Snowfall in the northeastern part of South Korea is the result of complex snowfall mechanisms due to a highly contrasting terrain combined with nearby warm waters and three synoptic pressure patterns. All these factors together create unique combinations, whose disentangling can provide new insights into the microphysics of snow on the planet. This study focuses on the impact of wind flow and topography on the microphysics drawing of 20 snowfall events during the ICE-POP 2018 (International Collaborative Experiment for PyeongChang 2018 Olympic and Paralympic winter games) field campaign in the Gangwon region. The vertical structure of precipitation and size distribution characteristics are investigated with collocated MRR (micro rain radar) and PARSIVEL (particle size velocity) disdrometers installed across the mountain range. The results indicate that wind shear and embedded turbulence were the cause of the riming process dominating the mountainous region. As the strength of these processes weakens from the mountainous region to the coastal region, riming became less significant and gave way to aggregation. This study specifically analyzes the microphysical characteristics under three major synoptic patterns: air–sea interaction, cold low, and warm low. Air–sea interaction pattern is characterized by more frequent snowfall and vertically deeper precipitation systems on the windward side, resulting in significant aggregation in the coastal region, with riming featuring as a primary growth mechanism in both mountainous and coastal regions. The cold-low pattern is characterized by a higher snowfall rate and vertically deep systems in the mountainous region, with the precipitation system becoming shallower in the coastal region and strong turbulence being found in the layer below 2 km in the mountainous upstream region (linked with dominant aggregation). The warm-low pattern features the deepest system: precipitation here is enhanced by the seeder–feeder mechanism with two different precipitation systems divided by the transition zone (easterly below and westerly above). Overall, it is found that strong shear and turbulence in the transition zone is a likely reason for the dominant riming process in the mountainous region, with aggregation being important in both mountainous and coastal regions.

The products from the Stable Water Isotope Intercomparison Group, Phase 2, are currently used for numerous studies, allowing water isotope model‐data comparisons with various isotope‐enabled atmospheric general circulation model (AGCMs) outputs. However, the simulations under this framework were performed using different parameterizations and forcings. Therefore, a uniform experimental design with state‐of‐the‐art AGCMs is required to interpret isotope observations rigorously. Here, we evaluate the outputs from three isotope‐enabled numerical models nudged by three different reanalysis products and investigate the ability of the isotope‐enabled AGCMs to reproduce the spatial and temporal patterns of water isotopic composition observed at the surface and in the atmospheric airborne water. Through correlation analyses at various spatial and temporal scales, we found that the model's performance depends on the model or reanalysis we use, the observations we compare, and the vertical levels we select. Moreover, we employed the stable isotope mass balance method to conduct decomposition analyses on the ratio of isotopic changes in the atmosphere. Our goal was to elucidate the spread in simulated atmospheric column δ¹⁸O, which is influenced by factors such as evaporation, precipitation, and horizontal moisture flux. Satisfying the law of conservation of water isotopes, this budget method is expected to explain various fractionation phenomena in atmospheric meteorological and climatic events. It also aims to highlight the spreads in modeled isotope results among different experiments using multiple models and reanalyses, which are primarily dominated by uncertainties in moisture flux and precipitation, respectively.

This study evaluates the performance of several cloud microphysics parameterizations in simulating surface precipitation for two snowstorm cases during the International Collaborative Experiment held at the PyeongChang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign. We compared four different schemes in the Weather Research and Forecasting (WRF) model, namely the Double-Moment 6-class (WDM6), the WRF Single-Moment 6-class (WSM6), and Thompson and Morrison parameterizations. Both WSM6 and WDM6 overestimated the precipitation amount for the shallow precipitation system because of the substantial amount of cloud ice, mostly generated by the deposition process. The simulated precipitation amount and distribution for the deep precipitation system showed no noticeable differences in the different cloud microphysics parameterizations. However, the simulated hydrometeor type at the surface using WSM6 and WDM6 showed good agreement with observations for all cases. The accuracy of the mean mass-weighted terminal velocity of cloud ice (⁠ $VI¯$⁠) applied in WSM6 and WDM6 is ±20%. The number concentration of cloud ice and the ice microphysics processes are newly retrieved with 1.2 times increased $VI¯$⁠. For the shallow snowstorm, the precipitation amount was reduced by approximately 8% because of the inefficient deposition and its effects on the subsequent ice microphysical processes, such as the accretion of cloud ice by snow and the conversion from cloud ice to snow.

In this study, the effect of a scale‐aware convective parameterization scheme (CPS) on the simulation of heavy precipitation in the gray‐zone was investigated using the Weather Research and Forecasting (WRF) model. We performed WRF simulations with the Kain–Fritsch (KF) scheme (non‐scale‐aware), Multiscale Kain–Fritsch (MSKF) scheme (scale‐aware), and explicit convection (i.e., no CPS). The MSKF scheme uses a scale‐aware parameter that modulates the convective available potential energy (CAPE) timescale and entrainment process in the KF scheme as a function of the horizontal grid spacing. The results of this study show that WRF simulations using explicitly resolved convection lead to overestimations and erroneous precipitation locations in the gray‐zone because the convection and atmospheric instability cannot be appropriately triggered and reduced. The CPS without scale‐awareness in the gray‐zone exaggerates the convection and distorts synoptic fields, leading to the erroneous simulation of heavy precipitation at high resolution. The MSKF scheme with scale‐awareness improves the simulation of convective cells‐related heavy rainfall by removing the atmospheric instability in the gray‐zone, reducing the role of the CPS, and increasing the role of the microphysics parameterization scheme (MPS) with decreasing grid spacing. In addition, the results of sensitivity experiments show that reducing the CAPE timescale leads to the faster development of convective cells, whereas decreasing the entrainment leads to precipitation overestimation. The modulated parameters in the scale‐aware MSKF scheme play a crucial role in balancing the effects of the CPS and MPS in the gray‐zone.

The net effective temperature (NET), an index that includes the combined effects of temperature, humidity, and wind, was used along with temperature to assess the impacts of climate change on the heat stress perception in East Asia, one of the regions considered most vulnerable to heat stress. The need for dynamic downscaling has been emphasized because the regional effects of climate change do not follow the global levels linearly. In this study, daily maximums calculated from the 3‐hourly data downscaled by five different regional climate models from four coupled general circulation models participating in Coordinated Regional Climate Downscaling Experiment‐East Asia phase 2 were utilized. To account for the fact human beings acclimate to their environments, 95th percentile of the maximum temperature and maximum NET was used along with the average boreal summer maximum temperatures/NETs. The performance of the models was assessed first, which showed that the models reproduced the current climate well. Future projections revealed an increase in both average and 95th percentile of the maximum temperature and NET over the entire domain for both the RCP8.5 and SSP5‐8.5 scenarios. The increase in heat stress (NET) was slightly larger than the temperature itself, with an increase of up to 7/10°C for temperature and 8/11°C for NET in RCP8.5/SSP5‐8.5, respectively. The overall increases in temperature and NET were projected to be higher in the higher latitudes, while the increase in the frequency of the temperature and NET extremes was predicted to be higher in the already vulnerable regions in the southern part of the domain.