Asia-Pacific Journal of Atmospheric Sciences最新文献

The aim of this study was to identify the sources of atmospheric pollutants in densely populated urban areas from a particle toxicity perspective. To this end, the Positive Matrix Factorization (PMF) model and vehicle flux analysis were used to identify the sources of atmospheric pollutants in an urban area based on the measured compounds and wind speed at the receptor site. Moreover, the toxicity of each emission source was compared with the dithiothreitol-oxidation potential normalized to 9,10-Phenanthrenequinone (QDTT-OP) analysis using the PMF source apportionment results. The study found that the dominant sources of atmospheric pollutants in the urban area examined were secondary product (43.7%), resuspended dust (25.4%), and vehicle emissions (14.4%). The vehicle flux analysis demonstrated that reducing the number of vehicles could directly reduce urban atmospheric pollutants. By comparing the time series of PMF source profiles with QDTT-OP, the QDTT-OP analysis showed an r² value of 0.9, thus indicating a strong correlation with biomass burning as the most harmful source of PM_2.5 based on emission sources. Overall, this study is expected to provide valuable guidance for managing atmospheric pollutants in densely populated urban areas, and the findings could serve as a helpful resource for improving urban air quality in the future.

Compound wind and precipitation extremes (CWPEs) are severe weather events that can have significant impacts on human health, ecological systems, and socioeconomic factors. Compared to isolated extreme events, CWPEs can result in higher economic losses and casualties. This study evaluates the ability of EC-Earth3, the sixth phase of the Coupled Model Intercomparison Project (CMIP6), to capture CWPEs by using ERA5 reanalysis as a reference dataset for model evaluation. Additionally, this study examines changes in CWPEs in the future, considering different Shared Socioeconomic Pathway (SSP) scenarios, including SSP1-2.6, SSP2-4.5, and SSP5-8.5. Our analysis indicates that EC-Earth3 accurately captures the spatial and temporal characteristics of global CWPEs during the historical period of 1979-2014. More CWPEs occur in the northern and southern hemispheres during their respective cold seasons, especially for the oceans. The frequency of CWPEs has increased over the historical period, with a greater increasing trend in the ocean than on land. The seasonal cycle of CWPEs differs significantly in land and ocean. Regarding future projections, the occurrence of CWPEs will change significantly with the increase of emissions, particularly in the late 21st century and over high latitudes. CWPEs will increase significantly at mid- and high-latitude regions and mainly decrease over low latitudes. The feature of more CWPEs occurring during the respective cold seasons will be more pronounced in the future.

The variability of the South Java Current (SJC) was observed by using reanalysis data spanning the years 1993 to 2021. This was done in order to determine whether or not the SJC was more influenced by the Indian Ocean Dipole (IOD), the El Niño-Southern Oscillation (ENSO), or a combination of the two. Employing empirical orthogonal function (EOF) analyses, we were able to determine that the time series of the principal component in the first mode (PC1) had an association with one of these occurrences. During the northwest monsoon in December, January, and February (DJF), it would appear that the IOD has a greater impact on the SJC than ENSO does, with a correlation of more than 0.8. During the first transition, which occurs in March, April, and May (MAM), the time series PC1 demonstrates that the SJC has a greater association with the ENSO (coefficient correlation more than 0.7). The study demonstrates that the PC1 has a negative association with both the IOD and the ENSO during the months of JJA, with a coefficient value less than 0.4. The JJA's SJC, however, is positively influenced by the coastal Kelvin wave in the vicinity of western Sumatra and southern Java. Moreover, the magnitude of the SJC, which was observed in DJF months, is affected by the Rossby wave that is moving in a westward direction south of 9˚S.

