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[1] This study examines the potential impact of future anthropogenic global warming on the Gulf of Mexico (GoM) by using a downscaled high-resolution ocean model constrained with the surface forcing fields and initial and boundary conditions obtained from the IPCC-AR4 model simulations under A1B scenario. The simulated volume transport by the Loop Current (LC) is reduced considerably by 20-25% during the 21st century, consistent with a similar rate of reduction in the Atlantic Meridional Overturning Circulation. The effect of the LC in the present climate is to warm the GoM, therefore the reduced LC and the associated weakening of the warm LC eddy have a cooling impact in the GoM, particularly in the northern basin. Due to this cooling influence, the northern GoM is characterized as the region of minimal warming. Low-resolution models, such as the IPCC-AR4 models, underestimate the reduction of the LC and its cooling effect, thus fail to simulate the reduced warming feature in the northern GoM. The potential implications of the reduced warming in the northern GoM on pelagic fish species and their spawning patterns are also discussed.

Muhling, B. A., Lee, S-K., Lamkin, J. T., and Liu, Y. 2011. Predicting the effects of climate change on bluefin tuna (Thunnus thynnus) spawning habitat in the Gulf of Mexico. – ICES Journal of Marine Science, 68: 1051–1062. Atlantic bluefin tuna (BFT) is a highly migratory species that feeds in cold waters in the North Atlantic, but migrates to tropical seas to spawn. Global climate-model simulations forced by future greenhouse warming project that upper-ocean temperatures in the main western Atlantic spawning ground, the Gulf of Mexico (GOM), will increase substantially, potentially altering the temporal and spatial extent of BFT spawning activity. In this study, an ensemble of 20 climate model simulations used in the Intergovernmental Panel for Climate Change fourth Assessment Report (IPCC-AR4) predicted mean temperature changes within the GOM under scenario A1B through to 2100. Associations between adult and larval BFT in the GOM and sea temperatures were defined using 20th century observations, and potential effects of warming on the suitability of the GOM as a spawning ground were quantified. Areas in the GOM with high probabilities of larval occurrence decreased in late spring by 39–61% by 2050 and 93–96% by the end of the 21st century. Conversely, early spring may become more suitable for spawning. BFT are therefore likely to be vulnerable to climate change, and there is potential for significant impacts on spawning and migration behaviours.

Although bluefin tuna are found throughout the Atlantic Ocean, spawning in the western Atlantic has been recorded predominantly in the Gulf of Mexico (GOM) in spring. Larval bluefin tuna abundances from the northern GOM are formulated into an index used to tune the adult stock assessment, and the variability of this index is currently high. This study investigated whether some of the variability in larval bluefin tuna abundances was related to environmental conditions, by defining associations between larval bluefin tuna catch locations, and a suite of environmental variables. We hypothesized that certain habitat types, as defined by environmental variables, would be more likely to contain bluefin tuna larvae. Favorable habitat for bluefin tuna larvae was defined using a classification tree approach. Habitat within the Loop Current was generally less favorable, as were warm‐core rings, and cooler waters on the continental shelf. The location and size of favorable habitat was highly variable among years, which was reflected in the locations of larval bluefin tuna catches. The model successfully placed bluefin tuna larvae in favorable habitat with nearly 90% accuracy, but many negative stations were also located within theoretically favorable habitat. The probability of collecting larval bluefin tuna in favorable habitat was nearly twice the probability of collecting bluefin tuna larvae across all habitats (35.5 versus 21.0%). This model is a useful addition to knowledge of larval bluefin tuna distributions; however, the incorporation of variables describing finer‐scale features, such as thermal fronts, may significantly improve the model’s predictive power.