The purpose of this study is to extract climate element affecting coffee yield by growth period using data of production and cultivation area of coffee and climate data for 2000-2018. During the analysis period, the production of coffee in Vietnam has been consistently increasing, but Ðăk Lăk in the Central Highlands, the main cultivation area for coffee production, has recently stagnated in the trend of increasing yield. The yield of Lâm Đồng, located in the relatively highlands of the Central Highlands, is steadily increasing. Coffee yields of Ðăk Lăk is negatively correlated with the temperature during flowering period, and is also significantly negatively correlated with the maximum temperature and precipitation during the late growing period. On the other hand, Lâm Đồng, located at a relatively high altitude, has a positive correlation with temperature during the late growing period. It is analyzed that the lower the altitude, the higher the temperature, the lower the coffee productivity due to the high temperature appearance, and the lower the low temperature appearance in the high altitude region.

This study evaluates the quality of surface air temperature, relative humidity, and precipitation detection observed by 22 internet of thing (IoT)-based mini-weather stations in Seoul in 2020 summer. The automatic weather station (AWS) closest to each IoT-based station is used as reference. The IoT-based observations show surface air temperature and relative humidity are about 0.2-4.0°C higher and about -1--22% lower than the AWS observations, respectively. However, they exhibit temporal variability similar to the AWS observations on both diurnal and daily time scales, with daily correlations greater than 0.90 for temperature and 0.82 for relative humidity. Given these strong linear relationships, it show that temperature and relative humidity biases can be effectively corrected by applying a simple bias correction method. For IoT-based precipitation detection, we found that precipitation conductivity value (PCV) during precipitation events is well separated from that during non-precipitation events, providing a basis for distinguishing precipitation events from non-precipitation events. When the PCV threshold is set to 250 for precipitation detection, the highest critical success index and the bias score index close to one, suitable for operational precipitation detection, are obtained. These results demonstrate that IoT-based mini-weather stations can successfully measure surface air temperature, relative humidity, and precipitation detection with appropriate bias corrections.

The purpose of this study is to elucidate the spatio-temporal characteristics of ultrafine dust generation in East Asia and the synoptic climate patterns related to its dispersal which has its adverse effects on public health across East Asia. To achieve this purpose, Level 3 monthly Aerosol Optical Depth (AOD) data extracted from MODIS satellite imagery (MOD08_M3) representing particle matters less than 2.5 micrometer (PM2.5) and the NCEP-NCAR reanalysis I upper-level climatic data associated with the exacerbation of ultrafine dust problem are analyzed for the recent 20-year (2001-2020) period. Analyses of long-term average MOD08 data show that high AOD value exceeding 0.5 or more frequently occurred in populous cities in East Asia but mainly in the vicinity of densely populated large rivers and the eastern lowlands in China between mid-winter and mid-spring, which is attributable to the accumulation effects of continuous fossil fuel consumption for heating and manufacturing. Despite the overall decreasing trend of ultrafine dust across China in the 2010s, the weakened westerlies in the warmer climate as well as its continuous generation from the densely populated industrial regions of China provide a favorable synoptic climate condition for frequent severe ultrafine dust problems across East Asia including South Korea. These results indicate that ultrafine dust from China is a long-lasting transboundary environmental problem across East Asia, which needs long-term international cooperation in developing the sustainable policies.

We projected the temperature changes in the mid-21st century with Representative Concentration Pathway (RCP) 4.5 and RCP8.5 using the temperature data simulated by four regional climate models (RCMs: WRF, CCLM, MM5, RegCM4) in Korea. The simulation area and spatial resolution of RCMs were the CORDEXEA (COordinated Regional Climate Downscaling Experiment-East Asia) area and 25 km, respectively. We defined the temperature change as the difference (ratio) between the average annual temperature (IAV: Interannual Variation) over the projected 25 years (2026-2050) and that over the present 25 years (1981-2005). The fact that the average annual temperature bias of the four RCMs is within ±2.5°C suggests that the RCM simulation level is reasonable in Korea. Across all RCMs, scenarios, and geographic locations, we observed increased temperatures (IAV) in the mid-21st century. In RCP4.5 and RCP8.5, 1.27°C and 1.57°C will be increased by 2050, respectively. The ensemble suggests that the temperature increase is higher in winter (RCP4.5: 1.36°C, RCP8.5: 1.75°C) than summer (RCP4.5: 1.25°C, RCP8.5: 1.49°C). Central Korea exhibited a higher temperature increase than southern Korea. A slightly larger IAV is expected in the southeastern region than in the Midwest of Korea. IAV is also expected to increase significantly in RCP4.5 (summer) than in RCP8.5 (winter).

