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The Simplified Wet Bulb Globe Temperature (sWBGT) is widely used in heat stress assessments for climate-change studies, but its limitations have not been thoroughly explored. Building on recent critiques of sWBGT's use for current climate on global scale, this study examines sWBGT's biases using dynamically-downscaled sub-daily climate projections under multiple future emission scenarios. The analysis is aimed at understanding caveats in the application of sWBGT and the uncertainties in existing climate change analysis dependent on sWBGT. Results indicate sWBGT's biases are heavily influenced by local near-surface air temperature, with overestimation of heat stress in East Asia regions, particularly hot and humid areas, due to static assumptions of radiation and wind speed. This overestimation is amplified in warmer climates, leading to exaggerated projected heat stress increases in future. In contrast, underestimations are found for heat stress levels attributed to low wind speeds and strong radiations, such as over the Tibetan Plateau and certain extreme events. Additionally, sWBGT underestimates variability in extreme heatwave events compared to WBGT in both current and future climates, irrespective of overestimation in absolute heatwave intensities. This study emphasizes the limitations of sWBGT, especially in future warmer climates. Importance of sub-daily data for capturing daily maximum heat stress level and reflecting diurnal variations in different components is also discussed. In conclusion, we recommend using Liljegren's model (i.e., physics-based calculation) with high-resolution sub-daily climate data for more accurate outdoor heat stress assessments in climate change studies.

This study examines future changes in extreme heat stress over East Asia and its sub-regions using wet bulb globe temperature (WBGT) based on the CORDEX East Asia Phase II multiple Regional Climate Model (RCM) simulations performed under Shared Socioeconomic Pathways (SSP) scenarios. Daily maximum WBGTs (WX) are obtained from 3-hourly bias-corrected WBGTs and their future changes in the late 21st century (2081–2100) are analyzed with respect to the current period (1979–2014). Summer mean WX is projected to increase by 3.2 °C (SSP1-2.6: low emission) to 7.6 °C (SSP5-8.5: high emission) over East Asia, dominated by temperature increases. Relative humidity decreases over many regions, slightly offsetting WX increases (up to −6%), while it increases in northeastern and northern China, intensifying WX increases (up to +14%). This humidity-induced WX increase becomes stronger during hottest WX days (summer top 5%) and also under low emission scenarios (up to +33%). For sub-regional projections, extreme heat stress day (EHD) is defined when WX exceeds its 95th percentile in at least 10% of the area. RCMs project on average a 10 times increase of EHD frequency under the SSP5-8.5 scenario. The EHD magnitude, which combines the intensity and area extent of EHD events, is also expected to increase dramatically throughout East Asia, reaching a range of 3.2–3.5 °C∙fraction compared to the current (0.1–0.2 °C∙fraction). Further, EHDs are projected to start earlier and end later, lasting much longer (85–140 days) than the current condition (5–6 days) in the SSP5-8.5 scenario. RCMs exhibit a good agreement in WX and EHD projections with some noticeable differences in in-land sub-regions. Our results indicate that severe heat stress will affect the whole East Asia throughout and beyond the summer season and, in particular, southern sub-regions will be affected by more-intense and longer-lasting extreme heat stress events. © 2023 The Authors

Statistical bias correction (BC) is a widely used tool topost-process climate model biases in heat-stress impact studies, which areoften based on the indices calculated from multiple dependent variables.This study compares four BC methods (three univariate and one multivariate)with two correction strategies (direct and indirect) for adjusting twoheat-stress indices with different dependencies on temperature and relativehumidity using multiple regional climate model simulations over SouthKorea. It would be helpful for reducing the ambiguity involved in thepractical application of BC for climate modeling and end-user communities.Our results demonstrate that the multivariate approach can improve thecorrected inter-variable dependence, which benefits the indirect correctionof heat-stress indices depending on the adjustment of individual components,especially those indices relying equally on multiple drivers. On the otherhand, the direct correction of multivariate indices using the quantile deltamapping univariate approach can also produce a comparable performance in thecorrected heat-stress indices. However, our results also indicate thatattention should be paid to the non-stationarity of bias brought by climatesensitivity in the modeled data, which may affect the bias-corrected resultsunsystematically. Careful interpretation of the correction process isrequired for an accurate heat-stress impact assessment.

