1. Introduction
Changes in weather extremes are a societally impactful manifestation of climate change. Several types of extreme weather can be associated with persistent anomalies (PAs1) in the midlatitude tropospheric flow. PAs can be associated with droughts (e.g., Dole et al. 2011), pluvials (e.g., Hong et al. 2011), heat waves (e.g., Matsueda 2011; Horton et al. 2016), and cold-air outbreaks (e.g., Carrera et al. 2004); these events can occur in all seasons and in diverse geographical locations. Despite their importance, however, a conclusive determination of how PAs and blocking will change in a warming climate remains elusive (e.g., Croci-Maspoli et al. 2007; Matsueda et al. 2009; Matsueda 2011; Masato et al. 2013; Dunn-Sigouin and Son 2013; de Vries et al. 2013; Francis and Vavrus 2012, 2015; Jaiser et al. 2012; Matsueda and Endo 2017; Woollings et al. 2018; Schiemann et al. 2020).
Several mechanisms motivate multiple hypotheses as to how and why blocking will respond to climate change. The sensitivity of blocking to the mean-state flow has been documented by Colucci (2001), Scaife et al. (2010), Berckmans et al. (2013), de Vries et al. (2013), and Hassanzadeh et al. (2014), among others. These results imply that changes in the mean jet stream location and/or intensity due to climate change could alter blocking distributions. Several early studies emphasize the role of transient eddies in the initiation and maintenance of blocking (e.g., Green 1977; Shutts 1983; Colucci 1985; Mullen 1987; Nakamura and Wallace 1993; Nakamura et al. 1997). Changes in the intensity or location of these transients could accompany climate change, for example owing to changes in baroclinicity or static stability. Another hypothesis that motivates our work stems from the expected increase in water vapor content and precipitation intensity in a warmer climate (e.g., Held and Soden 2006). Pfahl et al. (2015) and Steinfeld and Pfahl (2019) demonstrated that diabatic processes play a major role in blocking, especially for so-called moist diabatic blocks. Increased water vapor content in a warming atmosphere leads to stronger latent heat release and diabatic outflow, which may serve as an additional amplification or maintenance mechanism for PAs (e.g., Pfahl et al. 2015; Teubler and Riemer 2016; Steinfeld and Pfahl 2019). Specifically, increased diabatic heating in rising airstreams and the associated anticyclonic potential vorticity (PV) production and divergent outflow aloft could potentially increase the frequency, intensity, and longevity of PA events, all else equal. Of course, we understand that all else is not equal, and there are competing processes that could work against this mechanism, such as increased downstream energy dispersion, or changes in the mean-state flow that favor more progressive waves (Tierney et al. 2021).
Finally, numerous recent studies examining the influence of the Arctic amplification of global warming on the midlatitude flow suggest increases in flow persistence in response to sea ice loss (e.g., Overland and Wang 2010; Francis and Vavrus 2012, 2015; Overland et al. 2015; Francis et al. 2018, Overland et al. 2021). Other studies fail to find compelling supporting evidence of increased persistence associated with sea ice loss (e.g., Barnes 2013; Barnes et al. 2013; Barnes and Polvani 2015; Perlwitz et al. 2015; Barnes and Screen 2015; Cattiaux et al. 2016; Blackport and Screen 2020; Huguenin et al. 2020), leaving open the question of how Arctic amplification may affect the frequency, intensity, distribution, and duration of midlatitude PAs. It is difficult to separate the effects of Arctic amplification from other changes, such as tropical upper-tropospheric warming, which can exert opposing changes to the midlatitude circulation (e.g., Peings et al. 2017; Oudar et al. 2020). The proposed/possible mechanisms for changes in PAs owing to Arctic amplification are directly related to those outlined in the previous paragraph, namely modifications of the mean circulation, transient eddy activity, and the intensity and distribution of condensational heating.
