Search

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.

Background: The acute palliative care unit (APCU) bridges between active cancer treatment and hospice care. However, no study has proven the efficacy of APCU in Korea. Objective: To evaluate the first-year outcomes of the patients admitted to an APCU at a tertiary hospital in Korea. Design: The APCU admitted 205 patients between April 14, 2014, and April 30, 2015. Of these patients, 57 were evaluable for baseline and one-week follow-up Edmonton Symptom Assessment System (ESAS). Results: Of the 57 participants, 56.1% were male, with a median age of 60 years (range, 52.8?69.5 years). All patients had advanced cancer, and 42 out of 57 had terminal illnesses. The median APCU stay was 14 days (range, 10?17 days). The 42 (73.7%) patients were referred to the APCU after anticancer treatment was completed. Ten (17.5%) patients died during their stay, and 20 (35.1%) were discharged home. Among those who completed the ESAS, there were significant improvements in scores in the following symptoms: fatigue, depression, loss of appetite, and shortness of breath. Physical symptoms (pain, fatigue, nausea, drowsiness, appetite, and shortness of breath) and the total ESAS scores were significantly improved (p?=?0.002 and p?=?0.005, respectively). Each non-medical palliative care program, such as art and music therapy, yoga, foot massage, haircut, and body care, showed no significant differences between the group who received them and those who did not. Conclusion: During the APCU stay, the overall symptoms of inpatients were reduced. A comprehensive and multidisciplinary team approach is essential for patients who need palliative care.

As the incidence of extreme precipitation events attributable to global climate change increases, providing policymakers with accurate model predictions is of the utmost importance. However, model projections have inherent uncertainties. The present study attempted to distinguish the sources of the uncertainty of the mean and extreme precipitation projections in the East Asia region using the mean boreal summer precipitation, simple precipitation intensity index (SDII), maximum cumulative 5 day precipitation, and annual maximum daily precipitation (Rx1d). The results show that while the mean precipitation was projected to change very little regardless of the scenario, more extreme indices were projected to increase considerably by the end of the century, particularly in the high-emissions scenarios. On average, model uncertainty accounted for the largest part of the uncertainty. However, for Rx1d in the 2030s, as well as mean and SDII in some regions until the 2060s, the internal variability was the largest contributor. In addition, whilst scenario uncertainty accounted for a negligible proportion of average precipitation variability, for the more extreme the precipitation indices, scenario uncertainty contribution to total variability by the end of the century was significant; namely, the scenario uncertainty contribution was overall highest for the maximum one-day precipitation. Additionally, comparatively wetter regions had greater overall projection uncertainties, especially uncertainty arising from internal variability, likely due to the influence of interannual variability from the EA summer monsoon.

This study compares the bias correction techniques of empirical quantile mapping (QM) and the Long Short-Term Memory (LSTM) machine learning model for summertime daily rainfall simulation focusing on precipitation-dependent bias and temporal variation. Numerical experiments using Weather Research and Forecasting (WRF) were conducted over South Korea with lateral boundary conditions of ERA5 reanalysis data. For the spatial distribution of mean summertime rainfall, the bias-uncorrected WRF simulation (WRF_RAW) showed dry bias for most of the region of South Korea. The WRF results corrected by QM and LSTM (WRF_QM and WRF_LSTM, respectively) were improved for the mean summer rainfall simulation with the root mean square error values of 0.17 and 0.69, respectively, which were smaller than those of the WRF_RAW (1.10). Although the WRF_QM performed better than the WRF_LSTM in terms of the summertime mean and monthly precipitation, the WRF_LSTM presented a closer interannual rainfall variation to the observation than the WRF_QM. The coefficient of determination for calendar-day mean rainfall was the highest in the following order: the WRF_LSTM (0.451), WRF_QM (0.230), and WRF_RAW (0.201). However, the WRF_LSTM had a limitation in reproducing extreme rainfall exceeding 50 mm/day due to the few cases of extreme precipitation in training data. Nevertheless, the WRF_LSTM better simulated the observed light-to-moderate precipitation (10–50 mm/day) than the others.

This study examines the spatiotemporal characteristics of the summer monsoon rainy season over East Asia using six regional climate models (RCMs) participating in the Coordinated Regional Domain Experiment (CORDEX) East Asia Phase II project. The framework combining multiple global climate models (GCMs) with multiple RCMs produces a larger spread in summer monsoon characteristics than driving GCMs only, enabling a better quantification of uncertainty factors. On average, the RCM simulations reproduce the observed summer monsoon duration and area better than the corresponding boundary GCMs, implying the added values of downscaling. Both the area and duration of the East Asian summer monsoon are projected to increase by the late 21st century, more strongly in high emission scenarios than in low emission scenarios, particularly in China. Different responses between scenarios, which indicate warming mitigation benefits, only become significant in the late 21st century due to large intersimulation uncertainties. Analysis of variance results show that uncertainty in future monsoon area and duration is larger between boundary GCMs than between RCMs over East Asia and its coastal subregions. A strong intersimulation relationship between RCMs and GCMs supports that boundary GCMs substantially diversify downscaled RCM projections through different climate sensitivities. Furthermore, the distinct subregional responses in future monsoon area and duration emphasize the importance of fine-resolution projections with appropriate uncertainty measures for better preparing region-specific adaptation plans.

