1. Introduction
Atmospheric blocks are persistent extratropical anticyclones that are associated with numerous types of weather hazards (Carrera et al. 2004; Booth et al. 2021; Kautz et al. 2022; Narinesingh et al. 2023; White et al. 2023). Complex in their nature, blocks involve the dry and moist interactions of 5–30-day eddies with the atmospheric mean flow (Hoskins et al. 1983; Paradise et al. 2019; Martineau et al. 2022; Luo et al. 2023) as well as other eddies of similar and higher frequencies (Takaya and Nakamura 2001; Yamazaki and Itoh 2013; Steinfeld and Pfahl 2019; Martineau et al. 2022; Neal et al. 2022). Though numerous theoretical explanations for atmospheric blocking have been asserted [see aforementioned references, Woollings et al. (2018), and Lupo (2021) for a complete review], none so far have been fully comprehensive. As such, though much work has shown a reduction in wintertime blocking in warmer climates (e.g., Woollings et al. 2018; Davini and D’Andrea 2020), the causes of these reductions remain an open topic.
From CMIP3 to now CMIP6, general circulation models have shown that global warming leads to a decrease in wintertime atmospheric blocking throughout the Northern Hemisphere (Dunn-Sigouin and Son 2013, hereafter DS13; Davini and D’Andrea 2020; Fabiano et al. 2021; Trevisiol et al. 2022). These decreases are most concentrated in regions where blocking is most ubiquitous: across the north-central and northeastern Pacific, over northwestern North America, and stretching from northeastern Canada to northwestern Europe. The magnitude of decrease and regional variation, however, has been shown to be sensitive to the model used (Masato et al. 2013; Davini and D’Andrea 2020; Kleiner et al. 2021), magnitude of warming (Mokhov et al. 2014), and definition of blocking (e.g., Woollings et al. 2018).
Some studies have suggested that reduced atmospheric blocking in a warmer world is driven by changes in mean flow and eddy–eddy interactions (Barnes and Hartmann 2010, 2012; Masato et al. 2013; de Vries et al. 2013; DS13; Cheung and Zhou 2015). The work of Barnes and Hartmann (2010) and Barnes (2013), for example, suggests that higher jet latitudes in a warmer world lead to decreased anticyclonic wave breaking which in turn should lead to decreased blocking. On the other hand, other work has suggested that enhanced diabatic processes in a warmer, moister world should actually lead to stronger, more sustained, and ubiquitous blocking (Pfahl et al. 2015; Steinfeld et al. 2022). Much remains to be reconciled.
Taking a step back, atmospheric circulation changes in future climates are driven by a direct component related to radiative forcing from greenhouse gas concentrations and a more dominant indirect component due to changes in sea surface temperature (SST; Chen et al. 2013; Grise and Polvani 2014; Shaw 2019; Dong and Leung 2021). In simulations involving rising greenhouse gas concentrations, SST responses feature an overall mean state warming that amplifies in the poleward direction and El Niño–like changes in the SST pattern (Matsueda and Endo 2017; Dong et al. 2020). SST response can thus be decomposed into a uniform/constant, average warming response, plus a spatially dependent change in the SST gradient/pattern (Fig. 1), ΔSST = ΔSSTUniform + ΔSSTPattern. For the spatial distribution and prevalence of atmospheric blocking in climate projections, it is unclear which ΔSST component is most important and how this is linked to processes that drive or reduce blocking.
(a) Annual mean SST in the Hist simulation of CM4 (years 1980–2014). (b) Difference in annual mean SST, ΔSST, computed using CM4 SSP5-8.5 (years 2065–99) minus CM4 Hist. (c) Change in SST pattern, ΔSSTPTRN, relative to the mean SST change between 30°S and 30°N, and TM = 3.08 K.
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
Previous experiments have found that uniform warming of SSTs leads to an expansion of the Hadley cell and poleward-shifted jets (Chen et al. 2013; Graff and LaCasce 2012; e.g., Shaw 2019; Harvey et al. 2020; Oudar et al. 2020) embedded in a more stable atmosphere (Held 1993; Frierson 2006). This is important to blocking because jet latitude helps determine the orientation of high-frequency (1–5 days) eddies that are absorbed into and sustain atmospheric blocks (Shutts 1983; Wang and Kuang 2019). The relationship between blocking and jet latitude, however, is not yet completely clear as some studies suggest that there is more blocking for higher jet latitudes (Yeh 1949; Wang and Kuang 2019), while others suggest that there is less (Barnes and Hartmann 2010; Anstey et al. 2013) or that regional variations play an important role (Madonna et al. 2017).
The SST pattern could also play a critical role in atmospheric blocking and the stationary waves (Lee et al. 2011; Park and Lee 2019) that support them. For example, each phase of the El Niño–Southern Oscillation (ENSO) is associated with an induced Rossby wave pattern that permeates the extratropics (Kiladis and van Loon 1988). The cold phase, La Niña, induces a pattern that interferes constructively with the orographically induced (Wills et al. 2019; White et al. 2021) high-pressure stationary wave anomaly near the northeastern edge of the Pacific, while the warm phase, El Niño, does the opposite. Stationary wave amplitude has been shown to have a positive relationship with atmospheric blocking (Paradise et al. 2019; Narinesingh et al. 2020), and consistent with this, La Niña is associated with more blocking in northwestern North America (Renwick and Wallace 1996).
