1 Introduction

High pressure systems (anticyclones) are a common feature of weather in the global subtropics and midlatitudes, between around 30 and 50° of latitude (Pepler et al. 2019a). Distinct from the climatological anticyclones that can be identified in some areas of the subtropics (al Fahad et al. 2020), synoptic anticyclones are areas of high pressure, subsidence and weak wind speeds that can be identified from instantaneous pressure fields. These tend to be associated with hot, dry days and cold nights at the surface, with slow-moving or quasi-stationary anticyclones capable of causing prolonged periods of abnormally warm and cold conditions (Gibson et al. 2017; Marshall et al. 2013; Hatzaki et al. 2014; Ioannidou and Yau 2008). The eastward progression of anticyclones across land areas can contribute to similar movement of associated extreme temperature anomalies (King and Reeder 2021). Slow-moving and quasi-stationary or “blocked”  anticyclones can also act to block or deflect the movement of rain-bearing weather systems into an area, and periods with an abnormally high number of anticyclones and higher pressure can be an important factor in extended dry conditions and drought (Pepler et al. 2019b). In contrast, interactions with anticyclones can also enhance the rainfall associated with nearby weather systems such as cyclones by intensifying pressure gradients, winds and moisture transport (Mills et al. 2010).

When anticyclones are defined by closed contours of pressure they are most easily identified at the surface, but anticyclonic anomalies can extend well into the troposphere. At 500 hPa, the subset of long-lived quasi-stationary anticyclonic anomalies known as blocking highs have been a particular focus of research (Pook et al. 2013; Sousa et al. 2021; Drouard et al. 2021; Wiedenmann et al. 2002; Rohrer et al. 2020). While these events can be particularly important for surface impacts, such methods may not include the full range of anticyclones at the surface, and in cases such as heat lows an upper-level anticyclone may even be associated with cyclonic anomalies at the surface (Lavender 2017). Furthermore, blocking is often identified based on a reversal in the midlatitude upper-level westerlies (split-flow blocking), but this approach is designed for detecting blocking events close to the storm track, and may fail to detect the slow-moving anticyclones which are important in the subtropics (Marshall et al. 2013).

The frequency of synoptic anticyclones can be linked to a range of drivers of interannual variability (Pepler et al. 2019a). Anticyclones in the southern hemisphere are strongly correlated with the frequency and intensity of the subtropical ridge, with strong correlations also observed in the northern hemisphere oceans during the cool half of the year. In the southern hemisphere, the Southern Annular Mode is correlated with the average latitude of anticyclone activity in both seasons, with anticyclones shifting southward when SAM is positive. Anticyclones in the Pacific Ocean have also been linked to the El Niño-Southern Oscillation and the extent of the Hadley Cell (Pepler et al. 2019a), while the strength of tropical convection and the South Pacific Convergence Zone have been linked to the strength of the climatological mean anticyclone in the same region (al Fahad and Burls 2021; al Fahad et al. 2021).

Global warming is expected to lead to an increase in surface pressure in the southern hemisphere subtropics over the twenty-first century (Lee et al. 2021, section 4.5.1.6), including a strengthening of the subtropical ridge (Grose et al. 2015) and an expansion of regions with mean sea level pressure (MSLP) exceeding 1020 hPa (al Fahad et al. 2020). However, projections to date have focussed on changes in the mean anticyclones (al Fahad et al. 2020; He et al. 2017; Li et al. 2013) or specifically on changes in upper troposphere blocking (Woollings et al. 2018). A recent review (Woollings et al. 2018) found that confidence in future changes in upper troposphere blocking remains low, which is related to a lack of understanding of the physical mechanisms involved, large interannual variability, and poor simulation of blocking by global climate models (GCMs). The most likely future change is a decrease in blocking over much of the midlatitudes in both the northern (Woollings et al. 2018) and southern (Patterson et al. 2019) hemispheres, linked to changes in Rossby wave propagation and the eddy-driven jet (Barnes and Hartmann 2011). This projected decrease does not align with the observed increase in subtropical surface anticyclones in the southern hemisphere (Pepler et al. 2019a), suggesting that projections of surface anticyclones may differ considerably from projections based on 500 hPa data.

In this paper, surface anticyclones are tracked in 10 global climate models and separated into both slow-moving “quasi-stationary” anticyclones and faster-moving “mobile” anticyclones, to assess likely areas of change over the twenty-first century. The same approach is also applied to an ensemble of 12 regional climate models (RCMs) downscaled over the Australian domain, to assess the potential for regional models to decrease model biases in simulating anticyclones as well as whether projections from RCMs are consistent with GCMs. This research provides a new understanding of how anticyclones at the surface will change over the twenty-first century, including changes in their intensity and movement speeds.