An extreme heatwave, in terms of intensity and duration, is projected to occur at the end of the 21st century (2071–2100) over the whole of East Asia. The projection is calculated using daily maximum temperature data of 25 km horizontal resolution produced by 12 general circulation model-regional climate model chains participating in the CORDEX-East Asia Phase 2 project. An ‘extreme’ heatwave is defined as one in which the heatwave magnitude (HWM), which is the accumulated daily intensity of a heatwave during the heatwave period, is higher than the 95th percentile of the HWM for the reference period (1981–2005). In historical simulations, heatwaves have occurred mainly from April to June in India, in April and May in Indochina, from June to August in China and Mongolia, and in July and August in the Korean Peninsula and Japan; most heatwaves last three to four days. In India and Indochina, long-lasting and intense heatwaves occur more often than in other regions. In future, heatwave intensity will increase, the average duration of heatwaves will be approximately two to three weeks, and the heatwave season will be lengthened. Therefore, extreme heatwaves will occur more frequently and strongly. Under two representative concentration pathway scenarios (RCP2.6 and RCP8.5) and two shared socioeconomic pathway scenarios (SSP1-2.6 and SSP5-8.5), the proportion of extreme heatwaves to all heatwave events will increase from 5.0% (historical) to 8.0%, 20.8%, 19.3%, and 36.3%, and the HWM of the extreme heatwave will be 1.4, 3.5, 3.0, and 9.0 times stronger, respectively. The main reason for the increase in the HWM of extreme heatwaves is the increased duration rather than the daily intensity of the heatwaves. In East Asia, the temporal and regional disparities of heatwave damage will be much more prominent as extreme heatwaves become stronger and more frequent in these regions and during the periods that are more affected by heatwaves in the present day.

The predictability of the wintertime Western Pacific (WP) pattern is evaluated based on seasonal predictions from five models participating in the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) for the winters from 1982/1983 to 2021/2022. The temporal correlation coefficient (TCC) between the observed and MME-predicted WP indices was 0.61 (0.37–0.54 for individual models) for the entire series. However, when only three Super El Niño (SEN) years (Niño3.4 ≥ 2.0) out of the 40-year series were excluded, the TCC dropped down to 0.54 (0.27–0.42). During the SEN years, the WP was strongly affected by the SEN-excited anomalies via the PNA. In observations from non-SEN years, the WP pattern was strongly related to the dipole pattern in Northwestern Pacific SST (TCC = 0.8), for the description of which we suggested a Northwestern Pacific (NWP) index, and it was significantly weakly related to the ENSO and IOD, whereas in the model simulations, the main role was played by the ENSO (TCC = 0.6). The NWP index was well predictable in MME (TCC = 0.73) and individual models (0.56–0.71). We showed that the prediction of the WP index polarity is reliable when both predicted WP and NWP anomalies are significant and indicate the same WP sign that has implications for the seasonal forecasting.

The 2019/20 winter was extremely warm globally and in the Northern Hemisphere extratropics. The main cause of climate extremes particularly in East Asia, was the extreme positive Arctic Oscillation (AO) event superimposed on steady global warming. The negligible trend in the AO over the preceding 41 years makes it possible to distinguish the roles of AO and global warming in the observed extremes. We estimate and compare contributions to January–March 2020 climate extremes by the AO and global warming represented by local temperature trends using the ERA5 reanalysis data. Based on results from a preliminary study, we estimate the contribution by global warming using linear regression while that by the AO using cubic regression, which is more restrained for the high AO index values than linear. The results show that the temperature extremes were mainly caused by the extreme positive AO event which accounts for approximately 3/4 of the observed temperature anomalies in northern East Asia and 2/3 in eastern East Asia. In southern East Asia, the AO contributes negligibly and positive temperature anomalies are related to global warming and local and regional impacts, particularly extreme sea surface temperature, enhance south-westerlies and local radiative forcing. General conclusion is that the observed strong positive temperature anomalies including extreme anomalies over East Asia could have been achieved only as a combined effect of the extreme positive AO event and global warming. Quantification of the roles of the AO and global warming in climate extremes helps to estimate future anomalies caused by extreme AO events as well as assess uncertainties in climate model projections.

