Asia-Pacific Journal of Atmospheric Sciences最新文献

We investigated the microphysical characteristics of low-level Arctic clouds using four cloud microphysics parameterization schemes (Morrison, WDM6, NSSL, and P3) implemented in the Polar-optimized Weather Research and Forecasting (PWRF) model. Our assessment was based on a comparison with data collected during the Arctic Cloud Observations Using Airborne Measurements during the Polar Day (ACLOUD) experiment, which occurred near Svalbard between May and June 2017. During the ACLOUD campaign, a substantial number of clouds were observed, primarily influenced by adiabatic motions and sensible/latent heat fluxes that led to air masses warming up by 4 °C as they traversed over the sea ice and ocean transition zone. Among the parameterization schemes tested, the Morrison and WDM6 schemes demonstrated superior performance overall, showing frequency bias (FB) values closer to 1 (1.07 and 1.13) and high log-odds ratios (0.50 and 0.48) in cloud occurrence predictions, indicating good agreement with observed data. In contrast, the NSSL and P3 schemes exhibited higher FB values (1.30 and 1.56) with lower log-odds ratios (0.17 and 0.16), indicating an overestimation of cloud occurrence. The WDM6 scheme produced higher ice-mixing ratios compared to Morrison and NSSL schemes, while the latter two tended to generate more snow and graupel. The NSSL scheme showed the least bias in simulating ice water content (IWC) in mixed-phase clouds; however, all schemes generally underestimated both liquid water content (LWC) and IWC. Notably, significant deviations in IWC were observed at an altitude of 1.2 km compared to observations, attributed to differences in temperature thresholds for ice formation. This study emphasizes the importance of developing cloud parameterization in the Arctic based on observations to improve the accuracy of estimating cloud impacts on Arctic climate under rapid Arctic warming trends.

For climate risk assessments accurate gridded data sets are needed. An important aspect of such data sets is that they reliably represent the spatial and temporal characteristics of extreme events. This is particularly important for precipitation extreme events which are still not well represented in climate models. Here, we compare South Korean station data with two observation-based gridded data sets (APHRODITE and ERA5-Land) and data from global high-resolution Community Earth System Model (CESM) simulations with an atmospheric resolution of about 25km. We find that the two observation-based data sets have a lower level of the 99th percentile than the station data, but that CESM reproduces extreme events better. Our study provides evidence for an overall historical decrease in very large extreme events in the station data, which is not the case in the two gridded data sets. However, changes in extremes are locally dependent as shown by local quantile regression analysis; where local historical increases in precipitation extremes are statistically significant. The spatial dependence of extreme precipitation events is not well reproduced by the two gridded data sets but well by CESM. The temporal clustering of precipitation extremes is well reproduced by all data sets. Compared to the present day simulation, the CESM simulation of a warmer climate state shows an overall increase in mean precipitation and precipitation extremes and regionally dependent changes in temporal clustering. The model results also provide evidence for a change in spatial dependence in a warmer climate with spatially larger extreme precipitation systems possible. Our results highlight the need to produce better observation-based gridded data sets and also the need to adapt to more intense and frequent extreme precipitation events in the future in South Korea.

We developed a rice paddy model based on Noah land surface model (LSM) considering the standing water layer during the irrigation periods. In the model, we adopted a consistent subcanopy process from thin to thick canopy conditions and considered a small scalar roughness length of the water surface in the rice paddy fields. We evaluated the performance of the model using observations from three rice paddy sites with different leaf area index and water depth in Japan during the growing season. Two simulations were performed in an offline mode: a Noah LSM simulation with saturated soil moisture in the top two soil layers (IRRI) and a rice paddy model simulation (RICE). The average root mean squared errors of ground, sensible, and latent heat fluxes, and first soil layer temperature decreased by 20%, 16%, 17%, and 31%, respectively in the RICE simulation, compared to the IRRI simulation. The better performance of the RICE simulation was attributed to the consideration of the heat storage of the standing water layer during the irrigation periods and the realistic energy partitioning by the single-canopy model during the non-irrigation periods. Two sensitivity tests were performed related to the roughness length of the water and the constant mean water depth. When the small roughness length of the water surface during the irrigation periods was not considered, the subcanopy resistance decreased, which resulted in a cold bias in the daily mean ground and soil temperature and an overestimation of the daily mean latent heat flux under low leaf area index conditions. The use of constant mean water depth in the model did not significantly change simulated surface fluxes and ground and first soil layer temperature, implying that detailed information on temporally changing water depth is less important in the simulation.

