Ron K. Hoeke

The Common Oceanographer: Crowdsourcing the Collection of Oceanographic Data

We live on a vast, underexplored planet that is largely ocean. Despite modern technology, Global Positioning System (GPS) navigation, and advanced engineering of ocean vessels, the ocean is unforgiving, especially in rough weather. Coastal ocean navigation, with risks of running aground and inconsistent weather and sea patterns, can also be challenging and hazardous. In 2012, more than 100 international incidents of ships sinking, foundering, grounding, or being lost at sea were reported (http://en.wikipedia.org/wiki/List_of_shipwrecks_in_2012). Even a modern jetliner can disappear in the ocean with little or no trace [1], and the current costs and uncertainty associated with search and rescue make the prospects of finding an object in the middle of the ocean daunting [2]. Notwithstanding satellite constellations, autonomous vehicles, and more than 300 research vessels worldwide (www.wikipedia.org/wiki/List_of_research_vessels_by_country), we lack fundamental data relating to our oceans. These missing data hamper our ability to make basic predictions about ocean weather, narrow the trajectories of floating objects, or estimate the impact of ocean acidification and other physical, biological, and chemical characteristics of the worlds oceans. To cope with this problem, scientists make probabilistic inferences by synthesizing models with incomplete data. Probabilistic modeling works well for certain questions of interest to the scientific community, but it is difficult to extract unambiguous policy recommendations from this approach. The models can answer important questions about trends and tendencies among large numbers of events but often cannot offer much insight into specific events. For example, probabilistic models can tell us with some precision the extent to which storm activity will be intensified by global climate change but cannot yet attribute the severity of a particular storm to climate change. Probabilistic modeling can provide important insights into the global traffic patterns of floating debris but is not of much help to search-and-rescue personnel struggling to learn the likely trajectory of a particular piece of debris left by a wreck. Oceanographic data are incomplete because it is financially and logistically impractical to sample everywhere. Scientists typically sample over time, floating with the currents and observing their temporal evolution (the Langrangian approach), or they sample across space to cover a gradient of conditions—such as temperature or nutrients (the Eulerian approach). These observational paradigms have various strengths and weaknesses, but their fundamental weakness is cost. A modern ocean research vessel typically costs more than US

Spatially extensive microbial biogeography of the Indian Ocean provides insights into the unique community structure of a pristine coral atoll.

30,000 per day to operate—excluding the full cost of scientists, engineers, and the cost of the research itself. Even an aggressive expansion of oceanographic research budgets would not do much to improve the precision of our probabilistic models, let alone to quickly and more accurately locate missing objects in the huge, moving, three-dimensional seascape. Emerging autonomous technologies such as underwater gliders and in situ biological samplers (e.g., environmental sample processors) help fill gaps but are cost prohibitive to scale up. Similarly, drifters (e.g., the highly successful Argo floats program) have proven very useful for better defining currents, but unless retrieved after their operational lifetime, they become floating trash, adding to a growing problem. Long-term sampling efforts such as the continuous plankton recorder in the North Sea and North Atlantic [3] provide valuable data on decadal trends and leveraged English Channel ferries to accomplish much of the sampling. Modernizing and expanding this approach is a goal of citizen science initiatives. How do we leverage cost-effective technologies and economies of scale given shrinking federal research budgets?

Atmospheric Circulation Changes and their Impact on Extreme Sea Levels around Australia

Microorganisms act both as drivers and indicators of perturbations in the marine environment. In an effort to establish baselines to predict the response of marine habitats to environmental change, here we report a broad survey of microbial diversity across the Indian Ocean, including the first microbial samples collected in the pristine lagoon of Salomon Islands, Chagos Archipelago. This was the first large-scale ecogenomic survey aboard a private yacht employing a ‘citizen oceanography’ approach and tools and protocols easily adapted to ocean going sailboats. Our data highlighted biogeographic patterns in microbial community composition across the Indian Ocean. Samples from within the Salomon Islands lagoon contained a community which was different even from adjacent samples despite constant water exchange, driven by the dominance of the photosynthetic cyanobacterium Synechococcus. In the lagoon, Synechococcus was also responsible for driving shifts in the metatranscriptional profiles. Enrichment of transcripts related to photosynthesis and nutrient cycling indicated bottom-up controls of community structure. However a five-fold increase in viral transcripts within the lagoon during the day, suggested a concomitant top-down control by bacteriophages. Indeed, genome recruitment against Synechococcus reference genomes suggested a role of viruses in providing the ecological filter for determining the β-diversity patterns in this system.

Widespread inundation of Pacific islands triggered by distant-source wind-waves

Projections of sea level rise (SLR) will lead to increasing coastal impacts during extreme sea level events globally; however, there is significant uncertainty around short-term coastal sea level variability and the attendant frequency and severity of extreme sea level events. In this study, we investigate drivers of coastal sea level variability (including extremes) around Australia by means of historical conditions as well as future changes under a high greenhouse gas emissions scenario (RCP 8.5). To do this, a multi-decade hindcast simulation is validated against tide gauge data. The role of tide–surge interaction is assessed and found to have negligible effects on storm surge characteristic heights over most of the coastline. For future projections, 20-year-long simulations are carried out over the time periods 1981–1999 and 2081–2099 using atmospheric forcing from four CMIP5 climate models. Changes in extreme sea levels are apparent, but there are large inter-model differences. On the southern mainland coast all models simulated a southward movement of the subtropical ridge which led to a small reduction in sea level extremes in the hydrodynamic simulations. Sea level changes over the Gulf of Carpentaria in the north are largest and positive during austral summer in two out of the four models. In these models, changes to the northwest monsoon appear to be the cause of the sea level response. These simulations highlight a sensitivity of this semi-enclosed gulf to changes in large-scale dynamics in this region and indicate that further assessment of the potential changes to the northwest monsoon in a larger multi-model ensemble should be investigated, together with the northwest monsoon’s effect on extreme sea levels.