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    ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€,2020. 2. ์†์„์šฐ.์ตœ๊ทผ ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋ถ๋ฐ˜๊ตฌ ๊ฒจ์šธ์ฒ ์— ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์ด ์ œ์‹œ๋œ ๋ฐ” ์žˆ๋‹ค. ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋™ํ’์ผ ๋•Œ, ๊ฒจ์šธ์ฒ  ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ๋Œ€๋ฅ˜ ํ™œ๋™์ด ์„œํ’์ผ ๋•Œ์— ๋น„ํ•ด ํ™œ๋ฐœํ•ด์ง€๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋Š”๋ฐ, ์ด๋Š” ์ตœ๊ทผ์— ๋ฐœํ‘œ๋œ ์—ฐ๊ตฌ๋กœ์จ ๊ตฌ์ฒด์ ์ธ ํ˜„์ƒ๊ณผ ๊ทธ ์›์ธ์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ๋ถ€์กฑํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ํ˜„์—… ์˜ˆ์ธก ๋ชจํ˜•์˜ ๊ณ„์ ˆ๋‚ด ์‹œ๊ฐ„ ๊ทœ๋ชจ์˜ ์˜ˆ์ธก์„ฑ ํ–ฅ์ƒ์—๋„ ์ง์ ‘์ ์œผ๋กœ ์—ฐ๊ด€๋˜์–ด ์žˆ๋‹ค๋Š” ์ ์—์„œ ์ด ํ˜„์ƒ์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์ด์— ๋”ฐ๋ผ, ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์€ ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ทธ ์›์ธ์„ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์„ ๋ฐ”ํƒ•์œผ๋กœ ์ดํ•ดํ•ด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ด€์ธก ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์˜ ์œ„์ƒ์— ๋”ฐ๋ผ ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ํ™œ๋™ ํŠน์„ฑ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ์›๊ฒฉ ์ƒ๊ด€์„ฑ๊นŒ์ง€ ๋ณ€ํ™”ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋™ํ’์ผ ๋•Œ, ๋‹จ์ˆœํžˆ ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ๋Œ€๋ฅ˜ ํ™œ๋™ ๊ฐ•๋„๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋Œ€๋ฅ˜ ํ™œ๋™์˜ ๋™์ง„ ์†๋„๊ฐ€ ๋Š๋ ค์ง€๊ณ  ์ง€์†๊ธฐ๊ฐ„์ด ๊ธธ์–ด์ง€๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์œผ๋ฉฐ, ๋” ๋‚˜์•„๊ฐ€, ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ์ค‘์œ„๋„ ์›๊ฒฉ์ƒ๊ด€์„ฑ ๊ฐ•๋„๊นŒ์ง€ ๊ฐ•ํ™”์‹œํ‚ค๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์— ๋”ฐ๋ฅธ ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ์ฒด๊ณ„์ ์ธ ๋ณ€ํ™”๋Š” ํ˜„์ƒ์— ๋Œ€ํ•œ ๋‹น์œ„์„ฑ์„ ๋†’์ž„๊ณผ ๋™์‹œ์— ์ด์— ๋Œ€ํ•œ ๊ฒ€์ฆ ๋ฐ ์ดํ•ด์˜ ํ•„์š”์„ฑ์„ ๋”์šฑ ๋†’์˜€๋‹ค. ๋”ฐ๋ผ์„œ, ๋‹ค์–‘ํ•œ ์˜ˆ์ธก ๋ชจํ˜•์„ ํ™œ์šฉํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ํ˜„์ƒ์„ ์ดํ•ดํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™๊ณผ ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์„ ์ง์ ‘ ๋ชจ์˜ํ•˜๋Š” ๊ธฐํ›„ ๋ชจํ˜•์„ ์ด์šฉํ•˜์—ฌ ํ˜„์ƒ์ด ์กด์žฌํ•˜๋Š” ์ง€ ๊ฒ€์ฆํ•ด๋ณด๊ณ ์ž ํ•˜์˜€๊ณ , ๋Œ€๋ถ€๋ถ„์˜ ๋ชจํ˜•์—์„œ ๋ชจ์˜ํ•˜์ง€ ๋ชปํ•˜์˜€์œผ๋‚˜, ํ•˜๋‚˜์˜ ๊ธฐํ›„๋ชจํ˜•์—์„œ ์œ ์ผํ•˜๊ฒŒ ๊ด€์ธก์— ๋น„ํ•ด์„œ๋Š” ์•ฝํ•˜์ง€๋งŒ ํ˜„์ƒ์„ ๋ชจ์˜ํ•˜์˜€๋‹ค. ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์›์ธ์œผ๋กœ ํฌ๊ฒŒ ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์— ๋”ฐ๋ฅธ ๋Œ€๋ฅ˜๊ถŒ ์ƒ๋ถ€์™€ ์„ฑ์ธต๊ถŒ ํ•˜๋ถ€ ๊ฐ„ ๋™์„œ๋ฐฉํ–ฅํ‰๊ท  ์ •์  ์•ˆ์ •๋„์˜ ๋ณ€ํ™”, ๊ทธ๋ฆฌ๊ณ  ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ์—ฐ์ง ๊ตฌ์กฐ ๋ณ€ํ™”๋กœ ์ธํ•œ ์ •์ ์•ˆ์ •๋„์˜ ์ง€์—ญ์  ๋ณ€ํ™”๊ฐ€ ์ฃผ์š” ์›์ธ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋Š”๋ฐ, ๊ธฐํ›„ ๋ชจํ˜•์—์„œ ์ด๋ฅผ ์•ฝํ•˜์ง€๋งŒ ๋ชจ์˜ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ  ํ˜„์ƒ์„ ๋ชจ์˜ํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ–ˆ์„ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ๋Š” ์—ญํ•™์ฝ”์–ด๋ชจํ˜•์„ ๋ฐ”ํƒ•์œผ๋กœ ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ์—ฐ์ง ๊ตฌ์กฐ ๋ณ€ํ™”์— ์—ญํ•™์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š”์ง€ ๊ฒ€์ฆํ•ด๋ณด์•˜๊ณ , ์ •์  ์•ˆ์ •๋„ ์ด์™ธ์—๋„ ๋‹ค๋ฅธ ์—ญํ•™์  ์˜ํ–ฅ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ 10๊ฐœ์˜ ํ˜„์—… ๊ธฐ๊ด€ ์˜ˆ์ธก ๋ชจํ˜•์„ ๋ฐ”ํƒ•์œผ๋กœ ํ˜„์ƒ์— ๋Œ€ํ•ด ๊ฒ€์ฆํ•ด๋ณธ ๊ฒฐ๊ณผ, ํ˜„์ƒ์ด ๋ชจ์˜๋  ๋ฟ ์•„๋‹ˆ๋ผ ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ์˜ˆ์ธก์„ฑ์—๋„ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ชจํ˜•์— ๋”ฐ๋ผ ์ฐจ์ด๊ฐ€ ์žˆ์œผ๋‚˜, ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋™ํ’์ผ ๋•Œ, ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ์˜ˆ์ธก์„ฑ์ด 1-10์ผ ๋” ๋†’์€ ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ํ˜„์žฌ๊นŒ์ง€ ์—ฌ๋Ÿฌ ๋ชจํ˜•์—์„œ ๋‘ ํ˜„์ƒ์˜ ์ƒ๊ด€์„ฑ์„ ์•ฝํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ด๊ธด ํ•˜์˜€์œผ๋‚˜, ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋ชจํ˜•์—์„œ ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™๊ณผ ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™ ํ˜„์ƒ ๊ฐ๊ฐ์„ ๋ชจ์˜ํ•˜๋Š” ๋ฐ ํ•œ๊ณ„๊ฐ€ ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, ๊ด€์ธก์—์„œ ๋‚˜ํƒ€๋‚œ ๋งŒํผ ๋‘ ํ˜„์ƒ์˜ ๋†’์€ ์ƒ๊ด€์„ฑ์„ ๋ชจ์˜ํ•˜๊ธฐ์—๋Š” ์–ด๋ ค์›€์ด ์žˆ์—ˆ๋‹ค. ์ด๋ฅผ ํ–ฅํ›„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ ์ค‘ ํ•˜๋‚˜๋กœ์จ, ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ์ฃผ์š” ๋ฌผ๋ฆฌ ๊ณผ์ •์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ตฌ๋ฆ„-๋ณต์‚ฌ ํ”ผ๋“œ๋ฐฑ ์ž‘์šฉ๊ณผ ์ˆ˜๋ถ„์˜ ํ‰๊ท ์  ๊ณต๊ฐ„ ๋ถ„ํฌ ๋ชจ์˜ ๋Šฅ๋ ฅ์ด ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ๋ชจ์˜์— ์ค‘์š”ํ•œ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌผ๋ฆฌ ๊ณผ์ •์˜ ๋ชจ์˜ ๋Šฅ๋ ฅ์ด ํ–ฅ์ƒ๋œ๋‹ค๋ฉด, ํ–ฅํ›„ ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™๊ณผ ๋งค๋“ -์ค„๋ฆฌ์•ˆ ์ง„๋™์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ์ดํ•ดํ•˜๊ณ  ์˜ˆ์ธก์„ฑ ํ–ฅ์ƒ์—๋„ ํฌ๊ฒŒ ๊ธฐ์—ฌํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Recent studies have shown that the Quasi-Biennial Oscillation (QBO) affects the boreal winter Madden-Julian Oscillation (MJO). During the easterly phase of QBO (EQBO) winters, the MJO activity is amplified, and the opposite is shown during the westerly phase of QBO (WQBO) winters. Since this relationship is very recently reported with simple correlation analysis, it should be confirmed and understood in detail. This thesis is to investigate the QBO-MJO connection using a variety of datasets, such as the observations, dynamical core model, climate models, and subseasonal-to-seasonal (S2S) prediction models. Their possible mechanism(s) and the impacts on the MJO prediction are also evaluated and discussed. In the observational study, it is shown that the overall MJO characteristics are closely linked with the stratospheric QBO. The MJO activity around the Maritime Continent becomes stronger and more organized during EQBO than during WQBO winters. The QBO-related MJO change explains up to 40% of the interannual variation of the boreal winter MJO amplitude. During EQBO winters, the MJO convections propagate further eastward with a slower propagation, and more enhanced MJO teleconnection is also presented. These systematic changes in MJO activity confirm the QBO-MJO connection, emphasizing the stratospheric impact on the MJO. Due to the short analysis period of the observational data, the model outputs are helpful for a better understanding of this phenomenon. In the climate models, however, a weak hint of the QBO-MJO link is found only in the medium-resolution Max Planck Institute Earth System Model (MPI-ESM-MR) among four CMIP5 models that internally generate the QBO. In this model, the MJO anomalies become slightly stronger and more organized during EQBO than during WQBO winters. Overall differences, however, are still much weaker and less organized than the observation. When daily MJO-index amplitude is compared, their differences are not robust. The reasons for weak QBO-MJO connection might result from the weak QBO and MJO amplitudes, and weak static stability change in response to the