Near-surface temperature and relative humidity data over South Korea land (125.25-130.75E, 33.25-38.5N) in June-July-August, 1979-2014. The data is extracted from the CORDEX-East Asia domain climate projections. It is the downscaling product of UKESM1-0-LL (historical, r1i1p1,CMIP6) using CCLM, GRIMs, RegCM, WRF and HG3RA, respectively.

With the acceleration in global warming, extreme hot temperatures have emerged as one of the most prominent risks. In this study, we characterize the unprecedented extreme temperatures that occurred in Korea in summer 2018, and attempt to explain how this locally observed extreme event can be interpreted in the context of 2 degrees C and 3 degrees C global warming above the pre-industrial level. To better resolve geographically diverse climate features and enhance confidence in future changes, three global projections are dynamically downscaled using three regional climate models that are customized over Korea and the systematic biases are statistically corrected using quantile mapping. In July and August 2018, abnormally high maximum temperatures (Tmax) were observed over the entire territory of South Korea. Beyond the increase of mean value, Tmax at individual stations departed significantly from the typical Gaussian distribution of climatological Tmax due to the dramatic changes in the extent and shape of upper tails. The distinct behaviors of Tmax that appeared in 2018 largely represent the statistical analog of the distribution pattern expected under 3 degrees C global warming based on fine-scale climate projections. This implies that statistically extremely rare events like that of summer 2018 will become increasingly normal if global average temperature is allowed to increase by 3 degrees C. More importantly, the extreme heat stress measured by the wet-bulb globe temperature is projected to intensify the risks to a level never before seen in contemporary climate. This study is timely and relevant to the need to identify how the globally aggregated warming target temperature can be disaggregated into regional impacts.

With the acceleration in global warming, extreme hot temperatures have emerged as one of the most prominent risks. In this study, we characterize the unprecedented extreme temperatures that occurred in Korea in summer 2018, and attempt to explain how this locally observed extreme event can be interpreted in the context of 2 degrees C and 3 degrees C global warming above the pre-industrial level. To better resolve geographically diverse climate features and enhance confidence in future changes, three global projections are dynamically downscaled using three regional climate models that are customized over Korea and the systematic biases are statistically corrected using quantile mapping. In July and August 2018, abnormally high maximum temperatures (Tmax) were observed over the entire territory of South Korea. Beyond the increase of mean value, Tmax at individual stations departed significantly from the typical Gaussian distribution of climatological Tmax due to the dramatic changes in the extent and shape of upper tails. The distinct behaviors of Tmax that appeared in 2018 largely represent the statistical analog of the distribution pattern expected under 3 degrees C global warming based on fine-scale climate projections. This implies that statistically extremely rare events like that of summer 2018 will become increasingly normal if global average temperature is allowed to increase by 3 degrees C. More importantly, the extreme heat stress measured by the wet-bulb globe temperature is projected to intensify the risks to a level never before seen in contemporary climate. This study is timely and relevant to the need to identify how the globally aggregated warming target temperature can be disaggregated into regional impacts.

With the acceleration in global warming, extreme hot temperatures have emerged as one of the most prominent risks. In this study, we characterize the unprecedented extreme temperatures that occurred in Korea in summer 2018, and attempt to explain how this locally observed extreme event can be interpreted in the context of 2 degrees C and 3 degrees C global warming above the pre-industrial level. To better resolve geographically diverse climate features and enhance confidence in future changes, three global projections are dynamically downscaled using three regional climate models that are customized over Korea and the systematic biases are statistically corrected using quantile mapping. In July and August 2018, abnormally high maximum temperatures (Tmax) were observed over the entire territory of South Korea. Beyond the increase of mean value, Tmax at individual stations departed significantly from the typical Gaussian distribution of climatological Tmax due to the dramatic changes in the extent and shape of upper tails. The distinct behaviors of Tmax that appeared in 2018 largely represent the statistical analog of the distribution pattern expected under 3 degrees C global warming based on fine-scale climate projections. This implies that statistically extremely rare events like that of summer 2018 will become increasingly normal if global average temperature is allowed to increase by 3 degrees C. More importantly, the extreme heat stress measured by the wet-bulb globe temperature is projected to intensify the risks to a level never before seen in contemporary climate. This study is timely and relevant to the need to identify how the globally aggregated warming target temperature can be disaggregated into regional impacts.

