Comment on gmd-2021-278

doi:10.5194/gmd-2021-278-rc1

Abstract

Warm conveyor belts (WCBs) affect the atmospheric dynamics in midlatitudes and are highly relevant for total and extreme precipitation in many parts of the extratropics. Thus, these air streams and their effect on midlatitude weather should be well represented in numerical weather prediction (NWP) and climate models. This study applies newly developed convolutional neural network (CNN) models which allow the identification of footprints of WCB inflow, ascent, and outflow from a limited number of predictor fields at comparably low spatio-temporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different data sets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart which is most frequently used to objectively identify WCBs but requires data at higher spatio-temporal resolution which is often not available and is computationally more expensive. First, an application to reanalyses reveals that the well-known relationship between WCB ascent and extratropical cyclones as well as between WCB outflow and blocking anticyclones is also found for WCB footprints identified with the CNN models. Second, the application to Japanese 55-year reanalyses shows how the CNN models may be used to identify erroneous predictor fields that deteriorate the models' reliability. Third, a verification of WCBs in operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for three Northern Hemisphere winters reveals systematic biases over the North Atlantic with both the trajectory-based approach and the CNN models. The ensemble forecasts' skill tends to be lower when being evaluated with the trajectory approach due to the fine-scale structure of WCB footprints in comparison to the rather smooth CNN-based WCB footprints. A final example demonstrates the applicability of the CNN models to a convection permitting simulation with the ICOsahedral Nonhydrostatic (ICON) NWP model. Our study illustrates that deep learning methods can be used efficiently to support process-oriented understanding of forecast error and model biases, and opens numerous directions for future research.

Highlights

Extratropical cyclones are accompanied by coherent air streams which ascend cross-isentropically from the lower to the upper troposphere within two days – so-called warm conveyor belts (WCBs; Browning et al, 1973; Harrold, 1973; Carlson, 1980)
Warm conveyor belts (WCBs) of the trajectory-based climatology by Madonna et al (2014) are associated with extratropical cyclones. We investigate whether this relationship is found for WCBs identified with the convolutional neural network (CNN) models by matching objects of WCB ascent with cyclone objects
The climatological matching frequency of trajectory-based WCB ascent and extratropical cyclones reaches more than 90%

Summary

Introduction

Extratropical cyclones are accompanied by coherent air streams which ascend cross-isentropically from the lower to the upper troposphere within two days – so-called warm conveyor belts (WCBs; Browning et al, 1973; Harrold, 1973; Carlson, 1980). WCBs originate in the marine boundary layer of an extratropical cyclone’s warm sector and ascend poleward along the cyclone’s cold front (Wernli and Davies, 1997) This WCB ascent, which can be slantwise or convective in nature (e.g., Neiman and Shapiro, 1993; Rasp et al, 2016; Oertel et al, 2019), is accompanied by latent heat release on the order of 20 K due to phase changes during cloud formation (Eckhardt et al, 2004; Madonna et al, 2014). The latent heat release during the WCB ascent leads to a net cross-isentropic transport of lower-tropospheric low-PV air into the upper troposphere where it contributes along with its diabatically amplified divergent outflow to the formation of anticyclonic PV anomalies (e.g., Pomroy and Thorpe, 2000; Ahmadi-Givi et al, 2004; Grams et al, 2011; Bosart et al, 2017). Steinfeld and Pfahl (2019) found that almost 10% of air masses in blocking anticyclones had ascended in WCBs during the 7 days before reaching the blocking region

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