QBO in the model. To better simulate the QBO structure and to examine the dynamical process, the QBO-MJO connection is tested in an idealized experiment using a dynamical core model. It is found that the QBO can directly change the MJO-related vertical structure. The MJO-induced cold anomaly near the tropopause becomes colder, especially over the western Pacific in the EQBO-like experiment, which promotes the MJO activity. This result seems to be related to the Doppler shift effect by the QBO-related zonal wind, suggesting the potential impact of the dynamical process on the QBO-MJO connection. Considering both of dynamical and physical processes with a better QBO simulation, the capability of the QBO-MJO connection is evaluated in the S2S prediction models. Their relationship is also applied in the MJO prediction skill. Ten operational models participated in the S2S prediction project show a higher MJO prediction skill during EQBO winters than during WQBO winters, based on the QBO-MJO link. For the bivariate anomaly correlation coefficient of 0.5, the MJO prediction skill during EQBO winters is enhanced byย up to 10 days. This enhancement is insensitive to the initial MJO amplitude, indicating thatย the improved MJO prediction skill is not simply the result of aย stronger MJO. Instead, a longer persistence of theย MJO during EQBO winters likely induces a higher prediction skill by having a higher prediction limit. Even though the QBO modulates the MJO prediction skill, the QBO-MJO connection is not fully captured even in the S2S prediction models. To improve the simulation of the QBO-MJO connection in these models, the relationship of MJO prediction skill with model biases in the mean moisture fields and the longwave cloudโ€“radiation feedbacks are investigated, based on understanding the MJO processes. In most models, a notable dry bias develops within a few days of forecast lead time in the deep tropics, especially across the Maritime Continent. The dry bias weakens the horizontal moisture gradient over the Indian Ocean and western Paci๏ฌc, likely dampening the organization and propagation of the MJO. Most S2S models also underestimate the longwave cloudโ€“radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelope. In the S2S prediction project, the operational models with smaller bias in the mean horizontal moisture gradient and the longwave cloudโ€“radiation feedbacks show higher MJO prediction skills, suggesting that improving those biases would enhance MJO prediction skill and the simulation of the QBO-MJO connection.1. Introduction 1 2. QBO-MJO connection: observational features 7 2.1. Data and methods 7 2.2. Interannual variation of seasonal-mean tropical convection by the ENSO 11 2.3. Interannual modulation of subseasonal tropical convective activity by the QBO 15 2.3.1. MJO characteristics with the QBO 19 2.3.2. MJO teleconnection with the QBO 24 2.3.3. Lead-lag relationship 26 2.3.4. Seasonality 28 2.3.5. Possible mechanism(s) of the QBO-MJO connection 29 3. QBO-MJO connection in climate models 34 3.1. Data and methods 34 3.2. QBO and MJO simulations in CMIP5 models 38 3.3. QBO-MJO connection in MPI-ESM-MR simulations 44 4. A possible mechanism of the QBO-MJO connection 59 4.1. Model description and experimental design 59 4.2. Model results 63 5. QBO-MJO connection in the S2S prediction models 71 5.1. Data and methods 71 5.1.1. Data 71 5.1.2. Evaluation metrics 76 5.2. QBO prediction skill in S2S prediction models 77 5.3. MJO prediction skill with QBO 82 5.3.1. Sensitivity to initial MJO amplitude 91 5.3.2. Sensitivity to initial MJO phase 94 5.3.3. Limiting factors of MJO prediction skill 96 6. MJO prediction skill in the S2S prediction models: for improving the simulation of the QBO-MJO connection 102 6.1. Data and methods 102 6.1.1. Data 102 6.1.2. Evaluation metrics 103 6.2. MJO prediction skill 108 6.3. Mean-state biases and their impact on MJO prediction skill 125 6.3.1. Mean moisture field 126 6.3.2. Cloud-longwave radiation feedback 132 7. Summary and discussions 136 References 142 Abstract (Korean) 153Docto

