This study had two main objectives. We first investigated which weather phenomena people were most concerned about in the context of climate change or global warming. Then, we conducted content analysis to find which words were more commonly used with climate change or global warming. For this, we collected web data from Twitter, Naver, and Daum from April to October 2019 in the Republic of Korea. The results suggested that people were more concerned about air quality, followed by typhoons and heat waves. Because this study only considered one warm period in the year of 2019, winter-related weather phenomena such as cold wave and snowfall were not well captured. From Twitter, we were able to find wider range of terminologies and thoughts/opinions than Naver and Daum. Also, more life-relevant weather events such as typhoons and heat waves in Twitter were commonly mentioned compared to Naver and Daum. On the other hand, the comments from Naver and Daum showed relatively narrower and limited terms and thoughts/ opinions. Especially, most of the comments were influenced by headlines of articles. We found many comments about air quality and energy/economic policy. We hope this paper could provide background information about how to promote the climate change education and public awareness and how to efficiently interact with general audiences.

Meanwhile, reference evapotranspiration(ET0) is important information for agricultural management including irrigation planning and drought assessment, the database of reference evapotranspiration for future periods was rarely constructed especially at districts unit over the country. The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides several meteorological data such as precipitation, average temperature, humidity, wind speed, and radiation for long-term future period at daily time-scale. This study aimed to build a database for reference evapotranspiration using the climate forecasts at high resolution (the outputs of HadGEM3-RA provided by Korea Meteorological Administration (KMA)). To estimate reference evapotranspiration, we implemented four different models such as FAO Modified Penman, FAO Penman-Monteith, FAO Blaney-Criddle, and Thornthwaite. The suggested database system has an open architecture so that user could add other models into the database. The database contains 5,050 regions’ data for each four models and four Representative Concentration Pathways (RCP) climate change scenarios. The developed database system provides selecting features by which the database users could extract specific region and period data.

최근 전 세계적으로 지구온난화에 따른 기후변화로 태풍, 지진해일, 한파, 폭염 등과 같은 자연재해의 피해가 대규모로 확대됨에 따라 기후변화에 대한 관심이 증가하고 있다. 근본적으로 지구온난화를 유발하는 가장 큰 원인은 대기 중의 온실가스를 들 수 있다. 온실가스의 농도가 해마다 증가하는 것으로 조사되었으며, 특히 지난 2013년 5월경 미국 하와이 마우나로아 관측소에서 측정한 대기 중 이산화탄소 농도가 상징적 기준점으로 여겨지는 400ppm을 초과하였다. 지금까지 많은 연구에 의하여 온실가스의 대기 중 농도 증가와 지구온난화를 연관하여 어떠한 관계가 있는지 설명하기 위해 여러 기후모델을 사용하였으며, 모든 기후모델의 실험 결과 지구온난화의 주된 원인이 온실가스의 농도 증가라는 것을 증명하였다. 우리나라 국립기상연구소는 RCP에 기반한 전지구/지역 기후변화 시나리오 개발과 더불어 국가 차원의 기후변화 대응을 위한 국가 표준 기후변화 시나리오를 개발하고 있다. RCP 데이터는 총 4가지 시나리오를 포함하고 있으며 최근 온실가스 농도 증가의 추세를 반영한 시나리오는 RCP 8.5시나리오이다. 과거에 발생한 태풍의 정보에 대해서는 관측 자료를 이용하여 알 수 있지만, 미래의 태풍 발생 위치와 강도에 대해서는 알 수가 없으며, 기후변화 시나리오에서 역시 산출되지 않는다. Emanuel & Nolan(2004)은 태풍 발생과 연관된 기상요소(상대습도, wind shear 등)를 이용하여 계산하는 태풍발생지수를 개발하였다. 본 연구에서는 RCP 8.5 시나리오로부터 태풍발생지수를 계산하기 위한 기상요소를 산출하고 이를 바탕으로 1982년~2100년 기간 동안 태풍발생지수를 계산하였으며, 태풍발생지수와 이력태풍발생위도와 상관도가 높다고 판단, 태풍이 발생한 위도와 태풍발생지수에 기반한 태풍발생모형을 개발하였다.

In this study, the ultra-high resolution ground information database (30m × 30m), such as elevation map, facet map, coastal map, land cover map, was constructed over Korean Peninsula. ASTER GDEM with 30m resolution was used to generate elevation map, facet map, coastal map, and the accuracy of GDEM was validated using DEM constructed with 1:25,000 digital map. The facet map was generated with 8 direction and flat area using GDEM. The coastal map with 6 categories was generated by buffering of the distance from coast line, additionally considered with elevation. The land cover map was generated with Landsat ETM+ 24 scenes (around 2000’s) by supervised classification, the land cover classes was composed with urbanization, agriculture, green field, forest, tidal flat, bare land, water area. The file format of ground information database is 8-bit or 16-bit unsigned Geotiff, the image size is 27,331 × 40,858 pixels, and the file size is 1.04GB or 2.08GB. The coordinate system composed of UTM projection and WGS84 ellipsoid was applied to the database for the equal grid resolution. This ultra-high resolution ground information database will be able to provide a basis for regional climate modelling and forecasting accuracy enhancement.