Asia-Pacific Journal of Atmospheric Sciences最新文献

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.

During Octobers of 1970–2019, no tropical cyclones (TCs) affected Taiwan in 32 out of 50 years (64%). Suppressed TC activity in these years results from different modulating processes imposed by various climatic features. During Octobers of El Niño years, TC genesis in the western North Pacific (WNP) shifts eastward and decreases in the western WNP to the southeast of Taiwan. An anomalous anticyclone across the South China Sea (SCS) and Taiwan hinders TC movement toward Taiwan. In La Niña years, TC genesis increases in the region southeast of Taiwan. These TCs are guided by an anomalous cyclone centering in the SCS to have major TC tracks to the southwest of Taiwan toward the SCS. A year with a September–November value on the Oceanic Niño Index (ONI) of between 0°-0.5 °C (-0.5°-0 °C) is categorized as a positive (negative) Normal year. During the positive Normal years, an anomalous cyclone over the WNP enhances TC genesis in its southern section and guides these TCs northward along the regions east of Taiwan. An anomalous anticyclone across the SCS and Taiwan hinders TC movement toward Taiwan. During the negative Normal years, a westward elongation of warm sea surface temperature anomalies from the WNP into the eastern Indian Ocean forces an anomalous anticyclone to extend westward from the WNP toward the SCS. TC genesis to the south of this anomalous anticyclone decreases and is accompanied by reduced TC movement toward Taiwan.

Increasing heatwave frequency due to climate change threatens outdoor workers’ health. We aimed to assess the on-site heat strain level of outdoor workers using wearable sensors and identify the factors for consideration in developing individual-based heat adaptation strategies. Seven road construction workers were recruited and asked to wear necklace-form temperature loggers and smartwatches monitoring heart rate (HR). The questionnaire was delivered daily to ask about their psychological comfort level during work. Workers were exposed to up to 5.4 °C higher temperature than the official air temperature, indicating that the national heatwave alarm does not reflect on-site heat conditions. Based on the measured HR data, heat strain levels were defined. When HR exceeded the level of “180-age,” we assumed extreme heat strain occurred, which requires immediate cessation of work. When HR exceeded 40% of the individual heart rate reserve (the difference between the maximum and resting HR), we assumed high heat strain occurred, indicating a stressed condition. High heat strain occurred in all workers on 9 of the 13 monitored days, whereas the official heatwave alarms were issued only on four dates. Additionally, three workers experienced extreme heat strain on two dates. The main factor for workers experiencing extreme heat strain was age. Comparing the heat strain levels from HR with the survey results, we found that the older workers considered their condition comfortable even under extreme and high heat strain. Thus, an individual sensor-based early-warning system is needed to prevent heat strain not perceived by outdoor workers. The findings emphasize the need for a personalized adaptation strategy for heatwaves and will be a baseline for developing a new work manual that mainstreams climate change impacts.

Synoptic-scale weather systems play dominant roles in inducing high tropospheric dust over the Tibetan Plateau (TP). However, few studies have summarized the typical synoptic-scale weather patterns when high tropospheric dust occurs over the TP, as well as the difference between the distribution and transport methods of dust under weather patterns. Based on dust optical depth (DOD) from remote sensing data and reanalysis data during 2000 to 2019, two typical synoptic-scale weather patterns (T1 and T2) in the middle troposphere in association with high DOD in spring over the TP were obtained by using the self-organizing map (SOM) clustering method. The results show that the T1 features a deep trough over the Altai Mountains and the westerly wind increases over the TP. As a result, dust is transported from the Taklimakan Desert and Qaidam Basin to the upper troposphere and extends to the TP and northern China. T2 shows a low-pressure system over the western TP and decreased westerly winds over the TP, resulting in dust from the Taklimakan Desert, Qaidam Basin, and western TP to downstream areas. T1 (T2) contributes more to DOD over the eastern (western) TP. Therefore, we believe that a small increase (decrease) in DOD in the western (eastern) part of the TP from 2000 to 2019 may be related to an increase (decrease) in the occurrence of the T2 (T1). This work may provide a new possibility for projecting dust transport and its influence on tropospheric dust over the TP.