    Impact of Economic Crisis on Depression and Self-Rated Health

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌํ•™์ „๊ณต),2019. 8. ์ดํƒœ์ง„.๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ๊ฐœ์ธ์˜ ์šฐ์šธ๊ณผ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ฐํžˆ๋Š” ๋ฐ ๊ทธ ๋ชฉ์ ์ด ์žˆ๋‹ค. ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๋Š” 1997๋…„ ์™ธํ™˜์œ„๊ธฐ์™€ ๋น„๊ตํ•˜์—ฌ ๊ทธ ์˜ํ–ฅ๋ ฅ์ด ๋น„๊ต์  ํฌ์ง€ ์•Š์•˜์œผ๋‚˜ ์ง€์—ญ ๊ฐ„ ๋ณ€์ด๋Š” ํฌ๊ฒŒ ๋ฐœ์ƒํ•˜์˜€๋‹ค. ๊ธˆ์œต์œ„๊ธฐ์˜ ์˜ํ–ฅ์€ ๋„์‹œ๋‚˜ ์ง€์—ญ, ์ธ๊ตฌ ๊ทœ๋ชจ, ๋…ธ๋™๋ ฅ์˜ ์ˆ˜์ค€, ์‚ฐ์—… ๋“ฑ์— ๋”ฐ๋ผ ์„œ๋กœ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ์˜ ์˜ํ–ฅ์ด ์ง€์—ญ๋ณ„๋กœ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค๋Š” ์ ์„ ํ™œ์šฉํ•˜์—ฌ ์ง€์—ญ ๊ฐ„ ๊ฐœ์ธ์˜ ์šฐ์šธ๊ณผ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ ๋ณ€ํ™”๋ฅผ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ถ„์„์„ ์œ„ํ•ด ํ•œ๊ตญ๋ณต์ง€ํŒจ๋„ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ 20์„ธ ์ด์ƒ ๊ฐœ์ธ์„ ์—ฐ๊ตฌ ๋Œ€์ƒ์œผ๋กœ ์ •์˜ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ์™€ ๊ฐœ์ธ์˜ ๊ฑด๊ฐ• ๊ฒฐ๊ณผ ์‚ฌ์ด์˜ ์ธ๊ณผ์„ฑ์„ ๋ฐํžˆ๊ธฐ ์œ„ํ•ด ์ค€์‹คํ—˜์„ค๊ณ„ ์—ฐ๊ตฌ์ธ ์ด์ค‘์ฐจ์ด๋ถ„์„(Difference-in-Differences)์„ ํ™œ์šฉํ•˜์—ฌ ๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ์šฐ์šธ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ ๋ถ€๋ถ„์ ์œผ๋กœ ๊ทธ ์˜ํ–ฅ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์šฐ์šธ์˜ ๊ฒฝ์šฐ ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ ๋ฐœ์ƒ ์ „ํ›„๋ฅผ ๋น„๊ต ๋ถ„์„ํ–ˆ์„ ๊ฒฝ์šฐ ์˜ํ–ฅ์„ ๋ฐ›์€ ์ง€์—ญ ์—ฌ๋ถ€์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜์œผ๋‚˜ 7๊ฐœ ๊ถŒ์—ญ ๊ตฌ๋ถ„ ์ค‘ ์˜ํ–ฅ์ด ํ˜ผ์žฌ๋˜์–ด ์žˆ์ง€ ์•Š์€ ์ง€์—ญ์„ ๋”ฐ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋ถ„์„ํ–ˆ์„ ๊ฒฝ์šฐ ๋Œ€๊ตฌ/๊ฒฝ๋ถ์— ๊ฑฐ์ฃผํ•˜๋Š” ๊ฐœ์ธ์ด ๊ด‘์ฃผ/์ „๋‚จ/์ „๋ถ/์ œ์ฃผ ์ง€์—ญ์— ๊ฑฐ์ฃผํ•˜๋Š” ๊ฐœ์ธ๋ณด๋‹ค ์šฐ์šธํ•  ๊ฐ€๋Šฅ์„ฑ์ด ๋” ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋Œ€๊ตฌ/๊ฒฝ๋ถ ์ง€์—ญ์€ ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ ๋‹น์‹œ ์‹ค์งˆ ์ง€์—ญ๋‚ด์ด์ƒ์‚ฐ์ด ๋งˆ์ด๋„ˆ์Šค ์„ฑ์žฅ๋ฅ ์„ ๊ธฐ๋กํ•œ ์ง€์—ญ์œผ๋กœ์„œ ๊ด‘์ฃผ/์ „๋‚จ/์ „๋ถ/์ œ์ฃผ ์ง€์—ญ๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ์˜ ์˜ํ–ฅ์„ ๋น„๊ต์  ๋งŽ์ด ๋ฐ›์€ ์ง€์—ญ์œผ๋กœ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€ํ‘œ๊ฐ€ ์•…ํ™”๋  ๊ฒฝ์šฐ ๊ฐœ์ธ์˜ ์ •์‹ ๊ฑด๊ฐ•์ด ์•…ํ™”๋  ๊ฐ€๋Šฅ์„ฑ์ด ์กด์žฌํ•œ๋‹ค๋Š” ๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ฃผ๊ด€์  ๊ฑด๊ฐ•์˜ ๊ฒฝ์šฐ ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๋กœ ์ธํ•œ ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์ด ๋”์šฑ ๋ถ„๋ช…ํ•˜๊ฒŒ ๋“œ๋Ÿฌ๋‚ฌ๋‹ค. ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ ๋ฐœ์ƒ ์ „ํ›„๋ฅผ ๋น„๊ตํ–ˆ์„ ๋•Œ ๋ชจ๋‘ ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๋กœ ์˜ํ–ฅ์„ ๋” ํฌ๊ฒŒ ๋ฐ›์€ ์ง€์—ญ์—์„œ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ด ์ข‹์ง€ ์•Š์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋” ๋†’์•˜๋‹ค. ๋˜ํ•œ ์„œ์šธ์„ ๊ธฐ์ค€(reference)์œผ๋กœ 7๊ฐœ ๊ถŒ์—ญ์„ ๋น„๊ตํ•œ ๋ถ„์„์—์„œ๋„ ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ ์ „ํ›„ ๋Œ€๊ตฌ/๊ฒฝ๋ถ์˜ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ด ์ข‹์ง€ ์•Š์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋” ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์•ž์˜ ๋ถ„์„๊ณผ ๋™์ผํ•˜๊ฒŒ ์˜ํ–ฅ์ด ํ˜ผ์žฌ๋˜์–ด ์žˆ์ง€ ์•Š์€ ์ง€์—ญ๋งŒ์„ ๋”ฐ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋ถ„์„ํ–ˆ์„ ๊ฒฝ์šฐ์—๋„ ๋Œ€๊ตฌ/๊ฒฝ๋ถ์— ๊ฑฐ์ฃผํ•˜๋Š” ๊ฐœ์ธ์ด ์„œ์šธ, ๋Œ€์ „/์ถฉ๋‚จ, ๊ด‘์ฃผ/์ „๋‚จ/์ „๋ถ/์ œ์ฃผ์— ๊ฑฐ์ฃผํ•˜๋Š” ๊ฐœ์ธ๋ณด๋‹ค ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ด ์ข‹์ง€ ์•Š์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ๊ฐœ์ธ์˜ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์œผ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ๊ธˆ์œต์œ„๊ธฐ ์˜ํ–ฅ ์ง€์—ญ ๊ฑฐ์ฃผ ์—ฌ๋ถ€์— ๋”ฐ๋ผ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ์ฐจ์ด๊ฐ€ ์กด์žฌํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋กœ ๋„์ถœ๋œ ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ ์˜ํ–ฅ ์ง€์—ญ ๊ฐ„ ์šฐ์šธ๊ณผ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์˜ ์ฐจ์ด๋Š” ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ๊ฐœ์ธ์˜ ์šฐ์šธ๊ณผ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์š”์ธ์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ๊ฐœ์ธ์˜ ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ™•์ธํ–ˆ๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง€๋ฉฐ ๊ธˆ์œต์œ„๊ธฐ๋กœ ์ธํ•ด ๊ฐœ์ธ์˜ ์šฐ์šธ๊ณผ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ๋ณ€ํ™”๊ฐ€ ์กด์žฌํ•˜์˜€์Œ์„ ์‹ค์ฆ์ ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค๋Š” ๊ฒƒ์— ์˜์˜๊ฐ€ ์žˆ๋‹ค. ๊ฒฝ์ œ์œ„๊ธฐ๋ผ๋Š” ๋ถˆํ™•์‹ค์„ฑ์ด ๊ฐœ์ธ์˜ ๊ฑด๊ฐ•์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๊ณ  ์ง€์—ญ๋ณ„๋กœ ๊ฑด๊ฐ• ๊ฒฐ๊ณผ์— ์ฐจ์ด๊ฐ€ ์กด์žฌํ•  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ์ง€์—ญ ๊ฐ„ ๊ฑด๊ฐ• ๊ฒฉ์ฐจ๋ฅผ ํ•ด์†Œํ•˜๊ธฐ ์œ„ํ•ด ์ •์ฑ…์„ ์ˆ˜๋ฆฝํ•˜๋Š” ๋‹จ๊ณ„์—์„œ ์ง€์—ญ๋ณ„ ํŠน์„ฑ์„ ๊ณ ๋ คํ•  ํ•„์š”์„ฑ์ด ์ œ๊ธฐ๋œ๋‹ค.The purpose of this study is to identify the effects of the financial crisis on depression and self-rated health. Compared to the 1997 financial crisis, the 2008 financial crisis has not had a quite noticeable impact on the economy. However, interregional disparities have widened during this period. The impacts of the financial crisis vary depending on the cites, regions, density of population, labor force characteristics and industry characteristics. Therefore, this study used such variations to estimate the changes in individuals depression and self-rated health. This study used the Korean Welfare Panel data in 2006 to 2009, and individuals aged over 20 were included in the analysis. The difference-in-differences method was employed to prove causality between the financial crisis and health outcomes. Depression level was not significantly different before and after the financial crisis. But, the impacts of the financial crisis on depression were partially identified. Comparing the regions based on the degree of the effects of the financial crisis, individuals living in Daegu/Gyeongbuk were more likely to be depressed than those living in Gwangju/Jeonnam/Jeonbuk/Jeju. The Daegu/Gyeongbuk province is the region where the real GRDP(Gross Regional Domestic Product) growth rate was negative during the financial crisis. Compared with the Gwangju/Jeonnam/Jeonbuk/Jeju area, Daegu/Gyeongbuk can be classified as a region that is highly affected by the financial crisis. These results suggested that there is a possibility that the deterioration of the socioeconomic indicators may have negative effects on the depression level of individuals. In the case of self-rated health, the negative effects of the financial crisis are more evident. The odds ratios of poor self-rated health were higher in regions that are affected by the