• Atmosphere
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• v.18 no.2
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• pp.83-95
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• 2008

In this study we have investigated the preceding eighteen large-scale climate indices with a lead time from zero to twelve months that have an influence on the variability of temperature and precipitation in Korea in order to understand which climate indices are overall available as predictors for long-range forecasting. We also have studied the dynamic link between preceding large-scale climate indices and regional climate using singular value decomposition analysis (SVDA) and correlation analysis (CA). Based on the coupled mode between large-scale circulation and regional climate, and correlation pattern between the preceding large-scale climate indices and large-scale circulation, the level of significance on climate indices as a predictor for monthly mean temperature and precipitation was evaluated for 5 and 1% level.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.154-154
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• 2022

The frequency of natural disasters, including floods and drought events, driven by climate change has increased in recent times. Investigating the climate regimes and the roles of climate variables are indispensable to forestall future climate change-related disasters. This study compares the variability of two popular and widely used climate indices i.e., the United Nations Environment Programme (UNEP) aridity index and the Modified De-Martonne (MDM) index to assess the trend of climate change in the Chungcheong provinces of South Korea. The trend of annual and monthly climate indices was conducted using a non-parametric Mann-Kendall test and Kolmogorov-Smirnov normality test with daily climate data of 48 years (1978-2020) from 10 synoptic stations. The findings indicate that UNEP and MDM indices had a wet climate regime for the annual trend, with the UNEP index indicating a relatively humid trend of 60% humid, 20% semi-arid, and 10% sub-humid for the 48-years study period. However, the MDM index showed a high frequency of a severe wet climatic condition followed by the semi-arid condition. The months of July and August had the highest occurring frequency of the wet climatic condition (90%) for both UNEP and MDM indices. Comparing the two provinces, Chungnam showed a relatively wetter climatic condition using the UNEP index, while the MDM index indicated no significant regional difference in climate regime between the two provinces. The Kolmogorov-Smirnov normality test showed that all the 10 stations are normally distributed for monthly climate conditions at a 5% significant level in the two provinces except five stations for UNEP index and four stations for MDM index in the month of January.

• Environmental Engineering Research
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• v.16 no.3
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• pp.175-183
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• 2011

The climate of the $21^{st}$ century is likely to be significantly different from that of the 20th century because of human-induced climate change. An extreme weather event is defined as a climate phenomenon that has not been observed for the past 30 years and that may have occurred by climate change and climate variability. The abnormal climate change can induce natural disasters such as floods, droughts, typhoons, heavy snow, etc. How will the frequency and intensity of extreme weather events be affected by the global warming change in the $21^{st}$ century? This could be a quite interesting matter of concern to the hydrologists who will forecast the extreme weather events for preventing future natural disasters. In this study, we establish the extreme indices and analyze the trend of extreme weather events using extreme indices estimated from the observed data of 66 stations controlled by the Korea Meteorological Administration (KMA) in Korea. These analyses showed that spatially coherent and statistically significant changes in the extreme events of temperature and rainfall have occurred. Under the global climate change, Korea, unlike in the past, is now being affected by extreme weather events such as heavy rain and abnormal temperatures in addition to changes in climate phenomena.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.999-1009
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• 2021

As the frequency and intensity of extreme weather events due to climate change are increasing in recent years, it is very important to evaluate and analyze climate conditions to manage and respond to the negative effects of climate change in advance. In this study, the trends and characteristics of regional climate change were analyzed by calculating the climate indices for the Chungcheong Province. Annual and monthly UNEP-MP, UNEP-PM and MDM indices were calculated using daily data from 1973-2020 collected from 10 synoptic meteorological stations operated by the Korea Meteorological Administration. The normality of climate data was analyzed through the KS test, and the climate change trend was analyzed by applying the Spearman and Pearson methods. The Chungcheongnam-do region had a relatively humid climate than the Chungcheongbuk-do region, and the annual climate indices showed a dry climate trend in Cheongju and Chungju, while the climate of Seosan and Buyeo was becoming humid. Based on the monthly trend change analysis, a humid climate trend was observed in summer and autumn, while a dry climate trend was observed in spring and winter. Comparison of climate indices during the past (2001-2010) and the recent (2011-2020) years showed a higher decrease in the average climate indices during the last 10 years and a gradually drying climate change trend was recorded.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.339-346
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• 2017

The Korea Adaptation Center for Climate Change (KACCC) located in Korea Environment Institute has serviced a climate change vulnerability assessment support tool (VESTAP) since 2014 in order to help local governments to establish their own adaptation plans. Owing to its easy usage, the VESTAP has been utilized by not only local governments but also academia for examination of climate change vulnerability in various fields. However, the KACCC has not suggested a standard usage how to compose indices for climate exposure, sensitivity, and adaptation capacity which are main components of vulnerability although the KACCC manages operation and application of the VESTAP. Many users had no choice but to compose indices based on their own interpretation on the components of vulnerability. This technical note suggests the standard usage of VESTAP by reevaluating some vulnerability assessments previously developed. This may help users to correctly compose indices for climate change vulnerability assessment, and may minimize possibility of inter-user inconsistency in definition of vulnerability assessments.

