Comprehensive gridded dataset of photosynthetically active radiation in the upper ocean from 1958 to 2022

Photosynthetically Active Radiation (PAR) plays a crucial role in shaping marine ecosystems, influencing primary production, species interaction, and phytoplankton seasonal dynamics. However, comprehensive long-term (gap-free) datasets for both surface PAR and the diffuse Attenuation Coefficient of Photosynthetically Active Radiation (KdPAR) are currently lacking. In this study, we introduce two new extensive global 4D PAR gap-free datasets (Longitude x Latitude x Day x Depth) at a resolution of 0.25o latitude x 0.25o longitude from surface to 250 m covering the periods 1998-2022 and 1958-2022. The first dataset (1998-2022) is primarily derived from Globcolour (surface PAR and Chlorophyll-a), supplemented with missing surface PAR data estimated using the Environmental String Model (ESM) with key climatic ERA5 variables. Missing Chlorophyll-a data are interpolated by applying the DINEOF method (Data Interpolating Empirical Orthogonal Functions) and transformed into KdPAR. Visual and numerical evaluations closely approximate observations, demonstrating the accuracy of our approach. Subsequently, we extend our dataset back to 1958 using exclusively the ESM based on key climatic ERA5 variables. The ESM outperforms the Generalized Regression on Neural Network (GRNN) in computational efficiency while yielding similar results. Validation against in-situ measurements confirms the reliability of PAR and KdPAR surface products. Although the 1958-2022 dataset exhibits limited daily variability in PAR compared to the 1998-2022 dataset, it effectively captures critical spatial-temporal patterns, as demonstrated by correlative and comparative studies with El Nino indices. Furthermore, the similarity observed between euphotic depth (Zeu) derived from our ESM-based 4D PAR dataset (1958-2022), and the Mercator-Ocean hindcast model, along with in-situ data, underscores the robustness of our approach in capturing light availability at depth.