Previously, we modified the PA index of Dole and Gordon (1983) in order to increase its versatility in detecting events throughout the annual cycle and in different datasets (Miller et al. 2020). Advantages of this variable-threshold index are that it requires only a single variable (geopotential height at one isobaric level, such as 500 hPa), is able to capture the complete life cycle of PA events, and identifies a variety of PA types, including persistent shifts in the midlatitude jet stream, that are not blocking events. Here, toward the goal of analyzing changes in PA activity in response to climate change, we apply this index to output from a series of global time-slice simulations with the Model for Prediction Across Scales–Atmosphere (MPAS-A; Skamarock et al. 2012) that span a range of ENSO states, feature high spatial resolution, and use a novel method of future projection that retains high-resolution lower boundary conditions (Michaelis et al. 2019). The goal of this paper is to test the hypothesis that Northern Hemisphere PA activity will increase with climate warming. The following section presents data and methods, followed in section 3 by a comparison of PA activity in the present-day MPAS simulations with a reanalysis dataset. Section 4 presents results of the climate-change comparison between present-day and future MPAS simulations, followed by conclusions in section 5.
2. Data and methods
We use the high-resolution global time-slice simulations of Michaelis et al. (2019) to investigate changes in PA activity. These simulations include 10 present-day and 10 counterpart future climate simulations, performed globally with a 15-km grid mesh (~0.15°) in the Northern Hemisphere that expands to 60 km in the Southern Hemisphere. The benefit of high spatial resolution for representation of blocking has been identified by prior studies (e.g., Jung et al. 2012; Schiemann et al. 2017, 2020). With the elimination of a 2-month spinup period, each 14-month simulation provides 12 months (15 May of year 1 to 14 May of year 2) of analysis output at 6-hourly time increments. We selected nonconsecutive simulation years in order to sample the full range of present-day ENSO states: 1988/89, 1992/93, 1994/95, 1997/98, 2001/02, 2005/06, 2010/11, 2011/12, 2013/14, and 2015/16 (see Michaelis et al. 2019, their Table 1). We acknowledge that other sources of climate variability aside from ENSO could potentially exert a stronger influence on PA characteristics, but these MPAS simulations were also designed to represent tropical cyclones, whose distribution is strongly modulated by ENSO [see Michaelis and Lackmann (2019) for details]. We use daily varying SST fields from the Operational Sea Surface Temperature and Sea ice Analysis (OSTIA; Donlon et al. 2012).
In developing “future” simulations, we compute an altered version of the analyzed present-day SST pattern by adding a spatially varying “delta” SST calculated using an ensemble of 20 GCMs2 from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Delta fields are computed by subtracting 1980–99 averages from 2080–99 representative concentration pathway 8.5 (RCP8.5) averages. We adjust sea ice concentrations,3 CO2, and deep-soil temperatures in a consistent manner. This strategy replicates the same present-day range of ENSO states in the future simulations, but accounting for warming as depicted by GCM projections. An analogous delta value is applied to deep soil temperatures, below the deepest prognostic layer of the Noah land surface model. We set CO2 concentrations in the future simulations to 936 ppm, consistent with the RCP8.5 scenario for the year 2100 (Meinshausen et al. 2011); present-day CO2 concentrations are based on analyzed values set according to the year. We based daily-varying sea ice fields on a gridpoint climatology from the ensemble of twentieth-century CMIP5 simulations for present-day conditions, and for future simulations we used RCP8.5 output. Thus, the 10 present-day simulations each have the same annual Arctic sea ice progression, as do the 10 future simulations. Our experimental design is somewhat similar to the “pseudo-global warming” (PGW) method (e.g., Schär et al. 1996; Hara et al. 2008; Hill and Lackmann 2011; Rasmussen et al. 2011; Lackmann 2013; Mallard et al. 2013; Shepherd 2016), but only in the treatment of the lower boundary temperature field (SST and deep-soil temperature). Atmospheric conditions in these global model simulations evolve freely and are allowed ~1.5 months to equilibrate with the lower boundary fields. For additional details concerning the MPAS simulations, see Michaelis et al. (2019). In the late summer and fall, the future simulations are nearly devoid of Arctic sea ice (Fig. 1), consistent with strong Arctic amplification in the future simulations. Comparison of temperature-change cross sections between the MPAS simulations and CMIP5 GCMs demonstrate strong agreement, despite model differences (Michaelis et al. 2019, their Fig. 11).
