Spatio-temporal distribution of dissolved inorganic carbon and net community production in the Chukchi and Beaufort Seas

Bates, Nicholas R.; Best, M. H. P.; Hansell, Dennis A.

doi:10.1016/j.dsr2.2005.10.005

Cited by 85 publications

(106 citation statements)

References 58 publications

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“…Analyses of Chl a, particulate organic carbon (POC), and nutrient concentrations were performed as described in Arrigo et al (in press). Dissolved inorganic carbon (DIC) concentrations were measured as in Bates et al (2005). At a few select stations (Table 3), more detailed light profiles were measured as in Frey et al (2011; for the under-ice stations) and Arrigo et al (in press; for the open-water stations).…”

Section: Methodsmentioning

confidence: 99%

Light and nutrient control of photosynthesis in natural phytoplankton populations from the Chukchi and Beaufort seas, Arctic Ocean

Palmer

Dijken

Mitchell

et al. 2013

Limnology & Oceanography

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During the 2010-2011 'Impacts of Climate Change on the EcoSystems and Chemistry of the Arctic Pacific Environment' project, we measured photosynthetic parameters in natural Arctic phytoplankton assemblages from the Chukchi and Beaufort seas. Water-column samples were taken from the near surface (3.1 6 0.9 m) and subsurface (28 6 10.3 m) at , 85 stations each year representing a wide range of ecological conditions, including under sea ice (UI) and in open water (OW). The physiological response of phytoplankton to light was used to assess photo-acclimation, photosynthetic efficiency, and maximum chlorophyll a (Chl a) normalized rates of carbon fixation. Phytoplankton from the subsurface were acclimated to lower irradiance, as evidenced by higher photosynthetic efficiencies (a * ), reduced mean absorption spectra (ā * ) associated with heavy pigment packaging, higher maximum quantum yields of photosynthesis (W m ), increased Chl a content (Chl a : POC), and higher potential growth rates (m m ) than surface samples. In addition, phytoplankton growing in the UI subsurface had higher m m , increased W m , and higher Chl a content, as well as reduced ā * compared with those found in OW. P * m did not vary between habitats despite vastly different nutrient and light conditions (averaging , 1 mg C mg 21 Chl a h 21 ), except where nitrate exceeded . 10 mg m 23 , in which case P * m averaged 5-6 mg C mg 21 Chl a h 21 . Results from a stepwise regression analysis of photosynthetic parameters vs. environmental factors indicate that the concentration of inorganic nitrogen (significant relationships with P * m , a * , W m , m m , and Chl a : POC) and temperature at sample depth (a strong indicator of habitat type; significant relationship with b * , ā * , W m , m m , and Chl a : POC) are the best predictors of photosynthetic variables. In addition, the amount of light available at sample depth significantly predicted both E k and Chl a : POC. Our results suggest that it is the balance between light and nutrient availability in the various environments encountered in the seasonal sea ice zone that result in the pattern of photo-physiological data presented here. A significant proportion of primary production has now been observed to occur under the sea ice; therefore, our results may be a change from prior conditions in the region.

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Section: Methodsmentioning

confidence: 99%

Light and nutrient control of photosynthesis in natural phytoplankton populations from the Chukchi and Beaufort seas, Arctic Ocean

Palmer

Dijken

Mitchell

et al. 2013

Limnology & Oceanography

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“…We then use the seasonal drawdown of DIC in the mixed layer to estimate rates of net community production (NCP), which can be used as an indicator of ecosystem functionality (e.g. Bates et al, 2005. Because a number of processes impact the cycling and fate of carbon in the ecosystem including the timing of sea ice retreat, water temperature, stratification, and species abundance , NCP is a valuable tool in assessing net ecosystem production (NEP; Andersson et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Seasonal distribution of dissolved inorganic carbon and net community production on the Bering Sea shelf

