Abstract

This study investigates the sensitivity of the North Atlantic storm track to future changes in local and global sea surface temperature (SST) and highlights the role of SST changes remote to the North Atlantic. Results are based on three related coupled climate models: the Community Climate System Model, version 4 (CCSM4), the Community Earth System Model, version 1 (Community Atmosphere Model, version 5) [CESM1(CAM5)], and the Norwegian Earth System Model, version 1 (intermediate resolution) (NorESM1-M). Analysis reveals noticeable intermodel differences in projected storm-track changes from the coupled simulations [i.e., the difference in 200-hPa eddy activity between the representative concentration pathway 8.5 (RCP8.5) and historical scenarios]. In the CCSM4 coupled simulations, the North Atlantic storm track undergoes a poleward shift and eastward extension. In CESM1(CAM5), the storm-track change is dominated by an intensification and eastward extension. In NorESM1-M, the storm-track change is characterized by a weaker intensification and slight eastward extension. Atmospheric experiments driven only by projected local (North Atlantic) SST changes from the coupled models fail to reproduce the magnitude and structure of the projected changes in eddy activity aloft and zonal wind from the coupled simulations. Atmospheric experiments driven by global SST and sea ice changes do, however, reproduce the eastward extension. Additional experiments suggest that increasing greenhouse gas (GHG) concentrations do not directly influence storm-track changes in the coupled simulations, although they do through GHG-induced changes in SST. The eastward extension of the North Atlantic storm track is hypothesized to be linked to western Pacific SST changes that influence tropically forced Rossby wave trains, but further studies are needed to isolate this mechanism from other dynamical adjustments to global warming.

1. Introduction

Extratropical storm tracks, regions of enhanced baroclinic wave activity, exhibit variability over a range of spatial and temporal scales. From a regional perspective, the day-to-day passage of cyclones along the storm tracks can bring extreme weather and precipitation events to highly populated midlatitude regions in North America and Europe. Globally, storm tracks interact with the large-scale atmospheric circulation and account for a sizeable fraction of the poleward transports of momentum, heat, and moisture from the low latitudes. Given the societal and climatic importance of extratropical storm-track variability, this study focuses on future changes in the North Atlantic storm track and their sensitivity to changes in the physical boundary conditions.

The extratropical storm-track response to increasing greenhouse gas (GHG) concentrations has been examined within both phase 3 and phase 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively; Meehl et al. 2007; Taylor et al. 2012). From the zonal mean perspective, the most prominent feature is a poleward shift and upward expansion of the winter storm tracks in both hemispheres over the twenty-first century (Yin 2005; Bengtsson et al. 2006; Pinto et al. 2007; Chang et al. 2012). The future storm-track changes also have a zonally asymmetric component. Over the North Atlantic, the poleward shift is accompanied by an eastward extension of baroclinic wave activity across Europe that is not characteristic of storm-track changes over the Pacific or Southern Hemisphere (SH; Ulbrich et al. 2008; Chang et al. 2012; Woollings et al. 2012; Harvey et al. 2014, 2015).

While the CMIP multimember multimodel mean indicates a poleward shift and eastward extension of the North Atlantic storm track over the twenty-first century, considerable spread exists between individual CMIP models regarding the magnitude and spatial distribution of these future storm-track changes (Chang et al. 2012; Woollings et al. 2012). The poleward shift is a key feature of the upper-level storm track in most models (Chang et al. 2012), but it is less consistently evident in lower-level diagnostics (Chang et al. 2012; Harvey et al. 2012). The eastward extension is also featured in the upper- and lower-level ensemble mean, but there is still substantial variability from model to model (Woollings et al. 2012). In fact, the North Atlantic sector has the largest intermodel spread in the storm-track response to global warming, despite the actual multimodel mean response being weak in the Northern Hemisphere (NH) relative to the SH (Harvey et al. 2014).