Sea ice fraction (shaded as in legend at top of panels) on 15 Sep for (a) present-day MPAS simulations and (b) future MPAS simulations.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
Climatological aspects of the MPAS simulations are compared to the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis dataset (hereafter ERAI; Dee et al. 2011), which features an approximate grid spacing of ~0.7°. ERAI data are available beginning in 1979, but we use only the times matching the present-day MPAS simulations in our comparisons. The MPAS data were also interpolated to the ERAI grid to facilitate comparison, but this did not result in significant changes to the PA distribution, and so will not be presented here.
To identify PAs, we use the modified Dole and Gordon (1983) index of Miller et al. (2020); this algorithm identifies PAs as the geopotential height anomaly in a given grid cell that meets a temporally varying magnitude threshold and persists for a set duration of 5 days or longer. The variable magnitude threshold is computed from an 8-week low-pass Butterworth-filtered temporal average of the geopotential height standard deviation, spatially averaged from 20° to 90°N (Fig. 2). The magnitude threshold is set to ±2σ. We use simulation-dependent magnitude thresholds, derived from each respective data source; separate PA magnitude thresholds are derived for the ERAI and for the current and future MPAS simulations (Fig. 2). Tests using the same threshold applied to all of the different data sources yielded similar regions of maximized PA frequency, but with differences in the amplitude of the frequency maxima. For example, the ERAI variance (and therefore the PA threshold) is lower than that in the present-day MPAS simulations. Thus, when we use ERAI-based thresholds to identify PAs in the MPAS simulations, we find slightly enhanced frequency relative to results obtained using the MPAS-derived thresholds.
Derived PA magnitude thresholds from the spatial-averaged 500-hPa geopotential height standard deviation for the ERAI (black), MPAS present-climate (orange solid), and MPAS future-climate simulations (orange dashed).
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
To identify where changes in PA frequency fall outside of that expected from natural variability, we compare PA changes with the variability of the ERAI, computed from 100 random samples of 10-yr annual PA days from 38 years of ERAI reanalysis (1979–2016). When differences are greater than or equal to 2σ of the observed variability, we consider these “true” differences.
3. Comparison of MPAS and ERAI present-day PA activity
The 10-yr hemispheric average 500-hPa standard deviation computed from ERAI is generally lower than that in the corresponding MPAS simulations (Fig. 3). The larger standard deviation in MPAS output is likely due, in part, to its higher spatial resolution (0.15° vs 0.7°). However, interpolation of the MPAS output to the ERAI grid only resulted in a slight reduction of this difference (not shown). Miller et al. (2020) identify four PA frequency maxima in the 38-yr ERAI analysis period, including the North Pacific, the North Atlantic, Russia, and the Arctic (see Miller et al. 2020, their Fig. 3c). For the subset of 10 years corresponding to our MPAS simulations, the ERAI shows prominent PA frequency maxima over the Pacific and Atlantic (Fig. 4a), but with less distinct maxima over the Arctic and Russia relative to the longer-term ERAI analysis (not shown; see Miller et al. 2020, their Figs. 3a,b).
MPAS annual mean current-climate 500-hPa geopotential height standard deviation (m), based on a 10-yr average (contoured) and its difference from the corresponding ERAI value (shaded as in legend; MPAS minus ERAI). Simulation years include 1988/89, 1992/93, 1994/95, 1997/98, 2001/02, 2005/06, 2010/11, 2011/12, 2013/14, and 2015/16.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
Annual positive PA frequency (days yr−1) for (a) ERAI, for the 10 years corresponding to the current-climate MPAS simulations and (b) MPAS current-climate simulations, and (c) their difference (MPAS minus ERAI). Hatching in (c) indicates regions where differences exceed natural variability (2σ) in the 38-yr ERAI. Contours in (a) and (b) are annual mean 500-hPa height standard deviation, based on 10-yr averages for the respective data sources (interval: 25 m).