financial crisis. Also, the second analysis by region showed that the odds ratios of poor health were higher in Daegu/Gyeongbuk. When analyzing the regions where the effects of the financial crisis are not mixed, individuals living in Daegu/Gyeongbuk were more likely to have poor self-rated health than those living in Seoul, Daejeon/Chungnam, Gwangju/Jeonnam/Jeonbuk/ Jeju. These results showed that the financial crisis can lead to changes in self-rated health and the effects of the financial crisis were heterogeneous among the regions. The results of this study demonstrated that the financial crisis is a factor that can affect individuals depression and self-rated health. Also, this study provided empirical evidence of change in depression and self-rated health caused by the financial crisis. The results of this study indicated that there are differences in health outcomes between regions. Therefore, it is necessary to consider regional characteristics at the stage of formulating health policy to eliminate regional health disparities.I. ์„œ๋ก  1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 2. ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ์—ฐ๊ตฌ ์งˆ๋ฌธ II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 1. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 1.1 ๊ธˆ์œต์œ„๊ธฐ์˜ ์ •์˜ 1.2 ๊ฒฝ์ œ์œ„๊ธฐ์™€ ๊ฑด๊ฐ• 2. ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 2.1 ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ์šฐ์šธ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 2.2 ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ III. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 1. ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ์ž๋ฃŒ์› 2. ๋ถ„์„๋ฐฉ๋ฒ• 2.1 ์—ฐ๊ตฌ๋ชจํ˜• 2.2 ๋ถ„์„๋ชจํ˜• 2.3 ๋ณ€์ˆ˜์ •์˜ IV. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 1. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ 1.1 ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ(2007๋…„, 2008๋…„) 1.2 ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ(2007๋…„, 2009๋…„) 2. ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ์šฐ์šธ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 2.1 ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ์šฐ์šธ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 2.2 ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ์šฐ์šธ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ(์ง€์—ญ ๊ตฌ๋ถ„) 3. ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 3.1 ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 3.2 ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ๊ฐ€ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ(์ง€์—ญ ๊ตฌ๋ถ„) 4. ์ง€์—ญ๋ณ„ ๋น„๊ต 4.1 ๋Œ€๊ตฌ/๊ฒฝ๋ถ, ์„œ์šธ ๋น„๊ต 4.2 ๋Œ€๊ตฌ/๊ฒฝ๋ถ, ๋Œ€์ „/์ถฉ๋‚จ ๋น„๊ต 4.3 ๋Œ€๊ตฌ/๊ฒฝ๋ถ, ๊ด‘์ฃผ/์ „๋‚จ/์ „๋ถ/์ œ์ฃผ ๋น„๊ต 5. ๊ฐ•๊ฑด์„ฑ ๊ฒ€์ฆ V. ๊ณ ์ฐฐ ๋ฐ ๊ฒฐ๋ก  ์ฐธ๊ณ ๋ฌธํ—Œ ๋ถ€๋ก AbstractMaste

    ์„ฑ์ธต๊ถŒ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์˜ ๋ถํƒœํ‰์–‘ ๋Œ€๋ฅ˜๊ถŒ ์ˆœํ™˜์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ์˜ˆ์ธก์„ฑ ํ–ฅ์ƒ ๊ธฐ์—ฌ๋„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€, 2015. 2. ์†์„์šฐ.์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์€ ์„ฑ์ธต๊ถŒ์˜ ๋Œ€ํ‘œ์ ์ธ ์žฅ์ฃผ๊ธฐ ์ž์—ฐ๋ณ€๋™์„ฑ์œผ๋กœ์จ, ์—ด๋Œ€ ์„ฑ์ธต๊ถŒ ๋™์„œ๋ฐ”๋žŒ์˜ ๋ฐฉํ–ฅ์ด ๋™ํ’์—์„œ ์„œํ’์œผ๋กœ ๋ฒˆ๊ฐˆ์•„ ๋‚˜ํƒ€๋‚˜๋Š” ํ˜„์ƒ์ด๋‹ค. ์„ฑ์ธต๊ถŒ ์žฅ์ฃผ๊ธฐ ๋ณ€๋™์„ฑ๊ณผ ๊ด€๋ จ๋œ ๋ชจ๋ฉ˜ํ…€์€ ๊ณ ์œ„๋„๋กœ ์ด๋™ํ•˜๋ฉด์„œ, ์•„์—ด๋Œ€ ๋Œ€๋ฅ˜๊ถŒ์œผ๋กœ ๋‚ด๋ ค๊ฐ€๋Š” ํŠน์„ฑ์ด ์žˆ์–ด์„œ ์ง€ํ‘œ๋ฉด ๊ธฐํ›„๊นŒ์ง€ ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํŠนํžˆ ๋Šฆ๊ฒจ์šธ์— ๋™์•„์‹œ์•„ ์ง€์—ญ์˜ ์ง€ํ‘œ๊ธฐ์˜จ์ด ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋™ํ’์ผ ๋•Œ ํ‰๋…„๋ณด๋‹ค ๋”ฐ๋œปํ•œ ํŠน์ง•์ด ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™๊ณผ ๊ด€๋ จ๋œ ๊ธฐ์˜จ ๋ณ€ํ™”๋Š” ํ•œ๊ฒจ์šธ์— ์—˜๋‹ˆ๋‡จ ๋‚จ๋ฐฉ์ง„๋™์ด ๋™์•„์‹œ์•„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๋น„์Šทํ•˜๋‹ค. ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์ด ๋ถํƒœํ‰์–‘์ˆœํ™˜์— ์–ด๋–ป๊ฒŒ ์˜ํ–ฅ์„ ์ฃผ๋Š”์ง€ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด์„œ, ์—๋””-ํ‰๊ท ์žฅ ๋˜๋จน์ž„ํ˜„์ƒ๊ณผ ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™-๋Œ€๋ฅ˜ํ™œ๋™์˜ ์ƒํ˜ธ์ž‘์šฉ๊ณผ ๊ด€๋ จ๋œ ๋กœ์Šค๋น„ํŒŒ ํŠธ๋ ˆ์ธ์ด ๋ฐœ๋‹ฌํ•˜๋Š” ํ˜„์ƒ์„ ๋ฐ”ํƒ•์œผ๋กœ ์—ด๋Œ€ ์„ฑ์ธต๊ถŒ ๋™์„œ๋ฐ”๋žŒ์˜ ๋ฐฉํ–ฅ์— ๋”ฐ๋ฅธ ๋Œ€๋ฅ˜๊ถŒ ์ˆœํ™˜์žฅ์˜ ๋ณ€ํ™” ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•ด์„œ ์•Œ์•„๋ณด๊ณ ์ž ํ•œ๋‹ค. ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™๊ณผ ๊ด€๋ จ๋œ ์—ด๋Œ€์„ฑ์ธต๊ถŒ ๋™์„œ๋ฐ”๋žŒ์€ ํŒŒ๋™-ํ‰๊ท ์žฅ ์ƒํ˜ธ์ž‘์šฉ์— ์˜ํ•ด์„œ ๊ณ ์œ„๋„๋กœ ์ „ํŒŒํ•˜๋Š” ํŠน์ง•์ด ์žˆ๋‹ค. ์ค‘์œ„๋„ ์ด์ƒ์˜ ์œ„๋„๋Œ€์—์„œ ๊ฐ€์žฅ ๋šœ๋ ทํ•œ ์ž์—ฐ๋ณ€๋™์„ฑ์€ ์ œํŠธ์˜ ๋ณ€๋™์„ฑ์œผ๋กœ, ๋ถํƒœํ‰์–‘ ์ง€์—ญ์€ ํŠนํžˆ ์ œํŠธ์˜ ์ด๋™๊ณผ ๊ฐ•๋„๋ณ€ํ™”๊ฐ€ ๋™์‹œ์— ๋ฐœ์ƒํ•˜๋Š” ์ž์—ฐ๋ณ€๋™์„ฑ์„ ๊ฐ–๋Š” ์ง€์—ญ์ด๋‹ค. ๋ณ€๋™-์†Œ์‚ฐ ์ด๋ก ์— ์˜ํ•ด์„œ, ์—ด๋Œ€ ์„ฑ์ธต๊ถŒ์—์„œ ๊ณ ์œ„๋„ ์„ฑ์ธต๊ถŒ์œผ๋กœ ์ „ํŒŒ๋œ ๋™์„œ๋ฐ”๋žŒ์ด ๋Œ€๋ฅ˜๊ถŒ์˜ ์ž์—ฐ๋ณ€๋™์„ฑ ํŒจํ„ด์— ์ž˜ ์ ‘ํ•ฉ๋  ๋•Œ, ์„ฑ์ธต๊ถŒ ๋ฐ”๋žŒ์€ ์™ธ๋ถ€์—์„œ ์ถ”๊ฐ€๋œ ํ† ํฌ์˜ ์—ญํ• ์„ ํ•˜์—ฌ, ์ฆํญ๋œ ์ œํŠธ์˜ ๋ณ€๋™์„ฑ์ด ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™์˜ ๋ฐ˜์‘์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์„ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์œ„์™€ ๊ฐ™์€ ๋ฐฉ๋ฒ•์œผ๋กœ ๋Šฆ๊ฒจ์šธ์— ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™๊ณผ ๊ด€๋ จ๋œ ํƒœํ‰์–‘ ์ œํŠธ์˜ ๋‚จ๋ถ๋ฐฉํ–ฅ์ด๋™์— ๋Œ€ํ•ด์„œ ์„ค๋ช…ํ•˜๊ณ ์ž ํ•˜๋ฉฐ, ์ด๋Š” ์Šคํ†ฐํŠธ๋ž™์˜ ์ด๋™์„ ์œ ๋„ํ•˜๊ณ , ๋Œ€๋ฅ˜ ํ˜„์ƒ์— ์˜ํ–ฅ์„ ์ฃผ์–ด, ์ค€2๋…„์ฃผ๊ธฐ์ง„๋™๊ณผ ๊ด€๋ จ๋œ ๋Œ€๋ฅ˜๊ถŒ ์ˆœํ™˜์˜ ๋ณ€ํ™”๊ฐ€ ๋ถํƒœํ‰์–‘์ง€์—ญ์˜ ์ˆ˜๋ฌธํ•™์  ๋ณ€ํ™”์˜ ์›์ธ์ด ๋  ์ˆ˜ ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค.The stratospheric quasi-biennial oscillation (QBO) is the most prominent inter-annual variability of zonal wind in the tropical lower stratosphere. The associated QBO momentum anomalies tend to move poleward and downward, affecting surface climate in a major way. Particularly, it is found that the surface temperature over East Asia in late winter is anomalously warm during the easterly phase of QBO. This temperature anomaly is roughly comparable in amplitude to the one associated with El Nin ฬƒo - Southern Oscillation (ENSO) in mid-winter. In order to explore how the QBO affects the North Pacific circulation, we examine the role of eddy-mean flow interactions and local Rossby wave sources over the North Pacific. It is found that the response to QBO is consistent with the fluctuation-dissipation theorem (FDT). When additional torques, associated with poleward propagation of QBO-induced zonal wind, are well projected onto annular mode pattern in the troposphere, the tropospheric circulation pattern is changed to annular-mode-like dipole patterns, as suggested by FDT. It is also found that the modulation of storm tracks along with jet shift induces convection anomalies, indicating that QBO-related tropospheric circulation changes may cause hydrological changes in the North Pacific.Abstract i List of tables iv List of figures iv 1. Introduction 1 2. Data and methodology 6 2.1 Data 2.2 Methodology 3. QBO Impacts on surface climate 11 4. Potential mechanism of QBO-troposphere coupling 19 4.1 Tropospheric response to QBO 4.2 Possible mechanisms 4.3 Climate model simulation 5. Summary and discussion 46 Reference 50Maste

