From forecasting to fisheries: How AI is transforming the global ocean system

Digital Twin of the Ocean is a continuously updated virtual counterpart of the real ocean that exchanges data in real time with physical systems. Unlike conventional models that run offline and deliver delayed outputs, a true digital twin maintains a two-way connection with observations, allowing it to reflect current conditions and test future scenarios on demand.

The future of ocean monitoring, forecasting, and management will depend on AI-powered Digital Twins of the Ocean that can operate in near real time, integrate massive data streams, and support policy decisions at scale, according to a new review publsihed in the journal Climate.

Published amid growing concern over marine heatwaves, pollution, and ecosystem collapse, the study, titled Recent Advancements and Challenges in Artificial Intelligence for Digital Twins of the Ocean, frames the ocean as both a climate regulator and a data challenge. Traditional numerical models, long the foundation of oceanography, are struggling to keep pace with the volume, speed, and diversity of modern observations. Artificial intelligence, the authors argue, is no longer optional. It is becoming the enabling layer that allows digital ocean replicas to function as living, decision-ready systems rather than static simulations.

AI turns ocean models into real-time decision systems

Digital Twin of the Ocean is a continuously updated virtual counterpart of the real ocean that exchanges data in real time with physical systems. Unlike conventional models that run offline and deliver delayed outputs, a true digital twin maintains a two-way connection with observations, allowing it to reflect current conditions and test future scenarios on demand.

The study clearly states that this vision is only achievable through artificial intelligence. Satellite imagery, autonomous floats, underwater vehicles, coastal sensors, vessel tracking systems, and high-resolution numerical models now generate volumes of data that overwhelm traditional workflows. Machine learning methods, from classical algorithms to deep neural networks, offer a way to process these inputs quickly, identify hidden patterns, and generate forecasts at a fraction of the computational cost.

The review documents how AI-based surrogate models are replacing or augmenting physics-heavy simulations for global and regional ocean forecasting. Neural networks trained on reanalysis datasets can now predict temperature, salinity, currents, and sea level with accuracy comparable to established operational systems, while delivering results fast enough for near real-time use. This speed advantage is critical for applications such as extreme event forecasting, port safety, and emergency response.

Hybrid approaches emerge as a central theme. Rather than discarding physical laws, researchers are embedding them into AI architectures to improve stability, accuracy, and trust. These hybrid physics–machine learning models correct systematic biases, downscale coarse simulations, and maintain physical consistency, addressing long-standing weaknesses of purely data-driven systems. According to the authors, this combination represents the most promising path for operational Digital Twins of the Ocean.

Environmental monitoring shifts from observation to prediction

The review highlights a major transformation in how marine environmental health is monitored and managed. Water quality, biodiversity loss, oil spills, and plastic pollution are no longer treated as isolated observation problems. AI-enabled digital twins allow these threats to be tracked, predicted, and tested against intervention scenarios.

Water quality monitoring stands out as a key use case. Traditional methods rely on sparse in situ sampling or empirical satellite algorithms that often fail in complex coastal waters. Machine learning models now invert satellite signals to estimate chlorophyll, nutrients, dissolved oxygen, and light penetration more reliably across diverse environments. These outputs feed directly into water quality indices used by regulators, supporting continuous assessment rather than periodic reporting.

AI is also addressing one of the most persistent gaps in marine monitoring: missing data. Cloud cover, sensor failures, and uneven geographic coverage have long limited ocean observations. Neural networks trained on multi-source data can reconstruct missing fields and generate gap-free time series, enabling digital twins to maintain continuity even when measurements drop out.

Oil spill detection and forecasting illustrate how these capabilities translate into operational impact. Machine learning models automatically identify oil slicks from satellite imagery, while digital twins integrate these detections with wind and current forecasts to predict spill trajectories. This allows authorities to respond faster, coordinate across borders, and assess potential damage before oil reaches sensitive coastlines.

Marine litter monitoring follows a similar pattern. AI-driven image analysis detects floating plastics, coastal debris, and seafloor waste across vast areas that were previously unmanageable. Predictive models estimate accumulation hotspots and transport pathways, shifting cleanup strategies from reactive to proactive. The review positions these systems as essential tools for meeting international pollution targets and protecting marine ecosystems.

Ports, energy, and food systems become AI-driven ocean users

The influence of Digital Twins of the Ocean extends well beyond environmental protection. The review shows how AI-powered twins are reshaping economic sectors that depend on safe, efficient, and predictable ocean conditions.

Ports and shipping face rising risks from extreme weather, sea-level rise, and congestion. Digital twins integrate real-time environmental forecasts with operational data to support vessel routing, berth allocation, and hazard management. AI-enhanced routing systems reduce fuel use and emissions by identifying optimal paths under changing conditions, aligning maritime operations with climate targets while improving safety.

Marine renewable energy is another area undergoing rapid change. Wave, tidal, and offshore wind projects require precise forecasts of environmental forces, structural loads, and power output. AI surrogate models deliver fast predictions that support design optimization, real-time control, and predictive maintenance. The review documents how deep learning models forecast wave energy flux, optimize turbine layouts, and monitor structural health, lowering costs and improving reliability as deployment scales up.

Aquaculture and fisheries, sectors critical to global food security, are also being transformed. Computer vision systems count and measure fish, assess biomass, and detect disease without invasive sampling. Machine learning models forecast harmful algal blooms, identify productive fishing grounds, and optimize energy use in aquaculture facilities. Integrated into digital twins, these tools allow managers to simulate outcomes under different environmental and economic scenarios, supporting sustainable practices in the face of climate variability.

Across these sectors, the authors stress that AI does not operate in isolation. Its value comes from integration within broader digital twin frameworks that combine data ingestion, modeling, visualization, and user interaction. This integration turns raw predictions into actionable intelligence for operators, regulators, and policymakers.

Trust, data access, and governance remain unresolved

Despite its promise, the study delivers a clear warning: technical progress alone will not guarantee success. The authors identify data governance, model credibility, and transferability as major obstacles to widespread adoption.

Many high-value datasets remain restricted due to commercial sensitivity, national security, or privacy concerns. Fisheries data, vessel tracking records, and industrial sensor streams are often inaccessible, limiting model training and operational use. Fragmented data standards further complicate integration, increasing costs and slowing deployment.

Model portability is another challenge. AI systems trained in one region often perform poorly when applied elsewhere due to differences in physical conditions and human activity. While physics-informed models show improved generalization, retraining remains common, placing a burden on organizations with limited technical capacity.

Perhaps most critically, the review raises concerns about trust. Many AI models operate as black boxes, producing confident outputs without transparent reasoning. In high-stakes contexts such as disaster response or environmental regulation, errors can carry severe consequences. The authors argue that uncertainty quantification must become mandatory, with digital twins clearly distinguishing between model uncertainty and natural variability.

Explainable AI techniques are highlighted as a growing area of research, but the study makes clear that interpretability alone is not enough. Governance frameworks must define when human oversight is required and how AI-driven recommendations are validated before action.