Atmospheric Science Letters最新文献

On 7–8 September 2023, Hong Kong was hit by a historical and record-breaking rainstorm associated with the remnant of Tropical Cyclone Haikui (2311). The hourly rainfall recorded at the Hong Kong Observatory Headquarters once reached 158.1 mm, the highest since record began in 1884. The 24-h rainfall even exceeded 600 mm in some parts of the territory. The historical rainstorm resulted in heavy flooding and landslides, bringing significant societal impact to Hong Kong. This paper aims to review this unprecedented heavy rain event from the aspects of diagnosis, forecasting and nowcasting. Early indicators of such events over Hong Kong with substantial lead time are limited from the dynamics and thermodynamics consideration, the numerical weather prediction models, given the present technology. The only indication may come from the climatologically extreme total precipitable water. While recent research of developing a regional risk-based alerting system on the higher impact event of flooding associated with heavy rain might have potential to enhance the weather service, and emerging AI model showed some promising post-simulations, predicting historical and record-breaking rainstorms remains a challenge for operational weather forecasting and warning services.

With analysis of local climate zone (LCZ) classification, approximately 52.0% of underlying surfaces in Beijing are covered by buildings with LCZ 5 (open midrise) accounting for the highest proportion, and LCZ D (low plants) is the most distributed among natural surface types. Compared to natural underlying surfaces, building underlying surfaces have higher values in the high temperature (HT) and heat wave (HW) days, HW intensity, and maximum HW duration. In recent decades, HT days on building underlying surfaces in Beijing start earlier and end later than those on natural underlying surfaces. Building underlying surfaces make greater contribution to urban heat island intensity of apparent temperature than to that of temperature, yet it is opposite for natural underlying surfaces.

The boreal summer intraseasonal oscillation (BSISO) is a major mode of sub-seasonal variability that regulates the summer climate in East Asia. This study investigates the four possible effects of two different time-scale BSISOs on temperature and precipitation variations in South Korea. When active BSISO convection is positioned over the subtropical western Pacific, it induces anomalous anticyclonic circulation accompanied by subsidence, leading to significant positive temperature anomalies. Conversely, the anomalous cyclonic circulation near the Korean Peninsula, resulting from suppressed convection in the subtropical western Pacific, along with low-level cold advection anomalies, contributes to a decrease in temperature. The spatial distribution of BSISO convection, which drives precipitation variation, shows a distinctive pattern of three meridionally narrow cells extending from the Philippines to the Korean Peninsula. Suppressed (enhanced) convection to the north of 20°N in the western North Pacific (WNP) promotes the northwestward expansion (eastward contraction) of the WNP Subtropical High in conjunction with a southwesterly (northeasterly) moisture flux anomaly. Furthermore, enhanced (reduced) moisture flux convergence and intensified ascending (descending) motion create favorable conditions for positive (negative) precipitation anomalies in South Korea. The combined effect of BSISOs not only amplifies the mean temperature and precipitation anomalies compared to individual modes but also increases the frequency of warmer, wetter, and drier events. Therefore, monitoring both BSISO modes together is crucial for comprehending and predicting the anomalous summer climate in South Korea.

Vertical wind shear (VWS), as an important dynamic factor influencing tropical cyclone rainfall (TCR), has a remarkable diurnal cycle of variation over the East Asia–western North Pacific region. The magnitude of tropical cyclone (TC)-experienced VWS has enhanced amplitude but different phases over the South China Sea (SCS) and coastal East China (CEC) compared with that over the open ocean. Diurnal variation in TCR over the SCS shows statistically significant correlation with that of VWS. The convection concentrated in the downshear-left quadrant strengthens markedly when VWS becomes large, thereby delaying the peak rainfall in the inner core of the TC and enhancing the amplitude of the diurnal cycle of TCR at ~09 local standard time. Over CEC, the diurnal signal of TCR is very weak but statistically significant in the downshear-left and upshear-right quadrants with opposite phase, illustrating the change in asymmetry of the spatial distribution of TCR induced by the large VWS diurnal cycle. The findings of this study could provide reference for improved forecasting of TCR on the fine temporal scale.

The Pacific–Japan (PJ) teleconnection pattern, a dominant mode of atmospheric variability over the western North Pacific during boreal summer, is pivotal in shaping regional climate dynamics. Despite its important implications, accurately predicting the PJ pattern remains challenging due to inherent model biases and uncertainties. This study delves into the impact of model biases on the prediction skill of the PJ pattern and evaluates its predictability using outputs from three operational seasonal forecast models. Our findings elucidate that the spatial structure of the PJ pattern simulated by models introduces substantial diversities in prediction skills. By discerning the variance in PJ teleconnection simulation among models, we unveil the high predictability of the PJ pattern, showcasing its capability for accurate forecasts up to 3 months in advance within the current seasonal forecast models. The predictability of the PJ pattern stems from concurrent El Niño–Southern Oscillation-related sea surface temperature anomalies and its corresponding atmospheric teleconnection processes. Our research underscores the necessity of accounting for model biases in predicting the PJ pattern, and the potential for bolstering seasonal prediction skill through targeted mitigation of these biases.

