Drivers of Biases in the CMIP6 Extratropical Storm Tracks. Part II: Southern Hemisphere

Matthew D. K. Priestley aCollege of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom

Search for other papers by Matthew D. K. Priestley in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-5488-3959
,
Duncan Ackerley bMet Office, Exeter, United Kingdom

Search for other papers by Duncan Ackerley in
Current site
Google Scholar
PubMed
Close
,
Jennifer L. Catto aCollege of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom

Search for other papers by Jennifer L. Catto in
Current site
Google Scholar
PubMed
Close
, and
Kevin I. Hodges cDepartment of Meteorology, University of Reading, Reading, United Kingdom
dNational Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, United Kingdom

Search for other papers by Kevin I. Hodges in
Current site
Google Scholar
PubMed
Close
Free access

Abstract

The Southern Hemisphere storm tracks are commonly simulated too far equatorward in climate models for the historical period. In the latest generation of climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), the equatorward bias that was present in CMIP5 models still persists, although it is reduced considerably. A further reduction of the equatorward bias is found in atmosphere-only simulations. Using diagnostic large-scale fields, we propose that an increase in the midlatitude temperature gradients contributes to the reduced equatorward bias in CMIP6 and AMIP6 models, reducing the biases relative to ERA5. These changes increase baroclinicity in the atmosphere and are associated with a storm track that is situated farther poleward. In CMIP6 models, the poleward shift of the storm tracks is associated with an amelioration of cold midlatitude SST biases in CMIP5 and not through a reduction of the long-standing warm Southern Ocean SST bias. We propose that increases in midlatitude temperature gradients in the atmosphere and ocean are connected to changes in the cloud radiative effect. Persistent track density biases to the south of Australia are shown to be connected to an apparent standing-wave pattern originating in the tropics, which modifies the split jet structure near Australia and subsequently the paths of cyclones.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: M. Priestley, m.priestley@exeter.ac.uk

Abstract

The Southern Hemisphere storm tracks are commonly simulated too far equatorward in climate models for the historical period. In the latest generation of climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), the equatorward bias that was present in CMIP5 models still persists, although it is reduced considerably. A further reduction of the equatorward bias is found in atmosphere-only simulations. Using diagnostic large-scale fields, we propose that an increase in the midlatitude temperature gradients contributes to the reduced equatorward bias in CMIP6 and AMIP6 models, reducing the biases relative to ERA5. These changes increase baroclinicity in the atmosphere and are associated with a storm track that is situated farther poleward. In CMIP6 models, the poleward shift of the storm tracks is associated with an amelioration of cold midlatitude SST biases in CMIP5 and not through a reduction of the long-standing warm Southern Ocean SST bias. We propose that increases in midlatitude temperature gradients in the atmosphere and ocean are connected to changes in the cloud radiative effect. Persistent track density biases to the south of Australia are shown to be connected to an apparent standing-wave pattern originating in the tropics, which modifies the split jet structure near Australia and subsequently the paths of cyclones.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: M. Priestley, m.priestley@exeter.ac.uk

1. Introduction

Coupled climate models are the most sophisticated tools available for assessing potential changes to the climate in the coming century. The latest generation of models, part of phase 6 of the Coupled Model Intercomparison Project (CMIP6; Eyring et al. 2016), represents the most recent scientific and computational advancements to help with this scientific goal. To assess future projections, models with a good fidelity in reproducing historical climate variability are required. However, climate models have been marred with considerable biases in relation to historical variability (e.g., Wang et al. 2014; Menary et al. 2015; Flato et al. 2013) and an understanding of the origins of these biases is required in order to determine deficiencies in, and future directions for, model development. In this study the drivers of biases in the Southern Hemisphere (SH) storm tracks are investigated. This study serves as a follow up to Priestley et al. (2020) and an accompaniment to the authors’ investigation into drivers of Northern Hemisphere storm-track biases (Priestley et al. 2022).

Midlatitude cyclones, and the overall storm tracks, are vital components of Earth’s climate system as they act to transfer heat and momentum poleward (Kaspi and Schneider 2013). They are also responsible for considerable amounts of midlatitude precipitation and extreme winds (e.g., Hawcroft et al. 2012; Dowdy and Catto 2017; Clark and Gray 2018). In previous generations of coupled climate models (e.g., CMIP3 and CMIP5; Meehl et al. 2007; Taylor et al. 2012), the storm track, and the general SH midlatitude circulation has tended to feature significant biases. These biases have mainly been apparent as an equatorward bias of the midlatitude circulation (Kidston and Gerber 2010; Chang et al. 2012, 2013), a zonal bias of the storm track in winter (Lee 2015), and also an underestimation of cyclone intensity (Chang et al. 2013).

In the CMIP6 models, the latitude of the SH storm track, and also the peak intensity of cyclones, is much improved and closely matches that of various reanalysis products relative to CMIP5 (Priestley et al. 2020). Furthermore, the mean SH midlatitude circulation has also shown clear improvements, with reductions in biases from CMIP3 through to CMIP5 (Bracegirdle et al. 2013), and more recently in CMIP6, with the mean jet latitude in summer now being situated within 0.5° of that found in the latest fifth-generation ECMWF reanalysis (ERA5; Bracegirdle et al. 2020). It is not just the atmospheric circulation where improvements have been noted from CMIP5 to CMIP6. Improvements have been found in the surface temperature distribution, precipitation, structure of the intertropical convergence zone, and also the cloud radiative properties (Bock et al. 2020; Tian and Dong 2020), all of which may contribute to the reduction of storm-track biases in CMIP6.

The large equatorward bias in the SH circulation has been commonly linked to biases in sea surface temperatures (SST) and an underestimation of atmospheric temperature gradients across a number of generations of climate models (e.g., Trenberth and Fasullo 2010; Ceppi et al. 2012; Sallée et al. 2013; Wang et al. 2014). Recently, Garfinkel et al. (2020) linked the latitude of the eddy-driven jet to the representation of the Agulhas Current, with models that have a weak Agulhas Return Current featuring a more equatorward jet. Two other recent studies (Curtis et al. 2020; Wood et al. 2020) have offered differing hypotheses as to why there has been a reduction in jet latitude bias from CMIP5 to CMIP6. Curtis et al. (2020) discuss that it is a result of improvements in model resolution, whereas Wood et al. (2020) suggest that variations in SST are the leading driver of the change.

Biases in Southern Ocean SST have been shown to be driven by biases in the atmospheric net surface shortwave flux (Hyder et al. 2018) resulting from the misrepresentation of cloud properties, specifically the shortwave cloud radiative effect (SWCRE; Ceppi et al. 2012; Grise and Polvani 2014). The SWCRE is commonly too weak, leading to a net heating of the SH. Biases in the SWCRE modify the strength of SST and midlatitude temperature gradients and hence hemispheric baroclinicity (Ceppi et al. 2012), but have also been shown to have an impact on the temperature structure of the atmosphere through radiative absorption (Li et al. 2015). These long-standing biases have been noted as an area for specific improvement for CMIP6 (Stouffer et al. 2017), as the shortwave cloud feedback has significant implications for the strength of the equilibrium climate sensitivity (Zelinka et al. 2020). So far, several studies have demonstrated reduced biases in the newest model generations (Bock et al. 2020; Mauritsen et al. 2019; Kawai et al. 2019); however, despite some reductions, the CMIP6 multimodel mean has been shown to suffer from the same deficiencies as CMIP5 (Grise and Kelleher 2021).

Another long-standing bias that has not improved from CMIP5 to CMIP6 is the positive track density bias to the south of Australia (Priestley et al. 2020), which is associated with the split jet structure in this region and over New Zealand (Bals-Elsholz et al. 2001). The split jet is a feature that is simulated poorly in climate models, with an overly strong subtropical component, and a too weak polar component (Grose et al. 2017; Patterson et al. 2019). The representation of the split jet is partly driven by Antarctic orography (James 1988) and recently Patterson et al. (2020) linked the split jet bias in an idealized GCM to the representation of Antarctic orography affecting the eddy momentum fluxes in this region. Biases in the orographic wave drag have previously been linked to circulation biases in CMIP5 models (Pithan et al. 2016), as well as Rossby waves originating in the Indian Ocean, which have been shown to alter the structure of the storm track to the south of Australia (Inatsu and Hoskins 2004, 2006).

Understanding the origin of long-standing biases and reasons for their persistence is vital not just for future model development, but also for having confidence in model simulations and for understanding whether any systematic errors have an influence on future projections. The results presented herein aim to demonstrate linkages in the large-scale atmosphere–ocean system and to act as a framework for future scientific investigation. The science questions addressed in this study are as follows:

  1. Can the CMIP6 prescribed SST experiments (i.e., AMIP; Gates et al. 1999; Eyring et al. 2016) help to explain some of the coupled storm-track biases in the SH?

  2. Can reduced storm-track biases from CMIP5 to CMIP6 be associated with specific model developments?

The paper continues as follows. Section 2 describes the data and methods used for this work. Section 3 presents the results and findings. In section 4 the key points of this work and its implications in the wider scientific context will be discussed.

2. Data and methods

a. Datasets

1) CMIP6 models

In this study the CMIP6 models covering the historical period are used. The historical and amip model runs are analyzed covering the period from 1979 to 2014. Focus will be on the December, January, February (DJF) and June, July, August (JJA) periods, representing the SH summer and winter seasons, respectively. In total there are 24 models analyzed that have provided data from both a coupled atmosphere–ocean historical run and an atmosphere-only amip run for the required variables at 6-hourly temporal resolution. A full list of the models analyzed can be found in Table 1. The amip experiments are forced by observed SSTs and sea ice concentration and a full explanation of the differences between the experiments can be found in Eyring et al. (2016). Throughout this study the coupled models from the historical experiments will be referred to as the CMIP6 models, and the atmosphere-only models from the amip experiment will be referred to as the AMIP6 models. Monthly mean data are used to investigate biases in the large-scale fields. For all models only a single ensemble member (r1i1p1f1 or lowest available) is analyzed.