2 Methods

2.1 Anticyclone tracking

Anticyclones in this study are identified from 6-hourly gridded MSLP data using the University of Melbourne detection and tracking scheme (Murray and Simmonds 1991; Simmonds et al. 1999), following the approach of Pepler et al. (2019a). This method tracks anticyclones on a common polar stereographic grid with an effective resolution of 1.5° at 30°S, similar to the resolution of the global models, with areas above 1000 m on the model grid masked prior to tracking. Anticyclones are identified as local minima in the Laplacian of MSLP, with the average Laplacian within 10° of the anticyclone centre required to be below − 0.075 hPa (deg. lat.)−2, consistent with Jones and Simmonds (1994).

For anticyclones that persist for at least 24 h, the longitudinal (eastward) movement over a 24 h period is used to distinguish “mobile” anticyclones, that travel at least 4° over 24 h (~ 7 m/s), from “slow-moving” or “quasi-stationary” anticyclones. This threshold was chosen following Sinclair (1996), who considered a surface anticyclone to be blocked if it moved less than 20° over 5 days. A small proportion (< 3%) of anticyclones had westward movement (movement speed < − 4°/24 h); these anticyclones were included with quasi-stationary systems as they most likely represent a poorly defined centre within a semi-stationary area of high pressure.

The 6-hourly dataset of anticyclone centres is used to create a daily 1° grid of the area influenced by an anticyclone, which is defined using a 10° radius of influence around the anticyclone centre. This is slightly larger than the average anticyclone radius calculated by the low tracking software (7° in ERA5), but is consistent with the approach used for cyclones in Pepler and Dowdy (2022) and the region with closed MSLP contours and low rainfall around anticyclones in Pepler et al. (2019b). In cases where both a mobile and a quasi-stationary anticyclone centre are identified within a 10° radius of a grid point, the quasi-stationary anticyclone takes precedence. For completeness, the dataset also includes days where neither a quasi-stationary nor a long-lived mobile anticyclone is present, but a high pressure centre of less than 24 h duration is detected, which are considered short-lived anticyclones and contribute to the total number of days influenced by anticyclones.

2.2 Datasets

The ERA5 reanalysis (Hersbach et al. 2020) is used for evaluation of the anticyclone climatology as one of the most recent and widely-used reanalyses, noting that anticyclone climatologies vary little between reanalyses when identified on a common grid (Pepler et al. 2019a).

Anticyclones were tracked globally for the historical simulation as well as the RCP8.5 scenario (Representative Concentration Pathway of 8.5 W m−2 in 2100) using 10 models from the 5th Coupled Model Intercomparison Project (CMIP5, Taylor et al. 2012). These models were previously selected based on comprehensive analyses of their skill at simulating historical climate, particularly in the southern hemisphere midlatitudes, as well as model independence (CSIRO and Bureau of Meteorology 2015). For the Australian region, anticyclones were also tracked using a sparse matrix of 12 regional climate model (RCM) simulations, with models from among the 10 CMIP5 GCMs downscaled using four different RCM techniques: CCAM (Thatcher and McGregor 2011; Thatcher 2021), BARPA (Su et al. 2021), and two versions of WRF (Evans et al. 2020). The models used are summarised in Table 1.

Table 1 Models used in this study, noting their spatial resolution and their global mean temperature change between 1980–2009 and 2070–2099, as well as whether they are downscaled by any of the RCM approaches (CCAM, BARPA and WRF)
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Projected changes in anticyclone frequency are calculated between the periods 1980–2009 and 2070–2099, with RCP8.5 providing data for the years 2006–2099. Future emissions in this scenario are higher than current estimates of likely future emissions, but better match observed forcings over the early twenty-first century (Schwalm et al. 2020), while the stronger warming signal makes it easier to detect forced changes amid large interannual variability. Projections are then divided by the projected temperature change in each GCM to present results as the change per degree of global warming, to reduce uncertainties related to scenario or climate sensitivity (Pendergrass et al. 2015). RCM changes are divided by the global mean temperature change for the corresponding GCM. Projected changes are calculated for two 6-month seasons, May–October (MJJASO) and November–April (NDJFMA), as both the climatological frequency and historic trends can vary substantially between the warmer and cooler halves of the year (Pepler et al. 2019a).

We also assess the extent to which changes in southern hemisphere anticyclones can be explained by the strong positive trend in the Southern Annular Mode (SAM) which is projected for the twenty-first century (Lim et al. 2016). The SAM index was calculated for each model and ERA5 based on the difference in zonal mean pressure between 40° and 65°S, normalised by the 1979–2005 mean at each latitude, following the definition of Gong and Wang (1999).