This study assesses the hydroclimatic response to global warming over East Asia from multi-model ensemble regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical downscaling of the Hadley Centre Global Environmental Model version 2–Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981–2100) daily precipitation. Ensemble projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement. © 2016 The Author(s)

This study assesses the hydroclimatic response to global warming over East Asia from multi-model ensemble regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical downscaling of the Hadley Centre Global Environmental Model version 2–Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981–2100) daily precipitation. Ensemble projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement. © 2016 The Author(s)

This study assesses the hydroclimatic response to global warming over East Asia from multi-model ensemble regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical downscaling of the Hadley Centre Global Environmental Model version 2–Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981–2100) daily precipitation. Ensemble projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement. © 2016 The Author(s)

On the Korean peninsula, observation is being conducted so densely that the mean distance between stations of the extensive ground-based data network is only 12.7 km. Nevertheless, because of significant mountainous terrain and the fact that most observation sites are situated in low areas rather than mountain tops or ridges, the detailed topographical effect on temperature distribution is not reflected properly. A model using fine-scale grid spacing can represent such a topographical effect well, but due to systematic biases in the model, simulated temperature distribution will be different from the actual observation. This study therefore attempts to produce a detailed mean temperature distribution for South Korea through a method combining dynamical downscaling and statistical correction. For the dynamical downscaling, the Weather Research and Forecast (WRF) model developed by the U.S. National Center for Atmospheric Research (NCAR) is used. We applied a multi-nesting technique to obtain high-resolution climate information (3 km) with a focus on the Korean peninsula. The integration period was 10 years from January 1999 to December 2008. For the correction of systematic biases shown in downscaled temperature, a perturbation method divided into the mean and the perturbation part was used with a different correction method being applied to each part. The mean was corrected by a weighting function while the perturbation was corrected by the self-organizing maps method, which is one of the artificial neural networks method. The results with correction agree well with the observed pattern compared to those without correction, improving the spatial and temporal correlations as well as the root mean square error. In addition, they represented detailed spatial features of temperature including topographic signals, which cannot be expressed properly by gridded observation. Through comparison with in-situ observation with gridded values after objective analysis, it was fou

On the Korean peninsula, observation is being conducted so densely that the mean distance between stations of the extensive ground-based data network is only 12.7 km. Nevertheless, because of significant mountainous terrain and the fact that most observation sites are situated in low areas rather than mountain tops or ridges, the detailed topographical effect on temperature distribution is not reflected properly. A model using fine-scale grid spacing can represent such a topographical effect well, but due to systematic biases in the model, simulated temperature distribution will be different from the actual observation. This study therefore attempts to produce a detailed mean temperature distribution for South Korea through a method combining dynamical downscaling and statistical correction. For the dynamical downscaling, the Weather Research and Forecast (WRF) model developed by the U.S. National Center for Atmospheric Research (NCAR) is used. We applied a multi-nesting technique to obtain high-resolution climate information (3 km) with a focus on the Korean peninsula. The integration period was 10 years from January 1999 to December 2008. For the correction of systematic biases shown in downscaled temperature, a perturbation method divided into the mean and the perturbation part was used with a different correction method being applied to each part. The mean was corrected by a weighting function while the perturbation was corrected by the self-organizing maps method, which is one of the artificial neural networks method. The results with correction agree well with the observed pattern compared to those without correction, improving the spatial and temporal correlations as well as the root mean square error. In addition, they represented detailed spatial features of temperature including topographic signals, which cannot be expressed properly by gridded observation. Through comparison with in-situ observation with gridded values after objective analysis, it was fou

