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

===== 10.3.3.3.1 Mid- to high-latitude atmospheric variability phenomena: Blocking and extratropical cyclones =====

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Major large-scale meteorological phenomena for mid- to high latitude mean and extreme climate include atmospheric blocking and extratropical cyclones ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4|Section 2.3.1.4]] ). Atmospheric blocking is characterized by a quasi-stationary, long-lasting, high-pressure system that blocks and diverts the movement of synoptic cyclones ( [[#Woollings--2018|Woollings et al., 2018]] ). In regions where blocking occurs, it is known to lead to cold conditions in winter and warmth and drought during summer, defining the seasonal regional climate in certain years ( [[#Sousa--2017|Sousa et al., 2017]] , 2018b). Extratropical cyclones are storm systems that propagate preferentially in confined storm-track regions, characterized by large eddy kinetic energy, heat and momentum transports that shape regional weather at mid- to high latitudes ( [[#Shaw--2016|Shaw et al., 2016]] ). Given their importance in shaping mean and extreme regional climate (Sections 3.3.3.3, 11.3 and 11.4), an accurate representation of blocking and extratropical cyclones in global and regional climate models is needed to better understand regional climate variability and extremes as well as to project future changes ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.2|Section 11.7.2]] ; [[#Grotjahn--2016|Grotjahn et al., 2016]] ; [[#Mitchell--2017|Mitchell et al., 2017]] ; [[#Rohrer--2018|Rohrer et al., 2018]] ; [[#Huguenin--2020|Huguenin et al., 2020]] ). An overview of CMIP5 and CMIP6 model performance in simulating blocking and extratropical cyclones is given in [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.3|Section 3.3.3.3]] . CMIP6 models still suffer from long-standing blocking biases identified in previous generations of models. However, blocking location has improved compared to CMIP5, while comparable performance is seen for blocking frequency and persistence (Figure 10.7). Increasing horizontal model resolution to about 20 km in the HighResMIP experiments improves the representation of blocking frequency and its spatial pattern in most models, but no clear effect could be shown for blocking persistence. Biases associated with these two phenomena are highly region- and season-dependent and their amplitudes vary among CMIP models ( [[#Drouard--2018|Drouard and Woollings, 2018]] ; [[#Schaller--2018|Schaller et al., 2018]] ; [[#Woollings--2018|Woollings et al., 2018]] ; [[#Harvey--2020|Harvey et al., 2020]] ; [[#Schiemann--2020|Schiemann et al., 2020]] ).

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[[File:055ebbc14be5907922f77eee0f954f09 IPCC_AR6_WGI_Figure_10_7.png]]

'''Figure 10.7''' '''|''' '''Northern Hemisphere blocking performance in historical coupled simulations for different multi-model ensembles.''' Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6): CMIP5 and CMIP6 Diagnostic, Evaluation and Characterization of Klima (DECK) historical simulations, 1950–2005, LC/HC: Low- (LC)/high- (HC) resolution coupled simulations from the PRIMAVERA project, 1950–2014 following the hist-1950 experiment of the CMIP6 HighResMIP Protocol ( [[#Haarsma--2016|Haarsma et al., 2016]] ). (Top) blocking frequency, for example, fraction of blocked days; (middle) root-mean-squared error in blocking frequency; (bottom) 90th percentile of blocking persistence, aggregated over an Atlantic domain (left, ATL: 90°W–90°E, 50°–75°N) and a Pacific domain (right, PAC: 90°E–270°E, 50°–75°N). Results are for boreal winter (December–January–February, DJF) and summer (June–July–August, JJA). Box-and-whisker plots for CMIP5/6 follow the methodology used in Figure 10.6 and show median (line), mean (triangle), and interquartile range (box) across 29 models for each ensemble. The reference estimate (ERA, asterisk) is from a 50-year reanalysis dataset that merged ERA-40 (1962–1978) and ERA-Interim (1979–2011) reanalyses. An estimate of internal variability for each metric (IV) is shown as a box-and-whisker plot over the asterisk and is obtained from a single-model ensemble (ECMWF-IFS high-resolution hist-1950 experiment, 6 × 65 years). For details on the methodology see ( [[#Schiemann--2020|Schiemann et al., 2020]] ). Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

