• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.563-573
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• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1142-1146
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• 2009

A rainfall simulation and forecasting technique that can generate daily rainfall sequences conditional on multi-model ensemble GCMs is developed and applied to data in Korea for the major rainy season. The GCM forecasts are provided by APEC climate center. A Weather State Based Downscaling Model (WSDM) is used to map teleconnections from ocean-atmosphere data or key state variables from numerical integrations of Ocean-Atmosphere General Circulation Models to simulate daily sequences at multiple rain gauges. The method presented is general and is applied to the wet season which is JJA(June-July-August) data in Korea. The sequences of weather states identified by the EM algorithm are shown to correspond to dominant synoptic-scale features of rainfall generating mechanisms. Application of the methodology to seasonal rainfall forecasts using empirical teleconnections and GCM derived climate forecast are discussed.

• 대기
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• 제27권3호
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• pp.301-316
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• 2017

This study analyzes the spatio-temporal variability of terrestrial carbon flux and the response of land carbon sink with climate factors to improve of understanding of the variability of land-atmosphere carbon exchanges accurately. The coupled carbon-climate models of CMIP5 (the fifth phase of the Coupled Model Intercomparison Project) and CT (CarbonTracker) are used. The CMIP5 multi-model ensemble mean overestimated the NEP (Net Ecosystem Production) compares to CT and GCP (Global Carbon Project) estimates over the period 2001~2012. Variation of NEP in the CMIP5 ensemble mean is similar to CT, but a couple of models which have fire module without nitrogen cycle module strongly simulate carbon sink in the Africa, Southeast Asia, South America, and some areas of the United States. Result in comparison with climate factor, the NEP is highly affected by temperature and solar radiation in both of CT and CMIP5. Partial correlation between temperature and NEP indicates that the temperature is affecting NEP positively at higher than mid-latitudes in the Northern Hemisphere, but opposite correlation represents at other latitudes in CT and most CMIP5 models. The CMIP5 models except for few models show positive correlation with precipitation at $30^{\circ}N{\sim}90^{\circ}N$, but higher percentage of negative correlation represented at $60^{\circ}S{\sim}30^{\circ}N$ compare to CT. For each season, the correlation between temperature (solar radiation) and NEP in the CMIP5 ensemble mean is similar to that of CT, but overestimated.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.

• 대기
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• 제29권2호
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• pp.115-129
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• 2019

Surface winds over the ocean influence not only the climate change through air-sea interactions but the coastal erosion through the changes in wave height and direction. Thus, demands on a reliable projection of future changes in surface winds have been increasing in various fields. For the future projections, climate models have been widely used and, as a priori, their simulations of surface wind are required to be evaluated. In this study, we evaluate the climatological mean surface winds over the Korean Waters simulated by five regional climate models participating in Coordinated Regional Climate Downscaling Experiment (CORDEX) for East Asia (EA), an international regional climate model inter-comparison project. Compared with the ERA-interim reanalysis data, the CORDEX-EA models, except for HadGEM3-RA, produce stronger wind both in summer and winter. The HadGEM3-RA underestimates the wind speed and inadequately simulate the spatial distribution especially in summer. This summer wind error appears to be coincident with mean sea-level pressure in the North Pacific. For wind direction, all of the CORDEX-EA models simulate the well-known seasonal reversal of surface wind similar to the ERA-interim. Our results suggest that especially in summer, large-scale atmospheric circulation, downscaled by regional models with spectral nudging, significantly affect the regional surface wind on its pattern and strength.

• 한국기후변화학회지
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• 제9권2호
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• pp.133-142
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• 2018

Climate change will affect not only the crop productivity but also the pattern of rice disease epidemics in Korea. Impact assessments for the climate change are conducted using various climate change scenarios from many global climate models (GCM), such as a scenario from a best GCM or scenarios from multiple GCMs, or a combination of both. Here, we evaluated the feasibility of using a climate change scenario from the best GCM for the impact assessment on the potential epidemics of a rice leaf blast disease in Korea, in comparison to a multi?model ensemble (MME) scenario from multiple GCMs. For this, this study involves analyses of disease simulation using an epidemiological model, EPIRICE?LB, which was validated for Korean rice paddy fields. We then assessed likely changes in disease epidemics using the best GCM selected for individual agro?climatic zones and MME scenarios constructed by running 11 GCMs. As a result, the simulated incidence of leaf blast epidemics gradually decreased over the future periods both from the best GCM and MME. The results from this study emphasized that the best GCM selection approach resulted in comparable performance to the MME approach for the climate change impact assessment on rice leaf blast epidemic in Korea.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.314-314
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• 2023