Table 1

List of CMIP6/AMIP6 models that have been used in this study. Columns 3 and 4 indicate the horizontal and vertical resolution of the atmospheric component of the model. Any spectral models are first stated by their truncation type and number. Here, “T” stands for triangular truncation and “TL” stands for triangular truncation with linear Gaussian grid. The models with “C” refer to a cubed-sphere finite volumes model, with the following number being the number of grid cells along the edge of each cube face. Models with “N” refer to the total number of 2-gridpoint waves that can be represented in the zonal direction. Following any grid specification is the dimensions of the model output on a Gaussian longitude × latitude grid. The resolution stated in kilometers is the stated nominal resolution of the atmospheric component of the model from Taylor et al. (2018).

Table 1

In some instances, models will be separated between those of high and low resolution, for both the atmospheric and oceanic components. For the atmospheric resolution separation, the distinction of Priestley et al. (2020) is used and models with a nominal atmospheric resolution (see Taylor et al. 2017) of 100 km are classed as “high” resolution and those of 250 km are “low” resolution.

2) CMIP5 models

The CMIP5 models, which are the same as those used in Priestley et al. (2020) (see also Table S1 in the online supplemental material), provide a benchmark for the CMIP6 models. Of the 26 coupled CMIP5 models employed for this analysis, 19 of them have corresponding amip runs. For all models, data are used covering the period 1979–2005. Tests have been performed using the 1979–2005 period for the CMIP6 models, with no discernible differences found relative to the data period described above. As with the CMIP6 models, the coupled models will commonly be referred to as the CMIP5 models, with the atmosphere-only variants being referred to as the AMIP5 models.

3) Reanalysis

As a reference to real-world atmospheric variability, the ERA5 reanalysis (Hersbach et al. 2020) is used for comparison with the CMIP5 and CMIP6 models. ERA5 data span the period from January 1979 up to the near present, with the period 1979–2014 used to provide a consistent comparison period for the CMIP6/AMIP6 models. The ERA5 data are output at 0.28° × 0.28° (∼31 km) spatial resolution. Data are used at various output frequencies with feature tracking run on 6-hourly vorticity fields and monthly to seasonal averages used for other large-scale fields (see below). For ERA5 and the CMIP5 and CMIP6 models described above, all large-scale analyses are performed on the native grids that the data are provided on, the data are then interpolated onto a 1° × 1° grid for the purposes of visualization.

There are of course differences between numerous reanalysis products with regard to the storm tracks (Hodges et al. 2011) and other large-scale atmospheric variables (e.g., Mooney et al. 2011; Trenberth et al. 2011; Lindsay et al. 2014). Newer generation reanalysis products have been shown to be more consistent in their state of the storm track (Priestley et al. 2020), and therefore in most instances only ERA5 will be used as a reference. However, for a more comprehensive estimation of the real-world cyclogenesis rate and cyclogenesis latitude, both the MERRA-2 (Gelaro et al. 2017) and JRA-55 (Kobayashi et al. 2015) reanalyses have been employed alongside ERA5 for the same time period.

4) CERES

In calculations of the SWCRE the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product (Loeb et al. 2018) is used to validate the CMIP5 and CMIP6 data. Because of the reduced availability of CERES data and overlap with model data, this dataset is analyzed in monthly mean format covering the period 2000–14.

b. Feature tracking

For the identification and tracking of cyclones the method of Hodges (1995, 1999) is used. This method uses 850 hPa relative vorticity as the input variable, which allows for a reduced influence of the background state on cyclonic features and focuses on smaller spatial scales. The relative vorticity field is first truncated to T42 resolution with all planetary wavenumbers (5 and below) removed. This ensures tracking and cyclone identification is performed on a common resolution despite the varying input resolutions of the model data and reanalysis. Cyclones are initially identified as minima on a polar stereographic projection that exceed 1 × 10−5 s−1 (intensity scaled by −1). Following completion of the tracking cyclones are retained that travel at least 1000 km and have a lifetime of at least 48 h. This ensures the focus is on long-lived and mobile synoptic systems.

Cyclone track density is calculated using spherical nonparametric estimators from the individual cyclone tracks (Hodges 1996). In cases where cyclone genesis and lysis latitude are quantified this is taken as the latitude of the first and last (respective) time step that the cyclone is identified. For determining the poleward propagation of cyclones, the latitude difference between the 9th and 1st time step (first 48 h of life cycle) of the cyclone track is taken.

c. Metrics

To further explore the large-scale climate of the CMIP models a number of diagnostics are used that require manipulation of the raw model output. These metrics are the same as in Priestley et al. (2022) and are documented below.

1) Temperature gradients

Temperature gradients are calculated using the potential temperature θ on pressure levels. Gradients that are used are the meridional gradient of potential temperature as calculated by the Iris package (Met Office 2013) and gradients are quoted in units of kelvins per degree.

2) Static stability

The static stability is quantified in terms of the Brunt–Väisälä frequency N2 calculated on pressure levels:
N2=pg2RTθdθdp.
It is calculated for the lower troposphere, with N2 covering the 700–850-hPa layer; T and θ are the 700–850-hPa average temperature and potential temperature, respectively, and /dp is the vertical gradient in θ, calculated across the 700–850-hPa layer.

3) EGR

The Eady growth rate (EGR) is a description of the baroclinicity of the atmosphere and combines the two diagnostics described above. For this work the EGR will be a measure of the lower tropospheric baroclinicity and is defined as
EGR=0.31f|T/y|N2.
The temperature gradient |∂T/∂y| is calculated at 850 hPa, and the static stability N2 is calculated for the 700–850-hPa layer as detailed in Eq. (1).

3. Results

a. Cyclone track densities and statistics

In the CMIP6 models, a general improvement in the representation of the SH storm tracks, relative to ERA5, is seen when compared with CMIP5, particularly in DJF (Priestley et al. 2020). Priestley et al. (2020) found that the large equatorward storm-track bias of CMIP5 is reduced in CMIP6, with a near total elimination of this feature. Similar patterns are seen in JJA, with a reduction in the equatorward bias noted; however, some features still persist, such as an overestimation of track density to the south of Australia where cyclone tracks are too zonal.

Track density biases for the 24 CMIP6 and AMIP6 models are shown in Fig. 1. During DJF, biases in the CMIP6 models (Fig. 1a) are almost identical to those analyzed in Priestley et al. (2020) and indicate an underestimation of tracks in DJF and a slight equatorward bias relative to ERA5. In the AMIP6 models (Fig. 1b) the pattern of biases relative to ERA5 is generally consistent with CMIP6; however, there is a poleward shift in the track density (Fig. 1c). Consequently, the previous most evident equatorward biases in DJF across the South Pacific and to the south of New Zealand are mostly eradicated in AMIP6.

Fig. 1.
Fig. 1.

Track density biases for (a)–(c) DJF and (d)–(f) JJA for (a),(d) CMIP6 models and (b),(e) AMIP6 models relative to ERA5. Also shown is (c),(f) CMIP6 − AMIP6. Units are number of cyclones per 5° spherical cap per month. Stippling indicates where more than 80% of models agree on the sign of the error.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

The poleward shift of the track density in the AMIP6 models relative to CMIP6 is reflected in the regional cyclogenesis rates (Fig. 2a). During DJF the genesis rate for the whole SH (Fig. 2a) in the AMIP6 models has a very similar median to the CMIP6 models (255 and 256 cyclones per season, respectively, and not significantly different; Table S2 in the online supplemental material), with slightly larger intermodel spread. The similar genesis rate for the whole hemisphere can be broken down to slightly lower rates of genesis in the equatorward sector (30°–60°S) and higher rates in the poleward sector (60°–80°S) in the AMIP6 models relative to CMIP6 [Figs. 2a(ii),(iii)]. Despite these differences, the genesis density biases (relative to ERA5) are similar between AMIP6 and CMIP6 (Fig. S1d in the online supplemental material). Some of the differences in genesis density help explain the differences in track density between AMIP6 and CMIP6, for example, there is a reduction in cyclogenesis over New Zealand (Fig. S1d), which is collocated with a reduction in track density (and subsequent poleward shift of tracks, Fig. 1c).

Fig. 2.
Fig. 2.

Boxplots of regional cyclogenesis rates for (a) DJF and (b) JJA. Results are shown for all reanalyses, AMIP6, CMIP6, the two resolution groups of CMIP6, and CMIP5. Solid black lines indicate the uncertainty range of the reanalyses’s median, and dashed black lines signify the 25th–75th-percentile range of the reanalyses. Boxes extend to the 25th and 75th percentile, respectively, with yellow lines indicating the distribution median. Notches around the median show the uncertainty estimate based on 10 000 random samples, and whiskers extend to the 10th and 90th percentiles.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

The median location of cyclogenesis during DJF in ERA5 and the model groups are plotted in Fig. 3a. All model groups are biased by up to 0.5° equatorward relative to the reanalyses, with CMIP5 being the most biased. The CMIP6 models simulate genesis farther poleward than CMIP5 by ∼0.2° (p < 0.05), with AMIP6 another 0.1° farther poleward, although both are still biased significantly equatorward relative to the reanalyses. The lysis latitude is well simulated by the CMIP6 and AMIP6 models (Fig. 3b); however, the CMIP5 models simulate lysis significantly too equatorward according to this metric. The poleward displacement of cyclones is also well represented in the CMIP6 and AMIP6 models relative to the reanalyses (Fig. 3c). The CMIP5 and AMIP5 simulations tend to underestimate the poleward displacement in the first 48 h of the cyclone life cycle by ∼0.25°, which is significantly lower than the CMIP6 models. Across all measures, the CMIP6/AMIP6 models perform better than the CMIP5/AMIP5 models, with a poleward shift in AMIP relative to CMIP. This suggests that the large improvement seen in track density and the representation of the storm tracks shown in Priestley et al. (2020) has occurred through model developments from CMIP5 to CMIP6. Despite the similarities, there is a larger poleward shift from CMIP5 to CMIP6 than from AMIP5 to AMIP6, with shifts of 0.48° and 0.43°, respectively, in lifetime average latitude.

