295 research outputs found

    Influence of cross-shelf water transport on nutrients and phytoplankton in the East China Sea: a model study

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    A three dimensional coupled biophysical model was used to examine the supply of oceanic nutrients to the shelf of the East China Sea (ECS) and its role in primary production over the shelf. The model consisted of two parts: the hydrodynamic module was based on a nested model with a horizontal resolution of 1/18 degree, whereas the biological module was a lower trophic level ecosystem model including two types of phytoplankton, three elements of nutrients, and biogenic organic material. The model results suggested that seasonal variations occurred in the distribution of nutrients and chlorophyll <i>a</i> over the shelf of the ECS. After comparison with available observed nutrients and chlorophyll <i>a</i> data, the model results were used to calculate volume and nutrients fluxes across the shelf break. The annual mean total fluxes were 1.53 Sv for volume, 9.4 kmol s<sup>โˆ’1</sup> for DIN, 0.7 kmol s<sup>โˆ’1</sup> for DIP, and 18.2 kmol s<sup>โˆ’1</sup> for silicate. Two areas, northeast of Taiwan and southwest of Kyushu, were found to be major source regions of oceanic nutrients to the shelf. Although the onshore fluxes of nutrients and volume both had apparent seasonal variations, the seasonal variation of the onshore nutrient flux did not exactly follow that of the onshore volume flux. Additional calculations in which the concentration of nutrients in Kuroshio water was artificially increased suggested that the oceanic nutrients were distributed in the bottom layer from the shelf break to the region offshore of the Changjiang estuary from spring to summer and appeared in the surface layer from autumn to winter. The calculations also implied that the supply of oceanic nutrients to the shelf can change the consumption of pre-existing nutrients from rivers. The response of primary production over the shelf to the oceanic nutrients was confirmed not only in the surface layer (mainly at the outer shelf and shelf break in winter and in the region offshore of the Changjiang estuary in summer) but also in the subsurface layer over the shelf from spring to autumn

    Numerical study on the hydrodynamic background in coastal aquaculture dominated regions and corresponding interactions in the Yellow Sea

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    The Yellow Sea is an important region for aquaculture in China as the main production area for shellfish and seaweed. The aquaculture organisms sometimes can be the major group in a local ecosystem. e.g., in the Sanggou bay where about 84,500 tonnes of kelp (dry weight) and 100,000 tonnes of shellfish (wet weight) are produced annually from a surface area of ~144 km2 (Zhang et al., 2009; Mao et al., 2018). To maintain the development of the aquaculture industry at such scales and to minimize the negative impact on the natural ecosystem, the knowledge of the biological processes at different scales is necessary for decision-makers in the formulation of policy and management strategies. However, a comprehensive description of the biogeochemical process in the aquaculture-affected regions can be highly complicated. Observations are often limited in time and space to fully describe the environmental variations in the aquaculture areas. Numerical models are capable of resolving the ecosystem processes at an often sufficient spatial and temporal scale, but with an increasing complexity from current models describing the physical environment to ecosystem models trying to describe complicated and often less known processes. In this thesis, we have implemented a hydrodynamic model based on the Regional Ocean Modelling system (ROMS) to provide the background physical information for aquaculture related applications, the Yellow Sea Model. We have collected various observations to validate the model, and the results do reproduce reasonably well the ambient environment in aquaculture areas. The tide is the dominating current component in the Yellow Sea, moving the water back and forth continuously. The tide also provides energy on the shallow shelves creating usually well mixed water masses. In the summer, a tidal mixing front is established around the 20-50 m isobaths bordering on the Yellow Sea bottom cold water mass below the seasonal thermocline in the central Yellow Sea. An associated frontal jet flows along the tidal mixed front, transporting water masses along the shelf breaks. The tidal current also make the tidal mixing front oscillate laterally creating temporal temperature variations in the farm regions of bottom cultured scallops. The assessment index derived from these temperature oscillations is correlated to a massive scallop mortality found in the past years. Our model results are also applied to study the baroclinic tides in the northern Yellow Sea, with a semi-diurnal internal tide being present in the stratified waters in the tidal mixing front region. The baroclinic flow associated with this internal tide contributes to enhance the total current in the bottom layer, thus potentially being important for material transportation to farmed scallops. The baroclinic signals are mostly coherent with the barotropic tides, indicating a local generation and a rapid dissipation. Finally, we have established an ecosystem model for the integrated culture of Pacific oyster Crassostrea gigas and kelp Saccharina japonica in Sanggou bay based on a box model concept. The growth of oysters and kelp is simulated at the individual level based on the dynamic energy budget theory. The hydrodynamic information is included as forcing data to compute volume transportation and nutrient exchange. The model is validated with individual growth data recorded in the aquaculture field and water quality data for nutrients from cruises and mooring devices. The model results show that the intensive aquaculture of these low-trophic species is dominant in the local ecosystem and dramatically impacts the phytoplankton population and nutrient flux. The bay acts as a nitrogen sink during the rapid growth stage of kelp from early spring until the harvest in May. The model enables a stocking density adjustment of the culture organisms, thus providing a tool to predict the dynamic process under different scenarios. The model results support that the actual aquaculture stock density, with 50 oyster ind./m2 and 4 kelp ind./m2, is a balanced choice of production and cost based on decades of practical experience.Doktorgradsavhandlin

