FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution
Authors: Qiusheng Huang
Yuan Niu
Xiaohui Zhong
AnboyuGuo
Lei Chen
dianjun zhang
Xuefeng Zhang
Hao Li
Venue: NeurIPS 2025 oral ()
Keywords: AI For Science
Ocean Forecast System
Weather Forecast
Deep Learning
Primary area: machine_learning_for_sciences
TL;DR: The first deep-learning model for sub-daily ocean forecast

Abstract: Accurate, high-resolution ocean forecasting is crucial for maritime operations and environmental monitoring. While traditional numerical models are capable of producing sub-daily, eddy-resolving forecasts, they are computationally intensive and face challenges in maintaining accuracy at fine spatial and temporal scales. In contrast, recent data-driven approaches offer improved computational efficiency and emerging potential, yet typically operate at daily resolution and struggle with sub-daily predictions due to error accumulation over time.
We introduce FuXi-Ocean, the first data-driven global ocean forecasting model achieving six-hourly predictions at eddy-resolving 1/12° spatial resolution, reaching depths of up to 1500 meters. The model architecture integrates a context-aware feature extraction module with a predictive network employing stacked attention blocks. The core innovation is the Mixture-of-Time (MoT) module, which adaptively integrates predictions from multiple temporal contexts by learning variable-specific reliability
, mitigating cumulative errors in sequential forecasting. Through comprehensive experimental evaluation, FuXi-Ocean demonstrates superior skill in predicting key variables, including temperature, salinity, and currents, across multiple depths.

FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution
Qiusheng Huang H.2t Yuan Niu Xiaohui Zhong', Anboyu Guo', Lei Chen',
Dianjun Zhang', Xuefeng Zhang": Hao Li
Artificial Intelligence Innovation and Incubation Institute, Fudan University
Shanghai Innovation Institute
School of Marine Science and Technology,Tianjj University
Abstract
Accurate, high-resolution ocean forecasting is crucial for maritime operations and environmental monitoring. While traditional numerical models are capable of producing sub-daily, eddy-resolving forecasts, they are computationally intensive and face challenges in maintaining accuracy fine spatial and temporal scales. In contrast, recent data-driven approaches offer improved computational efficiency and emerging potential, yet typically operate at daily resolution and struggle with sub-daily predictions due to error accumulation over time. We introduce FuXi- Ocean, the first data-driven global ocean forecasting model achieving six-hourly predictions at addy-resolving 1/12" spatial resolution, reaching depths of up 1500 meters. The model architecture integrates a contexl-aware feature extraction module with predictive network employing stacked attention blocks. The core innovation is the Mixture-of Time (MoT) module, which adaptively integrates predictions from multiple temporal contexts by learning variable-speciff reliability .miigatting cumulative errors in sequential forecasting. Through comprehensive experimental evaluation, FuXi-Ocean demonstrates superior skill in predicting key variables, including temperature, salinity, and currents, across multiple depths.
1 Introduction
Ocean forecasting systems play vital role in maritime operations, providing critical information for navigation, search and rescue, fisheries management, and offshore energy production. These applications require increasingly accurate predictions at finer temporal and spatial resolution to capture rapidly evolving oceanic phenomena. For instance, high-resolution forecasts of ocean currents are indispensable for maritime search and rescue and oil spill tracking, where accurate backtracking and source identification are critical 4T). Despite notable advances in numerical modeling and computational capacity, achieving both high temporal resolution and global coverage remains challenging [16 [3].
Operational ocean forecasting traditionally relies on physics-based numerical models that solve the governing equations of ocean dynamics (15) While based on well-established physical laws, these models are computationally expensive, particularly when resolving mesoscale eddies, which require grid resolutions of 1/12" or finer. Even after decades of development, ocean general circulation models (OGCMs) still exhibit substantial uncertainties, due to numerical discretization errors [1 and uncertainties in parameterizing unresolved subgrid processes 34
"Corresponding author:
'These authors contributed equally to this work.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Recently, deep learning approaches emerge as promising alternatives to traditional OGCMs. Studies demonstrate that such models can achieve comparable or even superior accuracy at daily temporal resolution compared to operational numerical systems, and their operational efficiency can improved by a thousand times. However, extending these models to sub-daily resolutions presents unique challenges. Ocean variables exhibit diverse temporal dynamics that vary significantly across depths and spatial regions. Surface variables, such as sea surface temperature, often follow pronounced diurnal cycles similar to atmospheric variables, whereas deeper ocean currents evolve on considerably slower timescales. Unlike atmospheric forecasting, which is dominated by high- frequency variability, ocean prediction must accommodate broad spectrum of temporal behaviors, from fast-changing surface processes to slowly evolving deep-ocean processes. A key limitation of existing deep gearning-bused based ocean forecasting models lies in their predominant focus daily forecasts, which restricts their ability to adaptively model variable-specific temporal dynamics at sub-daily intervals. Lacking mechanisms to adaptively process temporal information across multiple timescales, these models often struggle with the complexity of high-frequency sub-daily predictions. Moreover, designing effective sub-daily models requires architectures capable of learning efficiently from limited historical data while avoiding overfitting to spurious correlations.
A5 43
In this work, we present FuXi-Ocean, the first deep learning- based global ocean forecasting model to achieve six- hour temporal resolution at eddy-resolving 1/12" spatial resolution. Our approach explicitly addresses the challenges of sub-daily ocean prediction through three key innovations: First, we design an autoregressive architecture specifically tailored to capture multi-scale temporal dependencies of different occanic variables across unprecedented depth range (0- m). Unlike previous models that treat all variables uniformly, our model adaptively learns temporal context appropriate for each variable and region, effectively distinguishing between fast-evolving surface processes (e.g., diurnal warming) and slowly varying deep-oceat dynamics. Second, we introduce (MoT) module that adaptively integrates predictions from multiple temporal windows based on their empirical reliability for each variable. This mechanism enables the model select the most informative temporal context, thereby mitigating the accumulation of forecast errors typically encountered in sequential prediction tasks. Third, we demonstrate remarkable data efficiency, achieving ofte-offthhe-rr performance with only 9 years of training datasignificantly less than required by comparable models. This efficiency stems from our architecture's 's ability to effectively leverage physical constraints and spatial coherence in the learning process. Our key contributions are as follows:
the Mixture-of-Ting
We propose FuXi-Ocean, the first data-driven global ocean forecasting model to achieve six-hour temporal resolution, 1/12" spatial resolution, and 0- m depth coverage.
We introduce the Mixture-of-Time (MoT) module, which adaptively integrates variable- specific temporal dependencies to reduce cumulative errors in sequential prediction.
We validate FuXi-Ocean with reanalysis and observational datasets, demonstrating superior performance over traditional numerical forecasting models sub-daily intervals.
2
Related Work
2.1
Numerical Models
Ocean forecasting traditionally relies on OGCMs that solve fundamental physical equations governing ocean dynamics. State-of-the-art operational systems, such as the HYbrid Coordinate Ocean Model (HYCOM)
8 3
Ocean Physical System (PSY4) [27], Global Ice Ocean Prediction System (GIOPS)
42 Forecast Ocean Assimilation Model (FOAM) [5 BLUElinK OceanMAPS (BLK) [40], Nucleus
for European Modelling of the Ocean (NEMO) 19, Modular Ocean Model (MOM) 177. Real-Time
Ocean Forecast System (RTOFS) 14] and GLORY12 (2], make significant advances in global ocean
prediction capabilities.
Despite their solid theoretical foundations, these models face persistent challenges. The computational cost increases dramatically with resolution, making global simulations at eddy-resolving scales particularly expensive [36). Additionally, uncertainties in parameterizing unresolved processes, such as vertical mixing and air-sea interactions, introduce systematic model biases 44 21.. The inherent
chaotic nature of ocean systems further complicates forecasting, as small errors initial conditions can rapidly amplify through nonlinear interactions. These limitations become particularly acute for

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fegh-freuenccy predictions, where sub-daily forecasts reveal the full spectrum of model deficiencies that might otherwise be obscured in daily averages.
2.2 Data-Driven Deep Learning Models
Recent advances in deep learning introduce promising new approaches for ocean forecasting and successfully applied to the global medium-range forecasts [ 26 33 6 37 .t present, deep learning based ocean forecasting technology is developing rapidly and can predict ocean variables such as satellite sea surface temperature, sea surface height, wave height, temperature, salinity, U and V 47 38), as well as scasonal or interannual ocean phenomena such as Indian Ocean Dipole (IOD) and EINiño Southern Oscillation (ENSO) 20 48 49
Wang et al. [43 developed "XiHe", a global forecasting model based on the Swin-Transformer architecture, incorporating specialized ocean-land mask mechanisms. Yang et [46] introduced "Langya", which mitigates cumulative forecast errors by employing Time Embedding module, enabling forecasts for up to 7 days without iterative steps. Both models achieve state-of-the-arr performance for daily predictions at 1/12" resolution. In addition, Cui et al. [I]] and Xiong et al. [45] also experimented with autoregressive forecasting architectures and achieved good performance.
Despite these advances, existing data-driven ocean forecasting approaches face three key limitations that constrain their practical utility. First, temporal resolution remains limited to daily intervals. Ocean parameters evolve on markedly different timescales, with surface temperatures exhibiting pronounced diurnal cycles while deep currents evolving more slowly over extended time periods. Without mechanisms to adaptively learn these multiscale temporal dependencies, current models are fundamentally limited in their ability to capture the full spectrum of ocean dynamics at sub-daily resolutions. Second, vertical coverage in existing models remains severely constrained, typically not extending beyond 700 m depth. The thermocline structure and deep water mass properties below this depth play crucial roles in energy transfer and climate dynamics, yet remain largely unaddressed in current data-driven frameworks. Third, most current approaches rely heavily on atmospheric forcing as input variables, creating additional computational overhead and external data dependencies.
3 Method
This section details our methodology for high-frequency ocean forecasting. We first describe our data preparation approach, then present the architecture of FuXi-Ocean, including our novel Mixture-of- Time module for adaptive temporal modeling, and finally outline our training strategy.
3.1 Problem Formulation
Ocean forecasting requires processing high- high-dimensional spatiotemporal data that captures the complex dynamics of global marine systems. FuXi-Ocean addresses the task of predicting fu- ture oceanic states from sequential historical observations with high spatial and temporal preci- sion. Formally, given a sequence of historical observations spanning N consecutive time points [xI-N+1 X
x E R NxCx xW we aim to predict the state at the subsequent time step X++1 E R Cxx Here, H 2160 and W = 4320 represent the spatial dimensions of our global grid at 1/120 resolution, providing eddy-resolvine resolving capability essential for capturing mesoscale ocean dynamics.
We focus on five fundamental ocean state variables that collectively characterize the physical state of the global ocean: temperature (T), salinity (S), zonal and meridional components of ocean currents (U and V), and sea surface height (SSH). While SSH is inherently a surface variable, the remaining four variables exhibit substantial vertical variability that necessitates prediction actoss multiple depth levels. We discretize the water column into 20 strategically selected depth levels from the surface (0m) to the deeper ocean (1500 m): 0, 2, 4. 6, 10, 20, 30, 40, 50, 60. 70, 80, 100, 125, 150, 200, 300, 500, 1000, and 1500 m. This vertical resolution is particularly dense near the surface to accurately capture upper-ocean dynamics that strongly influence air-sea interactions. Consequently, our input and output tensors comprise C' = 81 channels (4 variables x 20 depth levels + SSH).
The temporal resolution of our data is set at 6-hour intervals, aligning with operational oceanic forecasting standards and balancing computational feasibility with information density. We empiri- cally determine that N = 4 consecutive time steps (spanning 24 hours) provides sufficient historical
3