3 Evaluation

During the period 1980–2009, ERA5 recorded an average of 25,114 anticyclone centres per year, of which 18,786 (75%) were part of an anticyclone event that persisted for at least 24 h (1365 events/year). In the southern hemisphere, the highest frequency of anticyclones for any 1° latitude band was identified at 33°S during May–October and 38°S during November–April, consistent with results from Pepler et al. (2019a). Annually, the region with average anticyclone counts at least 20% of the peak value extends between 26°S and 50°S, while anticyclone centres are very uncommon outside of the region 20–60°S. Figure 1a shows that at least 40% of days have an anticyclone centre located within a 10° radius for much of the southern hemisphere midlatitudes.

Fig. 1
figure 1

Percentage of days per year with an anticyclone centre located within a 10° radius during 1980–2009 in ERA5 (a–c) and 10 CMIP5 models (d–f), and the difference (g–i). Plots show all anticyclone centres (a, d, g) as well as quasi-stationary (b, e, h) and mobile (c, f, i) anticyclones, with quasi-stationary taking precedence when both types are within 10°. Dots are shown every 5°, and indicate where fewer than 8/10 models have a bias of the same sign. See Figure S1 for a version with all anticyclones over elevated topography removed

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In the northern hemisphere, the highest frequency of anticyclones is observed at 34°N during November–April and 42 °N during May–October, indicating a stronger seasonal shift in latitude (not shown). While the total number of annual anticyclone centres is very similar between the two hemispheres, anticyclones can be detected much further poleward in the northern hemisphere, including at least 35 anticyclone centres per year at 82°N (Fig. 1). The maximum frequency for any latitudinal band in the northern hemisphere is half that in the southern hemisphere, but the region with annual counts at least 20% of the maximum ranges from 25 °N to 69 °N. Anticyclones are also more spatially heterogenous in the northern hemisphere, with highest frequencies over the midlatitude Atlantic and Pacific oceans as well as over the extratropical land areas (Fig. 1a).

When comparing mobile and quasi-stationary anticyclones, there is a tendency for faster-moving anticyclones in the west of each ocean basin, with quasi-stationary anticyclones occurring preferentially to the east (Fig. 1b, c), consistent with the locations of the climatological mean subtropical anticyclones (al Fahad et al. 2020). Hotspots where more than 40% of surface anticyclones are slow-moving include the northeast and southeast Pacific, the northeast Atlantic, and the Tasman Sea east of Australia. In these areas, as much as 20% of days are influenced by quasi-stationary anticyclones. The majority of surface anticyclones identified north of 50°N are also quasi-stationary, matching the region where 500 hPa blocking is most frequently identified (Woollings et al. 2018), although the total frequency of anticyclones is lower in these regions.

In comparison with ERA5, all CMIP5 models have a higher global frequency of surface anticyclone centres. The ensemble mean overestimates anticyclone frequency at all northern hemisphere latitudes, with the total number of northern hemisphere anticyclones overestimated by between 40 and 80% depending on the model. A large part of this can be attributed to differences in the effective topography between the high-resolution reanalyses and coarse-resolution climate models, with notable biases over areas of high topography such as in the western USA and Himalayas (Fig. 1d). However, anticyclone frequencies are also higher than in the reanalysis across many northern hemisphere land areas including in northern Africa and southern Europe. If all anticyclones located in grid cells with ERA5 topography over 1000 m are excluded, the CMIP5 models still overestimate the total number of northern hemisphere anticyclones by between 12 and 44% (Fig. S1).

In comparison, 7 of the 10 CMIP5 models underestimate the total number of anticyclones in the southern hemisphere, and all but one model (CanESM2) underestimate southern hemisphere anticyclone counts if anticyclone centres over high elevation are excluded. In particular the ACCESS and CNRM-CM5 models have total anticyclone frequencies more than 30% below ERA5. With the exception of HadGem2-CC, all models have the latitude of highest anticyclone frequency within 2° of ERA5, but the total number of anticyclone centres is underestimated by 20–30% across the whole region 24–53°S. This can be seen as a broad area of reduced anticyclone frequency across the southern hemisphere midlatitudes and extratropics, apart from parts of the African, South American and Antarctic land masses (Fig. 1g).

When anticyclones are separated into mobile and quasi-stationary systems, we find that the CMIP5 models have on average 41% more short-lived anticyclones and 36% more quasi-stationary anticyclones than the reanalysis. All 10 models have higher frequencies than ERA5, particularly in the northern hemisphere as well as at high altitudes over Antarctica (Fig. 1h). These types of systems are particularly common in areas of elevated topography, so the global mean biases decrease to + 15% and + 10% when such anticyclones are excluded. In contrast, 8 out of 10 CMIP5 models underestimate the frequency of mobile anticyclones, with an average bias of −16% (−21% when anticyclones at high elevation are excluded) and largest biases in the southern hemisphere midlatitudes (Fig. 1i). This pattern of positive biases in the northern hemisphere and negative biases in the southern hemisphere midlatitudes has also been identified for cyclones (Pepler and Dowdy 2021). This result suggests there may be some fundamental issues in the ability of CMIP5 models to represent MSLP variability in this region, perhaps related to land–atmosphere interactions, as well as negative biases in the frequency of wave packets in models (Trevisiol et al. 2022).

