Abstract. Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification as well as their impacts on marine organisms and ecosystems. So far ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim at developing a sustainable production chain of observation-derived global ocean carbonate system datasets at high space-time resolution. As the start of the long-term objective, this study introduces a new global 0.25° monthly reconstruction, namely CMEMS-LSCE, for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables including surface ocean partial pressure of CO₂ (pCO₂), total alkalinity (A_T), total dissolved inorganic carbon (DIC), surface ocean pH, and saturation states with respect to aragonite (Ω_ar) and calcite (Ω_ca). Reconstructing pCO₂ relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO₂ ATlas (SOCAT). Surface ocean A_T is estimated with a multiple linear regression approach, and the remaining carbonate variables are resolved by CO₂ system speciation given the reconstructed pCO₂ and A_T. 1σ-uncertainty associated with these estimates is also provided. Here, σ stands for either ensemble standard deviation of pCO₂ estimates or total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25°-resolution pCO₂ product outperforms a coarser spatial resolution (1°) thanks to a higher data coverage nearshore and a better description of horizontal and temporal variations in pCO₂ across diverse ocean basins, particularly in the coastal-open-ocean continuum. Product qualification with observation-based data confirms reliable reconstructions with root-of-mean–square–deviation from observations less than 8 %, 4 %, and 1 % relative to the global mean of pCO₂, A_T (DIC), and pH. The global average 1σ-uncertainty is below 5 % and 8 % for pCO₂ and Ω_ar (Ω_ca), 2 % for A_T and DIC, and 0.4 % for pH relative to their global mean values. Both model-observation misfit and model uncertainty indicate that coastal data reproduction still needs further improvement, wherein high temporal and horizontal gradients of carbonate variables and representative uncertainty from data sampling would be taken into account in priority. This study also presents a potential use case of the CMEMS-LSCE carbonate data product in tracking the recent state of ocean acidification.

中文翻译：

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CMEMS-LSCE：全球 0.25 度，表层海洋碳酸盐系统的每月重建

摘要。全球表层海洋碳酸盐系统变量的基于观测的数据重建在监测海洋碳吸收和海洋酸化的近期状况及其对海洋生物和生态系统的影响方面发挥着重要作用。到目前为止，正在进行的努力旨在探索新方法来描述完整的海洋碳酸盐系统并更好地恢复其精细尺度特征。在这方面，我们在哥白尼海洋环境监测服务 (CMEMS) 内的研究活动旨在开发一个可持续的高时空分辨率观测全球海洋碳酸盐系统数据集生产链。作为长期目标的开始，本研究引入了 1985-2021 年期间新的全球 0.25° 月重建，即 CMEMS-LSCE。₂ ( p CO ₂ )、总碱度 (AT ₎、总溶解无机碳 (DIC)、表面海洋p H 以及文石 (Ω _ar ) 和方解石 (Ω _ca )的饱和状态。重建p CO _{2依赖于映射由地表海洋 CO 2} ATlas (SOCAT)提供的基于网格观测的数据的神经网络模型集合。表层海洋 A _{T是用多元线性回归方法估算的，剩余的碳酸盐变量通过 CO 2}系统形态在给定重建p CO ₂的情况下解析和一个_T。1 σ -还提供了与这些估计相关的不确定性。此处，σ代表p CO ₂估计值的整体标准偏差或通过具有输入数据不确定性的处理链传播的五个其他变量中的每一个的总不确定性。我们证明了 0.25° 分辨率的p CO ₂产品优于较粗糙的空间分辨率 (1°)，这归功于更高的近岸数据覆盖率以及对p CO _{2水平和时间变化的更好描述}跨越不同的海洋盆地，特别是在沿海-公海连续体中。使用基于观测数据的产品鉴定确认了可靠的重建，均方根与观测值的偏差相对于p CO ₂、A _T (DIC) 和p H. 对于p CO ₂和 Ω _ar (Ω _ca )，全球平均 1 σ不确定性低于 5 % 和 8 % ，对于 A _T和 DIC 为 2 %，对于p为 0.4 %H 相对于它们的全局平均值。模型-观测失配和模型不确定性都表明沿海数据再现仍需要进一步改进，其中将优先考虑碳酸盐变量的高时间和水平梯度以及数据采样的代表性不确定性。本研究还介绍了 CMEMS-LSCE 碳酸盐数据产品在跟踪近期海洋酸化状态方面的潜在用例。