This study tried to reveal pattern of change in climatic normals during 1981-2010 and 1991-2020 from 28 weather stations in South Korea as well as its relationships with land cover change. Most of the weather stations showed temperature increase during 1991-2020 compared to 1981-2010, and positive correlations were found between temperature and urbanization in land cover, with the highest correlation coefficient in min. temperature. Temperature data also showed negative correlations with suburbanization in land cover, with lower correlation coefficients than urbanization in land cover. Overall decreases in relative humidity were revealed from the weather stations. However, clear relationships between relative humidity and land cover were not found in this study. The other climate data such as precipitation, sunshine and mean wind speed showed various ratios of change depending on the weather stations, without certain relationships with land cover.

The purposes of this study were to classify detailed climate types over the Republic of Korea (ROK) and to delineate their climate characteristics using the new normals of 1991-2020 for 219 weather stations. Total five climate types, Cfa, Cfb, Cwa, Dwa, and Dwb were identified in ROK based on the Köppen’s climate classification criteria. Subtropical climate types, Cfa or Cwa types were broadly covered with 79.9% of 219 stations and the most of remaining stations were included in Dwa types which had a very cold winter and hot summer with wet conditions. In the Trewartha classification, four climate types were identified, one subtropical Cfa, and three temperate Doa, Dca, and Dcb types. Dcb types were found at four stations (Daegwallyeong, Taebaek, Jinburyeong, and Sabuk) in Taebak mountains indicating the extent of cool summer climate types with more stations in mountain areas. The climate characteristics by climate types only were presented the results from the Trewartha classification with the new normals and 66 ASOS stations because Köppen’s climate classification was not appropriate for ROK. The annual mean precipitation of Cfa was the greatest while Dcb the lowest among four types. The annual range was the greatest at Dca types while the smallest at Cfa due to the geographical varieties. More detailed climate types were located in ROK with 219 weather stations and the new normals (1991-2020). However, there were some limitation applying the criteria of Köppen’s and Trewartha’s climate classification to a very complex topographical region.

In this study, an analysis were conducted to utilize the thermal infrared image using drone to present the temperature correction method of thermal infrared image and the thermal environment by the type of land cladding. The analysis was applied to the temperature correction of the thermal infrared image and total eight thermal infrared images were produced based on the land surface temperature. The thermal infrared image compared accuracy through RMSE calculation. Based on the result of RMSE, the thermal infrared image corrected by the land surface temperature was relatively accurate and contained at 2.26 to 3.58. According to the results, it is expected that the aggregation and waters will perform the functions of the green park sufficiently to improve the thermal comfort and improve the microclimate stability using the thermal infrared image and the reclassified land cover map. The results of this study obtained by Drone and the usability of the drone thermal infrared image in the detection of the thermal environment. Finally, it is expected to contribute to the improvement and management of the thermal environment in the city by being used as a basic data for the improvement and management policy of the thermal environment. Moreover, the macro view is expected to contribute to the mitigation of urban temperature reduction and heat island.

Heatwaves can affect human health and vegetation growth and bring about energy problems and socioeconomic damages, so the analysis and prediction of the heatwave is a crucial issue under a warming climate. This paper examines the production of STCI (Standard Temperature Condition Index) using ASOS (Automated Synoptic Observing System) in-situ observation data for the period of 1979-2020, and an STCI predictability assessment with an RF (Random Forest) model using ERA5 (ECMWF Reanalysis 5) meteorological variables. The accuracy was quite high with the MAE (Mean Absolute Error) of 0.365 and the CC (Correlation Coefficient) of 0.873, which corresponded to 7% to 10% difference for the range of STCI<1.5, and to 1% to 3% difference for the range of STCI>1.5, in terms of the probability density function. Also, we produced gridded maps for the summer STCI from 1979 to 2020 by utilizing the ERA5 raster data for the RF prediction model, which enables the spatial expansion of the ASOS point-based STCI to a continuous grid nationwide. The proposed method can be applied to forecasting of STCI by adopting future meteorological or climatic datasets.

