http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
표준강수지수 및 팔머가뭄지수를 이용한 남한 가뭄의 시공간적 특성
박창의 ( Park C. E. ) 강원대학교 농업생명과학연구원(구 농업과학연구소) 2017 강원 농업생명환경연구 Vol.29 No.3
This study investigated the occurrence and intensity of drought in South Korea between 1951 and 2012, using two drought indices; Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Spatial distributions of both drought indices over East Asia was examined to determine the possible association of drought occurrence in South Korea with its neighboring regions. Based on 3-month and 12-month SPI measured throughout the analysis period, drought occurs 34 and 14 times in South Korea. Drought frequency was significant at 8-10 months, and 2-3, 6-7, and 15 years for the 3-month and 12-month SPI, respectively, based on wavelet analysis. Drought events were also determined by PDSI and were similar to those evaluated by SPI before 2000; however, drought occurred more frequently after 2000. Across East Asia, 12-month SPI showed that the drought occurrence in South Korea was associated with the wet weather in the southern China, and based on PDSI, South Korea drought events were also associated with drought in central China. The difference between SPI and PDSI results indicates that multiple drought indices are necessary for better analysis and prediction of drought in South Korea.
김종호,정수종,박창의,박훈영,손성원,김상용 한국기상학회 2022 Asia-Pacific Journal of Atmospheric Sciences Vol.58 No.2
Phenological shifts associated with climate and environmental change are evident in temperate forests. Further dense and comprehensive observations are necessary to understand the species-specific and regional variation in the responses of plant phenology to climate change. This study first introduces the national-scale phenology network (NPN) over South Korea. By having newly established phenology datasets for the period 2009–2018, we analyze spatial and temporal variations of phenology of 21 deciduous species in spring and autumn as well as 4 evergreen species in spring. For deciduous species, the phenological responses are examined using the dates of budburst, leaf unfolding, leaf coloring, and leaf fall, whereas the times of pollen start and pollen peak are adopted to investigate the responses of evergreen species. The average dates of budburst and leaf unfolding are changed by -1.1 and − 1.3 day/year, respectively, in agreement with the observed trends in temperature. The pollen activity (start and peak) of evergreen species is significantly advanced by 1.4 day/year throughout Korea, suggesting apparent phenological response of evergreen species to the warming. Especially, the shrub had up to 45%larger temperature sensitivity than the subtree or tree in spring. By contrast, changes in the autumn phenology of deciduous species are not evident because of the large interannual variability in the dates of leaf coloring and leaf fall. Then, the large interannual variation in autumn phenology could constrain the interannual variability in the length of the growing season of temperate forest. We further expect on-going efforts on national-scale phenology monitoring will have an important contribution to understanding vegetation growing season changes related to warming.
이세현,정수종,박창의,김종호 한국기상학회 2022 Asia-Pacific Journal of Atmospheric Sciences Vol.58 No.2
Predicting plant phenology is considered the foundational for the forecast of ecosystem function and dynamics from species level to global level. However, the exact prediction of plant phenology remains limited because of the challenges associated with adding exact environmental and physiological cues to numerical models. In this study, we developed a simple data-based prediction model for leaf coloring dates of temperate deciduous trees by applying machine learning to datasets obtained from the newly established South Korean national-scale phenology network (NPN). Ground observations of spring leaf unfolding dates for 2009–2018 obtained from NPN together with data on the environmental drivers of leaf coloring (summer mean temperature, altitude) were utilized for the model. The model can be evaluated to have simulated the characteristics of observed leaf coloring dates relatively accurate, with only a two-day difference between the average observed and predicted leaf coloring dates. In addition, the model yielded an RMSE value of approximately 7 days, which is within the acceptable error criteria when compared to the sampling frequency, despite the use of only three input variables. Data-based machine learning using existing spring leaf unfolding data as an input help us predict autumn phenology better, even without precise species-specific physiological knowledge on leaf coloring mechanisms. Consequently, a phenology network across the globe based on steady observations will be favorable datasets for a phenology prediction model that can be applied widely.
허창회,박태원,전상윤,이민희,박창의,김진원,이석조,홍유덕,송창근,이재범 한국기상학회 2011 Asia-Pacific Journal of Atmospheric Sciences Vol.47 No.4
A series of coupled atmosphere-ocean-land global climate model (GCM) simulations using the National Center for Atmospheric Research (NCAR) Community Climate System Model 3 (CCSM3)has been performed for the period 1870-2099 at a T85 horizontal resolution following the GCM experimental design suggested in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). First, a hindcast was performed using the atmospheric concentrations of three greenhouse gases (CO2, CH4,N2O) specified annually and globally on the basis of observations for the period 1870-1999. The hindcast results were compared with observations to evaluate the GCM’s reliability in future climate simulations. Second, climate projections for a 100-year period (2000-2099) were made using six scenarios of the atmospheric concentrations of the three greenhouse gases according to the A1FI, A1T, A1B, A2,B1, and B2 emission profiles of the Special Report on Emissions Scenarios. The present CCSM simulations are found to be consistent with IPCC’s AR4 results in the temporal and spatial distributions for both the present-day and future periods. The GCM results were used to examine the changes in extreme temperatures and precipitation in East Asia and Korea. The extreme temperatures were categorized into warm and cold events: the former includes tropical nights, warm days,and heat waves during summer (June-July-August) and the latter includes frost days, cold days, and cold surges during winter (December-January-February). Focusing on Korea, the results predict more frequent heat waves in response to future emissions: the projected percentage changes between the present day and the late 2090s range from 294% to 583% depending on the emission scenario. The projected global warming is predicted to decrease the frequency of cold extreme events; however, the projected changes in cold surge frequency are not statistically significant. Whereas the number of cold surges in the A1FI emission profile decreases from the present-day value by up to 24%, the decrease in the B1 scenario is less than 1%. The frequency and intensity of extreme precipitation events year-round were examined. Both the frequency and the intensity of these events are predicted to increase in the region around Korea. The present results will be helpful for establishing an adaptation strategy for possible climate change nationwide, especially extreme climate events, associated with global warming.