In 2023, the average winter temperature in South Korea was around *** degrees Celsius, up more than two degrees Celsius compared to the previous year. The highest temperature since 2000 reached *** degrees Celsius in 2019, while the lowest temperature was **** degrees Celsius in 2012. Thus, the 2023 figure represents the second-highest average winter temperature in this century.

In June 2025, the average temperature in South Korea was **** degrees Celsius. August 2024 was the hottest month in the past five years, with a mean of around **** degrees Celsius. In the same period, December 2022 was the coldest month, with an average temperature of minus *** degrees Celsius.

In May 2025, the average temperature in Incheon, South Korea was 16.5 degrees Celsius. August 2024 was the city's hottest month in the past six years, while December 2022 was the coldest, with an average temperature of minus 2.6 degrees Celsius.

In May 2025, the average temperature in Seoul, South Korea was **** degrees Celsius. August 2024 was the hottest month in the city in the past six years, while December 2022 was the coldest, with an average temperature of minus *** degrees Celsius.

In May 2025, the average temperature in Jeju, South Korea, was 17.5 degrees Celsius. The island's hottest month was August 2024, while February 2022 was the coldest, with an average temperature of 5.2 degrees Celsius.

In May 2025, the average temperature in Busan, South Korea was 17.4 degrees Celsius. August 2024 was the city's hottest month in the past five years, while February 2025 was the coldest, with an average temperature of 2.9 degrees Celsius.

In May 2025, the average temperature in Gwangju, South Korea was 18.2 degrees Celsius. August 2024 was the city's hottest month in the past six years, while December 2022 and February 2025 were the coldest, with an average temperature of 1.1 degrees Celsius.

For extreme temperature, we used climate extreme indices provided by CLIVAR (Climate and Ocean-Variability, Predictability, and Change) ETCCDI (Expert Team on Climate Change Detection and Indices). ETCCDI has provided 27 climate extreme indices not only with global reanalysis datasets but with CMIP5 simulations. The indices data are available on-line and the results with CMIP5 simulations were summarized by Sillmann et al. [2013]. For our analysis, we downloaded a monthly minimum of daily minimum surface air temperature (TNn) and a monthly maximum of daily maximum temperature (TXx). Among the CMIP5, 27 model results available on their website, we used 23 model results containing both of the TNn and TXx for all of the historical, RCP 4.5 and 8.5 experiments.

Since our focus is on boreal-winter extreme temperature, we selected the lowest TNn and highest TXx among the three months of December-January-February every year from 1861 to 2005 for the historical simulation and from 2006 to 2099 for the RCP 4.5 and RCP 8.5 scenario. Before the spatial averaging over the analysis domain (34°N-43°N in latitude and 124°E-131°E in longitude including the Korean Peninsula), we had remapped all of the simulation data onto a 1.5° x 1.5° horizontal resolution.

The time of unprecedented climate (TUC) for extreme temperature is defined in this study as the beginning year when the extreme temperature projected for the future climate scenarios exceed a critical value in all subsequent years during the RCP scenario runs.

In this study, the critical value for extreme temperatures is specified as a 50-year return level which is rather arbitrary but refers to a rough estimate for the social lifetime of a man. One may find the return level empirically from historical data, but this study estimates it using a Generalized Extreme Value distribution function as suggested by Kharin et al. [2007]. Based on the CMIP5 historical simulation data using R, we obtained three parameters determining a GEV distribution for each model, respectively for TNn and TXx. The GEV distribution for each model and variable has been verified using a Q-Q (quantile-quantile) plot if it adequately describes the CMIP5 historical data. All of the models showed the Q-Q plot within the 95% confidence range (Figure 1a for GFDL-ESM2G TXx for an instance). Then, we estimated the return level from the distribution and TUC from the RCP scenario runs for the wintertime TNn and TXx averaged over Korea.

The daily minimum temperature record and the monthly heat sales record

In 2024, precipitation in Jeju in South Korea was the highest nationwide, with about 1928.9 millimeters. Gyeongnam followed with around 1713.6 millimeters.

This is climate data for major cities in South Korea (Seoul, Incheon, Daegu, Daejeon, Busan, and Jeju), originally provided by the Korea Meteorological Administration. I converted it to weekly data. Temperature and humidity have been standardized to show the highest, lowest, and average values for each week. The data covers the years from 2017 to 2024.

This data provides data on heated effluent discharge and temperature difference for each of Korea Western Power's plants (Taean, Pyeongtaek, Gunsan, and Seoincheon). These data are key indicators for assessing the environmental impact of cooling water discharged from power plants back into the water system. The monthly average temperature difference, calculated by comparing the water temperatures at the inlet and outlet, reflects seasonal variations. The temperature difference increases in summer due to increased cooling load and decreases in winter due to the increased temperature difference. Analysis of this monthly average data allows for a quantitative understanding of the thermal impact of power plant operations on aquatic ecosystems. This data serves as the basis for assessing compliance with environmental standards and developing efficient water resource management strategies.

In May 2025, the average temperature in Daegu, South Korea was 18.1 degrees Celsius. August 2024 was the hottest month in the city in the past six years, while December 2022 was the coldest, with an average temperature of 0.4 degrees Celsius.

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Köppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC).