This study analyzed how the impacts of major teleconnection patterns on December mean temperature in Korea have been changed during the period before and after the regime shift of 1986 for the last 61 years from 1958 to 2018. During the period before the regime shift, the teleconnection patterns originating from the North Atlantic mainly affected the temperature variability in Korea, but its influence almost disappeared after the regime shift. On the other hand, the Arctic Oscillation (AO) and warm Arctic and cold Eurasia (WACE) patterns played a more important role in the temperature variability in Korea after the regime shift. Regression analysis showed that the AO could explain about 12% of the total temperature variability before the regime shift, but about 22% after the regime shift. WACE pattern also explained about 4% before the regime shift, but after the regime shift, the importance increased by about 4.5 times to 18%. On the other hand, East Atlantic pattern (EA) and North Atlantic Oscillation (NAO), which are east-west teleconnection patterns, explained 27% and 11%, respectively, before the regime shift, but had little influence within 3% after the regime shift. This means that the influence of east-west teleconnection patterns disappeared after the regime shift, and teleconnection patterns by the Arctic Circle became more important.

Tropical cyclones (TCs) over the western North Pacific (WNP) mainly occur during June-October, and result in significant casualties and damages to property in East Asian countries (e.g., Korea, Japan, Taiwan, and China, etc.). Although the total number of TCs that occurred over WNP was similar to normal years, the numer of TCs that affected Korea in August and September 2019 was 3 times higher than with the same number of TCs in July. Therefore, this study examined why more TCs migrated into Korea in 2019 through analyzing four environmental conditions: steering flow, geopotential height at 500 hPa, vertical wind shear (VWS), and sea surface temperature (SST). Results showed that the tracks of TCs were significantly associated with steering flows from July to September. Furthermore, weaker VWS and warmer SST were distributed near the tracks of TCs during August and September, whereas strong VWS and lower SST were dominant in July. The environmental conditions in August and September were favorable for maintaining and developing TCs, explaining why more typhoons have affected Korea during August and September in 2019.

The present study aims to analyze climate change and trend of extreme temperature events occurred over the Republic of Korea. The observation data used are daily average, maximum, and minimum temperature from 6 weather stations for the period of 1912-2020. Seven extreme indices regarding frequency and extreme value of temperature are calculated in seasonal and annual time range. In addition, hot extremes and their changes by four physical terms that include information on the annual mean temperature, the amplitude of the annual cycle, the diurnal temperature range and the local temperature anomaly on the day of the extreme are analyzed. The climatology for the analysis is updated to the new normal year of 1991-2020. Consistent with the previous findings, statistically significant change was detected in the indices of annual lowest daily minimum temperature, annual extreme temperature range, frequency of daily minimum temperature below -12℃ and 10%ile(TN10p) during winter. Due to the gradual decrease of the occurrence days regarding the extreme minimum temperature during winter, the frequency calculated by the relative threshold in extreme high temperature during summer prevail since 1990s. Indices related with extreme high temperature had larger low-frequency variability than significant climate change during the analysis period. However, the assessment of hot extremes according to the terms describing mean, variability and tails during the new normal year of 1991-2020, significant increasing trend was detected not only in the annual mean and the amplitude of the seasonal cycle, but also in the daily hot extreme anomaly.

Climate change is considered as a major threat for agricultural production in Vietnam, particularly for coffee production, one of the most important agricultural sectors of the country. To effectively cope with the negative impacts of climate change on coffee production, the ways of understanding and also adequate responses by farmers on the climate change are very important. This paper aims to provide empirical insights into the farmers’ perception of climate change and their adaptation practices to its impacts on coffee production. The results from the questionnaire survey of 151 smallholder coffee farmers in Dak Nong province reveal the fact that the farmers understood the progress and impact of climate change through their observation into their surrounding areas. Coffee growers’ observation about the climate variability is remarkably consistent with the statistical data from the meteorological authority. However, this research revealed that their capacity to cope with climate change was relatively limited, mostly based on their own resources. This research suggests that more studies are required to examine the application of environmentally sustainable approaches to cope with climate changes.