Furthermore, the disproportionate warming of higher latitudes compared to lower latitudes (i.e., Arctic amplification) acts to weaken the meridional temperature gradient. This weakening has been thought to strongly modulate midlatitude circulation and extremes and potentially lead to more blocking (Francis and Vavrus 2012; Kug et al. 2015), though some studies suggest that this is not actually the case (Hassanzadeh and Kuang 2015; Riboldi et al. 2020; e.g., Cohen et al. 2020; Blackport and Screen 2020; Dai and Song 2020). With all this disagreement, it is unclear how important SST pattern changes in future climates might be for midlatitude circulation.
In previous prescribed SST experiments, Kennedy et al. (2016) imposed 0.87-K uniform warming to find a decrease in blocking with respect to their uniform cooling experiment. Expanding upon this, in the present study, we impose stronger forcing (i.e., +3.08 K) to simulate climate projections more comparable to the end of the twenty-first century in a shared socioeconomic pathway (SSP5-8.5) scenario. With regard to SST pattern, Matsueda and Endo (2017) used a suite of CMIP5 models to find decreased blocking for various +4-K global mean warming patterns, implying that differences in SST patterns did not matter as much as uniform warming. All models used in that study, however, featured El Niño–like pattern changes, making it unclear what to anticipate from the pattern change without the background warming.
As such, the present study aims to explicitly separate the response of blocking frequency to uniform warming versus SST pattern changes and to explain the physical processes that drive these responses as they relate to mean circulation and block energization. This is done using a series of experiments using NOAA GFDL’s atmosphere-only model (AM4) and coupled ocean model (CM4) [Zhao et al. (2018) and Held et al. (2019), respectively].
What follows this introduction (section 1) is a methods section (section 2) describing the model simulations, diagnostics, and block tracking criteria. After, the results are presented (section 3) starting with the response of blocking in the fully coupled GCM to SSP5-8.5 forcing, followed by atmosphere-only experiments decomposing SST forcing into uniform warming and pattern change components. Section 4 is a discussion and conclusions.
2. Methods
a. Model simulations
GCM simulations are used to investigate the general circulation and blocking response to warming and pattern changes in sea surface temperature. For this, we employ NOAA Geophysical Fluid Dynamics Laboratory’s coupled model CM4 (Held et al. 2019) and its atmosphere component AM4 (Zhao et al. 2018). CM4 is part of the CMIP6 suite and consists of AM4 coupled to the ocean and sea ice model (OM4) (Adcroft et al. 2019). Narinesingh et al. (2022) found that these models oversimulate blocking near the Pacific blocking maximum and undersimulate in the Euro-Atlantic. This is consistent with the multimodel mean of CMIP models (Woollings et al. 2018; Davini and D’Andrea 2020; Trevisiol et al. 2022).
In the experiments herein, the atmosphere is integrated using 33 vertical levels and roughly 100-km horizontal resolution. A single ensemble member historical control simulation of CM4, referred to as “Hist,” follows CMIP6 specifications for time-evolving solar irradiance, greenhouse gas concentrations, and precursor emissions. We focus on the years 1980–2014.
The SSP5-8.5 (Riahi et al. 2011) simulation of CM4, herein simply referred to as “SSP5-8.5,” is forced by increasing greenhouse gas emissions throughout the twenty-first century, and by the year 2100, it reaches a radiative forcing of +8.5 W m−2 relative to preindustrial forcing. We focus on the years 2065–99. One caveat to this scenario is that it is thought of as unrealistically extreme (Hausfather and Peters 2020). Still, SSP5-8.5 remains useful because it generates strong signals that are easier to discern from natural variability.
Four 50-yr simulations of AM4 forced by various SST patterns are also analyzed (Table 1). The only forcing difference between the AM4 experiments is SST, and all other conditions (i.e., sea ice concentration and aerosols) are held constant to isolate the response purely from SST. The control experiment of AM4, referred to as “CTL,” is derived from the Hist simulation of CM4 (1980–2014). For this, average sea ice concentration (SIC) and SST are computed for each month from January to December using CM4 Hist output. These average SIC and SST values are then used as prescribed boundary conditions in the CTL simulation of AM4. Figure 1a shows the annual average of SST in the entire CTL time series.
NOAA GFDL model simulations examined in this study. CM4 is a coupled ocean model. AM4 simulations utilize the atmospheric component of CM4 forced by prescribed SSTs and SICs. The term TM = 3.08 K and is the mean SST difference between the SSP5-8.5 and Hist simulations of CM4 averaged between 30°S and 30°N.
In the second AM4 simulation, referred to as “SSP,” prescribed SSTs are calculated using years 2065–99 of CM4’s SSP5-8.5 simulation. The difference in annual mean SST between SSP and CTL, ΔSST [Eq. (1)], is shown in Fig. 1b. The term ΔSST for CM4 is similar to other CMIP6 models (Dong et al. 2020). Note that sea ice and aerosol concentrations remain the same, just the SST is altered.