    Seamless integration of the coastal ocean in global marine carbon cycle modeling

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    We present the first global ocean-biogeochemistry model that uses a telescoping high resolution for an improved representation of coastal carbon dynamics: ICON-Coast. Based on the unstructured triangular grid topology of the model, we globally apply a grid refinement in the land-ocean transition zone to better resolve the complex circulation of shallow shelves and marginal seas as well as ocean-shelf exchange. Moreover, we incorporate tidal currents including bottom drag effects, and extend the parameterizations of the model's biogeochemistry component to account explicitly for key shelf-specific carbon transformation processes. These comprise sediment resuspension, temperature-dependent remineralization in the water column and sediment, riverine matter fluxes from land including terrestrial organic carbon, and variable sinking speed of aggregated particulate matter. The combination of regional grid refinement and enhanced process representation enables for the first time a seamless incorporation of the global coastal ocean in model-based Earth system research. In particular, ICON-Coast encompasses all coastal areas around the globe within a single, consistent ocean-biogeochemistry model, thus naturally accounting for two-way coupling of ocean-shelf feedback mechanisms at the global scale. The high quality of the model results as well as the efficiency in computational cost and storage requirements proves this strategy a pioneering approach for global high-resolution modeling. We conclude that ICON-Coast represents a new tool to deepen our mechanistic understanding of the role of the land-ocean transition zone in the global carbon cycle, and to narrow related uncertainties in global future projections

    Workshop on Conceptual/Theoretical Studies and Model Development including the MODEL Task Team Report; BASS Team Report; REX Task Team Reports

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    A workshop was convened by the MODEL Task Team and held June 23-28, 1996, in Nemuro, Japan, to develop the modeling requirements of the PICES Climate Change and Carrying Capacity (CCCC) Program. It was attended by over 40 scientists from all member nations of PICES. The principal objectives of the workshop were to โ€ข review the roles and limitations of modeling for the CCCC program; โ€ข propose the level of modeling required; and โ€ข provide a plan for how to promote these modeling activities. Secondary activities at the workshop included organisational meetings of the Regional comparisons (REX) and Basin-scale experiment (BASS) Task Teams, and a symposium by Japan-GLOBEC on โ€œDevelopment and application of new technologies for measurement and modeling in marine ecosystems.โ€ This report serves as a record of the proceedings of this workshop. (PDF contains 89 pages

    Stable isotopic evidence of nitrogen sources and C4 metabolism driving the worldโ€™s largest macroalgal green tides in the Yellow Sea