To better understand the intermodel spread in storm-track evolution under future climate change scenarios, the sources of and mechanisms driving the poleward shift and eastward extension of the North Atlantic storm track must be identified. Explanations for the future poleward shift in the storm tracks focus on thermodynamically driven adjustment processes internal to the atmosphere, particularly the warming of the tropical troposphere, cooling of the polar stratosphere, and subsequent changes in baroclinicity (e.g., Yin 2005; Butler et al. 2010). These changes have been hypothesized to affect the storm track by lifting the tropopause height (Lorenz and DeWeaver 2007), increasing tropospheric static stability (Frierson 2008; Lu et al. 2008), or altering the behavior of eddies (Chen et al. 2008; Kidston et al. 2011; Rivière 2011). The problem is complicated by the fact that some of these future changes have opposing influences on storm-track intensity. The combined effect has been evaluated using the concept of mean available potential energy (MAPE), the future changes of which are able to explain a fraction of the spread in the hemispheric mean response of storm-track intensity to global warming (O’Gorman 2010; Chang 2013).

In addition to the clear importance of thermodynamically driven adjustment processes internal to the atmosphere, recent evidence points toward drivers of future storm-track changes that are external to the atmosphere, particularly sea surface temperature (SST). Over the North Atlantic, the climatological storm track is anchored to the Gulf Stream, a region of strong surface baroclinicity that fuels storm-track growth. As such, variability in the extratropical SST gradients can cause fluctuations in the strength and location of the overlying jet and storm track (Nakamura et al. 2004; Brayshaw et al. 2008, 2011; Wilson et al. 2009). These SST gradients are expected to respond to greenhouse warming as well as the projected weakening of the Atlantic meridional overturning circulation (AMOC), but not necessarily in a straightforward way: while the former will warm SST, the latter could reduce the poleward transport of relatively warm water and thus cool SST along the Gulf Stream (Brayshaw et al. 2009; Frankignoul et al. 2013; Gastineau et al. 2013). The projected weakening in the AMOC differs widely between models and is potentially linked with the uncertainty in the low-level response of the North Atlantic storm track to global warming. However, it is not clear to what extent this statistical relationship reflects an atmospheric response to ocean circulation changes or vice versa (Woollings et al. 2012).

The North Atlantic atmospheric circulation, particularly in the upper troposphere, can also be influenced by remote SST changes. The tropical Pacific is an especially important source region for generating Rossby wave trains that propagate into the extratropics (Horel and Wallace 1981; Hoskins and Karoly 1981). Moreover, there is growing consensus that the mid-to-high-latitude climate is sensitive to changes in the tropical Pacific (Lee and Yoo 2014; Lee 2014; Feldstein and Lee 2014; Ding et al. 2014; Wettstein and Deser 2014; Baggett and Lee 2015). Indeed, the intermodel spread in El Niño–Southern Oscillation (ENSO)-like tropical Pacific SST changes is associated with a considerable fraction of the intermodel spread in upper-tropospheric circulation changes over the North Pacific and North Atlantic (Delcambre et al. 2013a,b).

Many aspects of the relationship between SST and North Atlantic storm-track changes remain unclear, particularly the relative importance of local and remote SST. The combination of tropical temperature and high-latitude sea ice changes affects equator-to-pole temperature gradients at upper and lower levels, and changes in these gradients have been shown to explain much of the projected multimodel mean response pattern in the low-level storm tracks in CMIP3 and CMIP5 (Harvey et al. 2014, 2015). The intermodel spread in the low-level storm track over the North Atlantic seems to be linked primarily to the spread in lower-tropospheric temperature gradient and, more specifically, to differences in the amount of lower-tropospheric warming in the high latitudes (Harvey et al. 2015). Yet intermodel spread in low-level storm-track activity is less understood over 30°–40°N, a region where the spread is not clearly associated with either the temperature gradient or the amount of globally averaged projected warming (Harvey et al. 2015). Furthermore, there has been less focus on the upper-level storm-track response in the North Atlantic, where nonlocal drivers may be more important (Graff and LaCasce 2014).

