1,397 research outputs found

    Report of Working Group 26 on Jellyfish Blooms around the North Pacific Rim: Causes and Consequences

    Get PDF

    ๊ฒจ์šธ์ฒ  ํ™ฉํ•ด ๋‚œ๋ฅ˜ ํ˜•์„ฑ ๋ฐ ๋‚œ๋ฅ˜๋ฅผ ํ†ตํ•œ ํ™ฉํ•ด ๋‚ด ์งˆ์‚ฐ์—ผ ์œ ์ž…์— ๊ด€ํ•œ ์ˆ˜์น˜ ์‹คํ—˜ ์—ฐ๊ตฌ

    Get PDF
    ํ•™์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

    Transport of FNPP1-derived radiocaesium from subtropical mode water in the western North Pacific Ocean to the Sea of Japan

    Get PDF
    This study investigated the spatio-temporal variations in activity concentrations in the Sea of Japan (SOJ) of 137Cs and these transport process from the North Pacific Ocean to the SOJ through the East China Sea (ECS) during 2012โ€“2016. The 137Cs activity concentrations in the SOJ have been increasing since 2012โ€“2013 and reached a maximum in 2015โ€“2016 of approximately 3.4&thinsp;Bq&thinsp;mโˆ’3, more than twice the pre-Fukushima accident 137Cs activity concentration of โ€‰โˆผโ€‰1.5&thinsp;Bq&thinsp;mโˆ’3. The 134Cs&thinsp;โˆ•&thinsp;137Cs activity ratios ranged from 0.36 to 0.51 in 2016. After taking into account radioactive decay and ocean mixing, we concluded that these 134Cs&thinsp;โˆ•&thinsp;137Cs activity ratios were evidence that the Fukushima accident caused the increase in the 137Cs activity concentrations. In the North Pacific south of Japan (NPSJ), the highest 137Cs activities in 2012โ€“2013 were observed in water from a depth of 300&thinsp;m, the potential water density anomaly (ฯƒฮธ) of which corresponded to subtropical mode water (STMW). In the ECS, a clear increase in the 137Cs activity concentration started at a depth of 140&thinsp;m (ฯƒฮธโ€‰=โ€‰&thinsp;25.2&thinsp;kg&thinsp;mโˆ’3) in April 2013, propagated to the surface layers at depths of roughly 0โ€“50&thinsp;m, reached a maximum in 2015 and decreased in subsequent years. In the ECS, the Fukushima-derived radiocaesium activity concentration in surface water reached a maximum in 2014โ€“2015, whereas the concentration in the SOJ reached a maximum in 2015โ€“2016. The propagation of Fukushima-derived radiocaesium in surface seawater from the ECS into the SOJ therefore required approximately 1ย year. These temporal changes in 137Cs activity concentrations and 134Cs&thinsp;โˆ•&thinsp;137Cs activity ratios indicated that part of the 137Cs and 134Cs derived from the Fukushima accident (FNPP1-derived 137Cs and134Cs) was transported within several years to the ECS and then to the SOJ via STMW from the NPSJ. The integrated amount of FNPP1-derived 137Cs that entered the SOJ before 2016 was estimated to be 0.21ยฑ0.01&thinsp;PBq, 5.0&thinsp;% of the estimated total amount of FNPP1-derived 137Cs in the STMW. The integrated amount of FNPP1-derived 137Cs that returned to the North Pacific Ocean through the Tsugaru Strait was estimated to be 0.09ยฑ0.01&thinsp;Bq, 43&thinsp;% of the total amount of FNPP1-derived 137Cs transported to the SOJ and 2.1&thinsp;% of the estimated total amount of FNPP1-derived 137Cs in the STMW.</p

    Satellite-Based Fog Detection: A Dynamic Retrieval Method for Europe Based on Machine Learning

    Get PDF
    Fog has many economic as well as ecological impacts and it directly affects human life in many ways. The large number of fog influence factors shows that a comprehensive understanding of its causes and a precise mapping of the spatio-temporal distribution patterns are of great interest. Since there are justifiable concerns about the general applicability of existing fog retrieval methods, this thesis investigates new techniques of satellite based fog detection and the derivation of spatio-temporal information on fog distribution in Europe. The central novelties of this study are: - No static assumptions about microphysical properties were used during fog retrieval. - A novel hybrid approach based on machine learning methods was developed that can be continuously applied 24 hours a day. - The algorithm covers all fog types. Areas of different fog types could also be differentiated indirectly from the generated product due to their typical diurnal and annual frequency cycles. - For the first time, fog frequency maps for Europe could be produced for different general weather situations separately for each fog type

