| RCP 시나리오기반 평균기온, 적산온도 불확실성 보완 프로그램 | |
| A Simple Program Improving Uncertainly Average Temperature and Growing Degree Days based on RCP Scenario | |
| 유동수*,**† ,권오창*** ,김홍기****† | |
| Yu, Dong-Su*,**†,Kwon, Oh-Chang*** and Kim, Hong-Gie****† | |
| Greenhouse gas released into the environment since the industrial revolution in the 18th century is resulting in global warming and is a critical issue with regard to climate change, which may result in problems such as influx of invasive alien plant/animal species, outbreak of endangered species, and spread of disease. In some studies on climate change and the ecosystem, average temperature and growing degree days (GDD) are basic and important climatic factors that are closely related to the conditions suited for growth and survival of animals and plants. When predicting climate change with average temperature and GDD, the Representative Concentration Pathway (RCP) is the main available scenario for the future climate. However, the RCP scenario has some errors because of its uncertainty caused by complex climate models, by inaccurate greenhouse gas emission, and by the physical natural environment. Thus, it is necessary to compensate the climatic data of the RCP scenario. We developed a simple program named RGI (RCP scenario, based Growing degree days Interpolation) for interpolating average temperature and GDD per day calculated from the RCP scenario (resolution 1 km) as supported by the Korea Meteorological Administration (KMA) in South Korea. Our program interpolates average temperature and GDD from a set of RCP scenario data using a quadratic model and nonlinear models such as the self, starting logistic, Gompertz, or/and Weibull functions. When we tested the RGI program against the actual temperatures in Seoul and Buyeo, South Korea, RGI was close to the observed temperature and had significantly less residual standard error in linear regression analysis than the RCP scenario (p, value < 0.05) and showed similar results for an additional 10 sites. Based on these results, we expect that RGI can improve the uncertainty of the RCP scenario. As RGI is coded using Perl script language and R open source, it can be easily used. The executive RGI source is available at https://sourceforge.net/projects/rgi/. | |
| Climate Change, Average Temperature, Growing Degree Day | |
 1102-03.pdf | |
| 2093-5919 | |
| 2586-2782 | |
| 2020-04 |
