Atmospheric Science Letters最新文献

Mediterranean cyclones can trigger severe weather hazards, including convective precipitation, lightning and hail, with implications for operational forecasting, risk assessment in the insurance industry, and societal preparedness. This work examines the climatological link between Mediterranean cyclones and atmospheric conditions conducive to severe convection. Using ATDnet lightning detections we find that, from autumn to spring, 20%–80% of lightning hours over the Mediterranean basin and adjacent land regions are associated with nearby cyclones. Based on reanalysis data, we demonstrate that severe convective environments and associated hazards predominantly occur in the warm sector of Mediterranean cyclones and to the north-east of their centres. Convective processes and hazards exhibit a peak approximately 6 h prior to the time of minimum pressure of the cyclone centre, consistent with previous studies. Additionally, we find a strong seasonal dependence of severe convection within cyclones. Severe convective environments are often detected in cyclone types typical of transition seasons (autumn especially) and summer, while they are rarer in deep baroclinic cyclones characteristic of winter. Finally, we provide novel insights regarding the dependence of convective activity on the presence of dynamical features around Mediterranean cyclones. Warm conveyor belts, characterised by large-scale ascent and high thermodynamic instability, emerge as the most favourable regions for lightning activity, with lightning potential being twice as high compared to cyclone cold fronts. These results advance our understanding of the interplay between cyclone dynamics and severe convection, offering valuable guidance for improving hazard prediction and for elaborating weather emergency strategies in the Mediterranean region.

The robust assessment of storm surge hazards induced by tropical cyclones in the Western North Pacific is constrained by only ca. 50 seasons of reliable observational data. This can be addressed by constructing physically consistent large tropical cyclone event sets, for example, from ensemble simulations. To allow efficient construction of these event sets, we propose a combination (M&S-WiTRACK) of two objective tracking methods relying solely on near-surface information and validate the performance of the combination on detecting tropical cyclones with potential for storm surge impact using ERA5. The M&S-WiTRACK is formed by a cyclone tracker (M&S) identifying tropical cyclone trajectories based on mean sea level pressure, with a wind-based storm impact identification algorithm (WiTRACK) determining potential storm surge impact areas using 10-m wind speed. For the first time, the general performance of the M&S for tracking tropical cyclones is evaluated and 84.9% of IBTrACS (with 18.3% false alarm rate) in the Western North Pacific from 1980 to 2019 are identified, which is well comparable to more data-intensive tropical cyclone trackers. Furthermore, M&S-WiTRACK successfully identifies over 85% of economic storm surge loss-related tropical cyclones in China. Nearly all tropical cyclones causing storm surges with economic losses exceeding 2 billion RMB are identified.

Data assimilation is a mathematical technique that uses observations to improve model predictions through consideration of their respective uncertainties. Observation error due to unresolved scales occurs when there is a difference in scales observed and modeled. To obtain an optimal estimate through data assimilation, the error due to unresolved scales must be accounted for in the algorithm. In this work, we derive a novel ensemble transform formulation of the Schmidt–Kalman filter (ETSKF) to compensate for observation uncertainty due to unresolved scales in nonlinear dynamical systems. The ETSKF represents the small-scale variability through an ensemble sampled from the representation error covariance. This small-scale ensemble is added to the large-scale forecast ensemble to obtain an ensemble representative of all scales resolved by the observations. We illustrate our new method using a simple nonlinear system of ordinary differential equations with two timescales known as the swinging spring (or elastic pendulum). In this simple system, our novel method performs similarly to another method of compensating for uncertainty due to unresolved scales. Indeed, the use of small-scale ensemble statistics has potential as a new approach to compensate for uncertainty due to unresolved scales in nonlinear dynamical systems but will need further testing using more complicated systems.

We analyzed environmental fields for moderate clear air turbulence (CAT) reported above 400 hPa over Japan between 2010 and 2017. We divided the year into five periods, with June separate from summer. In June, a stagnant front called the Baiu front is often present over Japan, making meteorological conditions different from summer. Using reanalysis data, we analyzed environmental fields for each period. It is shown that the environmental fields of aviation turbulence around Japan differ depending on altitude and period. In winter, CAT tends to occur on the west side of the trough of the jet stream axis. The jet stream is often weak when CAT occurred. CAT is likely to occur around trough in spring and fall, suggesting that the trough-enhanced deformation and strengthened vertical wind shear caused the CAT. CAT likely occurs in a field with cyclonic circulation and active convection in the lower levels in summer. In June, it is suggested that it tends to occur north of the Baiu front and around troughs associated with the jet stream. The jet stream to the north of the Baiu front suggests that the Baiu front is related to the onset of CAT.

El Niño-Southern Oscillation (ENSO) is the strongest mode of interannual climate variability, characterized by fluctuations in sea surface temperature (SST) with diverse spatial structures. Recent research has identified two main flavors of El Niño events: Eastern Pacific (EP) and Central Pacific (CP) types, each associated with different physical processes and climatic impacts. However, the causes of El Niño diversity remain widely debated, with westerly wind bursts (WWBs) recognized as a major contributing factor. This study investigates the relationship between WWBs and El Niño diversity, focusing on decadal variations in the cumulative intensity (CWI) and longitudinal center (LonCen) of WWBs. Analyses of CWI and LonCen throughout the year and in spring show that CWI exhibits significant decadal variations corresponding to changes in El Niño diversity, with stronger CWI favoring the occurrence of EP El Niño events. While LonCen exhibits a trend of shifting gradually toward the Western Pacific, which aligns with an increased frequency of CP El Niño events. These results further highlight the importance of WWBs, emphasizing not only their intensity but also their spatial pattern, in influencing El Niño evolution and diversity.

