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Recent Developments in Artificial Intelligence in Oceanography
With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications. In this paper, these applications are reviewed from the perspectives of identifying, forecasting, and parameterizing ocean phenomena. Specifically, the usage of AI algorithms for the identification of mesoscale eddies, internal waves, oil spills, sea ice, and marine algae are discussed in this paper. Additionally, AI-based forecasting of surface waves, the El Niño Southern Oscillation, and storm surges is discussed. This is followed by a discussion on the usage of these schemes to parameterize oceanic turbulence and atmospheric moist physics. Moreover, physics-informed deep learning and neural networks are discussed within an oceanographic context, and further applications with ocean digital twins and physics-constrained AI algorithms are described. This review is meant to introduce beginners and experts in the marine sciences to AI methodologies and stimulate future research toward the usage of causality-adherent physics-informed neural networks and Fourier neural networks in oceanography.
Stressing over the Complexities of Multiple Stressors in Marine and Estuarine Systems
Aquatic ecosystems are increasingly threatened by multiple human-induced stressors associated with climate and anthropogenic changes, including warming, nutrient pollution, harmful algal blooms, hypoxia, and changes in CO2 and pH. These stressors may affect systems additively and synergistically but may also counteract each other. The resultant ecosystem changes occur rapidly, affecting both biotic and abiotic components and their interactions. Moreover, the complexity of interactions increases as one ascends the food web due to differing sensitivities and exposures among life stages and associated species interactions, such as competition and predation. There is also a need to further understand nontraditional food web interactions, such as mixotrophy, which is the ability to combine photosynthesis and feeding by a single organism. The complexity of these interactions and nontraditional food webs presents challenges to ecosystem modeling and management. Developing ecological models to understand multistressor effects is further challenged by the lack of sufficient data on the effects of interactive stressors across different trophic levels and the substantial variability in climate changes on regional scales. To obtain data on a broad suite of interactions, a nested set of experiments can be employed. Modular, coupled, multitrophic level models will provide the flexibility to explore the additive, amplified, propagated, antagonistic, and/or reduced effects that can emerge from the interactions of multiple stressors. Here, the stressors associated with eutrophication and climate change are reviewed, and then example systems from around the world are used to illustrate their complexity and how model scenarios can be used to examine potential future changes.
Contribution of Wind Speed and Sea-Air Humidity Difference to the Latent Heat Flux-SST Relationship
This study investigates contributions of wind speed and sea-air humidity difference (dq) terms to the seasonal change and time scale dependence in the relationship between surface latent heat flux (LHF) and sea surface temperature (SST) using daily data. Generally, the dq term is dominant in the SST effect on LHF in the midlatitude SST frontal zones and tropical Indo-western Pacific, and the wind speed term is dominant in the LHF effect on SST in the subtropical gyres and tropical Indo-western Pacific. The seasonal change in the dq term accounts for a larger SST effect in winter than in summer in the midlatitude frontal zones, and that of the wind speed term explains a larger LHF effect in summer than in winter in the subtropical gyres. In the tropical Indo-western Pacific, the dq term is dominant in the SST effect in summer, and the wind speed term is dominant in the LHF effect in winter. The contribution of the dq term to the SST effect increases with the time scale. The contribution of the wind speed term to the SST effect varies regionally: It is supplementary in the midlatitude frontal zones in winter and summer and in the Arabian Sea in summer, but it is opposite in the Philippine Sea in winter and summer and in the South China Sea and Bay of Bengal in summer. The contribution of the wind speed term to the LHF effect is confined to short time scales in most of the tropical Indo-western Pacific regions.