Figure 2 shows the seasonal mean anticyclone frequency for each latitude band during 1980–2009 in both CMIP5 and ERA5, separated into quasi-stationary and mobile anticyclones. The poleward migration of the latitude of highest anticyclone frequency during the warm months is present for both mobile and quasi-stationary anticyclones in the southern hemisphere and is well simulated by CMIP5, although the mean frequencies are underestimated. While there is no difference in the total frequency of anticyclones between seasons, the warm season has slightly fewer mobile anticyclones on average, and slightly more quasi-stationary anticyclones. A poleward migration in the warm season (May–October) is also observed for northern hemisphere mobile anticyclones, but rather than a seasonal shift in the location of quasi-stationary anticyclones there is a strong increase in frequency during the warm season (May–October), particularly in the midlatitudes. For both hemispheres, the average central pressure of both quasi-stationary and mobile anticyclones is higher during the cool half of the year, consistent with Pepler et al. (2019a) (not shown).

Fig. 2
figure 2

The average number of anticyclones per year and latitude in May–October (purple) and November–April (green), during 1980–2009 (centres/degree2), split into quasi-stationary (a) and mobile (b) anticyclones. Solid lines indicate ERA5 and dashed the CMIP5 ensemble mean, with shading showing the full CMIP5 ensemble range

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Over the Australian region (110–155 °E, 10–45 °S), the CMIP5 GCMs tended to underestimate both the number of days with anticyclone conditions (Fig. 3d) as well as the total number of identified anticyclone centres (mean = 1019 p.a.) compared to the reanalysis (1267 centres p.a.). In contrast, the RCM ensemble generated more than twice as many anticyclone centres per year (range: 2719–3426) than either the reanalysis or the CMIP5 ensemble (range: 480–1424). This likely reflects higher spatial variability in mean sea level pressure in high resolution models, particularly over land, leading to multiple anticyclone “centres” identified for a single high.

Fig. 3
figure 3

(a–c) % of days in ERA5 with an anticyclone within a 10° radius during 1980–2009 (as in Fig. 1), but focussed on the Australian region. (d–f) Mean bias in anticyclone frequency from 10 GCMs and (g–i) 12 RCMs. Plots show all anticyclone centres (a, d, g) as well as quasi-stationary (b, e, h) and mobile (c, f, i) anticyclones. Dots indicate where fewer than 80% (8/10 or 10/12) models have biases of the same sign

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When we instead calculate the number of days influenced by an anticyclone, biases in the RCM ensemble are comparable in magnitude to the CMIP5 ensemble. The RCMs tend to overestimate anticyclone frequency over land areas, particularly quasi-stationary systems (Fig. 3h), and underestimate anticyclone frequency over the surrounding ocean (Fig. 3g). Averaged over 113–153 °E, 20–38 °S, approximately 34% of days have an anticyclone in ERA5, compared to 29% of days in the CMIP5 ensemble and 36% of days in the RCM ensemble. While the RCM ensemble has a similar frequency of mobile anticyclones to ERA5 in this region, the frequency of quasi-stationary anticyclones increases from 6% of days to 10% of days. No RCM underestimates the frequency of quasi-stationary anticyclones when compared to ERA5, with blocking most overestimated in BARPA-ACCESS1.0 and closest to ERA5 in CCAM-MIROC5. The increased frequency of quasi-stationary anticyclones in the RCMs is linked to a lower average movement speed of 478 km/24 h, compared to 638 km/24 h in the GCM ensemble and 702 km/24 h in ERA5.

The frequency of mobile anticyclones remains slightly underestimated near Tasmania and near the western edge (Fig. 3i). This negative bias near the western border of the downscaled region is expected, as the smaller model domain means that the early part of an anticyclone track may be missed, decreasing the number of anticyclones that have persisted for at least 24 h at the edge of the model domain. These results suggest that RCMs can change the climatology of anticyclones when compared to the parent models, particularly slow-moving and quasi-stationary systems which are less sensitive to edge effects and potentially more sensitive to land–atmosphere interactions, but the current generation of RCMs does not give uniform improvements in skill.

4 GCM projections

The CMIP5 GCMs produce a robust and coherent signature of projected change in the southern hemisphere midlatitudes (Fig. 4). Averaged across the southern hemisphere there are on average 7% fewer anticyclones in 2070–2099 compared to 1980–2009 under a high emissions scenario (range: −12% to −4%). At least 8/10 CMIP5 models project an increase in the average number of anticyclone centres between 38–47°S, associated with a small poleward shift in the latitude of highest anticyclone frequency (average movement: −0.7°). There is a slightly larger −1.1° shift in the equatorward edge of the region of high anticyclone activity, contributing to an average 17% decline in the frequency of southern hemisphere anticyclones north of 35°S in 2070–2099 (range: −6% to −27%). Averaged across all southern hemisphere anticyclones there is also a projected 0.7 hPa increase in mean central pressure, noting that climatological average SLP is lower towards the poles, and an 11% increase in movement speed.