    Report of Working Group 29 on Regional Climate Modeling

    Get PDF

    Methane dynamics in the coastalโ€“Continental shelf transition zone of the Gulf of Cadiz

    Get PDF
    The concentration of CH4 in water was measured along five transects in the Gulf of Cadiz (Trafalgar, Sancti Petri, Guadalquivir, Tinto - Odiel and Guadiana) during four cruises throughout the months of March, June, September and December 2016. For two of the cruises water overlying three mud volcanoes situated in the Gulf of Cadiz were also sampled (San Petersburgo, 860 m; Pipoca, 460 m; and Anastasya, 500 m. In addition, the stable carbon isotopic compositions of dissolved CH4 (ฮด13C) were measured in the study area in June and December 2016. The mean CH4 value for the whole water column was 10.5 ยฑ 4.3 nmol Lโˆ’1, with large spatial and temporal variations. The highest values were found in June 2016 and the lowest in March 2016. In surface waters, the mean dissolved methane concentration was of 9.6 ยฑ 2.6 nmol Lโˆ’1. In most of the sampling area, high concentrations of CH4 were found in subsurface waters at depths close to the thermocline and at the coastal stations. The highest concentrations were obtained from the bottom waters above the Anastasya mud volcano (125 nmol Lโˆ’1). The stable carbon isotope compositions ranged between - 29.2 and - 58.4โ€ฐ, and the least negative values were associated with the highest CH4 concentrations in samples from above the mud volcanoes, related to thermogenic values. The sea-air fluxes of CH4 ranged from 12.4 to 37.7 ฮผmol mโˆ’2 dโˆ’1, showing that the study area acts as a source of CH4 to the atmosphere

    Marine Nitrogen Fixation and Phytoplankton Ecology

    Get PDF
    Many oceans are currently undergoing rapid changes in environmental conditions such as warming temperature, acidic water condition, coastal hypoxia, etc. These changes could lead to dramatic changes in the biology and ecology of phytoplankton and consequently impact the entire marine ecosystems and global biogeochemical cycles. Marine phytoplankton can be an important indicator for the changes in marine environments and ecosystems since they are major primary producers that consolidate solar energy into various organic matter transferred to marine ecosystems throughout the food-webs. Similarly, the N2 fixers (diazotrophs) are also vulnerable to changing environmental conditions. It has been found that the polar regions can be introduced to diazotrophic activity under warming conditions and the increased N availability can lead to elevated primary productivity. Considering the fundamental roles of phytoplankton in marine ecosystems and global biogeochemical cycles, it is important to understand phytoplankton ecology and N2 fixation as a potential N source in various oceans. This Special Issue provides ecological and biogeochemical baselines in a wide range of geographic study regions for the changes in marine environments and ecosystems driven by global climate changes

    Do bacteria thrive when the ocean acidifies? Results from an off-ยญshore mesocosm study

    Get PDF
    Marine bacteria are the main consumers of the freshly produced organic matter. In order to meet their carbon demand, bacteria release hydrolytic extracellular enzymes that break down large polymers into small usable subunits. Accordingly, rates of enzymatic hydrolysis have a high potential to affect bacterial organic matter recycling and carbon turnover in the ocean. Many of these enzymatic processes were shown to be pH sensitive in previous studies. Due to the continuous rise in atmospheric CO2 concentration, seawater pH is presently decreasing at a rate unprecedented during the last 300 million years with so-far unknown consequences for microbial physiology, organic matter cycling and marine biogeochemistry. We studied the effects of elevated seawater pCO2 on a natural plankton community during a large-scale mesocosm study in a Norwegian fjord. Nine 25m-long Kiel Off-Shore Mesocosms for Future Ocean Simulations (KOSMOS) were adjusted to different pCO2 levels ranging from ca. 280 to 3000 ยตatm by stepwise addition of CO2 saturated seawater. After CO2 addition, samples were taken every second day for 34 days. The first phytoplankton bloom developed around day 5. On day 14, inorganic nutrients were added to the enclosed, nutrient-poor waters to stimulate a second phytoplankton bloom, which occurred around day 20. Our results indicate that marine bacteria benefit directly and indirectly from decreasing seawater pH. During both phytoplankton blooms, more transparent exopolymer particles were formed in the high pCO2 mesocosms. The total and cell-specific activities of the protein-degrading enzyme leucine aminopeptidase were elevated under low pH conditions. The combination of enhanced enzymatic hydrolysis of organic matter and increased availability of gel particles as substrate supported higher bacterial abundance in the high pCO2 treatments. We conclude that ocean acidification has the potential to stimulate the bacterial community and facilitate the microbial recycling of freshly produced organic matter, thus strengthening the role of the microbial loop in the surface ocean
    • โ€ฆ
    corecore