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Yeon-Hee Kim
and
Jong-Jin Baik

Abstract

The maximum urban heat island (UHI) intensity in Seoul, Korea, is investigated using data measured at two meteorological observatories (an urban site and a rural site) during the period of 1973–96. The average maximum UHI is weakest in summer and is strong in autumn and winter. Similar to previous studies for other cities, the maximum UHI intensity is more frequently observed in the nighttime than in the daytime, decreases with increasing wind speed, and is pronounced for clear skies. A multiple linear regression analysis is performed to relate the maximum UHI to meteorological elements. Four predictors considered in this study are the maximum UHI intensity for the previous day, wind speed, cloudiness, and relative humidity. The previous-day maximum UHI intensity is positively correlated with the maximum UHI, and the wind speed, cloudiness, and relative humidity are negatively correlated with the maximum UHI intensity. Among the four predictors, the previous-day maximum UHI intensity is the most important. The relative importance among the predictors varies depending on time of day and season. A three-layer back-propagation neural network model with the four predictors as input units is constructed to predict the maximum UHI intensity in Seoul, and its performance is compared with that of a multiple linear regression model. For all test datasets, the neural network model improves upon the regression model in predicting the maximum UHI intensity. The improvement of the neural network model upon the regression model is 6.3% for the unstratified test data, is higher in the daytime (6.1%) than in the nighttime (3.3%), and ranges from 0.8% in spring to 6.5% in winter.

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Yeon-Hee Kim
and
Jong-Jin Baik

Abstract

The spatial and temporal structure of the urban heat island in Seoul, Korea, is investigated using near-surface temperature data measured at 31 automatic weather stations (AWSs) in the Seoul metropolitan area for the 1-yr period from March 2001 to February 2002. The urban heat island in Seoul deviates considerably from an idealized, concentric heat island structure, mainly because of the location of the main commercial and industrial sectors and the local topography. Relatively warm regions extend in the east–west direction and relatively cold regions are located near the northern and southern mountains. Several warm cores are observed whose intensity, size, and location are found to vary seasonally and diurnally. Similar to previous studies, the urban heat island in Seoul is stronger in the nighttime than in the daytime and decreases with increasing wind speed and cloud cover, but it is least developed in summer. The average maximum urban heat island intensity is 2.2°C over the 1-yr period and it is 3.4°C at 0300 local standard time (LST) and 0.6°C at 1500 LST. The reversed urban heat island is occasionally observed in the afternoon, but its intensity is very weak. An empirical orthogonal function (EOF) analysis is performed to find the dominant modes of variability in the Seoul urban heat island. In the analysis using temperature data that are averaged for each hour of the 1-yr period, the first EOF explains 80.6% of the total variance and is a major diurnal mode. The second EOF, whose horizontal structure is positive in the eastern part of Seoul and is negative in the western part, explains 16.0% of the total variance. This mode is related to the land use type and the diurnal pattern of anthropogenic heat release. In the analysis using temperature data at 0300 LST, the leading four modes explain 72.4% of the total variance. The first EOF reflects that the weakest urban heat island intensity is in summer. It is found that the urban heat island in Seoul is stronger on weekdays than weekends.

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Jonghun Kam
,
Seung-Ki Min
,
Yeon-Hee Kim
,
Byeong-Hee Kim
, and
Jong-Seong Kug
Open access
Jong-Jin Baik
,
Yeon-Hee Kim
, and
Hye-Yeong Chun