Seasonal prediction of atmospheric rivers (ARs) in the western north Pacific (WNP) is examined using a seasonal prediction model with and without atmospheric initialisation. A 20-year seasonal prediction was conducted to evaluate the model's prediction skill, particularly focusing over the Japan area. The prediction skill of the present model indicated that the seasonal AR frequency is predictable with a lead time of up to 7–10 months, and the atmospheric initialisation further improved the skill. An additional investigation was conducted to identify the source of predictability for seasonal ARs. One significant source is the predictability of the Pacific-Japan (PJ) pattern, which is influenced by the model's skill in predicting tropical sea surface temperature (SST) variability. The anticyclonic circulation southeast of Japan is well predicted when the tropical SST variability and PJ pattern are accurately predicted. Another source of predictability difference originated from the subsurface sea temperature (SBT) beneath the subtropical high in the North Pacific. When the SBT prediction is improved with atmospheric initialisation, it enhances the air-sea interactions over the subtropical high in the WNP and southeast of Japan, leading to better predictability of seasonal ARs.

In September 2024, the Yangtze River basin experienced a supremely extreme heatwave that broke historical records from at least 1961 and could have a severe impact on outdoor health of school children. This paper provides a timely analysis of the characteristics of the extreme heatwave in the Yangtze River basin in September 2024, its exposure to the population aged 14 years and below, and the causes that led to its occurrence, as well as its future projections. In September 2024, the regional average heatwave days in the Yangtze River basin reached 7.57 days, and the average daily maximum temperature (T_max) reached 31.53°C, both of which are much higher than the climatology and exceed the historical records. This supremely heatwave resulted in high exposure of the population aged 14 years and under, with the provinces of Sichuan, Chongqing, Hunan, and Jiangxi exposed to more than 100 million person-days. The extreme expansion of the South Asian High (SAH) and the Western Pacific Subtropical High (WPSH) may have directly contributed to this supremely heatwave. The CMIP6 projections show that the frequency of extreme heatwaves in September similar to that in 2024 will increase in the future.

The wintertime central Arctic atmosphere comprises a radiatively clear and a radiatively opaque state, which are linked to synoptic forcing and mixed-phase clouds. Weather and climate models often lack process representations surrounding these states, but prior work mostly treated the problem as an aggregate of synoptic conditions, resulting in partially overlapping biases. Here, we disaggregate the Arctic states and confront ERA5 reanalysis with observations from the MOSAiC campaign over the central Arctic sea ice during winter 2019/2020. Low-level winds and liquid water path (LWP) are combined to derive different synoptic classes. Results show that the clear state is primarily formed by weak/moderate winds and the absence of liquid-bearing clouds, while strong winds and enhanced LWP primarily form the radiatively opaque state. ERA5 struggles to reproduce these basic statistics, shows too weak sensitivity of thermal radiation to synoptic forcing, and overestimates thermal radiation for similar LWP amounts. The latter is caused by a warm bias, which has a pronounced inversion structure and is largest in clear and calm conditions. Under strong synoptic forcing, the warm bias is constant with height and discrepancies in mixed-phase cloud altitude appear. Separating synoptic conditions is regarded as useful for process-oriented evaluation of the Arctic troposphere in models.

Global numerical weather prediction (NWP) models often face challenges in providing the fine spatial resolution required for accurate prediction of localized phenomena and extreme precipitation events due to computational constraints and the chaotic nature of atmospheric dynamics. Downscaling models address this limitation by refining forecasts to higher resolutions for specific regions. Recently, machine learning (ML) based weather forecasting models demonstrate superior efficiency and accuracy compared to traditional NWP models. However, these ML models generally operate with a temporal resolution of 6 h and a spatial resolution of 0.25°. Furthermore, they predominantly rely on the fifth generation of the European Center for Medium-Range Weather Forecasts Reanalysis (ERA5) data, which is notorious for its precipitation biases. In this study, we utilize the High-Resolution China Meteorological Administration Land Data Assimilation System dataset, which provides more accurate precipitation data, as the target for downscaling and bias correction. This study pioneers the application of a transformer-based super-resolution model, SwinIR, to downscale and correct biases in precipitation forecasts generated by FuXi-2.0, a state-of-the-art ML weather forecasting model trained on ERA5 with a temporal resolution of 1 h. Our results demonstrate that the downscaled forecasts outperform the high-resolution forecasts from the ECMWF in terms of both accuracy and computational efficiency. However, the study also underscores the persistent challenge of underestimating high-intensity rainfall and extreme weather events, which remain critical areas for future improvement.

The occurrence of blocking weather patterns over Europe is analysed in a large ensemble of simulations of a climate model with perturbed physical parameters. The experiments were performed with HadGEM3-GC3 for the UK Climate Change Projections, and comprise a set of 15 coupled simulations supported by a larger suite of 505 atmosphere-only simulations. Despite the systematic perturbation of 47 different physical constants in the atmosphere-only experiments, only three were found to have any impact on European blocking frequencies. These reveal the sensitivity of European blocking to orographic drag in winter and to convective entrainment in summer. However, these sensitivities cannot be traced through to the coupled simulations, due to the smaller and more realistic range of perturbations used and likely also to coupled dynamical effects. Overall, we find that although physical sensitivity to the parameterisations exists, adjustment of the parameters is no replacement for further structural improvement in the representation of these processes in the model.