Radon-222 and 226Ra activities were measured in the waters and sediment of the southeastern Bering Sea shelf to evaluate the use of radon as a tracer of gas exchange, water column mixing and sediment-water exchange. Cross-shelf distributions of 222Rn and 226Ra are presented. Gas transfer coefficients were estimated using near-surface 222Rn deficiency measurements. A statistically significant linear relationship between averaged wind speed and transfer coefficient was found. Vertical eddy diffusivities were evaluated by applying a one-dimensional model to near-bottom excess 222Rn distributions; these diffusivities were compared to independently determined values. The one-dimensional model applied to the near-bottom 222Rn data was found to be inadequate and a two-dimensional model was applied to improve the fit between model and data. Exchange across the sediment-water interface was computed from the deficiency of 222Rn measured in sediment cores, standing crop estimates of excess 222Rn in the water column and 222Rn production rates of sediment surface grab samples. Biological irrigation of the sediments appeared to be the primary exchange mechanism between the sediment and water columns. Distributions in the water column showed finestructure reported previously and suggested biological removal of 226Ra. © 1987.

A global marine ecosystem mixed-layer model is used to study iron cycling and nutrient-limitation patterns in surface waters of the world ocean. The ecosystem model has a small phytoplankton size class whose growth can be limited by N, P, Fe, and/or light, a diatom class which can also be Si-limited, and a diazotroph phytoplankton class whose growth rates can be limited by P, Fe, and/or light levels. The model also includes a parameterization of calcification by phytoplankton and is described in detail by Moore et al. (Deep-Sea Res. II, 2002). The model reproduces the observed high nitrate, low chlorophyll (HNLC) conditions in the Southern Ocean, subarctic Northeast Pacific, and equatorial Pacific, and realistic global patterns of primary production, biogenic silica production, nitrogen fixation, particulate organic carbon export, calcium carbonate export, and surface chlorophyll concentrations. Phytoplankton cellular Fe/C ratios and surface layer dissolved iron concentrations are also in general agreement with the limited field data. Primary production, community structure, and the sinking carbon flux are quite sensitive to large variations in the atmospheric iron source, particularly in the HNLC regions, supporting the Iron Hypothesis of Martin (Paleoceanography 5 (1990) 1-13). Nitrogen fixation is also strongly influenced by atmospheric iron deposition. Nitrogen limits phyuoplankton growth rates over less than half of the world ocean during summer months. Export of biogenic carbon is dominated by the sinking particulate flux, but detrainment and turbulent mixing account for 30% of global carbon export. Our results, in conjunction with other recent studies, suggest the familiar paradigm that nitrate inputs to the surface layer can be equated with particulate carbon export needs to be expanded to include multiple limiting nutrients and modes of export. © 2001 Elsevier Science Ltd. All rights reserved.

A new marine ecosystem model designed for the global domain is presented, and model output is compared with field data from nine different locations. Field data were collected as part of the international Joint Global Ocean Flux Study (JGOFS) program, and from historical time series stations. The field data include a wide variety of marine ecosystem types, including nitrogen- and iron-limited systems, and different physical environments from high latitudes to the mid-ocean gyres. Model output is generally in good agreement with field data from these diverse ecosystems. These results imply that the ecosystem model presented here can be reliably applied over the global domain. The model includes multiple potentially limiting nutrients that regulate phytoplankton growth rates. There are three phytoplankton classes, diatoms, diazotrophs, and a generic small phytoplankton class. Growth rates can be limited by available nitrogen, phosphorus, iron, and/or light levels. The diatoms can also be limited by silicon. The diazotrophs are capable of nitrogen fixation of N2 gas and cannot be nitrogen-limited. Calcification by phytoplankton is parameterized as a variable fraction of primary production by the small phytoplankton group. There is one zooplankton class that grazes the three phytoplankton groups and a large detrital pool. The large detrital pool sinks out of the mixed layer, while a smaller detrital pool, representing dissolved organic matter and very small particulates, does not sink. Remineralization of the detrital pools is parameterized with a temperature-dependent function. We explicitly model the dissolved iron cycle in marine surface waters including inputs of iron from subsurface sources and from atmospheric dust deposition. © 2001 Elsevier Science Ltd. All rights reserved.

