Editing IPCC:AR6/WGI/Chapter-10 (section)

===== 10.3.3.4.4 Fronts =====

<div id="h4-11-siblings" class="h4-siblings"></div>

Weather fronts are two-dimensional surfaces separating air masses of different characteristics and are a key element of mid-latitude cyclones. In particular cold fronts are regions of relatively strong uplift and hence often associated with severe weather (e.g., [[#Schemm--2016|Schemm et al., 2016]] ). Stationary or slowly moving fronts may cause extended heavy precipitation. The evaluation of how climate models represent fronts, however, remains limited. [[#Catto--2014|Catto et al. (2014)]] found in both ERA-Interim and CMIP5 models that frontal frequency and strength were realistically simulated, albeit with some biases in the location. Follow-up investigations, for boreal and austral winter ( [[#Catto--2015|Catto et al., 2015]] ) found frontal precipitation frequency to be too high and the intensity too low, but these compensating biases resulted in only a small total precipitation bias. [[#Blázquez--2018|Blázquez and Solman (2018)]] found similar results for Southern Hemisphere (SH) winter, and also showed that CMIP5 models typically overestimate the fraction of frontal precipitation compared to total precipitation. As for the reference, the ERA-Interim reanalysis misrepresents conditional symmetric instability associated with fronts, and the corresponding precipitation ( [[#Glinton--2017|Glinton et al., 2017]] ). Only a few studies evaluating fronts in RCMs have been conducted. [[#Kawazoe--2013|Kawazoe and Gutowski (2013)]] diagnosed strong temperature gradients associated with extreme winter precipitation in the North American Regional Climate Change Assessment Program (NARCCAP) RCM ensemble ( [[#Mearns--2012|Mearns et al., 2012]] ) and found the models agreed well with gradients in a reanalysis. De Jesus et al. (2016) diagnosed the representations of cold fronts over southern Brazil in two RCMs, finding that they were only underestimated by about 5% across the year, but in one RCM, summer cold fronts were underestimated by 17%. An RCM-based reanalysis suggests that high-resolution RCM simulations improve the representation of orographic influences on fronts ( [[#Jenkner--2009|Jenkner et al., 2009]] ).

<div id="10.3.3.5" class="h3-container"></div>

<span id="performance-at-simulating-regional-feedbacks"></span>