On the Korean peninsula, observation is being conducted so densely that the mean distance between stations of the extensive ground-based data network is only 12.7 km. Nevertheless, because of significant mountainous terrain and the fact that most observation sites are situated in low areas rather than mountain tops or ridges, the detailed topographical effect on temperature distribution is not reflected properly. A model using fine-scale grid spacing can represent such a topographical effect well, but due to systematic biases in the model, simulated temperature distribution will be different from the actual observation. This study therefore attempts to produce a detailed mean temperature distribution for South Korea through a method combining dynamical downscaling and statistical correction. For the dynamical downscaling, the Weather Research and Forecast (WRF) model developed by the U.S. National Center for Atmospheric Research (NCAR) is used. We applied a multi-nesting technique to obtain high-resolution climate information (3 km) with a focus on the Korean peninsula. The integration period was 10 years from January 1999 to December 2008. For the correction of systematic biases shown in downscaled temperature, a perturbation method divided into the mean and the perturbation part was used with a different correction method being applied to each part. The mean was corrected by a weighting function while the perturbation was corrected by the self-organizing maps method, which is one of the artificial neural networks method. The results with correction agree well with the observed pattern compared to those without correction, improving the spatial and temporal correlations as well as the root mean square error. In addition, they represented detailed spatial features of temperature including topographic signals, which cannot be expressed properly by gridded observation. Through comparison with in-situ observation with gridded values after objective analysis, it was fou

This study investigates the elevation dependency of the present-day climate and future climate change in temperature and precipitation over Korea. A dynamically downscaled fine-scale climate simulation (20 km) shows reasonable agreement with two types of observations maintained by the Korea Meteorological Administration. The model exactly captures the strong relationship between the elevation and local climatology as seen in observed temperature and precipitation patterns. The behavior of the elevation dependency shown by the present-day climate simulation is also appeared in the climate change signal. The warming amplification is highly correlated with elevation. The warming is more pronounced at higher elevations than at lower elevations during winter, and maximum warming occurs at minimum temperature, showing an asymmetric response between minimum and maximum temperature. A noticeable differential rate of winter warming in response to the elevation can be explained by the snow-albedo feedback. Precipitation and snow changes also show the relevant topographical modulation under global warming. This study clearly demonstrates the importance of a refined topography for improving the accuracy of the local climatology and suitably reflecting the altitudinal distribution.

This study investigates the elevation dependency of the present-day climate and future climate change in temperature and precipitation over Korea. A dynamically downscaled fine-scale climate simulation (20 km) shows reasonable agreement with two types of observations maintained by the Korea Meteorological Administration. The model exactly captures the strong relationship between the elevation and local climatology as seen in observed temperature and precipitation patterns. The behavior of the elevation dependency shown by the present-day climate simulation is also appeared in the climate change signal. The warming amplification is highly correlated with elevation. The warming is more pronounced at higher elevations than at lower elevations during winter, and maximum warming occurs at minimum temperature, showing an asymmetric response between minimum and maximum temperature. A noticeable differential rate of winter warming in response to the elevation can be explained by the snow-albedo feedback. Precipitation and snow changes also show the relevant topographical modulation under global warming. This study clearly demonstrates the importance of a refined topography for improving the accuracy of the local climatology and suitably reflecting the altitudinal distribution.

This study investigates the elevation dependency of the present-day climate and future climate change in temperature and precipitation over Korea. A dynamically downscaled fine-scale climate simulation (20 km) shows reasonable agreement with two types of observations maintained by the Korea Meteorological Administration. The model exactly captures the strong relationship between the elevation and local climatology as seen in observed temperature and precipitation patterns. The behavior of the elevation dependency shown by the present-day climate simulation is also appeared in the climate change signal. The warming amplification is highly correlated with elevation. The warming is more pronounced at higher elevations than at lower elevations during winter, and maximum warming occurs at minimum temperature, showing an asymmetric response between minimum and maximum temperature. A noticeable differential rate of winter warming in response to the elevation can be explained by the snow-albedo feedback. Precipitation and snow changes also show the relevant topographical modulation under global warming. This study clearly demonstrates the importance of a refined topography for improving the accuracy of the local climatology and suitably reflecting the altitudinal distribution.