Asia-Pacific Journal of Atmospheric Sciences最新文献

In early January 2016, Storm Frank, an extreme winter storm with a peak intensity of 928 hPa, intruded into the Atlantic sector of the Arctic. This led to unprecedented warming and significant sea ice loss in the Barents-Kara (B-K) Sea. Following this extreme warming event, a series of extreme weather events occurred in mid- and late-January across Eurasia, including a persistent blocking pattern near the Ural mountains and extreme cold wave events over Mongolia, China, and Korea. This study utilizes the Korean Integrated Model (KIM), coupled with an ocean-sea ice model, to reproduce this event and to examine its extended medium-range forecasting performance. While the control model effectively captures the initial Arctic warming, it struggles to reproduce the observed sustained warming that lasted over two weeks. Here, we identified that the model significantly overestimates the sea ice concentration in the B-K Sea, where the initial warming is more pronounced in observations. Through sensitivity experiments, we found that reducing the sea ice strength parameter, which governs the ice resistance to pressure and deformation, effectively alleviated this overestimation. This adjustment facilitates easier sea ice melting, strengthens the ocean-atmosphere interactions, and extends the duration of simulated Arctic warming. Our findings emphasize the crucial role of accurate Arctic sea ice representation in extended medium-range forecasting for East Asia, particularly for extreme weather events.

In this study, GloSea6 hindcast (HCST) from the UK Met Office is used to investigate the model prediction skill for the impacts on the East Asian summer associated with two extreme El Niño cases (1997/1998 and 2015/2016). For the 1998 case, we found that GloSea6 model is able to predict the ocean–atmosphere circulations one to two seasons ahead, including the anomalously positive sea surface temperature (SST) in the Tropical Indian Ocean (TIO) and the anomalous anticyclone (AAC) in the Western North Pacific during the spring and summer seasons. However, for the 2016 case it fails to capture the observed fast cooling of the spring TIO SST and the rapid decaying of the summer AAC, due to an overestimated linkage between the summer TIO and the precedent winter El Niño. Physically, the exaggerated model SST warming over both the eastern and western Indian Ocean suppresses the development of the surface westerly wind that enhances the summer monsoon flow in the TIO and cools the warmed SST as in the real world. According to further analysis, the sensitivity of the TIO is linked to the formation of the spring AAC, which is influenced by the longitudinal position of warm Pacific SST, causing the HCST to display a more idealized El Niño-TIO-AAC teleconnection than the observations. Thus, simulating the decaying El Niño and its teleconnection to the TIO is crucial for reliable seasonal forecasts of East Asian climate during post-El Niño summers.

In this study, the microphysical characteristics of snowfall in Seoul, South Korea and their changes with meteorological conditions are examined using about 6-year observation data from a Parsivel disdrometer. The snow particle size distribution (PSD) exhibits convex-down shapes, being better represented by gamma distributions than exponential distributions. As snowfall rate increases, the snow PSD broadens and its peak rises. The changes in gamma PSD parameters with snowfall rate differ between the mean PSD and 1-min PSDs. The volume-weighted mean diameter D_m much more rapidly increases with snowfall rate in comparison with D_m in Beijing, China and Pyeongchang, South Korea, suggesting the relative importance of aggregation in Seoul. 77% of snowfall in Seoul occurs when northwesterly blows at the 850-hPa level. This snowfall is associated with west-high/east-low pressure patterns, large air–sea temperature differences (~ 19 °C), and shallow (≤ 2.5 km) precipitation systems, suggesting a large contribution of sea-effect snowfall from the Yellow Sea. The northwesterly-type snowfall with lower temperatures (≤ 25th percentile, COLD) and with higher temperatures (≥ 75th percentile, WARM) at the 850-hPa level is compared in the same intensity range of 0.5–1 mm h⁻¹. Compared with the WARM snowfall, the COLD snowfall has relatively broad PSDs and less-rimed snow particles. The COLD snowfall is associated with relatively large wind shear, small static stability, low temperatures of − 21 to − 9 °C, and low humidity in the lower atmosphere, which is attributed to relatively strong northwesterly resulting in relatively strong cold and dry advection. This implies that enhanced aggregation by stronger turbulence and dendritic growths can contribute to the broader PSDs and that weakened riming for the lower temperatures might be associated with the less-rimed snow particles.

