• 대기
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• 제22권3호
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• pp.331-344
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• 2012

The changes in the Hadley circulation during the second half of the 20th century were examined using observations and the 20C3M (Twentieth Century Climate in Coupled Models) simulations by the 21 IPCC AR4 models. Multi-model ensemble (MME) mean shows that the mean features of the Hadley circulation, such as the intensity, magnitude, and the seasonal variations, are very realistically reproduced, compared to the ERA40 reanalysis. But the long-term trends of the Hadley circulation in 20C3M MME are quite different to those of observations. The observed intensity of the Hadley cell is persistently enhanced, particularly during boreal winter. In comparison, the meridional overturning circulations reproduced in the MME mean remains invariant in time, and even weakened in boreal summer. This discrepancy between the ERA40 and 20C3M MME is consistently shown in the overall structure of the Hadley circulations, such as mass streamfunction, the velocity potential, the vertical shear of meridional wind, and the vertical velocity in the tropical region. This results indicate that the current climate models are skill-less to capture the long-term trend of Hadley circulation yet, and should be improved in simulation of the large-scale features to enhance the confidence level of future climate change projection.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.171-171
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• 2020

Decision making strategies should consider both adaptiveness and robustness in order to deal with two main characteristics of climate change: non-stationarity and deep uncertainty. Especially, robust strategies are different from traditional optimal strategies in the sense that they are satisfactory over a wider range of uncertainty and may act as a key when confronting climate change. In this study, a new framework named Robust Stochastic Dynamic Programming (R-SDP) is proposed, which couples previously developed robust optimization (RO) into the objective function and constraint of SDP. Two main approaches of RO, feasibility robustness and solution robustness, are considered in the optimization algorithm and consequently, three models to be tested are developed: conventional-SDP (CSDP), R-SDP-Feasibility (RSDP-F), and R-SDP-Solution (RSDP-S). The developed models were used to derive optimal monthly release rules in a single reservoir, and multiple simulations of the derived monthly policy under inflow scenarios with varying mean and standard deviations are undergone. Simulation results were then evaluated with a wide range of evaluation metrics from reliability, resiliency, vulnerability to additional robustness measures. Evaluation results were finally visualized with advanced visualization tools that are used in multi-objective robust decision making (MORDM) framework. As a result, RSDP-F and RSDP-S models yielded more risk averse, or conservative, results than the CSDP model, and a trade-off relationship between traditional and robustness metrics was discovered.

• 한국수자원학회논문집
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• 제40권2호
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• pp.147-158
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• 2007

본 연구는 기후변화에 따른 수자원 영향평가를 위해 적합한 수문모형을 선택하고 지역기후모형인 SNURCM에서 생성한 모의기상자료로 유출량을 생성하여 모의정확성을 평가하였다. 4개의 월 물수지모형과 두개의 일 유출모형을 이용하여 대청댐 상류유역의 유입량 모의능력을 비교한 결과 abcd모형이 월 물수지모형 중에서는 가장 뛰어났고, 국내에서 널리 사용되고 있는 일 유출모형인 SSARR와 비슷한 모의정확성을 보였다. 다음으로 abcd모형을 금강유역의 12개 소유역에 적용하기 위하여 매개변수 지역화기법을 사용하였다. 9개의 다목적댐에서 구한 매개변수를 미계측유역으로 가정한 4개 다목적댐에 대하여 지역화기법으로 매개변수를 추정하여 유출량을 모의한 결과 모든 지역에서 효율성계수가 최소 87% 이상으로 모의능력이 우수하였다. 마지막으로 금강유역 12개 소유역의 SNURCM모의강수를 실측강수와 비교한 결과 모든 소유역에서의 효율성계수는 60% 이상이었으며, SNURCM 모의강수자료를 abcd모형에 입력하여 생성한 모의유출량을 대청댐 실측유입량과 비교한 결과 효율성계수가 80% 이상으로 기후변화 연구에 활용 가능함을 확인하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.138-142
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• 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.

• 대기
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• 제25권2호
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• pp.249-260
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• 2015

The changes in thermodynamic and dynamic aspects on near (2025~2049) and long-term (2075~2099) future climate changes between the historical run (1979~2005) and the Representative Concentration Pathway (RCP) 4.5 run with 20 coupled models which employed in the phase five of Coupled Model Inter-comparison Project (CMIP5) over East Asia (EA) and the Korean Peninsula are investigated as an extended study for Moon et al. (2014) study noted that the 20 models' multi-model ensemble (MME) and best five models' multi-model ensemble (B5MME) have a different increasing trend of precipitation during the boreal winter and summer, in spite of a similar increasing trend of surface air temperature, especially over the Korean Peninsula. Comparing the MME and B5MME, the dynamic factor (the convergence of mean moisture by anomalous wind) and the thermodynamic factor (the convergence of anomalous moisture by mean wind) in terms of moisture flux convergence are analyzed. As a result, the dynamic factor causes the lower increasing trend of precipitation in B5MME than the MME during the boreal winter and summer over EA. However, over the Korean Peninsula, the dynamic factor causes the lower increasing trend of precipitation in B5MME than the MME during the boreal winter, whereas the thermodynamic factor causes the higher increasing trend of precipitation in B5MME than the MME during the boreal summer. Therefore, it can be noted that the difference between MME and B5MME on the change in precipitation is affected by dynamic (thermodynamic) factor during the boreal winter (summer) over the Korean Peninsula.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.268-272
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• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.

