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Arthur Coquereau
,
Florian Sévellec
,
Thierry Huck
,
Joël J.-M. Hirschi
, and
Antoine Hochet

Abstract

As well as having an impact on the background state of the climate, global warming due to human activities could affect its natural oscillations and internal variability. In this study, we use four initial-condition ensembles from the CMIP6 framework to investigate the potential evolution of internal climate variability under different warming pathways for the twenty-first century. Our results suggest significant changes in natural climate variability and point to two distinct regimes driving these changes. The first is a decrease in internal variability of surface air temperature at high latitudes and all frequencies, associated with a poleward shift and the gradual disappearance of sea ice edges, which we show to be an important component of internal variability. The second is an intensification of the interannual variability of surface air temperature and precipitation at low latitudes, which appears to be associated with El Niño–Southern Oscillation (ENSO). This second regime is particularly alarming because it may contribute to making the climate more unstable and less predictable, with a significant impact on human societies and ecosystems.

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Timothy A. Coleman
,
Richard L. Thompson
, and
Gregory S. Forbes

Abstract

Recent articles have shown that the long-portrayed “tornado alley” in the central plains is not an accurate portrayal of current tornado frequency over the United States. The greatest tornado threat now covers parts of the eastern United States. This paper shows that there has been a true spatial shift in tornado frequency, dispelling any misconceptions caused by the better visibility of tornadoes in the Great Plains versus the eastern United States. Using F/EF1+ tornadoes (the dataset least affected by increasing awareness of tornado locations or by changing rating methods), a 1° × 1° grid, and data for the two 35-yr periods 1951–85 and 1986–2020, we show that since 1951, by critical measures (tornadogenesis events, tornado days, and tornado pathlength), tornado activity has shifted away from the Great Plains and toward the Midwest and Southeast United States. In addition, tornadoes have trended away from the warm season, especially the summer, and toward the cold season since 1951. Annual trends in tornadoes by season (winter, spring, summer, and autumn) confirm this. All of the increase in F/EF1+ tornadoes in the eastern United States is due to an increase in cold season tornadoes. Tornadoes in the western United States decreased 25% (from 8451 during 1951–85 to 6307 during 1986–2020), while tornadoes in the eastern United States. increased 12% (from 9469 during 1951–85 to 10 595 during 1986–2020). The cities with the largest increases and decreases in tornado activity since 1951 are determined.

Significance Statement

This paper quantifies in many ways (tornadoes, tornado days, and pathlength) the geographical shift in tornadoes from the central to the eastern United States and from the warm season to the cold season, since 1951. Where and when tornadoes most frequently occur is significant not only for the research and operational meteorology communities but also for public perception and risk awareness. Some research studies have shown that tornado casualties are more likely in the eastern United States and the cold season because of preconceived notions of a “tornado alley” in the Great Plains and a “tornado season” in the spring. Publication of the results of this research might help ameliorate this problem.

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Sining Ling
and
Riyu Lu

Abstract

The climatological western North Pacific summer monsoon onset, so called convection jump, occurs around 41th pentad, corresponding to an abrupt northeastward extension of strong convection. This study investigates the process of convection jump from a local perspective. Composite analyses are performed based on the onset dates that are identified in individual years. The results show that the convective inhibition (CIN) decreases dramatically around the onset dates, while the convective available potential energy (CAPE) reaches its maximum long before the onset, suggesting that the CIN, rather than CAPE, plays a dominant role in triggering convection. Further analysis indicates that the reduction of CIN is induced by the increased low-lever relative humidity, which is the result of enhanced water vapor convergence. The moisture transportation is primarily contributed by the wind transfer from easterlies to southeasterlies or southerlies along the southern boundary of convection jump region, in accordance with the monsoon trough establishment. The present observational results may be used to evaluate climate models in simulating stepwise evolution of summer monsoon.

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Dylan J. Dodson
and
William A. Gallus Jr.