Fig. 3.
Fig. 3.

Boxplots of (a),(d) annual mean cyclogenesis latitude; (b),(e) cyclolysis latitude; and (c),(f) cyclone 48-h latitude change for reanalyses, CMIP6, AMIP6, CMIP5, and AMIP5 in (a)–(c) DJF and (d)–(f) JJA. Horizontal colored lines indicate the median value for each model distribution. Boxes extend to the 25th and 75th percentile, respectively, with yellow lines indicating the distribution median. Notches around the median show the uncertainty estimate based on 10 000 random samples, and whiskers extend to the 10th and 90th percentiles. In the labels, a star indicates where the model group is significantly different from the reanalyses, a dagger indicates where AMIP6 and CMIP6 are significantly different, and a caret indicates where CMIP6 and CMIP5 are significantly different. Significance tests were performed using a Mood’s median test and quoted at the 5% level.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

In the winter season (JJA), a similar pattern of track density biases in the CMIP6 and AMIP6 models is evident (Figs. 1d–f). However, the highest track density, as in DJF, is improved in AMIP6 through a poleward shift relative to CMIP6 (Fig. 1f). Other features such as the overly high track density to the southeast of South Africa are reduced. The persistent overestimation of tracks to the south of Australia in the CMIP6 models (as described in Priestley et al. 2020) is also present in the AMIP6 models, although to a lesser extent. Therefore, it is likely that this bias depends upon both the atmosphere–land components of the models and is being amplified through the coupling to an interactive ocean.

In JJA the AMIP6 models also have lower genesis rates than the CMIP6 models from 30° to 60°S [Fig. 2b(ii)] and higher genesis rates from 60° to 80°S [Fig. 2b(iii)]. Overall, there are significantly (p < 0.05) fewer cyclones in JJA in AMIP6 models than in CMIP6 [medians of 342 and 346 cyclones per season, respectively; Fig. 2b(i)]. In JJA there is an underestimation of track density from the east coast of South America along 40°S toward South Africa in AMIP6 relative to CMIP6 (Fig. 1f), which represents an even larger underestimation of track density relative to ERA5. This underestimation of track density coincides with a robust underestimation of genesis density (Fig. S1h in the online supplemental material). Interestingly, there are minimal differences in genesis rate to the south of Australia in either CMIP6 or AMIP6 relative to ERA5, or from CMIP6 to AMIP6 (Figs. S1f–h). This suggests that the robust track density bias in this region (Figs. 1d,e) is unrelated to the number of cyclones and instead may be driven by errors in cyclone paths being too zonal.

In JJA the differences in genesis latitude, lysis latitude, and cyclone poleward movement between the CMIP5, CMIP6, and their AMIP counterparts is similar to DJF (Figs. 3d–f). The median genesis latitude continues to be biased equatorward in CMIP5 and CMIP6, although genesis in CMIP6 occurs significantly farther poleward than CMIP5, with less than half the bias. The cyclogenesis latitude is displaced significantly poleward in AMIP6 relative to CMIP6, which agrees with Figs. 1 and 2, although the genesis latitude in the AMIP6 models is ∼0.1° poleward of the reanalyses (Fig. 3d). The lysis latitude is very well represented in CMIP5 and CMIP6, with a continued poleward bias in AMIP6 relative to ERA5 (Fig. 3e). Finally, for the poleward displacement of the cyclones, all model groups perform similarly but are biased with up to ∼0.2° more poleward movement than the reanalyses (Fig. 3f). For most of the metrics in Figs. 3d–f the models produce good results relative to the reanalyses and, at all times, there is considerable overlap in their interquartile ranges.

b. Poleward shift of the storm tracks

The largest change from CMIP5 to CMIP6 is the large improvement in the latitudinal bias of the storm track (particularly for DJF) leading to a storm track that is almost unbiased in latitude relative to ERA5 (Priestley et al. 2020; Bracegirdle et al. 2020; Curtis et al. 2020). The drivers of this improvement from CMIP5 to CMIP6, and the further reduction in the bias in AMIP6 models, will be explored below.

In DJF there is a large positive SST bias around Antarctica in the CMIP6 models (Fig. 4b) relative to ERA5 (Fig. 4a), which has persisted from CMIP5 (Fig. 4c). The SST biases in Fig. 4 are substantially different than those shown in Ocean Model Intercomparison Project (OMIP) experiments (Tsujino et al. 2020), indicating it is likely that the SST biases are driven by processes occurring in the atmospheric component of the models. For CMIP5 models, the warm bias in the high-latitude Southern Ocean has been demonstrated to arise from positive biases in the cloud-related shortwave fluxes, which result in associated errors in atmospheric net heat flux (Ceppi et al. 2012; Hyder et al. 2018). These biases have been shown to be linked to insufficient cloudiness and optical depth within the cold sectors of cyclones (Grise and Polvani 2014; Williams et al. 2013; Bodas-Salcedo et al. 2014; Govekar et al. 2014; Williams and Bodas-Salcedo 2017). As the CMIP6 models continue to have a high-latitude Southern Ocean that is too warm relative to observations, it is likely that the same biases in the cloud-related shortwave fluxes are still present. To demonstrate this the zonal mean differences in the SWCRE for CMIP6 (red line) and CMIP5 (blue line) relative to CERES are plotted in Fig. 5a. In the CMIP6 models the SWCRE is still too weak1 relative to CERES at high latitudes (i.e., poleward of 55°S, red line in Fig. 5a), and therefore the process driving the warm SSTs in the CMIP5 models appears unimproved in the CMIP6 models.

Fig. 4.
Fig. 4.

DJF averaged SST (°C) for (a) ERA5 (b) CMIP6 − ERA5, (c) CMIP5 − ERA5, and (d) CMIP6 − CMIP5. Stippling indicates where there is 80% model agreement on the sign of the bias. Data are taken from the SST (“tos”) CMIP variable.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

Fig. 5.
Fig. 5.

The ensemble zonal mean difference in (a) SWCRE (W m−2), (b) zonal mean SST gradient (kelvins per degree of latitude), and (c) zonal mean 850-hPa potential temperature gradient (kelvins per degree of latitude) for CMIP6 (red line), AMIP6 (orange line), CMIP5 (blue line), and AMIP5 (green line). The differences in (a) are for model ensemble mean minus CERES.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

At mid-to-lower latitudes (from approximately 40° to 50°S) CMIP6 SSTs are generally up to 1°C higher than in CMIP5 (Fig. 4d), particularly in the South Atlantic and Indian Ocean sectors. The SSTs in this sector are particularly important for modulating the latitude of the storm track in CMIP6 models (as indicated by the significant linear regression in Fig. 6a), with warmer SSTs associated with a more poleward storm track. This is not something that is seen in CMIP5 models (Fig. 6b). This 40°–50°S latitude band is where the largest differences in the magnitude of the SWCRE bias from CMIP5 to CMIP6 are seen, with CMIP6 models having a smaller bias and less negative SWCRE relative to CMIP5 (Fig. 5a). This reduced bias is likely contributing to the higher midlatitude SSTs in CMIP6. It is worth noting here that the increase in SSTs (CMIP6 relative to CMIP5) is largely the result of the amelioration of cold biases present in the CMIP5 models (particularly in the region of the Agulhas Current retroflection; see Fig. 4).

Fig. 6.
Fig. 6.

Linear least squares gridpoint regression slope maps of DJF seasonal mean storm-track density against area-averaged SST from 40° to 50°S (cyclones per month per kelvin) for (a) CMIP6 and (b) CMIP5. Regression is performed across the model means of the CMIP6 and CMIP5 ensembles, respectively. Stippling indicates where regressions are significant at the 5% level. The black-outlined area in (a) indicates the region of SSTs used in the regression calculations.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

The pattern of positive high-latitude and negative midlatitude SST biases in CMIP6 and CMIP5 (discussed above) causes the SST gradient to be weaker poleward of 40°S in both ensembles relative to ERA5 (Fig. 5b). Nevertheless, the midlatitude SST gradient in CMIP6 is stronger than CMIP5 between approximately 40° and 60°S (see Fig. 5b). A good representation of the strong SH midlatitude SST gradient in the models is important as it acts to maintain baroclinicity in the atmosphere (Nakamura et al. 2008; Nakayama et al. 2021). A weak temperature gradient may reduce the midlatitude baroclinicity and thereby reduce the strength of the storm track (Graff and LaCasce 2014; Garfinkel et al. 2020; Kajtar et al. 2021; Nakayama et al. 2021). It is therefore important to evaluate whether the biases in the SST gradient (Fig. 5b) are also apparent in the atmosphere. The zonal mean 850 hPa potential temperature (θ850) gradient is plotted for ERA5 (black line), CMIP5 (blue line), and CMIP6 (red line) in Fig. 5c. The CMIP6 and CMIP5 models generally feature a weaker atmospheric temperature gradient in the midlatitudes relative to ERA5 (Fig. 5c) as a result of temperatures being too high surrounding Antarctica. As with the SST gradient, the biases in the θ850 gradient are smaller in CMIP6 than CMIP5, relative to ERA5. The stronger θ850 gradient in CMIP6 relative to CMIP5 between 40° and 60°S (Fig. 5c, red vs blue lines) is likely to be driven by the higher θ850 values equatorward of approximately 50°S rather than the (smaller magnitude) reduction in 850 hPa θ adjacent to Antarctica (Fig. S3e in the online supplemental material).

