In this Part 2 article of a two-part series, observations based on satellite missions were used to evaluate the empirical consistency of model ensembles generated via stochastic modelling of ocean physics and biogeochemistry. A high-resolution Bay of Biscay configuration was used as a case study to explore the model error subspace in both the open and coastal ocean. In Part 1 of this work, three experiments were carried out to generate model ensembles by perturbing only physics, only biogeochemistry, and both of them simultaneously. In Part 2 of this work, empirical consistency was checked, first by means of rank histograms projecting the data onto the model ensemble classes, and second, by pattern-selective consistency criteria in the space of “array modes” defined as eigenvectors of the representer matrix. Rank histograms showed large dependency on geographical region and on season for sea surface temperature (SST), sea-level anomaly (SLA), and phytoplankton functional types (PFT), shifting from consistent model-data configurations to large biases because of model ensemble underspread. Consistency for SST array modes was found to be verified at large, small and coastal scales soon after the ensemble spin-up. Array modes for the along-track sea-level showed useful consistent information at large scales and at the mesoscale; for the gridded SLA was verified only at large scale. Array modes showed that biogeochemical model uncertainties generated by stochastic physics, were effectively detected by PFT measurements at large scales, as well as at mesoscale and small-scale. By contrast, perturbing only biogeochemistry, with an identical physical forcing across the ensemble, limits the potential of PFT measurements at detecting and possibly correcting small-scale biogeochemical model errors. When an ensemble was found to be inconsistent with observations along a particular direction (here, an array mode), a plausible reason is that other error processes must have been active in the model, in addition to the ones at work across the ensemble.

在这个由两部分组成的系列文章的第2部分中，基于卫星任务的观测结果用于评估通过海洋物理和生物地球化学的随机建模生成的模型集合的经验一致性。使用高分辨率的比斯开湾配置作为案例研究，以探索开放海洋和沿海海洋中的模型误差子空间。在这项工作的第1部分中，进行了三个实验，通过仅扰动物理，仅扰动生物地球化学以及同时扰动两者来生成模型合奏。在这项工作的第2部分中，检查了经验一致性，首先是通过将数据投影到模型集成类上的秩直方图，其次是通过在定义为代表特征向量的“数组模式”空间中的模式选择一致性标准矩阵。等级直方图显示，地理区域和季节对海表温度（SST），海平面异常（SLA）和浮游植物功能类型（PFT）的依赖性很大，由于模型集成不足，因此从一致的模型数据配置转变为较大偏差。发现整体旋转后不久，SST阵列模式的一致性已在大，小和沿海规模得到验证。沿海平面的阵列模式在大尺度和中尺度上显示出有用的一致信息。网格化的SLA仅在大规模验证。阵列模式表明，通过随机，大尺度，中尺度和小尺度的测量可以有效地检测出随机物理学产生的生物地球化学模型的不确定性。相比之下，仅干扰生物地球化学，在整个集合中具有相同的物理作用力，限制了PFT测量在检测和可能纠正小规模生物地球化学模型错误时的潜力。当发现某个集合与沿特定方向的观察结果不一致（此处为阵列模式）时，一个合理的原因是，除了在整个集合中起作用的过程外，其他错误过程也必须在模型中处于活动状态。