• Journal of Korea Water Resources Association
• /
• v.34 no.4
• /
• pp.347-356
• /
• 2001

Even though the increase of greenhouse gases such as $CO_2$ is thought to be the main cause for global warming, its impact on global climate has not been revealed clearly in rather quantitative manners. However, researches using Genral Circulation Model(GCM) has shown that the accumulation of greenhouse gases increases the global mean temperature, which in turn impacts on the global water circulation pattern. A climate predictive capability is limited by lack of understanding of the different process governing the climate and hydrologic systems. The prediction of the complex responses of the fully coupled climate and hydrologic systems can be achieved only through development of models that adequately describe the relevant process at a wide range of spatial and temporal scales. These models must ultimately couple the atmospheres, oceans, and lad and will involve many submodels that properly represent the individual processes at work within the coupled components of systems. So far, there are no climate and related hydrologic models except local rainfall-runoff models in Korea. The purpose of this research is to predict the change of temperature in Korean Peninsula using regional scale model(IRSHAM96 model) and GCM data obtained from the increasing scenarios of $CO_2$ Korean Peninsula increased by $2.5^{\circ}C$ and the duration of Winter in $lxCO_2$ condition would be shorter the $2xCo_2$ condition due to global warming.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.138-142
• /
• 2005

The change of precipitation and temperature due to the global. warming eventually caused the variation of water availability in terms of potential evapotranspiration, soil moisture, and runoff. In this reason national long-term water resource planning should be considered the effect of climate change. Study of AOGCM-based scenario to proposed the plausible future states of the climate system has become increasingly important for hydrological impact assessment. Future climate changes over East Asia are projected from the coupled atmosphere-ocean general circulation model (AOGCM) simulations based on Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 and B2 scenarios using multi-model ensembles (MMEs) method (Min et al. 2004). MME method is used to reduce the uncertainty of individual models. However, the uncertainty increases are larger over the small area than the large area. It is demonstrated that the temperature increases is larger over continental area than oceanic area in the 21st century.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.89-89
• /
• 2023

인간 활동에 의해 발생한 전 지구적 기후변화는 다양한 분야에 영향을 미치고 있다. 특히, 군락을 기반으로 서식하는 동식물은 기후변화에 가장 취약하며, 대부분의 군락 위치가 북상하거나 멸종 위기에 처해있다. 2022년에 발표된 IPCC(Intergovernmental Panel on Climate Change) 보고서는 섭씨 5도 이상 상승하면 생물군의 60%가 멸종될 것이라고 보고하였으며, 고위도와 고도로 이동하여 봄철 식물 성장이 과거보다 더욱 가속화 될 것으로 예측하였다. 따라서, 온실가스 농도에 따른 전 지구적 기후변화 분석은 다양한 분야에서 지속가능한 완화 및 적응 정책을 결정하는데 필요하다. 본 연구는 SSP2-4.5와 SSP5-8.5를 이용하여 Koppen-Geiger의 기후대 분류에 따른 전 지구 규모(아시아, 유럽, 남아메리카, 북아메리카, 오세아니아, 아프리카)의 과거 및 미래 기후대에 대한 변화를 분석하였다. 과거 기간의 기후대를 추정하기 위해 25개 CMIP6(Coupled Model Intercomparison Project 6) GCM(General circulation model)의 월 단위 강수량과 표면 온도를 사용하였으며 6개의 기간으로 구분하여 기후대 변화를 비교하였다. 더 나아가, 미래 기후대를 예측하기 위해 SSP(Shared Socioeconomic Pathways)2-4.5와 SSP5-8.5의 미래 기후변수를 사용하였으며, 전망 기간을 7개로 구분하여 전망 기간의 기후대를 변화를 비교하였다. 본 연구의 결과로는 온실가스 농도가 높은 시나리오에서는 북아메리카, 아시아, 유럽의 툰드라와 영구동토층이 가파르게 감소하였으며, 온대 기후 중 습한 아열대 기후대의 면적이 급속도로 증가하였다. 더 나아가, 남아메리카의 경우 대륙성 기후대가 지속적으로 감소하는 반면에 열대 우림 기후대는 증가한다. 오세아니아의 미래 기후대는 몬순의 영향을 받는 아열대 기후대가 증가하고 열대 우림은 증가할 것으로 예측하였다.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2011.11a
• /
• pp.20-27
• /
• 2011

