Global space-time variability in primary production and carbon flux using the Argo float network

据预测，全球气候变化将改变海洋的生物生产力，对渔业和气候产生影响。海洋耦合物理-生物地球化学模型预测的浮游植物丰度的长期变化预测全球平均浮游植物丰度下降，相关的碳通量和广泛的脱氧（Kwiatkowski等，2020）。然而，可用于验证当前气候趋势的观测数据在空间和时间上都是有限的，特别是在海洋表面以下。bgc-Argo计划（https://biogeochemical-argo.org/）的出现大大增加了可用于分析的生物地球化学数据的数量（自2002年以来，目前已部署了bbb1500个浮标）。然而，这些数据在空间和时间上是分散的，因此很难推断出生物地球化学特性的全球平均三维分布，也很难推断出它们是如何随季节或逐年变化的。这导致我们在当代海洋生物地球化学知识和未来对气候变化的潜在反应方面存在一些根本性的差距。例如，地表下叶绿素最大值的全球分布是怎样的？颗粒有机碳在海洋中的储存效率在全球范围内是如何变化的（例如Dall'Olmo和Mork， 2014）？该项目旨在利用bgc-Argo网络的全部潜力，通过使用时空建模（Hammond et al., 2017）来推断关键生物地球化学过程的全球时空变化，从而回答这些问题。

Global climate change is predicted to alter the ocean's biological productivity with implications for fisheries and climate. Long-term changes in phytoplankton abundance projected by ocean coupled physical-biogeochemical models predict declining globally averaged phytoplankton abundance, associated carbon fluxes, and widespread deoxygenation (Kwiatkowski et al., 2020). However, observational data that could be used to verify current climate trends is limited in both space and time, particularly below the ocean surface. The advent of the bgc-Argo programme (https://biogeochemical-argo.org/) has vastly increased the quantity of biogeochemical data available for analysis (currently > 1500 floats have been deployed since 2002). The data are, however, scattered in space and time, making it difficult to deduce either the global mean 3-D distribution of biogeochemical properties, or how they may change seasonally or from year to year. This results in some fundamental gaps in our knowledge of contemporary ocean biogeochemistry, and the potential future response to climate change. For example, what is the global distribution of the subsurface chlorophyll maximum? How does the efficiency with which particulate organic carbon is stored in the ocean vary globally (e.g. Dall'Olmo and Mork, 2014)? This project aims to exploit the full potential of the bgc-Argo network to answer these questions by using space-time modelling (Hammond et al., 2017) to infer global spatial and temporal variability in critical biogeochemical processes.