(a) Zonal-mean temperature during boreal winter for the CM4 Hist simulation (contours). The contour closest to the surface in the tropics is 290 K, and the contour interval is 10 K. Shading shows the difference between the SSP5-8.5 and Hist simulations of CM4. (b) Zonal-mean zonal wind during boreal winter for the CM4 Hist simulation. The outer solid contour is 5 m s−1, the outer dotted contour is 0 m s−1, and the contour intervals are 5 and −5 m s−1, respectively. Shading shows the difference between CM4 SSP5-8.5 and CM4 Hist. Stippling in all plots denotes significant differences between simulations at the 95% confidence level.
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
If higher latitudes were included to calculate TM, the tropical mean of the resulting pattern would be negative. Compared to using all latitudes to calculate TM, decomposing ΔSSTPTRN into TM defined using a tropical mean yields a tropical SST pattern change that is, by construction, 0 on average, making it possible to distinguish the thermodynamic and dynamic components of SST-induced tropical changes (Hill et al. 2015).
In the UNFRM simulation, the prescribed SST is the CTL SST plus a uniform warming of TM = 3.08 K. In the PTRN simulation, the prescribed SST is the CTL SST plus ΔSSTPTRN. The warming in the tropical eastern Pacific in ΔSSTPTRN has been previously noted as forcing similar to an El Niño pattern (Cai et al. 2021) and is similar to other CMIP6 models (Dong et al. 2020).
To isolate changes purely due to SST, the RCP, UNFRM, and PTRN simulations use the same SIC as CTL. Table 1 contains a summary of all simulations. Aerosol concentrations in the prescribed SST experiments are also held constant. The present study concentrates only on winter (December–February) in the Northern Hemisphere. Winter is when blocking is most ubiquitous (Woollings et al. 2018) and most dominantly forced by eddy–mean flow interactions (Martineau et al. 2022). All analyses are carried out using daily mean data interpolated onto 2.0° × 2.5° latitude by longitude grids. This resolution was used based on data availability and avoiding errors associated with data interpolation onto finer grids.
b. Circulation features
The prevalence of atmospheric blocking is largely controlled by mean state circulation features (Hoskins et al. 1983; Paradise et al. 2019; Kleiner et al. 2021; Martineau et al. 2022; Luo et al. 2023). As such, to better understand the response of blocking in the present study, we document the response of the time-mean zonal wind at 250 hPa
(a) Wintertime 250-hPa zonal wind climatology
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
c. Block tracking and climatology
Blocking is identified using the 500-hPa geopotential height (Z500) metric described in DS13. This metric is considered a hybrid metric because it searches for persistent anomalies that are strongly positive and also reverses the meridional gradient of Z500. Various block tracking metrics exist (i.e., see Barnes 2013 for a comprehensive review) and the DS13 metric is one that is most commonly used (e.g., Woollings et al. 2018; Lupo 2021), yielding similar results to other metrics. The DS13 metric does well in capturing the climatology, dynamical features, and extreme weather association of atmospheric blocking (DS13; Chan et al. 2019; Booth et al. 2021; Narinesingh et al. 2023).
Details of the DS13 algorithm can be found therein, but here, we provide a brief overview. First, at each grid point, the anomaly
For each simulation, the climatology of blocking is defined as the percentage of winter days that blocking is present at each grid point. To calculate this, the tracking algorithm yields binary latitude–longitude grids for each time step where 1 denotes the presence of blocking and 0 denotes the absence. These grids are then averaged over all winter time steps to yield the blocking climatology (i.e., Fig. 3c).
To determine the significance of differences in
The results presented herein hold even when the meridional gradient reversal criterion is ignored, and blocks are tracked solely as positive, high-amplitude geopotential height anomalies.
d. Blocking energetics
To explain the climatological blocking response to changes in SST, we utilize the blocking energetics considerations presented in Martineau et al. (2022), which build upon early theoretical work by Oort (1964). This formalism explicitly describes the energization of blocking by various processes. Herein, overbars denote the DJF climatology and primes denote the differences between composited 10-day low-pass-filtered fields on days with blocking and those without.
In 20°- × -20° subdomains (i.e., Figs. 8–11), 〈CP〉, 〈CK〉, 〈CPHF〉, and 〈CKHF〉 are calculated when at least 30 days of blocking geopotential height anomaly maxima fall within that region. This 30-day criterion is set to get a sampling of blocking events during all stages of their life cycles: genesis, maintenance, and decay. Process efficiencies are calculated in each AM4 integration to compare the relative contribution of each process across experiments. Bootstrapping is used to determine significance with a 95th-percentile confidence interval.
3. Results
We begin by documenting the response of the general circulation and blocking climatology to SSP5-8.5 forcing in CM4. This serves as a baseline of comparison for the prescribed SST and SIC experiments using AM4. Afterward, the results from the prescribed experiments involving SSP5-8.5 warming, uniform warming, and SST pattern change are presented.
a. Coupled ocean simulations
In the zonal mean, the SSP5-8.5 simulation of CM4 features an alternating warming pattern with both meridional and vertical components (Fig. 2a), consistent with previous findings (Lu et al. 2008; Chen et al. 2013; Shaw 2019). A warming maximum is generated in the tropical upper troposphere. Near the Northern Hemisphere’s descending edge of the Hadley cell, warming becomes more uniformly distributed in the vertical direction. Moving poleward, a minimum in tropospheric warming occurs between 40°–55°N and 300–925 hPa. This is followed by an amplification maximum of over 6 K near the surface at higher latitudes.