The Korean Peninsula frequently experiences localized torrential rainfall (LTR) in the summer. However, on August 8, 2022, a peculiar LTR occurred by the continuous generation of convective clouds within a few hours, numerical weather prediction model was hard to forecast such a high intensity of LTR. This study explores the possibility of uncovering potential precursory signals using remote sensing techniques in both Geostationary Korea Multi-Purpose Satellite 2A (GK2A) and the operational RKSG (Camp Humphreys) Weather Surveillance Radar 88 Doppler (WSR-88D). Using cloud properties from GK2A, cloud top temperature showed a decrease and maintained low values below 220 K 1–1.5 h before the LTR events. However, discerning the exact onset of LTR in already mature stage clouds using only GK2A variables proved challenging. Instead, liquid water content from RKSG sharply increased before the LTR started. Our calculation of the LTR potential from a combination of GK2A and RKSG cloud properties shows a more accurate precursory signal of LTR than from GK2A cloud properties solely or RKSG either. This study highlights the synergistic benefits of combining geostationary satellite and radar observations to understand and predict early precursors of LTR events.

The zonal gradients in sea surface temperature and convective heating across the tropical Pacific play a pivotal role in setting the weather and climate patterns globally. Under global warming, the current generation of climate models predict that the zonal gradients will decrease, but the trajectory of the observed trends is the opposite. Theories supporting either of the two projections exist, but there are many relevant processes whose net effect is unclear. In this study, a global constraint – the maximum material entropy production (maxMEP) hypothesis—is considered to help close the gap. The climate system considered here is comprised of a one-layer atmosphere and surface in six regions that represent the western tropical Pacific, eastern tropical Pacific, northern and southern midlatitudes, and northern and southern polar regions. The model conserves energy but does not explicitly include dynamics. The model input is observation-based radiative parameters. The radiative effect of greenhouse gas (GHG) loading is mimicked by prescribing increases in the longwave absorptivity (epsilon). The model solutions predict that zonal contrasts in surface temperature, convective heat flux, and surface pressure increase with increasing (epsilon). While maxMEP solutions in general cannot provide a definite answer to the problem, these model results strengthen the possibility that the trajectory of the observed trend reflects the response to increasing GHG loading in the atmosphere.

High concentration Asian Dust Storms (ADSs) significantly impact health and economic activities by increasing atmospheric particulate matter. This study aims to understand the mechanisms, migration paths, and activity patterns of ADSs, which are essential for issuing timely warnings and aiding in atmospheric environment research. Using unsupervised learning methods, including the principal component analysis (PCA) and K-means clustering, we analyzed the mega ADS events from 2002 to 2022 based on the ECMWF reanalysis (ERA5) data. We identified key meteorological factors, including geopotential height and temperature at lower levels (800–1000 hPa), and classified synoptic patterns associated to the mega ADSs during the origination stages in the source regions and the peak concentration stages in Korea. Findings highlight that, during the origination stage, enhanced troughs and high temperature at low levels are primary factors affecting atmospheric instability and consequently strong updrafts that lift dust particles, combined with high planetary boundary layer heights, ranging 1400─2950 m, and strong pressure gradients at the source regions. It is further noted that low-level temperature and specific humidity are critical during the peak stages in Korea, with contributions from higher atmospheric levels. Variability in atmospheric conditions among different patterns affects dust concentrations, with certain patterns experiencing sharp declines in humidity leading to peak dust events. Noting also that the mega ADSs occur under specific synoptic patterns classified at both the origination stages and the peak concentration stages in Korea, this comprehensive analysis provides crucial insights into the dynamics and prediction of mega ADSs in Korea.

Atmospheric rivers (ARs) are closely associated with extreme precipitation and hydrological events in East Asia. Predicting the impacts of climate change on ARs is crucial for preventing the damage caused by extreme precipitation and ensuring the effective operation of water management facilities. We aimed to conduct future projections (2080–2099) of annual and seasonal changes based on the assessment of East Asian AR and AR-related precipitation, using the Coupled Model Intercomparison Project Phase 6 (CMIP6) Multi-model ensemble (MME). The annual average integrated vapor transport (IVT) in East Asia in 2080–2099 will increase by approximately 32.5% compared to 1995–2014. Meanwhile, the annual average AR frequency (F_AR) will increase by approximately 111%. Examination of the water vapor and moist wind components of the IVT revealed that the future increase in the IVT was primarily from increases in water vapor. The increase in IVT is largely responsible for the increase in AR frequency. Changes in AR following global warming have also affected precipitation, increasing the total precipitation for East Asia. An examination of the changes in AR characteristics shows that the frequency of intense AR events will also increase owing to global warming. Increases in the frequency of strong AR events during the East Asian summer monsoon season are projected to occur. Projections regarding the frequency and intensity of AR events vary substantially by region, such as Korean Peninsula, Southern China and Western Japan.