This study examined the observed PM_2.5 concentration across 247 underground stations  consisting of Line-1 to Line-8 of the Seoul Metro from April 2021 to March 2023 in order to understand general characteristics of underground PM_2.5 air quality. Approximately, in one-thirds of underground stations (85 stations), annual averaged PM_2.5 concentration are over 35 µg m⁻³. Moreover, in 30 underground stations (approximately 12%), it exceeds 50 µg m⁻³, the recommended 24-hour maintenance standard for PM_2.5 concentration in underground stations. We found that PM_2.5 concentration is considerably influenced by both internal and external factors. Among the internal factors (i.e., depth, the number of passengers and operation frequency), the frequency of subway operation significantly affects changes in PM_2.5 concentration however, various internal factors may act in combination. In terms of external factor, there are positive correlation coefficients (r = 0.15–0.95) between daily averaged PM_2.5 concentration in underground station and that of the outdoor observatory closest to each underground station. In particular, in underground stations with high PM_2.5 concentration, the correlation with outdoor PM_2.5 air quality was low, suggesting that for better air quality in underground stations, we need to focus more on reducing the inherent emission from underground stations in highly polluted stations, but for less polluted stations, we need to improve outdoor air quality as well. We believe that this study may provide insights for effective future PM_2.5 air quality management in underground stations.

Variations in the wind speed intensity significantly impact evapotranspiration, water cycle processes, air quality and wind utilization. Previous studies have focused primarily on changes in mean wind speed, with little research on variations in different wind speed intensities. In this paper, we defined five wind speed indices to quantify the changes in different wind speed intensities and analyzed their associations with atmospheric circulation based on daily wind speed data collected from 601 meteorological stations across China from 1960 to 2018. The wind speed indices we defined include the annual mean wind speed, the annual maximum daily mean wind speed, the number of heavy wind days, the number of gentle breeze days and the number of light breeze days. The results showed that from 1960 to 2018, the annual mean wind speed, the annual maximum daily mean wind speed, the number of heavy wind days and the number of gentle breeze days exhibited significant decreasing trends (P < 0.05). The number of light breeze days exhibited a significant increasing trend (P < 0.001) in China during the same period. Large-scale atmospheric circulation patterns were one of the main factors affecting the changes in wind speed intensity. The Arctic Oscillation (AO) and the West Pacific Subtropical High Intensity Index (WPSHI) were significantly negatively correlated with the annual mean wind speed, the annual maximum daily mean wind speed, the number of heavy wind days and the number of gentle breeze days (P < 0.01), and the Asian Polar Vortex Intensity Index (APVI) was extremely significantly positively correlated with these four wind speed indices (P < 0.001). This suggests that monitoring and analyzing these atmospheric circulation indices can enable more accurate predictions of wind speed. These findings will provide information for climate change forecast, air pollution risk assessments and wind energy utilization.

Exponential-random vertical overlap of clouds is applied for radiative processes in a research version of the Korean Integrated Model (KIM) to replace the maximum-random vertical overlap of clouds. The cloud radiative effect (CRE) increases overall when the exponential-random overlap is used. This is because vertically continuous clouds, which are assumed to overlap maximally under the maximum-random overlap assumption, can be relaxed to random overlap depending on the vertical distance between cloud layers and the specified decorrelation length of clouds. CRE is more enhanced by considering the latitudinal dependency of cloud decorrelation length based on previous observational studies. This alleviates biases in CRE, which is underestimated overall, except in the low latitudes where the CRE is overestimated in the present simulations. The interaction between radiative and convective processes plays a role in decreasing CRE over the tropical western Pacific region, where strong convections develop, although the direct impact of applying the exponential-random overlap is to decrease the vertical overlap between ice clouds. The simulation of temperature in the lower troposphere is improved owing to the changes in cloud overlap. The warm bias over the Eurasian continent, in particular, is alleviated as more shortwave fluxes are reflected due to increased CRE.

The Weather Research and Forecasting (WRF) model has numerous model parameters that significantly affect rainfall forecasts. However, the multitude of parameters makes it challenging to identify which of these are critical for rainfall forecasting and optimization. This study utilizes the Morris One-At-a-Time (MOAT) Global Sensitivity Analysis (GSA) to ascertain the sensitivity of the simulated rainfall and other key atmospheric variables to 23 tunable model parameters across seven physics schemes in the WRF model. The MOAT mean and standard deviation were used as sensitivity measures and calculated for two Tropical Cyclone (TC)-enhanced southwest monsoon events in August 2012 and 2013 that resulted in catastrophic flooding over Metro Manila, Philippines. Results show that of the 23 model parameters, the ones more critically important to simulating rainfall are parameters that are related to cumulus schemes such as the multiplier for downdraft mass flux rate (P3), multiplier for entrainment mass flux rate (P4), starting height of downdraft over updraft source layer (P4), and mean consumption time of convective available potential energy (P6). To investigate the optimum parameter for the simulation of rainfall for each of the two events, the root mean square error (RMSE) is computed between the simulated rainfall over Metro Manila and observed data from the Global Satellite Mapping of Precipitation (GSMaP). The best performing set of parameters was able to reduce the RMSE of rainfall over Metro Manila by about 42% and 27% for the 2012 and 2013 enhanced monsoon events, respectively, relative to the default runs. For the first time, this study provides insight into which model parameters in the WRF model are critically important to the simulation of enhanced monsoon events. The results of this study may serve as a basis for future optimization studies of extreme weather events over the Philippines.