Abstract

Ten bow echo events were simulated using the Weather Research and Forecasting (WRF) Model with 3- and 1-km horizontal grid spacing with both the Morrison and Thompson microphysics schemes to determine the impact of refined grid spacing on this often poorly simulated mode of convection. Simulated and observed composite reflectivities were used to classify convective mode. Skill scores were computed to quantify model performance at predicting all modes, and a new bow echo score was created to evaluate specifically the accuracy of bow echo forecasts. The full morphology score for runs using the Thompson scheme was noticeably improved by refined grid spacing, while the skill of Morrison runs did not change appreciably. However, bow echo scores for runs using both schemes improved when grid spacing was refined, with Thompson runs improving most significantly. Additionally, near storm environments were analyzed to understand why the simulated bow echoes changed as grid spacing was changed. A relationship existed between bow echo production and cold pool strength, as well as with the magnitude of microphysical cooling rates. More numerous updrafts were present in 1-km runs, leading to longer intense lines of convection which were more likely to evolve into longer-lived bow echoes in more cases. Large-scale features, such as a low-level jet orientation more perpendicular to the convective line and surface boundaries, often had to be present for bow echoes to occur in the 3-km runs.

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Hanzhao Yu
,
Tianjun Zhou
, and
Linqiang He

Abstract

The zonal wavenumber-5 circumglobal teleconnection pattern (CGT) is one of the most critical atmospheric teleconnection patterns during boreal summer over the Northern Hemisphere (NH). CGT can exert significant climatic impact across NH including Europe, East Asia and North America but how reliable coupled climate models simulate the characteristics of CGT is poorly understood. Here, twenty coupled models with their respective versions in Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 are selected to evaluate their performance on CGT simulation. We find that while both CMIP5 and CMIP6 models are able to capture the basic features of CGT in multi-model mean (MMM), there are large inter-model discrepancies in the simulation of CGT pattern among CMIP5 and CMIP6 models. High-skill models exhibit strong action center over west-central Asia, coinciding with the pattern derived from reanalysis, while the corresponding action center in low-skill models are weaker. Further analyses demonstrate that high-skill models are capable of simulating more realistic Indian Summer Monsoon (ISM) precipitation anomalies related to CGT. The resultant anomalous upper-tropospheric divergence over west-central Asia, acting as a Rossby wave source, can therefore excite the above-mentioned action center. This high- and low-skill model difference on CGT-ISM relationship is consistent in both CMIP5 and CMIP6. It is also found that high-skill models tend to simulate more realistic CGT-ENSO relationship. The relationship between simulation skills of CGT-ENSO correlation and CGT spatial pattern is attributed to the remote impact of ENSO on CGT wavetrain through affecting ISM precipitation anomalies.

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Matthew C. Brown
,
Geoffrey R. Marion
, and
Michael C. Coniglio

Abstract

Observational and modeling efforts have explored the formation and maintenance of mesovortices, which contribute to severe hazards in quasi-linear convective systems (QLCS). There exists an important interplay between environmental shear and cold pool-induced circulations which, when balanced, allow for upright QLCS updrafts with maximized lift along storm outflow boundaries. Numerical simulations have primarily tested the sensitivity of squall lines to zonally-varying low-level (LL) shear profiles (i.e., purely line-normal, assuming a north-south oriented system), but observed near-storm environments of mesovortex-producing QLCSs exhibit substantial LL hodograph curvature (i.e., line-parallel shear). Therefore, previous QLCS simulations may fail to capture the full impacts of LL shear variability on mesovortex characteristics. To this end, this study employs an ensemble of idealized QLCS simulations with systematic variations in the orientation and magnitude of the ambient LL shear vector, all while holding 0–3-km line-normal shear constant. This allows for a nuanced examination of how line-parallel shear modulates system structure, as well as mesovortex strength, size, and longevity. Results indicate that hodographs with LL curvature support squall lines with prominent bowing segments and wider, more intense rotating updrafts. Shear orientation also impacts mesovortex characteristics, with curved hodographs favoring cyclonic vortices that are stronger, wider, deeper and longer-lived than those produced with straight-line wind profiles. These results provide a more complete physical understanding of how LL shear variability influences the generation of rotation in squall lines.