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
The current-climate MPAS simulations replicate the main PA regions (Fig. 4b), albeit with a Pacific maximum that is stronger and displaced to the east of that in the ERAI, and with an Atlantic maximum that is weaker than that in the ERAI (Fig. 4). Regions of excessive +PA activity in MPAS relative to the ERAI generally coincide with regions of enhanced variance in MPAS relative to ERAI, while regions with a deficit of activity coincide with regions of reduced variance (cf. Figs. 3 and 4c). Generally, the MPAS simulations exhibit a dearth of PA activity relative to the ERAI at high latitudes, especially in the North Atlantic (Fig. 4c). This is a region of small or negative difference in standard deviation between the MPAS simulations and ERAI. This difference in variance is consistent with analysis of these simulations presented by Michaelis et al. (2019, their Fig. 4), which indicates that the MPAS Pacific storm track extends too far east, while the Atlantic storm track does not extend as far east as that in ERAI.
For −PA occurrence, differences between the present-day MPAS simulations and the ERAI are noisy, but again exhibit excessive activity in the eastern North Pacific (Fig. 5). As was the case with +PAs, differences in −PA activity between the ERAI and MPAS simulations also match differences in variance.
Annual negative PA frequency (days yr−1) for (a) ERAI and (b) MPAS current climate, and (c) their difference (MPAS minus ERAI). Hatching in (c) indicates regions where differences exceed natural variability (2σ) in the 38-yr ERAI. Contours in (a) and (b) indicate annual mean 500-hPa height standard deviation, based on 10-yr averages for the respective data sources (interval: 25 m).
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
Summed over the 10 present-day simulation years, the MPAS simulations produce a number of +PA events similar to that found in ERAI, but >20% more −PA events (Fig. 6). On seasonal scales, the MPAS simulations fail to capture the summertime Arctic PA maximum for PAs of either sign (not shown). Overall, the differences in PA activity between MPAS and ERAI are similar in magnitude to those between CMIP5 GCM output and ERAI (Miller 2019, ch. 4.1). Interpretations of the future PA changes in these MPAS simulations should take into account their less than perfect representation of present-day PAs.
Total number of PA events over the 10 simulation years; “Current” and “Future” are MPAS values. Events defined with dataset-relative amplitude thresholds. Error bars for ERAI represent variability (2σ) in the 38-yr ERAI sample.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
The mean and maximum magnitudes of both positive and negative PA events are shifted toward slightly higher values in the MPAS simulations relative to ERAI (Fig. 7; mean magnitude distribution not shown). This is especially true for −PA event magnitude; there were 10 MPAS −PAs with a maximum absolute magnitude greater than or equal to 700 gpm; such large maximum magnitude values are not observed in ERAI (Fig. 7d), likely due to coarser spatial resolution in the reanalysis relative to the MPAS simulations. Surprisingly, analysis of these 10 large-amplitude −PA events demonstrates that they occur over tropical oceans just north of 20°N latitude, and correspond to strong tropical cyclones with slow movement or looping tracks. One method of accounting for resolution differences is to interpolate the MPAS data to the coarser ERAI grid. However, this exercise resulted in modest differences, and so we opted to present the analyses at their original resolutions, in order to preserve information in the higher-resolution MPAS output.
Maximum event magnitude histograms, based on (a),(c) ERAI and (b),(d) the MPAS present climate. Positive PAs are shown in (a) and (b), with negative PAs in (c) and (d). The 5th and 95th percentiles are represented as vertical dashed lines.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
4. Changes between current and future MPAS simulations
a. Overall trends
The annual mean 500-hPa standard deviation exhibits a general decrease between the present-day and future simulations, with the exception of Scandinavia and its vicinity and scattered other regions (Fig. 8). Analysis of the changes in standard deviation by season indicates that the largest decreases occur in winter, with the summer showing an overall increase (Fig. 2). Given that we are using simulation-relative PA detection thresholds, the future decrease in MPAS 500-hPa standard deviation lowers the threshold for identifying future PA events, especially during the winter. Despite this, future changes indicate a slight decrease in the number of +PA events, with a stronger decrease in −PA occurrence (Fig. 6).
MPAS future 500-hPa geopotential height standard deviation (contoured) and the difference from the MPAS current-climate simulations (future − present; shaded).