Fig. 4
figure 4

Change in total anticyclone frequency between 1980–2009 and 2070–2099 in RCP8.5 (days per degree of warming) in May–October (a) and November–April (d). b, e Projected change in days with quasi-stationary anticyclones. c, f Projected change in days with mobile anticyclones. Stippling indates where fewer than 8/10 models agree on the sign of the change. See Figure S2 for a version with all anticyclones over elevated topography removed

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Consistent with the seasonal movement of the climatological anticyclone frequency, increases in the number of days influenced by an anticyclone are projected for around 35–45 °S during the cool season and 40–55 °S during the warm season, with decreases in frequency to the north and south of this (Fig. 4a,d). However, while these changes are robust between climate models they are generally small in the context of the mean climate, with a projected increase of 1–2 anticyclone days per degree of warming. This amounts to at most a 4% increase in frequency between 1980–2009 and 2070–2099 during the cool season, and a 13% increase in frequency during the warm season.

Projected increases in anticyclone frequency are predominantly due to projected increases in short-lived and mobile anticyclones, which have a poleward shift in latitude of approximately 1° in both seasons between 1980–2009 and 2070–2099. In contrast, there are few areas of robust increases in the frequency of quasi-stationary anticyclones but several areas of decrease, including during the cool season in South Africa and over southeast Australia in both seasons, consistent with projections of decreases in the speed of Rossby wave packets (Trevisiol et al. 2022).

The CMIP5 ensemble also projects a small decline in the total number of anticyclones in the northern hemisphere between 1980–2009 and 2070–2099, with an average decline of 4% (range: − 7% to + 1%). Compared to the southern hemisphere, trends in northern hemisphere anticyclones are less zonally aligned (Fig. 4), but decreases in anticyclone frequency are more common than increases, particularly during the cool season. Areas of note include the Mediterranean region, which has a robust decrease in anticyclones projected for May–October and an increase in November–April, while increases in anticyclone days over the UK are projected for both seasons. Decreases in anticyclones are also projected for both seasons in Japan and east Asia as well as the western USA, with areas of decrease expanding poleward during November–April. Decreases are also projected for northern Africa and the middle east during the cool season. Projected changes in quasi-stationary anticyclones are generally weak and inconsistent between models, but with areas of robust decreases projected in western Europe and the Mediterranean. There is also a projected increase in quasi-stationary anticyclones over Greenland in the cool season, and a decrease in the warm season, but this is an area of high elevation and high model bias (Fig. S2). Mobile anticyclones, in contrast, are projected to increase in frequency during November–April in many areas of the northern hemisphere midlatitudes, particularly over western Europe and the Mediterranean.

The average central pressure of identified anticyclones is projected to increase across much of the southern hemisphere midlatitudes (Fig. 5), particularly in the South Pacific where projected increases exceed 0.5 hPa per degree of warming. This is consistent with projected changes in mean sea level pressure (Lee et al. 2021), as well as projections that show an increase in the spatial extent of regions with climatological mean sea level pressure exceeding 1020 hPa (al Fahad and Burls 2021). In contrast, a slight decrease is projected in the average central pressure of anticyclones over much of the northern hemisphere extratropics, including declines of 0.4 hPa per degree of warming across much of North America.

Fig. 5
figure 5

Change in the average central pressure of all anticyclone centres identified at each grid cell between 1980–2009 and 2070–2099 in RCP8.5 (hPa per degree of warming) in May–October (a) and November–April (b). 2 degrees of spatial smoothing as been applied to make patterns clearer, and results are only shown for cells with at least 3 anticyclone centres (1 per decade) identified during both periods. Stippling indates where fewer than 8/10 models agree on the sign of the change

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5 Congruence with the Southern Annular Mode

The zonally-aligned patterns of change in the southern hemisphere (Fig. 4) raise the question of the links between these patterns and the Southern Annular Mode, which is projected to shift poleward over the twenty-first century as a result of anthropogenic climate change (Arblaster et al. 2011).

Figure 6b, f shows the seasonal correlations between SAM and the zonal mean frequency of anticyclone days. Positive SAM is associated with an increase in anticyclone activity around the subtropical ridge and a contraction of the region influenced by anticyclones, with decreased anticyclones in the subtropics (i.e. the latitudes equatorward of the subtropical ridge) and poleward of 60°S. The majority of models are well able to replicate the correlations seen in ERA5. The exception to this pattern is CCSM4, which is unable to replicate observed correlations with SAM; this is likely related to deficiencies in the representation of southern hemisphere circulation in this model, which has an anomalously strong and poleward eddy-driven jet and an abnormally high mean meridional pressure gradient over the southern hemisphere midlatitudes (G Boschat, pers. comm.).