A system for fine-resolution long-range weather forecast is introduced in this study. The system is basically consisted of a global-scale coupled general circulation model (CGCM) and Weather Research and Forecast (WRF) regional model. The system makes use of a data assimilation method in order to reduce the initial shock or drift that occurs at the beginning of coupling due to imbalance between model dynamics and observed initial condition. The long-range predictions are produced in the system based on a non-linear ensemble method. At the same time, the model bias are eliminated by estimating the difference between hindcast model climate and observation. In this research, the predictability of the forecast system is studied, and it is illustrated that the system can be effectively used for the high resolution long-term weather prediction. Also, using the system, fine-resolution climatological data has been produced with high degree of accuracy. It is proved that the production of agrometeorological variables that are not intensively observed are also possible.

• Atmosphere
• /
• v.25 no.2
• /
• pp.353-366
• /
• 2015

The impact of land and ocean initial condition on coupled general circulation model seasonal predictability is assessed in this study. The CGCM used here is Pusan National University Couple General Circulation Model (PNU CGCM). The seasonal predictability of the surface air temperature and ocean potential temperature for boreal winter are evaluated with 4 different experiments which are combinations of 2 types of land initial conditions (AMI and CMI) and 2 types of ocean initial conditions (DA and noDA). EXP1 is the experiment using climatological land initial condition and ocean initial condition to which the data assimilation technique is not applied. EXP2 is same with EXP1 but used ocean data assimilation applied ocean initial condition. EXP3 is same with EXP1 but AMIP-type land initial condition is used for this experiment. EXP4 is the experiment using the AMIP-type land initial condition and data assimilated ocean initial condition. By comparing these 4 experiments, it is revealed that the impact of data assimilated ocean initial is dominant compared to AMIP-type land initial condition for seasonal predictability of CGCM. The spatial and temporal patterns of EXP2 and EXP4 to which the data assimilation technique is applied were improved compared to the others (EXP1 and EXP3) in boreal winter 2m temperature and sea surface temperature prediction.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.12 no.1
• /
• pp.11-22
• /
• 2010

According to IPCC 4th Assessment Report, concentration of carbon dioxide has been increasing by 30% since Industrial Revolution. Most of IPCC $CO_2$ emission scenarios estimate that the concentration will reach up to double of its present level within 100-year if the current tendency continues. The global warming has resulted in the agro-climate change over the Korean Peninsula as well. Accordingly, it is necessary to understand the future agro-climate induced by the increase of greenhouse gases in terms of the agro-climatic indices in the Korean peninsula. In this study, the future climate is simulated by an atmosphere/ocean/land surface/sea ice coupled general circulation climate model, Pusan National University Coupled General Circulation Model(hereafter, PNU CGCM), and by a regional weather prediction model, Weather Research and Forecasting Model(hereafter, WRF) for the purpose of a dynamical downscaling. The changes of the vegetable period and the crop growth period, defined as the total number of days of a year exceeding daily mean temperature of 5 and 10, respectively, have been analyzed. Our results estimate that the beginning date of vegetable and crop growth periods get earlier by 3.7 and 17 days, respectively, in spring under the $CO_2$-doubled climate. In most of the Korean peninsula, the predicted frost days in spring decrease by 10 days. Climatic production index (CPI), which closely represent the productivity of rice, tends to increase in the double $CO_2$ climate. Thus, it is suggested that the future $CO_2$ doubled climate might be favorable for crops due to the decrease of frost days in spring, and increased temperature and insolation during the heading date as we expect from the increased CPI.

• Atmosphere
• /
• v.22 no.4
• /
• pp.455-464
• /
• 2012

This study investigates a 12 month-lead predictability of PNU Coupled General Circulation Model (CGCM) V1.1 hindcast, for which an oceanic data assimilated initialization is used to generate ocean initial condition. The CGCM, a participant model of APEC Climate Center (APCC) long-lead multi-model ensemble system, has been initialized at each and every month and performed 12-month-lead hindcast for each month during 1980 to 2011. The 12-month-lead hindcast consisted of 2-5 ensembles and this study verified the ensemble averaged hindcast. As for the sea-surface temperature concerns, it remained high level of confidence especially over the tropical Pacific and the mid-latitude central Pacific with slight declining of temporal correlation coefficients (TCC) as lead month increased. The CGCM revealed trustworthy ENSO prediction skills in most of hindcasts, in particular. For atmospheric variables, like air temperature, precipitation, and geopotential height at 500hPa, reliable prediction results have been shown during entire lead time in most of domain, particularly over the equatorial region. Though the TCCs of hindcasted precipitation are lower than other variables, a skillful precipitation forecasts is also shown over highly variable regions such as ITCZ. This study also revealed that there are seasonal and regional dependencies on predictability for each variable and lead.