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context to accurately capture diurnal cycles and inertial oscillations while maintaining computational efficiency. To preserve physical coherence in predictions, we apply land-sea mask all variables, ensuring the model only processes and predicts values for ocean grid points.
Xt-3
E
X
Prediction
Module
X++1
Figure I: Architecture of our ocean forecasting framework. Our model processes sequential ocean states Xt-3 through X to predict X *+1 (a) The main pipeline consists of a shared encoder that transforms input states into latent representations, modulated by spatiotemporal features F's from network M. The fused representations feed into the prediction module, whose outputs are processed by decoders D with skip connections, (b) The prediction module employs stacked attention blocks with adaptive layer normalization (AdaLN) and feed-forward networks (FFN), capturing complex temporal dynamics. (c) The Mixture-of of-Time (MoT) module performs channel-wise selection across the four decoder outputs from different temporal skip connections. For each channel, MoT identifies the top-K temporal dependencies using matrix V (derived from spatially MAE metrics and softmax) and computes the optimal weighted average to synthesize the final prediction X x++1
3.2 Model Architecture
FuXi-Ocean employs autoregressive architecture designed capture the multiscale temporal dynamics of oceanic variables. The model comprises three primary components: feature extraction module that encodes multi- multi-temporal inputs, prediction module that captures temporal evolution patterns, and feature remapping module that reconstructs the target variables. Figure provides an overview of the model architecture.
3.2.1 Feature Extraction
Ocean forecasting requires robust feature representations that capture both spatial dependencies and temporal correlations. We design a feature extraction pipeline that combines context-aware encoding with efficient feature fusion, shown in Fig. Ta).
Our design employs shared encoder E for patch embedding. This encoder convolutional layers with matched kernel size and stride for spatial downsampling, followed by layer normalization. This approach balances computational efficiency with representation power when processing global ocean data. To leverage spatiotemporal context, we implement a prior information network M that processes:
Fs = M(S)
(1)
where S contains temporal information (diurnal, seasonal patterns) and spatial data (coordinates, bathymetry). The network M uses sinusoidal positional encodings for temporal components and learnable embeddings for spatial coordinates. We then modulate the encoder weights using these contextual features through:
F'= Fs) Norm(Conv(X",, W W Fs))
(2)

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where W represents the learnable convolution parameters and @ denotes element-wiss multiplication. This modulation enhances the encoder's sensitivity region-specific patterns, such as boundary currents or seasonal mixed layer dynamics. The encoder processes each input time step L - (i E {0, 2, 3)) to produce feature tensors F E I xx", Here, C" is the feature dimension while H' and W' represent the downsampled spatial dimensions.
Our feature fusion module concatenates these tensors along the channel dimension, preserving their temporal characteristics. We then apply 1 x convolutions with normalization layers to integrate information while maintaining spatial coherence. This fusion approach enables the model capture complex spatiotemporal patterns essential for accurate forecasting, without compromising computational efficiency.
3.2.2 Prediction Module
Building upon the extracted features, our prediction process involves a sequence of specialized components designed to model oceanic dynamics across multiple temporal scales, as illustrated in Figure lb).
The prediction module processes the fused representations to model the nonlinear evolution of oceanic states, which consists of stacked attention blocks (8 paired with feed-forward networks. Each attention block captures dependencies across spatiotemporal features, while adaptive layer normalization (Ad incorporates contextual information Fş to modulate normalization parameters. This design allows the model to focus selectively on relevant oceanic patterns while maintaining computational efficiency.
For feature reconstruction, we employ a shared decoder D that transforms latent representations back into the physical variable space. The decoder consists of transposed convolutional layers with normalization operations. We establish skip connections between encoder features and corresponding decoder layers, preserving fine- ane-grained spatial details often lost during predictiona critical factor when modeling mesoscale ocean dynamics.
3.2.3 Mixture-of-Time Module
A key innovation in our framework is the Mixture-of-Timm (MoT) module, illustrated in Tigureme). that adaptively integrates predictions from multiple temporal windows. This module addresses a fundamental challenge in ocean forecastinddifferent oceanic variables exhibit distinct temporal evolu- tion characteristics, from fast-changing surface processes to the relatively slow and stable subsurface variability extending down to the twilight zone ( 1500 m). The MoT module performs channel-wist adaptive selection across predictions derived from different temporal windows. Specifically, we maintain a selection matrix V E Cx4 that quantifies prediction reliability across temporal scales for each channel (variable and depth combination). For a given channel c and spatial location (h, w), the final prediction is computed as:
3
X
(c, h
Σ TopK (c.)) X (c, h, w)
(3)
K
Here, the TopK operator selects the K most reliable temporal windows (corresponding smallest values in V) for each channel. Specifically, for each channel c, it assigns a value of I to the K smallest elements in the vector V(c,-) and 0 to all others. x represents the forecast for time + 1 generated using historical context beginning at time - i. This approach enables variable-specific temporal context selection without increasing model complexity. During training, we update the selection matrix based on prediction performance:
1+1 V(c, 1) = a ((c, + (1 a1) softmax(AvPPolaan (M,
(4)
where a controls update momentum, and MAE measures prediction error. The spatially averaged MAE captures the reliability of each temporal window for each variable, which is then normalized through softmax to ensure the selection weights sum to one. This adaptive approach enables FuXi- Ocean to dynamically adjust its reliance on different temporal windows for each variable and region. For example, deeper ocean current predictions might benefit from recent observations that capture immediate flow evolution, while surface temperature predictions might rely more on longer temporal contexts that effectively represent diurnal cycles and day-night transitions.
5

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3.3 Training Strategy
Training deep learning models for ocean forecasting presents unique challenges due to Earth's spherical geometry. To address the varying grid cell areas across latitudes, we implement latitude- weighted Charbonnier loss
CH
J
W a.
pred
1
EEΣ
ERs x2
(5)
CxHxW
where 0; H E cos d:, is the deeeudeddependent weighting factor at latitude P,, and a small constant ensuring differentiability. This approach prevents bias toward high-latitude regions in error calculations, which would otherwise be overrepresented the rectangular grid projection.
Besides, we employ a two-stage training approach to enhance model stability and performance. In the first stage, pre-train the model using single-step prediction objective. In the second stage, we fine-tune the model with a multi-step loss that penalizes predictions across multiple consecutive time steps, mitigating error accumulation in autoregressive forecasting. This approach follows recent advances in autoregressive modeling [TO, [T]. which demonstrate the effectiveness of multi-step training for improving long-term forecast stability.
4 Experiment
4.1 Data
HYCOM-RD. We train and evaluate FuXi-Ocean using the HYCOM Reanalysis Data 88, the only publicly available ocean dataset with 6-hour temporal resolution. This dataset features 1/12e horizontal resolution with up 40 vertical layers from sea surface to 5000m depth. We selecte approximately 8.5 years (January 2006 to June 2014) of data for training, a six-month period (July to December 2014) for validation, and one year (January 2015 December 2015) for testing, focusing on 20 strategically chosen vertical layers down to 1500 m that capture essential ocean dynamics. IV-TT Framework. For evaluation against real-worlh observations, we employ the GODAE Ocean View Intercomparison and Validation Task Team (IV-TT) Class 4 framework [39,, which is obtained from publicly available sources. Details are in the appendix. This framework provides observational datasets from drifting buoys for 2022, alongside interpolated outputs from operational numerical forecasting systems. In addition, we apply filter to remove anomalous points. Specifically, we compare HYCOM reanalysis data with observational data and remove outliers with high MAE from the observations to ensure fairness in the comparison process. However, after evaluation, the raw data errors for salinity and temperature were too large to be used directly. We primarily evaluate sea surface temperature (SST) forecasts, as this variable exhibits high variability and effectively demonstrates forecasting skill. To ensure consistent comparison, we transform the irregularly distributed observational data to a regular grid matching FuXi-Ocean's 's output format using nearest- neighbor interpolation. For operational evaluation, FuXi-Ocean is initialized with six-hou hourly analysis fields from HYCOM.
4.2 Implementation Details
The FuXi-Ocean employs the PyTorch framework (35] with the AdamW 23 optimizer, config- ured with 3, = 0.9, 32 0.95, and cosine annealing learning rate schedule 130 that decays from 2.5 x 10-4 to 10-8, We train on a cluster of NVIDIA H100 GPUs for 60, 60,000 iterations with a batch size of 1 per GPU, requiring approximately 81 hours to complete. Similar to other autoregressive models [10 IT] FuXi adopts an autoregressive approach during inference, feeding the output back into the input to iteratively generate results for the next time step. For further details, plcase refer to the supplementary materials.
4.3 Metrics
Following standard practices operational ocean forecast evaluation 11, we use latitude-weighted root mean square error (RMSE) our primary evaluation metrics to account for the varying grid cell

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1175
24
120
240 Lead Time (hours)
Figure 2: Global RMSE distribution of sea surface. From top to bottom, the results represent salinity (psu), temperature (fC), and ocean current U/V components (m/s), and from left to right correspond to different forecast lead times. Each subplot represents the average RMSE (lower is better) for the test
sizes across different latitudes, formulated as follows:
RMSE(c.
1
Σ
ΣΣ
a Gid
xlo+T)2
(6)
D H
foED
==
where to denotes the forecast initialization time in the testing dataset D, and 7 represents the lead time steps added to to-
Results
We first evaluate FuXi-Ocera the 2015 HYCOM set derived from HYCOM reanalysis data to assess its ability to generate high-frequency (6-hourly) predictions. We then compare our model with operational forecasting systems using 2022 observational data through the GODAE Ocean View IV-TT framework. This dual evaluation approach allows us to comprehensively assess both the model's intrinsic predictive capabilities and its real-world operational performance relative established systems.
5.1 Performance testing
Horizontal Spatial Error Analysis. Fig. shows the global spatial distribution of RMSE for sea surface variables (temperature, salinity, and currents) at different lead times (6, 24, 120, and 240 hours). For temperature (T), the model maintains particularly low errors in the tropical and subtropical regions across all forecast horizons, with RMSE values typically below 0.3FC for 6-hour predictions. Error patterns intensify primarily in western boundary current regions (Gulf Stream, Kuroshio Current) and the Antarctic Circumpolar Current, where intense mesoscale eddy activity creates inherent predictability challenges [3I [2]. Notably, due the high uncertainty of changes in these regions, even observational data from satellites and other sources can be significantly affected, leading to instability in model results [2]. Salinity (S) forecasts display similar spatial pattern but with larger relative errors in regions of significant freshwater influence, such major river outflows and high-precipitation zones 255 The equatorial Pacific shows particularly strong performance across all lead times, maintaining low error values even at 10-day forecasts. At shorter forecast times, the error distribution locations the current velocity components U and are highly similar. As the forecast lead time increases, significant differences in U and V near the equator