• Atmosphere
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• v.25 no.2
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• pp.323-337
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• 2015

This study explores the 6-month lead prediction skill of several climate indices that influence on East Asian climate in the GloSea5 hindcast experiment. Such indices include Nino3.4, Indian Ocean Diploe (IOD), Arctic Oscillation (AO), various summer and winter Asian monsoon indices. The model's prediction skill of these indices is evaluated by computing the anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) for ensemble mean values over the period of 1996~2009. In general, climate indices that have low seasonal variability are predicted well. For example, in terms of ACC, Nino3.4 index is predicted well at least 6 months in advance. The IOD index is also well predicted in late summer and autumn. This contrasts with the prediction skill of AO index which shows essentially no skill beyond a few months except in February and August. Both summer and winter Asian monsoon indices are also poorly predicted. An exception is the Western North Pacific Monsoon (WNPM) index that exhibits a prediction skill up to 4- to 6-month lead time. However, when MSSS is considered, most climate indices, except Nino3.4 index, show a negligible prediction skill, indicating that conditional bias is significant in the model. These results are only weakly sensitive to the number of ensemble members.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.108-125
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• 2015

The agro-climatic index is one of the ways to assess the climate resources of particular agricultural areas on the prospect of agricultural production; it can be a key indicator of agricultural productivity by providing the basic information required for the implementation of different and various farming techniques and practicalities to estimate the growth and yield of crops from the climate resources such as air temperature, solar radiation, and precipitation. However, the agro-climate index can always be changed since the index is not the absolute. Recently, many studies which consider uncertainty of future climate change have been actively conducted using multi-model ensemble (MME) approach by developing and improving dynamic and statistical downscaling of Global Climate Model (GCM) output. In this study, the agro-climatic index of Korean Peninsula, such as growing degree day based on $5^{\circ}C$, plant period based on $5^{\circ}C$, crop period based on $10^{\circ}C$, and frost free day were calculated for assessment of the spatio-temporal variations and uncertainties of the indices according to climate change; the downscaled historical (1976-2005) and near future (2011-2040) RCP climate sceneries of AR5 were applied to the calculation of the index. The result showed four agro-climatic indices calculated by nine individual GCMs as well as MME agreed with agro-climatic indices which were calculated by the observed data. It was confirmed that MME, as well as each individual GCM emulated well on past climate in the four major Rivers of South Korea (Han, Nakdong, Geum, and Seumjin and Yeoungsan). However, spatial downscaling still needs further improvement since the agro-climatic indices of some individual GCMs showed different variations with the observed indices at the change of spatial distribution of the four Rivers. The four agro-climatic indices of the Korean Peninsula were expected to increase in nine individual GCMs and MME in future climate scenarios. The differences and uncertainties of the agro-climatic indices have not been reduced on the unlimited coupling of multi-model ensembles. Further research is still required although the differences started to improve when combining of three or four individual GCMs in the study. The agro-climatic indices which were derived and evaluated in the study will be the baseline for the assessment of agro-climatic abnormal indices and agro-productivity indices of the next research work.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.207-215
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• 2016

In this study, it is analyzed how large scale climate variation has an effect on climate systems over Korea using correlation analysis between climate indices and Intrinsic Mode Functions (IMFs) of precipitation and temperature. For this purpose, the estimated IMFs of precipitation and temperature from the accompanying paper were used. Furthermore, cross correlation coefficients and lag time between climate indices and IMFs were calculated considering periodicities and tendencies. As results, more accurate correlation coefficients were obtained compared with those between climate indices and raw precipitation and temperature data. We found that the Korean climate is closely related with climate variations of $El-Ni{\tilde{n}}o$ in terms of periodicity and its tendency is followed with increasing sea surface temperature due to climate change.

• Atmosphere
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• v.31 no.5
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• pp.607-623
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• 2021

This study presents projections of future extreme climate over the Korean Peninsula (KP), using bias-corrected data from multiple regional climate model (RCM) simulations in CORDEX-EA Phase 2 project. In order to confirm difference according to degree of greenhouse gas (GHG) emission, high GHG path of SSP5-8.5 and low GHG path of SSP1-2.6 scenario are used. Under SSP5-8.5 scenario, mean temperature and precipitation over KP are projected to increase by 6.38℃ and 20.56%, respectively, in 2081~2100 years compared to 1995~2014 years. Projected changes in extreme climate suggest that intensity indices of extreme temperatures would increase by 6.41℃ to 8.18℃ and precipitation by 24.75% to 33.74%, being bigger increase than their mean values. Both of frequency indices of the extreme climate and consecutive indices of extreme precipitation are also projected to increase. But the projected changes in extreme indices vary regionally. Under SSP1-2.6 scenario, the extreme climate indices would increase less than SSP5-8.5 scenario. In other words, temperature (precipitation) intensity indices would increase 2.63℃ to 3.12℃ (14.09% to 16.07%). And there is expected to be relationship between mean precipitation and warming, which mean precipitation would increase as warming with bigger relationship in northern KP (4.08% ℃^-1) than southern KP (3.53% ℃^-1) under SSP5-8.5 scenario. The projected relationship, however, is not significant for extreme precipitation. It seems because of complex characteristics of extreme precipitation from summer monsoon and typhoon over KP.