Abstract

This study numerically investigates dry and moist convection forced by an urban heat island using a two-dimensional, nonhydrostatic, compressible model with explicit cloud microphysical processes (Advanced Regional Prediction System). The urban heat island is represented by specified heating. Extensive numerical experiments with various heating amplitudes, representing the intensity of the urban heat island, uniform basic-state wind speeds, and basic-state relative humidities, are performed to examine their roles in characterizing urban-induced convection. Two flow regimes can be identified in dry simulations. One regime is characterized only by stationary gravity waves near the heating region and is revealed when the urban heat island intensity is very weak. The other regime is characterized both by stationary gravity waves near the heating region and by a downwind updraft cell that moves in the downstream direction. The intensity of the downwind updraft cell increases as the heat island intensity increases or the basic-state wind speed decreases. Results of moist simulations demonstrate that the downwind updraft cell induced by the urban heat island can initiate moist convection and result in surface precipitation in the downstream region when the basic-state thermodynamic conditions are favorable. As the urban heat island intensity increases, the time required for the first cloud water (or rainwater) formation decreases and its horizontal location is closer to the heating center. It is shown that for the same basic-state wind speed and heat island intensity a stronger dynamic forcing—that is, a stronger downwind updraft—is needed to trigger moist convection in less favorable basic-state thermodynamic conditions.

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Junsu Kim
,
Yeon-Hee Kim
,
Hyejeong Bok
,
Sungbin Jang
,
Eunju Cho
, and
Seung-Bum Kim

Abstract

We developed an advanced post-processing model for precipitation forecasting using a micro-genetic algorithm (MGA). The algorithm determines the optimal combination of three general circulation models: the Korean Integrated Model, the Unified Model, and the Integrated Forecast System model. To measure model accuracy, including the critical success index (CSI), probability of detection (POD), and frequency bias index, the MGA calculates optimal weights for individual models based on a fitness function that considers various indices. Our optimized multi-model yielded up to 13% and 10% improvement in CSI and POD performance compared to each individual model, respectively. Notably, when applied to an operational definition that considers precipitation thresholds from three models and averages the precipitation amount from the satisfactory models, our optimized multi-model outperformed the current operational model used by the Korea Meteorological Administration by up to 1.0% and 6.8% in terms of CSI and false alarm ratio performance, respectively. This study highlights the effectiveness of a weighted combination of global models to enhance the forecasting accuracy for regional precipitation. By utilizing the MGA for the fine-tuning of model weights, we achieved superior precipitation prediction compared to that of individual models and existing standard post-processing operations. This approach can significantly improve the accuracy of precipitation forecasts.

Open access
Seung-Ki Min
,
Yeon-Hee Kim
,
Seungmok Paik
,
Maeng-Ki Kim
, and
Kyung-On Boo
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Seungmok Paik
,
Seung-Ki Min
,
Yeon-Hee Kim
,
Baek-Min Kim
,
Hideo Shiogama
, and
Joonghyeok Heo

Abstract

In 2015, the sea ice extent (SIE) over the Sea of Okhotsk (Okhotsk SIE) hit a record low since 1979 during February–March, the period when the sea ice extent generally reaches its annual maximum. To quantify the role of anthropogenic influences on the changes observed in Okhotsk SIE, this study employed a fraction of attributable risk (FAR) analysis to compare the probability of occurrence of extreme Okhotsk SIE events and long-term SIE trends using phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations performed with and without anthropogenic forcing. It was found that because of anthropogenic influence, both the probability of extreme low Okhotsk SIEs that exceed the 2015 event and the observed long-term trends during 1979–2015 have increased by more than 4 times (FAR = 0.76 to 1). In addition, it is suggested that a strong negative phase of the North Pacific Oscillation (NPO) during midwinter (January–February) 2015 also contributed to the 2015 extreme SIE event. An analysis based on multiple linear regression was conducted to quantify relative contributions of the external forcing (anthropogenic plus natural) and the NPO (internal variability) to the observed SIE changes. About 56.0% and 24.7% of the 2015 SIE anomaly was estimated to be attributable to the external forcing and the strong negative NPO influence, respectively. The external forcing was also found to explain about 86.1% of the observed long-term SIE trend. Further, projections from the CMIP5 models indicate that a sea ice–free condition may occur in the Sea of Okhotsk by the late twenty-first century in some models.