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emerge, which aligns with the physical phenomenon of the inconsistent rotation directions of warm currents in the Northern and Southern Hemispheres. Westemn boundary currents show the highest enror magnitudes, consistent with their intrinsic variability. Nevertheless, FuXi-Ocean maintains reasonable accuracy in these challenging regions, with errors remaining within operational tolerance thresholds even at longer lead times. Crucially, our 6-hour predictions show substantially lower emooss than daily forecasts across all variables, validating the model's ability to capture sub-daily ocean dynamics.
Vertical Performance Analysis. The vertical profile of the ocean environment is a crucial aspect for
1.4
170
170
120
240 Lead Time (hour)
Depth-dependens RMSE Distributions of salinity and temperature varying with lead time. Each subplot represents the RMSE (lower is better) varying with depth at the current lead time. Blue represents salinity results, while red represents temperature results.
Figure 3:
evaluating the effectiveness of ocean prediction systems. In Fig. 3 we present the variation of RMSE for salinity and temperature with depth. First, FuXi-Ocean maintains consistent prediction skill throughout the water column, with RMSE degrading gradually with depth. Second, the thermocline region (approximately 100-300m) shows relatively higher errors compared both surface and deep layers, reflecting the inherent difficulty in predicting this dynamically complex interface. Despite this challenge, FuXi-Ocean's RMSE still maintains effective values. Additionally, the error growth with increasing forecast lead time shows an interesting depth-dependent pattern. Thanks to our Mixture- of-Time (MoT) approach, which adaptively weights temporal dependencies for each variable, even though surface layers (0-300m) are sensitive to rapidly changing conditions while deeper layers (beyond 500m) are not, the error growth across layers remains relatively stable without any layer performing significantly worse
5.2 Comparison
RMSE. (2C)
0.
FCN
BUX
0.9
Lead Time (days)
Figure 4: The SST comparison of different methods based on the IV-TT evaluation framework.
The x-axis represents the forecast time, and the y-axis represents the RMSE (lower is better).
To evaluate the actual operational performance, we used sea surface temperature observation data from 2022 within the IV-TT framework to compare FuXi-Ocean with the HYCOM, BLK, and FOAM methods. For this comparison, we averaged our 6-hourly outputs to produce daily means that can be directly compared with the daily forecasts of other methods. To ensure fair comparison, we also evaluated the RMSE at the initial time (0 day). As shown in Fig. 4 the RMSE of FuXi-Ocean is

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lower than that of other methods. and the cumulative error growth is significantly smaller. Note that the most recent training data for FuXi-Ocean does not exceed 2014, and uses only oceanic variables as input information, highlighting that FuXi-Ocean effectively captures the intrinsic patterns of ocean system changes.
5.3
Ablation
Salinity
T Temperauure
midilty leve
4.351
SSH
10
U-component of octenn currents
Y-component of occan currents
Load 2
190
Tlae
Figure 5: The ablation study of different methods the HYCOM-RD. The five subplots respectively show salinity, temperature, ocean current UV components, and sea surface height. The x-axis represents the forecast lead time, and the y-axis represents the RMSE (lower is better). Note that for each variable, we average the RMSE over depth.
To evaluate the contribution of our key innovations, compare FuXi-Ocean with two ablated variants: (1) "woMoT," which removes the MoT module, and (2) "womoT((times). which removes MoT and reduces input time steps from four to two.
Figure 5 presents the results across all five forecasted variables. For salinity, temperature, and SSH, removing the MoT module leads to substantial performance degradation, with the increasing of RMSE peaked at nearly 40% across forecast lead times. This degradation is particularly pronounced for short-term forecasts, where adaptive temporal context selection proves critical for capturing rapidly evolving processes. These findings confirm that variable-specific temporal modeling significantly im- proves prediction accuracy for thermohaline variables that exhibit complex spatiotemporal dynamics. Furthermore, reducing the historical context from four to two time steps (woMoT((times) causes additional performance deterioration, especially for temperature and salinity predictions beyond 48 hours. This contrasts with experiences in atmospheric forecasting models, where shorter historical windows often suffice. Interestingly, ocean current components (U and V) show less sensitivity to both the removal of MoT' and the reduction of temporal context. This observation aligns with the physical reality that surface currents largely driven by recent wind forcing and geostrophic balance rather than their own history. The minimal performance difference between ablated models for current forecasting suggests that specialized architectures may be warranted for different ocean variablesa direction we leave for future work.
6 Conclusion
We present FuXi-Ocean, the first data-driven global ocean forecasting system achieving 6-hour temporal resolution at 1/12" spatial scale with coverage from surface to 1500 m depth. Our Mixture-of-Time module adaptively captures different temporal dependencies across ocean variables. addressing key limitation of previous models. FuXi-Ocean outperforms operational numerical systems in predicting sea surface temperature while requiring significantly fewer computational resources and relying only on ocean variables as input. Its 6-hour forecasts demonstrate superior

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accuracy than daily-averaged predictions from state-of-the-an numerical models, confirming the effectiveness of our approach for high high-frequency ocean prediction.
The successful application of deep learning to sub-daily ocean forecasting opens several promising avenues for future research directions. First, while current model covers depths up to 1500 m, extending its range to abyssal depths would provide more complete representation of the ocean system. Second, incorporating physical conservation laws as additional constraints could further improve prediction stability and ensure physical consistency. Third, increasing the forecast frequency beyond hours toward hourly predictions could enable applications requiring even finer temporal resolution, such as tidal forecasting and coastal hazard warning systems.
Acknowledgments and Disclosure of Funding
This work was supported by Al for Science Program (ID: Municipal Commission of Economy and Information
Shanghai
References
[1] Adcroft, A., Hallberg, R., Harrison, M., 2008 A finite volume discretization of the pressure gradient force using analytic integration. Ocean Modelling 22, 106-113. doi 2008.02.000
1tte//0.cceoooeedd ocemod
[2] Aouni, A.F.,, Gaudel, Q., Regnier, C., Gennip, S.V., Drevillon, M., Drillet, Y., Lellouche, J.M., 2024. Glonet: Mercator's s end-to-end neural forecasting system. Preprint at https//aaxivvag//b/22222222445.
[3] Barton, N., Metzger, E.J., Reynolds, C.A., Ruston, B., Rowley, C., Smedstad, O.M., Ridout, J.A., Wallcraft, A., Frolov, S., Hogan, P., et al., 2021. The navy's earth system prediction capability: global coupled atmosphere-ocean- ice prediction system designed for daily to subseasonal forecasting. Earth and Space science 8, e2020EA001 199.
[4] Bi, K., Xie, I.., Zhang, H., Chen, X., Gu, X., Tian, Q. 2023. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533-538.
[5] Blockley, E., Martin, M., McLaren, A., Ryan, A., Waters, J., Lea, D., Mirouze, I., Peterson, K., Sellar, A., Storkey, D., 2014. Recent development of the met office operational occan forecasting system: an overview and assessment of the new global foam forecasts. Geoscientific Model Development 2613-2638.
[6] Bodnar, C., Bruinsma, W.P., Lucic, A., Stanley, M., Brandstetter, J., Garvan, P., Riechert, M., Weyn, J., Dong, H., Vaughan. A., et al., 2024. Aurora: A foundation model of the atmosphere. arXiv preprint arXiv:2405.1300 130633....
Charbonnier, P., Blanc-Feraud, L., Aubert, G., Barlaud, M.. 1994. Two deterministic half-quadratic regularization algorithms for computed imaging. Proceedings International Conference on Image Processing, pp. 168-17 172 vol.2. doi (0..100..1/////11.111 1109/ICIIIIIP 1994.41335333
[8] Chassignet, E.P. Hurlburt, H.F., Smedstad, O.M., Halliwell, G.R., Hogan, PJ., Wallcraft, A.J. Baraille, R., Bleck, R., 2007. The hycom (hybrid coordinate ocean model) data assimilative system. Journal of Marine Systems 65, 60-83 83. URL: http:///wwwwwcccccccceeeeeee wom/sccince//rtic./...... jmars/s....aaarasssa.a... 2005 09. 016 marine Environ-
$0924796306002888 doi mental Monitoring and Prediction.
[9] Chen, K., Han, T., Gong, J., Bai, L.,, Ling, F,, Luo, JJ., Chen, X., Ma, L., Zhang, T., Su, R., el al., 2023a. Fengwu: Pushing the skillful global medium maniu weather forecast beyond 10 days lead, arXiv preprint arXiv:230.00294..
[10] Chen, I.., al., 2023h. Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast. npj Climate and Atmospheric Science. 1-11.
[11] Cui, Y., Wu, R., Zhang, X., Zhu, Z., Liu, B., Shi, J., Chen, J., Liu, H., Zhou, S., Su, L., et al., 2025. Forecasting the eddying ocean with deep neural network. Nature Communications 16, 2268.
[12] Dong, C., You. Z., Dong, J., Ji, J., Sun. W., Xu, G., Lu, X., Xie, H., Teng, F., Liu, Y., et al., 2025, Oceanic mesoscale eddies. Leean-Land--e Atmosphere Research 4, 0081.
10

---

[13] Fox-Kemper, B., et al., 2019. Challenges and prospects in ocean circulation models. Frontiers in Marine Science Volume 6- 2019. doi 3389/fmars 2019. 00065
[14] Garrafto, Z.D., Cummings, J.A., Paturi, S., Hao, Y. Iredell, D., Spindler, T., al., 2020. Rtof'-da: real time ocean-sea ice coupled three dimensional variational global data assimilative ocean forecast system. Research activities Earth system modelling.
[15] Griffies, S., 2018, Fundamentals of ocean climate models. Princeton university press.
[16] Griffies, S., Adcroft, A., Banks, H., Böning, C., Chassignet, E., Danabasoglu, G., Danilov, S., Deleersnijder, E., Drange, H., England, M., et al., 2009. Problems and prospects in large-scale ocean circulation models. Proceedings of OceanObs 9, 410-431.
[17] Griffies, S.M., Harrison, M.J., Pacanowski, R.C., Rosati, A., et al., 2004. A technical guide to mom4. GFDL Occan Group Tech, Rep 371.
[18] Guo, Y., Wang, C., Yu, S.X., McKenna, F., Law, 2022 Adaln: vision transformer for multidomain learning and predisaster building information extraction from images. Journal of Computing Civil Engineering 36, 04022024.
[19] Gurvan, M., Bourdallé- BBdii,, R., Bricaud, C., Bruciaferri, D., Calvert, D., Chanut, J., Clementi, E., Coward, A., Delrosso, D., Ethé, C., et al., 2017. Nemo ocean engine.
[20] Ham, Y.G., Kim, J.H., Luo, J.J., 2019. Deep learning for multi-year enso forecasts. Nature 573, 568-5768- 572.
[21] Jayne, S.R., 2009, The impact of abyssal mixing parameterizations in an occan general circulation model. Journal of Physical Occanography 39, 1756-1775.
[22] Jean-Michel, L.,, Eric, G., Romain, B.B., Gilles, G., Angélique, M., Marie, D., Clément, B., Mathicu, H., Olivier, I.G., Charly, R., et al., 2021. The copernicus global 1/12 occanic and sea ice glorys reanalysis. Frontiers in Earth Science 9, 698876.
[23] Kingma, D.P., Ba, 2017. Adam: A method for stochastic optimization. arXiv: 1412. 6980 preprint https:/aaxivvorr/aah//11122 2.1.......
[24] Kurth, T.. Subramanian, S., Harrington, P., Pathak, Mardani. M.. Hall, D., Miele, A., Kashinath, K., Anandkumar, A., 2023. Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators, in: Proceedings of the platform for advanced scientific computing conference, pp. 1-11.
[25] Lagerloef, G., Schmitt, R., Schanze, J., Kao, H.Y., 2010. The ocean and the global water cycle. Oceanog- raphy 23, 82-93.
[26] Lam, R., T., Eaton-Rosen, Z., Hu, W., et al., 2023, Learning skillful medium-range global weather forecasting. Science 382, 1416-1421.
A., Willson, M., Wirnsberger. P., Fortunato, M., Alet, F., Ravuri, S., Ewalds,
[27] Lellouche, J.M., Greiner, E., Le Galloudec, O., Regnier, C., Benkiran, M., Testut, C.E., Bourdalle-Badte R., Drevillon, M., Garric, G., Drillet, Y., 2018. The mercator ocean global high-resolution monitoring and forecasting system. New Frontiers in Operational Occanography 1, 563-592.
[28] Liu, Z., et al., 2022. Swin transformer v2: Scaling up capacity and resolution, in: 2022 IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 11999-12009.
[29] Loshchilov. I., Hutter, F., 2017a. Decoupled weight decay regularization, in: International Conference Learning Representations.
[30] Loshchilov, I., Hutter, F,, 2017b. Sgdr: Stochastic gradient descent with warm restarts. Preprint https://anivvorr//bb//1693 03388333.
[31] Morrow, R., Fu, L.I.,, Farrar, J.T., Seo, H., Le Traon, P.Y., 2017. Ocean eddies and mesoscale variability, in: Satellite altimetry over oceans and land surfaces. CRC press, pp. 315-342.
[32] Morrow, R., Le Traon, P.Y., 2012. Recent advances in observing mesoscale ocean dynamics with satellite altimetry. Advances in Space Research 50, 1062-1076.
[33] Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J.K., Grover, A., 2023, Climax: A foundation model for weather and climate. arXiv preprint arXiv:2301.10343.