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    ยฉ The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 8 (2018): 17437, doi:10.1038/s41598-018-35309-3.During recent years, rapid seasonal growth of macroalgae covered extensive areas within the Yellow Sea, developing the worldโ€™s most spatially extensive โ€œgreen tideโ€. The remarkably fast accumulation of macroalgal biomass is the joint result of high nitrogen supplies in Yellow Sea waters, plus ability of the macroalgae to optionally use C4 photosynthetic pathways that facilitate rapid growth. Stable isotopic evidence shows that the high nitrogen supply is derived from anthropogenic sources, conveyed from watersheds via river discharges, and by direct atmospheric deposition. Wastewater and manures supply about half the nitrogen used by the macroalgae, fertiliser and atmospheric deposition each furnish about a quarter of the nitrogen in macroalgae. The massive green tides affecting the Yellow Sea are likely to increase, with significant current and future environmental and human consequences. Addressing these changing trajectories will demand concerted investment in new basic and applied research as the basis for developing management policies.This work was supported by the State Key Project of Research and Development Plan (2016YFC1402106)

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€,2020. 2. ์กฐ์–‘๊ธฐ.The Yellow Sea (YS) is a shallow, semi-enclosed marginal sea surrounded by China and the Korean Peninsula. A deep trough is located in the central YS. The northwesterly wind drives the Yellow Sea Warm Current (YSWC) flows into the YS along the deep trough and two southward coastal currents occurs along the Chinese and Korean coasts in winter. Previous observations have shown that the path of the YSWC has shifted to the west from the deep trough one or two days after northerly wind bursts. However, exact evolution process of the YSWC remains unclear. Model results in this study suggested that the YSWC occurs along the deep trough one day after the wind burst. It shifts to the west of the trough two days later, which phenomenon is generally