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Bo-Joung Park
,
Yeon-Hee Kim
,
Seung-Ki Min
, and
Eun-Pa Lim

Abstract

Observed long-term variations in summer season timing and length in the Northern Hemisphere (NH) continents and their subregions were analyzed using temperature-based indices. The climatological mean showed coastal–inland contrast; summer starts and ends earlier inland than in coastal areas because of differences in heat capacity. Observations for the past 60 years (1953–2012) show lengthening of the summer season with earlier summer onset and delayed summer withdrawal across the NH. The summer onset advance contributed more to the observed increase in summer season length in many regions than the delay of summer withdrawal. To understand anthropogenic and natural contributions to the observed change, summer season trends from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations forced with the observed external forcings [anthropogenic plus natural forcing (ALL), natural forcing only (NAT), and greenhouse gas forcing only (GHG)] were analyzed. ALL and GHG simulations were found to reproduce the overall observed global and regional lengthening trends, but NAT had negligible trends, which implies that increased greenhouse gases were the main cause of the observed changes. However, ALL runs tend to underestimate the observed trend of summer onset and overestimate that of withdrawal, the causes of which remain to be determined. Possible contributions of multidecadal variabilities, such as Pacific decadal oscillation and Atlantic multidecadal oscillation, to the observed regional trends in summer season length were also assessed. The results suggest that multidecadal variability can explain a moderate portion (about ±10%) of the observed trends in summer season length, mainly over the high latitudes.

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Siyan Dong
,
Ying Sun
,
Chao Li
,
Xuebin Zhang
,
Seung-Ki Min
, and
Yeon-Hee Kim

Abstract

While the IPCC Fifth Assessment Working Group I report assessed observed changes in extreme precipitation on the basis of both absolute and percentile-based extreme indices, human influence on extreme precipitation has rarely been evaluated on the basis of percentile-based extreme indices. Here we conduct a formal detection and attribution analysis on changes in four percentile-based precipitation extreme indices. The indices include annual precipitation totals from days with precipitation exceeding the 99th and 95th percentiles of wet-day precipitation in 1961–90 (R99p and R95p) and their contributions to annual total precipitation (R99pTOT and R95pTOT). We compare these indices from a set of newly compiled observations during 1951–2014 with simulations from models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). We show that most land areas with observations experienced increases in these extreme indices with global warming during the historical period 1951–2014. The new CMIP6 models are able to reproduce these overall increases, although with considerable over- or underestimations in some regions. An optimal fingerprinting analysis reveals detectable anthropogenic signals in the observations of these indices averaged over the globe and over most continents. Furthermore, signals of greenhouse gases can be separately detected, taking other forcing into account, over the globe and over Asia in these indices except for R95p. In contrast, signals of anthropogenic aerosols and natural forcings cannot be detected in any of these indices at either global or continental scales.

Open access
Min-Gyu Seong
,
Seung-Ki Min
,
Yeon-Hee Kim
,
Xuebin Zhang
, and
Ying Sun

Abstract

This study conducted a detection and attribution analysis of the observed global and regional changes in extreme temperatures during 1951–2015. HadEX3 observations were compared with multimodel simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) using an optimal fingerprinting technique. Annual maximum daily maximum and minimum temperatures (TXx and TNx; warm extremes) and annual minimum daily maximum and minimum temperatures (TXn and TNn; cold extremes) over land were analyzed considering global, continental, and subcontinental scales. Response patterns (fingerprints) of extreme temperatures to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AA), and natural (NAT) forcings were obtained from CMIP6 forced simulations. The internal variability ranges were estimated from preindustrial control simulations. A two-signal detection analysis where the observations are regressed onto ANT and NAT fingerprints simultaneously reveals that ANT signals are robustly detected in separation from NAT over global and all continental domains (North and South America, Europe, Asia, and Oceania) for most of the extreme indices. ANT signals are also detected over many subcontinental regions, particularly for warm extremes (more than 60% of 33 subregions). A three-signal detection analysis that considers GHG, AA, and NAT fingerprints simultaneously demonstrates that GHG signals are detected in isolation from other external forcings over global, continental, and several subcontinental domains especially for warm extremes, explaining most of the observed warming. Moreover, AA influences are detected for warm extremes over Europe and Asia, indicating significant offsetting cooling contributions. Overall, human influences are detected more frequently, compared to previous studies, particularly for cold extremes, due to the extended period and the improved spatial coverage of observations.

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