This study investigated the physicochemical properties of PM_2.5, especially among secondary aerosols, based on the particulate matter and gaseous precursors in Yongin, Korea measured between February and June 2022. A comparative analysis of PM composition across two seasons highlighted the atmospheric characteristics of this suburban area. As observed, the average PM_2.5 concentrations in February and March were higher than those in May and June, with NO₃⁻ being particularly predominant during the colder months when PM_2.5 levels were elevated. During this period, the high levels of gaseous precursors such as NO_X, HNO₃, and NMHC likely contributed to secondary aerosol formation. The intermediate oxidation states of organic matter in Yongin indicate its suburban characteristic, which is intermediate between urban and rural areas. Inorganic aerosols were enriched with (NH₄)₂SO₄ with sufficient NH₃ availability, and then the formation of NH₄NO₃ was promoted through the reaction of the same phase (gas–gas) HNO₃ with NH₃. Additionally, the temperature variations influenced the PM_2.5 composition, promoting the production of NH₄NO₃ in February–March. In Yongin, HNO₃ acted as a limiting factor in NH₄NO₃ production. Thus, the management of precursor gases such as HNO₃ and NO₂ is crucial during periods of high PM_2.5 in the colder seasons.

After making landfall, Typhoon In-Fa (2021) moved slowly, resulting in heavy rainfall and flooding across fourteen provinces in China. This extreme precipitation was primarily linked to the evolution of active mesoscale convective systems. This study analyzes the characteristics and causes of mesoscale rainbands during In-Fa’s slow northward-moving period, aiming to identify the key factors that influence the detailed evolution of typhoon rainbands and to enhance typhoon quantitative precipitation forecasting skill. In-Fa’s mesoscale asymmetric rainbands can be categorized into three types: mesoscale spiral rainbands, a convective rainband to the east of In-Fa, and a rainband to the north of In-Fa. Mesoscale low-level jets are a critical factor in the development of mesoscale spiral rainbands. The wind speed gradient near these jets, along with the convergence of wind directions between two jets, fosters low-level convergence and upward motion, triggering the evolution of several mesoscale rainbands. The convective rainband to the east of In-Fa flourishes under conditions of high humidity and energy, displaying distinct diurnal variations. This is due to the strengthening of low-level jets at night, which enhances both dynamic convergence and water vapor availability. The presence of moderate to strong convective available potential energy (600–1500 J kg⁻¹), substantial whole-layer water vapor (relative humidity exceeding 90–95%), and a high 0 °C-layer favors the development of efficient warm-cloud convective precipitation, leading to intense hourly rainfall. The rainband to the north of In-Fa is primarily associated with cold air intrusion in the lower troposphere, although the interaction between typhoon and mid-latitude systems has not yet occurred. At the interface between cold and warm air, the colder air to the north side sinks while the warmer air to the south side rises, forming a secondary circulation that supports the development and persistence of precipitation on the north side of the typhoon. These findings offer a conceptual model for accurately predicting precipitation associated with typhoons that move slowly northward after landfall.

Over the past few years, sudden high swells (SHSs) have often occurred on the east coast of the Korean Peninsula (KP), especially during the winter season, causing many casualties and considerable property damage. High waves can be generated suddenly even in the absence of strong winds, sweeping away unsuspecting people on breakwaters or causing damage to properties such as ports and fish farms located on coasts. In this study, we developed a detection and warning system for SHSs on the KP’s east coast. First, we developed a method for separating waves into the wind sea and swell components based on one-dimensional wave spectra, wind speed, wind direction, and mean wave direction data obtained from coastal buoys. Using the calculated significant wave height difference between swells and wind seas, as well as wind speed, we developed a SHS alert system with three levels: “Attention,” “Watch,” and “Warning.” This system successfully detected three recent swell events on the east coast of the KP. Applying this system to an operational wave prediction model, it successfully issued an alert 72 h before the SHS reached the coast. The proposed system can provide consistent quantitative forecast information that can greatly contribute to preventing casualties and property damage caused by SHSs.