referred to as the westward shift of the YSWC. Previous studies have proposed a possibility that the westward shift can be driven by continental shelf waves (CSWs). Idealized models were performed to figure out the relationship between CSWs and the westward shift, and generation mechanism of the CSWs driving the shift. The westward shift appeared from the north to south with the propagation of sea surface height at a speed of 3 m/s that was consistent with the phase speed of the first mode CSW. CSWs driving the westward shift were generated on the northern slope primarily by scattering of barotropic Kelvin waves that developed due to northerly wind and propagated poleward into the YS along the eastern boundary off Korea. The YSWC plays an important role in the ecosystem of the YS, because it provides an external water mass in winter. A physical-biogeochemical coupled model and several sensitivity experiments were performed to reveal the role of the YSWC in nitrate (NO3) budget of the YS and quantify contributions of external sources and biological process to NO3 in the YS. Multiple sensitivity experiments revealed quantitative contribution of NO3 from the Changjiang River, Kuroshio Current (KC), run-off in the YS, and Taiwan Warm Current (TWC). 51 percent of total NO3 in the YS was estimated from the Changjiang River and 25 percent from the KC and rivers in the YS, respectively. The TWC contributed 8 percent. Change by nitrification process due to the biological activity was estimated less than 1 percent of the total NO3 in the YS. The estimation of NO3 flux into the YS suggested that 20 percent of total mass NO3 in the YS was supplied by the YSWC during winter. Relative NO3 contributions of the Changjiang River, KC, and TWC through the YSWC to the total inflow to the YS were 64, 29, and 10 percent, respectively.