This study was undertaken to find out national level changes in area, production and yield of two major staple crops wheat and potato in Bangladesh. The time series of secondary data was collected from yearbook of agricultural statistics under Bangladesh Bureau of Statistics (BBS) and used for the statistical analysis during the thirty-year period of 1989/90-2018/19. Moreover, selected data were divided into two groups and regarded as segment (1989/90-2003/04, 2005/06-2018/19) to examine the significant level in each crop. The results of different statistical techniques showed that wheat cultivated area and production were satisfactory level but yield was not too much standard in context of country demand. In the case of potato, yield as well as cultivated area and production were crossed the significant level and fulfilled the demand of population. In recent few years, the ratio of potato production rapidly increased, compared with the cultivation area. Based on segment (period) analysis, at the first half wheat production was always below, compared with the area but second half nine years saw slightly improved. On the other hand, in both segment potato growth rate in area, production and yield were increased throughout the study period. The highest instability was also shown in area, production and yield of potato during whole as well as segmented period. There was always a positive relationship between country’s demand and supply. Both wheat and potato are considered as staple crops and based on the productive capability over cultivated area, potato showed the higher productivity for the country of Bangladesh. In consequences, potato consuming demand also rapidly increased all over the country, compared with past respectively.

This paper has presented not only the spatial coverage change of climate extreme events in summer and winter seasons during the period of 2000-2017, but also their future projections in 2021-2100, South Korea through analysis of a Combined Climate Extreme Index (CCEI). The CCEI quantifies the spatial coverage of climate extreme events based on a set of five indicators. MK (Modified Korean)-PRISM (Parameter-elevation Regression on Independent Slopes Model)v1.2 (1×1km) and RCP scenario data (1×1km) were applied to CCEI. Results indicated that in average, 21.7% of the areas in the summer and 23.6% in the winter experienced climate extremes from 2000 to 2017 regardless of types of climate extreme events in South Korea. The summer of 2003 and 2009 was relatively cool and humid, while the summer of 2014 and 2015 was cool and dry and the summer of 2016 was warm and dry. The extreme events with much above normal maximum and minimum temperature during the study period were detected but not much below normal maximum and minimum temperature after 2015. For RCP2.6 and RCP8.5 scenarios, there were statistically significant trends with spatial coverage expansion of climate extreme events in the future. It might be concluded that climate extreme events in the summer and winter seasons were affected simultaneously by two or more indicators than a single indicator in South Korea.

Soil temperature (SoT) is one of the important climate elements for the land-atmosphere interactions. In this study, the climatic characteristics and trends of SoT were analyzed at nine locations for the period of 1966 to 2020. In addition, effect of precipitation on the interannual variations of SoT was also addressed. The monthly average of the SoT shows seasonal and interannual variations regardless of location. While there are some differences depending on the location and depth, the interannual variations of SoT occur more strongly at the shallow layer during summer comparatively other seasons. The strong negative correlation between SoT and precipitation of summer shows that the large interannual variations of SoT in summer is closely related to the strong interannual variations of precipitation of same season. According to the depth the range of SoT is larger in winter and summer and smaller in spring and autumn. Generally, air temperature and SoTs are increasing due to global warming. However, the increasing trend varies and depends on the location and depth of the study area. Most of investigation stations are revealed a strong increasing trend, particularly, at the shallow layer during summer season.

We examined the ocean forcing, associated with the different trends in precipitation between North and South Korea, using GPCC V2018 precipitation and OISST V2 sea surface temperature (SST) for the recent thirty years of 1982-2011. As a result of linear regression, the precipitation trends in the monsoon (June and July) and post-monsoon (August and September) seasons were different between North and South Korea, respectively, with increased and decreased trends, during the both monsoon seasons. During the monsoon season, the results of detrended correlation and composite analysis showed the opposite relationships of precipitation with SSTs in the equatorial Pacific and Arctic Oceans between North and South Korea. It was identified that large-scale atmospheric circulation linked to ENSO can differently affect the Korean Peninsula. On the other hand, during the post-monsoon season, the correlation and composite patterns across the oceans in the Northern Hemisphere were generally similar for the two Koreas. It was suggested that near the ocean of the Korean Peninsula and the land surface forcings might affect the precipitation variability during the post-monsoon season, especially in North Korea.

