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Statistical bias correction (BC) is a widely used tool to post-process climate model biases in heat-stress impact studies, which are often 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 two heat-stress indices with different dependencies on temperature and relative humidity using multiple regional climate model simulations over South Korea. It would be helpful for reducing the ambiguity involved in the practical application of BC for climate modeling and end-user communities. Our results demonstrate that the multivariate approach can improve the corrected inter-variable dependence, which benefits the indirect correction of heat-stress indices depending on the adjustment of individual components, especially those indices relying equally on multiple drivers. On the other hand, the direct correction of multivariate indices using the quantile delta mapping univariate approach can also produce a comparable performance in the corrected heat-stress indices. However, our results also indicate that attention should be paid to the non-stationarity of bias brought by climate sensitivity in the modeled data, which may affect the bias-corrected results unsystematically. Careful interpretation of the correction process is required for an accurate heat-stress impact assessment.

GCM ensembles indicate that the October 2021 South Korean heatwave was extremely unlikely to occur without human influences, which corresponds to 2060s’ new normal without ambitious greenhouse gas mitigation.

This study evaluates tropical cyclone (TC) rainfall structures in the CMIP6 HighResMIP global climate model (GCM) simulations against satellite rainfall retrievals. We specifically focus on TCs within the deep tropics (25°S–25°N). Analysis of TC rain rate composites indicates that in comparison to the satellite observations at the same intensity, many HighResMIP simulations tend to overproduce rain rates around TCs, in terms of both maximum rain rate magnitude and area-averaged rain rates. In addition, as model horizontal resolution increases, the magnitude of the peak rain rate appears to increase. However, the area-averaged rain rates decrease with increasing horizontal resolution, partly due to the TC eyewall being located closer to the TC center, thus occupying a smaller area and contributing less to the area-averaged rain rates. The effect of ocean coupling is to lower the TC rain rates, bringing them closer to the satellite observations, due to reduced horizontal moisture flux convergence and surface latent heat flux beneath TCs. Examination of horizontal rain rate distributions indicates that vertical wind shear–induced rainfall asymmetries in HighResMIP-simulated TCs are qualitatively consistent with the observations. In addition, a positive relationship is observed between the area-averaged inner-core rainfall and TC intensification likelihoods across the HighResMIP simulations, as GCM simulations producing stronger TCs more frequently have the greater rainfall close to the center, in agreement with previous theoretical and GCM simulation results.

To assess the risk gradient of chromosomal abnormalities and fetal or neonatal death across a socioeconomic spectrum of pregnant women.We used the data from the Korean Prenatal Diagnosis Study (KPDS), which included singleton pregnancies who were candidates for fetal aneuploidy screening enrolled from the Seoul Capital Area from December 2016 to April 2018. We analyzed chromosomal abnormalities which were diagnosed pre- or postnatally, and fetal or neonatal death. The highest level of education among the women and the average monthly household income were used as proxies for socioeconomic status.Among the 6,715 women, the majority of were 30-39 years old and university graduates, with a reported household income higher than the national median. Chromosomal abnormalities occurred in 45 women (6.7 per 1,000). Fetal or neonatal death occurred in 70 (11.3 per 1,000), excluding pregnancies affected by chromosomal abnormality diagnosis. The adjusted odds ratio for chromosomal abnormalities was higher when household income was 4,484 USD per month. For fetal or neonatal death, the risk estimates for lower education and lower household income were generally positive but remained imprecise.We observed some evidence of an inverse association between the risk of fetal chromosomal abnormality and level of household income in a prospective cohort of pregnant women. Interventions to reduce socioeconomic disparities in perinatal health should focus on those with a low household income.

