간행물
기후연구 KCI 등재 Journal of Climate Research
- 발행기관 건국대학교 기후연구소
- 자료유형 학술지
- 간기 계간
- ISSN 1975-6151 (Print)2288-8772 (Online)
- 수록기간 2006 ~ 2021
- 주제분류 사회과학 > 지리학 사회과학 분류의 다른 간행물
- 십진분류KDC 453DDC 551
권호리스트/논문검색
제14권 제2호 (2019년 6월) 6건
1.
2019.06
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This study aims to analyze the change of onset and end dates of extreme temperature events and examine their relationships with global warming. The data used for this study are daily maximum temperature, daily minimum temperature, and global mean temperature anomaly. Results were similar to the trend of global temperature, showing that the onset date of extreme high temperature is advanced while the end date of extreme high temperature is delayed. Also, the change of onset (end) dates of extreme low temperature were clear, with coming later (earlier). There is more distinct change in extreme low temperature than extreme high temperature. The length between onset date and end date of extreme high (low) temperature is significantly longer (shorter). The onset (end) date of extreme high temperature has a negative (positive) relationship with global mean temperature. The onset (end) date of extreme low temperature has a positive (negative) relationship with global mean temperature. It might be concluded that the change of onset and end date of extreme temperature in South Korea has been affected by global warming.
2.
2019.06
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We estimated changes in temperature-related extreme events over South Korea for the mid and late 21st Century using the 122 years (1979-2100) data simulated by RegCM4 with HadGEM2-AO data as boundary conditions. We analyzed the four extreme events (Hot day: HD, Tropical day: TD, Frost day: FD, Icing Day: ID) and five extreme values (Maximum temperature 95/5 percentile: TX95P/TX5P, Minimum temperature 95/5 percentile: TN95P/TN5P, Daily temperature range 95 percentile: DTR95P) based on the absolute and relative thresholds, respectively. Under the global warming conditions, hot extreme indices (HD, TD, TX95P, TN95P) increase, suggesting more frequent and severe extreme events, while cold extreme indices (FD, ID, TX5P, TN5P) decrease their frequency and intensities. In the late 21st Century, changes in extremes are greater in severe global warming scenario, RCP8.5 rather than RCP4.5. HD and TD (FD and ID) are expected to increase (decrease) in the mid 21st Century. The average HD is expected to increase by 14 (17) days in RCP4.5 (8.5). All the percentile indices except for DTR95P are expected to increase in both RCP4.5 and RCP8.5. In the late 21st Century, HD and TD are significantly increased in RCP8.5 compared to RCP4.5, but FD and ID are expected to be significantly reduced. HD is expected to increase mainly in the southwestern region, twice (+41 days) in RCP8.5. TD is expected to increase by 17 days in RCP8.5, which is 5 times greater than that in RCP4.5. TX95P, TN95P and TX5P are expected to increase by about 2°C and 4°C in RCP4.5 and RCP8.5, respectively. TN5P is expected to increase significantly by 4°C and 7°C in RCP4.5 and RCP8.5, respectively.
3.
2019.06
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This study examined the efficiency of satellite images in terms of detecting wheat cultivation areas, and then analyzed the possibility of climate change through an correlation analysis of time series climate data from the western regions of Gyeongnam province, Korea. Furthermore, we analyzed the effect of climate change on wheat production through a multiple regression analysis with the time series wheat production and climate data. A relatively accurate distribution was achieved on the wheat cultivation area extracted through satellite image classification with an error rate of less than 10% in comparison to the statistical data. Upon correlation analysis with time series climate data, significant results were displayed in the following changes: the monthly mean temperature of the seedling stage, the monthly mean duration of sunshine, the monthly mean temperature of the growing period, the monthly mean humidity, the monthly mean temperature of the ripening stage, and the monthly mean ground temperature. Accordingly, in the study area, the monthly mean temperature, precipitation, and ground temperature generally increased whereas the monthly mean duration of sunshine and humidity decreased. The monthly mean wind speed did not display a particular change. In the multiple regression analysis results, the greatest effect on the production and productivity of wheat as climate factors included the annual mean humidity of the seedling stage, the annual mean temperature of the wintering period, and the annual mean ground temperature of the ripening stage. These results demonstrate that there is a change in wheat production depending on the climate change in the study area. in addition, it is determined that this study will be used as important basic data in the resolution of food security problems based on climate change.
4.
2019.06
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Cell based grid data of future temperature and precipitation produced with four RCP scenarios were converted into polygon based data for administrative districts using three simple vectorizing methods; (1) KMA Dong-Nae forecast point based, (2) areal ratio based and (3) central point based methods. The results were compared the existed KMA areal weight based methods to identify which methods were more efficient than others. Simple statistical methods such descriptive statistics, correlation coefficient, and Bland & Altman plots (B&A) were used to compare agreements between them. When central point and areal ratio based methods were applied to administrative districts of Eup-Myeon-Dong or some Gus, NULLs were found because their sizes are smaller than the cell of 1x1 km. Therefore, KMA Dong-Nae forecast point based methods were better when sizes of administrative districts are smaller than the cell size. For Do and Metropolitan cities, there were no greater differences among methods except for the KMA Dong- Nae forecast points. The greater the areas of administrative districts the more distortions from the KMA Dong-Nae forecast points because only KMA Dong-Nae forecast one point were used for the calculation. In conclusion, the KMA Dong-Nae forecast point based method was appropriate when sizes of administrative districts are smaller than the grid cell. For the greater areal sizes such as Do and Metropolitan cities, areal ratio and central point based methods were better.
5.
2019.06
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In this study, we compared the prediction performances according to the bias and dispersion of temperature using ensemble machine learning. Ensemble machine learning is meta-algorithm that combines several base learners into one prediction model in order to improve prediction. Multiple linear regression, ridge regression, LASSO (Least Absolute Shrinkage and Selection Operator; Tibshirani, 1996) and nonnegative ride and LASSO were used as base learners. Super learner (van der Lann et al ., 1997) was used to produce one optimal predictive model. The simulation and real data for temperature were used to compare the prediction skill of machine learning. The results showed that the prediction performances were different according to the characteristics of bias and dispersion and the prediction error was more improved in temperature with bias compared to dispersion. Also, ensemble machine learning method showed similar prediction performances in comparison to the base learners and showed better prediction skills than the ensemble mean.
단보
6.
2019.06
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The purpose of this study is to examine the effect of climatic elements on the arabica coffee yield during the various growth stages of coffee plant for the period of 1996-2017 over Costa Rica. For the future scenario, change rate of arabica coffee yield is also estimated using the data of production and cultivated area. The cultivation area and the yield of arabica coffee has been decreasing since the 1990s in Costa Rica. The decreasing trend in arabica coffee yield could have a negative effect on Costa Rica’s coffee industry in the future. During the dry season, the yield of arabica coffee has significant negative correlation with precipitation at the stage of flowering in the month of February. In case of wet season, coffee yield and temperature were negatively correlated while precipitation showed positive association with coffee yield at the stage of growing period in the month of August. However, the observation revealed the excessive precipitation drastically reduced arabica coffee yield in 2013 during August, the month of wet season. According to the RCP 4.5 and RCP 8.5 climate change scenarios, due to high temperature and fluctuated precipitation the yield of arabica coffee is unstable and the future of coffee industries is also insecure.