To further evaluate the role of the SST biases on the atmospheric temperature gradient and storm tracks, data from the AMIP6 simulations are used. As the SSTs in AMIP6 simulations are prescribed from observations, the biases in the midlatitude SST gradient should be negligible and any errors should be primarily the result of atmospheric processes. In the AMIP6 models, a stronger θ850 gradient is seen relative to CMIP6 models (Fig. 5c, orange vs red lines), although the θ850 gradients are weaker relative to ERA5 poleward of 50°S (Fig. 5c, black line). The larger midlatitude temperature gradient in AMIP6 is the likely driver of the more poleward location of the storm track relative to CMIP6 (see Fig. 1c and Priestley et al. 2020) and the largest increases in the 850 hPa zonal wind (Fig. S2c in the online supplemental material). The increase in θ850 gradient in AMIP6 relative to CMIP6 is driven by reducing the high-latitude temperature bias (Fig. S3c in the online supplemental material) and not through increasing lower-latitude temperatures, as is the case from CMIP5 to CMIP6 (Fig. S3e in the online supplemental material). Nevertheless, there are still clearly biases in the representation of the SH storm track in the AMIP6 simulation (Fig. 1c), which are not resolved by using observed SSTs. Moreover, as both the AMIP5 and AMIP6 models are forced by the same prescribed SSTs, there should be minimal influence from the ocean state and therefore one would not expect large differences in the midlatitude temperature gradient. However, there is a clear increase in the temperature gradient in AMIP6 models, relative to AMIP5 (Fig. 5c). This increase in temperature gradient is associated with higher temperatures in the lower troposphere from 40° to 50°S in similar locations to the biases in the coupled models (black contours; Figs. S3e,f). Radiative processes have been shown to influence the temperature structure of atmosphere-only models (Li et al. 2015) and, as with the coupled models, the AMIP6 models feature a smaller bias in SWCRE than the AMIP5 models in the midlatitudes (40°–50°S, Fig. 5a). The temperature (and gradient) change from AMIP5 to AMIP6 is geographically very similar to the CMIP5 to CMIP6 change yet is smaller in magnitude. Therefore, the temperature change from 40° to 50°S has its origins in the atmospheric component of the models, which is then amplified further by the SST biases in the coupled models (as in Hyder et al. 2018).

Overall, there has been an improvement in the SH midlatitude temperature gradients (both θ850 and SST) from CMIP5 to CMIP6 (Figs. 5b,c). This improvement appears to be the result of reducing biases in the SWCRE in the midlatitudes (Fig. 5a). Furthermore, when SSTs are prescribed from observations (AMIP6), the representation of the temperature gradients improve further. These results (SST and θ850 gradient improvement) are consistent with the better representation of the storm tracks in CMIP6 relative to CMIP5 (also noted by Bracegirdle et al. 2020), and also AMIP6 relative to CMIP6. There is also a clear improvement of the temperature gradient (Fig. 5c) and jet (Fig. S2f in the online supplemental material) in AMIP6 relative to AMIP5, despite both experiments using the same SST dataset (as also noted by Curtis et al. 2020). Therefore, improvements in the SST alone cannot explain the improved midlatitude circulation in CMIP6 relative to CMIP5. However, while the location of the jet and mean latitude of cyclogenesis (Fig. 2a) are simulated better in CMIP6/AMIP6 relative to CMIP5/AMIP5, there is still a lack of cyclogenesis events (Fig. 1a; Table S2 in the online supplemental material) in the SH during DJF. Therefore, there are still clear problems with the representation of extratropical cyclones in the SH midlatitudes. Further interpretation is given in section 4.

c. Winter cyclogenesis rate

Despite improvements in modeling capabilities from CMIP5 to CMIP6, CMIP6 models continue to underestimate cyclogenesis rate in the SH (Fig. 2b). Unlike in DJF, radiative processes do not have a dominant influence on baroclinicity and therefore the cyclogenesis rate and storm-track latitude in JJA. Consequently, we use the EGR to examine rates of cyclogenesis in the SH winter. The EGR broadly indicates where the largest track and genesis densities are likely to occur. Positive or negative EGR biases are respectively a proxy for higher or lower cyclone track and genesis densities.

Eady growth rate

In JJA the CMIP6 models feature negative biases of EGR in the South Atlantic sector and to the south of New Zealand and generally higher values around the rest of the hemisphere (Fig. 7b), with a similar pattern of biases in CMIP5 (Fig. 7d). However, the CMIP6 models tend to have lower EGR values around a majority of the SH, relative to CMIP5 (Fig. 7e). The smaller EGR of CMIP6 is consistent with the lower cyclogenesis rate in CMIP6 relative to CMIP5, equatorward of 60°S (Fig. 2b). In the AMIP6 models the EGR is higher than in CMIP6 (Fig. 7c), which is not consistent with the cyclogenesis rate (Fig. 2b).

Fig. 7.
Fig. 7.

EGR (s−1) for JJA for (a) ERA5, (b) CMIP6 − ERA5, (c) AMIP6 − CMIP6, (d) CMIP5 − ERA5, and (e) CMIP6 − CMIP5. Stippling indicates where there is 80% model agreement on the sign of the change.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

The differences in EGR, and the related cyclogenesis rates can be understood through inspecting the two components that make up the EGR, the gradient of potential temperature at 850 hPa and the lower tropospheric static stability [see Eq. (2)]. For both CMIP5 and CMIP6, a positive relationship is seen between the average static stability from 40° to 70°S and the rate of cyclogenesis in the same region (Fig. 8a). Furthermore, these two fields are positively correlated, which implies that models that are more stable have higher levels of cyclogenesis. This is counterintuitive as cyclogenesis will typically occur in less stable environments. It therefore seems unlikely that the static stability is the driving factor behind the lack of cyclogenesis in the CMIP6 models.

Fig. 8.
Fig. 8.

Scatterplots of (a) lower-tropospheric static stability (s−2) and (b) 850-hPa potential temperature gradient (kelvins per degree) against seasonal cyclogenesis rate. Large-scale fields are averages from 40° to 70°S for CMIP6 (blue), CMIP5 (orange), AMIP6 (green), and ERA5 (black star) in JJA.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

The other component of the EGR is the potential temperature gradient associated with vertical wind shear. All model groups, both CMIP and AMIP feature a positive relationship between the θ850 gradient and number of cyclones (Fig. 8b), with models that have more cyclones having a stronger temperature gradient. Unlike the stability relationships, this is what would be expected, as stronger temperature gradients that more closely match the reanalysis values would result in a greater number of cyclones. Therefore, it appears that the strength of the temperature gradient, and not the atmospheric stability, is the primary factor controlling the cyclogenesis rate.

This result also suggests that correcting the cause of SST biases in coupled models might improve the stability of the models, but as the stability does not appear to be the controlling factor on cyclogenesis rate, this may not yield any additional cyclogenesis. However, a decrease in SSTs in the most poleward regions would also increase the large-scale temperature gradient (as in Fig. S3c in the online supplemental material), and therefore increase the rate of cyclogenesis.

d. Persistent South Australian track density overestimation

One bias that has persisted from CMIP5 to CMIP6, and is also present in the AMIP6 models (Figs. 1d–f), is the overestimation of the track density to the south of Australia during JJA. This bias is associated with the bifurcation of the split subtropical and polar front jet located in this region (Fig. 9), which models represent poorly (Grose et al. 2017; Patterson et al. 2019). Models tend to have biases from ∼90° to 180°E with a too strong subtropical jet along 40°S (most visible at 250 hPa; Fig. S4a in the online supplemental material), and a polar jet that is too weak along 60°S (most visible at 850 hPa; Fig. 9a). Despite the two jets generally being identified at different pressure levels, the two biases are notable in the CMIP6 and CMIP5 models (Figs. 9b,d and supplemental Figs. S4b–d) at all pressure levels.

Fig. 9.
Fig. 9.

JJA-averaged zonal wind u at 850 hPa for (a) ERA5, (b) CMIP6 − ERA5, (c) AMIP6 − CMIP6, (d) CMIP5 − ERA5, (e) CMIP6 − CMIP5, and (f) AMIP6 − AMIP5. Overlaid black contours in (a) indicate the 250-hPa ERA5 zonal wind magnitude, with contours of 24, 36, and 48 m s−1.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

The bias in the zonal wind reflects that of the track density bias of CMIP6 models in Figs. 1d–f and also of CMIP5 models in Fig. 9 of Priestley et al. (2020). The CMIP6 models simulate only a slight poleward shift in the zonal wind south of Australia (Fig. 9e), as in the track density, relative to CMIP5. The AMIP6 models feature a more poleward circulation than in CMIP6 (Fig. 9c), although the zonal bias in this sector still remains (not shown), suggesting that this error may be amplified by SST biases in the coupled models, but ultimately has its roots in the atmosphere or land component of the models.