Also consistent with previous findings, the zonal-mean zonal wind in SSP5-8.5 features an oscillating response in the meridional direction when compared to the historical simulation (Fig. 2b) showing that imposing this forcing effectively introduces a stationary wave perturbation to the background flow. From 0° to 20°N, a minimum change in zonal wind speed occurs in the upper troposphere which then extends down to the surface from 20° to 30°N. Poleward of this, an increase in zonal flow occurs between 40° and 60°N, maximizing near the tropopause. This zonal wind speed decrease to the south followed by an increase just north of the subtropical jet is indicative of a poleward jet shift and Hadley cell expansion. At higher latitudes, another minimum in zonal wind response is exhibited and the mean zonal flow decreases.
Now shifting focus to the horizontal structure of the mean circulation, Figs. 3a and 3b show the 250-hPa zonal wind and 500-hPa stationary wave, respectively. Note that for an in-depth assessment of winter circulation in CM4 compared to reanalysis, the reader is referred to Narinesingh et al. (2022). There, the authors find that CM4 generates realistic jet structures in terms of location, tilt, and magnitude, albeit with an equatorward shift relative to the observed flow.
The SSP5-8.5 simulation exhibits a meridionally varying pattern of decreasing and increasing zonal wind over Asia and the Pacific (Fig. 3a) compared to Hist. This is consistent with the zonal-mean response (Fig. 2b). The jet maximum also extends further downstream. From North America to Europe, the jet features alternating increases and decreases following along the North American and Greenland coastlines in agreement with Brayshaw et al. (2009), who found the angle of the eastern North American coastline to be important for shaping mean flow features.
The difference in the stationary wave in the 500-hPa streamfunction
In terms of blocking, the SSP5-8.5 simulation produces a significant reduction in frequency compared with the historical simulation (Fig. 3c). Hemispherically averaging the blocking climatology of the historical integration yields 3.3%, whereas SSP5-8.5 has a value of 2.8%. Near the Pacific blocking maximum in the Hist simulation, a block is present on 12%–14% of winter days. In SSP5-8.5, however, this percentage decreases to 8%–10%, a reduction of nearly one-third. Over North America, blocking occurs up to 6% of winter days, but in SSP5-8.5, it decreases to about 3%. Over parts of Eurasia, there is an increase in blocking, but this signal is mostly not significant.
The overall Northern Hemisphere decrease in blocking in CM4 is consistent with the multimodel mean of CMIP models (Woollings et al. 2018; Davini and D’Andrea 2020). The regional variation is also well captured in North America and near Greenland compared to the results of Davini and D’Andrea (2020), which examined the multimodel mean of CMIP6 models. Further compared to its generation, however, CM4 does not produce a clear reduction in blocking over Europe and the reduction in blocking over the northwestern Pacific is too equatorward compared to the multimodel mean. However, these discrepancies could be due to disagreements in block tracking methodologies, which have been a well-documented but unresolved issue in the blocking community (Barnes et al. 2012; Woollings et al. 2018; Davini and D’Andrea 2020).
Putting this all together, the enhanced and zonally extended zonal flow over the Pacific in CM4 SSP5-8.5 is accompanied by less blocking. This result is consistent with the results of Paradise et al. (2019), who found that the prevalence of blocking decreases in stronger zonal flows using a channel model. Inconsistent with their results about the direct relationship between stationary waves and blocking, however, is that SSP5-8.5’s enhanced stationary wave over North America is not collocated with enhanced block occurrence. This suggests that other factors here could play an important role in the decreased blocking, such as diabatic effects, the meridional gradient of the mean flow, or eddy–eddy interactions (Martineau et al. 2022). Over the Atlantic basin, a combination of increased stationary wave plus enhanced zonal flow could be canceling each other out, leading to insignificant changes in the blocking climatology. Over eastern Europe, the enhanced stationary wave is consistent with the suggestion of enhanced blocking.
With the response of CM4 now documented as a benchmark, we next examine the prescribed sea surface temperature experiments of AM4.
b. Prescribed SST simulations
We begin with the zonal-mean response (Fig. 4). AM4’s SSP simulation produces a similar temperature and zonal wind response to CM4’s SSP5-8.5 simulation (Figs. 2a,b and 4a,e). In terms of temperature, a vertically and meridionally alternating pattern of warming maximizes near the tropopause in the deep tropics, minimizes throughout the midlatitude troposphere, and maximizes again near the polar surface. In terms of the zonal wind response, an alternating pattern of decreasing and increasing wind is generated in the poleward directions.
Difference from the AM4 CTL simulation of the wintertime zonal-mean temperature (shading) for the (a) SSP, (b) UNFRM, and (c) PTRN simulations. (d) The sum of UNFRM and PTRN. Contours in (a)–(d) show the zonal-mean temperature in the CTL simulation. The inner contour is 290 K, and the contour interval is 10 K. (e)–(h) As in (a)–(d), but for the zonal-mean zonal wind. The outer solid and dotted contours show the zonal-mean zonal wind in the CTL simulation and are 5 and 0 m s−1, respectively. The contour intervals are ±5 m s−1. Stippling in all plots denotes significant differences from CTL.
