• Journal of Climate Change Research
• /
• v.6 no.2
• /
• pp.61-72
• /
• 2015

In this study, we have investigated monthly changes in temperature extremes in South Korea for the past (1921~2010) and the future (2011~2100). We used seven stations' (Gangneung, Seoul, Incheon, Daegu, Jeonju, Busan, Mokpo) data from KMA (Korea Meteorological Administration) for the past. For the future we used the closest grid point values to observations from the RCP8.5 scenario of 1 km resolution. The Expert Team on Climate Change Detection and Indices (ETCCDI)'s climate extreme indices were employed to quantify the characteristics of temperature extremes change. Temperature extreme indices in summer have increased while those in winter have decreased in the past. The extreme indices are expected to change more rapidly in the future than in the past. The number of frost days (FD) is projected to decrease in the future, and the occurrence period will be shortened by two months at the end of the $21^{st}$ century (2071~2100) compared to the present (1981~2010). The number of hot days (HD) is projected to increase in the future, and the occurrence period is projected to lengthen by two months at the end of the $21^{st}$ century compared to the present. The annual highest temperature and its fluctuation is expected to increase. Accordingly, the heat damage is also expected to increase. The result of this study can be used as an information on damage prevention measures due to temperature extreme events.

• The Korean Journal of Applied Statistics
• /
• v.21 no.4
• /
• pp.705-716
• /
• 2008

The objectives of this study are the detection and forecast of climate change signal in the annual mean of surface temperature data, which are generated by MRI/JMA CGCM over the Korean Peninsula. MRI/JMA CGCM outputs consist of control run data(experiment with no change of $CO_2$ concentration) and scenario run data($CO_2$ 1%/year increase experiment to quadrupling) during 142 years for surface temperature and precipitation. And ECMWF reanalysis data during 43 years are used as observations. All data have the same spatial structure which consists of 42 grid points. Two statistical models, the Bayesian fingerprint method and the regression model with autoregressive error(AUTOREG model), are separately applied to detect the climate change signal. The forecasts up to 2100 are generated by the estimated AUTOREG model only for detected grid points.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.4
• /
• pp.45-53
• /
• 2013

Global warming causes sea levels to rise and global changes apparently taking place including coastline changes. Coastline change due to sea level rise is also one of the most significant phenomena affected by global climate change. Accordingly, Coastline change detection can be utilized as an indicator of representing global climate change. Generally, Coastline change has happened mainly because of not only sea level rise but also artificial factor that is reclaimed land development by mud flat reclamation. However, Arctic coastal areas have been experienced serious change mostly due to sea level rise rather than other factors. The purposes of this study are automatic extraction of coastline and identifying change. In this study, in order to extract coastline automatically, contrast of the water and the land was maximized utilizing modified NDWI(Normalized Difference Water Index) and it made automatic extraction of coastline possibile. The imagery converted into modified NDWI were applied image processing techniques in order that appropriate threshold value can be found automatically to separate the water and land. Then the coastline was extracted through edge detection algorithm and changes were detected using extracted coastlines. Without the help of other data, automatic extraction of coastlines using LANDSAT was possible and similarity was found by comparing NLCD data as a reference data. Also, the results of the study area that is permafrost always frozen below $0^{\circ}C$ showed quantitative changes of the coastline and verified that the change was accelerated.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.21 no.2
• /
• pp.49-57
• /
• 2016