The better representation of the jet and storm-track structure in AMIP6 is a result of a more poleward location of the circulation in JJA relative to CMIP6 (similar but smaller magnitude as discussed in Bracegirdle et al. 2020). In JJA the SST anomalies of the CMIP6 models are consistent with those in DJF (Fig. 4) and therefore the AMIP6 models have considerably lower atmospheric potential temperature relative to CMIP6 (south of South Africa, poleward of 50°S) when the SSTs are corrected (Fig. S5c in the online supplemental material). This decrease in potential temperature poleward of 50°S leads to an increase in the temperature gradient from 40° to 50°S in AMIP6 relative to CMIP6 in a very similar pattern to the increase in EGR shown in Fig. 7c from 20° to 100°W. The stronger temperature gradient contributes to shifting the circulation poleward, which may then have downstream impacts on the South Australian sector. Despite this shift, the dominant split jet bias clearly still has an impact on the circulation and track density bias of this region in the AMIP6 models.

The driver of the positive bias in the zonal wind from 100° to 160°E cannot be directly investigated due to the limited output of CMIP6 models and inability to perform interactive experiments; however, insight can be gained through examination of the seasonal mean meridional wind (υ), which appears to display an anomalous standing-wave pattern in the CMIP6 models (Fig. 10). Directly to the west of the zonal jet anomaly (Fig. 9b) there is a region of anomalously northward (positive υ) motion (along 100°E; Fig. 10a), which contributes to reducing the poleward motion of cyclones downstream of the anomaly. There also appears to be a wave train (denoted by the opposing meridional wind anomalies) extending in an arc from Madagascar to the west coast of South America (Fig. 10a). The origins of this apparent wave train can be estimated from biases in the divergence and velocity potential (Figs. 10b,c). To the southeast of the horn of Africa, positive divergence, and negative velocity potential (relative to ERA5), indicates a possible source of this standing-wave pattern.

Fig. 10.
Fig. 10.

Circulation biases of CMIP6 relative to ERA5 for (a) meridional wind, (b) divergence, and (c) velocity potential. All variables are shown at 250 hPa and for JJA.

Citation: Journal of Climate 36, 5; 10.1175/JCLI-D-20-0977.1

The presence of the split jet has been shown to be associated with Rossby waves originating from upper-level divergence over the Indian Ocean (Inatsu and Hoskins 2004, 2006) and it has been shown that Rossby wave sources are incorrectly modeled in the CMIP5 models (Nie et al. 2019). Based on the divergence pattern and origin of the wave train it is also possible that this wave train could have its origins in the equatorial Atlantic Ocean, where biases in the location of the intertropical convergence (present in CMIP6 models, see Tian and Dong 2020), affect the source of planetary waves. Divergence anomalies across southern Africa could also be a result of incorrect orographic interaction with the mean flow, which is a long-standing problem with GCMs (Inatsu and Hoskins 2004). Recently, Patterson et al. (2020) associated the split-jet bias to the representation of Antarctic orography, and therefore the incorrect representation of the orographic impact on the circulation in the CMIP6 models may be contributing to this bias. The stationary wave pattern in the meridional wind, the divergence, and velocity potential is evident in all models and also in the atmosphere-only simulations for both phase 5 and phase 6 models.

4. Discussion and conclusions

In this study the state of the Southern Hemisphere storm tracks has been examined in both coupled and atmosphere-only model simulations from both phase 5 and phase 6 of the Coupled Model Intercomparison Projects. The influence of ocean biases on model errors and also other large-scale features has been investigated. Furthermore, reasons for improvements from CMIP5 to CMIP6 have been explored. The main conclusions of this work are detailed below:

  • AMIP6 models generally show reduced storm-track biases relative to CMIP6 models (Fig. 1). AMIP6 models also tend to simulate storm tracks that are located farther poleward than in CMIP6, eliminating some of the equatorward bias relative to ERA5 (Figs. 2 and 3). Despite these improvements, the overall cyclogenesis rate is biased even lower than CMIP6 (Fig. 2).

  • The improved location of the storm track due to a poleward shift from CMIP5 to CMIP6 is associated with increased SSTs and temperatures in the lower troposphere from 40° to 50°S (Fig. 4; Fig. S3 in the online supplemental material), which increases the midlatitude temperature gradients (Fig. 5; Fig. S3). The AMIP6 models simulate an improvement of the storm-track location relative to the AMIP5 models due to increases in the tropospheric temperature with no influence from the underlying ocean state (Fig. 5c).

  • The biases in cyclogenesis rates in the SH are primarily associated with increases in atmospheric temperature gradients rather than static stability (Fig. 8). Models that have lower cyclogenesis rates relative to reanalyses have midlatitude temperature gradients that are too weak.

  • The overestimation of tracks to the south of Australia in JJA is related to biases in the split jet in the same region, where the subtropical jet is too strong. The jet appears to be modulated by a planetary wave train that likely has origins in the equatorial Indian and/or Atlantic Oceans, which weakens the polar component of the split jet (Fig. 10). The presence of the wave train decreases the poleward movement of cyclones forming to the southwest of Australia, driving the track density bias.

One finding from this work is that the CMIP6 models offer an improved representation of the storm-track latitude relative to CMIP5 as a result of a poleward shift in temperature gradients and jet latitude. However, despite this apparent improvement, the shift in temperature gradients and circulation appears to be driven by an amelioration of preexisting CMIP5 biases, particularly in the region of the Agulhas Current retroflection, and not through improving the long-standing high-latitude Southern Ocean warm biases. Therefore, further attention is required to eliminate these compensating biases that may yield further improvements in storm-track representation and have implications for future projections (Kajtar et al. 2021).

With respect to the improvement in the representation of the latitude of maximum zonal wind and cyclogenesis in CMIP6 relative to CMIP5, we have explored three plausible reasons for this:

  1. The representation of SSTs within the models, particularly with respect to their variability and geographical distribution of warm and cold areas, are driving the changes in the SH circulation in CMIP6 relative to CMIP5 (Wood et al. 2020).

  2. Increased resolution in the CMIP6/AMIP6 generation of models, relative to their CMIP5/AMIP5 counterparts, leads to the SH jet being located more poleward (i.e., better) in the higher-resolution CMIP6/AMIP6 simulations (Curtis et al. 2020).

  3. Improvements in the representation of clouds and their radiative properties within the models may lead to an improvement in the temperature structure of the whole atmosphere, which leads to a better representation of the preferred location of baroclinic eddy growth. This has been shown in atmosphere-only models by Li et al. (2015).

It is clear from the analysis provided in section 3a that it is difficult to truly separate the impact of SST (Wood et al. 2020), resolution (Curtis et al. 2020) and clouds (Li et al. 2015) on the preferred location for extratropical cyclone development and it is likely that each process is playing a role in causing errors in the SH storm tracks. Given the changes in the 850 hPa temperature gradient and the mean cyclogenesis latitude seen in the AMIP6 runs relative to AMIP5 (Figs. 5c and 3a), it is more likely that the processes suggested by Curtis et al. (2020) and Li et al. (2015) (i.e., points 2 and 3 above) are likely to be the main causes. Nevertheless, there is some impact from the SST distribution (Figs. 5b and 6a), which agrees with Wood et al. (2020). Furthermore, the representation of other physical processes (either parameterized or explicitly represented) not discussed in the study may be contributing to the changes in mean state from CMIP5 to CMIP6. The representation of surface drag (Pithan et al. 2016), cloud radiative heating (Voigt and Shaw 2015), and the resolution of the stratosphere (Wilcox et al. 2012) have all been shown to influence the mean state and response of the SH circulation. All these features likely influence the model biases, yet understanding which is playing the dominant role may be key to understanding the biases in extratropical cyclone formation in the SH; however, this is beyond the scope of this paper and is an area for future work.

The atmospheric temperature gradient has been shown to be important for correctly representing the cyclogenesis rate in the Southern Hemisphere (Fig. 8b). As the CMIP6 and AMIP6 models still tend to underestimate the cyclogenesis rate (Fig. 2) it is likely that improvements will be made with increased horizontal resolution. Increasing resolution would likely have beneficial impacts on SST and atmospheric temperature gradients and therefore storm-track latitude and cyclogenesis rate, as was suggested by Curtis et al. (2020). However, resolution is not the only important factor when representing extratropical cyclones, which can be seen by comparing cyclogenesis rates in the midlatitude and high-latitude bands (Fig. 2 and Priestley et al. 2020) and indicates that the low-resolution CMIP6 (∼250 km) models perform similarly to the high-resolution (∼100 km) CMIP6 models. Therefore, further work is needed to identify how resolution plays a role in improving the representation of the storm tracks and whether that improvement might be negated by the configuration of a given model.

Going forward, further investigation into the drivers of the SH circulation and storm-track biases can be performed utilizing the HighResMIP experiments (Haarsma et al. 2016). These models feature horizontal resolutions of ∼25–50 km and ocean resolutions of 0.25° and have yielded improvements in numerous global model biases (e.g., Baker et al. 2019; Gutjahr et al. 2019; Roberts et al. 2019). Despite the aforementioned limited improvements with horizontal atmospheric resolution of 100 km (Priestley et al. 2020), it may be that increases in resolution to 25–50 km in the HighResMIP models yields some benefits. Willison et al. (2013) noted improvements in cyclone moist processes at 20 km resolution, which can have feedbacks on large-scale circulation (e.g., Tamarin and Kaspi 2016). Furthermore, orographic features are more accurately represented in high-resolution models (Sandu et al. 2019), and improved orographic representation has been shown to reduce circulation biases in the SH (Patterson et al. 2020) and NH (Davini et al. 2022), and may be significantly improved in HighResMIP models.

An additional research avenue would be to investigate the processes leading to the changes in radiative forcing in the latest generation of coupled and atmosphere-only models. Creating specific experiments from which a wide array of variables can be output, the influence from longwave and shortwave radiation and the rates of atmospheric absorption can be quantified. Finally, it has recently been documented by Kajtar et al. (2021) that models with reduced latitudinal biases have less “capacity for change” under future climate conditions and that models with greater latitudinal storm track/jet biases have stronger climate responses (e.g., Chang et al. 2012; Kidston and Gerber 2010). Therefore, as the CMIP6 models in this study have a reduced equatorward bias, it may be that the magnitude of the poleward shift of storm tracks in the Southern Hemisphere is smaller in CMIP6 than in CMIP5.

