WAP-1D-VAR v1.0: Development and Evaluation of a One-Dimensional Variational Data Assimilation Model for the Marine Ecosystem Along the West Antarctic Peninsula
WAP-1D-VAR v1.0: Development and Evaluation of a One-Dimensional Variational Data Assimilation Model for the Marine Ecosystem Along the West Antarctic Peninsula
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DOI：
10.5194/gmd-14-4939-2021
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发表时间：
2021-02
影响因子：
5.1
通讯作者：
H. Kim;Ya‐Wei Luo;H. Ducklow;O. Schofield;D. Steinberg;S. Doney
中科院分区：
文献类型：
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作者：
H. Kim;Ya‐Wei Luo;H. Ducklow;O. Schofield;D. Steinberg;S. Doney
抽象的。西南极半岛(WAP)是一个快速变暖的地区，过去几十年来，帕尔默南极长期生态研究(LTER)计划的数十年观测揭示了该地区对气候变化和多变性的大量生态和生物地球化学响应。这些长期观测的丰富为生态系统建模提供了重要的资源，但缺乏对开发数值模型的关注，这些模型模拟了沿沿海WAP的澳大利亚生长季浮游生物的时间演变动态。本文开发了一个一维数据同化浮游生态系统模式(即WAP-1D-VAR模式v1.0)，该模式配备了变分伴随和模式参数优化方案。我们首先演示了对WAP-1D-VAR模型中已有的食物网和生物地球化学成分的修改和新增的模型方案，包括诊断海冰强迫和特定于WAP区域的营养相互作用。然后，我们通过同化来自Palmer LTER生长季(2002年10月至2003年3月)的11种不同类型的数据进行了模式实验，这些数据类型与相应的模式状态变量和舱间气流直接相关。迭代数据同化程序通过优化72个自由参数中的14个参数，将观测结果和模型结果之间的不匹配减少了80%。优化的模式结果捕捉到了WAP的关键生态特征，如季节性海冰消退期间的水华，缺乏常量营养素限制，同化和非同化模式状态变量的可比值和流向其他研究的流量，以及几个重要的生态系统指标。一个例外是略微低估了颗粒输出通量，我们讨论了完全潜在的潜在原因。WAP-1D-VAR模式的数据同化方案使现有的观测数据能够约束先前知之甚少的过程，包括不同浮游植物种群对初级生产的分配、WAP浮游植物群落的最佳叶绿素与碳的比率，以及不同稳定性的溶解有机碳库的分配。WAP-1D-VAR模式被成功地应用于将一系列可用数据集的快照粘合在一起，以解释和理解沿沿海WAP的观测动态。
Abstract. The West Antarctic Peninsula (WAP) is a rapidly warming region, with substantial ecological and biogeochemical responses to climate change and variability for the past decades, revealed by multi-decadal observations from the Palmer Antarctica Long-Term Ecological Research (LTER) program. The wealth of these long-term observations provides an important resource for ecosystem modelling, but there has been a lack of focus on the development of numerical models that simulate time-evolving plankton dynamics over the Austral growth season along the coastal WAP. Here we developed a one-dimensional, data assimilation planktonic ecosystem model (i.e., the WAP-1D-VAR model v1.0) equipped with a variational adjoint and model parameter optimization scheme. We first demonstrate the modified and newly added model schemes to the pre-existing food-web and biogeochemical components of the WAP-1D-VAR model, including diagnostic sea-ice forcing and trophic interactions specific to the WAP region. We then conducted model experiments by assimilating eleven different data types from an example Palmer LTER growth season (October 2002–March 2003) directly related to corresponding model state variables and intercompartmental flows. The iterative, data assimilation procedure reduced by 80 % the misfits between observations and model results, compared to before optimization, via an optimized set of 14 parameters out of total 72 free parameters. The optimized model results captured key WAP ecological features, such as blooms during seasonal sea-ice retreat, the lack of macronutrient limitation, and comparable values of the assimilated and non-assimilated model state variables and flows to other studies, as well as several important ecosystem metrics. One exception was slightly underestimated particle export flux, for which we discuss fully potential underlying reasons. The data assimilation scheme of the WAP-1D-VAR model enabled the available observational data to constrain previously poorly understood processes, including the partitioning of primary production by different phytoplankton groups, the optimal chlorophyll to carbon ratio of the WAP phytoplankton community, and the partitioning of dissolved organic carbon pools with different lability. The WAP-1D-VAR model was successfully employed to glue the snapshots from a range of the available data sets together to explain and understand the observed dynamics along the coastal WAP.