Changes in summer heat extremes have been recorded all over the globe in recent years. East Asia is one of the most vulnerable areas for climate change, owing to a combination of natural and anthropogenic factors. The human experience of heat, however, does not only depend on the temperature itself but rather the combination of factors that regulate the exchange of heat with the environment. The present study uses Net Effective Temperature (NET), an index that combines the effects of temperature, humidity, and wind, to assess heat stress perception and its potential changes by the end of the century over East Asia. The data used in the study are maximums calculated from the three-hourly output of the 10 Coupled General Circulation Model (CGCM) - Regional Climate Model (CGM) chains participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX)-East Asia phase 2. As human beings can acclimate to their environment, not only maximum values but also 95th percentiles of maximum values have been used. The assessment of the models showed that all of the models reproduced the current climate reasonably. The present study utilized two different scenarios, RCP8-5, and SSP5-8.5. In both scenarios, the increase in averages and 95th percentiles of both maximum temperatures and NETs over the whole domain has been projected. The increase in NET was projected to be higher than the increase in temperature itself, and the increase in the SSP5-8.5 scenario was projected to be higher than the increase in the RCP8.5 scenario, but the details of the warming patterns were dependent on the choice of model. Additionally, the Korean Peninsula and Japan have been shown to have the largest difference in the increase between heat stress and temperature, while the highest overall increase of both temperature and NET was projected over the North-Western part of the domain in both scenarios.

Tropical Cyclone (hereafter, TC), a most destructive weather phenomenon that causes enormous socio-economic damage, occurs around 25 times every year in the western North Pacific, of which Korea is directly or indirectly affected by about 3 to 4 TCs every year. Even if it is affected by a small number of TCs, the damage could be unimaginably large. To preemptively prepare and respond to TCs, predictability on the sub-seasonal to seasonal (S2S) time-scale, over two weeks to two months is being emphasized. In this study, the characteristics of TCs in sub-seasonal forecasting with the Global Seasonal Forecast System 5 (GloSea5) of the Korea Meteorological Administration (KMA) were assessed over the western North Pacific (WNP). The predictability of GloSea5 was examined for its ability to reproduce observed TC climatology as well as changes in TC genesis with the El Niño-Southern Oscillation (ENSO) and a 1998/1999 climate regime shift. GloSea5 showed skilful performance in simulating the frequency and genesis spatial distribution of TCs in climatology and both extreme ENSO phases. Synoptic fields related to TC genesis were also reasonably captured, despite some systematic biases in those. GloSea5 performed well in terms of characteristics of changes in TC genesis due to the climate regime shift. However, there were biases in TC frequency before the regime shift and in changes in TC-related environmental fields. This study implies that GloSea5, which has a good predictive skill for TC genesis over the WNP, can be used as an operational model for sub-seasonal TC forecasting, although it requires continuous improvements to reduce systematic errors

East Asian Winter Monsoon (EAWM) is an important system, which has impact on extreme winter phenomena, in East Asia including the Korean Peninsula. EAWM is characterized by the Siberian high with cold air and the Aleutian low with warm air, low-level northerly wind between them, East Asian trough in the middle troposphere, and East Asian jet stream in the upper troposphere. According to some previous studies, the variability of EAWM, which can be influenced by human activity and climate change, is strongly correlated with the occurrence of cold waves in East Asia. Additionally, EAWM variability has been shown to affect the precipitation during winter on the Korean Peninsula. Extreme boreal winter phenomena such as cold waves and heavy snow related to EAWM can induce disruptions to regular life and socio-economic damage. Regional climate models (RCMs) have added value in the simulations of extreme climate phenomena, but also have internal variability, which can increase uncertainty in future climate projections. Therefore, in this study, to closely understand EAWM and reduce the uncertainty in future climate projection, the performance of two RCMs (SNURCM and WRF) for EAWM simulation was evaluated. The results showed that two RCMs had the ability to capture the interannual variability and climatological spatial pattern of EAWM despite the systematic biases of EAWM in the lower troposphere (e.g., comparatively low correlation and larger temporal-spatial variability than reanalysis). Additionally, two RCMs presented significant cold bias over the Manchuria and wet bias over the Kuroshio Current. In further analysis, their systematic errors were positively correlated with EAWM, which had a larger pressure gradient between the Siberian high and the Aleutian low and colder advection over Manchuria than those of the ERA5. Wet biases over the Kuroshio Current could be related to unreasonable simulation EAWM induced by uncoupled air-sea interaction in RCMs.

