Primary production in the Bering and Chukchi Seas is strongly influenced by the annual cycle of sea ice. Here pelagic and sea ice algal ecosystems coexist and interact with each other. Ecosystem modeling of sea ice as...Primary production in the Bering and Chukchi Seas is strongly influenced by the annual cycle of sea ice. Here pelagic and sea ice algal ecosystems coexist and interact with each other. Ecosystem modeling of sea ice associated phytoplankton blooms has been understudied compared to open water ecosystem model applications. This study introduces a general coupled ice-ocean ecosystem model with equations and parameters for 1-D and 3-D applications that is based on 1-D coupled ice-ocean ecosystem model development in the landfast ice in the Chukchi Sea and marginal ice zone of Bering Sea. The biological model includes both pelagic and sea ice algal habitats with 10 compartments: three phytoplankton (pelagic diatom, flagellates and ice algae: D, F, and Ai) , three zooplankton (copepods, large zooplankton, and microzooplankton : ZS, ZL, ZP) , three nutrients ( nitrate + nitrite, ammonium, silicon : NO3 , NH4, Si) and detritus (Det). The coupling of the biological models with physical ocean models is straightforward with just the addition of the advection and diffusion terms to the ecosystem model. The coupling with a multi-category sea ice model requires the same calculation of the sea ice ecosystem model in each ice thickness category and the redistribution between categories caused by both dynamic and thermodynamic forcing as in the physical model. Phytoplankton and ice algal self-shading effect is the sole feedback from the ecosystem model to the physical model.展开更多
Nitrate is considered the nutrient that limits new primary production in the southeastern Bering Sea shelf. Nitrate regenerated through biological nitrification has the potential to significantly support primary produ...Nitrate is considered the nutrient that limits new primary production in the southeastern Bering Sea shelf. Nitrate regenerated through biological nitrification has the potential to significantly support primary production as well. Here we use measurements of the specific rate of water column nitrification in a 1-D ecosystem model to quantify the resupply of nitrate from nitrification in the middle shelf of the southeastern Bering Sea. Model sensitivity studies reveal nitrification rate is an important control on the dominant phytoplankton functional type, and the amount of nitrate in su mer bottom waters and in the winter water column. Evaluation of nitrification using the model supports the hypothesis that increases in late-summer nitrate concentrations observed in the southeastern Bering Sea bottom waters are due to nitrification. Model results for nitrate replenishment exceed previously estimated rates of 20-30% based on observations. The results of this study indicate that nitrification, potentially the source of up to - 38% of the springtime water column nitrate, could support - 24% of the annual primary production.展开更多
基金supported by North Pacific Research Board(NPRB) grant 607(paper contribution number 202)NSF grant ARC-0652838+1 种基金DOE/EPSCoR grant DE-FG02-08ER46502.This is GLERL Contribution No.1499 and DOE/EPS-CoRInternational Arctic Research Center,University of Alaska Fairbanks supported this study through the JAMSTEC-IARC Research Agreement.
文摘Primary production in the Bering and Chukchi Seas is strongly influenced by the annual cycle of sea ice. Here pelagic and sea ice algal ecosystems coexist and interact with each other. Ecosystem modeling of sea ice associated phytoplankton blooms has been understudied compared to open water ecosystem model applications. This study introduces a general coupled ice-ocean ecosystem model with equations and parameters for 1-D and 3-D applications that is based on 1-D coupled ice-ocean ecosystem model development in the landfast ice in the Chukchi Sea and marginal ice zone of Bering Sea. The biological model includes both pelagic and sea ice algal habitats with 10 compartments: three phytoplankton (pelagic diatom, flagellates and ice algae: D, F, and Ai) , three zooplankton (copepods, large zooplankton, and microzooplankton : ZS, ZL, ZP) , three nutrients ( nitrate + nitrite, ammonium, silicon : NO3 , NH4, Si) and detritus (Det). The coupling of the biological models with physical ocean models is straightforward with just the addition of the advection and diffusion terms to the ecosystem model. The coupling with a multi-category sea ice model requires the same calculation of the sea ice ecosystem model in each ice thickness category and the redistribution between categories caused by both dynamic and thermodynamic forcing as in the physical model. Phytoplankton and ice algal self-shading effect is the sole feedback from the ecosystem model to the physical model.
基金supported by North Pacific Research Board(NPRB) grant 607 awarded to Jin,Deal,and WangThe International Arctic Research Center supported this study through the JAMSTEC-IARC Research Agreement and NSF-IARC Cooperative AgreementWe thank NPRB and PMEL/NOAA for supporting the collection of data at site 2.This is GLERL Contribution No.1498
文摘Nitrate is considered the nutrient that limits new primary production in the southeastern Bering Sea shelf. Nitrate regenerated through biological nitrification has the potential to significantly support primary production as well. Here we use measurements of the specific rate of water column nitrification in a 1-D ecosystem model to quantify the resupply of nitrate from nitrification in the middle shelf of the southeastern Bering Sea. Model sensitivity studies reveal nitrification rate is an important control on the dominant phytoplankton functional type, and the amount of nitrate in su mer bottom waters and in the winter water column. Evaluation of nitrification using the model supports the hypothesis that increases in late-summer nitrate concentrations observed in the southeastern Bering Sea bottom waters are due to nitrification. Model results for nitrate replenishment exceed previously estimated rates of 20-30% based on observations. The results of this study indicate that nitrification, potentially the source of up to - 38% of the springtime water column nitrate, could support - 24% of the annual primary production.