Marine heatwaves in the Gulf of Mexico 1983‒2021: Statistics, recent intensifications, and threats on coral reefs

There is the current lack of comprehensive understanding of the hotspots,frequency,duration,spatiotemporal trends,and physical drivers of marine heatwaves(MHWs)within the Gulf of Mexico(GoM).Here,a series of high-resolution satellite and reanalysis products are used to examine their spatiotemporal characteristics,trends,and possible geophysical triggers of MHWs.Possible impacts of the MHW on coral reefs are also discussed.Results reveal an increasing trend in their frequency,duration,and intensities from 1983–2021,particularly after 2016.It identifies MHWs hotspots within the GoM,notably the northern and western shelves and the Loop Current.The study further documents an intense MHW event from late 2020 to early 2021 near the Yucatan Channel,south of 24°N,attributing its development to oceanic processes such as wind anomalies,anticyclonic eddies,and current-driven heat transport anomalies.The occurrence of this MHW event potentially increased thermal stress on the Campeche and Tuxtlas Reef Systems.This research illuminates the increasing trends and impacts of MHWs in the GoM,providing valuable insights for understanding and predicting the effects of climate change on marine ecosystems.

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.