In this study, the linear and non-linear multivariate relationships between 25 teleconnection indices (tele-indices) as independent variables and annual P24max as the dependent variable were analyzed using multivariate linear regression (MLR) and decision tree regression models (M5), in selected synoptic weather stations of Iran over a statistical period of 30 years (1992–2021). No strong and statistically significant correlation between each tele-index and P24max was observed. Therefore, it is not appropriate to attribute climate changes in the region to a single factor such as El Niño, but rather consider the combined influence of multiple factors. The M5 model demonstrated higher performance, indicating a non-linear relationship between tele-indices and P24max. The stepwise execution of the M5 model tree showed that the algorithm follows a greedy approach, and it is not necessary to use all variables to predict P24max. The normalized root mean square error (NRMSE) of P24max estimation was found to be 15%, 13%, 15%, 8%, 20%, 14%, and 12% with the coefficients of determination of 0.78, 0.79, 0.72, 0.85, 0.81, 0.82, and 0.84 in Hashemabad-Gorgan, Rasht, Kermanshah, Ahvaz, Bandar Abbas, Isfahan, and Birjand, respectively. Finally, it is possible to forecast P24max using tele-indices measured in the previous year.

We assess the effects of the North Atlantic Sea surface temperature multidecadal variability on Northeast Asia using a set of sensitivity experiments (with a total of 530 ensemble members). We show that a warming of the North Atlantic Ocean leads to a strong and robust increase in temperature over Northeast Asia, which is replicated by a large majority of ensemble members. We show that the effect of the North Atlantic on Northeast Asia is model and season-dependent. We focus on two seasons, for which response to the North Atlantic Ocean is the most robust (autumn) and the less robust (spring) as indicated by the number of models that simulate a statistically significant change in surface air temperature. We use a clustering method to identify the sources of differences between models in simulating the effects of warming in the North Atlantic. We find that the primary mechanism linking the North Atlantic to Northeast Asia is a perturbation of the circumglobal teleconnection pattern (i.e., of the upper tropospheric atmospheric circulation), which allows modulation of the near-surface atmospheric circulation and an increase in temperature over East Asia. A second mechanism is related to the influence of the North Atlantic on the Pacific Ocean and the resulting effects on atmospheric circulation over Northeast Asia.

The interdecadal change in the relationship between the Antarctic Oscillation (AAO) and autumn rainfall in the Yunnan–Guizhou Plateau of Southwest China is investigated by using the observed autumn rainfall data at 119 stations and the National Centers for Environment Prediction/National Center for Atmospheric Research reanalysis data for the period 1961–2021. Results show the AAO correlates well with the autumn rainfall in the Yunnan–Guizhou Plateau for the second period (2002–2021) because the AAO becomes stronger. The possible influencing mechanism of AAO on autumn rainfall in the Yunnan–Guizhou Plateau during 2002–2021 is related to the meridional teleconnection pattern and associated convection over the Philippine Sea. The positive AAO can trigger a meridional teleconnection pattern in the upper troposphere to propagate from the southern Pacific to northern Pacific and cause anomalous westerly over the tropical west Pacific, which inhibits the convection over the Philippine Sea. On the one hand, the weakened convection over the Philippine Sea causes the anomalous ascending motion over the Yunnan–Guizhou Plateau; on the other hand, it results in an anomalous anticyclone over the tropical Northwest Pacific and strengthens the transport of water vapor from the tropical Pacific to the Yunnan–Guizhou Plateau.

Wildfires are significant contributors to atmospheric gases and aerosols, impacting air quality and composition. This pollution from fires also affects radiative forcing, influencing short-term weather patterns and climate dynamics. Our research employs the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to investigate the repercussions of wildfires on aerosol abundances and associated immediate weather responses. We examine the summer season of 2021, a period marked by severe wildfire events in the country during a heatwave period. We conducted sensitivity experiments including and excluding wildfire emissions to measure their effects on aerosol optical depth (AOD), radiative forcing, and weather features such as temperature, humidity, clouds, and atmospheric circulation. Our findings demonstrate that the radiative impacts of wildfires negatively influence the local temperature over the fire smoke plume-affected areas. Conversely, neighbouring areas of continental Greece experience increases in temperature due to remote effects of wildfire emissions, caused by meteorological feedbacks that reduce atmospheric humidity. Crucially, including fire emissions significantly improves the simulated surface temperatures predicted by the model over the Greek domain. Our work demonstrates that wildfire-generated aerosols can significantly impact weather conditions and highlights the importance of including both local radiative effects and remote feedback for achieving more accurate weather prediction.

This study applies dynamic mode decomposition (DMD) to three-dimensional simulation results of urban heat island circulation (UHIC, which is horizontal circulation) and thermals (vertical convections). The aim of this study is to revisit how these phenomena coexist based on the characteristics of temporal changes in the flow field. We used DMD to obtain the dominant spatial patterns and information on temporal changes. One of the modes of horizontal wind, which does not change temporally (no oscillation or amplification), exhibits a spatial UHIC pattern. The unique feature of this UHIC mode is that there are small-scale striated structures (150–200 m) and large-scale convergence. The other modes are time-varying (oscillating and decaying) and represent smaller spatial-scale phenomena (150–250 m), such as thermals. The frequency of each mode takes various values, some of which are lower than the lifetime of thermals in accordance with the Deardorff convective scale (~10 min). These low-frequency modes showed striated structures similar to that observed in the UHIC modes. These results suggest that UHIC and thermals deform each other through components that vary in long temporal scales.