CENP-A (CID in flies) is the histone H3 variant essential for centromere specification, kinetochore formation, and chromosome segregation during cell division. Recent studies have elucidated major cell cycle mechanisms and factors critical for CENP-A incorporation in mitosis, predominantly in cultured cells. However, we do not understand the roles, regulation, and cell cycle timing of CENP-A assembly in somatic tissues in multicellular organisms and in meiosis, the specialized cell division cycle that gives rise to haploid gametes. Here we investigate the timing and requirements for CID assembly in mitotic tissues and male and female meiosis in Drosophila melanogaster, using fixed and live imaging combined with genetic approaches. We find that CID assembly initiates at late telophase and continues during G1 phase in somatic tissues in the organism, later than the metaphase assembly observed in cultured cells. Furthermore, CID assembly occurs at two distinct cell cycle phases during male meiosis: prophase of meiosis I and after exit from meiosis II, in spermatids. CID assembly in prophase I is also conserved in female meiosis. Interestingly, we observe a novel decrease in CID levels after the end of meiosis I and before meiosis II, which correlates temporally with changes in kinetochore organization and orientation. We also demonstrate that CID is retained on mature sperm despite the gross chromatin remodeling that occurs during protamine exchange. Finally, we show that the centromere proteins CAL1 and CENP-C are both required for CID assembly in meiosis and normal progression through spermatogenesis. We conclude that the cell cycle timing of CID assembly in meiosis is different from mitosis and that the efficient propagation of CID through meiotic divisions and on sperm is likely to be important for centromere specification in the developing zygote.

We quantify the mechanisms governing interannual variability in the global, upper-ocean inorganic carbon system using a hindcast simulation (1979-2004) of an ecosystem-biogeochemistry model forced with time-evolving atmospheric physics and dust deposition. We analyze the variability of three key, interrelated metrics-air-sea CO2 flux, surface-water carbon dioxide partial pressure pCO2, and upper-ocean dissolved inorganic carbon (DIC) inventory-presenting for each metric global spatial maps of the root mean square (rms) of anomalies from a model monthly climatology. The contribution of specific driving factors is diagnosed using Taylor expansions and linear regression analysis. The major regions of variability occur in the Southern Ocean, tropical Indo-Pacific, and Northern Hemisphere temperate and subpolar latitudes. Ocean circulation is the dominant factor driving variability over most of the ocean, modulating surface dissolved inorganic carbon that in turn alters surface-water pCO2 and air-sea CO2 flux variability (global integrated anomaly rms of 0.34 Pg C yr-1). Biological export and thermal solubility effects partially damp circulation-driven pCO2 variability in the tropics, while in the subtropics, thermal solubility contributes positively to surface-water pCO2 and air-sea CO2 flux variability. Gas transfer and net freshwater inputs induce variability in the air-sea CO2 flux in some specific regions. A component of air-sea CO2 flux variability (global integrated anomaly rms of 0.14 Pg C yr-1) arises from variations in biological export production induced by variations in atmospheric iron deposition downwind of dust source regions. Beginning in the mid-1990s, reduced global dust deposition generates increased air-sea CO2 outgassing in the Southern Ocean, consistent with trends derived from atmospheric CO2 inversions. © 2008 Elsevier Ltd.

We present a generalized framework for assessing the skill of global upper ocean ecosystem-biogeochemical models against in-situ field data and satellite observations. We illustrate the approach utilizing a multi-decade (1979-2004) hindcast experiment conducted with the Community Climate System Model (CCSM-3) ocean carbon model. The CCSM-3 ocean carbon model incorporates a multi-nutrient, multi-phytoplankton functional group ecosystem module coupled with a carbon, oxygen, nitrogen, phosphorus, silicon, and iron biogeochemistry module embedded in a global, three-dimensional ocean general circulation model. The model is forced with physical climate forcing from atmospheric reanalysis and satellite data products and time-varying atmospheric dust deposition. Data-based skill metrics are used to evaluate the simulated time-mean spatial patterns, seasonal cycle amplitude and phase, and subannual to interannual variability. Evaluation data include: sea surface temperature and mixed layer depth; satellite-derived surface ocean chlorophyll, primary productivity, phytoplankton growth rate and carbon biomass; large-scale climatologies of surface nutrients, pCO2, and air-sea CO2 and O2 flux; and time-series data from the Joint Global Ocean Flux Study (JGOFS). Where the data is sufficient, we construct quantitative skill metrics using: model-data residuals, time-space correlation, root mean square error, and Taylor diagrams. © 2008 Elsevier B.V. All rights reserved.