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
The uniform warming experiment of AM4, UNFRM (Figs. 4b,f), captures most of the SSP response (Figs. 4a,e), despite not including changes in SST gradient. The differences, however, are that in UNFRM, the midlatitude warming minimum (Figs. 4a,b) and subtropical zonal wind decrease (Figs. 4e,f) do not extend through the lower troposphere as in SSP.
When changing the prescribed SST pattern, PTRN (Figs. 4c,g), a global net cooling effect is found in a vertically integrated sense. However, parts of the Northern Hemisphere extratropics exhibit warming. In terms of zonal wind, the Southern Hemisphere subtropics exhibit an enhancement, while the Northern Hemisphere exhibits diminution. Poleward of the subtropics, however, the zonal circulation does not change appreciably.
Combining UNFRM and PTRN (Figs. 4d,h) compensates for some of the discrepancies between UNFRM and SSP (Figs. 4a,e). For example, the width and extension to the surface of SSP’s zonal wind decrease in the Northern Hemisphere are not apparent in UNFRM, but adding in PTRN restores it. The same goes for the enhancement of zonal flow in the tropical Southern Hemisphere. Thus, in the zonal-mean sense, the decomposition of the SSP forcing into UNFRM and PTRN components yields behavior that is approximately linear.
The
Difference from the AM4 CTL simulation of the wintertime 250-hPa zonal wind climatology
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
In the PTRN simulation (Fig. 5c), an oscillating
Table 2 shows E calculated for
Area-averaged RMS error of
Uniform warming is the dominant mode of explaining the SSP response. Comparing the UNFRM, PTRN, and UNFRM+PTRN responses to SSP yields E values of 2.2, 3.7, and 2.0 m s−1, respectively (Table 2). As one can see, adding UNFRM and PTRN is only a slight improvement of ∼10% compared to UNFRM alone in explaining the SSP signal.
Calculating E for the CM4 SSP5-8.5 yields a value of 1.4 m s−1 (Table 2), even less than the AM4 experiments. This result emphasizes how similar the
Shifting focus to the
Difference from the AM4 CTL simulation of the wintertime 500-hPa stationary wave in the streamfunction
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
In fact, E in the CM4 case is much bigger than the atmosphere-only experiments (Table 2). This suggests that when it comes to the stationary wave, aerosols, sea ice, and/or ocean variability could play an important role in generating the phase of the response.
Like the zonal wind response, the AM4 UNFRM simulation (Fig. 6b) produces a similar
Despite some differences in the stationary wave response between CM4 SSP5-8.5 (Fig. 3b) and AM4 SSP (Fig. 7a), the blocking decrease in both simulations compared to their respective control simulations remains similar. Decreased blocking is especially prevalent over North America and the North Pacific. The UNFRM response (Fig. 7b) is also in agreement with the SSP5-8.5 and SSP simulations. This shows that, regardless of sea ice and aerosol concentrations, and coupled or prescribed SSTs, in an SSP5-8.5 type of warming scenario, there is a general decrease in blocking over these regions driven by uniform warming.
Difference from the AM4 CTL simulation of the wintertime blocking climatology (percentage of all winter time steps with blocking; shading), for the (a) SSP, (b) UNFRM, and (c) PTRN simulations. (d) The sum of UNFRM and PTRN. The outer contour is 2%, and the interval is 2%. Stippling denotes significant differences from CTL.
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
There are some differences between the CM4 and AM4 responses, however. Over parts of Eurasia, for example, AM4 SSP generates more blocking (Fig. 7a) compared to its respective control run, whereas CM4 does not. This discrepancy over Eurasia could be linked to the aforementioned differences between the atmosphere-only and coupled simulations. Still, in most places, the atmosphere-only and coupled GCMs yield similar responses. In terms of the area-averaged blocking climatology, AM4 CTL has a value of 3.1%, whereas AM4 SSP yields a value of 2.6%—a decrease of roughly 1/6; for reference, CM4 control yielded an area-averaged blocking frequency of 3.3% and SSP5-8.5 yielded a value of 2.8%.
Just as for the general circulation (Figs. 5 and 6), the UNFRM simulation (Fig. 7b) demonstrates a similar response to SSP when it comes to blocking. Hemispherically averaging the blocking climatology of UNFRM yields a value of 2.5%, nearly the same as the SSP response. A decrease in blocking extends from the polar regions near Greenland equatorward over northeastern North America. In northwestern North America as well as over the northern Pacific, the blocking response also minimizes. The response near western Europe, however, is dissimilar, where SSP generates more blocking and UNFRM does not. This difference could be driven by the pattern response, as the PTRN simulation does have an increase in blocking near western Europe (Fig. 7c), albeit more south.
The PTRN simulation also features decreased blocking over North America and the Pacific (Fig. 7c). This is consistent with El Niño type of SST forcing that acts to destructively interfere with the high-pressure stationary wave anomaly and is associated with decreased blocking (Renwick and Wallace 1996). Combining the UNFRM and PTRN results, however, yields nonlinear behavior where there is too much of a decrease in blocking in some regions. In terms of E, UNFRM yields a value of 0.64%, PTRN yields a value of 0.96%, and UNFRM+PTRN yields a value of 1.01% (Table 2). CM4 SSP5-8.5 yields an E of 0.77%, again downplaying the importance of differences between the atmosphere-only and coupled ocean experiments.