The present study explores the intraseasonal variability of the west Pacific subtropical high (WPSH) and its relation with Indian summer monsoon rainfall (ISMR) based on the IMD rainfall data and NCEP-NCAR reanalysis data sets for the 1950–2021 period. The longitudinal position of the western edge of WPSH around 20° N is about 139.3° E in June, which gradually extends eastward up to 151° E by September end. The zonal movement in the western edge of WPSH exhibits a 30–60 day periodicity, which is prominent in July -August months during WPSH expansion. In contrast, the western edge of WPSH shows a periodicity of about 10–25 days, which is dominant from mid-June to early September. These two periodicities are significant at a 90% confidence level. As compared to the climatology, the WPSH shifted about 11° (10°) westward (eastward) along with an intensification (weakening) at the center of WPSH during expansion (contraction) cases. The surplus (deficit) rainfall occurred over entire India (east central India) during the WPSH expansion. In WPSH contraction, surplus (deficit) rainfall was noticed over the east-central and northern India (southern peninsular and northwest India). The sea surface temperature (SST) anomalies during expansion (contraction) follows the Modoki type of La Niña (El Niño) patterns over the central Pacific Ocean. During WPSH expansion, an intense mid-tropospheric updraft, abundance of atmospheric moisture along with its convergence over the ISM regions are observed and these are conducive to above normal rainfall.

In 2022, South Korea experienced a series of climate extremes, among which the August 8 extreme rainfall event stands out due to its considerable damage to Seoul, with daily precipitation exceeding 380 mm/d. This study aimed to examine the contributions of dynamic and thermodynamic components in the moisture budget to two major extreme rainfall events occurring on June 27–30 and August 8–11 in 2022. Our analysis revealed the distinctive roles of wind and moisture content during these extreme rainfall events. In both events, the changes in the wind (dynamic components) played a crucial role, mainly attributed to the northward or westward shift of the subtropical high. On the other hand, the moisture content (thermodynamic component) contributed to approximately 30% of the rainfall but only for the period from August 8 to 11. The subtropical thermal forcings, positive North Atlantic Oscillation, and the intensified rainfall in Pakistan induced circulation changes that redistributed the thermodynamic characteristics. Consequently, substantial meridional pressure gradients developed, giving rise to zonally elongated rainfall patterns that appeared to be characteristics of both extreme rainfall events. These findings shed light on the factors that influenced extreme rainfall events in South Korea in 2022 and highlight the crucial role of remote forcing in predicting such events.

The concentration of particulate matter (PMs) is governed by complex processes such as long-range transport, vertical diffusion, and local emissions. Therefore, thus it is relatively difficult to accurately forecast high PM concentration events. As the application of artificial intelligence (AI) techniques to air-quality prediction has increased, optimal input variables for AI models have become critical. The purpose of this study was to suggest combined and synoptic variables, in addition to conventional surface meteorological and air quality variables, for AI-based high PM event prediction models. In Seoul and four cities in China, the observed surface meteorological and air quality data, upper air meteorological data, planetary boundary layer height, and temperature gradients between the surface and 850 hPa were tested. The east–west geopotential index (EWGI) and Korean Region Blocking Index (KRBI) have been suggested as regional-scale blocking indices. A concentration-wind (CW) variable was introduced to represent the effects of long-range transport from China. The usefulness of the suggested variables was tested using random forest (RF) and support vector machine (SVM) for 2017–2020. As the forecasting days progressed, the importance of surface variables decreased, whereas those of the EWGI, KRBI, CW, and stability variables increased. The stability variables increased the accuracy, probability of detection, and F1 scores, while decreasing the false alarm rate on the 3‒5 forecasting days. EWGI and KRBI improved the prediction performance after the third forecast day, and CW was important for predicting the 3‒4 forecast days. Newly introduced variables, such as EWGI, KRBI, CW, and stability tended to increase the 1‒4 day forecast hit rate for high PM_2.5 events and were found to be useful input data for machine learning or artificial intelligence-based air quality prediction models.

