Description: In this paper, we review the state of the art and major challenges in current efforts to incorporate biogeochemical functional groups into models that can be applied on basin-wide and global scales, with an emphasis on models that might ultimately be used to predict how biogeochemical cycles in the ocean will respond to global warming. We define the term “biogeochemical functional group” to refer to groups of organisms that mediate specific chemical reactions in the ocean. Thus, according to this definition, “functional groups” have no phylogenetic meaning—these are composed of many different species with common biogeochemical functions. Substantial progress has been made in the last decade toward quantifying the rates of these various functions and understanding the factors that control them. For some of these groups, we have developed fairly sophisticated models that incorporate this understanding, e.g. for diazotrophs (e.g. Trichodesmium), silica producers (diatoms) and calcifiers (e.g. coccolithophorids and specifically Emiliania huxleyi). However, current representations of nitrogen fixation and calcification are incomplete, i.e., based primarily upon models of Trichodesmium and E. huxleyi, respectively, and many important functional groups have not yet been considered in open-ocean biogeochemical models. Progress has been made over the last decade in efforts to simulate dimethylsulfide (DMS) production and cycling (i.e., by dinoflagellates and prymnesiophytes) and denitrification, but these efforts are still in their infancy, and many significant problems remain. One obvious gap is that virtually all functional group modeling efforts have focused on autotrophic microbes, while higher trophic levels have been completely ignored. It appears that in some cases (e.g., calcification), incorporating higher trophic levels may be essential not only for representing a particular biogeochemical reaction, but also for modeling export. Another serious problem is our tendency to model the organisms for which we have the most validation data (e.g., E. huxleyi and Trichodesmium) even when they may represent only a fraction of the biogeochemical functional group we are trying to represent. When we step back and look at the paleo-oceanographic record, it suggests that oxygen concentrations have played a central role in the evolution and emergence of many of the key functional groups that influence biogeochemical cycles in the present-day ocean. However, more subtle effects are likely to be important over the next century like changes in silicate supply or turbulence that can influence the relative success of diatoms versus dinoflagellates, coccolithophorids and diazotrophs. In general, inferences drawn from the paleo-oceanographic record and theoretical work suggest that global warming will tend to favor the latter because it will give rise to increased stratification. However, decreases in pH and Fe supply could adversely impact coccolithophorids and diazotrophs in the future. It may be necessary to include explicit dynamic representations of nitrogen fixation, denitrification, silicification and calcification in our models if our goal is predicting the oceanic carbon cycle in the future, because these processes appear to play a very significant role in the carbon cycle of the present-day ocean and they are sensitive to climate change. Observations and models suggest that it may also be necessary to include the DMS cycle to predict future climate, though the effects are still highly uncertain. We have learned a tremendous amount about the distributions and biogeochemical impact of bacteria in the ocean in recent years, yet this improved understanding has not yet been incorporated into many of our models. All of these considerations lead us toward the development of increasingly complex models. However, recent quantitative model intercomparison studies suggest that continuing to add complexity and more functional groups to our ecosystem models may lead to decreases in predictive ability if the models are not properly constrained with available data. We also caution that capturing the present-day variability tells us little about how well a particular model can predict the future. If our goal is to develop models that can be used to predict how the oceans will respond to global warming, then we need to make more rigorous assessments of predictive skill using the available data.

Description: In this paper we used a steady-state ecosystem model that simulates both dissolved organic carbon (DOC) and nitrogen (DON) cycling to study how the planktonic community structure, nutrient availability, and dissolved organic matter (DOM) loading affect these cycles in idealized oceanic, coastal, and estuarine surface waters. The model was able to reproduce DOM and planktonic biomass distributions, uptake rates, and production rates (including DOM) that fell within ranges reported for oceanic, coastal, and estuarine systems. Using a sensitivity analysis we show that DOM cycling was intricately tied to the biomass concentration, distribution, and productivity of plankton. The efficiency of nutrient remineralization and the availability of inflowing nutrients and DON also played a large role in DOM cycling. In these simulations the largest autochthonous source of DOC was always phytoplankton exudation while important sources of DON varied considerably. In the oceanic simulations heterotrophic bacteria were particularly important for mediating DOM cycling because they were the primary agents that controlled nutrient recycling and supply (i.e., strong bottom-up control). In contrast, in the estuarine simulations mortality (mainly from grazing and viral lysis) had the most influence on DOM production. However, DOM cycling was generally less dependent on interactions between plankton in the estuarine case because of high nutrient and DOM loading. The coastal simulations were somewhere in between. In all simulations competition between different size classes of phytoplankton also played an important role in DOM cycling.

Description: Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(11), (2020): E1891-E1913, https://doi.org/10.1175/BAMS-D-19-0209.1

Description: The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a 3-yr international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better-resolved upper ocean processes to improve understanding of air–sea coupling and yield better subseasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basinwide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.

Description: We thank the World Climate Research Programme (WCRP) and its core project on Climate and Ocean: Variability, Predictability and Change (CLIVAR), the Indian Ocean Global Ocean Observing System (IOGOOS), the Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO), the Integrated Marine Biosphere Research (IMBeR) project, the U.S. National Oceanic and Atmospheric Administration (NOAA), and the International Union of Geodesy and Geophysics (IUGG) for providing the financial support to bring international scientists together to conduct this review. We thank the members of the independent review board that provided detailed feedbacks on the review report that is summarized in this article: P. E. Dexter, M. Krug, J. McCreary, R. Matear, C. Moloney, and S. Wijffels. PMEL Contribution 5041. C. Ummenhofer acknowledges support from The Andrew W. Mellon Foundation Award for Innovative Research.

Description: 2021-05-01