A combined global ocean pCO2 climatology combining open ocean and coastal areas (NCEI Accession 0209633)

This dataset contains the partial pressure of carbon dioxide (pCO2) climatology that was created by merging 2 published and publicly available pCO2 datasets covering the open ocean (Landschützer et. al 2016) and the coastal ocean (Laruelle et. al 2017). Both fields were initially created using a 2-step neural network technique. In a first step, the global ocean is divided into 16 biogeochemical provinces using a self-organizing map. In a second step, the non-linear relationship between variables known to drive the surface ocean carbon system and gridded observations from the SOCAT open and coastal ocean datasets (Bakker et. al 2016) is reconstructed using a feed-forward neural network within each province separately. The final product is then produced by projecting driving variables, e.g., surface temperature, chlorophyll, mixed layer depth, and atmospheric CO2 onto oceanic pCO2 using these non-linear relationships (see Landschützer et. al 2016 and Laruelle et. al 2017 for more detail). This results in monthly open ocean pCO2 fields at 1°x1° resolution and coastal ocean pCO2 fields at 0.25°x0.25° resolution. To merge the products, we divided each 1°x1° open ocean bin into 16 equal 0.25°x0.25° bins without any interpolation. The common overlap area of the products has been merged by scaling the respective products by their mismatch compared to observations from the SOCAT datasets (see Landschützer et. al 2020).

本数据集汇聚了由公开发布且可供公众获取的二氧化碳分压（pCO2）气候学数据，其中涉及开阔海域（Landshützer 等人，2016年）以及近海海域（Laruelle 等人，2017年）。两套数据集均通过两阶段神经网络技术构建。首先，利用自组织图将全球海洋划分为16个生物地球化学区域。其次，在各个区域内部，采用前馈神经网络重建了已知驱动表层海洋碳循环的变量与来自 SOCAT 开放和近海海洋数据集（Bakker 等人，2016年）的网格观测数据之间的非线性关系。最终产品通过将这些驱动变量，例如表层温度、叶绿素、混合层深度和大气二氧化碳，投影至海洋pCO2，并利用这些非线性关系生成（详见 Landshützer 等人，2016年和 Laruelle 等人，2017年的详细描述）。由此，产生了每月的开阔海域pCO2场，分辨率为1°x1°，以及近海海域pCO2场，分辨率为0.25°x0.25°。为合并这些产品，我们将每个1°x1°的开阔海域网格细分为16个等面积的0.25°x0.25°网格，且未进行任何插值。通过将各自产品按其与 SOCAT 数据集观测值的不匹配程度进行缩放，合并了产品共有的重叠区域（见 Landshützer 等人，2020年的相关内容）。