ํ™ฉํ•ด๋Š” ํ•œ๋ฐ˜๋„์™€ ์ค‘๊ตญ ์‚ฌ์ด์— ์กด์žฌํ•˜๋Š” ๋ฐ˜ํ์‡„์„ฑ ํ•ด์—ญ์ด๋ฉฐ ํ•ด์—ญ ์ค‘์•™์—๋Š” ์ˆ˜์‹ฌ 90 m ์ด์ƒ์˜ ๊นŠ์€ ๊ณจ์ด ์กด์žฌํ•œ๋‹ค. ๊ฒจ์šธ์ฒ  ํ™ฉํ•ด์— ๋‚˜ํƒ€๋‚˜๋Š” ์ฃผ์š” ํ•ด๋ฅ˜๋Š”, ๋ถ์„œ๊ณ„์ ˆํ’์œผ๋กœ ์ธํ•ด ์ค‘๊ตญ ๋ฐ ํ•œ๊ตญ ์—ฐ์•ˆ์—๋Š” ๋‚จํ–ฅํ•˜๋Š” ํ๋ฆ„์ด ์กด์žฌํ•˜๋ฉฐ ๊นŠ์€ ๊ณจ์—๋Š” ๋ฐ”๋žŒ์˜ ๋ฐ˜๋Œ€ ๋ฐฉํ–ฅ์œผ๋กœ ๋ถํ–ฅํ•˜๋Š” ํ™ฉํ•ด ๋‚œ๋ฅ˜๊ฐ€ ๋‚˜ํƒ€๋‚œ๋‹ค. ๊ด€์ธก์„ ํ†ตํ•ด ํ™ฉํ•ด ๋‚œ๋ฅ˜์˜ ๊ฒฝ๋กœ๋Š” ํ™ฉํ•ด ๋‚ด๋ถ€ ๊นŠ์€ ๊ณจ์˜ ์„œ์ชฝ์œผ๋กœ ์น˜์šฐ์ณ์ ธ ์žˆ๊ณ  ๋ฐ”๋žŒ์ด ๋ถ„ ๋’ค ํ•˜๋ฃจ๋‚˜ ์ดํ‹€ ๋’ค ๊ฐ•ํ•ด์ง„๋‹ค๊ณ  ๋ณด๊ณ ๋˜์—ˆ์œผ๋‚˜, ํ˜•์„ฑ๊ณผ์ •์— ๋Œ€ํ•ด์„  ์ž์„ธํžˆ ๋ณด๊ณ ๋œ ๋ฐ”๊ฐ€ ์—†๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชจ๋ธ ์ˆ˜ํ–‰ ๊ฒฐ๊ณผ, ๊ฒจ์šธ์ฒ  ํ™ฉํ•ด์—๋Š” ํ‰๊ท ์ ์œผ๋กœ ์‹œ๊ณ„๋ฐฉํ–ฅ์˜ ์ˆœํ™˜์ด ๋ฐœ์ƒํ•˜์˜€์œผ๋ฉฐ, ์‹œ๊ฐ„์ง€์—ฐ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ ๊ฒฐ๊ณผ, ํ™ฉํ•ด ๋‚œ๋ฅ˜๋Š” ๋ฐ”๋žŒ์ด ๋ถ„ ํ•˜๋ฃจ ๋’ค ๊นŠ์€ ๊ณจ์— ์ถœํ˜„ํ•˜์˜€์œผ๋ฉฐ ์ดํ‹€ ๋’ค ๊ณจ์˜ ์„œ์ชฝ์œผ๋กœ ํŽธํ–ฅ๋˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์„ ํ–‰์—ฐ๊ตฌ๋Š” ํ™ฉํ•ด ๋‚œ๋ฅ˜์˜ ์„œ์ชฝ ํŽธํ–ฅ ํ˜„์ƒ์ด ๋Œ€๋ฅ™๋ถ•ํŒŒ์— ์˜ํ•ด ๋ฐœ์ƒ ํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์ฃผ์žฅํ•˜๊ณ  ์žˆ๋‹ค. ์ง€ํ˜•์„ ๊ฐ„์†Œํ™”ํ•œ ๋ชจ๋ธ์„ ํ†ตํ•ด ํ™ฉํ•ด ๋‚œ๋ฅ˜์˜ ์„œ์ชฝ ํŽธํ–ฅ ํ˜„์ƒ๊ณผ ํŽธํ–ฅ ํ˜„์ƒ์˜ ์ฃผ ์š”์ธ์ธ ๋Œ€๋ฅ™๋ถ•ํŒŒ์˜ ์—ญํ•  ๋ฐ ํ˜•์„ฑ ๊ธฐ์ž‘์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ํ™ฉํ•ด ๋‚œ๋ฅ˜์˜ ์„œ์ชฝ ํŽธํ–ฅ ํ˜„์ƒ์€ ๋Œ€๋ฅ™๋ถ•ํŒŒ์˜ ์ „ํŒŒ์™€ ํ•จ๊ป˜ ๋ฐœ์ƒํ•˜์˜€์œผ๋ฉฐ ํŽธํ–ฅ ํ˜„์ƒ์˜ ์ „ํŒŒ ์†๋„๋Š” 2.99 m/s ๋กœ ๋Œ€๋ฅ™๋ถ•ํŒŒ์˜ 1๋ฒˆ ๋ชจ๋“œ ์ „ํŒŒ ์†๋„์™€ ์ผ์น˜ํ•˜์˜€๋‹ค. ๋Œ€๋ฅ™๋ถ•ํŒŒ๋Š” ๋ฐ”๋žŒ์— ์˜ํ•ด ๊ฒฝ์‚ฌ๊ฐ€ ์žˆ๋Š” ์ง€์—ญ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ํŽธํ–ฅ ํ˜„์ƒ์„ ์ฃผ๋„ํ•˜๋Š” ๋Œ€๋ฅ™๋ถ•ํŒŒ๋Š” ๊ฒจ์šธ์ฒ  ๋ถํ’์œผ๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ์บ˜๋นˆ ํŒŒ๊ฐ€ ํ™ฉํ•ด์˜ ๋ถ์ชฝ ์‚ฌ๋ฉด์—์„œ ์‚ฐ๋ž€ ๋˜๋ฉด์„œ ๋ฐœ์ƒํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ™ฉํ•ด ๋‚œ๋ฅ˜๋Š” ์™ธ๋ถ€์˜ ์ˆ˜๊ดด๋ฅผ ํ™ฉํ•ด ๋‚ด๋ถ€๋กœ ์œ ์ž…์‹œํ‚ค๊ธฐ ๋•Œ๋ฌธ์— ํ™ฉํ•ด ์ƒํƒœ๊ณ„์—๋„ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ๋ฌผ๋ฆฌ-์ƒ์ง€ํ™”ํ•™ ์ ‘ํ•ฉ ๋ชจ๋ธ์„ ํ†ตํ•ด ํ™ฉํ•ด ๋‚œ๋ฅ˜๋กœ ์ธํ•œ ์งˆ์‚ฐ์—ผ ์œ ์ž…๋Ÿ‰์„ ์—ฐ๊ตฌํ•˜์˜€์œผ๋ฉฐ, ์™ธ๋ถ€ ์œ ์ž…์› ๋ฐ ์งˆํ™” ์ž‘์šฉ์„ ํ†ตํ•œ ์ƒ๋ฌผํ•™์ ์ธ ๊ณต๊ธ‰๋Ÿ‰์„ ์ •๋Ÿ‰ํ™” ํ•˜์˜€๋‹ค. ๊ด€์ธก ์ž๋ฃŒ๋ฅผ ํ†ตํ•ด ํ™ฉํ•ด ์ฃผ๋ณ€์— ์งˆ์‚ฐ์—ผ ๋†๋„๊ฐ€ ๋†’์€ ์ˆ˜๊ดด๋ฅผ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ํ™ฉํ•ด ๋‚ด๋ถ€์˜ ํ™ฉํ•ด์ €์ธต๋ƒ‰์ˆ˜์™€ ์ฟ ๋กœ์‹œ์˜ค ํ•ด๋ฅ˜๊ฐ€ ๊ธฐ์›์ธ ๋Œ€ํ•œํ•ดํ˜‘ ์ค‘์ธต์ˆ˜์˜ ์งˆ์‚ฐ์—ผ ๋†๋„๊ฐ€ ๋†’์•˜๋‹ค. ๋ฏผ๊ฐ๋„ ์‹คํ—˜์„ ํ†ตํ•ด ์–‘์ž๊ฐ•, ์ฟ ๋กœ์‹œ์˜ค ํ•ด๋ฅ˜, ๋Œ€๋งŒ ๋‚œ๋ฅ˜, ํ™ฉํ•ด ๋‚ด๋ถ€ ๊ฐ•๋“ค์˜ ํšจ๊ณผ, ๊ทธ๋ฆฌ๊ณ  ์งˆํ™” ์ž‘์šฉ์˜ ๊ธฐ์—ฌ๋„๋ฅผ ๊ฐ๊ฐ ์‚ฐ์ •ํ•˜์˜€๋‹ค. ํ™ฉํ•ด ์งˆ์‚ฐ์—ผ์˜ 51%๊ฐ€ ์–‘์ž๊ฐ• ๊ธฐ์›์ด์—ˆ์œผ๋ฉฐ, ์ฟ ๋กœ์‹œ์˜ค ํ•ด๋ฅ˜ ๊ทธ๋ฆฌ๊ณ  ํ™ฉํ•ด ๋‚ด๋ถ€ ๊ฐ•๋“ค์ด ์ „์ฒด ์งˆ์‚ฐ์—ผ์˜ ์•ฝ 25%์”ฉ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ์—ˆ๊ณ  ๋Œ€๋งŒ๋‚œ๋ฅ˜๋กœ๋ถ€ํ„ฐ ๊ธฐ์ธํ•œ ์งˆ์‚ฐ์—ผ์€ ์ „์ฒด์˜ 5% ์ •๋„๋กœ ์ ์€ ์–‘์ด ์œ ์ž…๋˜๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์งˆํ™” ์ž‘์šฉ์„ ํ†ตํ•œ ์œ ์ž…์€ 1% ์•„๋ž˜๋กœ ๋‚ฎ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ด๋Š” ํ™ฉํ•ด์— ์กด์žฌํ•˜๋Š” ์งˆ์‚ฐ์—ผ์€ ์ƒ๋ฌผํ•™์ ์ธ ๊ณต๊ธ‰๋ณด๋‹ค ํ•ด๋ฅ˜๋“ค๋กœ ์ธํ•ด ์™ธ๋ถ€๋กœ๋ถ€ํ„ฐ ์œ ์ž…๋˜๋Š” ๊ฒƒ์ž„์„ ์˜๋ฏธํ•œ๋‹ค. ๊ฐ ๊ธฐ์›์˜ ๊ณต๊ฐ„์ ์ธ ๊ธฐ์—ฌ๋„๋ฅผ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ์–‘์ž๊ฐ• ๊ธฐ์› ์งˆ์‚ฐ์—ผ์€ ํ™ฉํ•ด ์ค‘์•™๋ถ€ ๋ฐ ์„œ์ชฝ์—์„œ ๊ธฐ์—ฌ๋„๊ฐ€ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ฟ ๋กœ์‹œ์˜ค ํ•ด๋ฅ˜ ๊ธฐ์› ์งˆ์‚ฐ์—ผ์€ ํ™ฉํ•ด ์ค‘์•™๋ถ€์— ์กด์žฌํ•˜๋Š” ์งˆ์‚ฐ์—ผ์— ๋ถ€์ฐจ์ ์ธ ๊ธฐ์—ฌ๋ฅผ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ™ฉํ•ด ๋‚ด๋ถ€ ๊ฐ•๋ฌผ์— ์˜ํ•œ ์งˆ์‚ฐ์—ผ ์œ ์ž…์€ ํ™ฉํ•ด ๋™์ชฝ ์—ฐ์•ˆ๊ณผ ์‚ฐ๋‘ฅ๋ฐ˜๋„ ์ฃผ๋ณ€ ์—ฐ์•ˆ์— ๊ตญํ•œ๋˜์–ด ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค. ํ™ฉํ•ด ๋‚œ๋ฅ˜๋กœ ์ธํ•ด ์œ ์ž…๋˜๋Š” ์งˆ์‚ฐ์—ผ ์–‘์€ 0.137 Tg N ๋กœ ๊ฒจ์šธ์ฒ  ํ™ฉํ•ด ๋‚ด๋ถ€ ํ‰๊ท  ์งˆ์‚ฐ์—ผ ์งˆ๋Ÿ‰์˜ ์•ฝ 20% ์ •๋„๊ฐ€ ํ™ฉํ•ด ๋‚œ๋ฅ˜๋กœ ์ธํ•ด ์œ ์ž…๋˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ™ฉํ•ด ๋‚œ๋ฅ˜๋กœ ์ธํ•ด ์œ ์ž…๋˜๋Š” ์งˆ์‚ฐ์—ผ ์ค‘ ์–‘์ž๊ฐ•, ์ฟ ๋กœ์‹œ์˜ค ํ•ด๋ฅ˜, ๋Œ€๋งŒ๋‚œ๋ฅ˜ ๊ธฐ์›์„ ์ •๋Ÿ‰ํ™”ํ•œ ๊ฒฐ๊ณผ, ๊ฐ๊ฐ 64%, 29%, ๊ทธ๋ฆฌ๊ณ  10%๋ฅผ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค.1. General Introduction ๏ผ‘ 2. Evolution of wind-driven flows in the Yellow Sea during winter 7 2.1. Introduction 7 2.2. Model configuration 8 2.3. Model validation 10 2.4. Model results 13 2.4.1 Mean flow in winter 13 2.4.2 Temporal variation of the YSWC 15 2.4.3 Correlation between northwesterly wind and meridional flow 19 2.4.4 Correlation between northwesterly wind and zonal flow 23 2.4.5 Evolution of actual flow in response to northwesterly wind burst 25 2.5. Summary 29 3. Numerical