Fig. 6
figure 6

a, e Average number of anticyclone days per season in May–October (top) and November–April (bottom) in ERA5 (black) and the 10 CMIP5 models for each latitude in the southern hemisphere in 1980–2009 (blue) and 2070–2099 using RCP8.5 (red), with thin lines showing the 10 models and solid lines the ensemble median. b, f Correlation between SAM and the zonal mean number of anticyclones for each season. c, g Change in the seasonal mean number of anticyclone days between 1980–2009 and 2070–2099 (blue), as well as the change that would be expected based on a linear regression between SAM and anticyclone frequency during 1980–2009 and the change in SAM to 2070–2099 (red). d, h Percentage of the total change in anticyclone frequency at each latitude that can be explained by the change congruent with trends in SAM

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All models project SAM to become more positive by the end of the twenty-first century under RCP8.5, with a slightly larger mean change in May–October for 2070–2099, noting that summer changes in SAM are sensitive to the opposing contributions from both greenhouse gas emissions and ozone recovery (Arblaster et al. 2011). However, the zonal correlation patterns between SAM and anticyclone frequency remain broadly consistent between the present and future periods (Fig. 6b, f).

The latitudes of largest projected changes in anticyclone frequency in the CMIP5 models are consistent with the latitudes of strongest correlations between SAM and anticyclones, such that the pattern of change is broadly similar to that which would be expected from changes in SAM alone (Fig. 6c,g). Consequently, greater than 80% of the projected change is congruent with changes in SAM at most latitudes (Fig. 6d,h), noting larger uncertainties at latitudes where correlations are close to zero. With CCSM4 excluded, the magnitude of projected changes in the midlatitudes and subtropics in both seasons are correlated with the strength of the projected change in SAM. Interestingly, despite being unable to represent the link between SAM and anticyclones, CCSM4 has a very similar pattern of anticyclone change to other models.

During summer, the models with the strongest projected warming also have the strongest projected trend in the SAM; this is not true during the cool season, when there is a weak negative relationship between projected changes in seasonal SAM and the annual global mean temperature change. During winter, the strength of projected changes in SAM is a better predictor of projected changes in anticyclone frequency than the projected change in global mean temperature.

Changes in the SAM are strongly linked to changes in mobile anticyclones across the southern hemisphere (Fig. 7). However, the projected poleward shift in SAM is insufficient to explain the projected decrease in quasi-stationary anticyclones in the subtropics, and even opposes the projected change at some latitudes. These changes could potentially be linked to changes in tropical forcing; in simulations where only tropical diabatic heating was increased, there was a decrease in mean sea level pressure across much of the southern hemisphere subtropics including a reduction in the size of the climatological mean subtropical anticyclones, which are observed in the regions where quasi-stationary anticyclones are most common (al Fahad and Burls 2021). It is also possible that land–atmosphere feedbacks play a role, noting the projected increase in the frequency of surface heat lows across most southern hemisphere land areas (Pepler and Dowdy 2021).

Fig. 7
figure 7

Change in the seasonal mean number of anticyclone days between 1980–2009 and 2070–2099 under RCP8.5 (blue), as well as the change that would be expected based on a linear regression between SAM and anticyclone frequency during 1980–2009 and the change in SAM to 2070–2099 (red). Results are shown for May–October (top) and November–April (bottom), and separated into changes in the zonal mean number of days with quasi-stationary (a, c) and mobile (b, d) anticyclones

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6 RCM projections

The very different patterns of biases between the RCM and GCM ensembles, including the different representation of quasi-stationary anticyclones, raise the question of how sensitive projections of anticyclones and their characteristics may be to how they are represented within models, and whether RCMs offer any potential added value in projecting large-scale synoptic systems. We find that the projected changes in anticyclone frequency for the Australian region are very consistent between the GCM and RCM ensembles, particularly when the RCM ensemble is compared against just those GCMs that contributed to downscaling. During May–October (Fig. 8), there is very little change projected in the frequency of anticyclones over the southern Australian mainland (113–153 °E, 20–38 °S), with a mean change of −1.1 anticyclone days/K in GCMs and − 0.7 days/K in RCMs and the ensemble range spanning both positive and negative changes. There is a more robust decrease in anticyclone frequency north of 30°S, while RCMs project an increase in anticyclone frequency on parts of the southern coast. In southeast Australia (140–150 °E, 30–40 °S) little change is projected in the overall frequency of anticyclones, with an increase in mobile anticyclones (+ 0.94 days/K) balanced by a decrease in quasi-stationary anticyclones (− 0.88 days/K), consistent across 90% of GCMs and RCMs.