1

Because the “background state” SWCRE is negative in both the models and CERES, a positive bias implies that CMIP6 SWCRE is “less negative” (i.e., weaker) than that of CERES and vice versa.

Acknowledgments.

Authors M. D. K. Priestley and J. L. Catto are supported by the Natural Environment Research Council (NERC) Grant NE/S004645/1. Author D. Ackerley was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. Author K. I. Hodges was funded by the United Kingdom’s NERC as part of the National Centre for Atmospheric Science. We thank the ECMWF for their ERA5 reanalysis, which is available from the Copernicus Climate Change Service Climate Data Store (https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset). CMIP6 data are publicly available through the Earth System Grid Federation (https://esgf-node.llnl.gov/projects/cmip6/). We are very grateful to the three anonymous reviewers and the editor, whose comments greatly improved the quality of this paper.

REFERENCES

  • Baker, A. J., and Coauthors, 2019: Enhanced climate change response of wintertime North Atlantic circulation, cyclonic activity, and precipitation in a 25-km-resolution global atmospheric model. J. Climate, 32, 77637781, https://doi.org/10.1175/JCLI-D-19-0054.1.

    • Search Google Scholar
    • Export Citation
  • Bals-Elsholz, T. M., E. H. Atallah, L. F. Bosart, T. A. Wasula, M. J. Cempa, and A. R. Lupo, 2001: The wintertime Southern Hemisphere split jet: Structure, variability, and evolution. J. Climate, 14, 41914215, https://doi.org/10.1175/1520-0442(2001)014<4191:TWSHSJ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bock, L., and Coauthors, 2020: Quantifying progress across different CMIP phases with the ESMValTool. J. Geophys. Res. Atmos., 125, e2019JD032321, https://doi.org/10.1029/2019JD032321.

    • Search Google Scholar
    • Export Citation
  • Bodas-Salcedo, A., and Coauthors, 2014: Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models. J. Climate, 27, 4156, https://doi.org/10.1175/JCLI-D-13-00169.1.

    • Search Google Scholar
    • Export Citation
  • Bracegirdle, T. J., E. Shuckburgh, J.-B. Sallee, Z. Wang, A. J. S. Meijers, N. Bruneau, T. Phillips, and L. J. Wilcox, 2013: Assessment of surface winds over the Atlantic, Indian, and Pacific Ocean sectors of the Southern Ocean in CMIP5 models: Historical bias, forcing response, and state dependence. J. Geophys. Res. Atmos., 118, 547562, https://doi.org/10.1002/jgrd.50153.

    • Search Google Scholar
    • Export Citation
  • Bracegirdle, T. J., C. R. Holmes, J. S. Hosking, G. J. Marshall, M. Osman, M. Patterson, and T. Rackow, 2020: Improvements in circumpolar Southern Hemisphere extratropical atmospheric circulation in CMIP6 compared to CMIP5. Earth Space Sci., 7, e2019EA001065, https://doi.org/10.1029/2019EA001065.

    • Search Google Scholar
    • Export Citation
  • Ceppi, P., Y.-T. Hwang, D. M. W. Frierson, and D. L. Hartmann, 2012: Southern Hemisphere jet latitude biases in CMIP5 models linked to shortwave cloud forcing. Geophys. Res. Lett., 39, L19708, https://doi.org/10.1029/2012GL053115.

    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., Y. Guo, and X. Xia, 2012: CMIP5 multimodel ensemble projection of storm track change under global warming. J. Geophys. Res., 117, D23118, https://doi.org/10.1029/2012JD018578.

    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., Y. Guo, X. Xia, and M. Zheng, 2013: Storm-track activity in IPCC AR4/CMIP3 model simulations. J. Climate, 26, 246260, https://doi.org/10.1175/JCLI-D-11-00707.1.

    • Search Google Scholar
    • Export Citation
  • Clark, P. A., and S. L. Gray, 2018: Sting jets in extratropical cyclones: A review. Quart. J. Roy. Meteor. Soc., 144, 943969, https://doi.org/10.1002/qj.3267.

    • Search Google Scholar
    • Export Citation
  • Curtis, P. E., P. Ceppi, and G. Zappa, 2020: Role of the mean state for the Southern Hemispheric jet stream response to CO2 forcing in CMIP6 models. Environ. Res. Lett., 15, 064011, https://doi.org/10.1088/1748-9326/ab8331.

    • Search Google Scholar
    • Export Citation
  • Davini, P., F. Fabiano, and I. Sandu, 2022: Orographic resolution driving the improvements associated with horizontal resolution increase in the Northern Hemisphere winter mid-latitudes. Wea. Climate Dyn., 3, 535553, https://doi.org/10.5194/wcd-3-535-2022.

    • Search Google Scholar
    • Export Citation
  • Dowdy, A. J., and J. L. Catto, 2017: Extreme weather caused by concurrent cyclone, front and thunderstorm occurrences. Sci. Rep., 7, 40359, https://doi.org/10.1038/srep40359.

    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Search Google Scholar
    • Export Citation
  • Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866.

  • Garfinkel, C. I., I. White, E. P. Gerber, and M. Jucker, 2020: The impact of SST biases in the tropical east Pacific and Agulhas Current region on atmospheric stationary waves in the Southern Hemisphere. J. Climate, 33, 93519374, https://doi.org/10.1175/JCLI-D-20-0195.1.

    • Search Google Scholar
    • Export Citation
  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 2956, https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Search Google Scholar
    • Export Citation
  • Govekar, P. D., C. Jakob, and J. Catto, 2014: The relationship between clouds and dynamics in Southern Hemisphere extratropical cyclones in the real world and a climate model. J. Geophys. Res. Atmos., 119, 66096628, https://doi.org/10.1002/2013JD020699.

    • Search Google Scholar
    • Export Citation
  • Graff, L. S., and J. H. LaCasce, 2014: Changes in cyclone characteristics in response to modified SSTs. J. Climate, 27, 42734295, https://doi.org/10.1175/JCLI-D-13-00353.1.

    • Search Google Scholar
    • Export Citation
  • Grise, K. M., and L. M. Polvani, 2014: Southern Hemisphere cloud–dynamics biases in CMIP5 models and their implications for climate projections. J. Climate, 27, 60746092, https://doi.org/10.1175/JCLI-D-14-00113.1.

    • Search Google Scholar
    • Export Citation
  • Grise, K. M., and M. K. Kelleher, 2021: Midlatitude cloud radiative effect sensitivity to cloud controlling factors in observations and models: Relationship with Southern Hemisphere jet shifts and climate sensitivity. J. Climate, 34, 58695886, https://doi.org/10.1175/JCLI-D-20-0986.1.

    • Search Google Scholar
    • Export Citation
  • Grose, M. R., J. S. Risbey, A. F. Moise, S. Osbrough, C. Heady, L. Wilson, and T. Erwin, 2017: Constraints on southern Australian rainfall change based on atmospheric circulation in CMIP5 simulations. J. Climate, 30, 225242, https://doi.org/10.1175/JCLI-D-16-0142.1.

    • Search Google Scholar
    • Export Citation
  • Gutjahr, O., D. Putrasahan, K. Lohmann, J. H. Jungclaus, J.-S. von Storch, N. Brüggemann, H. Haak, and A. Stössel, 2019: Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP). Geosci. Model Dev., 12, 32413281, https://doi.org/10.5194/gmd-12-3241-2019.

    • Search Google Scholar
    • Export Citation
  • Haarsma, R. J., and Coauthors, 2016: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev., 9, 41854208, https://doi.org/10.5194/gmd-9-4185-2016.

    • Search Google Scholar
    • Export Citation
  • Hawcroft, M. K., L. C. Shaffrey, K. I. Hodges, and H. F. Dacre, 2012: How much Northern Hemisphere precipitation is associated with extratropical cyclones? Geophys. Res. Lett., 39, L24809, https://doi.org/10.1029/2012GL053866.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., 1995: Feature tracking on the unit sphere. Mon. Wea. Rev., 123, 34583465, https://doi.org/10.1175/1520-0493(1995)123<3458:FTOTUS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., 1996: Spherical nonparametric estimators applied to the UGAMP model integration for AMIP. Mon. Wea. Rev., 124, 29142932, https://doi.org/10.1175/1520-0493(1996)124<2914:SNEATT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., 1999: Adaptive constraints for feature tracking. Mon. Wea. Rev., 127, 13621373, https://doi.org/10.1175/1520-0493(1999)127<1362:ACFFT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., R. W. Lee, and L. Bengtsson, 2011: A comparison of extratropical cyclones in recent reanalyses ERA-interim, NASA MERRA, NCEP CFSR, and JRA-25. J. Climate, 24, 48884906, https://doi.org/10.1175/2011JCLI4097.1.

    • Search Google Scholar
    • Export Citation
  • Hyder, P., and Coauthors, 2018: Critical Southern Ocean climate model biases traced to atmospheric model cloud errors. Nat. Commun., 9, 3625, https://doi.org/10.1038/s41467-018-05634-2.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., and B. J. Hoskins, 2004: The zonal asymmetry of the Southern Hemisphere winter storm track. J. Climate, 17, 48824892, https://doi.org/10.1175/JCLI-3232.1.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., and B. J. Hoskins, 2006: The seasonal and wintertime variability of the split jet and the storm-track activity minimum near New Zealand. J. Meteor. Soc. Japan, 84, 433445, https://doi.org/10.2151/jmsj.84.433.