investigation of the generation of continental shelf waves and their role in the westward shift of the YSWC 31 3.1. Introduction 31 3.2. Model configuration 33 3.3. Results and Discussion 36 3.3.1 Westward shift of upwind flow with equatorward propagation of CSWs along the Chinese coast 36 3.3.2 Characteristics of CSWs driving westward shifting 42 3.3.3 Generation of CSWs driving the westward shift of the YSWC 47 3.3.4 Effects of periodic wind and the Shandong Peninsula on the westward shift of the upwind flow 57 3.3.5 Model application to realistic topography in the YS 64 3.4. Conclusion 71 4. Contribution of the YSWC to nitrate flux in the YS based on a 3-D physical-biogeochemical coupled model 73 4.1. Introduction 73 4.2. Data and Model configuration 75 4.3. Results and Discussion 86 4.3.1 Seasonal variations in temperature, salinity, chlorophyll, and NO3 in the YS 86 4.3.2 NO3 fluxes in the ECS 92 4.3.3 Water mass analysis to figure out the sources of NO3 94 4.3.4 Contribution of each origin of NO3 in the YS 97 4.3.5 Estimation of NO3 flux by the YSWC 103 4.3.6 Limitations of this study and future works 108 4.4. Conclusion 109 5. Summary and conclusion 112 References 115 Abstract (in Korean) 138Docto

    PICES-GLOBEC International Program On Climate Change And Carrying Capacity: Report of the 2000 BASS, MODEL, MONITOR and REX workshops, and the 2001 BASS/MODEL workshop

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    Table of Contents [pdf, 0.07 Mb] Executive Summary [pdf, 0.05 Mb] Report of the 2000 BASS Workshop on The Development of a conceptual model of the Subarctic Pacific basin ecosystems [pdf, 0.71 Mb] Report of the 2000 MODEL Workshop on Strategies for coupling higher and lower trophic level marine ecosystem models [pdf, 3.62 Mb] Report of the 2000 MONITOR Workshop on Progress in monitoring the North Pacific [pdf, 1.21 Mb] Report of the 2000 REX Workshop on Trends in herring populations and trophodynamics [pdf, 4.22 Mb] Report of the 2001 BASS/MODEL Workshop on Higher trophic level modeling [pdf, 0.29 Mb] (Document pdf contains 119 pages
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