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
A pronounced increase in the frequency of +PA days occurs in the eastern North Pacific, with decreases evident in the central North Pacific and over Siberia (Fig. 9c); several of these regions exhibit changes that exceed natural variability (hatching in Fig. 9). Weak increases in +PA frequency are evident over Scandinavia and Russia (Fig. 9c), corresponding approximately to the aforementioned increases in 500-hPa standard deviation in these regions (Fig. 8). There is cancellation between regions of +PA increase and decrease, leaving a weak overall difference across the Northern Hemisphere (Fig. 6). Similar to what was noted for the ERAI–MPAS comparison, the +PA difference field appears to coincide with differences in 500-hPa standard deviation (Figs. 8 and 9c). The spatial correlation between these two fields is only 0.34, however.
Annual positive PA frequency (days yr−1) for (a) MPAS current climate and (b) MPAS future climate, and (c) their difference (future minus present). Hatching in (c) indicates regions where differences exceed natural variability (2σ) in the 38-yr ERAI. Contours in (a) and (b) are annual mean 500-hPa height standard deviation, based on 10-yr averages for the respective data sources (interval: 25 m).
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
Future changes in the frequency of −PAs are somewhat noisy (Fig. 10c) but reveal a prominent decrease near the northeastern North Pacific maximum; this region drives the overall decrease in the hemispheric count (Fig. 6). The decrease over the North Pacific exceeds natural variability. This region of decrease in −PA frequency over the central North Pacific is collocated with the decrease in +PA frequency (Fig. 9) and in 500-hPa geopotential height standard deviation (Fig. 8). Unlike the changes in +PAs, there is no increase in −PA activity over the eastern North Pacific (Fig. 10). Decreases outweigh increases for −PA activity, leading to the substantial decrease in total −PA frequency seen in Fig. 6.
Annual negative PA frequency (days yr−1) for (a) MPAS current climate and (b) MPAS future climate, and (c) their difference (future − present). Hatching in (c) indicates regions where differences exceed natural variability (2σ) in the 38-yr ERAI. Contours in (a) and (b) are annual mean 500-hPa height standard deviation, based on 10-yr averages for the respective data sources (interval: 25 m).
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
b. Seasonal analysis
The nearly complete absence of Arctic sea ice during the late summer and fall seasons in the future MPAS simulations motivates examination of seasonal changes in PA activity, although we acknowledge that Arctic amplification is not the only climate change signal in these simulations. For the total number of events by season, +PA events show essentially no trend in the summer, decreases in the spring and fall, and an increase during winter (Fig. 11a). For −PA, a future decrease is evident in all seasons, with strongest decreases in spring and fall (Fig. 11b). Analysis of regional changes reveals either small changes or decreases in both positive and negative PA activity in each region (Fig. 12). The regional boundaries are defined as in Miller et al. (2020, their Fig. 3c). Positive PA events decrease most markedly over the North Pacific, with generally small changes in other regions (Fig. 12a). Negative PA events also exhibit the largest decrease in the North Pacific, but show decreases in all regions (Fig. 12b). Recall that we are using simulation-relative PA amplitude thresholds, and that the future thresholds for PA amplitude are lower than for the present-day simulations. Had we applied the present-day thresholds to the future simulations, the decreases would have been more substantial.
Total number of PA events by season for the ERAI, MPAS current, and MPAS future simulations. Error bars for ERAI represent variability (2σ) in the 38-yr ERAI sample.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
Total number of PA events by region for the ERAI, MPAS current, and MPAS future simulations. Region abbreviations are North Pacific (PAC), North Atlantic (ATL), Russia (RUS), and Arctic (ARC). Error bars for ERAI represent variability (2σ) in the 38-yr ERAI sample.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
Spatial analysis of seasonal changes in +PA activity (Fig. 13) reveals an eastward shift in the North Pacific positive PA activity in every season except summer, with strong central North Pacific decreases, particularly during winter and autumn (Figs. 13a,d). There is a suggestion of increased +PA activity across parts of Scandinavia in the summer and fall (Figs. 13c,d). A region of increased +PA activity is evident across northeastern Canada during winter (Fig. 13a), as well as over the North Atlantic in spring (Fig. 13b). Figure 14 reveals that the largest decrease in −PA events takes place in the autumn over the central and eastern North Pacific (Fig. 14d).