Even if an external forcing that will drive a climate change is given uniformly over the globe, the corresponding climate change and the feedbacks by the climate system differ by region. Thus the detection of global warming signal has been made on a regional scale as well as on a global average against the internal variabilities and other noises involved in the climate change. The purpose of this study is to estimate a timing of unprecedented climate due to global warming and to analyze the regional differences in the estimated results. For this purpose, unlike previous studies that used climate simulation data, we used an observational dataset to estimate a magnitude of internal variability and a future temperature change. We calculated a linear trend in surface temperature using a historical temperature record from 1880 to 2014 and a magnitude of internal variability as the largest temperature displacement from the linear trend. A timing of unprecedented climate was defined as the first year when a predicted minimum temperature exceeds the maximum temperature record in a historical data and remains as such since then. Presumed that the linear trend and the maximum displacement will be maintained in the future, an unprecedented climate over the land would come within 200 years from now in the western area of Africa, the low latitudes including India and the southern part of Arabian Peninsula in Eurasia, the high latitudes including Greenland and the mid-western part of Canada in North America, the low latitudes including Amazon in South America, the areas surrounding the Ross Sea in Antarctica, and parts of East Asia including Korean Peninsula. On the other hand, an unprecedented climate would come later after 400 years in the high latitudes of Eurasia including the northern Europe, the middle and southern parts of North America including the U.S.A. and Mexico. For the ocean, an unprecedented climate would come within 200 years over the Indian Ocean, the middle latitudes of the North Atlantic and the South Atlantic, parts of the Southern Ocean, the Antarctic Ross Sea, and parts of the Arctic Sea. In the meantime, an unprecedented climate would come even after thousands of years over some other regions of ocean including the eastern tropical Pacific and the North Pacific middle latitudes where an internal variability is large. In summary, spatial pattern in timing of unprecedented climate are different for each continent. For the ocean, it is highly affected by large internal variability except for the high-latitude regions with a significant warming trend. As such, a timing of an unprecedented climate would not be uniform over the globe but considerably different by region. Our results suggest that it is necessary to consider an internal variability as well as a regional warming rate when planning a climate change mitigation and adaption policy.

• Journal of Korean Society of Forest Science
• /
• v.108 no.1
• /
• pp.10-20
• /
• 2019

The purpose of this study is to identify the long-term area changes in the subalpine coniferous forests in Korea in order to understand the changes in the subalpine forest ecosystems vulnerable to climate change. We analyzed 20 years of time-series Landsat satellite images (mid 1990s, mid 2010s) for change detection of coniferous forests and compared with the long term changes of climate information to identify their relationship in the study area. As a result, the area of coniferous forests in the study region decreased by 25% over 20 years. The regions with largest changes are Seoraksan, Baegunsan-Hambaeksan-Jangsan, Jirisan, and Hallasan. The region with the largest decrease in area was Baegunsan (reduced area: 542 ha), and the region with large decrease in area and the largest rate of decrease was Hallasan (rate of decrease: 33.3%). As the Jeju region has the most rapid temperature rise, it is projected that Hallasan is the most vulnerable forest ecosystem affected by climate change. The result of this study shows that from a long-term perspective the overall coniferous forests in the subalpine region are declining, but the trend varies in each region. This national and long-term information on the change of coniferous forests in the subalpine region can be utilized as baseline data for the detailed survey of endangered subalpine coniferous trees in the future.

• Journal of Korea Water Resources Association
• /
• v.47 no.8
• /
• pp.671-684
• /
• 2014

Change point analysis is a efficient tool to understand the fundamental information in hydro-meteorological data such as rainfall, discharge, temperature etc. Especially, this fundamental information to change points to future rainfall data identified by reasonable detection skills can affect the prediction of flood and drought occurrence because well detected change points provide a key to resolve the non-stationary or inhomogeneous problem by climate change. Therefore, in this study, the comparative study to assess the performance of the 3 change point detection skills, cumulative sum (CUSUM) method, Bayesian change point (BCP) method, and segmentation by dynamic programming (DP) was performed. After assessment of the performance of the proposed detection skills using the 3 types of the synthetic series, the 2 reasonable detection skills were applied to the observed and future rainfall data at the 5 rainfall gauges in South Korea. Finally, it was suggested that BCP (with 0.9 posterior probability) could be best detection skill and DP could be reasonably recommended through the comparative study. Also it was suggested that BCP (with 0.9 posterior probability) and DP detection skills to find some change points could be reasonable at the North-eastern part in South Korea. In future, the results in this study can be efficiently used to resolve the non-stationary problems in hydrological modeling considering inhomogeneity or nonstationarity.