---

[34] Palmer, T.N., Buizza, R., Deblas-Reyes, F., Jung. T., Leutbecher, M. Shutts, G.J. Steinheimer, M..
Weisheimer, A., 2009. Stochastic parametrization and model uncertainty.
[35] Paszke, A., al., 2017. Automatic differentiation in pytorch, in: NIPS 2017 Workshop on Autodiff.
[36] Piomelli, U., 2014. Large eddy simulations in 2030 and beyond. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, 20130320.
[37] Price, I,, Sanchez-Gonzalez, A., Alet, F,, Andersson, T.R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., al., 2025. Probabilistic weather forecasting with machine learning. Nature 637, 84-90.
[38] Quach, B., Glaser, Y., Stopa, J.E., Mouche, A.A., Sadowski, P., 2020. Deep learning for predicting significant wave height from synthetic aperture radar. IEEE Transactions Geoscience and Remote Sensing 59, 1859-1867.
[39] Ryan, A., Régnier, C., Divakaran, P. Spindler, T., Mehra, A.. Smith, G.C., Davidson, F., Hernandez, F., Maksymezuk, J., Liu, Y.. 2015. Godae oceanview class forecast verification framework: global ocean inter-comparison. Journal of Operational Oceanography 8, s1l1 - >98. URL: https://api semanticscholar org/Corpus ID: 118680054
[40] Schiller, A.. Brassington, G.B., Oke, P., Cahill, M., Divakaran, P., Entel, M., Freeman, J., Griffin, D., Herzfeld, M.. Hoeke, R., et al., 2020. Bluelink ocean forecasting australia: 15 years of operational ocean service delivery with socictal, cconomic and environmental benefits. Journal of Operational Oceanography 13, 1-18.
[41] Schiller, A.. Davidson, F.J.M. DiGiacomo, P.M.. Wilmer-Beckes K., 2016. Better informed rine operations and management: Multidisciplinary efforts ocean forecasting research socioe- conomic henefit. Bulletin hwwpaaar.orr/ooppuaa055551----
American Metcorological Society 97, 1553-1559.
URL.: https
[42] Smith, G.C., Roy, F. Reszka, M., Surcel Colan, D., Hc, Z., Deacu, D., Belanger, J.M., Skachko, S., Liu, Y., Dupont, F,, et al., 2016. Sea ice forecast verification in the canadian global ice ocean prediction system. Quarterly Journal of the Royal Metcorological Society 142, 659-671.
[43] Wang, X., Wang, R., Hu, N., Wang, P., Huo, P., Wang, G., Wang. H., Wang, S., Zhu, J., Xu, J., et al., 2024. Xihe: A data-driven model for global occan redy-vesolving forccasting. arXiv preprint arXiv:2402.02995
[44] Wunsch, C., Ferrari, R., 2004. Vertical mixing, energy, and the general circulation of the oceans. Annu. Rev. Fluid Mech. 36, 281-314.
[45] Xiong, W., Xiang, Y., Wu, H., Zhou, S., Sun, Y., Ma, M., Huang, X., 2023. Ai-goms: Large ai-driven global occan modeling system. arxiv. arXiv preprint arXiv:2308.0311
[46] Yang, N., Wang, C., Zhao, M., Zhao, Z., Zheng, H., Zhang, B., Wang. J. Li, X., 2024. Langya: Revolutionizing cross-spatiotempprall ocean forecasting. arXiv preprint arXivv2412.1 18097.
[47] Zheng, G., Li, X., Zhang, R.H., Liu, B., 2020. Purely satellite data driven deep learning forecast of complicated tropical instability waves. Science advances cabal482.
[48] Zhou, L., Zhang, H.H., 2022. A hybrid neural network model for enso prediction in combination with principal oscillation pattern analyses. Advances in Atmospheric Sciences 39, 289-902.
[49] Zhou, L., Zhang, R.H., 2023. A self-attention based neural network for diree-dimensional multivariate modeling and its skillful enso predictions. Science Advances 9, cad12827.
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A Limitations
Despite these promising results, our approach has several limitations that warrant further investigation. The reliance reanalysis data for training, while necessary given data availability constraints, introduces potential biases from the underlying numerical models. This challenge is compounded by the scarcity of publicly available high-quality sub-daily ocean reanalysis data. In future work, plan to address this limitation by integrating HYCOM analysis fields with Argo observational data and other in-situ measurements create more robust dataset. For subsurface validation, this will involve a rigorous data matching process, where observational profiles are spatio-lemporally collocated with the model grid points, and quality control filters are applied to remove outliers and inconsistent measurements before comparison. The filters use HYCOM analysis field data as an anchor to remove outlier data with high variability. Additionally, our evaluation thus far focus primarily on standard RMSE metrics, which, while useful for comparing forecast systems, may not fully capture the model's s ability to represent specific ocean phenomena like mesoscale eddies, western boundary currents, or seasonal thermocline transitions. Future analysis will incorporate phenomenon-specife evaluation metrics and physical consistency measures to provide deeper insights for model improvement. Finally, while our system's is forecast accuracy remains robust through 10 days, performance for longer-term predictions (seasonal to interannual) remains unexplored, representing another important direction for future research.
B Broader Impacts
FuXi-Ocean represents a significant advancement with far-reaching implications across multiple domains. By enabling high-resolution, sub-daily ocean forecasting at global scales, our work creates opportunities for numerous scientific and societal applications. In the realm of maritime safety and operations, the 6-hour temporal resolution provides critical advantages for shipping navigation, offshore energy production, and search and rescue missions. Particularly for emergency response scenarios such as oil spill tracking and marine accident response, the ability to forecast ocean currents at 6-hour intervals significantly improves source identification and trajectory prediction, potentially saving lives and reducing environmental damage. For marine resource management, FuXi-Ocean can enhance fisheries operations through improved forecasting of ocean conditions that influence fish migration and aggregation patterns. The comprehensive vertical coverage (0- 1500 m) is especially valuable for this application, as it captures the habitats of commercially important species that undergo diel vertical migration. For coastal communities and small island nations, FuXi-Ocean provides enhanced capability for predicting coastal hazards like storm surge, coastal flooding, and harmful algal blooms, all of which can develop rapidly and require high- high-frequency forecasting systems for adequate warning. The eddy-resolving capabilities of our model are particularly important for these applications, smaller-scale coastal processes often drive the most damaging impacts. From computational efficiency perspective, FuXi-Ocean demonstrates that high-quality forecasts can be generated with significantly reduced computational resources compared to traditional numerical models. This efficiency makes sophisticated ocean forecasting more accessible to researchers and institutions with limited computational infrastructure, potentially democratizing access to advanced oceanographic tools.
However, our work also presents potential risks that warrant careful consideration. Relying on deep learning models for critical occan forecasting applications requires robust verification systems, particularly when extending predictions to new or unprecedented scenarios not represented in the training data. The reliance on reanalysis data means that biases in the underlying numerical models could be perpetuated or even amplified by our approach. Additionally, as with any forecasting system, there is risk in over-reliance on model outputs for critical decision-making without appropriate uncertainty quantification.
C Additional results
C.1 Performance Test Supplement
We present our test results for ocean current UV in Fig and 7 Similar to Fig in the main text, the RMSE here is tested the HYCOM reanalysis data for 2015. From the results, it can be seen that
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0300
116671
12
18
Lead Time (hour)
Figure 6: Depth-dependent RMSE Distributions of ocean current UV components varying with lead time (6 24 hours). Each subplot represents the RMSE (lower is better) varying with depth at the current lead time.
$540
48
96
240
Lead (hour)
Figure 7: Depth-dependent RMSE Distributions of ocean current UV components varying with lead time (48 240 hours). Each subplot represents the RMSE (lower is better) varying with depth the current lead time.
the RMSE values for ocean current UV maintain high accuracy and exhibit high stability in error accumulation. As lead time increases, the eтror growth is gradual and uniform, without significant differences between intra-day and daily intervals. Notably, in the 6-hour forecast, the stratification of change characteristics near the ocean surface is evident, indicating that the changes in ocean currents are more easily influenced by recent changes, such as sea surface winds.
C.2 Performance Test Supplement
To comprehensively evaluate the model's performance, conducted tests components under the observation data (Buoy data on CMEMS). The WenHai* and Glory* shown in the fig and direct comparisons based on descriptions from paper [T]. Although this comparison may not be entirely fair (mainly due to inconsistent testing years), FuXi-Ocean's performance can still serve as simple reference.
current UV
D Data
As shown in the Tab compared with the long-term training data used by mainstream ocean models such as Xihe, Wenhai, Langya, etc., we used shorter time range data for training and achieved better prediction results. For data-driven deep learning methods, we can accurately capture ocean phenomena and features by relying solely on pure ocean variables such as T. S, U, V and SSH. Observation Data. For the IV-TT data, we list the available public links in Fig which include salinity, temperature, and SST variables. Through experimentation, it was found that due practical
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WenHall
0.20
Glory*
FuXi-Orean
0.18
RMSE: (m/s)
0.16
0.14
0.12
Lead Time (hour)
Figure 8: The ocean current U comparison of different methods based on the observation data. The x-axis represents the forecast time, and the y-axis represents the RMSE (lower is better). Note that the results of WenHai and Glory are derived from their papers, hence marked with asterisk (*).
0.20
Wenlfai
Glory*
0.19
FuXi-Ocean
0.18
RMSE (mis)
0.17
0.16
0.15
0.14
0.13
0.12
2
10
Lead Time (hour)
Figure 9: The ocean current V comparison of different methods based on the observation data. The x-axis represents the forecast time, and the y-axis represents the RMSE (lower is better). Note that the results of WenHai and Glory are derived from their papers, hence marked with an asterisk (").
issues, only partial data is available. Firstly, due the presence of outliers in the temperature and salinity vertical profile data from IV-TT, these anomalies significantly impact the evaluation results, causing the errors to be abnormally large. Secondly, because the ocean contains thermoclines and haloclines, where temperature and salinity change rapidly and non-linearly, using IV-TT to perform linear interpolation in the vertical direction to align with the model grid introduces excessive errors, thereby reducing the reliability of the evaluation results. Therefore, we ultimately use SST for
Table 1: Comparison of Training Data for Existing Ocean Models
Name of the Model Training Data Time Range
Input Variables
Xihe
1993-2020
SST, UIO, V10, T. S, U, V. SSH
Langya
0093-2021
SST, UTO, V10, T, S. U. V, SSH
WenHai
1993-2020
SST, UTO, V10, T, S. U, V, SSH T, S. U. V. SSH
FuXi-Ocean
2006-2015
22