The average temperature in South Korea in 2024 was **** degrees Celsius. The average temperature in South Korea has risen steadily over the years, which is shown in the graph. Extreme weather South Korea has a distinct four-season climate. Generally, summer in South Korea is humid and hot, while winter is dry and cold. However, the summer climate, which usually lasts from June to August, is getting longer and can last from May through to September. Especially in summer, extreme weather such as tropical nights, typhoons, and heatwaves occur. Recently, there was an increase in the consecutive days in which heatwaves reached temperatures above ** degrees. Greenhouse gas emissions South Korea is suffering from air pollution problems, such as yellow dust and fine dust, that have increased rapidly over recent years. In addition, as the carbon dioxide concentration has continued to rise, the average annual temperature has also risen steadily, resulting in abnormal climates, such as heatwaves in summer or extreme cold in winter. South Korea is one of the countries that produces a lot of greenhouse gases. Due to the manufacturing-oriented industrial structure, greenhouse gas emissions from energy use account for a large portion.

Understanding climatic effect on wildlife is essential to prediction and management of climate change’s impact on the ecosystem. The climatic effect can interact with other environmental factors. This study aimed to determine effects of climate and altitude on Siberian roe deer (Capreolus pygargus) activity in temperate forests of South Korea. We conducted camera trapping to investigate roe deer’s activity level from spring to fall. Logistic regressions were used to determine effects of diel period, temperature, rain, and altitude on the activity level. A negative relationship was noted between temperature and the activity level due to thermoregulatory costs. Roe deer activity exhibited nocturnal and crepuscular patterns during summer and the other seasons, respectively, possibly due to heat stress in summer. In addition, the effect of temperature differed between high- and low-altitude areas. In low-altitude areas, temperature affected negatively the activity level throughout the study..., The camera trapping method was used to observe temporal variations in roe deer capture (sampling days: September to October 2021 and April to August 2022). In the study area, a 5 × 6 grid design (interval = 600 m) was established, and one trail camera (Spec Ops Elite HP4; Browning Co., USA) was deployed corresponding to each cell of the grid. The study period was divided into five seasons, and further analyses were performed for each season: spring (15 April to 15 May, 960 trap-days), early summer (16 May to 30 June, 1380 trap-days), summer (1 July to 31 August, 1860 trap-days), early fall (September, 900 trap-days) and fall (October, 810 trap-days). The camera-plot altitudes were categorised into four classes: 600 (600–800 m asl, n = 3), 800 (800–1,000 m asl, n = 10), 1,000 (1,000–1,200 m asl, n = 11) and 1,200 (1,200–1,400 m asl, n = 6). We created a roedeer variable as presence/absence of observation per 2-h in each altitude class. In order to account for sampling effort depending on..., , This README file was generated on 2023-09-22 by Tae-Kyung Eom.

GENERAL INFORMATION

1. Title of Dataset: Adaptive response of Siberian roe deer (Capreolus pygargus) to climate and altitude in the temperate forests of South Korea

2. Author Information A. Principal Investigator Contact Information Name: Tae-Kyung Eom Institution: Chung-Ang University Address: Ansung, South Korea Email: xorud147@naver.com

   B. Associate or Co-investigator Contact Information Name: Jae-Kang Lee Institution: Chung-Ang University Address: Ansung, South Korea

   Name: Dong-Ho Lee Institution: Chung-Ang University Address: Ansung, South Korea

   Name: Hyeongyu Ko Institution: Chung-Ang University Address: Ansung, South Korea

   Name: Shin-Jae Rhim Institution: Chung-Ang University Address: Ansung, South Korea

3. Date of data collection (single date, range, approximate date): 2021-2022

4. Geographic location of data collection: Mt. Gariwang, Pyeo...

Spring mean temperatures were calculated by averaging daily temperatures from March to May, summer mean temperature from June to August, fall mean temperature from September to November, and winter mean temperature from December to February.23 climate variables: 19 bioclimate and 4 seasonal mean temperature variables considered in this study.

Geographical information and the number of records of 54 weather stations.

The constant temperature and humidity air conditioner market is experiencing robust growth, driven by increasing demand across various sectors. The market's expansion is fueled by several key factors. Firstly, the rising adoption of precision climate control in industries like pharmaceuticals, electronics, and data centers is a significant driver. These sectors require extremely stable environmental conditions for optimal product performance and data integrity, leading to a substantial increase in demand for high-precision air conditioning systems. Secondly, advancements in technology are contributing to improved energy efficiency and reduced operational costs, making these systems more attractive to businesses. Smaller, more efficient units, coupled with smart controls and remote monitoring capabilities, are enhancing the overall value proposition. Furthermore, growing awareness of the potential damage caused by temperature and humidity fluctuations on sensitive products is further propelling market expansion. We can estimate the market size in 2025 to be around $5 billion, based on a conservative projection considering industry growth trends and competitive landscape. Assuming a CAGR of 7% (a reasonable estimate given technological advancements and industry growth in similar sectors), the market is expected to reach approximately $8 billion by 2033. However, market growth is not without its challenges. High initial investment costs can be a significant barrier to entry for smaller businesses. The complexity of installation and maintenance also necessitates specialized expertise, adding to overall expenses. Furthermore, stringent environmental regulations regarding refrigerants are influencing product design and manufacturing, potentially impacting cost and availability. Despite these restraints, the long-term outlook for the constant temperature and humidity air conditioner market remains positive, driven by consistent technological improvements and the increasing demand from sectors that require precise climate control. Key players like H.Stars Group, Shenling, HICON, Hi Air Korea, Socoman, and Shitusi are expected to continue shaping the market landscape through innovation and strategic expansion.

In May 2025, the average temperature in Daejeon, South Korea was 18 degrees Celsius. August 2024 was the city's hottest month in the past five years, while December 2022 was the coldest, with an average temperature of minus two degrees Celsius.