Simulating North American mesoscale convective systems with a convection-permitting climate model
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Date
2020
Publication Type
Journal Article
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no
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Abstract
Deep convection is a key process in the climate system and the main source of precipitation in the tropics, subtropics, and mid-latitudes during summer. Furthermore, it is related to high impact weather causing floods, hail, tornadoes, landslides, and other hazards. State-of-the-art climate models have to parameterize deep convection due to their coarse grid spacing. These parameterizations are a major source of uncertainty and long-standing model biases. We present a North American scale convection-permitting climate simulation that is able to explicitly simulate deep convection due to its 4-km grid spacing. We apply a feature-tracking algorithm to detect hourly precipitation from Mesoscale Convective Systems (MCSs) in the model and compare it with radar-based precipitation estimates east of the US Continental Divide. The simulation is able to capture the main characteristics of the observed MCSs such as their size, precipitation rate, propagation speed, and lifetime within observational uncertainties. In particular, the model is able to produce realistically propagating MCSs, which was a long-standing challenge in climate modeling. However, the MCS frequency is significantly underestimated in the central US during late summer. We discuss the origin of this frequency biases and suggest strategies for model improvements.
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published
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Journal / series
Volume
55 (1-2)
Pages / Article No.
95 - 110
Publisher
Springer
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Subject
Mesoscale convective systems; Convection-permitting climate modeling; Strom tracking; North America; Percipitation extremes
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09844 - Prein, Andreas Franz / Prein, Andreas Franz