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Hawcroft, Matt; Dacre, Helen; Forbes, Richard; Hodges, Kevin; Shaffrey, Len; Stein, Thorwald (2017)
Publisher: Springer
Journal: Climate Dynamics
Languages: English
Types: Article
Subjects: Atmospheric Science
Extratropical cyclones are a key feature of the weather in the extratropics, which climate models need to represent in order to provide reliable projections of future climate. Extratropical cyclones produce significant precipitation and the associated latent heat release can play a major role in their development. This study evaluates the ability of a climate model, HiGEM, to represent latent heating in extratropical cyclones. Remote sensing data is used to investigate the ability of both the climate model and ERA-Interim (ERAI) reanalysis to represent extratropical cyclone cloud features before latent heating itself is assessed. An offline radiance simulator, COSP, and the ISCCP and CloudSat datasets are used to evaluate comparable fields from HiGEM and ERAI. HiGEM is found to exhibit biases in the cloud structure of extratropical cyclones, with too much high cloud produced in the warm conveyor belt region compared to ISCCP. Significant latent heating occurs in this region, derived primarily from HiGEM’s convection scheme. ERAI is also found to exhibit biases in cloud structure, with more clouds at lower altitudes than those observed in ISCCP in the warm conveyor belt region. As a result, latent heat release in ERAI is concentrated at lower altitudes. CloudSat indicates that much precipitation may be produced at too low an altitude in both HiGEM and ERAI, particularly ERAI, and neither capture observed variability in precipitation intensity. The potential vorticity structure in composite extratropical cyclones in HiGEM and ERAI is also compared. A more pronounced tropopause ridge evolves in HiGEM on the leading edge of the composite as compared to ERAI. One future area of research to be addressed is what impact these biases in the representation of latent heating have on climate projections produced by HiGEM. The biases found in ERAI indicate caution is required when using reanalyses to study cloud features and precipitation processes in extratropical cyclones or using reanalysis to evaluate climate models’ ability to represent their structure.
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