• Journal of the Korean earth science society
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• v.32 no.7
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• pp.750-760
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• 2011

In this study, the reproducibility of the simulated current climate by using two regional climate models, such as Seoul National University Regional Climate Model (SNURCM) and Weather Resuearch and Forecasting (WRF), is evaluated in advance to produce the standard regional climate scenario of future climate. Within the evaluation framework of a COordinated Regional climate Downscaling EXperiment (CORDEX), 28-year-long (1978-2005) regional climate simulation was conducted by using the Hadley Centre Global Environmental Model (HadGEM2-AO) global simulation data of the National Institute of Meteorological Research (NIMR) as a lateral boundary forcing. The simulated annual surface temperatures were in good agreement with the observation; the spatial correlation coefficients between each model and observation were over 0.98. The cold bias, however, were shown over the northern boundary in the both simulated results. In evaluation of the simulated precipitation, the skill was reasonable and good. The spatial correlation coefficients for the precipitation over the land area were 0.85 and 0.79 in SNURCM and WRF, respectively. It is noted that two regional climate models (RCMs) have different characteristics for the distribution of precipitation over equatorial and midlatitude areas. SNURCM shows better distribution of the simulated precipitation associated with the East Asia summer monsoon in the mid-latitude areas, but WRF shows better in the equatorial areas in comparison to each other. The simulated precipitation is overestimated in summer season (JJA) rather than in spring season (MAM), whereas the spatial distribution of the precipitation in spring season corresponds to the observation better than in summer season. Also the RCMs were capable of reproducing the annual variability of the maximum amount and its timing in July, in which the skills over the inland area were in better agreement with the observation than over the maritime area. The simulated regional climates, however, have the limitation to represent the number of days for extremely hot temperature and heavy rainfall over South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.312-317
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• 2006

지역규모의 기후변화 모의결과를 이용하여 금강유역 용담댐의 홍수기 치수안전도에 대한 민감도분석을 수행하였다. 기후변화 모의에 사용된 SNURCM(Seoul National University Regional Climate Model)은 미국 National Center for Atmospheric Research의 Community Climate System Model의 전지구모형을 기반으로 spectral nudging 기법을 사용한 공간해상도 30 km, 연직 21층의 지역기후모형이다. 기후변화 시나리오로는 SRES 'B1'이 사용되었으며 과거 control run에 대한 기후모의 정확도 분석을 통하여 SNURCM 기상자료를 관측치와 비교한 결과 면적강우량을 다소 과소추정하였고 이점을 감안하여 SNURCM의 일 모의결과에 보정 계수를 적용하였다. 하천유출량은 SSARR 모형을 이용하여 SNURCM 모의가 수행된 전체기간을 $1980{\sim}1999$년과 $2000{\sim}2019$년으로 20년씩 나누어 용담댐 일 유입량을 산정하여 통계분석을 실시하였고 과거와 미래 20년 동안을 비교하여 본 결과 (1) 유량의 평균보다는 분산이 미래 20년 동안 증가하여 가뭄과 홍수에 대한 위험도가 증가함을 알 수 있었고, (2) 특히 연최대유량 또한 미래 20년 동안 상당히 증가하여 홍수기 치수대책이 더욱 중요해질 것으로 판단되었다. 마지막으로 용담댐 운영은 범용 시스템분석 도구인 STELLA(System Thinking Experimental Learning Laboratory with Animation) 상에서 GUI로 구현하여 유입량 변화에 따른 용담댐 치수안전도 변화를 모의해 보았다. 용담댐의 홍수기 운영은 저수지 수위가 제한수위를 초과하기 시작하면 Rigid ROM 발효하여 방류량을 결정하도록 구성하였고, 무효방류(spill)가 일어나는 현상을 실패로 가정하여 이에 대한 신뢰도(reliability), 회복도(resiliency), 그리고 심도(vulnerability)를 치수안전도 지표로 계산하였다. 전체기간을 1980년${\sim}$1999년, 2000년${\sim}$2019년, 2000년${\sim}$2009년, 그리고 2010년${\sim}$2019년까지 총 4구간으로 나누어 결과를 도출하였으며 예상한 바와 같이 후반기 20년 동안에 세 가지 지표가 취약해 지는 것을 확인할 수 있었고, 특히 2000년부터 2009년까지 10년 동안에는 더욱 취약해짐을 확인할 수 있었다.