Extreme climate events can have a large impact on human life by hampering social, environmental, and economic development. Global circulation models (GCMs) are the widely used numerical models to understand the anticipated future climate change. However, different GCMs can project different future climates due to structural differences, varying initial boundary conditions and assumptions about the physical phenomena. The multi-model ensemble (MME) approach can improve the uncertainties associated with the different GCM outcomes. In this study, a comprehensive rating metric was used to select the best-performing GCMs out of 11 CMIP5 and 13 CMIP6 GCMs, according to their skills in terms of four temporal and five spatial performance indices, in replicating the 21 extreme climate indices during the baseline (1975-2017) in South Korea. The MME data were derived by averaging the simulations from all selected GCMs and three top-ranked GCMs. The random forest (RF) algorithm was also used to derive the MME data from the three top-ranked GCMs. The RF-derived MME data of the three top-ranked GCMs showed the highest performance in simulating the baseline extreme climate which was subsequently used to project the future extreme climate indices under both the representative concentration pathway (RCP) and the socioeconomic concentration pathway scenarios (SSP). The extreme cold and warming indices had declining and increasing trends, respectively, and most extreme precipitation indices had increasing trends over the period 2031-2100. Compared to all scenarios, RCP8.5 showed drastic changes in future extreme climate indices. The coasts in the east, south and west had stronger warming than the rest of the country, while mountain areas in the north experienced more extreme cold. While extreme cold climatology gradually declined from north to south, extreme warming climatology continuously grew from coastal to inland and northern mountainous regions. The results showed that the socially, environmentally and agriculturally important regions of South Korea were at increased risk of facing the detrimental impacts of extreme climatology.

• 한국기후변화학회지
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• 제9권1호
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• pp.31-45
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• 2018

This study implemented the prediction of drought properties (number of drought events, intensity, duration) using the user-oriented systematical procedures of downscaling climate change scenarios based the multiple global climate models (GCMs), AIMS (APCC Integrated Modeling Solution) program. The drought properties were defined and estimated with Effective Drought Index (EDI). The optimal 10 models among 29 GCMs were selected, by the estimation of the spatial and temporal reproducibility about the five climate change indices related with precipitation. In addition, Simple Quantile Mapping (SQM) as the downscaling technique is much better in describing the observed precipitation events than Spatial Disaggregation Quantile Delta Mapping (SDQDM). Even though the procedure was systematically applied, there are still limitations in describing the observed spatial precipitation properties well due to the offset of spatial variability in multi-model ensemble (MME) analysis. As a result, the farther into the future, the duration and the number of drought generation will be decreased, while the intensity of drought will be increased. Regionally, the drought at the central regions of the Korean Peninsula is expected to be mitigated, while that at the southern regions are expected to be severe.

• 대기
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• 제28권1호
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• pp.15-24
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• 2018

Sea ice plays an important role in modulating surface conditions at high and mid-latitudes. It reacts rapidly to climate change, therefore, it is a good indicator for capturing these changes from the Arctic climate. While many models have been used to study the predictability of climate variables, their performance in predicting sea ice was not well assessed. This study examines the predictability of the Arctic sea ice extent from ensemble prediction systems. The analysis is focused on verification of predictability in each model compared to the observation and prediction in particular, on lead time in Sub-seasonal to Seasonal (S2S) scales. The S2S database now provides quasi-real time ensemble forecasts and hindcasts up to about 60 days from 11 centers: BoM, CMA, ECCC, ECMWF, HMCR, ISAC-CNR, JMA, KMA, Meteo France, NCEP and UKMO. For multi model comparison, only models coupled with sea ice model were selected. Predictability is quantified by the climatology, bias, trends and correlation skill score computed from hindcasts over the period 1999 to 2009. Most of models are able to reproduce characteristics of the sea ice, but they have bias with seasonal dependence and lead time. All models show decreasing sea ice extent trends with a maximum magnitude in warm season. The Arctic sea ice extent can be skillfully predicted up 6 weeks ahead in S2S scales. But trend-independent skill is small and statistically significant for lead time over 6 weeks only in summer.

• 한국수자원학회논문집
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• 제44권8호
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• pp.653-666
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• 2011

본 연구에서는 기후변화에 따른 홍수 취약성 평가기법을 제안하고 국내 5대강 유역에 적용 및 평가하고자 하였다. 특히 Multi-Model Ensemble 시나리오를 이용하여 평가 시 발생하는 불확실성을 제시하고자 하였다. 취약성 평가를 위해 우선 유역의 기상, 수문 자료를 비롯한 지형, 인문 사회 정보를 수집, 지표를 산정하여 현재 기후상태 하에서의 홍수 취약성을 평가하였다. 또한 기후변화에 따른 미래 홍수 취약성을 평가하기 위해 기존에 3개 온실가스 배출시나리오, 13개 GCMs (Global Climate Models), 3개 수문모형(2~3개 증발산량 산정방법)으로 생산된 39개 미래 기후시나리오 및 312개 미래 수문시나리오를 이용하여 기준 S0 (1971~2000년) 기간 대비 미래 S1 (2010~2039년), S2 (2040~2069년), S3 (2070~2099년)기간의 홍수 취약성의 시공간적 변화 및 불확실성을 평가하였다. 평가 결과 현재 기후상황에서 홍수에 취약한 지역은 한강, 섬진강, 영산강 하류 지역으로 나타났으며, 미래 기후변화 시나리오를 고려한 결과 낙동강, 금강, 한강 권역에서의 민감도가 가장 크게 변할 것으로 분석되었으나, 기본적으로 섬진강 유역의 적응능력이 낮기 때문에 미래에도 섬진강 유역이 홍수에 가장 취약할 것으로 분석되었다.