Tropical cyclones (TCs), which often form over the western North Pacific (WNP), have a large socioeconomic impact and result in destructive damages in East Asian countries. Therefore, it is critical to estimate future changes in TCs characteristics under the global warming. In this study, future characteristics of TCs using five regional climate models (RCMs) (i.g., RegCM4, GRIMs, WRF, CCLM, and HadGEM3-RA) were investigated in Coordinated Regional Climate Downscaling Experiment (CORDEX) East Asia domain. Five RCMs were forced by the UK Earth System Model (UKESM) under the historical and two Shared Socioeconomic Pathways (SSP) scenarios (SSP1-2.6 and SSP5-8.5). The simulation experiments were conducted at about 25-km horizontal resolution. The multi-RCM ensemble mean was used for analysis to reduce the uncertainty of a single RCM. In the historical period (1985-2014) during the TC season (June-November), the ensembled RCMs properly reproduced the TC genesis over the WNP comparable to the best track data. In the future climate, the RCMs tended to migrate the core region of TCs genesis more northward compared in the present climate. This migration could be related to weakening of vertical wind shear over the mid-latitudes, due to decreased meridional sea surface temperature gradient.

The tropical cyclone (TC) tracks are usually simulated with the numerical models, which have an intrinsic error, although the performance of numerical models is continuously improving. Recently, machine learning has been suggested as a good tool to correct the intrinsic error of the model outputs. This study used an artificial neural network (ANN) to correct the error of TC tracks hindcasted by the Weather Research and Forecasting (WRF) model over the western North Pacific (WNP). TCs whose intensity was higher than tropical depression (i.e., tropical storm, severe tropical storm, and typhoon) from June to November were hindcasted, and TC positions at 72 h were set as the target of bias correction. WRF model output, best track data, and wind field of reanalysis were used as input variables of ANN. The structure of ANN was optimized for TCs during 2006-2015, and the optimized ANN was verified for TCs from 2016-2018. In the verification of ANN, TCs were classified using k-mean clustering to analyze the results of bias correction because the performance of the numerical model for the TC track varied depending on the region of WNP. The ANN corrected the error of WRF by 8.81% for four clusters where ANN was most effective. Moreover, the post-processing was applied to other clusters with less effect of ANN. Consequently, ANN with post-processing improved the accuracy of WRF by 4.34%.

Studies have shown that regional climate models (RCMs) can simulate local climates at a higher resolution for specific regions compared to global climate models (GCMs), making dynamic downscaling using RCMs a more effective approach. Therefore, RCMs have become valuable tools for evaluating the potential impacts of climate change on specific regions and for informing local adaptation strategies. To fully understand the added value (AV) of RCMs, it is essential to understand how the characteristics differ between land and ocean. The complex topography of East Asia, including land and sea, makes it a suitable region for evaluating the AV of RCMs. In this study, we compared two regional simulations that integrated the same RCMs but employed different GCMs from the Coordinated Regional Climate Downscaling Experiment for their ability to simulate storm tracks in East Asia. The results of the RCMs over a historical period were compared with their host Coupled Model Intercomparison Project GCM projections and high-resolution reanalysis. In mountainous regions, the AV of the RCMs weakened the bias of the GCM and improved its agreement with the reanalysis. In plains and coastal areas, owing to the increase in horizontal resolution in RCMs, small-scale phenomena are well represented, and the storm track of RCMs shows similar values to that of the GCM in maritime regions. This study demonstrates the value of RCMs for improving the accuracy of climate projections in East Asia, informing adaptation strategies, and enhancing climate research.

The quantitative assessment of the uncertainty components of future climate projections is critical for decision-makers and organizations to establish climate change adaptation and mitigation strategies at regional or local scales. This is the first study in which the changes in the uncertainty components of future temperature and precipitation projections are quantitatively evaluated using multiple regional climate models over East Asia, vulnerable to future climate change. For temperature, internal variability and model uncertainty were the main factors affecting the near-term projections. The scenario uncertainty continued to increase and was estimated to be the dominant factor affecting the uncertainty after the mid-term projections. Although precipitation has the same main uncertainty factors as the temperature in the near-term projections, it significantly differs from temperature because the internal variability notably contributes to the fraction to the total variance, even in the long-term projections. The internal variability of the temperature and precipitation in the near-term projections was predicted to be larger in Korea than that in East Asia. This was confirmed by regional climate models as well as previous studies using global climate models as to the importance of internal variability at smaller regional scales during the near-term projections. This study is of significance because it provides new possibilities with respect to the consideration of climate uncertainties to the establishment of climate change policies in more detail on the regional scale.