---

Table 2: The source of observation data
Variable
Data type situ Argo profiles
Source data
Argo GDAC (from http://tpp/t usgodae org/
prgo. html http://www www.://oooo.i..o.. org/
In situ Argo proiiees In sim Argo profiees
Argo GDAC (as above)
U/V
Argo, Ocean Sites, GOSUD, EGO
https:
500 PUU_HHYGY DISCRETE, MaaL 013_
SLA
Satellite altimeter from Jason- I, Jason-2 and Envisal In situ surface drifler data
CLS Aviso Level 3 data
SST
From USGODAE server: http: /vwu. ungodae org/
Eg bttibi//aaaaait.pl112. Tnnoe abe sfcobel
Sunmary
evaluation and comparison. The observational data from ARGO is not gridded, and its degree of discreteness shown in Fig 10 Globally, the distribution of stations at key locations is relatively uniform and comprehensive.
Figure 10: Global distribution map of ARGO and other buoys.
E
Supplementary details
In practice, K achieve the best performance of the MoT module. In the module design, we assume that there will always be an optimal moment of information that can optimize the results. Of course, the number historical moments involved, N, increases significantly. K 1 is unlikely to be good choice.
23


Reviews:
[{"review_id": "2ueIEx84OM", "reviewer": "Reviewer_TuNU", "summary": "The paper introduces FuXi-Ocean, a data-driven global ocean-forecasting system that delivers six-hourly predictions at 1/12° horizontal resolution from the surface down to 1500 m.\nKey ingredients include\n- an encoder–decoder backbone with attention blocks,\n- a Mixture-of-Time (MoT) module that, for every variable-depth channel, adaptively selects the most reliable temporal context among the last four six-hour states, and\n- a latitude-weighted Charbonnier loss plus two-stage (single -- then multi-step) training.\n\nTrained on 8.5 years of HYCOM reanalysis, FuXi-Ocean outperforms both traditional numerical systems (HYCOM, BLK, FOAM) and recent deep-learning baselines on RMSE for SST, T, S and currents, while using only pure ocean fields as input. Ablation studies show that MoT and longer input windows are critical, especially for thermohaline variables. The authors discuss limitations, broader impacts and commit to releasing code and data.", "questions": "- Q1: How sensitive is FuXi-Ocean to the choice K = 1 in MoT? Would adaptive K or soft aggregation outperform hard Top-K?\n- Q2: Can the authors provide probabilistic or ensemble extensions to quantify forecast uncertainty? If so, what compute overhead do they expect?\n- Q3: The reanalysis training end date is mid-2014, yet testing on 2015 HYCOM and 2022 observations shows strong skill. Could the authors clarify whether covariate shift (e.g., strong El Niño 2015–16) affects performance?\n- Q4: Please report bias metrics (mean error) in addition to RMSE, especially for temperature and salinity below 300 m.\n- Q5: What is the inference wall-clock time for a single 10-day forecast on one A100/H100, and how does this compare to OGCMs when identical hardware is used?", "limitations": "The authors explicitly discuss training-data bias, limited phenomenon-specific metrics and unexplored seasonal predictions. This coverage is adequate.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- First published DL model to achieve sub-daily, eddy-resolving global forecasts; extends beyond the daily cadence of XiHe/Langya.\n- Practical 6-hour lead times are valuable for search-and-rescue, spill tracking, fisheries and coupled Earth-system modelling.\n- MoT is a simple yet effective mechanism for channel-wise temporal selection, reducing autoregressive drift without heavy ensembling.\n- Comprehensive evaluation: horizontal error maps, depth-profiles, 10-day lead times, IV-TT comparisons and carefully designed ablations.\n- Achieves state-of-the-art skill using only 9 years of training data and no atmospheric forcings, which lowers data barriers.\n- Implementation details, loss weighting, optimiser schedule and training hardware are described; code and checkpoints promised.\n\nWeaknesses\n- W1: Real-world validation is limited to SST in 2022; salinity, subsurface temperature and currents are not cross-checked against in-situ observations.\n- W2: Forecasts are deterministic; no ensemble or probabilistic output, so users cannot gauge confidence.\n- W3: No error bars or significance tests. Authors justify this by cost, but it hampers comparison.\n- W4: Model inherits biases from HYCOM reanalysis; the impact of these biases on predictions is not fully quantified.\n- W5: Operational model outputs are daily; FuXi-Ocean’s six-hour forecasts are averaged to daily means, but differing analysis/assimilation dates may still advantage the proposed method.\n- W6: Section 3.2 mixes architectural and algorithmic details; a schematic of tensor shapes and MoT update rule would aid comprehension for non-experts.", "was_revised": true, "final_justification": "I appreciate the plan to add subsurface validation with ARGO or mooring data. In the final version, please mention how you filter and match the data. Overall, my concerns are addressed, and I will keep my current score.", "rebuttal": "Dear Reviewer TuNU,\n\nThank you for your thorough review and positive evaluation. We particularly appreciate your recognition of FuXi-Ocean as the first sub-daily deep learning model for global ocean forecasting, and your acknowledgment of the practical value of the 6-hour predictions and the MoT module innovation. We have carefully addressed all your questions in the responses below.\n\n## Q1. How sensitive is FuXi-Ocean to the choice of K = 1 in MoT? Would adaptive K or soft aggregation outperform hard Top-K?\n\nA: Following the reviewer’s suggestion, we conducted additional experiments with K = 2, 3, 4, and also tested a soft aggregation approach. The table below shows the relative change in RMSE compared to K=1, calculated as $\\frac{\\text{RMSE} - \\text{RMSE}_{K=1}}{\\text{RMSE}_{K=1}} \\times 100\\%$. Positive values indicate that K=1 yields better performance. For each variable, results are averaged across the depth dimension. The \"attn\" group refers to the soft aggregation on the four temporal features using attention mechanisms.\n\n| Lead Time (h) | S (K=2) | S (K=3) | S (K=4) | S (attn) | T (K=2) | T (K=3) | T (K=4) | T (attn) | U (K=2) | U (K=3) | U (K=4) | U (attn) | V (K=2) | V (K=3) | V (K=4) | V (attn) | SEL (K=2) | SEL (K=3) | SEL (K=4) | SEL (attn) |\n|------|-------|-------|-------|---------|--------|--------|--------|----------|--------|--------|--------|----------|--------|--------|--------|----------|---------|---------|---------|-----------|\n| 6    | +1.07% | +1.05% | +1.06% | +0.90%   | +1.11%  | +1.10%  | +1.12%  | +1.10%    | +1.21%  | +1.43%  | +1.53%  | +0.51%    | +1.21%  | +1.32%  | +1.42%  | +0.39%    | +1.12%   | +1.11%   | +1.12%   | +1.11%     |\n| 24   | +1.00% | +2.32% | +3.00% | +2.49%   | +1.18%  | +1.60%  | +1.78%  | +1.82%    | +1.10%  | +0.93%  | +0.95%  | +0.80%    | +1.10%  | +0.92%  | +1.01%  | +0.71%    | +1.21%   | +1.52%   | +1.67%   | +1.19%     |\n| 54   | +1.01% | +1.05% | +1.01% | +1.09%   | +1.01%  | +1.10%  | +1.12%  | +1.22%    | +1.07%  | +1.88%  | +1.52%  | +0.51%    | +1.06%  | +1.81%  | +1.53%  | +0.49%    | +0.97%   | +1.32%   | +1.11%   | +1.12%     |\n| 72   | +1.03% | +1.10% | +1.04% | +1.41%   | +1.03%  | +1.24%  | +1.21%  | +1.19%    | +1.13%  | +2.62%  | +1.79%  | +0.71%    | +1.12%  | +2.42%  | +1.75%  | +0.60%    | +1.00%   | +1.70%   | +1.26%   | +1.29%     |\n| 120  | +1.09% | +1.35% | +1.14% | +1.19%   | +1.10%  | +1.79%  | +1.42%  | +1.41%    | +1.48%  | +3.94%  | +2.40%  | +0.40%    | +1.47%  | +3.44%  | +2.30%  | +0.30%    | +1.06%   | +2.64%   | +1.55%   | +1.21%     |\n| 240  | +1.03% | +1.62% | +1.03% | +1.51%   | +1.35%  | +2.80%  | +1.56%  | +1.59%    | +2.23%  | +5.09%  | +2.87%  | +0.69%    | +2.32%  | +4.61%  | +2.78%  | +0.82%    | +1.77%   | +3.94%   | +2.19%   | +1.39%     |\n\n*Units: S (PSU), T (°C), U/V (m/s), SEL (m)*\n\nThese results confirm that K=1 is the optimal choice. All alternatives, including K>1 and soft aggregation, lead to degraded performance, particularly at the 24-hour forecast horizon. This supports our hypothesis that selecting a single most reliable temporal context is crucial for capturing diurnal cycles. Mixing multiple temporal features, whether through higher K or soft aggregation, introduces interference that hinders the model's ability to learn periodic patterns.\n\nFor ocean currents (U, V), while soft aggregation shows some improvement over K>1, it still cannot match the performance of K=1. This suggests that even for variables with different temporal characteristics, adaptively selecting a single optimal temporal window remains the most effective strategy.\n\n## Q2. Quantitative demonstration of forecast uncertainty.\n\nA: This is an important question about uncertainty quantification. While we were unable to complete ensemble model training within the rebuttal period due to computational constraints, we followed WenHai's approach and computed the continuous ranked probability score (CRPS) between our deterministic forecasts and observational data for sea surface variables.\n\nThe CRPS is defined as:\n$$CRPS_{t,l}^i = \\int_{-\\infty}^{+\\infty} \\left[F(x) - H\\left(x \\geq V_{t+l}^i\\right)\\right]^2 dx$$\n\nwhere $F(x)$ is the cumulative distribution function of the pseudo ensemble forecast, $H$ is the Heaviside function, and $V_{t+l}^i$ is the observed value at time $t+l$. The pseudo ensemble is constructed from FuXi-Ocean predictions within a 1° × 1° box centered on the grid point nearest to $V_{t+l}^i$.\n\nGiven known biases in observational data, we used HYCOM full analysis fields for validation, as they provide the most accurate available estimates of ocean state. The results are shown below:\n\n| Variable | Day 0  | Day 1  | Day 2  | Day 3  | Day 4  | Day 5  | Day 6  | Day 7  | Day 8  | Day 9 | Day 10 |\n|----------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|\n| S0 (PSU)  | 0.0423 | 0.0441 | 0.0445 | 0.0448 | 0.0449 | 0.0455 | 0.0453 | 0.0455 | 0.0465 | 0.0468 | 0.0471 |\n| T0 (°C)  | 0.2207 | 0.2291 | 0.2301 | 0.2362 | 0.2351 | 0.2363 | 0.2372 | 0.2379 | 0.2382 | 0.2403 | 0.2443 |\n| U0 (m/s)  | 0.0482 | 0.0485 | 0.0484 | 0.0484 | 0.0485 | 0.0486 | 0.0488 | 0.0489 | 0.0488 | 0.0490 | 0.0493 |\n| V0 (m/s)  | 0.0473 | 0.0475 | 0.0477 | 0.0477 | 0.0477 | 0.0478 | 0.0479 | 0.0480 | 0.0482 | 0.0482 | 0.0483 |\n\nThese results demonstrate that FuXi-Ocean maintains stable probabilistic performance over forecast horizons, with gradual and predictable error growth. The relatively low CRPS values, particularly for ocean currents, indicate that our deterministic forecasts provide reliable central estimates.\n\nRegarding computational cost, a single-member forecast requires 72 seconds on one H100 GPU. Thus, a 10-member ensemble would take approximately 12 minutes on a single GPU, or significantly less if parallelized across multiple GPUs.\n\n## Q3. The extent to which covariate shift affects performance.\n\nA: Thank you for raising this important question about potential covariate shift. To assess its impact, we retrained a model ($M_{new}$) using data from 2006.01 to 2013.06, validated it on 2013.07-2013.12, and tested it on 2014 reanalysis data. We then compared its performance with our original model ($M_{best}$) which was trained through 2014.06 and tested on 2015. The table below shows the relative RMSE difference, computed as $\\frac{\\text{RMSE}_{M_{new}} - \\text{RMSE}_{M_{best}}}{\\text{RMSE}_{M_{best}}} \\times 100\\%$.