• 한국제4기학회지
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• 제20권1호
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• pp.9-27
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• 2006

이 연구는 동아시아 (중국, 한국, 그리고 일본) 여름몬순과 그 변동성을 MME (multi-model ensemble)을 이용하여 IPCC AR4 (Intergovernmental Panel on Climate Change Fourth Assessment Report) 실험의22개 접합 기후모델 결과 자료로 분석하였다. 결과자료들은 사용 가능한 모든 모델의 평균값을 이용하였다. 여름 몬순 기간 동안 최대 강수를 가지는 연주기는 모델에 의해 모의되었으나 장마(Meiyu-Changma-Baiu) 강수밴드의 이동(북쪽)과 연관되어 7월에 나타나는 최소값은 모의하지 못했다. MME 강수 패턴은 북태평양아열대 고기압과 장마전선대의 위치와 연관된 강수의 공간적 분포를 잘 나타내었다. 그러나 중국, 한반도, 그리고 일본의 동해와 인근 해역의 강수는 과소 예측되었다. 마지막으로 $CO_2$ 농도 배증시나리오의 복사 강제에 대한 미래예측을 분석하였다. MME는 $CO_2$ 농도가 배증될 때 동아시아지역에서 강수는 평균 7.8%로 나타났고, $5{\sim}10%$의 변화폭을 보였다. 그러나 이러한 강수의 증가는 통계적으로 한반도와 일본, 그리고 인근 북중국 지역에서만 중요한 의미를 가진다. 강수 예측에서 나타난 변화는 아열대 고기압의 강도 변화에 비례하는 것으로 나타났다. 그리고 봄에서 초가을까지 여름 몬순의 지속기간이 길어짐을 확인하였다.

• 한국지구과학회지
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• 제42권4호
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• 대기
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• 제30권4호
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• pp.405-420
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• 2020

In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia-Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the σ-value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NN-BN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.

• 한국농림기상학회지
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• 제18권1호
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• pp.42-54
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• 2016

최근 국내에서 재배면적이 증가하고 있는 골드키위 해금의 개화시기를 예측할 수 있는 휴면시계모형의 모수를 추정하고 해금 주산지에서 미래 기후변화에 의한 개화시기의 변화와 불확실성을 전망하고자 본 연구를 수행하였다. 해금 개화시기 예측을 위한 휴면시계모형의 모수는 $6.3^{\circ}C$(base temperature, $T_b$), 102.5(chill requirement, $R_c$), 575(heat requirement, $R_h$)로 추정되었다. 2가지 방법으로 추정된 모수를 검증하였는데, 4개 표준기상관측소의 3년 동안(2013-2015)의 기상자료로부터 해금의 개화시기를 예측하고 25개 해금 노지 재배농가에서 수집된 2년 동안(2014-2015)의 관측 개화일과 비교한 결과 5.2일의 추정오차를 보였다. 또한 격자형 기후표면에 의해 계산된 격자형 개화시기 표면으로부터 25개 해금 노지 재배농가가 위치한 격자들의 예상 개화시기를 추출하여 비교한 결과, 3.4일의 추정오차를 보였다. 이 모수를 2021-2040년 동안의 6개 GCMs의 미래 기후변화 시나리오와 결합하여 해금의 미래 개화시기를 예측하였다. 전남 키위 주산지역에서 가장 빠른 개화시기는 4월 21일(111일), 가장 늦은 개화시기는 6월 2일(153일)로 나타났다. 6개 개별 GCM 중에서 RCP 4.5의 CanESM2과 GFDL-ESM2G, RCP 8.5의 HadGEM2-AO에서 20년 후 전남 키위 주산지역에서 해금의 개화시기는 현재보다 2-3일 단축될 뿐 현재와 큰 차이가 발생하지 않는 것으로 전망되었다. 그러나 RCP 4.5와 RCP 8.5의 6개 GCMs의 평균 미래 개화시기에서 현재보다 10일 이상 단축되고 현재와 같은 개화시기는 전북 및 충남 해안지역 등 북쪽으로 약 150km 이상까지 확대될 수 있는 것으로 전망되었다. 본 연구의 예비 결과는 국내 육종 과수의 생장발육 및 개화시기 예측 등을 위한 생물계절 연구와 기후변화에 대한 영향평가 개선에 기여할 수 있을 것으로 기대한다.