The changes of agro-climate and heat extremes, and their impact on rice cultivation are assessed over South Korea in context of 2 and 3 °C global warming levels (GWL) compared to pre-industrial levels, with ensemble regional climate model projection produced under the Coordinated Regional Climate Downscaling Experiment–East Asia (CORDEX-EA) phase 2 protocols. It is found that the mean temperature increase under global warming has not only positive effects such as the extension of vegetable and crop periods and the widening of the cultivatable regions but also negative effects due to the shortening of the reproductive growth period. On the other hand, extreme heat changes in the future clearly show a negative effect on rice cultivation via the increase of hot days during heat-sensitive stages (27.16% under 2 °C GWL, 54.59% under 3 °C GWL) among rice phenology which determines the rice yield in tandem with rice flowering, ripening, and sterility problems. The major type of heat extreme is dominated by nationwide warm anomalies covering entire S. Korea, and the proportion of this type is projected to increase from 35.8% to 49.5% (57.4%) under 2 °C (3 °C) GWL in association with the thermal expansion of atmosphere which links to the favorable environment for occurring barotropic anti-cyclonic system.

Crop growth models and remote sensing are useful tools for predicting crop growth and yield, but each tool has inherent drawbacks when predicting crop growth and yield at a regional scale. To improve the accuracy and precision of regional corn yield predictions, a simple approach for assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) products into a crop growth model was developed, and regional yield prediction performance was evaluated in a major corn-producing state, Illinois, USA. Corn growth and yield were simulated for each grid using the Crop Environment Resource Synthesis (CERES)-Maize model with minimum inputs comprising planting date, fertilizer amount, genetic coefficients, soil, and weather data. Planting date was estimated using a phenology model with a leaf area duration (LAD)-logistic function that describes the seasonal evolution of MODIS-derived leaf area index (LAI). Genetic coefficients of the corn cultivar were determined to be the genetic coefficients of the maturity group [included in Decision Support System for Agrotechnology Transfer (DSSAT) 4.6], which shows the minimum difference between the maximum LAI derived from the LAD-logistic function and that simulated by the CERES-Maize model. In addition, the daily water stress factors were estimated from the ratio between daily leaf area/weight growth rates estimated from the LAD-logistic function and that simulated by the CERES-Maize model under the rain-fed and auto-irrigation conditions. The additional assimilation of MODIS data-derived water stress factors and LAI under the auto-irrigation condition showed the highest prediction accuracy and precision for the yearly corn yield prediction (R2 is 0.78 and the root mean square error is 0.75 t ha-1). The present strategy for assimilating MODIS data into a crop growth model using minimum inputs was successful for predicting regional yields, and it should be examined for spatial portability to diverse agro-climatic and agro-technology regions.

Reanalysis data are known to have relatively large uncertainties in the polar region than at lower latitudes. In this study, we used a single sea-ice model (Los Alamos’ CICE5) and three sets of reanalysis data to quantify the sensitivities of simulated Arctic sea ice area and volume to perturbed atmospheric forcings. The simulated sea ice area and thickness thus volume were clearly sensitive to the selection of atmospheric reanalysis data. Among the forcing variables, changes in radiative and sensible/latent heat fluxes caused significant amounts of sensitivities. Differences in sea-ice concentration and thickness were primarily caused by differences in downward shortwave and longwave radiations. 2-m air temperature also has a significant influence on year-to-year variability of the sea ice volume. Differences in precipitation affected the sea ice volume by causing changes in the insulation effect of snow-cover on sea ice. The diversity of sea ice extent and thickness responses due to uncertainties in atmospheric variables highlights the need to carefully evaluate reanalysis data over the Arctic region.

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 °C and 3 °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 °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 °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.

Although gastrointestinal stromal tumors (GISTs) are rare disease and rectal GISTs is only 5% of total GISTs, they have the worst prognosis. Due to narrow pelvis, tumor rupture or positive resection margin are common in the management of rectal GISTs. The impact of neoadjuvant treatment on the clinical outcomes of rectal gastrointestinal stromal tumors (GISTs) remains unclear. Thus, we conducted a retrospective study to investigate the impact of neoadjuvant imatinib on rectal GIST. The cohort comprised 33 patients; of them, 10 and 23 belonged to the neoadjuvant (i.e., those who underwent neoadjuvant imatinib treatment) and the control group (i.e., those who underwent surgery without prior imatinib treatment), respectively. Neoadjuvant group was associated with more common levator ani muscle displacement (P = 0.002), and showed significantly larger radiologic tumor size (P = 0.036) than the control group. The mean tumor size was significantly decreased after imatinib treatment (6.8 cm to 4.7cm, P = 0.006). There was no significant difference in resection margin involvement (P >0.999), and sphincter preservation rates (P = 0.627) between the two groups. No difference was observed with respect to morbidities, hospital stay, local recurrence and disease-free survival. Neoadjuvant imatinib treated group had similar propensity with control group after treatment. We thought reduced tumor sized could enhance resectability and provide more chance to preserve sphincter for rectal GIST patients. Considering large tumor size and higher rate of sphincter invasion in the neoadjuvant group, imatinib treatment could be helpful as a conversion strategy to make huge and low-lying rectal GIST operable and achieve better surgical outcomes.