\n\n| Lead Time (h) | S (PSU) | T (°C) | U (m/s) | V (m/s) | SEL (m) |\n|---------------|---------|---------|---------|---------|---------|\n| 6             | +1.71%  | -0.04%  | +0.01%  | -0.02%  | +0.50%  |\n| 24            | -1.44%  | +1.02%  | -0.02%  | -0.18%  | -0.42%  |\n| 54            | +1.05%  | +0.12%  | +0.12%  | +0.12%  | +0.33%  |\n| 72            | +0.99%  | +0.03%  | +0.03%  | +0.05%  | +0.03%  |\n| 120           | +1.11%  | +0.21%  | +0.25%  | +0.22%  | +0.34%  |\n| 240           | +1.15%  | +0.18%  | +0.20%  | +0.17%  | +0.17%  |\n\nThe performance differences are minimal across all lead times. Interestingly, $M_{best}$ (trained with 2014 data) performed slightly better on the 2015 test dataset, despite the occurrence of a strong El Niño event. This suggests that:\n\n1. FuXi-Ocean generalizes well to climate anomalies not seen during training\n2. The impact of missing one year of El Niño training data (2014) is actually larger than the covariate shift from testing on a strong El Niño year (2015)\n\n## Q4. The mean bias error metrics (MBE).\n\nA: We computed the mean bias error (MBE) for FuXi-Ocean and compared it with operational systems using IV-TT observations. As MBE does not show consistent trend with lead time, we present the average over all 11 forecast steps (from initialization to day 10) for clarity. FOAM does not provide surface salinity data, resulting in missing values at 0 m depth.\n\n**Salinity MBE (PSU):**\n| Model | 0m      | 300m     | 500m     | 1000m    | 1500m    |\n|-------|---------|----------|----------|----------|----------|\n| BLK   | -0.0133 | +0.0001  | +0.0150  | +0.0135  | -0.0103  |\n| FOAM  | N/A     | +0.0012  | -0.0058  | -0.0092  | -0.0093  |\n| HYCOM | -0.0327 | -0.0197  | +0.0363  | +0.0277  | -0.0232  |\n| Ours  | -0.0155 | +0.0151  | +0.0064  | +0.0046  | +0.0106  |\n\n**Temperature MBE (°C):**\n| Model | 0m      | 300m     | 500m     | 1000m    | 1500m    |\n|-------|---------|----------|----------|----------|----------|\n| BLK   | -0.0546 | +0.1624  | +0.1936  | +0.1770  | +0.1399  |\n| FOAM  | -0.0065 | +0.0704  | +0.0504  | +0.0096  | +0.0032  |\n| HYCOM | +0.0936 | -0.0640  | +0.2741  | +0.4324  | +0.1979  |\n| Ours  | +0.0402 | -0.3221  | -0.4865  | -0.0809  | +0.0782  |\n\nKey observations:\n1. All models show systematic biases of similar magnitude, reflecting uncertainties inherent in ocean observations and reanalyses\n2. FuXi-Ocean's biases are comparable to or smaller than those of operational systems across most depths\n3. The larger temperature bias at 300-500m in FuXi-Ocean is localized near the thermocline, where sharp gradients increase prediction difficulty\n\nThese biases represent systematic offsets rather than forecast errors. When considered alongside FuXi-Ocean’s superior RMSE performance, the results demonstrate the model's forecast accuracy and the effectiveness of our approach.\n\n## Q5. Inference time comparison.\n\nA: We benchmarked the inference time for a full 10-day forecast (40 autoregressive steps):\n\n| Model     | Time        | Hardware Configuration |    \n|-----------|-------------|------------------------|\n| BLK       | 6-10 hours  | >1000 CPU cores       |\n| FOAM      | 5-8 hours   | >3000 CPU cores       |\n| HYCOM     | 4-8 hours   | >5000 CPU cores       |\n| FuXi-Ocean| 72 seconds  | 1 × H100 GPU          |\n\n## Others.\n\nWe will incorporate all suggested revisions in the updated manuscript, including enhanced figure readability and corrected typos. Upon acceptance, we will release the code and model checkpoints to ensure reproducibility."}, {"review_id": "yYZVCKHg4m", "reviewer": "Reviewer_8ZnV", "summary": "The paper presents an advancement in ocean forecasting by introducing a deep learning model that achieves high temporal and spatial resolution. FuXi-Ocean is a data-driven global ocean forecasting model achieving six-hourly predictions at eddy-resolving 1/12° spatial resolution, reaching depths of up to 1500 meters. The model architecture integrates a context-aware feature extraction module with a predictive network employing stacked attention blocks. The experimental results shows a better results compared with other models.", "questions": "1. Both XiHe and FuXi claimed that it is the first data-driven model for 1/12◦ resolution global ocean prediction model. Can you please explain it?\n\n2. Can you compare with other models, such as XiHe, with a more complete manner? While FuXi shows better accuracy for short-period prediction, less than 5 days, the slope of RMSE increases more rapidly than XiHe. Can you explain why? Both have a similar RMSE range for the 10-day period.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. Comprehensive experimental evaluations demonstrate that FuXi-Ocean outperforms traditional numerical forecasting models in predicting key ocean variables such as temperature, salinity, and currents across multiple depths.\n2. The model is evaluated using both reanalysis data (HYCOM-RD) and observational data (IV-TT framework), providing a thorough assessment of its performance in both synthetic and real-world conditions.\n3. The six-hour temporal resolution and 1/12° spatial resolution make FuXi-Ocean highly valuable for maritime operations, environmental monitoring, and coastal hazard warning systems.\n\nWeaknesses:\n1. The reliance on reanalysis data for training introduces potential biases from the underlying numerical models. This challenge is compounded by the scarcity of publicly available high-quality sub-daily ocean reanalysis data.\n2. While RMSE is a useful metric for comparing forecast systems, it may not fully capture the model's ability to represent specific ocean phenomena like mesoscale eddies, western boundary currents, or seasonal thermocline transitions. \n3. While FuXi-Ocean demonstrates robust forecast accuracy through 10 days, its performance for longer-term predictions (seasonal to interannual) remains unexplored. This represents an important direction for future research.\n4. Comparison with others might not be complete. There is a recent paper, XiHe [43], written by X. Wang, et. al. and posted in the following link: https://arxiv.org/pdf/2402.02995. The authors also claimed \"it is the first data-driven 1/12◦ resolution global ocean eddy-resolving forecasting model named XiHe...\" If so, which one is the first data-driven model? \nAdditionally, on page 9 of this paper, the authors present the RMSE of temperature and other metrics. Looks like the overall range of RMSE is similar to FuXi. While FuXi shows better accuracy for short-period prediction, less than 5 days, the slope of RMSE increases more rapidly than XiHe.", "was_revised": true, "final_justification": "After reading the rebuttals and other reviews, I raised the rating by 1.", "rebuttal": "Dear Reviewer 8ZnV,\n\nThank you for your thoughtful review and constructive feedback. We appreciate the opportunity to clarify several aspects of our work and provide additional context to address your concerns. Below, we have provided a more detailed and structured response.\n\n## Q1. The \"First\" Claim - Important Clarification.\n\nA: We acknowledge the concern regarding the claim that FuXi-Ocean is the \"first\" data-driven 1/12° resolution global ocean forecasting model, especially in light of the *XiHe* model. To clarify, FuXi-Ocean is indeed the first to achieve **sub-daily (6-hourly)** predictions at **1/12° spatial resolution** across a **depth range of 0-1500m**, making it a unique contribution to the field. While *XiHe* also operates at 1/12° resolution, it focuses on **daily** forecasting, which poses fundamentally different challenges. \nTo better emphasize these differences, we have updated the comparison table to highlight additional technical specifications:\n\n| Method | Lead Time | Spatial Resolution | Depth Range | Model Category | Forecast Interval | information dependency |\n|--------|-----------|-------------------|-------------|----------------|------------------|------------------|\n| XiHe | 10 days | 1/12° | <700m | Deep Learning (one step) | daily | atmosphere and ocean |\n| LangYa | 7 days | 1/12° | <600m | Deep Learning (one step) | daily | atmosphere and ocean |\n| WenHai | 10 day | 1/12° | <700m | Deep Learning (autoregressive) | daily | atmosphere and ocean |\n| FuXi-Ocean | 10 days | 1/12° | 0-1500m | Deep Learning (autoregressive) | 6 hours | ocean only |\n\n**Technical Challenges of Sub-Daily Prediction**:\n- **Diurnal Cycle Modeling**: Predicting ocean variables at sub-daily intervals requires capturing rapid changes in surface temperature, salinity, and currents driven by daily atmospheric and oceanic processes. Sub-daily prediction models must handle diurnal cycles and inertial oscillations, which are not as significant in daily models. FuXi-Ocean incorporates this by modeling diurnal dynamics with **higher temporal resolution**.\n  \n- **Higher Frequency Ocean Dynamics**: At 6-hour intervals, the model can track faster oceanic processes, such as **mesoscale eddies** and **surface current fluctuations**, which are not resolvable in daily predictions. This leads to more accurate short-term forecasts for applications such as maritime operations and coastal hazard monitoring.\n\nNote that ocean variable forecasting tasks are significantly different from video generation. The time interval gap from daily to sub-daily is substantial, and many variables such as temperature have significant diurnal cycle characteristics. Direct migration of daily models by simply increasing parameter size would cause serious prediction errors, and this is a discovery made for the first time.\n\n## Q2. Compare with other models.\nA: We appreciate the reviewer’s interest in comparing our model with others in the field. While we acknowledge that comparing sub-daily resolution models with daily resolution models is not entirely fair—due to the inherent difficulty of sub-daily forecasting—we have nevertheless conducted a direct comparison. Specifically, we calculated the RMSE using observed ground truth from the IVTT dataset for Sea Surface Temperature (SST) in 2022 and compared the results with those from WenHai, a state-of-the-art daily model. SST is a critical ocean variable and is typically the most accurately measured in the IVTT dataset, which is why we focused our comparison primarily on SST (issues with other variables are discussed in the supplementary materials).\n\n| Method | 0 day | 1 day | 2 day | 3 day | 4 day | 5 day | 6 day | 7 day | 8 day | 9 day | 10 day |\n|--------|---|---|---|---|---|---|---|---|---|---|-----|\n| WenHai | 0.371 | 0.4048 | 0.4734 |0.4785 | 0.497 | 0.5308 | 0.5486 | 0.5869 | 0.6039 | 0.6214 | 0.6384 \n| FuXi-Ocean | 0.236 | 0.272 | 0.277 | 0.295 | 0.317 | 0.319 | 0.339 | 0.359 | 0.386 | 0.404 | 0.428 |\n\nThe unit is ℃, the lower is the better. \n\nTo more intuitively demonstrate the slope of interest, we further convert the table results into daily error increments, shown as follows:\n\n| Method | 1 day | 2 day | 3 day | 4 day | 5 day | 6 day | 7 day | 8 day | 9 day | 10 day | average error growth |\n|--------|-------|-------|-------|-------|-------|-------|-------|-------|-------|--------|--------|\n| WenHai | 0.0338 | 0.0686 | 0.0051 | 0.0185 | 0.0338 | 0.0178 | 0.0383 | 0.017 | 0.0175 | 0.017 | 0.0267\n| FuXi-Ocean | 0.036 | 0.005 | 0.018 | 0.022 | 0.002 | 0.02 | 0.02 | 0.027 | 0.018 | 0.024 | 0.0192|\n\nThe unit is ℃, the lower is the better. \n\nIt is important to highlight that daily models, such as WenHai, forecast daily mean values, whereas sub-daily models like FuXi-Ocean predict instantaneous values at specific time steps. For a fair comparison, we averaged the sub-daily predictions within a day, which inevitably introduces some loss of accuracy and potential error. Despite this, the table shows that FuXi-Ocean demonstrates either similar or even lower error growth slopes compared to WenHai. A common challenge in autoregressive models is that the error tends to grow with each iteration. FuXi-Ocean, with its 40 autoregressive iterations over a 10-day forecast period, naturally experiences more error accumulation compared to daily models like WenHai, which only require 10 iterations. Nevertheless, the results indicate that FuXi-Ocean remains competitive and even superior in terms of error growth, especially over shorter forecast periods."