    • Search Google Scholar
    • Export Citation
  • James, I. N., 1988: On the forcing of planetary-scale Rossby waves by Antarctica. Quart. J. Roy. Meteor. Soc., 114, 619637, https://doi.org/10.1002/qj.49711448105.

    • Search Google Scholar
    • Export Citation
  • Kajtar, J. B., A. Santoso, M. Collins, A. S. Taschetto, M. H. England, and L. M. Frankcombe, 2021: CMIP5 intermodel relationships in the baseline Southern Ocean climate system and with future projections. Earth’s Future, 9, e2020EF001873, https://doi.org/10.1029/2020EF001873.

    • Search Google Scholar
    • Export Citation
  • Kaspi, Y., and T. Schneider, 2013: The role of stationary eddies in shaping midlatitude storm tracks. J. Atmos. Sci., 70, 25962613, https://doi.org/10.1175/JAS-D-12-082.1.

    • Search Google Scholar
    • Export Citation
  • Kawai, H., S. Yukimoto, T. Koshiro, N. Oshima, T. Tanaka, H. Yoshimura, and R. Nagasawa, 2019: Significant improvement of cloud representation in the global climate model MRI-ESM2. Geosci. Model Dev., 12, 28752897, https://doi.org/10.5194/gmd-12-2875-2019.

    • Search Google Scholar
    • Export Citation
  • Kidston, J., and E. P. Gerber, 2010: Intermodel variability of the poleward shift of the austral jet stream in the CMIP3 integrations linked to biases in 20th century climatology. Geophys. Res. Lett., 37, L09708, https://doi.org/10.1029/2010GL042873.

    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Search Google Scholar
    • Export Citation
  • Lee, R. W., 2015: Storm track biases and changes in a warming climate from an extratropical cyclone perspective using CMIP5. Ph.D. thesis, University of Reading, 411 pp.

  • Li, Y., D. W. J. Thompson, and S. Bony, 2015: The influence of atmospheric cloud radiative effects on the large-scale atmospheric circulation. J. Climate, 28, 72637278, https://doi.org/10.1175/JCLI-D-14-00825.1.

    • Search Google Scholar
    • Export Citation
  • Lindsay, R., M. Wensnahan, A. Schweiger, and J. Zhang, 2014: Evaluation of seven different atmospheric reanalysis products in the Arctic. J. Climate, 27, 25882606, https://doi.org/10.1175/JCLI-D-13-00014.1.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., and Coauthors, 2018: Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. J. Climate, 31, 895918, https://doi.org/10.1175/JCLI-D-17-0208.1.

    • Search Google Scholar
    • Export Citation
  • Mauritsen, T., and Coauthors, 2019: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. J. Adv. Model. Earth Syst., 11, 9981038, https://doi.org/10.1029/2018MS001400.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 13831394, https://doi.org/10.1175/BAMS-88-9-1383.

    • Search Google Scholar
    • Export Citation
  • Menary, M. B., D. L. R. Hodson, J. I. Robson, R. T. Sutton, R. A. Wood, and J. A. Hunt, 2015: Exploring the impact of CMIP5 model biases on the simulation of North Atlantic decadal variability. Geophys. Res. Lett., 42, 59265934, https://doi.org/10.1002/2015GL064360.

    • Search Google Scholar
    • Export Citation
  • Met Office, 2013: Iris: A Python library for analysing and visualising meteorological and oceanographic data sets v1.2 ed. https://scitools-iris.readthedocs.io/en/stable/.

  • Mooney, P. A., F. J. Mulligan, and R. Fealy, 2011: Comparison of ERA-40, ERA-Interim and NCEP/NCAR reanalysis data with observed surface air temperatures over Ireland. Int. J. Climatol., 31, 545557, https://doi.org/10.1002/joc.2098.

    • Search Google Scholar
    • Export Citation
  • Nakamura, H., T. Sampe, A. Goto, W. Ohfuchi, and S.-P. Xie, 2008: On the importance of midlatitude oceanic frontal zones for the mean state and dominant variability in the tropospheric circulation. Geophys. Res. Lett., 35, L15709, https://doi.org/10.1029/2008GL034010.

    • Search Google Scholar
    • Export Citation
  • Nakayama, M., H. Nakamura, and F. Ogawa, 2021: Impacts of a midlatitude oceanic frontal zone for the baroclinic annular mode in the Southern Hemisphere. J. Climate, 34, 73897408, https://doi.org/10.1175/JCLI-D-20-0359.1.

    • Search Google Scholar
    • Export Citation
  • Nie, Y., Y. Zhang, X.-Q. Yang, and H.-L. Ren, 2019: Winter and summer Rossby wave sources in the CMIP5 models. Earth Space Sci., 6, 18311846, https://doi.org/10.1029/2019EA000674.

    • Search Google Scholar
    • Export Citation
  • Patterson, M., T. Bracegirdle, and T. Woollings, 2019: Southern Hemisphere atmospheric blocking in CMIP5 and future changes in the Australia-New Zealand sector. Geophys. Res. Lett., 46, 92819290, https://doi.org/10.1029/2019GL083264.

    • Search Google Scholar
    • Export Citation
  • Patterson, M., T. Woollings, T. Bracegirdle, and N. T. Lewis, 2020: Wintertime Southern Hemisphere jet streams shaped by interaction of transient eddies with Antarctic orography. J. Climate, 33, 10 50510 522, https://doi.org/10.1175/JCLI-D-20-0153.1.

    • Search Google Scholar
    • Export Citation
  • Pithan, F., T. G. Shepherd, G. Zappa, and I. Sandu, 2016: Climate model biases in jet streams, blocking and storm tracks resulting from missing orographic drag. Geophys. Res. Lett., 43, 72317240, https://doi.org/10.1002/2016GL069551.

    • Search Google Scholar
    • Export Citation
  • Priestley, M. D. K., D. Ackerley, J. L. Catto, K. I. Hodges, R. E. McDonald, and R. W. Lee, 2020: An overview of the extratropical storm tracks in CMIP6 historical simulations. J. Climate, 33, 63156343, https://doi.org/10.1175/JCLI-D-19-0928.1.

    • Search Google Scholar
    • Export Citation
  • Priestley, M. D. K., D. Ackerley, J. L. Catto, and K. I. Hodges, 2022: Drivers of biases in the CMIP6 extratropical storm tracks. Part I: Northern Hemisphere. J. Climate, 36, 14511467, https://doi.org/10.1175/JCLI-D-20-0976.1.

    • Search Google Scholar
    • Export Citation
  • Roberts, M. J., and Coauthors, 2019: Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments. Geosci. Model Dev., 12, 49995028, https://doi.org/10.5194/gmd-12-4999-2019.

    • Search Google Scholar
    • Export Citation
  • Sallée, J.-B., E. Shuckburgh, N. Bruneau, A. J. S. Meijers, T. J. Bracegirdle, Z. Wang, and T. Roy, 2013: Assessment of Southern Ocean water mass circulation and characteristics in CMIP5 models: Historical bias and forcing response. J. Geophys. Res. Oceans, 118, 18301844, https://doi.org/10.1002/jgrc.20135.

    • Search Google Scholar
    • Export Citation
  • Sandu, I., and Coauthors, 2019: Impacts of orography on large-scale atmospheric circulation. npj Climate Atmos. Sci., 2, 10, https://doi.org/10.1038/s41612-019-0065-9.

    • Search Google Scholar
    • Export Citation
  • Stouffer, R. J., V. Eyring, G. A. Meehl, S. Bony, C. Senior, B. Stevens, and K. E. Taylor, 2017: CMIP5 scientific gaps and recommendations for CMIP6. Bull. Amer. Meteor. Soc., 98, 95105, https://doi.org/10.1175/BAMS-D-15-00013.1.

    • Search Google Scholar
    • Export Citation
  • Tamarin, T., and Y. Kaspi, 2016: The poleward motion of extratropical cyclones from a potential vorticity tendency analysis. J. Atmos. Sci., 73, 16871707, https://doi.org/10.1175/JAS-D-15-0168.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., and Coauthors, 2018: CMIP6 global attributes, DRS, filenames, directory structure, and CV’s v6.2.7. Program for Climate Model Diagnosis and Intercomparison Doc., 29 pp., https://goo.gl/v1drZl.

  • Tian, B., and X. Dong, 2020: The double-ITCZ bias in CMIP3, CMIP5, and CMIP6 models based on annual mean precipitation. Geophys. Res. Lett., 47, e2020GL087232, https://doi.org/10.1029/2020GL087232.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first-century energy budgets of the Southern Oceans. J. Climate, 23, 440454, https://doi.org/10.1175/2009JCLI3152.1.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., J. T. Fasullo, and J. Mackaro, 2011: Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J. Climate, 24, 49074924, https://doi.org/10.1175/2011JCLI4171.1.

    • Search Google Scholar
    • Export Citation
  • Tsujino, H., and Coauthors, 2020: Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2). Geosci. Model Dev., 13, 36433708, https://doi.org/10.5194/gmd-13-3643-2020.

    • Search Google Scholar
    • Export Citation
  • Voigt, A., and T. A. Shaw, 2015: Circulation response to warming shaped by radiative changes of clouds and water vapour. Nat. Geosci., 8, 102106, https://doi.org/10.1038/ngeo2345.

    • Search Google Scholar
    • Export Citation
  • Wang, C., L. Zhang, S.-K. Lee, L. Wu, and C. R. Mechoso, 2014: A global perspective on CMIP5 climate model biases. Nat. Climate Change, 4, 201205, https://doi.org/10.1038/nclimate2118.

    • Search Google Scholar
    • Export Citation
  • Wilcox, L. J., A. J. Charlton-Perez, and L. J. Gray, 2012: Trends in austral jet position in ensembles of high- and low-top CMIP5 models. J. Geophys. Res., 117, D13115, https://doi.org/10.1029/2012JD017597.

