Super-resolution reconstruction of ocean dissolved oxygen via conditional diffusion modeling with auxiliary physical fields

A Variationally Constrained Attention Model for Sea Surface Height Reconstruction With Multisource Observations

Sea surface height (SSH) is a key variable for characterizing ocean dynamics, yet its high-resolution reconstruction remains challenging due to sparse satellite observations and the limited ability of conventional methods to represent multiscale nonlinear processes. This study proposes a physics-constrained SSH reconstruction model, 4-D variational (4DVar) Attention, built upon a 4DVar framework. The model approximates gradient-based updates using neural networks and jointly assimilates satellite altimeter and in situ pressure-recording inverted echo sounder (PIES) observations, while combining a dual-scale U-Net and a Vision Transformer to model cross-scale spatiotemporal dependencies. Experiments in the Gulf of Mexico demonstrate that 4DVarAttention outperforms several representative methods in terms of reconstruction accuracy and spatiotemporal continuity, and further confirm that incorporating PIES observations significantly enhances the resolution of the reconstructed SSH fields, providing a foundation for extending physics-constrained deep learning-based data assimilation frameworks to broader oceanic regions.

4DVarGen: A 4D Variational-Inspired Generative Model for Eddy-Resolving Surface Ocean Reconstruction

4DVarGen is proposed, a 4DVar-inspired generative framework for reconstructing sea surface variable fields at eddy-resolving scales from sparse remote-sensing observations that establishes a mathematical equivalence between 4DVar and an observation-guided denoising process.

Hybrid Graph Learning Reconstructs Global Ocean Oxygen Spatiotemporal Changes

Climate change and anthropogenic activities have exacerbated hypoxic conditions in the global ocean, posing a serious threat to marine ecosystems. Quantifying changes in dissolved oxygen levels is crucial for understanding the impact of these factors on marine life and earth sustainability. However, dissolved oxygen observations are severely sparse, limiting comprehensive analysis. Here we present Jingwei, a 4D spatiotemporal graph transfer learning model that reconstructs ocean oxygen levels globally over the past six decades on a 1◦ x 1◦ grid, covering depths of 0-5500 meters. Jingwei simultaneously leverages intra-profile knowledge transfer using a pattern bank from simulations and captures inter-profile correlations through zoning-varying message passing among observations. It significantly reduces reconstruction error by 27.26% compared to CMIP6, demonstrates high consistency with cruise surveys, and provides satisfactory visual quality. Furthermore, Jingwei produces interpretable results, effectively identifying vertical profile patterns and distinguishing fine-grained spatial distributions. Jingwei provides cartography and quantitative analysis of oxygen minimum zones (OMZ) evolution since 1960. We foresee Jingwei revolutionizing observation-based ocean modeling and deepening our understanding of the breathless ocean. Alongside, we have released an open-source online platform (https://jingwei.acemap.info/ https://jingwei.acemap.info/), providing data visualization, resource sharing and ongoing updates.

The Spatiotemporal Distribution of Dissolved Inorganic Carbon in the Global Ocean Interior: Reconstructed through Machine Learning

Global Daily XCO2 Reconstruction at 0.05° Resolution for 2021–2024 Using Satellite and Reanalysis Data

Accurate and seamless reconstruction of column-averaged dry-air CO2 mole fraction (XCO ${}_{2}$ ) at high spatial and temporal resolution remains challenging because satellite observations are sparse, unevenly distributed, and frequently interrupted by clouds and retrieval-related limitations. In this study, we propose Trans-XCO2, a spatiotemporal deep learning (DL) framework for reconstructing global daily XCO2 at 0.05° resolution during 2021–2024 by integrating transport background fields with multisource environmental predictors. The framework incorporates instantaneous environmental covariates and short-term atmospheric memory to represent temporal transport effects, and uses a wavelet-enhanced Transformer (WET) to improve multiscale spatial representation. A dual-resolution decoding strategy is further introduced to preserve large-scale background structures while refining fine-scale spatial details. The model is trained using data from 2021 to 2023 and evaluated through strict temporal extrapolation in 2024. Independent validation against measurements from the Total Carbon Column Observing Network (TCCON) shows strong agreement, with an R2 of 0.93 and an RMSE of 1.04 ppm. Compared with coarse-resolution background products, Trans-XCO2 achieves lower reconstruction errors while better preserving spatial gradients. The reconstructed dataset reveals a persistent global XCO2 growth rate of approximately 2.36 ppm yr−1 during 2021–2024, with similar temporal trends over land and ocean. The resulting seamless high-resolution XCO2 dataset provides valuable support for atmospheric carbon monitoring, satellite-model data fusion, and carbon cycle analysis.

Dual-Branch State-Displacement Network for Sea Surface Temperature Super-Resolution

Sea surface temperature (SST) is a critical indicator of global climate change, yet satellite-derived SST imagery often suffers from coarse spatial resolution, limiting the ability to capture fine-scale thermal structures such as ocean fronts. To address this, we propose a Dual-Branch State-Displacement Network (DBSD-Net) for SST super-resolution. DBSD-Net adopts a dual-branch architecture: a wavelet frequency branch that explicitly separates low and high-frequency components via discrete wavelet transform for targeted processing, and a VGGUNet branch that extracts multi-scale semantic features from a frozen pre-trained VGG backbone. Within the wavelet branch, we introduce a Structural State Space Module (SSSM) with a Gated Structure Refinement (GSR) unit to efficiently capture long-range dependencies and enhance structural integrity, and a Displacement Gate Module (DGM) that learns a displacement field for geometry-aware modulation of high-frequency details, thereby mitigating spatially varying degradation. Experiments on multiple public SST datasets demonstrate that DBSD-Net outperforms existing state-of-the-art methods.