• Atmosphere
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• v.18 no.4
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• pp.387-395
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• 2008

The impact of horizontal resolution on a regional climate model was investigated by simulating precipitation over the Korean Peninsula. As a regional climate model, the SNURCM(Seoul National University Regional Climate Model) has 21 sigma layers and includes the NCAR CLM(National Center for Atmospheric Research Community Land Model) for land-surface model, the Grell scheme for cumulus convection, the Simple Ice scheme for explicit moisture, and the MRF(Medium-Range Forecast) scheme for PBL(Planetary Boundary Layer) processing. The SNURCM was performed with 20 km resolution for Korea and 60 km resolution for East Asia during a 20-year period (1980-1999). Although the SNURCM systematically underestimated precipitation over the Korean Peninsula, the increase of model resolution simulated more precipitation in the southern region of the Korean Peninsula, and a more accurate distribution of precipitation by reflecting the effect of topography. The increase of precipitation was produced by more detailed terrain data which has a 10 minute terrain in the 20 km resolution model compared to the 30 minute terrain in the 60 km resolution model. The increase in model resolution and more detailed terrain data played an important role in generating more precipitation over the Korean Peninsula. While the high resolution model with the same terrain data resulted in increasing of precipitation over the Korean Peninsula including the adjoining sea, the difference of the terrain data resolution only influenced the precipitation distribution of the mountainous area by increasing the amount of non-convective rain. In conclusion, the regional climate model (SNURCM) with higher resolution simulated more precipitation over the Korean Peninsula by reducing the systematic underestimation of precipitation over the Korean Peninsula.

• Journal of Korea Water Resources Association
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• v.40 no.2 s.175
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• pp.147-158
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• 2007

This study attempted to determine a suitable hydrologic model for assessing the impact of climate change on water resources, and to assess the accuracy of streamflow scenarios simulated by the selected hydrologic model using the meteorological scenarios of the Seoul National University Regional Climate Model(SNURCM). Comparison of four water balance models and two daily conceptual rainfall-runoff models for the simulation capability of the Daecheong Dam inflow indicated that the abcd model performs the best among the tested water balance models and performs as well as SSARR that is popular as a daily rainfall-runoff model in Korea. Parameters of the abcd model were then estimated for 12 ungauged subbasins of the Geum River by the regionalization method. The model parameters were first calibrated at nine multi-purpose dams and were then regionalized using catchment characteristics for another four multi-purpose dams, which were assumed to be ungauged sites. The model efficiency(ME) coefficients of the simulated inflows for these four dams were at least 87%. The MEs of the hindcasted meteorological rainfall scenarios of the 12 subbasins of the Geum River were more than 60%. Moreover, the ME of the Daecheong Dam inflow simulated by the abcd model using the SNURCM rainfall scenarios was more than 80%. Therefore, this research concluded that the abcd model coupled with the SNU-RCM meteorological scenarios can be used for impact assessment studies of climate change on water resources.

• Atmosphere
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• v.25 no.4
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.2
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• pp.79-88
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• 2015

Preparations need to be made for Korean pine(Pinus koraiensis) in anticipation of climate change because Korean pine is an endemic species of South Korea and the source of timber and pine nut. Therefore, climate change adaptation policy has been established to conduct an impact assessment on the distribution of Korean pine. Our objective was to predict the distribution of Korean pine while taking into account uncertainty and afforestation conditions. We used the 5th forest types map, a forest site map and BIOCLIM variables. The climate scenarios are RCP 4.5 and RCP 8.5 for uncertainty and the climate models are 5 regional climate models (HadGEM3RA, RegCM4, SNURCM, GRIMs, WRF). The base period for this study is 1971 to 2000. The target periods are the mid-21st century (2021-2050) and the end of the 21st century (2071-2100). This study used the MaxEnt model, and 50% of the presences were randomly set as training data. The remaining 50% were used as test data, and 10 cross-validated replicates were run. The selected variables were the annual mean temperature (Bio1), the precipitation of the wettest month (Bio13) and the precipitation of the driest month (Bio14). The test data's ROC curve of Korean pine was 0.689. The distribution of Korean pine in the mid-21st century decreased from 11.9% to 37.8% on RCP 4.5 and RCP 8.5. The area of Korean pine at an artificial plantation occupied from 32.1% to 45.4% on both RCPs. The areas at the end of the 21st century declined by 53.9% on RCP 4.5 and by 86.0% on RCP 8.5. The area of Korean pine at an artificial plantation occupied 23.8% on RCP 4.5 and 7.2% on RCP 8.5. Private forests showed more of a decrease than national forests for all subsequent periods. Our results may contribute to the establishment of climate change adaptation policies for considering various adaptation options.