Taken together, the results from the simulations show that the blocking response is nonlinear. The UNFRM blocking response is similar to SSP, but adding in the PTRN simulation takes one further away from reconstructing the SSP response. Furthermore, when comparing AM4 to CM4, our results suggest that changes in blocking and general circulation in SSP5-8.5 are primarily driven by a uniform warming of SST, not changes in the SST pattern, sea ice concentrations, direct aerosol effects, or ocean variability. Next, we will examine the mechanistic drivers of the blocking response in the prescribed SST simulations.
c. Physical driving mechanisms of the blocking response to SST changes
Previous studies have found the prevalence of atmospheric blocking to be modulated by the strength and latitude of the jet (Yeh 1949; Barnes and Hartmann 2010; Anstey et al. 2013; Wang and Kuang 2019; Paradise et al. 2019), as well as the magnitude of the background stationary waves (Renwick and Wallace 1996; Paradise et al. 2019; Narinesingh et al. 2020). In the SSP and UNFRM experiments, poleward-shifted jets (Figs. 4e,f) and decreased blocking (Figs. 7a,b) are consistent with the findings of Barnes and Hartmann (2010) who found a similar relationship in CMIP3 models. The PTRN simulation, however, features no clear jet shift (Fig. 4g), but still yields less blocking (Fig. 7c).
Instead, the decrease in blocking over northwestern North America in the PTRN simulation (Fig. 7c) is more consistent with the weakened stationary wave anomaly (Fig. 6c) over that region, in agreement with the results of Paradise et al. (2019) and Narinesingh et al. (2020); in the SSP and UNFRM simulations, however, although there is less blocking (Figs. 7a,b), the stationary wave is actually more anticyclonic (Figs. 6a,b). As such, considering the jet and stationary wave alone is not sufficient to explain the blocking response and we turn to examining the various energy transformation processes that drive blocking.
Building off the work of Oort (1964), Martineau et al. (2022) used an energetics framework on reanalysis data to document how eddy–mean flow interactions, baroclinic conversion and barotropic conversion, and eddy–eddy interactions drive blocking in different magnitudes depending on the region. The energization of blocking in the control run of AM4 agrees with their results (Fig. 8). Baroclinic conversion 〈CP〉 dominates and maximizes for blocking near the Bering Strait and southwest of Greenland (Fig. 8a). Block energization by barotropic conversion 〈CK〉 maximizes near the central Pacific and western Atlantic (Fig. 8b), but is 2–3 times less strong than contributions from 〈CP〉.
In the AM4 CTL simulation, block energization efficiencies due to (a) 〈CP〉, (b) 〈CK〉, (c) 〈CPHF〉, (d) 〈CKHF〉, and (e) Res. to the blocking energy budget.
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
In terms of contributions from high-frequency eddies, the conversion to eddy available potential energy of blocking 〈CPHF〉 acts to dissipate blocking (Fig. 8c), while the conversion to eddy kinetic energy of blocking 〈CKHF〉 is most positive across the Atlantic and over the central Pacific (Fig. 8d). There is some suggestion, however, that the model is underestimating 〈CKHF〉, especially over the Atlantic where the contributions of this process are more dominant (Martineau et al. 2022), and models tend to underestimate blocking (Woollings et al. 2018; Davini and D’Andrea; Narinesingh et al. 2022).
Averaged over entire blocking life cycles, the change in total eddy energy of blocking
Next, we shift focus to differences in energy conversion efficiencies between the experimental (SSP, UNFRM, and PTRN) and CTL simulations of AM4 in the context of climatological blocking response. In agreement with decreases in blocking, the strongest driver 〈CP〉 decreases almost everywhere (Fig. 9a) in SSP. Notice, however, that near the central Pacific and southern Greenland, there is actually enhanced 〈CP〉, which suggests that other processes are driving decreases in blocking in those regions, as we will discuss.
Difference in (a) 〈CP〉, (b) 〈CK〉, (c) 〈CPHF〉, (d) 〈CPHF〉, and (e) Res. between the SSP and CTL simulations. White (black) contours indicate reductions (increases) in the blocking climatology; the outer contour and contour interval are −1% (1%).
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
In terms of 〈CK〉, barotropic conversion is enhanced over the central and northeastern Pacific and northern Europe, but nearly unchanged over the western Atlantic where blocking decreases (Fig. 9b). The term 〈CPHF〉 shows only modest changes (Fig. 9c), and 〈CKHF〉 is slightly enhanced over parts of the ocean basins (Fig. 9d). Over the Pacific Ocean and near the southern tip of Greenland, where blocking decreases the most, Res. acts to dissipate more blocking energy (i.e., SSP Res. − CTL Res. < 0) and exhibits less dissipation almost everywhere else (i.e., SSP Res. − CTL Res. > 0). We also note an increase in blocking near western Europe that is accompanied by enhanced 〈CK〉 and 〈CKHF〉.
Taken together, these results suggest that near northwestern and northeastern North America, decreases in blocking are driven by decreased baroclinic conversion, whereas over the central Pacific and the tip of Greenland, enhanced dissipation by diabatic processes is driving blocking decreases. Greater blocking energy dissipation in these regions could be linked to cooling from enhanced evaporation and/or melting, or other nonconservative processes induced by blocking, but here, we cannot say for certain.