The main objective of this study is to compare the influence of local and remote SST changes on the upper-level North Atlantic storm track in the context of future climate change. To that end, the analysis aims to address two related questions: 1) to what extent is the North Atlantic storm track sensitive to local and remote SST changes over the twenty-first century; and 2) to what extent is the North Atlantic storm track sensitive to different realizations of SST changes over the twenty-first century? The remainder of the paper is organized as follows: section 2 describes the three coupled CMIP5 models and the experimental design used in this study. Section 3 compares future changes of upper-level storm-track changes within the coupled simulations from the three models. Section 4 examines the atmospheric response to future changes in local North Atlantic SST in uncoupled experiments. Section 5 examines the atmospheric response to projected changes in remote SST and other nonlocal changes in uncoupled experiments. Section 6 provides a summary and discussion of key results.

2. CMIP5 models and experimental design

The sensitivity of future changes in the North Atlantic storm track to future changes in SST is examined using a combination of coupled and uncoupled climate simulations. First, simulations from three coupled CMIP5 models are analyzed in order to characterize differences in projected North Atlantic storm-track changes. The analysis focuses on climatological periods from two CMIP5 scenarios: 1980–99 from the historical scenario and 2080–99 from the representative concentration pathway 8.5 (RCP8.5) scenario. The RCP8.5 scenario corresponds to a future projection with an increase in radiative forcing of +8.5 W m−2 by 2100 relative to preindustrial values (van Vuuren et al. 2011). Projected changes in the twenty-first-century climate are defined as the difference between the RCP8.5 and historical periods. The significance of projected changes is determined by comparing the projected changes to the internal variability in each model. The amount of internal variability is calculated by applying a bootstrapping method to 500 years of each model’s preindustrial control simulation. Consecutive 20-yr periods were sampled (with replacement) 1000 times, and the standard deviation of their means were calculated. A projected change is considered significant if it is at least two standard deviations of the internal variability from the preindustrial control simulation (IPCC 2013). The sensitivity of the North Atlantic storm track to SST changes is then assessed in a series of uncoupled atmospheric general circulation model (AGCM) experiments. These AGCM experiments test different realizations of local (North Atlantic) SST, remote (global) SST, and other nonlocal changes derived from the coupled simulations.

The analysis characterizes upper-level storm-track activity using the variance of 200-hPa meridional wind vv200, which represents eddy activity aloft during a mature stage of the baroclinic eddy life cycle when perturbations are well developed [as shown using the variance of 300-hPa meridional wind vv300 in Wettstein and Wallace (2010)]. A thirteenth-order 2–6-day bandpass Butterworth filter is applied to daily averages to isolate the high-frequency baroclinic wave activity (Blackmon 1976). Qualitatively similar results are obtained using several different bandpass filters as well as a 24-h time difference filter (Wallace et al. 1988). To complement the upper-level eddy activity, the 200-hPa zonal wind u200 and geopotential height z200 fields are also analyzed, providing an indication of the mean flow and low-frequency wave responses to prescribed SST changes. Hereinafter, the terms “storm track” and “jet” refer to the upper-level features defined by vv200 and u200, respectively. The results discussed here focus on boreal winter (December–February), when the North Atlantic storm track and its variability are most pronounced.

a. Model description

The current study analyzes results from a set of related coupled climate models, providing a unique opportunity to compare and contrast simulations from models with several common components. The three models are the Community Climate System Model, version 4 (CCSM4); Community Earth System Model, version 1 (Community Atmosphere Model, version 5) [CESM1(CAM5)]; and the Norwegian Earth System Model, version 1 (intermediate resolution) (NorESM1-M). This subset of the CMIP5 suite utilizes the same coupler and sea ice and land components. As described below, the atmospheric components are similar, with each model employing a different version of the Community Atmosphere Model (CAM). Two entirely different ocean components are used. This suite of models allows us to consider the following two sources of intermodel differences: 1) differences in the version of CAM and 2) differences in projected surface ocean changes [i.e., SST and sea ice concentration (SIC)] produced by the two ocean components. The model systems described in detail below are summarized in Fig. 1.