RCMs have a very limited ability to reduce large-scale circulation errors of the driving GCM ( [[#Hall--2014|Hall, 2014]] ). In a study of five ERA-Interim-driven RCMs, [[#Jury--2018|Jury et al. (2018)]] showed that RCMs typically simulate fewer blocking events over Europe than are present in the driving data, irrespective of the RCM horizontal resolution. Based on a simple blocking bias-decomposition method, they suggest that blocking frequency biases can contribute to the RCM mean surface biases. Over some large domains, reanalysis-driven RCMs can significantly improve the representation of storm characteristics compared to the driving reanalysis near regions with complex orography and/or large water masses ( [[#Poan--2018|Poan et al., 2018]] ). However, this is not necessarily true if the domain is large enough because the RCM and its biases will then control the circulation leading to a biased performance with regard to storm characteristics ( [[#Pontoppidan--2019|Pontoppidan et al., 2019]] ). An ensemble of 12 RCMs with and without air-sea coupling reasonably reproduced the climatology of Mediterranean cyclones, and air-sea coupling had a rather weak impact ( [[#Flaounas--2018|Flaounas et al., 2018]] ). Over the Gulf Stream, however, air-sea coupling played an important role in representing cyclone development ( [[#Vries--2019|Vries et al., 2019]] ). [[#Sanchez-Gomez--2018|Sanchez-Gomez and Somot (2018)]] showed that the effect of RCM internal variability on density of cyclone tracks is very significant and larger than for other variables such as precipitation. It is larger in summer than in winter, in particular over the Iberian Peninsula, northern Africa and the eastern Mediterranean, which are regions of enhanced cyclogenesis during the warm season.

Biases in the representation of large-scale atmospheric circulation can result in biased representation of regional climate. While, in principle, the connection between large-scale and regional biases is obvious, given the strong control of regional climate by large-scale phenomena, research on this connection is still limited. [[#Munday--2018|Munday and Washington (2018)]] relate CMIP5 model rainfall biases over South Africa to anomalous low-level moisture transport across high topography due to upstream wind biases and inaccurate representation of unresolved orographic drag effects. [[#Addor--2016|Addor et al. (2016)]] show that the overestimated frequency of westerly synoptic situations was a significant contributor to the wet bias in several RCMs in winter over Switzerland. Pepler et al. (2014, 2016) suggest that better capturing westerly-driven synoptic systems such as cold fronts and cut-off lows in climate models could be key in simulating the observed pattern correlation between rainfall and zonal wind in southern south-east Australia. [[#Cannon--2020|Cannon (2020)]] shows global improvement in performance going from CMIP5 to CMIP6 for both frequency and persistence of circulation types.

The robust quantification of the influence of atmospheric circulation errors on regional climate remains a challenge as many parametrized processes such as cloud radiative effects and soil moisture or snow feedbacks can also contribute and interact with the circulation errors. Atmospheric nudging experiments where the simulated circulation is constrained to be close to that observed have been used to separate the circulation effect from other contributions to regional climate biases ( [[#Wehrli--2018|Wehrli et al., 2018]] ). The nudging approach requires detailed and careful implementation in order to limit detrimental effects due to the added tendency term in the model equations ( [[#Zhang--2014|Zhang et al., 2014]] ; [[#Lin--2016|Lin et al., 2016]] ). Based on single-model experiments, [[#Wehrli--2018|Wehrli et al. (2018)]] show that the circulation induced biases are often not the main contributors to mean and extreme temperature and precipitation biases for many regions and seasons.

There is ''high confidence'' that atmospheric circulation biases can deteriorate the model representation of regional land surface climate. Assessing the relative contributions of atmospheric circulation and other sources of bias remains a challenge due to the strong coupling between the atmosphere and other components of the climate system, including the land surface.

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