• Atmosphere
• /
• v.33 no.1
• /
• pp.1-20
• /
• 2023

Holding the longest observation data from April 1904, Busan is one of the essential points to understand the climate variability of the Korean Peninsula without missing data since implementing the modern weather observation of the South Korea. Busan is featured by coastal areas and affected by various climate factors and fluctuations. This study aims to investigate climate variability and changes in climatic variables, extremes, and several weather indexes. The statistically significant change points in daily mean rainfall intensity and temperature were found in 1964 and 1965. Based on the change point detection, 117 years were divided into two periods for daily mean rainfall intensity and temperature, respectively. In the long-term temperature analysis of Busan, the increasing trend of the daily maximum temperature during the period of 1965~2021 was larger than the daily mean temperature and the daily minimum temperature. Applying Ensemble Empirical Mode Decomposition, daily maximum temperature is largely affected by the decadal variability compared to the daily mean and minimum temperature. In addition, the trend of daily precipitation intensity from 1964~2021 shows a value of about 0.50 mm day^-1, suggesting that the rainfall intensity has increased compared to the preceding period. The results in extremes analysis demonstrate that return values of both extreme temperatures and precipitation show higher values in the latter than in the former period, indicating that the intensity of the current extreme phenomenon increases. For Wet-Bulb Globe Temperature (effective humidity), increasing (decreasing) trend is significant in Busan with the second (third)-largest change among four stations.

• Korean Journal of Remote Sensing
• /
• v.35 no.5_1
• /
• pp.637-647
• /
• 2019

The detection of crop stress is an important issue for the accurate assessment of yield decline. The photochemical reflectance index (PRI) was developed as a remotely sensed indicator of light use efficiency (LUE). The PRI has been tested in crop stress detection and a number of studies demonstrated the feasibility of using it. However, only few studies have focused on the use of PRI from remote sensing imagery. The monitoring of PRI using drone and satellite is made difficult by the low spectral resolution image captures. In order to estimate PRI from multispectral sensor, we propose a band fusion method using adjacent bands. The method is applied to the drone-based hyperspectral and multispectral imagery and estimated PRI explain 79% of the original PRI. And time series analyses showed that two PRI data (drone-based and SRS sensor) had very similar temporal variations. From these results, PRI from multispectral imagery using band fusion can be used as a new method for evaluation of crop stress.

• International Journal of Advanced Culture Technology
• /
• v.10 no.4
• /
• pp.511-517
• /
• 2022

Recently, due to rapid climate change, the population of honey bees has decreased, posing a great threat to the existence of the Earth's ecosystem. In particular, the colony collapse phenomenon in which bees disappeared nationwide in early 2022 had devastating consequences for beekeepers. In order to solve the problems of beekeeping due to climate change, it is urgent to develop a system that can monitor the situation inside the hive through various IoT sensors. This paper develops a system that can measure the activity of bees inside the hive and uses it to measure the number of times of entry and exit of the hive. The data measured by the developed system can be monitored in real time on a smartphone through the cloud server. The system developed in this paper can monitor the ecology of bees according to climate change and measure internal and external bee activities. Using this method, it is possible to check in advance for the colony collapse phenomenon in which bees disappeared in early 2022. This is very meaningful in that it presents an alternative that can identify the cause of the problem through early detection.

• Journal of Climate Change Research
• /
• v.7 no.1
• /
• pp.1-8
• /
• 2016

In this study, we investigated the applicability of a short-term carbon dioxide ($CO_2$) passive sampler using turbidity change in a solution containing barium hydroxide ($Ba(OH)_2$). The mass of $CO_2$ introduced into the $Ba(OH)_2$ aqueous solution was strongly correlated ($r^2=0.9565$) to the change in turbidity caused by its reaction with the solution. The sampling rates calculated for 1 h and 24 h were $42.4{\pm}5.4mL\;min^{-1}$ and $2.3{\pm}0.3mL\;min^{-1}$, respectively. Both unexposed (blank) and exposed samplers remained stable during the storage period of at least two weeks. The detection limits of the passive sampler for $CO_2$ were 81.5 ppm for 1 h and 61.5 ppm for 24 h. Based on the results, the passive sampler using the change of turbidity in the $Ba(OH)_2$ aqueous solution appears to be a suitable tool for measuring short-term atmospheric concentrations of $CO_2$.