Fig. 8
figure 8

Change in the number of days influenced by an anticyclone in May–October between 1980–2009 and 2070–2099 in RCP8.5 (days per degree of warming) for all anticyclones (top) as well as quasi-stationary (middle row) and mobile (bottom) anticyclones. Columns show the mean change for the 10 GCMs (a–c), 12 RCMs (g–i), and the GCMs weighted by their representation within the RCM ensemble (d–f). Stippling indates where fewer than 80% of models agree on the sign of the change

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During November–April (Fig. 9), all 22 models project a decrease in the frequency of anticyclones across southern mainland Australia between 1980–2009 and 2070–2099, by − 1.4 days/K in the GCM ensemble and − 2.0 days/K in the RCM ensemble. Both quasi-stationary and mobile anticyclones are projected to decline in this season, particularly in southeast Australia, with larger declines in frequency for the RCM ensemble. Quasi-stationary anticyclones are less common than mobile anticyclones in the current climate and this pattern is projected to intensify, with the proportion of long-lived southeast Australian anticyclones that are quasi-stationary decreasing from 35.6 to 32.9%. The decrease in the frequency of anticyclones over Australian land areas during the warm season coincides with a projected increase in the frequency of shallow surface cyclones and heat lows over the same period, which has been attributed to the enhanced warming of the land surface (Pepler and Dowdy 2022).

Fig. 9
figure 9

Change in the number of days influenced by an anticyclone in November–April between 1980–2009 and 2070–2099 in RCP8.5 (days per degree of warming) for all anticyclones (top) as well as quasi-stationary (middle row) and mobile (bottom) anticyclones. Columns show the mean change for the 10 GCMs (a–c), 12 RCMs (g–i), and the GCMs weighted by their representation within the RCM ensemble (d–f). Stippling indates where fewer than 80% of models agree on the sign of the change

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Results are broadly consistent when shown for all anticyclone centres identified in southern Australia (20–45 °S, 105–160 °E) for the four meteorological seasons, with at least 85% of models projecting a decrease in frequency during DJF and MAM but more mixed changes in JJA and SON (Fig. 10). In both JJA and SON, at least 90% of models project an increase in the average distance an anticyclone travels over a 24 h period, with an average increase of about 2.5% per degree of warming. Average anticyclone central pressure is also projected to increase by an average of 0.4 hPa/degree of warming in JJA and SON and 0.3 hPa/K in DJF. Projected changes in central pressure are weakest and least robust during MAM (0.2 hPa/K), a season which has seen considerable increases in pressure during recent decades (Timbal and Drosdowsky 2013). Despite the very different biases in the frequency of detected anticyclone centres, the projected changes are generally consistent between the RCM and GCM ensembles.

Fig. 10
figure 10

Boxplot showing the projected change between 1980–2009 and 2070–2099 in RCP8.5 for all 10 GCMs (purple) and 12 RCMs (yellow) in the four meteorological seasons: a Total number of anticyclone centres detected in the region 20–45°S, 105–160°E, in %/K. b Average 24 h movement speed of all anticyclones in this region (%/K). c Average central pressure of all anticyclones in this region (hPa/K)

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7 Discussion

Recent decades have seen an increase in the frequency of anticyclones in the southern hemisphere midlatitudes, between 30–40 °S in May–October and 35–45 °S in November–April (Pepler et al. 2019a). This has been linked with changes to the mean atmospheric circulation, including increasing MSLP in the subtropics, positive trends in the SAM, and an intensifying subtropical ridge (Timbal and Drosdowsky 2013). However, while the observed changes in mean climate are projected to continue in the twenty-first century under a warming climate (Grose et al. 2015), GCMs project a decrease in the frequency of surface anticyclones in many of the same southern hemisphere regions where there has been an increase in anticyclone frequency over recent decades in reanalyses (Pepler et al. 2019a,b). Moreover, this projected decrease is consistent in sign but stronger in magnitude using an ensemble of regionally downscaled models, suggesting it is not a consequence of biases in the ability of CMIP5 models to simulate anticyclones.

In the southern hemisphere, the overall projected change is for a slight southward shift in the latitude where anticyclones are most common, by approximately 1° between 1980–2009 and 2070–2099 under RCP8.5. The frequency of anticyclones is projected to increase slightly near these latitudes but decrease at latitudes to the north and south, indicating a small contraction of the width of the subtropical ridge. In particular, the frequency of slow-moving or quasi-stationary anticyclones, which travel less than 4° of longitude over 24 h, is projected to decrease in many areas of the southern hemisphere midlatitudes including southern Africa during May–October and southeastern Australia and New Zealand year-round. These results are consistent with Patterson et al. (2019) and Grose et al. (2016, 2019) who also projected decreases in 500 hPa blocking in the Australian-New Zealand sector under RCP8.5, particularly during the winter months of June–August. The southward shift in anticyclone frequency, as well as an increase in MSLP, is also consistent with projected changes in the subtropical ridge (Grose et al. 2015) and in the climatological mean subtropical anticyclones (Al Fahad et al. 2020).