    A Study on the Presentation Form of Education Content in the 2015 Revised National Subject Curricula: Focused on Content Framework

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    ๋ณธ ์—ฐ๊ตฌ๋Š” 2015 ๊ฐœ์ • ๊ต์œก๊ณผ์ • ๊ฐœ๋ฐœ์— ์žˆ์–ด์„œ ํ•ต์‹ฌ๊ฐœ๋…, ์ผ๋ฐ˜ํ™”๋œ ์ง€์‹ ์ค‘์‹ฌ์˜ ๋‚ด์šฉ ์ฒด๊ณ„๋ฅผ ๋ชจ๋“  ๊ต๊ณผ์— ๊ณตํ†ต์ ์œผ๋กœ ์ ์šฉํ•œ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚ด๋Š” ๊ต๊ณผ๋ณ„ ํƒ€๋‹น์„ฑ๊ณผ ๋ฌธ์ œ์ ์„ ๊ฒ€ํ† ํ•˜๊ณ  ๋…ผ์˜ํ•˜๋Š” ๋ฐ ๋ชฉ์ ์ด ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด 10๊ฐœ ์ฃผ์š” ๊ต๊ณผ๋ฅผ ์ง€์‹๊ณผ ๊ฐœ๋…์˜ ์ดํ•ด๊ฐ€ ์ค‘์‹ฌ์ด ๋˜๋Š” ๊ต๊ณผ, ๊ธฐ๋Šฅ์˜ ์ˆ™๋‹ฌ์ด ์ค‘์‹ฌ์ด ๋˜๋Š” ๊ต๊ณผ, ๊ฐ€์น˜์™€ ํƒœ๋„์˜ ๋‚ด๋ฉดํ™”๊ฐ€ ์ค‘์‹ฌ์ด ๋˜๋Š” ๊ต๊ณผ๋กœ ๋Œ€๋ณ„ํ•˜๊ณ  ๋‚ด์šฉ ์ฒด๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ๊ณผํ•™์ด๋‚˜ ์‚ฌํšŒ ๊ต๊ณผ์™€ ๊ฐ™์€ ๊ต๊ณผ๋Š” ํ•ต์‹ฌ๊ฐœ๋…๊ณผ ์ผ๋ฐ˜ํ™”๋œ ์ง€์‹์˜ ๊ตฌ์กฐ๊ฐ€ ๋น„๊ต์  ํƒ€๋‹นํ•˜์˜€์œผ๋‚˜, ๊ธฐ๋Šฅ๊ณผ ํƒœ๋„ ์ค‘์‹ฌ์˜ ๊ต๊ณผ์—์„œ๋Š” ํ•ต์‹ฌ๊ฐœ๋…๊ณผ ์ผ๋ฐ˜ํ™”๋œ ์ง€์‹์œผ๋กœ ๋‚ด์šฉ ์ฒด๊ณ„๋ฅผ ์ง„์ˆ ํ•˜๋Š” ๊ฒƒ์€ ํƒ€๋‹น์„ฑ์ด ์•ฝํ–ˆ๊ณ  ์˜คํžˆ๋ ค ๊ต๊ณผ ํŠน์„ฑ์„ ์ƒ์‹คํ•˜๋Š” ํ˜•ํƒœ๊ฐ€ ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ํ•™์ƒ๋“ค์˜ ์—ญ๋Ÿ‰ ํ•จ์–‘์„ ๊ฐ•์กฐํ•˜๋Š” ๊ต์œก๊ณผ์ • ๊ฐœ๋ฐœ ๋ฐฉํ–ฅ๊ณผ๋Š” ๋‹ฌ๋ฆฌ ๋‚ด์šฉ ์ฒด๊ณ„๊ฐ€ ์ œ์‹œํ•˜๊ณ  ์žˆ๋Š” ๊ธฐ๋Šฅ์€ ํ•ด๋‹น ๊ต๊ณผ์˜ ๊ธฐ๋Šฅ์œผ๋กœ ๋ณด๊ธฐ ์–ด๋ ค์šธ ์ •๋„๋กœ ์ง€๋‚˜์น˜๊ฒŒ ์ผ๋ฐ˜์ ์ด๊ฑฐ๋‚˜ ํ•™๋…„๊ตฐ์— ๋”ฐ๋ฅธ ๋ฐœ๋‹ฌ ์ •๋„์™€ ํ•™์Šต ์˜์—ญ์„ ๊ตฌ๋ถ„ํ•˜์ง€ ์•Š๋Š” ๋“ฑ ๋ฏธํกํ•จ์ด ์žˆ์—ˆ๋‹ค. ์ด์— ๋ชจ๋“  ๊ต๊ณผ์˜ ๊ต์œก๋‚ด์šฉ ์กฐ์ง์— ์žˆ์–ด์„œ ์ง€์‹์ด ์ ˆ๋Œ€์  ์šฐ์œ„๋ฅผ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ๋Š” ํ˜•์‹์„ ๊ฐœ์„ ํ•˜์—ฌ, ๊ฐ ๊ต๊ณผ์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ๊ต๊ณผ์—์„œ ๊ฐ€์žฅ ๊ฐ€์น˜ ์žˆ๊ฒŒ ๋‹ค๋ฃจ์–ด์ ธ์•ผ ํ•˜๋Š” ๋‚ด์šฉ์ด ๋ฌด์—‡์ธ๊ฐ€๋ฅผ ๊ณ ๋ คํ•  ํ•„์š”๊ฐ€ ์žˆ์œผ๋ฉฐ, ์ฐจ๊ธฐ๊ต์œก๊ณผ์ • ๊ฐœ๋ฐœ์— ์žˆ์–ด์„œ๋Š” ๊ต๊ณผ(๊ตฐ)๋ณ„ ํŠน์ˆ˜์„ฑ์„ ์ดํ•ดํ•˜๊ณ  ๊ต๊ณผ ๋‚ด์  ์ ํ•ฉ์„ฑ์„ ๊ฐ–์ถœ ์ˆ˜ ์žˆ๋Š” ๋‚ด์šฉ ์ฒด๊ณ„์˜ ํ˜•์‹์  ๊ตฌ์กฐ์™€ ์ง„์ˆ  ๋ฐฉ์‹์˜ ๋Œ€์•ˆ์„ ๋ชจ์ƒ‰ํ•ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์ œ์–ธํ•˜์˜€๋‹ค. The purpose of this study is to analyze and discuss the adequacy or problems of each subjects content framework that is based on key concepts and generalized knowledge and uniformly applied to all subjects in the 2015 revised curriculum. For this purpose, this study has classified 10 major subjects into 3 clustersโ€”subjects focused on understanding of knowledge and concepts, subjects focused on mastering skills, and subjects focused on internalization of values and attitudesโ€”and analyzed their content framework. As a result, it is revealed that subjects such as science and social studies have relatively high adequacy in their key concepts and generalized knowledge structure, but subjects focused on skills and attitudes have low adequacy and lost their distinct characteristics. Besides, unlike a vision of curriculum development focusing on cultivating students competencies, skills presented by the content framework have some problems including the fact that they are overly general to the point where it is hard to see them as skills relevant to the subjects and the degree of development and learning domains are not divided by each grade. This study suggests that there is a need to adjust the existing form which is dominated by knowledge in the organization of the education content in every subject and to consider the most valuable content in each subject according to its unique features and goals. In addition, the content framework and statement ways of education content should have suitability based on each subjects specificity
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