The purpose of this study is to explore spatio-temporal patterns of extreme low human-sensible temperature (HST) across Mt Halla (1,950m), Korea. To do this, decadal (2011/12-2020/21) averages of daily or hourly windchill index (WCI), which quantifies HST considering the combined effects of low temperature and strong wind, are calculated for 24 weather stations in Mt Halla. Time series of decadal average daily mean show that extreme low HST events with moderate risk level (-27~-10°C) occur in mid-winter (mid-January~early February) around the high mountainous areas of Mt Halla, while such risk does not exist in the low-elevated coastal regions of Jeju Island under subtropical climate. Strong wind around the subalpine climate belt lowers HST by 5°C than air temperature in mid-winter. In extreme cases when the advection of northerly cold wind is intensified by the west high-east low pressure pattern in East Asia, the HST around the peak of Mt Halla can be lowered to high risk level (-39~-28°C) in the early morning times of mid-winter days. These information about mountain bioclimate may help establish mountain extreme climate warning systems, which are needed to protect mountaineers from potential life-threatening accidents caused by extreme low HST events over high mountains such as Mt Halla.

In this study, a high-resolution daily data set of surface weather were obtained from PRIDE(PRISMbased Dynamic downscaling Error correction) model for the period of 2000 to 2017 over South Korea. The simulation data of five RCM(Regional Climate Model) were also used which are forced by the CMIP6 participating model UK-ESM as the boundary condition under historical period (2000-2014) and SSP 5-8.5 period (2015- 2017). Here we compared the RCM data and the PRIDE data with MK-PRISM data in terms of ensemble mean and ensemble spread. Results show that the PRIDE model effectively eliminates systematic error in the RCM up to 63.0% for daily average temperature, 72.2% for daily maximum temperature, 68.2% for daily minimum temperature, and 28.7% for daily precipitation when evaluated from the RMSE perspective. Overall, the ensemble spread of the PRIDE model is significantly decreased from 1.46°C to 0.36°C for daily temperature and from 2.0 mm/day to 0.72 mm/day for daily precipitation compared to the RCM ensemble spread, indicating that the largest systematic error of the RCMs is effectively removed in the PRIDE model.

As global warming continues, we expect extreme climate events to occur more frequently and intensively and the extremes to become normal eventually. Such timing of when the climate extremes in the present climate become normal in a future climate has been estimated in previous studies, but these studies used different methods and definitions, making an interpretation and application of the results difficult. Defining the year of beginning of a new normal climate as the timing of unprecedented climate (TUC), this study suggests a new estimation method using a return level of extreme temperatures. The TUC was estimated using CMIP5 climate model simulation data with an application to the wintertime daily maximum and minimum temperatures in Korea. With a 50-year return level obtained from Generalized Extreme Value distribution of the CMIP5 historical experiment data, overall in the models, TUC was estimated to come in the 2070s under RCP 8.5 (a business- as-usual) scenario and in the 2090s under RCP 4.5 (an intermediate level of emissions) scenario. Using this new method, TUC can be estimated for various fields globally or regionally in different seasons with different variables, providing a useful guideline for climate change mitigation and adaption policy and also a timetable for the actions.

Because of the population growth and industrialization in recent decades, the air quality over the world has been worsened with the increase of PM10 concentration. Korea is located near the eastern part of China which has many industrial complexes, so the consideration of China’s air quality is necessary for the PM10 prediction in Korea. This paper examines a machine learning-based modeling of the prediction of tomorrow’s PM10 concentration in the form of a gridded map using the AirKorea observations, Chinese cities’ air quality index, and NWP (numerical weather prediction) model data. A blind test using 23,048 cases in 2019 produced a correlation coefficient of 0.973 and an MAE (mean absolute error) of 4.097㎍/㎥, which is high accuracy due to the appropriate selection of input variables and the optimization of the machine learning model. Also, the prediction model showed stable predictability irrespective of the season and the level of PM10. It is expected that the proposed model can be applied to an operative system if a fine-tuning process using a larger database is accomplished.