Seasonal +PA frequency difference (days yr−1; shaded as in legend) between the MPAS future-climate and MPAS current-climate simulations.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
Seasonal −PA frequency difference (days yr−1; shaded as in legend) between the MPAS future-climate and MPAS current-climate simulations.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
As was the case for changes in annual PA activity (Figs. 9c and 10c), there are no strong, systematic changes in seasonal PA activity evident in these simulations (Figs. 13 and 14).
c. Changes in meridional circulation index
Spatial plots of the averaged absolute value of the 300-hPa MCI over the 10-yr period for both present and future MPAS simulations exhibit minima across the western North Pacific and adjacent eastern Asia, and maxima at higher latitudes and in the eastern storm-track regions (Figs. 15a,b). There is a substantial decrease in future 300-hPa |MCI| relative to present-day values across much of the North Pacific, with smaller increases over eastern North America and parts of the Arctic (Fig. 15c). Spatially averaging the 300-hPa |MCI| from 20° to 80°N for the 10-yr simulations indicates an overall |MCI| decrease from 0.398 to 0.389. This result is not sensitive to the latitude band over which the average is computed; averaging from 30°–60°N yields a decrease from 0.370 to 0.355. At the 700-hPa level, the patterns of |MCI| and its changes are broadly similar to those seen at the 300-hPa level, except for a greater increase in future |MCI| in low-latitude regions of the Eastern Hemisphere (Fig. 16c). The 10-yr temporal and spatial averages also show decreases in the absolute value of MCI using either latitudinal band; 20°–80°N yields a decrease from 0.438 to 0.431, and a 30°–60°N averaging band gives a decrease from 0.415 to 0.402. Patterns and trends of MCI at the 500-hPa level are consistent with those at the 300- and 700-hPa levels (not shown). To facilitate comparison with the results of Francis and Vavrus (2015), we examined seasonal changes; decreases were found for each season and for both of the spatial averages, with the smallest decrease in autumn (not shown).
Absolute value of the meridional circulation index (MCI) waviness metric evaluated at the 300-hPa level for (a) present-day MPAS and (b) future MPAS, and (c) their difference (future − current MPAS). Average computed from daily MCI computations.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
Absolute value of meridional circulation index (MCI) waviness metric evaluated at the 700-hPa level for (a) present-day MPAS and (b) future MPAS, and (c) their difference (future − current MPAS). Average computed from daily MCI computations.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0465.1
The changes in MCI in our MPAS simulations differ markedly from those presented by Francis and Vavrus (2015, their Figs. 4c, 5c, 6c, and 7c), which were based on trends in the NCAR–NCEP reanalysis. Across our results, there is a strong association between changes in variance and changes in PA activity. Reduced future variance, especially in the cool season, is therefore consistent with a reduction, or for +PAs, a lack of increase in PA activity, as well as with a decrease in the average absolute value of MCI. We speculate that the reduction in future variance is the result of reduced lower-tropospheric baroclinicity associated with Arctic amplification (e.g., Barnes and Screen 2015) or that other climate change signals, such as tropical upper-tropospheric warming, result in compensating trends (e.g., Peings et al. 2017; Oudar et al. 2020).
5. Discussion and concluding remarks
We analyze PA activity in 20 years of high-resolution time-slice global model simulations from the Model for Prediction across Scales–Atmosphere (MPAS-A; Michaelis et al. 2019). The set of 10 present-day simulations spans ENSO phases from strong El Niño to strong La Niña events since 1988, and features high-resolution SST analyses at the lower boundary. Ten counterpart future simulations use the same basic SST pattern, but modified with a delta field computed from an average of 20 GCMs between the twentieth century and RCP8.5 end-of-century projections, and increased carbon dioxide concentrations consistent with this emissions pathway. Sea ice for the present-day simulations is based on twentieth-century GCM averages, and follows the same daily-varying annual progression in each of the 10 present-day runs. Future sea ice is based on the RCP8.5 GCM ensemble mean, and again features the same annual progression in each of the 10 future simulations. In the future simulations, Arctic sea ice is nearly absent in the late summer and fall (Fig. 1), consistent with strong Arctic amplification. The simulations also feature strong upper-tropospheric warming in the tropics.