• Journal of Environmental Science International
• /
• v.7 no.1
• /
• pp.27-35
• /
• 1998

The present study intends to investigate the transient response of an atmosphere /ocean general circulation model to a gradual Increase of atmospheric carbon dioxide. To detect the climatic change of the for 1% $CO_2$ run with increasing $CO_2$ and the control run with fried $CO_2$ are compared. From response of the surface air temperature due to the gradual increase of atmospheric carbon dioxide is slowly increased with latitudes and is clearly larger over continents than oceans. The annual goffal mean temperature is continuously increased with 0.03552 per one year with strong SIN ratio and distinguished from the natural variability The time dependent response of the gradual increasing $CO_2$ has the strong seasonal variability with small change In summer and large change in winter, and the strong regional In the Asian and the American continents. It has been suggested that the direct and the feedback processes in the climate systems should be investigated by the detailed sensitivity runs to get the meaninguul estimate of the $CO_2$ forced variability.

• Atmosphere
• /
• v.17 no.4
• /
• pp.365-379
• /
• 2007

Global warming may shift the properties and dynamics of ENSO. We study the changes in ENSO characteristics in a coupled general circulation model, ECHO-G/S. First, we analyse the mean state changes by comparing present day simulation and various high $CO_2$ climates. The model shows a little El Nino-like changes in the sea surface temperature and wind stress in the eastern tropical Pacific. As the mean temperature rises, the ENSO amplitude and the frequency of strong El Ninos and La Nina decrease. The analysis shows that the weakening of the oceanic sensitivities is related to the weakening of ENSO. In addition to the surface changes, the remote subsurface sea temperature response in the western Pacific to the wind stress in the eastern Pacific influences the subsequent ENSO amplitude. However, ENSO amplitude does not show linear response to the greenhouse gas concentrations.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.224-224
• /
• 2019

국제기상기구의 Global Framework for Climate Services (GFCS)의 관점에서 살펴보면 국내의 기상 기후 정보는 기상청을 중심으로 관측 자료와 중장기 예측 및 기후변화 시나리오 정보 등의 다양한 시간규모로 생산되고 있다. 하지만 사용자가 직접적으로 다양한 기후정보를 상세화하여 활용하기 위해서는 기후정보의 구축 및 전처리를 수행해야하는 어려움이 있다. 따라서 APEC Climate Center (APCC)에서 다학제 융합 기반 기후정보 서비스를 중심으로 사용자 인터페이스 플랫폼 (User Interface Platform: UIP)의 기술적 플랫폼으로 APCC Integrated Modeling Solution (AIMS)를 개발하였다. AIMS는 사용자의 관점으로 상세화를 수행할 수 있고, 다양한 응용 분야에 적용하기 쉽게 데이터를 생성하여 연구에 도움을 주고 있다. 본 연구는 AIMS에서 제공하고 있는 기존의 국가별로 제공하는 제 5차 결합 기후모델 비교사업 (The $5^{th}$ phase of the coupled model intercomparision project, CMIP5)에서 해석한 전구기후모델 (General Circulation Model, GCM)의 통계적 상세화 방법인 Simple Quantile Mapping (SQM)과 Spatial Disaggregation Quantile Delta Mapping (SDQDM)를 포함하여 AIMS에 새롭게 추가 된 통계적 상세화 방법인 Bias Correction and Stochastic Analog (BCSA) 방법을 소개하고자 한다. 또한 60개의 종관기상관측 (Automated Surface Observing System, ASOS)자료를 중심으로 생성한 세 가지 통계적 상세화방법의 과거재현성과 RCP4.5, RCP8.5 시나리오를 활용한 미래 불확실성 평가 결과를 이용하여 연구자들의 맞춤형 자료를 생산하고 평가하는데 도움을 줌으로써 다양한 기후자료의 효과적인 활용이 가능할 것으로 기대된다.