A light-driven, one-dimensional dimethylsulfide biogeochemical cycling model for the Sargasso Sea

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 113 (2008): G02009, doi:10.1029/2007JG000426.We evaluate the extent to which dimethylsulfide (DMS) cycling in an open-ocean environment can be constrained and parameterized utilizing emerging evidence for the significant impacts of solar ultraviolet radiation (UVR) on the marine organic sulfur cycle. Using the Dacey et al. (1998) 1992–1994 Sargasso Sea DMS data set, in conjunction with an offline turbulent mixing model, we develop and optimize a light driven, one-dimensional DMS model for the upper 140 m. The DMS numerical model is primarily diagnostic in that it incorporates observations of bacterial, phytoplankton, physical, and optical quantities concurrently measured as part of the Bermuda Atlantic Time-series Study (BATS) and Bermuda Bio-Optical Project (BBOP) programs. With the exception of sea-to-air ventilation, each of the sulfur cycling terms is explicitly parameterized or altered by the radiation field. Overall, the model shows considerable skill in capturing the salient features of the DMS distribution, specifically the observed DMS summer paradox whereby peak summer DMS concentrations occur coincident with annual minima in phytoplankton pigment biomass and primary production. The dominant processes controlling the upper-ocean DMS concentrations are phytoplankton UVR-induced DMS release superimposed upon more surface oriented processes such as photolysis and sea-to-air ventilation. The results also demonstrate that mixing alone is not enough to parameterize DMS distributions in this environment. It is critical to directly parameterize the seasonal changes in the flux and attenuation of solar radiation in the upper water column to describe the DMS distribution with depth and allow for experimentation under a variety of climate change scenarios.This work was supported by NASA under an Earth System Science Fellowship, a WHOI Ocean and Climate Change Institute Postdoctoral scholarship, and NSF OCE-0525928

Revising upper-ocean sulfur dynamics near Bermuda : new lessons from 3 years of concentration and rate measurements

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Environmental Chemistry 13 (2016): 302-313, doi:10.1071/EN15045.Oceanic biogeochemical cycling of dimethylsulfide (DMS), and its precursor dimethylsulfoniopropionate (DMSP), has gained considerable attention over the past three decades because of the potential role of DMS in climate mediation. Here we report 3 years of monthly vertical profiles of organic sulfur cycle concentrations (DMS, particulate DMSP (DMSPp) and dissolved DMSP (DMSPd)) and rates (DMSPd consumption, biological DMS consumption and DMS photolysis) from the Bermuda Atlantic Time-series Study (BATS) site taken between 2005 and 2008. Concentrations confirm the summer paradox with mixed layer DMS peaking ~90 days after peak DMSPp and ~50 days after peak DMSPp : Chl. A small decline in mixed layer DMS was observed relative to those measured during a previous study at BATS (1992–1994), potentially driven by long-term climate shifts at the site. On average, DMS cycling occurred on longer timescales than DMSPd (0.43 ± 0.35 v. 1.39 ± 0.76 day–1) with DMSPd consumption rates remaining elevated throughout the year despite significant seasonal variability in the bacterial DMSP degrader community. DMSPp was estimated to account for 4–5 % of mixed layer primary production and turned over at a significantly slower rate (~0.2 day–1). Photolysis drove DMS loss in the mixed layer during the summer, whereas biological consumption of DMS was the dominant loss process in the winter and at depth. These findings offer new insight into the underlying mechanisms driving DMS(P) cycling in the oligotrophic ocean, provide an extended dataset for future model evaluation and hypothesis testing and highlight the need for a reexamination of past modelling results and conclusions drawn from data collected with old methodologies.The authors acknowledge funding from the National Science Foundation (NSF) (OCE-0425166) and the Center for Microbial Oceanography Research and Education (CMORE) a NSF Science and Technology Center (EF-0424599)

Environmental, biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Environmental Microbiology 14 (2012): 1210-1223, doi:10.1111/j.1462-2920.2012.02700.x.Dimethylsulfide (DMS) is a climatically relevant trace gas produced and cycled by the surface ocean food web. Mechanisms driving intraannual variability in DMS production and dimethylsulfoniopropionate (DMSP) degradation in open-ocean, oligotrophic regions were investigated during a 10 month time-series at the Bermuda Atlantic Time-series Study site in the Sargasso Sea. Abundance and transcription of bacterial DMSP degradation genes, DMSP lyase enzyme activity, and DMS and DMSP concentrations, consumption rates, and production rates were quantified over time and depth. This interdisciplinary dataset was used to test current hypotheses of the role of light and carbon supply in regulating upper-ocean sulfur cycling. Findings supported UV-A dependent phytoplankton DMS production. Bacterial DMSP degraders may also contribute significantly to DMS production when temperatures are elevated and UV-A dose is moderate, but may favor DMSP demethylation under low UV-A doses. Three groups of bacterial DMSP degraders with distinct intraannual variability were identified and niche differentiation was indicated. The combination of genetic and biochemical data suggest a modified ‘bacterial switch’ hypothesis where the prevalence of different bacterial DMSP degradation pathways is regulated by a complex set of factors including carbon supply, temperature, and UV-A dose.This research was funded by National Science Foundation (NSF) grants OCE- 0525928, OCE-072417, and OCE-042516. Additional funding was provided by the NSF Center for Microbial Oceanography Research and Education (CMORE), the Gordon and Betty Moore Foundation, the Scurlock Fund, the Ocean Ventures Fund, a National Defense Science and Engineering Graduate Fellowship, and an Environmental Protection Agency STAR Graduate Fellowship

Light-driven cycling of dimethylsulfide (DMS) in the Sargasso Sea : closing the loop

Author Posting. © American Geophysical Union, 2004. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 31 (2004): L09308, doi:10.1029/2004GL019581.The factors driving dimethylsulfide (DMS) cycling in oligotrophic environments are isolated using a time-series of DMS sampled in the Sargasso Sea. The observed distribution of DMS is inconsistent with bottom-up processes related to phytoplankton production, biomass, or community structure changes. DMS concentrations and estimates of net biological community production are most highly correlated with physical and optical properties, with the dose of ultraviolet radiation (UVR) accounting for 77% of the variability in mixed layer DMS concentrations. Physiological stresses associated with shallow mixed layers and high UVR are the first order determinant of biological production of DMS, indicating that DMS cycling in open-ocean regions is fundamentally different than in eutrophic regions where phytoplankton blooms provide the conditions for elevated DMS concentrations. The stress regime presented here effectively closes the DMS-climate feedback loop for open-ocean environments. This response may also provide a climatic role for phytoplanktonic processes in response to anthropogenic forcing.This work was supported by a NASA Earth System Science Fellowship

SeaWiFS Postlaunch Technical Report Series

Volume 11 continues the sequential presentation of postlaunch data analysis and algorithm descriptions begun in Volume 9. Chapters 1 and 2 present the OC2 (version 2) and OC4 (version 4) chlorophyll a algorithms used in the SeaWiFS data second and third reprocessings, August 1998 and May 2000, respectively. Chapter 3 describes a revision of the K(490) algorithm designed to use water-leaving radiances at 490 nm which was implemented for the third reprocessing. Finally, Chapter 4 is an analysis of in situ radiometer calibration data over several years at the University of California, Santa Barbara (UCSB) to establish the temporal consistency of their in-water optical measurements

Pelagic functional group modeling: Progress, challenges and prospects

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