We selected the land area over East Asia (100°E–150°E and 20°N–50°N) as the analysis domain, which includes Korea, Japan, China (except for the western arid region), and parts of Mongolia and Russia. The major analysis variables are the daily mean surface temperature (TAS) and daily precipitation (PR). Multiple RCM datasets with 25 km horizontal spatial resolution forced by three Climate Model Intercomparison Project phase 5 (CMIP5) GCMs (HadGEM2-AO, MPI-ESM-LR, and GFDL-ESM-2M) included in the CORDEX-East Asia phase Ⅱ project were used to estimate the uncertainties of future climate projections. The historical experiment for 1981–2005 and Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 scenarios for 2006–2099 were applied in this study. The variance scaling (VS) and quantile mapping of the entire period (QME) were applied to the TAS and PR for each grid, respectively.

This study identifies diverse synoptic weather patterns of warm-season heavy rainfall events (HREs) in South Korea. The HREs not directly connected to tropical cyclones (TCs) (81.1%) are typically associated with a midlatitude cyclone from eastern China, the expanded North Pacific high, and strong southwesterly moisture transport in between. They are frequent both in the first (early summer) and second rainy periods (late summer) with impacts on the south coast and west of the mountainous region. In contrast, the HREs resulting from TCs (18.9%) are caused by the synergetic interaction between the TC and meandering midlatitude flow, especially in the second rainy period. The strong south-southeasterly moisture transport makes the southern and eastern coastal regions prone to the TC-driven HREs. By applying a self-organizing map algorithm to the non-TC HREs, their surface weather patterns are further classified into six clusters. Clusters 1 and 3 exhibit a frontal boundary between the low and high with differing relative strengths. Clusters 2 and 5 feature an extratropical cyclone migrating from eastern China under different background sea level pressure patterns. Cluster 4 is characterized by the expanded North Pacific high with no organized negative sea level pressure anomaly, and cluster 6 displays a development of a moisture pathway between the continental and oceanic highs. Each cluster exhibits a distinct spatiotemporal occurrence distribution. The result provides useful guidance for HRE prediction by depicting important factors to be differently considered depending on their synoptic categorization.

This study investigates the impact of the sea surface temperature (SST) on the forecast of two typhoons, which consecutively hit South Korea in 2020. SST data were obtained from the Daily Optimum Interpolation Sea Surface Temperature (OISST) version 2 and HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA; GLBy0.08/expt_93.0). When verified using in situ observational data, the OISST data did not accurately estimate the changes in SST during each typhoon’s landfall period compared to the HYCOM data since it has a relatively low temporal resolution. To investigate the impact of these two SST data on typhoon forecasts, we conducted sensitivity experiments using the Weather Research and Forecasting (WRF) model. The results showed that simulated typhoon intensities were significantly improved in the simulations with HYCOM data (HY runs), while typhoon track forecast performances were similar in both runs. In addition, the forecast performances of the maximum wind speed at 10 m during the typhoon landfall period were improved in the HY runs. Therefore, this study showed that the overall typhoon intensity and forecast performances during the landfall period could be improved when the higher temporal-resolution SST data were prescribed in the model boundary conditions for a better representation of typhoon-induced SST changes.

As the resolution of regional climate models has increased with the development of computing resources, Added Values (AVs) have always been a steady research topic. Most previous studies examined AVs qualitatively by comparing model results with different model resolutions qualitatively. This study tried to quantitatively investigate the AV of the high-resolution regional climate model for precipitation by analyzing the distribution of kinetic energy according to the different wavelengths at two different resolutions (36 km vs. 4 km), away from the traditional comparative analysis. In addition, the experiment that the low-resolution topography was forced to the high-resolution model was additionally conducted to separate the AVs associated with the topographic effect. Among the three experiments, two with the same topography and two with the exact horizontal resolution were compared separately. With identical topography, the high-resolution model simulated amplified precipitation intensity more than the low-resolution model in all quantiles, especially for extreme precipitation. The precipitation generated by mesoscale or smaller scale weather/climate events was also simulated with greater intensity in the high-resolution model. With the same grid spacing, the more detailed topography model showed AV for increasing spatial variability of precipitation, especially in mountainous regions. The AVs identified in this study were related to kinetic energy with wavelengths at the meso-beta or smaller scale. On the other hand, the kinetic energy above the meso-alpha or larger scale has no significant correlation with the AV of precipitation.