The UNFRM simulation yields a similar response to SSP in terms of blocking energetics (Fig. 10): 1) decreased 〈CP〉 and blocking throughout most of the Northern Hemisphere midlatitudes, except over the central Pacific and the southern tip of Greenland (Fig. 10a); 2) 〈CK〉 is enhanced over the northeastern Pacific (Fig. 10b); 3) 〈CPHF〉 and 〈CKHF〉 change only modestly (Figs. 10c,d); and 4) over the Pacific where blocking decreases, UNFRM also exhibits more negative Res. term (Fig. 10e), again pointing to enhanced dissipation from diabatic processes driving the decreases in blocking over this region. Unlike SSP, however, in UNFRM over the southern tip of Greenland, a decrease in 〈CK〉 is most strongly associated with less blocking. Still, two possible explanations of the reduction in blocking from the SSP simulation hold: Decreased blocking over northwestern and northeastern North America is driven by decreased baroclinic conversion, whereas the central Pacific decrease is driven by enhanced dissipation by nonconservative processes.
Difference in (a) 〈CP〉, (b) 〈CK〉, (c) 〈CPHF〉, (d) 〈CKHF〉, and (e) Res. between the UNFRM and CTL simulations.
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
Compared to the SSP (Fig. 9) and UNFRM (Fig. 10) simulations, changes in blocking for the PTRN simulation (Fig. 11) are driven by different changes in energy conversion efficiencies: 1) The response of 〈CP〉 is more spatially heterogeneous (Fig. 11a); 2) the reduction in blocking over the central Pacific is most associated with decreased 〈CK〉 and 〈CPHF〉 (Figs. 11b,c), not Res.; and 3) decreased blocking over northwestern North America is associated with enhanced energy dissipation (Fig. 11e). On the other hand, similar to SSP, there is an increase in blocking near western Europe that is accompanied by enhanced 〈CKHF〉 (Fig. 11d).
Difference in (a) 〈CP〉, (b) 〈CK〉, (c) 〈CPHF〉, (d) 〈CKHF〉, and (e) Res. between the PTRN and CTL simulations.
Citation: Journal of Climate 37, 17; 10.1175/JCLI-D-23-0371.1
To summarize, in the warming simulations (SSP and UNFRM), decreases in blocking over northwestern and northeastern North America are most associated with blocks being less able to extract mean state potential energy through baroclinic conversion. This is consistent with a warmer, moister, more stable atmosphere (i.e., higher stability parameter γ) with poleward-shifted jets (Held 1993; Frierson 2006; Barnes and Hartmann 2010). Over the Pacific Ocean, both warming simulations also yielded more block dissipation by the Res. term, suggesting that block decreases in that region are driven by diabatic effects. Furthermore, although the PTRN simulation yields a reduction in blocking like SSP does, the response is driven by different changes in the regional variation of block energization processes, emphasizing the dominance of uniform warming in generating the full SSP response.
4. Summary and discussion
We have investigated whether projected winter general circulation changes and decreases in blocking in SSP5-8.5 are dominated by a uniform warming of SST or changes in the SST pattern. This is done using coupled ocean and prescribed SST simulations (Fig. 1; Table 1).
First, to investigate the baseline warming response to both SST warming and pattern changes, historical and SSP5-8.5 coupled ocean simulations are compared (CM4 Hist and CM4 SSP5-8.5, respectively, in Table 1). The end of the twenty-first century is characterized by an overall warming of the troposphere which maximizes in the tropical upper troposphere and polar lower troposphere (Fig. 2a). In agreement with the multimodel mean of CMIP models, the SSP5-8.5 exhibits a wider Hadley cell (Fig. 2b), poleward-shifted jets (Fig. 3a) (Harvey et al. 2020; Oudar et al. 2020), and less blocking over the Pacific and North America (Fig. 3c) (Woollings et al. 2018; Davini and D’Andrea 2020).
In prescribed SST experiments, the SSP5-8.5 (SSP) response is then decomposed into parts representing a uniform warming of SST (UNFRM) and changes in SST pattern (PTRN). Within tropical and subtropical regions, changes in SST pattern add linearly with uniform warming to produce the zonal-mean SSP5-8.5 response (Fig. 4). Outside of these regions, however, the circulation response is dominated by uniform warming (Figs. 4–6), especially over the Pacific.
The uniform warming experiment also produces similar blocking decreases across North America and the Pacific Ocean as both the coupled and atmosphere-only SSP experiments (Figs. 3c and 7), dominating over the SST pattern change. This shows that the blocking response can be thought of as by-product of the general circulation response, or in other words, uniform warming drives the mean circulation change, not the SST pattern changes, and blocking eddies follow suit. One caveat region, however, is near western Europe, where uniform warming (Fig. 7b) does not capture the SSP response (Fig. 7a) and the PTRN response may be more influential (Fig. 7), though the enhanced blocking found over that region in SSP and PTRN is not found in the coupled model (Fig. 3c).
Jet and stationary wave changes explain much of the blocking response, but not all. Over the Pacific Ocean and near northeastern North America in SSP and UNFRM, there is stronger zonal background flow, consistent with less blocking. However, over northwestern North America, there is little change in zonal flow but still less blocking. Furthermore, northwestern North America has an enhanced anticyclonic stationary wave anomaly, in contradiction with previous studies using idealized simulations to suggest that enhanced stationary waves lead to more blocking (Paradise et al. 2019; Narinesingh et al. 2020). This inconsistency prompted a closer look at the processes driving the blocking response using an energetics approach (Martineau et al. 2022).
