Jingwei - Nutrients: A global spatiotemporal reconstruction of ocean nutrients (1965–2023) using multi-task deep learning

Dissolved nitrate, phosphate, and silicate are fundamental drivers of marine primary productivity and the biological carbon pump. However, the development of continuous, long-term global datasets has long been severely hindered by extreme historical data sparsity and complex biogeochemical dynamics. Statistical interpolation methods struggle to simultaneously fill the severely sparse data gaps and capture the non-linear interactions, necessitating advanced artificial intelligence (AI) to explicitly learn and leverage their underlying relationships. Nevertheless, most existing Al methods reconstruct nutrients independently (i.e., Single-Task Learning), failing to exploit the synergistic effects inherent in cross-nutrients stoichiometry. In this study, we present Jingwei-Nutrients, a global monthly dataset at 1°×1° resolution from 0 to 2000 m depth spanning 1965 to 2023, reconstructed using a Transformer-based Multi-Task Learning (MTL) framework trained on a comprehensive, quality-controlled multi-source observational database. Evaluation on the validation set yields R² values of 0.980, 0.961, and 0.983, with RMSEs of 2.21, 0.23, and 6.35 µmol/kg for nitrate, phosphate, and silicate, respectively. Temporal K-fold cross-validation reveals that the MTL framework consistently achieves higher R² and lower RMSE for all three nutrients compared to single-task models, with larger accuracy gains in data-sparse earlier decades such as 1965-1975. Our dataset reproduces consistent global climatology patterns and seasonal cycles with World Ocean Atlas (WOA). Furthermore, independent evaluations against long-term monitoring stations (HOT and KERFIX) and GO-SHIP cruise sections (P16N, P16S, and P06E) demonstrate our effectiveness across multi-decadal temporal trend, spatial variability and vertical changes. Additionally, an ensemble-based uncertainty analysis reveals interpretable spatial heterogeneities and a long-term decreasing trend in global uncertainty, which directly mirrors the historical transition from sparse early sampling to modern observing networks. This dataset fills a critical gap in historical ocean biogeochemical observations, providing a reliable, physically consistent foundation for marine biogeochemical modeling and climate change studies.溶解态硝酸盐、磷酸盐与硅酸盐是海洋初级生产力及生物碳泵（biological carbon pump）的核心驱动因子。然而，长期以来，受限于历史观测数据极度匮乏与复杂的生物地球化学动力学过程，连续、长期的全球海洋营养盐数据集构建始终面临严重阻碍。统计插值方法难以同时填补极度稀疏的数据空白并捕捉非线性相互作用，因此亟待借助先进人工智能（AI）来显式学习并利用其内在关联。然而，绝大多数现有AI方法均采用独立式营养盐重构（即单任务学习，Single-Task Learning），未能充分利用跨营养盐化学计量比所固有的协同效应。 本研究提出经纬营养盐数据集（Jingwei-Nutrients）：该数据集为全球逐月格点产品，空间分辨率为1°×1°，垂向覆盖0至2000米，时间跨度为1965年至2023年，其重构基于经全面质控的多源观测数据库训练的基于Transformer的多任务学习（Multi-Task Learning, MTL）框架。验证集评估结果显示，硝酸盐、磷酸盐与硅酸盐的决定系数（Coefficient of Determination, R²）分别为0.980、0.961与0.983，均方根误差（Root Mean Square Error, RMSE）分别为2.21、0.23与6.35 µmol/kg。时序K折交叉验证结果表明，相较于单任务模型，多任务学习（MTL）框架在三种营养盐的重构中均稳定实现更高的R²与更低的RMSE，在1965-1975年等数据极度匮乏的早期年代，精度提升尤为显著。 本数据集与世界海洋图集（World Ocean Atlas, WOA）所呈现的全球气候态格局与季节循环特征高度吻合。进一步而言，通过对长期观测站（HOT与KERFIX）及GO-SHIP航次断面（P16N、P16S与P06E）的独立验证，证实本数据集在年代际时间趋势、空间变异性与垂向变化特征的重构上均表现优异。除此以外，基于集成方法的不确定性分析揭示了可解释的空间异质性特征，以及全球不确定性的长期下降趋势，这一趋势直接反映了海洋观测从早期稀疏采样到现代观测网络的历史演进历程。本数据集填补了历史海洋生物地球化学观测中的关键空白，可为海洋生物地球化学模拟与气候变化研究提供可靠且物理自洽的基础支撑。