The stakes in the region are rising. Colombia’s Caribbean waters hold promising offshore hydrocarbon prospects, and the country’s National Hydrocarbon Agency has said Colombia could position itself as a regional leader in offshore energy. Yet the same sea and its coastline — fishing ports, tourism beaches, mangrove-fringed lagoons — remain highly vulnerable to spills, whose environmental, social and economic consequences can ripple for years. Earlier research has tied Caribbean oil pollution to damage in mangroves and fisheries, and Colombia’s oceanographic community has spent two decades building spill-prediction tools for its own waters. In response, DIMAR has been developing a new ecosystem of climate services for Colombia that, unusually for the field, extends to maritime oil spills. The effort sits within SIPSEM, the Integrated Forecasting System for Maritime Safety, an initiative designed to turn observations and numerical models into products that port captains, naval commanders and environmental agencies can actually use during emergencies. The work, carried out as part of the lead author’s doctoral research, needed a rigorous test case, and the Amuay spill provided one: a real, well-documented transboundary event whose evolution could be checked years later against independent satellite records and coastal measurements.

At the heart of the system sits GNOME, a Lagrangian trajectory model developed by NOAA’s Office of Response and Restoration and used by spill responders worldwide. The model has been applied to incidents from Mumbai’s harbor to the Gulf of Mexico, but rather than solving the full equations of ocean motion for the oil itself, GNOME treats a slick as thousands of virtual particles — tiny, independent tracers — that are pushed across the sea surface by whatever current and wind fields the modeler supplies. A stochastic term mimics sub-grid turbulence, smearing each particle cloud in a way that reflects the chaos of real eddies, while the model also propagates uncertainty in the forcing data, generating a best-estimate trajectory flanked by minimum- and maximum-regret bounds. Those bounds matter operationally: a responder deciding where to place containment booms cares less about a single predicted path than about the envelope of plausible ones. GNOME does not attempt detailed oil chemistry — evaporation, emulsification and other weathering processes are handled separately — but speed is its virtue. Within minutes it can address the urgent, deceptively simple question that dominates the first hours of a spill: where is the oil going, and when will it get there?

The quality of any trajectory forecast, however, lives and dies by its inputs, and the Colombian team threw an unusually diverse set at the problem. For winds, they drew on the Climate Forecast System version 2 (CFSv2), NOAA’s coupled atmosphere-ocean modeling system, and on satellite scatterometer winds archived by CERSAT, the Centre ERS d’Archivage et de Traitement, France’s satellite oceanography data center. For ocean currents, they tested three very different engines: the Copernicus Global Ocean Physics Reanalysis (GLORYS), an eddy-resolving global reanalysis built on the NEMO ocean model; the Hybrid Coordinate Ocean Model (HYCOM), which blends vertical coordinate systems to represent everything from shallow shelves to open-ocean gyres; and the Navy Coastal Ocean Model (NCOM), a coastal circulation model developed for U.S. Navy operations. The rationale for this shotgun approach is that oil at the sea surface obeys two masters. Currents supply most of the drift, but wind pushes the slick along — responders often estimate wind-driven drift at a few percent of the wind speed — and no single dataset captures every eddy, jet and upwelling filament in a basin as energetic as the Caribbean. Running GNOME under multiple atmospheric and oceanic combinations is, in effect, a homegrown ensemble forecast.

The Caribbean makes that hedging essential. The basin is flushed from east to west by the Caribbean Current, but its surface circulation is far more intricate than a conveyor belt: mesoscale eddies shed and drift across the region, interacting with the narrow, wind-driven coastal upwelling off Colombia’s Guajira Peninsula and with the Caribbean Counter Current, which can reverse the flow along the shelf edge. Decades of research — from early descriptions of eddy development and motion in the Caribbean Sea to recent analyses of the basin’s eddy variability — have documented how these swirling features can trap drifting material, spin it in circles for weeks or eject it toward the coast in sudden bursts. For an oil slick, the difference between beaching in one fishing village and missing the coast entirely can hinge on an eddy a hundred kilometers offshore. Global ocean models that resolve such structures imperfectly can therefore diverge quickly, which is precisely why the researchers wanted to quantify how sensitive their forecasts were to the choice of current and wind fields.

To judge the simulations, the team assembled an independent record of what the oil actually did. Radar imagery from the European Sentinel-1 satellites formed the backbone: synthetic aperture radar is acutely sensitive to oil because thin crude films damp the millimeter-scale capillary waves that normally roughen the sea surface, leaving slicks as dark, smoky patches against a brighter ocean. Radar has its pitfalls — calm-wind patches, rain cells and natural films can masquerade as oil — so the team cross-checked it with optical imagery from Landsat 8 and from Planet’s high-resolution constellation, though cloud cover limited the optical sensors at times. On the ground, laboratory and field reports from DIMAR’s marine environmental protection program documented hydrocarbon contamination along the Colombian coast as the slick arrived. Stitching optical and radar satellites to in-situ sampling gave the researchers something rare in spill science: a multi-sensor, ground-truthed picture of a transboundary spill against which model trajectories could be scored.

The verdict was encouraging. Run across the different atmospheric and oceanic forcing combinations, the GNOME-based system effectively replicated the spill’s arrival date on the Colombian coast and the areas affected, producing trajectories that aligned closely with the satellite observations. That timing matters most of all: the moment oil makes landfall is the moment that determines whether containment equipment reaches the right beach before the slick does. The model’s skill, however, was not uniform. Its performance proved contingent on the spill’s initial conditions — where, when and how much oil was released — and on which forcing data were used. That sensitivity is both a warning and a lesson: a trajectory forecast is a chain whose weakest link may be the assumed release point of a slick that no one observed in its first hours, or a current field that misplaces a single eddy. For the Colombian-Venezuelan Caribbean, at least, the study concluded that the chain held together well enough to be trusted for the decisions that matter most in an emergency, even as the authors caution that ongoing efforts to sharpen prediction accuracy remain essential.

For responders, the practical meaning is direct. A validated configuration gives Colombian authorities a defensible way to decide, within hours of a reported spill, where to deploy containment booms, which beaches and mangrove forests to prioritize for protection, and where to stage skimmers and dispersants. The tool’s potential reach is unusual, too: the researchers note that the approach could support extended forecast horizons at sub-seasonal timescales, effectively turning spill forecasting into a branch of the growing discipline of ocean-weather prediction, in which marine current outlooks are issued much like meteorological forecasts. That framing places the work squarely within the broader climate-services movement, which aims to convert raw scientific data into information tailored to real decisions — from farmers choosing planting dates to coast guards positioning equipment before a storm. A spill model that only scientists can operate is of little use at three in the morning during a refinery accident; one wired into an operational maritime-safety system, the authors argue, can serve everyone.

None of this removes the uncertainty inherent in a turbulent sea, and the authors are explicit that continued work to improve accuracy is essential to strengthening oil-spill emergency response in the years ahead. Better estimates of spill volume and location in the critical first hours, denser satellite coverage and higher-resolution coastal currents would all tighten the envelope of predicted shorelines. But the broader message of the Amuay retrospective is difficult to miss: the ingredients for credible transboundary spill forecasting already exist, and they are inexpensive — GNOME is free, and its atmospheric and oceanic forcing datasets are openly archived — so any maritime nation with the will can assemble them. As offshore development accelerates across the Caribbean, the region’s oceanographers have demonstrated that when the next tanker fails or the next refinery leaks, the question of where the oil will go no longer needs to be answered with guesswork. It can be answered, with measured caveats, by physics.