In the warming simulations (SSP and UNFRM), decreases in blocking over northwestern and northeastern North America appear to be driven by reduced baroclinic conversion from mean flow potential energy to the eddy available potential energy (Figs. 9a and 10a); over the Pacific Ocean, decreases in blocking appear to be driven by increases in diabatic dissipation processes. Here, the exact sources of these dissipation changes are yet to be determined, but further work is planned to investigate the separate contributions from latent heating, radiation, vertical mixing, and friction. For example, enhanced evaporative cooling over the Pacific Ocean in the warming simulations (SSP and UNFRM) could be acting to dampen the warm temperature anomalies induced by blocking.
On the other hand, the blocking reductions in the PTRN simulation are driven by other processes. Over the Pacific, blocking reduction is most associated with reduced barotropic conversion, whereas over northwestern North America, reduction appears to be driven by nonconservative processes. Taken together, these results further emphasize the dominance of the warming of SST compared to SST gradient change when considering the full SST response in an SSP5-8.5 scenario.
Adding blocking climatology changes from the uniform and pattern change experiments (Figs. 7b–d) moves away from converging to the full SSP response (Fig. 7a) compared to the uniform experiment alone: 1) Over North America and the Pacific, there is too much of a blocking decrease; and 2) over Eurasia, UNFRM+PTRN produces a different pattern completely from SSP. Thus, changes in the prevalence of atmospheric blocking have a nonlinear response when decomposing SST projections into a mean warming response plus a spatially dependent pattern change.
The nonlinear response of the SSP simulation could be a result of cancellations between the various processes that drive blocking. Over the Pacific in PTRN, for example, decreased barotropic conversion (Fig. 11b) helps to reduce blocking, whereas in UNFRM, that region has enhanced barotropic conversion and the decrease in blocking is more associated with the Res. It could be that the decrease in barotropic conversion from the PTRN simulation is cancelled out by the increase induced by UNFRM, leading to a blocking response in SSP that is dominated by decreased Res. Furthermore, over northwestern North America, the PTRN simulation’s blocking decrease seems to be driven by greater energy dissipation from Res., whereas UNFRM and SSP show an opposite Res. response and are more associated with decreased baroclinic conversion. This could imply that the Res. response over that region is different for a warmer background temperature.
Putting these results all together, it is clear that through various processes, the uniform SST warming dominates both the mean state (i.e., the zonal winds and stationary wave) and eddy (i.e., blocking) responses of the circulation in the full SST change signal (uniform warming + pattern change). These experiments downplay the importance of changes in the SST gradient in contradiction with several studies emphasizing the importance of Arctic amplification to midlatitude circulation (Francis and Vavrus 2012; Kug et al. 2015; Francis et al. 2017; e.g., Vavrus 2018 and the references therein).
Arctic amplification tends to decrease the meridional temperature gradient and thus weaken the zonal background flow via the thermal wind relation. Francis and Vavrus (2012) found Arctic amplification to be linked to increases in extreme event frequency and asserted that a weakened zonal flow could lead to weakened eastward wave propagation and an increase in blocking events.
Our PTRN simulation does not show this. Despite having enhanced warming closer to the poles (Fig. 1c), the midlatitude zonal flow does not decrease appreciably (Fig. 4g) and the blocking decreases (Fig. 7c) instead of increasing as predicted by Francis and Vavrus (2012). Instead, our results are in better agreement with more recent work downplaying the importance of Arctic amplification on blocking (Barnes 2013; Hassanzadeh and Kuang 2015; Riboldi et al. 2020) and midlatitude circulation (e.g., Cohen et al. 2020; Blackport and Screen 2020; Dai and Song 2020) compared to background warming and natural variability.
However, we acknowledge that by following SSP5-8.5 types of experiments, we generate large uniform warming values compared to the SST pattern changes (Fig. 1b). For example, the warming maximum in the tropical pattern is only about half the magnitude of uniform warming. Based on differences we see between the SSP and PTRN simulations, it could be that the effects of the pattern could be modulated by how warm the background is, which would be relevant to milder SSP projections. Future work could examine this by varying the strength of the pattern change and also embedding SST pattern changes onto different magnitudes of uniform warming.
Finally, we comment on the role of sea ice. The SSP and UNFRM simulations of the atmosphere-only model produce similar regional decreases in blocking compared to the SSP5-8.5 simulation of the coupled model. This is despite the sea ice in the SSP and UNFRM simulations being the same as the CTL simulation, whereas the sea ice in the coupled model decreases in the SSP5-8.5 simulation. This further underscores uniform warming of SST being the main driver of blocking decreases, not SST gradient changes in the tropics or high latitudes, nor sea ice. We note, though, that the blocking response of the atmosphere-only and coupled warming simulations diverges over Europe, leaving open questions about the influence of sea ice, aerosols, and ocean variability on the region.
Acknowledgments.
This report was prepared by Veeshan Narinesingh under Award NA18OAR4320123 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration or the U.S. Department of Commerce.
Data availability statement.
Model output data are available upon request.
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