We identify atmospheric persistent anomalies using the modified PA detection method of Miller et al. (2020). Comparing PA activity in present-day MPAS simulations to that in the corresponding years from the ERAI demonstrates that the MPAS simulations were able to identify the basic regions of enhanced PA frequency, but with the North Pacific PA frequency maximum shifted too far east, a region that also exhibited greater 500-hPa height variance in MPAS relative to ERAI. The North Atlantic +PA maximum evident in the ERAI was weaker in the MPAS simulations, and the Arctic maximum was underdone in the MPAS simulations, though this feature was muted during these particular simulation years relative to the complete reanalysis period. Differences in PA frequency between the MPAS and ERAI appear to be correlated with differences in the 500-hPa height variance between the datasets. Despite the imperfect representation of PAs in the present-day climate, results are sufficiently realistic to warrant comparison of present and future PA activity in the MPAS simulations.
The spatially averaged 500-hPa geopotential height standard deviation, which we used to compute the PA amplitude threshold, is reduced in the future MPAS simulations relative to the present-day values. Despite the lowered PA threshold, no systematic hemisphere-averaged increases in +PA activity are evident between the present-day and future MPAS simulations, and there is a consistent decrease in −PA frequency. The lack of change in PA activity is evident in each season and in most geographical regions; however, there are increases in +PA frequency over the eastern North Pacific and Scandinavia, with substantial decreases across the Gulf of Alaska, central North Pacific, and Bering Sea regions. During winter, there are +PA increases across parts of eastern Canada, and during winter and spring, in the northeastern North Pacific (Figs. 13a,b). The increase in +PA activity in Scandinavia, though modest, is generally consistent with recent results from Overland et al. (2021), who find that Scandinavian anticyclone activity during early winter increases under low-ice conditions in the Arctic.
For −PA frequency, the overall negative trend is driven by substantial decreases across the central North Pacific (Fig. 10). While it is speculative to infer changes in potential high-impact weather from a small set of simulations, the eastward shift in +PA activity in the northeastern North Pacific could correspond to increased weather extremes along the U.S. West Coast, and for +PA activity, these could correspond to persistent anticyclonic conditions over this region. Changes in future PA activity relative to present-day values correlate positively with changes in the 500-hPa height variance (r = 0.34). In regions characterized by future PA activity increases, such as in the northeastern North Pacific and Scandinavia, future 500-hPa height variance also increased (Figs. 8 and 9c). In both the North Pacific and North Atlantic, MPAS-simulated storm tracks extended farther eastward in the warmer future simulation (Boaggio 2019). For the North Atlantic, this result is consistent with the limited-area modeling study of Willison et al. (2015).
Using an idealized channel model, Tierney et al. (2021) examined both PA activity and variance as a function of baroclinicity and absolute temperature across a range of climate states in a bivariate experiment designed to separate responses to baroclinicity and temperature. Across these differing climates, they found that PA activity was strongly associated with the overall geopotential variance and that variance diminished in response to polar amplification (reduced baroclinicity), but increased with the absolute temperature. Because PA changes scaled nearly linearly with geopotential variance, when they used a simulation-relative amplitude threshold, as we have done here, they found that simulation-relative PA activity varied only weakly across model climates.
