‐climate data assimilation system used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires two steps. First, we find that tightening constraints on atmospheric data assimilation maintains a better equatorial wind stress and pressure gradient balance. This reduces spurious vertical velocities, but those remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of model‐data weights that removed spurious biogeochemical signals while benefitting from off‐equatorial constraints that still substantially improve equatorial physical ocean simulations. Compared to the unconstrained control run, the optimally constrained model reduces equatorial biogeochemical biases and markedly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach described herein offers a means of advancing earth system prediction in parallel with continued data assimilation advances aimed at fully considering equatorial data constraints.

中文翻译:

利用物理数据同化为全球海洋生物地球化学建模:赤道不稳定性的务实解决方案

对历史和当前生物地球化学的可靠估计对于了解过去的生态系统可变性和预测未来的变化至关重要。然而,由于对物理数据同化过程中出现的瞬时动量不平衡的高度生物地球化学敏感性,阻碍了将改进的物理海洋状态估计值转化为改进的生物地球化学估计值的努力。最值得注意的是,赤道地区对地转约束的数据同化的破坏可能导致虚假的上升流,从而导致赤道生产力和生物地球化学通量过大。这阻碍了人们理解和预测厄尔尼诺和拉尼娜的生物地球化学后果的努力。我们制定了将海洋生物地球化学模型与用于季节到十年全球气候预测的整体耦合气候数据同化系统进行稳固整合的策略。解决虚假的垂直速度需要两个步骤。首先,我们发现加强对大气数据同化的约束条件可以保持更好的赤道风应力和压力梯度平衡。这降低了虚假的垂直速度,但是剩余的速度仍然会产生很大的生物地球化学偏差。其余部分则通过对赤道附近的数据约束施加更严格的保真度来对动力学进行建模来解决。我们确定了模型数据权重的最佳选择,该模型权重消除了虚假的生物地球化学信号,同时受益于赤道以外的约束条件,这些约束条件仍大大改善了赤道物理海洋模拟。与无约束控制运行相比,最优约束模型减少了赤道生物地球化学偏差,并显着提高了赤道地下硝酸盐浓度和低氧区域。本文所述的实用方法提供了一种与地球数据预测并行的,旨在充分考虑赤道数据约束的,连续的数据同化进展的方法。
更新日期:2018-03-30
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