We investigated the strengthened ocean surface heat flux at Ieodo Ocean Research Station (Ieodo ORS) during the passage of Typhoon Lingling. On September 6, 2019, when Typhoon Lingling approached the Ieodo ORS, we observed a dramatic drop in the sea surface temperature (SST) of 5.0 °C along with intense turbulent mixing. Although the decline of the SST was much weaker than the observed data, the SST simulated by the regional air-sea coupled model exhibited a decrease of about 2.0 °C in a few hours due to turbulent mixing by current shear. At the same time, the latent heat flux showed a robust downward transfer. We also investigated the case with the air-sea coupled model and atmosphere-only model, but this negative latent heat flux was not captured. The sensible heat flux was better captured by the coupled model than the uncoupled model. And the coupled model simulated the latent heat flux closer to downward than the uncoupled model. We conducted a sensitivity test by altering the flux estimation methods used to estimate ocean heat fluxes. We found that the latent heat flux was more sensitive to the choice of estimation and showed much room for improvement. In this regard, the joint model-observation study using Ieodo ORS data provides an invaluable opportunity for improved typhoon simulation.

Anemia during pregnancy is known to be associated with an increased risk of antenatal and/or postnatal depression, as well as adverse pregnancy outcomes. However, there are few studies evaluating psychological health throughout the antepartum and postpartum periods in women with anemia in early pregnancy. This study analyzed data collected by the Korean Pregnancy Outcome Study, a multicenter prospective cohort study conducted in South Korea, to determine the impact of anemia during the first trimester on birth outcomes and maternal mental health during pregnancy and postpartum. Hemoglobin levels were measured during the first trimester, and psychological health was evaluated at 12, 24, and 36 gestational weeks and 4–6 weeks postpartum. Anxiety and depression were defined using the Hospital Anxiety and Depression Scale and the Edinburgh Postnatal Depression Scale, respectively. Among 4067 Korean participants, 119 (2.9%) were diagnosed with anemia during the first trimester. Incidences of anxiety and depression did not differ over the pregnancy period between those with and without anemia during the first trimester. However, postpartum anxiety and depression were significantly more common in participants with anemia than in those without (p < 0.05, both). Hence, obstetricians should pay attention to postpartum mental health in women with anemia during the first trimester.

Typhoon Chaba made landfall on the Korean Peninsula in the fall of 2016, resulting in record-breaking rainfall in southeastern Korea. In particular, the Ulsan metropolitan region experienced the most severe floods due to heavy rainfall of 319 mm for just 3 h. The heavy rainfall was possibly associated with the mountainous southeastern Korea topography and the warm sea surface temperature (SST) anomaly in the East China Sea. In this study, the Weather Research and Forecasting (WRF) model was used to investigate the effects of topography and SST anomalies through high-resolution numerical experiments. Simulation using original topography showed more rainfall on the windward and less on the leeward slope compared to the experiment with reduced topography around Ulsan. The moist flow in the typhoon was raised by orographic uplift, enhancing precipitation on the windward side and summits of the mountains. The orographically induced updraft extended to the mid-troposphere and contributed to the upward vertical moisture flux associated with rainfall. Therefore, the mountainous topography around Ulsan affected the local change in rainfall induced by the simulated typhoon. In addition, SST on the track of the typhoon controlled storm intensity and caused extreme precipitation changes. The experiment using the original SST in the East China Sea simulated less decayed typhoons and produced more precipitation compared to the experiment wherein the positive SST anomaly in the East China Sea was removed. The warm SST anomaly hindered the weakening of the typhoon moving northward to the mid-latitudes. At landfall, the stronger typhoon contained more water vapor, transported more moisture inland, and generated increased precipitation.