Projected changes in anticyclones in the southern hemisphere are strongly linked with a poleward shift of the SAM (Arblaster et al. 2011), which was found to explain > 80% of projected changes across most latitudes in the southern hemisphere where the sign of the projected change is consistent between climate models. This is particularly true for mobile anticyclones, whereas the projected decline in quasi-stationary anticyclones in the subtropics remains poorly explained. Projected changes in SAM during the warm season are sensitive to the balance between greenhouse gas emissions and ozone recovery, and thus strongly correlated with projected changes in global mean temperature. This is not true of the cool season May–October, when projected changes in the SAM are a stronger indicator of projected anticyclone changes than projected global mean temperature anomalies. This result highlights that, while normalising projections by model projected warming is a useful approach to reduce uncertainty, a storyline approach that considers the role of additional factors in potential climate futures can offer valuable additional insight (Mindlin et al. 2020; Shepherd and Lloyd 2021).

In comparison to the southern hemisphere, projected changes in anticyclone frequency are more regional in the northern hemisphere. However, the CMIP5 models project a robust decrease in quasi-stationary anticyclones over much of western Europe throughout the year, with areas of projected decreases in parts of the North Pacific during the cool season, which is broadly consistent with previous studies that assessed projections of 500 hPa blocking (Woollings 2018). Few areas of the northern hemisphere have increases projected in quasi-stationary cyclones, with the exception of some high elevation areas where the models produce too many anticyclones in the current climate. However, an increase in the frequency of mobile anticyclones is projected for much of the northern hemisphere midlatitudes including the Mediterranean and adjacent areas of Europe during the cool months, coinciding with the latitudes where little change in cyclone frequency is projected during this season (Fig. S3 of Pepler and Dowdy 2021).

While the CMIP5 models broadly replicate the historical climatology of anticyclones from the ERA5 reanalysis, there are notable areas of bias. These include a tendency to produce too many anticyclones over northern hemisphere land areas, including both lower-elevation areas such as Europe and areas of high elevation where the coarser topography on the model grid is below the threshold of 1000 m used for filtering. The GCMs also tend to underestimate the frequency of anticyclones in the southern hemisphere midlatitudes. This pattern of biases is very similar to the biases found for simulations of cyclones using the same tracking method and GCMs in Pepler and Dowdy (2021), which may suggest a broader tendency for GCMs to underestimate the temporal and spatial variability of MSLP in the southern hemisphere midlatitudes. This cannot be attributed to model resolution itself, as reanalyses of resolutions from 0.75° to 2.5° produce very similar anticyclone frequencies when tracked on the same grid (Pepler et al. 2019a). Using an ensemble of regionally downscaled models at 0.5° or higher resolution over Australia changed the pattern of biases but did not alter the broad patterns of projected changes.

With the exception of CCSM4, the models are able to reproduce the observed correlations between anticyclones and SAM, which is a major driver of projected changes in anticyclones in the southern hemisphere. Future work should also consider the potential role of changes to tropical convection and the Hadley Cell in projected anticyclone change, which may be particularly important for quasi-stationary anticyclones in the southern hemisphere (al Fahad and Burls 2021; al Fahad et al. 2021).

This paper drew on relatively small subsets of both global and regional climate models from the previous model generation, and employed a single RCP. While the high level of consistency between models in projected changes indicates a strong signal, future work should assess how consistent these are with the newer generation of climate models from CMIP6, particularly those run at higher resolution that may better represent interactions with topography. Assessment of the area influenced by anticyclones was also represented by a consistent radius; this could be improved by using additional tracking procedures that directly identify the region enclosed by closed contours of surface pressure, as has been done for extratropical cyclones (Wernli and Schwierz 2006).

This paper presents the first projections of global anticyclones identified at the surface level, as distinct from projections of blocking at 500 hPa; while results are similar in many cases they will not necessarily coincide. Importantly, this paper presents the first projections of faster-moving mobile anticyclones, which can also contribute to anomalies of rainfall and temperature, although their impacts are less extreme than from periods when multiple days of blocking leads to extended periods of anomalous heat or cold. In many areas mobile anticyclones may increase even where quasi-stationary anticyclones become less common, linked to an increase in the average movement speed (in degrees/day) of anticyclones in a warmer climate. While the changes presented in this paper are relatively small in most cases, even under a high emissions scenario, they may nonetheless lead to important changes in the location, frequency or intensity of severe events such as heatwaves, cold outbreaks, and drought in some regions. The ability of climate models to represent links between anticyclones and surface weather, as well as the implications of projected changes in anticyclones for other variables, will be a focus of future work.