Making ecosystem modelling operational - selected EcoOcean output

Marine Ecosystem Models (MEMs) are increasingly driven with Earth System Models (ESMs) to better understand marine ecosystem dynamics, and to analyse the effects of alternative management efforts for marine ecosystems under potential scenarios of climate change. However, policy and commercial activities typically occur on seasonal-to-decadal time scales, a time span widely used in the global climate modelling community but where the skill level assessments of MEMs are in their infancy. This is mostly due to technical hurdles that prevent the global MEM community from performing large ensemble simulations with which to undergo systematic skill assessments. Here, we developed a novel distributed execution framework constructed of low-tech and freely available technologies to enable the systematic execution and analysis of linked ESM / MEM prediction ensembles. We apply this framework on the seasonal-to-decadal time scale, and assess how retrospective forecast uncertainty in an ensemble of initialised decadal Earth System Model predictions affects a mechanistic and spatiotemporal explicit global MEM. Our results indicate that ESM internal variability has a relatively low impact on the MEM variability in comparison to the broad assumptions related to reconstructed fisheries. We also observe that the results are also sensitive to the ESM specificities. Our case study warrants further systematic explorations to disentangle the impacts of climate change, fisheries scenarios, MEM internal ecological hypotheses, and ESM variability. Most importantly, our case study demonstrates that a simple and free distributed execution framework has the potential to empower any modelling group with the fundamental capabilities to operationalize marine ecosystem modelling.This data set constitutes the selected output for 385 EcoOcean executions, when driven by two ESMs, for fished and non-fished oceans, aggregated as ttime series across FAO sub-oceans and FAO statistical areas. Only focus data used in the manuscript - functional groups 1-6 (small, med and large pelagics, and small, med and large demersals) and the Atlantic zones - are included here

海洋生态系统模型（Marine Ecosystem Models, MEMs）正越来越多地与地球系统模型（Earth System Models, ESMs）耦合驱动，以更深入地理解海洋生态系统动力学，并分析气候变化潜在情景下海洋生态系统的各类替代管理措施的影响。然而，政策与商业活动通常发生在季节至年代际时间尺度上——这一时间跨度在全球气候建模领域被广泛应用，但当前对MEMs的技能水平评估仍处于起步阶段。这主要是因为技术瓶颈阻碍了全球MEM研究团队开展可用于系统性技能评估的大集合模拟实验。为此，我们开发了一套由低技术门槛且可免费获取的技术构建的新型分布式执行框架，以实现耦合的ESM/MEM预测集合的系统性执行与分析。我们在季节至年代际时间尺度上应用该框架，并评估了初始化的年代际地球系统模型预测集合中的回溯预报不确定性，如何对一个具有机理性、显式时空分布特征的全球海洋生态系统模型产生影响。研究结果表明，相较于与重建渔业相关的宽泛假设，地球系统模型的内部变率对海洋生态系统模型变率的影响相对较小。我们同时发现，研究结果对地球系统模型的具体特性也较为敏感。本案例研究还需开展进一步的系统性探索，以厘清气候变化、渔业情景、海洋生态系统模型内部生态学假设以及地球系统模型变率各自产生的影响。尤为重要的是，本案例研究证明，一套简单免费的分布式执行框架，具备赋能任何建模团队掌握海洋生态系统建模核心能力并实现业务化运行的潜力。本数据集包含了385次EcoOcean运行的筛选输出结果：该运行由两类地球系统模型驱动，涵盖捕捞与非捕捞海洋场景，并按联合国粮食及农业组织（Food and Agriculture Organization, FAO）次大洋区域与FAO统计区域聚合为时间序列。本数据集仅包含论文中使用的核心数据：即功能群1-6（小型、中型、大型中上层鱼类，以及小型、中型、大型底栖鱼类）与大西洋海域相关数据。