The same family four single-moment microphysics schemes (WSM3, WSM5, WSM6, and WSM7) were selected to simulate the tropical cyclone (TC) Mujigae in 2015 over the South China Sea using the Weather Research and Forecasting (WRF) model. The effect of the species number of hydrometeors (SNH) used in these schemes on the track, intensity, precipitation, and structure of the TC is investigated. SNH has a slight impact on the TC track, while a significant effect on the TC intensity. The WSM6 scheme has the best skill to reproduce the minimum sea level pressure (MSLP). The WSM3 scheme has the highest simulation score for the maximum surface wind (MSW) speed. In general, the simulated TC intensity is strengthened as SNH increased, while weakened with the addition of hail. SNH affects structure and thus the TC intensity. The TC simulated by WSM6 scheme, with the smallest eye area and the radius of maximum wind, the strongest cloud wall convection, warm core, convergence in the lower layer, and divergence in the upper layer, simulates the minimum MSLP, which is closest to the observation. The four schemes can well reproduce precipitation distribution. The relationship between the total hydrometeor content and the TC intensity is non-linear. The total hydrometeor content simulated by the WSM3 scheme is the most while that by the WSM6 scheme is the least. However, the cloud ice simulated by the WSM6 scheme is the most. The graupel simulated by the WSM6 scheme is more than that by the WSM7 scheme. SNH modifies the microphysical conversion process and latent heat efficiency, and further affects the structure and intensity of TC.

Abstract

The Indian Summer Monsoon Rainfall (ISMR) plays a significant role in India’s agriculture and economy. Our understanding of the climate dynamics of the Indian summer monsoon has been enriched with general circulation models (GCMs) and regional climate models (RCMs). Systematic bias associated with these numerical simulations, however, needs to be corrected before we can obtain accurate or reliable projections of the future. Therefore, this study applies two state-of-the-art deep-learning (DL)-based super-resolution bias correction (SRBC) methods, viz. Autoencoder-Decoder (ACDC) and a deeper network Residual Neural Network (ResNet) to perform spatial downscaling and bias-correction on high-resolution CORDEX-SA climatic simulations of precipitation. To do so, we obtained eight meteorological variables from CORDEX-SA RCM simulations along with a digital elevation model at a spatial resolution of 0.25°×0.25° as input. Indian Monsoon Data Assimilation and Analysis, precipitation reanalysis re-grided to 0.05°×0.05° spatial resolution is chosen as output for the training period 1979–2005. To evaluate the DL algorithms, the RCP 2.6 scenario of CORDEX-SA future simulations for the period 2006–2020 is chosen. Moreover, we also conducted a performance assessment of the representation of mean, variability, extreme, and frequency of rainfall associated with ISMR. The results of the experiments show that the DL method ResNet a highly efficient in (i) improving the spatial resolution of the climatic simulations from 0.25°×0.25° to 0.05°×0.05°, (ii) reducing the systematic biases of the extreme rainfall of ISMR from 21.18 mm to -7.86 mm, and (iii) providing a robust bias-corrected climate simulation of ISMR for future climate mitigation and adaptation studies.

Multi-system seasonal hindcasts supporting operational seasonal forecasts of the Copernicus Climate Change Service (C3S) are examined to estimate probabilities that El Niño and La Niña episodes more extreme than any in the reliable observational record could occur in the current climate. With 184 total ensemble members initialized each month from 1993 to 2016, this dataset greatly multiplies the realizations of ENSO variability during this period beyond the single observed realization, potentially enabling a detailed assessment of the chances of extreme ENSO events. The validity of such an assessment is predicated on model fidelity, which is examined through two-sample Cramér–von Mises tests. These do not detect differences between observed and modeled distributions of the Niño 3.4 index once multiplicative adjustments are applied to the latter to match the observed variance, although differences too small to be detected cannot be excluded. Statistics of variance-adjusted hindcast Niño 3.4 values imply that El Niño and La Niña extremes exceeding any that have been instrumentally observed would be expected to occur with a > 3% chance per year on average across multiple realizations of the hindcast period. This estimation could also apply over the next several decades, provided ENSO variability remains statistically similar to the hindcast period.