}, {"review_id": "y7WFyi7IbN", "reviewer": "Reviewer_7qdU", "summary": "The paper proposes an autoregressive machine learning (ML) emulator of the surface ocean, including state variables up to 1500m depth, at 1/12° resolution, and six-hourly time steps. The proposed method is based on an encoder-processor-decoder framework with a newly proposed \"mixture-of-time\" component that is intended to modulate the temporal context for each prognostic variable. The method is trained on reanalysis and evaluated against the reanalysis and SSTs from an observational dataset using RMSE.", "questions": "- How does the method improve on existing methods in the comparison on reanalysis?\n- Why does the model not use atmospheric forcings as input?\n- What is the maximum temporal history of the model?\n- Is Figure 4 a fair comparison?", "limitations": "- Several limitations were left unclear. It help to discuss limitations regarding blurring due to RMSE, lack of surface forcings, maximum timescales, and observational errors in temperature and salinity. \n- The ethical concerns are well written. It would have been helpful to mention the risks of overfishing in the sentence on using this model for guiding large-scale fishing vessels.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The work is very well motivated and ML ocean emulators are certainly a key topic of interest in the research community. Ocean emulators are necessary for subseasonal to seasonal forecasting and coupled ocean-atmosphere clime model emulators.\n- The choice to use more than two previous steps as inputs is well motivated for the ocean. Other work has shown that the time-step size and history are important hyperparameters (e.g., https://doi.org/10.22541/essoar.170110658.85641696/v1) and it is helpful to see the authors spend a significant amount of effort thinking about this. \n- The dataset, experiment setup, and evaluation metrics are very clearly described making is easy to understand the results section. \n\nWeaknesses\n- Most importantly the paper needs a baseline for Figure 2 and 3. It is entirely unclear to me if these results are improving on existing methods, and it is hard to tell if e.g., 0.5 salinity is a high or low error. I would ask the authors to add a 'persistence' baseline, i.e., a forecast that assumes the state at t=0 to be constant throughout the prediction window,  into the paper. I would also recommend to add a climatology baseline, and - I don't think it's required - but the authors would greatly improve the impact of the paper if they were to add HYCOM, BLK, or FOAM hindcasts into the comparison vs reanalysis.\n- Why does this forecasting problem work if there are no forcings provided from the atmosphere? As far as I understand, SST, salinity, U and V at the ocean surface layer are largely influenced by atmospheric winds, temperatures, and rainfall. I would have assumed that the model should take these forcings as inputs, but they don't seem to be used. Could the authors please detail why these forcings are not necessary? \n- The arguments on \"long-term deep ocean memory\" and \"slow-evolving deep-water phenom­ena\" seemed a bit of a stretch to me. Does the model have the capability to leverage information further than 24hrs, i.e., four time steps ago? There are deep ocean processes that require history windows of over 50 years and the current model architecture doesn't seem like it would capture those. Thus, I recommend to rephrase those sections in the paper and clarify the timescales that the paper can pick up on.\n\nMinor comments:\n- Can the authors change the wording from \"deep ocean\" into \"twilight zone\" or \"deeper ocean\". I find the denomination deep ocean quite misleading as it typically includes all depth beyond 200m, incorrectly implying that FuXi-Ocean would contain state variables from the abyssal and benthic zone. \n- 3.2.3 was a bit hard to understand for me. For example how is a \"temporal window\" defined here? Given that the MoT layer is such an important piece of this paper it would have helped to expand a bit on this layer in the appendix. For example, are there similar approaches in other papers? What are the limitations of this layer? How is K determined and why did the authors choose K=1?\n- There a sentence in the introduction: \"designing [...] models requires [...] while avoiding overfitting to spurious correlations\". This is very interesting, as I believe it is a common issue of ML autoregressive emulators that they do not learn correct sensitivities to each input variables. It is not necessary, but would make this paper excellent if the authors included a study to test if the model has learned correct sensititivites to each input variable.\n- The paper includes a sentence \"after evaluation, the raw data errors for salinity and temperature were too large to be used directly\". There is detail provided on this in the appendix, but it would be helpful to have one additional sentence on this in the main text. \n- Figure 2 plot is missing units\n- Un Fig 4, what are the references for HYCOM, BLK, and FOAM methods. Is HYCOM in this case a reanalysis or a forecast? If it's a reanalysis than there is likely an issue in the put because a forecast almost never outperforms a reanalysis. \n- The results in Figure 8 and 9 seem suspiciously good. Could the authors please detail in which ways this is NOT a fair comparison? In particular I would have expected Glory and WenHai to have better RMSE scores due to their access to U10 and V10 wind forcing fields. Do I understand correctly that they're using UV10 as forcing and not as prognostic variables? I would probably recommend\n- Evaluation on RMSE only seems quite limiting. It would significantly increase the impact of this paper if the evaluation of spectra were included. It's unclear if the model predicts blurred fields.\n- Typos:\nresolutions deep learning unique challenges.\npreivous models\nand relying only ocean variables as input\nImplement Details\nB BoraderImpacts\n\nI am willing to raise my score if the proposed changes are adequately addressed.", "was_revised": true, "final_justification": "The authors have addressed all my questions and exceeded expectations in doing so. The paper proposes a global ocean emulator that outperforms a persistence baseline, which is much less trivial than it may sound. This ocean emulator will be useful for next steps towards seasonal and climate emulators.", "rebuttal": "Dear Reviewer 7qdU,\n\nThank you for your insightful review and constructive feedback. We appreciate your recognition of our work's strong motivation and clear experimental setup. Your expertise in ocean dynamics is evident from your thoughtful questions about atmospheric forcing and temporal memory. We have conducted additional experiments and analyses to address all your concerns comprehensively.\n\n## Q1. Compare with baseline.\n\nA: We have added persistence baseline comparisons as requested. Following WenHai's approach, we present results on observational data for sea surface variables to avoid introducing HYCOM data biases. The baseline uses HYCOM initial fields to calculate persistence RMSE.\n\n|Method|Variable|Day 0|Day 1|Day 2|Day 3|Day 4|Day 5|Day 6|Day 7|Day 8|Day 9|Day 10|\n|-------------|----------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|\n|Persistence|S0|0.110|0.115|0.116|0.121|0.124|0.123|0.127|0.130|0.135|0.136|0.139|\n||T0|0.236|0.274|0.310|0.356|0.396|0.438|0.487|0.522|0.573|0.615|0.664|\n||U0|0.119|0.145|0.154|0.156|0.155|0.157|0.159|0.161|0.162|0.161|0.163|\n||V0|0.120|0.146|0.154|0.156|0.157|0.159|0.159|0.161|0.163|0.161|0.162|\n|FuXi-Ocean|S0|0.110|0.113|0.115|0.118|0.119|0.121|0.118|0.123|0.122|0.125|0.126|\n||T0|0.236|0.272|0.277|0.295|0.317|0.319|0.339|0.359|0.386|0.404|0.428|\n||U0|0.119|0.123|0.127|0.132|0.133|0.136|0.136|0.137|0.140|0.141|0.143|\n||V0|0.120|0.124|0.128|0.131|0.133|0.136|0.135|0.135|0.138|0.141|0.140|\n\n*Units: S0 (PSU), T0 (°C), U0/V0 (m/s)*\nThe results clearly demonstrate that FuXi-Ocean consistently outperforms persistence for all variables, with the advantage increasing over time. \n\n## Q2. Atmospheric forcings.\n\nA: We strongly agree that atmospheric forcing is crucial for ocean forecasting. To quantify this, we conducted experiments incorporating five atmospheric variables from ERA5 (10-m zonal/meridional winds, mean sea level pressure, 2-m temperature, and 2-m dewpoint temperature) using an additional encoder network for atmosphere-ocean fusion.\n\nThe table shows relative RMSE improvement when adding atmospheric forcing: $\\frac{\\text{RMSE}_{with\\_atm} - \\text{RMSE}_{FuXi-Ocean}}{\\text{RMSE}_{FuXi-Ocean}} \\times 100\\%$ (negative values indicate improvement):\n\n|Lead Time (h)|S|T|U|V|SEL|\n|---------------|---------|---------|---------|---------|---------|\n|6|-2.52%|-4.30%|-1.22%|-1.20%|-1.71%|\n|24|-0.65%|-6.64%|-2.61%|-2.50%|-3.06%|\n|54|-1.19%|-3.88%|-3.56%|-3.14%|-2.22%|\n|72|-3.41%|-5.66%|-3.06%|-2.46%|-2.15%|\n|120|-6.36%|-5.85%|-5.21%|-4.43%|-4.40%|\n|240|-9.00%|-11.05%|-8.06%|-8.85%|-12.82%|\n\n*Units: S (PSU), T (°C), U/V (m/s), SEL (m)*\nSpecifically, we conducted comprehensive testing on the 2015 HYCOM reanalysis data and average the results across the depth dimension. As expected, atmospheric forcing improves all variables, particularly temperature at longer lead times. However, our key contribution is demonstrating that effective sub-daily forecasting is achievable using ocean variables alone, with performance exceeding daily numerical models. This approach:\n\n1. Reduces data dependencies and computational overhead\n2. Leverages the ocean's inherent predictability from its large heat capacity and momentum\n3. Provides a strong baseline that can be enhanced with atmospheric coupling\n\nThis represents a different paradigm from traditional ocean forecasting—utilizing the ocean's internal dynamics rather than treating it as purely atmospherically driven. Our upcoming work will present a fully coupled ocean-atmosphere system building on this foundation, but the current results establish the surprising effectiveness of ocean-only deep learning approaches for short-term forecasting.\n\n## Q3. The maximum temporal history of the model.\n\nA: Thank you for pointing out the ambiguity in our terminology. We will revise \"temporal windows\" to \"historical time points\" for clarity.\n\nRegarding the temporal extent accessible to FuXi-Ocean: While the architecture could theoretically accommodate longer histories, our design choices are guided by the specific requirements of sub-daily ocean forecasting:\n\n1. **Physical considerations**: The 6-hour interval with 4 historical time points (24 hours total) captures the complete diurnal cycle—the dominant mode of variability for sub-daily forecasting. \n\n2. **Practical constraints**: Extending the temporal history significantly increases:\n   - Memory requirements (linear with time points)\n   - Computational cost during training and inference\n   - Data loading overhead in operational settings\n\n3. **Task-specific design**: FuXi-Ocean targets medium-range (1-10 day) forecasting where 24-hour history provides optimal information content. For seasonal/subseasonal prediction, different architectural choices would be appropriate, as you correctly note.