Enhancement of the summer extreme precipitation over North China by interactions between moisture convergence and topographic settings

Zhao, Yang; Chen, Deliang; Li, Jiao; Chen, Dandan; Chang, Yi; Li, Juan; Qin, Rui

doi:10.1007/s00382-020-05139-z

Cited by 41 publications

(26 citation statements)

References 63 publications

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“…Following such results, the elevation dependency of the turbulent gusts found in the current study across both Sweden and Norway points to the need of including unresolved topography in the WG parametrization. This can be better achieved in future studies only if the possible mechanisms by which gustiness is affected by adjacent topography are explained and the terrain influence can be quantitatively addressed, similarly to what has been done by Zhao et al (2020) in relation to the roles of moisture budget under the influence of topography on summer extreme precipitation over Northern China.…”

Section: Possible Explanation Of the Elevation-dependency In Turbulenmentioning

confidence: 98%

Near-surface mean and gust wind speeds in ERA5 across Sweden: towards an improved gust parametrization

et al. 2020

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The ERA5 reanalysis product has been compared with hourly near-surface wind speed and gust observations across Sweden for 2013-2017. ERA5 shows closer agreement than the previous ERA-Interim reanalysis with regard to both mean wind speed and gust measurements, although significant discrepancies are still found for inland and mountainous regions. Therefore, attempts have been made to improve formulations of the gust parametrization used in ERA5 by adding an elevationdependency and by adjusting the convective gust contribution. Major improvements, especially over mountain regions, are achieved when the elevation differences among the stations are considered. Closer agreement between the observed and parametrized gusts is reached when the convective gust contribution is also tuned. The newly designed gust parametrization was also tested for Norway, which is characterized by more complex topography. Wind gusts from the selected Norwegian stations are more realistically simulated when both the elevation-dependency and the tuned convective contribution are implemented, although the parametrized gusts are still negatively biased. Such biases are not explained by the different in gust duration in recorded wind gusts between Sweden and Norway.

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Section: Possible Explanation Of the Elevation-dependency In Turbulenmentioning

confidence: 98%

Near-surface mean and gust wind speeds in ERA5 across Sweden: towards an improved gust parametrization

et al. 2020

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“…( 3) are responsible for future summer precipitation changes, as shown in Eqs. ( 4) and ( 5) , Zhao et al (2019Zhao et al ( , 2020 where Δ denotes the difference between future and historical simulations (future minus historical). In Eq.…”

Section: Moisture Budgetmentioning

confidence: 99%

Future precipitation changes in three key sub-regions of East Asia: the roles of thermodynamics and dynamics

Zhao

Chen

et al. 2021

Clim Dyn

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Previous studies have projected an increase in future summer precipitation across East Asia (EA). This study investigates the relative contributions of thermodynamic and dynamic components to future precipitation changes in three key sub-regions of EA where the maximum centers of the historical precipitation are located (the tropical region, East China, and the Japan and Korea sector), and analyzes the causes of the changes in thermodynamic and dynamic components. Outputs from 30 climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) are used. From these, the five best-performing models for historical summer precipitation climatology for EA are selected. The future summer precipitations in the three sub-regions over the near- to mid-term (2020–2069) and the long-term (2070–2095) are then examined using the multi-model ensemble mean of the five models selected (MMM05). The projections were driven by four combined scenarios of the Shared Socioeconomic Pathways (SSPs) and forcing levels of the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results show that long-term precipitations under SSP5-8.5 are greater than those under the other scenarios across all sub-regions. After the 2070s under SSP5-8.5, a marked precipitation intensification is identified in all three sub-regions, but with different rates of increase. The projected precipitation increase is primarily attributed to the thermodynamic component, while the dynamic component related to circulation changes is relatively weak. Further analysis indicates that the pattern of the thermodynamic component in the three sub-regions is dominated by the climatological upward motion, mediated by an increase in moisture.

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“…1. The potential application of VMFC in weather prediction due to its ability to calculate precipitation in mesoscale systems (Banacos and Schultz, 2005) and for extreme events (Zhao et al, 2020). Detailed calculation of precipitation from VMFC is provided in Appendix.…”

Section: Vertically Integrated Moisture Flux Convergencementioning

confidence: 99%

Understanding the relationship of storm‐ to large‐scale environment in the monsoon trough region: results inferred from long‐term radar and reanalysis datasets

Jha

Das

Deshpande

et al. 2021

Quart J Royal Meteoro Soc

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Monsoon trough (MT) convection exhibits multiscale spatial variability ranging from cellular storms to large organized convective systems. Understanding the relationships between storm-scale convection and the large-scale environment is crucial for accurately representing the properties and effect of convection in climate models. We investigate these relationships using nine wet seasons (June-September of 2009-2017) of S-band radar observations over a tropical location (Kolkata) in the Indian summer monsoon trough region. A Lagrangian-based objective cell-tracking method, Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN), is applied to the volumetric reflectivity data to identify and track the convective storms. ERA5 hourly reanalysis products from the European Centre for Medium-Range Weather Forecasts (ECMWF) are utilized for the field variables representing the large-scale environment. We focus on the relationships of radar-derived storm properties with large-scale dynamic, thermodynamic, and stability variables such as convergence, mid-level humidity, convective available potential energy (CAPE), and convective inhibition (CIN). Storm-scale convection shows a selective nature, characterized as embedded cells in their unique environment. Furthermore, storms are classified as storm cells (simple and intense) and multicellular (linear and nonlinear) based on their geometrical properties and intensity. MT convection is predominantly composed of storm cells and characterized by an environment of moist mid-level, low-to-moderate CAPE, low CIN, and strong dynamic forcing, that is, vertical ascent. In dry and weak dynamic forcing (i.e., subsiding atmosphere) with high CAPE and CIN, the highest rain intensity is associated with a few linear and nonlinear multicellular storms. In the development of such major linear and nonlinear multicellular storms over the eastern flank of the MT, the mid-troposphere acts as a dry capping inversion. It is observed that the storm-scale convection varies with the large-scale dynamic forcing. During weak (strong) dynamic forcing conditions, the CAPE (mid-level humidity) relates well with the observed storm-scale convection.

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Enhancement of the summer extreme precipitation over North China by interactions between moisture convergence and topographic settings

Cited by 41 publications

References 63 publications

Near-surface mean and gust wind speeds in ERA5 across Sweden: towards an improved gust parametrization

Near-surface mean and gust wind speeds in ERA5 across Sweden: towards an improved gust parametrization

Future precipitation changes in three key sub-regions of East Asia: the roles of thermodynamics and dynamics

Understanding the relationship of storm‐ to large‐scale environment in the monsoon trough region: results inferred from long‐term radar and reanalysis datasets

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