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ZiJian Chen
and
Yu Du

Abstract

A significant diurnal offshore propagation of rainfall is observed extending from the eastern coast of India to the central Bay of Bengal. This study focuses on understanding the influence of topography over the Indian subcontinent on this rainfall propagation through a series of semi-idealized mesoscale numerical simulations. These simulations with varying topography highlight the crucial role of inertia–gravity waves, driven by diurnal mountain–land–sea thermal contrast between India and the Bay of Bengal, in initiating and promoting the offshore propagation of convective systems in the Bay. These waves’ phase speed of around 14.8 m s−1 aligns well with the speed of diurnal rainfall propagation. Even after eliminating the impact of Indian topography, the offshore propagating signal persists, suggesting a secondary rather than dominant effect of terrain on offshore rainfall propagation. Furthermore, the topography affects the depth of diurnal heating within the land’s boundary layer, which thus influences the amplitude, phase, and speed of the inertia–gravity waves. Specifically, the presence of higher mountains along the coastal area drives faster waves by increasing heating depth, resulting in faster rainfall propagation.

Significance Statement

This study advances our comprehension of the fundamental driver behind diurnal offshore rainfall propagation and the manner in which coastal terrain influences the rainfall pattern. We demonstrate that the diurnal offshore propagation of rainfall is closely related to inertia–gravity waves generated by thermal contrasts, and we successfully distinguish these waves in our study. Furthermore, our findings indicate that elevated coastal topography contributes to a greater heating depth near the coastline, which plays a crucial role in driving faster gravity waves and, consequently, leading to faster rainfall propagation. These outcomes provide a deeper insight into the mechanism governing offshore rainfall propagation and underscore the impact of real-world topography.

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Mohammad Hadavi
and
Djordje Romanic

Abstract

Thunderstorms are recognized as one of the most disastrous weather threats in Canada because of their power to cause substantial damage to human-made structures and even result in fatalities. It is therefore essential for operational forecasting to diagnose thunderstorms that generate damaging downdrafts of negatively buoyant air, known as downbursts. This study develops several machine learning models to identify environments supportive of downbursts in Canada. The models are trained and evaluated using 38 convective parameters calculated based on ERA5 reanalysis vertical profiles prior to thunderstorms with (306 cases) and without (19 132 cases) downbursts across Canada. Various resampling techniques are implemented to adjust data imbalance. An increase in the performance of the random forest (RF) model is observed when the support vector machine synthetic minority oversampling technique is utilized. The RF model outperforms other tested models, as indicated by model performance metrics and calibration. Several model interpretability methods highlight that the RF model has learned physical trends and patterns from the input variables. Moreover, the thermodynamic parameters are deemed to have higher impacts on the model outcomes compared to parcel, kinematic, and composite variables. For example, a considerable rise in the downburst probability is detected with an increase in cold pool strength. This study serves as one of the earliest attempts toward the fledgling field of machine learning applications in weather forecasting systems in Canada. The findings suggest that the developed model has the potential to enhance the effectiveness of issuing severe thunderstorm warnings in Canada, although further assessment with operational meteorologists is needed to validate its practical application.

Significance Statement

Severe thunderstorms demand particular attention in forecasting as their outflow can pose a serious threat to both structures and human life. This study uses machine learning techniques to predict whether or not a thunderstorm generates a damaging outflow in Canada. Atmospheric conditions that could trigger a severe thunderstorm are identified and discussed. Results show that the models have the potential to assist forecasters in better analyzing and predicting thunderstorms that generate destructive winds. Consequently, taking advantage of promising machine learning tools can yield more reliable forecasts of damaging thunderstorms, thereby mitigating the economic and societal burdens of these storms on Canadian communities.

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