\n\nWe acknowledge that processes like deep ocean memory spanning decades exist, but these operate on timescales beyond our forecast horizon and would require fundamentally different modeling approaches. \n\n## Q4. Sensitivity of K in the MoT module.\n\nA: We conducted extensive experiments with K = 2, 3, 4, and also tested a soft aggregation approach. The table below shows the relative RMSE change compared to K=1, calculated as $\\frac{\\text{RMSE} - \\text{RMSE}_{K=1}}{\\text{RMSE}_{K=1}} \\times 100\\%$. For each variable, we averaged the results across the depth dimension. The \"attn\" group performs soft aggregation on the four temporal features using attention mechanisms.\n\n|Lead Time (h)|S (K=2)|S (K=3)|S (K=4)|S (attn)|T (K=2)|T (K=3)|T (K=4)|T (attn)|U (K=2)|U (K=3)|U (K=4)|U (attn)|V (K=2)|V (K=3)|V (K=4)|V (attn)|SEL (K=2)|SEL (K=3)|SEL (K=4)|SEL (attn)|\n|-|--|----|---|---|----|---|--|----------|--------|--------|--------|----------|--------|--------|--------|----------|---------|---------|---------|-----------|\n|6|+1.07%|+1.05%|+1.06%|+0.90%|+1.11%|+1.10%|+1.12%|+1.10%|+1.21%|+1.43%|+1.53%|+0.51%|+1.21%|+1.32%|+1.42%|+0.39%|+1.12%|+1.11%|+1.12%|+1.11%|\n|24|+1.00%|+2.32%|+3.00%|+2.49%|+1.18%|+1.60%|+1.78%|+1.82%|+1.10%|+0.93%|+0.95%|+0.80%|+1.10%|+0.92%|+1.01%|+0.71%|+1.21%|+1.52%|+1.67%|+1.19%|\n|54|+1.01%|+1.05%|+1.01%|+1.09%|+1.01%|+1.10%|+1.12%|+1.22%|+1.07%|+1.88%|+1.52%|+0.51%|+1.06%|+1.81%|+1.53%|+0.49%|+0.97%|+1.32%|+1.11%|+1.12%|\n|72|+1.03%|+1.10%|+1.04%|+1.41%|+1.03%|+1.24%|+1.21%|+1.19%|+1.13%|+2.62%|+1.79%|+0.71%|+1.12%|+2.42%|+1.75%|+0.60%|+1.00%|+1.70%|+1.26%|+1.29%|\n|120|+1.09%|+1.35%|+1.14%|+1.19%|+1.10%|+1.79%|+1.42%|+1.41%|+1.48%|+3.94%|+2.40%|+0.40%|+1.47%|+3.44%|+2.30%|+0.30%|+1.06%|+2.64%|+1.55%|+1.21%|\n|240|+1.03%|+1.62%|+1.03%|+1.51%|+1.35%|+2.80%|+1.56%|+1.59%|+2.23%|+5.09%|+2.87%|+0.69%|+2.32%|+4.61%|+2.78%|+0.82%|+1.77%|+3.94%|+2.19%|+1.39%|\n\n\n*Units: S (PSU), T (°C), U/V (m/s), SEL (m)*\n\nOur experiments reveal that K=1 consistently yields optimal performance. The degradation at 24-hour lead time is particularly revealing—it coincides with the diurnal cycle period, supporting our hypothesis that single-context selection prevents interference between incompatible temporal signals. The attention-based soft aggregation, despite its theoretical appeal, underperforms because it forces the model to reconcile conflicting temporal patterns. Ocean currents show somewhat different behavior, suggesting variable-specific temporal dependencies, though K=1 remains optimal overall.\n\n## Q5. Fairness of Figure 4.\n\nA: In Fig 4, the initial fields (0 days) of all methods are analysis data, with no reanalysis data included. All methods at subsequent lead times are forecast results rather than analysis or reanalysis results, ensuring fairness of comparison.\n\n## Permutation Feature Importance and Sensitivity Analysis.\n\nTo address the reviewer's question about whether the model has learned the correct sensitivities to each input variable, we performed a **Permutation Feature Importance (PFI) analysis**. In this approach, we perturb each input variable independently by replacing it with randomly selected values from other time steps, and then calculate the **RMSE** after each perturbation. The RMSE is then normalized to show the relative impact of each perturbation compared to the original, unperturbed predictions. This method helps assess how well the model relies on the most important variables and whether it has overfitted to spurious correlations. The results are presented in a **5×5 matrix**, where each row corresponds to a perturbed variable, and each column shows the degraded performance defined as relative change in RMSE. The **relative RMSE**,\n$\\text{relative RMSE} = \\frac{RMSE_{\\text{perturbed}} - RMSE_{\\text{original}}}{RMSE_{\\text{original}}} \\times 100\\%$ \nreflects how sensitive the model's predictions are to changes in each variable.\n\n|Perturbed Variable|s(%)|t(%)|u(%)|v(%)|sel(%)|\n|-|-|-|-|-|-|\n|S|23.330|1.223|0.288|0.467|0.934|\n|T|4.273|12.242|1.442|1.948|3.992|\n|U|9.476|9.326|15.896|6.549|9.162|\n|V|9.128|8.954|8.610|16.612|11.746|\n|SEL|3.569|2.058|1.918|1.979|8.815|\n\n**Leadtime=120h**\n\nWhen perturbing temperature and salinity separately, these two variables are more sensitive to each other relative to other variables. Ocean current components (U and V) exhibit minimal cross-sensitivity to each other, consistent with their orthogonal relationship as zonal and meridional velocity components. This low interdependence validates that the model has correctly learned the independent nature of these directional flow components. However, due to time constraints, we only randomly sampled half a month's worth of samples for averaging, requiring further analysis in subsequent work.\n\n## Other non-technical issues.\n\nA: We acknowledge your suggestions for improving clarity:\n\n1. We will refine our terminology regarding ocean depth ranges and temporal scales to avoid ambiguity\n2. Figure 2 will be updated with clear unit labels for all variables\n3. The discussion of IV-TT data quality issues will be incorporated concisely into the main text"}, {"review_id": "hN90YlLnaT", "reviewer": "Reviewer_sFCf", "summary": "In this paper, the authors introduce FuXi-Ocean, a new data-driven global ocean forecasting model that delivers six-hourly predictions at an eddy-resolving 1/12° spatial resolution—offering a big step up in temporal detail compared to earlier approaches. What’s more, while many previous models only go down to about 700 meters, FuXi-Ocean dives much deeper, reaching 1500 meters to capture complex ocean dynamics like thermocline shifts and deep-water mass behavior.\nOne of the standout features is its Mixture-of-Time (MoT) module, which smartly learns how reliable different variables are across various time scales. Experimental results demonstrate that FuXi-Ocean can achieve state-of-the-art accuracy across key variables like temperature, salinity, ocean currents, and sea surface height.", "questions": "To further solidify the novelty and impact of the Mixture-of-Time (MoT) module , I recommend adding a dedicated discussion comparing MoT with temporal attention mechanisms , which are commonly used in sequence modeling (e.g., Transformers in weather prediction). This comparison would help readers better understand the unique advantages of MoT in the context of ocean forecasting.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "This paper presents a well-structured deep learning framework, FuXi-Ocean, featuring novel architectural components such as the Mixture-of-Time (MoT) module and context-aware feature extraction. The authors conduct a fairly comprehensive evaluation using both HYCOM reanalysis data and real-world observational datasets (via the IV-TT framework). Comparisons with operational numerical models (HYCOM, BLK, FOAM) and other data-driven approaches (XiHe, Langya, WenHai) highlight FuXi-Ocean’s strong performance.\n\nHowever, I remain somewhat unconvinced about the effectiveness of the MoT module. As shown in Figure 5, for the U and V components of ocean currents, there appears to be little to no performance gain when MoT is applied. This suggests that MoT may not be the right solution for these variables—at least in its current form. As the authors themselves note, future work should consider designing specialized modules that can better capture inter-variable interactions, rather than relying solely on historical temporal patterns.", "was_revised": true, "final_justification": "Thank you to the authors for their time and detailed responses during the rebuttal. After reviewing all the comments, I would like to raise my score.", "rebuttal": "Dear Reviewer sFCf,\n\nThank you for your comments and suggestions on our work. We appreciate your recognition of the novel contributions of our work and are grateful for the opportunity to address your concerns.\n\n## The effectiveness of the MoT module.\n\nA: We appreciate your interest in understanding the effectiveness of the **Mixture-of-Time (MoT)** module in ocean sub-daily forecasting models. To evaluate its performance, we replace the MoT module with temporal attention mechanisms, which force the model to consider the influence of all historical temporal features for each variable. We test on HYCOM 2015 reanalysis data, and average the results across the depth dimension for each variable. While we cannot provide intuitive line graphs to illustrate the comparison, we present the **normalized RMSE** to highlight the impact of the MoT module on forecasting accuracy.\n\nThe normalized RMSE is calculated as:\n\n$\\frac{RMSE - RMSE_{FuXi-Ocean}}{RMSE_{FuXi-Ocean}} *100\\%$\n\nThe results are shown in the table below:\n| Time | S     | T    | U    | V    | SEL   |\n|------|-------|-------|-------|-------|-------|\n| 6    | 0.897 | 1.103 | 0.512 | 0.394 | 1.108 |\n| 24   | 2.486 | 1.821 | 0.795 | 0.713 | 1.194 |\n| 54   | 1.089 | 1.217 | 0.506 | 0.491 | 1.115 |\n| 72   | 1.412 | 1.185 | 0.708 | 0.597 | 1.287 |\n| 120  | 1.193 | 1.408 | 0.403 | 0.296 | 1.206 |\n| 240  | 1.507 | 1.592 | 0.694 | 0.815 | 1.389 |\n\nIt is evident from the table that while the model was provided with all temporal features, **more feature information does not always lead to better performance**, particularly in the case of **ocean sub-daily forecasting tasks**. The performance of the model significantly deteriorates at **24-hour forecasts**, suggesting that the inclusion of more temporal features **interferes with the learning of diurnal cycle characteristics**. This is an important observation that highlights the challenge of balancing the complexity of temporal information in sub-daily ocean forecasting models.\n\n## The U and V components of ocean currents.\n\nA: As we mentioned in our paper, while the MoT module has shown **significant improvements** for variables with **diurnal cycles** (e.g., temperature and salinity), its application to **ocean currents** presents a different set of challenges due to the **slower timescales** of these variables. We acknowledge that **U and V currents** are more **complex** and may require additional design considerations. However, the model’s limitations in this regard do not detract from the broader impact of our work.\n\nOur objective was not to fully solve the challenge of predicting ocean currents but to **introduce the first global ocean forecasting model with sub-daily resolution**. This was the primary aim of our paper. The improvements we’ve achieved for surface variables with diurnal cycles demonstrate that FuXi-Ocean **paves the way** for future advancements in this field. We believe that **specialized techniques for ocean currents** can be developed as a natural extension of our work, and we plan to explore this in future research."}]

Author rebuttal:

Meta-review: The paper presents the first sub-daily, data-driven global ocean forecasting system at 1/12° horizontal resolution, extending from the surface to 1500 m depth. The approach leverages an encoder–decoder backbone with attention blocks and introduces a novel mixture-of-time component designed to modulate the temporal context for each prognostic variable.

The authors have thoroughly addressed reviewer concerns by providing additional validation and experiments, including permutation feature importance, K-sensitivity analyses, and retraining with confidence intervals. These efforts confirmed the robustness of their method and were positively received by reviewers.

Therefore, I recommend acceptance of this paper.
Decision: Accept (oral)