OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations

Simon Donike; Ruben Cartuyvels; Antonino Ian Ferola; Elisa Carli; Diego Fernandez Prieto; Marie-Helene Rio

cs.LGAug 17, 2026

OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations

Authors: Simon Donike, Ruben Cartuyvels, Antonino Ian Ferola, Elisa Carli, Diego Fernandez Prieto, Marie-Helene Rio

Organizations: Image Processing Laboratory, University of Valencia, Av. de Blasco Ibáñez 13, 46010, Spain · ESA-ESRIN, Via Galileo Galilei 1, 00044 Frascati RM, Italy

Abstract

Despite comprising over 70% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere. Understanding the global ocean requires jointly observing its surface and subsurface structure, yet no standardized, high-resolution dataset couples satellite surface fields to co-located in situ depth profiles in an AI-ready format. Existing resources either consist of model-reconstructed gridded products rather than observations, cover only a single variable or basin, or operate at resolutions too coarse for mesoscale dynamics. We introduce OceanDepths, the first open, global, regridded AI-ready dataset that pairs satellite-derived sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) L4 products with co-located EN4 subsurface temperature and salinity profiles, complemented by matched GLORYS12 ocean reanalysis data to support comparisons or multi-stage learning. The dataset spans 2000-2024 at 0.1 degrees x 0.1 degrees spatial resolution and at weekly temporal resolution, covering the entire globe's sea surface and with over 9.5 million paired profiles interpolated to 50 standardized depth levels. We provide a configurable system to split the globe in equally sized spatial patches. The 4D multivariate structure, high resolution, long temporal extent, and extreme sparsity of subsurface observations (approximately 0.01% per depth level) make OceanDepths a challenging testbed for novel AI methods. We demonstrate subsurface state reconstruction as an example task with simple baseline models, but also envision OceanDepths to support the development of observation-based forecast methods and other related tasks. Available at: https://huggingface.co/datasets/ESA-philab/OceanDepths.

Figures & tables

Figure 1: Global distribution of the EN4 density per week and examples of surface observations and reanalysis data. OceanDepths aggregates all profiles gathered between 2000 and 2024 with sea surface temperature, salinity and height observational products accompanied by the GLORYS12V1 reanalysis product. The SST, ADT, SSS, GLORYS12, and profile T and S are shown for the white square around New York on the map in the middle.

Dataset	Surface	Subsurf.	Depth (m)	Levels	SR	TR	Period
Interpolation / Analysis
ARMOR3D [ 13 ]	–	T, S	1500	50	1/8 ∘	D	1993–…
ISAS [ 15 ]	–	T, S	5500	187	0.5 ∘	M	2002–2020
WOA [ 19 ]	–	T, S, B	5500	102	0.25–1 ∘	M	1971–2022
IAPv4 [ 6 ]	–	T	6000	119	1 ∘	M	1940–2023
RG-Clim [ 24 ]	–	T, S	2000	58	1 ∘	M	2004–2018

Table 1: Comparison of existing public global ocean subsurface–surface datasets. Variables: T = temperature, S = salinity, U/V = currents, H = sea surface height or ADT (absolute dynamic topography), SST = sea surface temperature, SSS = sea surface salinity, W = wind, B = biological variables, nutrients, oxygen, SR/TR = spatial/temporal resolution G = Global. † : Preserves native sensor resolutions (2 km–0.25 ∘ ) without resampling to a common grid.

Property	Value
Spatial coverage	Global, 3,600 $\times$ 1,800 px at $0.1\text{\,}\mathrm{\SIUnitSymbolDegree}$ resolution (4,338,138 ocean cells)
Temporal coverage	January 2000 – July 2024, weekly resolution (1283 target weeks)
Total Argo profiles	9,485,977 (9.4 M valid temperature; 7.0 M valid salinity)
Surface variables per sample	SST (OSTIA), SSS & surface density (MULTIOBS), ADT (DUACS)
Depth levels	50 GLORYS standard levels (0.49 m to 5,728 m)
Patch dataset	358 K patch–date samples ( $128^{2}$ px, no overlap) – 343 K train / 15 K eval

Table 2: Dataset summary statistics.

Figure 2: EN4–GLORYS12 profile alignment example. Example from the South Atlantic in September 2018. The depth-interpolated profiles closely track the GLORYS12 reanalysis across the full water column for both modalities, with occasional discrepancies (which is expected due to limitations of the reanalysis method and/or uncertainties in the profile measurements).

Figure 3: Global overlapping patch grid coverage for the training dataset. Retained patches ( $\leq$ 30% land, transparent blue) and force-included patches for enclosed basins (in red boxes) cover all major ocean regions. Discarded patches ( $>$ 30% land) are excluded. Transparency indicates overlap density from the 75% overlap tiling.

Figure 4: Patch land-fraction filtering with land areas (black), EN4 locations and SST. a: Kept by default. b: Argentinian coast, rejected by default. c: Aegean Sea, included due to relaxed region-based rules.

	Temperature			Salinity
Method	RMSE	MAE	$R^{2}$	RMSE	MAE	$R^{2}$
Climatology	0.974	0.510	0.964	0.543	0.170	0.717
IDW	0.979	0.521	0.964	0.566	0.174	0.678
LSTM	2.420	1.645	0.701	0.623	0.295	0.633
1D CNN	3.073	2.377	0.370	0.797	0.536	0.338
3D U-Net	1.101	0.645	0.937	0.518	0.177	0.738

Table 3: Subsurface reconstruction results (evaluation year 2018, week 25,

|\mathcal{P}_{\mathrm{val}}|=0.2|\mathcal{P}|

). Performance is reported for temperature and salinity against held-out EN4 profiles, averaged across depth levels no deeper than 2000 m. Best values highlighted in bold.

Figure 5: RMSE per depth against held-out EN4 samples. T and S reconstruction error by depth level, averaged over the global 2018 week-25 validation set.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6: Random selection of surface-level training patches from the dataset, illustrating the diversity of ocean regions, SST gradients, and coastline configurations encountered during training.

Figure 7: EN4 depth index compared to depth in meters. Heatmap of corrected depth (m, log scale) vs. EN4 level index, with median (white) and P10/P90 (pink dotted lines) curves. The irregular, profile-dependent depth sampling motivates the interpolation onto the fixed 50-level GLORYS12 coordinate.

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Conditional Diffusion Models

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The ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware generative framework for reconstructing high-resolution three-dimensional ocean states from extremely sparse surface data. Our approach employs a conditional denoising diffusion probabilistic model (DDPM) trained on sea surface height and temperature observations with up to 99.9 percent sparsity, without reliance on a background dynamical model. By incorporating continuous depth embeddings, the model learns a unified vertical representation of the ocean states and generalizes to previously unseen depths. Applied to the Gulf of Mexico, the framework accurately reconstructs subsurface temperature, salinity, and velocity fields across multiple depths. Evaluations using statistical metrics, spectral analysis, and heat transport diagnostics demonstrate recovery of both large-scale circulation and multiscale variability. These results establish generative diffusion models as a scalable approach for probabilistic ocean reconstruction in data-limited regimes, with implications for climate monitoring and forecasting.

Abstract

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