et al. 2010

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Abstract. In order to assess the current state of net community production (NCP) in the southeastern Bering Sea, we measured the spatio-temporal distribution and controls on dissolved inorganic carbon (DIC) concentrations in spring and summer of 2008 across six shelf domains defined by differing biogeochemical characteristics. DIC concentrations were tightly coupled to salinity in spring and ranged from ∼1900 µmoles kg −1 over the inner shelf to ∼2400 µmoles kg −1 in the deeper waters of the Bering Sea. In summer, DIC concentrations were lower due to dilution from sea ice melt, terrestrial inputs, and primary production. Concentrations were found to be as low ∼1800 µmoles kg −1 over the inner shelf. We found that DIC concentrations were drawn down 30-150 µmoles kg −1 in the upper 30 m of the water column due to primary production and calcium carbonate formation between the spring and summer occupations. Using the seasonal drawdown of DIC, estimated rates of NCP on the inner, middle, and outer shelf averaged 28 ± 9 mmoles C m −2 d −1 . However, higher rates of NCP (40-47 mmoles C m −2 d −1 ) were observed in the "Green Belt" where the greatest confluence of nutrient-rich basin water and iron-rich shelf water occurs. We estimated that in 2008, total NCP across the shelf was on the order of ∼96 Tg C yr −1 . Due to the paucity of consistent, comparable productivity data, it is impossible at this time to quantify whether the system is becoming more or less productive. However, as changing climate continues to modify the character of the Bering Sea, we have shown that NCP can be an important indicator of how the ecosystem is functioning.

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“…We generated monthly composite satellite SST and chla data for July, August, and September of 2010 and 2011 based on the inversion models (Equations (8), (9), and (13)). The accuracy of satellite-derived chla might be not good at the Bering Sea, and showed systematic overestimation for the SeaWiFS OC4V4 algorithm (i.e., [66,67]).…”

Section: Results and Validationmentioning

confidence: 99%

Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)

et al. 2016

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Abstract:The Bering Sea, one of the largest and most productive marginal seas, is a crucial carbon sink for the marine carbonate system. However, restricted by the tough observation conditions, few underway datasets of sea surface partial pressure of carbon dioxide (pCO 2 ) have been obtained, with most of them in the eastern areas. Satellite remote sensing data can provide valuable information covered by a large area synchronously with high temporal resolution for assessments of pCO 2 that subsequently allow quantification of air-sea carbon dioxide 2 flux. However, pCO 2 in the Bering Sea is controlled by multiple factors and thus it is hard to develop a remote sensing algorithm with empirical regression methods. In this paper pCO 2 in the Bering Sea from July to September was derived based on a mechanistic semi-analytical algorithm (MeSAA). It was assumed that the observed pCO 2 can be analytically expressed as the sum of individual components controlled by major factors. First, a reference water mass that was minimally influenced by biology and mixing was identified in the central basin, and then thermodynamic and biological effects were parameterized for the entire area. Finally, we estimated pCO 2 with satellite temperature and chlorophyll data. Satellite results agreed well with the underway observations. Our study suggested that throughout the Bering Sea the biological effect on pCO 2 was more than twice as important as temperature, and contributions of other effects were relatively small. Furthermore, satellite observations demonstrate that the spring phytoplankton bloom had a delayed effect on summer pCO 2 but that the influence of this biological event varied regionally; it was more significant on the continental slope, with a later bloom, than that on the shelf with an early bloom. Overall, the MeSAA algorithm was not only able to estimate pCO 2 in the Bering Sea for the first time, but also provided a quantitative analysis of the contribution of various processes that influence pCO 2 .

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Spatio-temporal distribution of dissolved inorganic carbon and net community production in the Chukchi and Beaufort Seas

Cited by 85 publications

References 58 publications

Light and nutrient control of photosynthesis in natural phytoplankton populations from the Chukchi and Beaufort seas, Arctic Ocean

Light and nutrient control of photosynthesis in natural phytoplankton populations from the Chukchi and Beaufort seas, Arctic Ocean

Seasonal distribution of dissolved inorganic carbon and net community production on the Bering Sea shelf

Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)

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