Search for: All records

The Mid-Atlantic Cold Pool is a seasonal mass of cold bottom water that extends throughout the Mid-Atlantic Bight (MAB). Formed from rapid vernal surface warming, the Cold Pool dissipates in the fall due to mixing events such as storms. The Cold Pool supports a myriad of MAB coastal ecosystems and economically valuable commercial and recreational fisheries. Offshore wind energy has been rapidly developing within the MAB in recent years. Studies in Europe demonstrate that offshore wind farms can impact ocean mixing and hence seasonal stratification; there is, however, limited information on how MAB wind development will affect the Cold Pool. Seasonal overlap between the Cold Pool and pre-construction wind lease areas at varying distances from shore in the MAB was evaluated using output from a data-assimilative ocean model. Results highlight overlap periods as well as a thermal gradient that persists after bottom temperatures warm above the threshold typically used to identify the Cold Pool. These results also demonstrate cross-shelf variability in Cold Pool evolution. This work highlights the need for more focused ocean modeling studies and observations of wind farm effects on the MAB coastal environment.

Ocean acidification alters the oceanic carbonate system, increasing potential for ecological, economic, and cultural losses. Historically, productive coastal oceans lack vertically resolved high‐resolution carbonate system measurements on time scales relevant to organism ecology and life history. The recent development of a deep ion‐sensitive field‐effect transistor (ISFET)‐based pH sensor system integrated into a Slocum glider has provided a platform for achieving high‐resolution carbonate system profiles. From May 2018 to November 2019, seasonal deployments of the pH glider were conducted in the central Mid‐Atlantic Bight. Simultaneous measurements from the glider's pH and salinity sensors enabled the derivation of total alkalinity and calculation of other carbonate system parameters including aragonite saturation state. Carbonate system parameters were then mapped against other variables, such as temperature, dissolved oxygen, and chlorophyll, over space and time. The seasonal dynamics of carbonate chemistry presented here provide a baseline to begin identifying drivers of acidification in this vital economic zone.

Hurricane Michael formed on October 7, 2018, in the Northwestern Caribbean Sea, and quickly traveled northward through the Gulf of Mexico, making landfall on the Florida panhandle as a devastating Category 5 hurricane only 3 days later. Before landfall, Michael underwent rapid intensification, despite unfavorable atmospheric conditions. Using observations, we characterized the key ocean features encountered by Michael along its track, which are known for favoring hurricane intensification: high sea surface temperatures, upper ocean heat content and low salinity barrier layer conditions. Ocean observations were consistent with suppressed hurricane‐induced upper ocean cooling, which could only be observed by underwater gliders, and showed that Hurricane Michael constantly experienced sea surface temperatures above 28°C. We carried out ocean Observing System Experiments, which demonstrate that the combined assimilation of in situ and satellite ocean observations into a numerical ocean model led to the most realistic representation of the ocean conditions. They also suggest that, when using the Cooper‐Haines (1996) method to assimilate altimetry observations, assimilating temperature observations is necessary to constrain the model upper ocean vertical structure. We also performed coupled hurricane‐ocean simulations to assess the impact of ocean initial conditions on forecasting Michael. These simulations demonstrate that the ocean conditions, in particular the high sea surface temperatures north of 24°N, played a crucial role in the intensification of Michael. Coupled simulations initialized with the most realistic ocean conditions, constrained by field and satellite observations, show a ∼56% error reduction in wind intensity prior to landfall compared to simulations initialized without data assimilation.