In the idealized model experiments of Tierney et al. (2021), in the present model results, and in the observational analyses of Miller et al. (2020), we find that PA activity tends to follow overall geopotential variance geographically, across seasons, and across climate states. This reinforces the suggestion made by Miller et al. that “most PAs have no ‘special’ dynamics, but rather are manifestations of the high-amplitude low-frequency tails of a broad spectrum of atmospheric macroturbulence” (p. 57). An exception to this may be in the wintertime blocking region in the eastern North Atlantic, where present-day +PA activity is not well represented in the MPAS simulations, and where previous research (cf. Evans and Black 2003) suggests that nonlinear processes are especially important for PA or block evolution. The behavior of temporally persistent blocks may also suggest that “special” dynamics are in play. To the extent that PA activity increases in proportion to the overall variability of the flow, it could be expected to follow the behavior of a red noise model. This idea was explored by Masato et al. (2009), who constructed a longitudinally dependent red noise model for the frequency of blocking in the Northern Hemisphere, using the Pelly and Hoskins (2003) potential-vorticity based blocking index. The Masato et al. model captured the observed longitudinal variability in the frequency of blocking, but it underestimated the frequency of blocks persisting for longer than 5 days by 25% to 30% at most longitudes. Overall, therefore, while the present results appear to support the inference of Miller et al. that most PAs have no special dynamics, we emphasize that “most” does not mean “all.”
Our study is in part inspired by recent research exploring linkages between Arctic sea ice loss and increased persistence of midlatitude flow patterns (e.g., Francis and Vavrus 2012, 2015; Francis et al. 2020; Overland and Wang 2010). We acknowledge that the time-slice model simulations analyzed here include other strong climate-change signals in addition to Arctic amplification, and so we are not able to isolate responses to a single factor. To provide a direct comparison to these previous studies, we compute the meridional circulation index (MCI) of Francis and Vavrus (2015) at several isobaric levels, and for seasonal and annual averages. This measure shows little change between the present-day and future MPAS simulations, exhibiting a slight but consistent midlatitude decrease in the 10-yr averages at all isobaric levels examined (700, 500-, and 300-hPa) and in all seasons. Neither PA activity nor the MCI index show increases, despite the significant reductions in Arctic sea ice in the summer and fall seasons. Given the positive correlation between PA activity and variance, and reductions in future variance owing to Arctic amplification and reduced lower-tropospheric baroclinicity, weak overall changes in +PA activity and decreases in −PA activity can be interpreted as consistent responses to weakened storm tracks.
There are several limitations of this study, perhaps the most serious being that we examine only one set of model simulations, each using the same physical parameterizations. Our novel time-slice method for generating realistic future simulations is based on an even spread across the ENSO spectrum, which could shift in a changing climate. Further, the MPAS representation of present-day PA activity is imperfect, and additional model experiments are needed to identify the cause. For example, we speculate that the dearth of simulated +PA activity in the North Atlantic in our MPAS simulations relative to observations could benefit from improved stratospheric representation in our simulations.
Acknowledgments
This research was supported by NSF Grants AGS-1546743 and AGS-1560844, awarded to North Carolina State University (NCSU). Thoughtful and constructive comments from three anonymous reviewers were helpful in improving this manuscript. The MPAS-A and NCAR Command Language (NCL) are provided by the National Center for Atmospheric Research (NCAR), sponsored by the NSF. High-performance computing included use of Cheyenne (doi:10.5065/D6RX99HX), from NCAR’s Computational and Information System Laboratory (CISL), sponsored by the NSF. Michael Duda of NCAR created our custom MPAS-A grid. Chunyong Jung obtained the CMIP5 GCM ensemble data from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archive, and provided interpolation codes. We acknowledge the NCSU HPC facility for their role in this project. This paper represents a portion of R. Miller’s MS thesis.
Data availability statement
Model output from the simulations presented in this manuscript is located on the NCAR Cheyenne High Performance Storage System (HPSS) and on the NCSU Henry2 Cluster. Please contact the corresponding author for details on access to these data. The European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis is available through the NCAR CISL RDA facility (https://rda.ucar.edu/) and through the ECMWF (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim).
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As defined in the AMS Glossary of Meteorology, the term “blocking” may involve cyclonic as well as anticyclonic systems. The term “persistent anomaly” is broader than “blocking”; blocks are a subset of persistent anomalies. However, given the large body of prior research on blocking and the useful correspondence between blocking and PA characteristics, we cite the blocking literature extensively here.
See Table 2 of Michaelis et al. (2019) for a list of the GCMs used.
Our MPAS-A version 5.1 simulations were completed before an additional MPAS core, MPAS-Seaice, became available in version 6.0. Our simulations rely on changes in GCM-based sea ice concentration; we recognize the limitations associated with incomplete treatment of sea ice.
