Improved Representation of Extratropical Cyclone Structure in HighResMIP Models

Abstract

General circulation models are broadly able to capture the shape and structure of extratropical cyclones. Increased atmospheric resolution has been shown to improve the representation of cyclone structure. However, the intensity of cyclones, and the strength of their winds, are commonly underestimated in models. Using a cyclone compositing technique applied to the new generation HighResMIP and CMIP6 models, the representation of cyclone wind speeds and airstreams is quantified. CMIP6 models are able to capture the structure of cyclones, however winds associated with various airstreams within the cyclones are too weak. HighResMIP models show considerable improvements, with a majority of cyclone‐scale biases present in the CMIP6 models reduced in both winter and summer seasons in the Northern and Southern hemispheres. HighResMIP and CMIP6 models have similar biases on the large‐scale, with both struggling to represent the width of the upper‐level jet.

Toward Eliminating the Decades‐Old “Too Zonal and Too Equatorward” Storm‐Track Bias in Climate Models

“…As a result, the sign of, for example, changes in fall but also annual mean precipitation under the SSP5‐8.5 scenario over western and central Europe remains unclear which can be conveniently verified using the new IPCC WGI Interactive Atlas accessible via https://interactive-atlas.ipcc.ch/regional-information (last accessed March 2022). In addition, climate models tend to underestimate in particular the cases of rapid cyclone intensification (Priestley & Catto, 2022 ). Overall, the biases are thus best summarized by “too zonal, too equatorward and too weak for extreme cases.”…”

Machine Learning‐Based Detection of Weather Fronts and Associated Extreme Precipitation in Historical and Future Climates

“…(2022) found that CMIP6 models were able to well represent the spatial patterns of daily precipitation and duration of dry spells, implying these models would also be able to capture precipitation extremes. Continued model improvements in resolution (Bador et al., 2020; Wehner et al., 2014), calibration (Yang et al., 2012) and process representation such as convective precipitation (Harding et al., 2013) and atmospheric circulation (Priestley & Catto, 2022; Shepherd, 2014) may help address these biases and aid in the assessment of extreme precipitation associated with fronts.…”

Machine learning-based detection of weather fronts and associated extreme precipitation in historical and future climates

“…Leung et al (2022) found that CMIP6 models were able to well represent the spatial patterns of daily precipitation and duration of dry spells, implying these models would also be able to capture precipitation extremes. Continued model improvements in resolution (Wehner et al, 2014;Bador et al, 2020), calibration (Yang et al, 2012) and process representation such as convective precipitation (Harding et al, 2013) and atmospheric circulation (Shepherd, 2014;Priestley & Catto, 2022) may help address these biases and aid in the assessment of extreme precipitation associated with fronts.…”