    • Search Google Scholar
    • Export Citation
  • Williams, K. D., and A. Bodas-Salcedo, 2017: A multi-diagnostic approach to cloud evaluation. Geosci. Model Dev., 10, 25472566, https://doi.org/10.5194/gmd-10-2547-2017.

    • Search Google Scholar
    • Export Citation
  • Williams, K. D., and Coauthors, 2013: The Transpose-AMIP II experiment and its application to the understanding of Southern Ocean cloud biases in climate models. J. Climate, 26, 32583274, https://doi.org/10.1175/JCLI-D-12-00429.1.

    • Search Google Scholar
    • Export Citation
  • Willison, J., W. A. Robinson, and G. M. Lackmann, 2013: The importance of resolving mesoscale latent heating in the North Atlantic storm track. J. Atmos. Sci., 70, 22342250, https://doi.org/10.1175/JAS-D-12-0226.1.

    • Search Google Scholar
    • Export Citation
  • Wood, T., C. M. McKenna, A. Chrysanthou, and A. C. Maycock, 2020: Role of sea surface temperature patterns for the Southern Hemisphere jet stream response to CO2 forcing. Environ. Res. Lett., 16, 014020, https://doi.org/10.1088/1748-9326/abce27.

    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., T. A. Myers, D. T. McCoy, S. Po-Chedley, P. M. Caldwell, P. Ceppi, S. A. Klein, and K. E. Taylor, 2020: Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save
  • Baker, A. J., and Coauthors, 2019: Enhanced climate change response of wintertime North Atlantic circulation, cyclonic activity, and precipitation in a 25-km-resolution global atmospheric model. J. Climate, 32, 77637781, https://doi.org/10.1175/JCLI-D-19-0054.1.

    • Search Google Scholar
    • Export Citation
  • Bals-Elsholz, T. M., E. H. Atallah, L. F. Bosart, T. A. Wasula, M. J. Cempa, and A. R. Lupo, 2001: The wintertime Southern Hemisphere split jet: Structure, variability, and evolution. J. Climate, 14, 41914215, https://doi.org/10.1175/1520-0442(2001)014<4191:TWSHSJ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bock, L., and Coauthors, 2020: Quantifying progress across different CMIP phases with the ESMValTool. J. Geophys. Res. Atmos., 125, e2019JD032321, https://doi.org/10.1029/2019JD032321.

    • Search Google Scholar
    • Export Citation
  • Bodas-Salcedo, A., and Coauthors, 2014: Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models. J. Climate, 27, 4156, https://doi.org/10.1175/JCLI-D-13-00169.1.

    • Search Google Scholar
    • Export Citation
  • Bracegirdle, T. J., E. Shuckburgh, J.-B. Sallee, Z. Wang, A. J. S. Meijers, N. Bruneau, T. Phillips, and L. J. Wilcox, 2013: Assessment of surface winds over the Atlantic, Indian, and Pacific Ocean sectors of the Southern Ocean in CMIP5 models: Historical bias, forcing response, and state dependence. J. Geophys. Res. Atmos., 118, 547562, https://doi.org/10.1002/jgrd.50153.

    • Search Google Scholar
    • Export Citation
  • Bracegirdle, T. J., C. R. Holmes, J. S. Hosking, G. J. Marshall, M. Osman, M. Patterson, and T. Rackow, 2020: Improvements in circumpolar Southern Hemisphere extratropical atmospheric circulation in CMIP6 compared to CMIP5. Earth Space Sci., 7, e2019EA001065, https://doi.org/10.1029/2019EA001065.

    • Search Google Scholar
    • Export Citation
  • Ceppi, P., Y.-T. Hwang, D. M. W. Frierson, and D. L. Hartmann, 2012: Southern Hemisphere jet latitude biases in CMIP5 models linked to shortwave cloud forcing. Geophys. Res. Lett., 39, L19708, https://doi.org/10.1029/2012GL053115.

    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., Y. Guo, and X. Xia, 2012: CMIP5 multimodel ensemble projection of storm track change under global warming. J. Geophys. Res., 117, D23118, https://doi.org/10.1029/2012JD018578.

    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., Y. Guo, X. Xia, and M. Zheng, 2013: Storm-track activity in IPCC AR4/CMIP3 model simulations. J. Climate, 26, 246260, https://doi.org/10.1175/JCLI-D-11-00707.1.

    • Search Google Scholar
    • Export Citation
  • Clark, P. A., and S. L. Gray, 2018: Sting jets in extratropical cyclones: A review. Quart. J. Roy. Meteor. Soc., 144, 943969, https://doi.org/10.1002/qj.3267.

    • Search Google Scholar
    • Export Citation
  • Curtis, P. E., P. Ceppi, and G. Zappa, 2020: Role of the mean state for the Southern Hemispheric jet stream response to CO2 forcing in CMIP6 models. Environ. Res. Lett., 15, 064011, https://doi.org/10.1088/1748-9326/ab8331.

    • Search Google Scholar
    • Export Citation
  • Davini, P., F. Fabiano, and I. Sandu, 2022: Orographic resolution driving the improvements associated with horizontal resolution increase in the Northern Hemisphere winter mid-latitudes. Wea. Climate Dyn., 3, 535553, https://doi.org/10.5194/wcd-3-535-2022.

    • Search Google Scholar
    • Export Citation
  • Dowdy, A. J., and J. L. Catto, 2017: Extreme weather caused by concurrent cyclone, front and thunderstorm occurrences. Sci. Rep., 7, 40359, https://doi.org/10.1038/srep40359.

    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Search Google Scholar
    • Export Citation
  • Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866.

  • Garfinkel, C. I., I. White, E. P. Gerber, and M. Jucker, 2020: The impact of SST biases in the tropical east Pacific and Agulhas Current region on atmospheric stationary waves in the Southern Hemisphere. J. Climate, 33, 93519374, https://doi.org/10.1175/JCLI-D-20-0195.1.

    • Search Google Scholar
    • Export Citation
  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 2956, https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Search Google Scholar
    • Export Citation
  • Govekar, P. D., C. Jakob, and J. Catto, 2014: The relationship between clouds and dynamics in Southern Hemisphere extratropical cyclones in the real world and a climate model. J. Geophys. Res. Atmos., 119, 66096628, https://doi.org/10.1002/2013JD020699.

    • Search Google Scholar
    • Export Citation
  • Graff, L. S., and J. H. LaCasce, 2014: Changes in cyclone characteristics in response to modified SSTs. J. Climate, 27, 42734295, https://doi.org/10.1175/JCLI-D-13-00353.1.

    • Search Google Scholar
    • Export Citation
  • Grise, K. M., and L. M. Polvani, 2014: Southern Hemisphere cloud–dynamics biases in CMIP5 models and their implications for climate projections. J. Climate, 27, 60746092, https://doi.org/10.1175/JCLI-D-14-00113.1.

    • Search Google Scholar
    • Export Citation
  • Grise, K. M., and M. K. Kelleher, 2021: Midlatitude cloud radiative effect sensitivity to cloud controlling factors in observations and models: Relationship with Southern Hemisphere jet shifts and climate sensitivity. J. Climate, 34, 58695886, https://doi.org/10.1175/JCLI-D-20-0986.1.

    • Search Google Scholar
    • Export Citation
  • Grose, M. R., J. S. Risbey, A. F. Moise, S. Osbrough, C. Heady, L. Wilson, and T. Erwin, 2017: Constraints on southern Australian rainfall change based on atmospheric circulation in CMIP5 simulations. J. Climate, 30, 225242, https://doi.org/10.1175/JCLI-D-16-0142.1.

    • Search Google Scholar
    • Export Citation
  • Gutjahr, O., D. Putrasahan, K. Lohmann, J. H. Jungclaus, J.-S. von Storch, N. Brüggemann, H. Haak, and A. Stössel, 2019: Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP). Geosci. Model Dev., 12, 32413281, https://doi.org/10.5194/gmd-12-3241-2019.

    • Search Google Scholar
    • Export Citation
  • Haarsma, R. J., and Coauthors, 2016: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev., 9, 41854208, https://doi.org/10.5194/gmd-9-4185-2016.

    • Search Google Scholar
    • Export Citation
  • Hawcroft, M. K., L. C. Shaffrey, K. I. Hodges, and H. F. Dacre, 2012: How much Northern Hemisphere precipitation is associated with extratropical cyclones? Geophys. Res. Lett., 39, L24809, https://doi.org/10.1029/2012GL053866.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., 1995: Feature tracking on the unit sphere. Mon. Wea. Rev., 123, 34583465, https://doi.org/10.1175/1520-0493(1995)123<3458:FTOTUS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., 1996: Spherical nonparametric estimators applied to the UGAMP model integration for AMIP. Mon. Wea. Rev., 124, 29142932, https://doi.org/10.1175/1520-0493(1996)124<2914:SNEATT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., 1999: Adaptive constraints for feature tracking. Mon. Wea. Rev., 127, 13621373, https://doi.org/10.1175/1520-0493(1999)127<1362:ACFFT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., R. W. Lee, and L. Bengtsson, 2011: A comparison of extratropical cyclones in recent reanalyses ERA-interim, NASA MERRA, NCEP CFSR, and JRA-25. J. Climate, 24, 48884906, https://doi.org/10.1175/2011JCLI4097.1.

    • Search Google Scholar
    • Export Citation
  • Hyder, P., and Coauthors, 2018: Critical Southern Ocean climate model biases traced to atmospheric model cloud errors. Nat. Commun., 9, 3625, https://doi.org/10.1038/s41467-018-05634-2.

    • Search Google Scholar
    • Export Citation