Fig. 1.

Schematic representation of the atmospheric and oceanic components of the (top) CCSM4 (blue), (middle) CESM1(CAM5) (cyan), and (bottom) NorESM1-M (magenta) models used in the analysis.

Fig. 1.

Schematic representation of the atmospheric and oceanic components of the (top) CCSM4 (blue), (middle) CESM1(CAM5) (cyan), and (bottom) NorESM1-M (magenta) models used in the analysis.

The CCSM4 is a fully coupled global climate model (Gent et al. 2011). The atmospheric component—CAM, version 4 (CAM4; Neale et al. 2013)—has a latitude–longitude resolution of 0.9° × 1.25° in the horizontal. The model has 26 vertical levels that extend to 2.917 hPa (Neale et al. 2012), thus not fully resolving the stratosphere. The ocean component of CCSM4 is the Parallel Ocean Program, version 2 (POP2; Danabasoglu et al. 2012), developed at Los Alamos National Laboratory. The horizontal grid in POP2 is in spherical coordinates and has nominal 1° resolution with a displaced pole over Greenland and a pole over Antarctica. POP2 has 60 vertical (depth) levels, 20 of which are in 10-m increments in the upper ocean. The sea ice component of CCSM4 is the Community Ice Code, version 4 (CICE4), based on the Los Alamos National Laboratory Sea Ice Model (Hunke and Lipscomb 2008). The land component in CCSM4 is the Community Land Model, version 4 (CLM4; Lawrence et al. 2011).

The CESM1(CAM5) uses many of the same model components as the CCSM4: namely, the ocean (POP2), land (CLM4), and sea ice (CICE4) components. The primary difference between the CESM1(CAM5) and the CCSM4 is the newer version of CAM (i.e., CAM5; Neale et al. 2012). Some of the improvements in CAM5 include enhancements to the radiative transfer code, boundary layer parameterizations, and a prognostic aerosol scheme. In particular, CAM5 incorporates both direct and indirect aerosol effects, whereas CAM4 includes only direct effects. CAM5 has the same horizontal resolution as CAM4 (0.9° × 1.25° latitude–longitude mesh) and the same model top, but the vertical resolution in CAM5 increases from 26 to 30 levels (Neale et al. 2012).

The third coupled climate model is the NorESM1-M (Bentsen et al. 2013; Iversen et al. 2013), which utilizes the coupler and the land (CLM4) and sea ice (CICE4) components from CCSM4 and CESM1(CAM5). The atmospheric component of the NorESM1-M is CAM4-Oslo, a modified version of the CAM4. Most of the CAM4-Oslo modifications to CAM4 are related to aerosols and their interactions with radiation and cloud microphysics (Kirkevåg et al. 2013). CAM4-Oslo has 26 vertical levels, like CAM4, but the horizontal resolution is coarser, at 1.9° latitude × 2.5° longitude. The ocean component of the NorESM1-M is distinct from that of CCSM4 and CESM1(CAM5), as it is based on the Miami Isopycnic Coordinate Ocean Model (MICOM; Bentsen et al. 2013), which uses potential density rather than geometric depth as in POP2. The MICOM version used in NorESM1-M has 53 vertical levels and a displaced horizontal grid of approximately 1° resolution with poles in Greenland and Antarctica.

b. Experimental design

In the first set of AGCM experiments (SST_NATL experiments; Table 1), the objective is to evaluate the sensitivity of the North Atlantic storm track to projected local SST changes, which are derived from the three coupled climate simulations. Each AGCM experiment is run with CAM5 and consists of a 30-member ensemble in which each member is a 12-month integration forced with the seasonal cycle in climatological mean SST and SIC from the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST; Rayner et al. 2003) dataset, initialized with slightly different atmospheric conditions. Results are calculated as the mean of the 30 separate 12-month simulations. In the perturbation experiment (SST_NATLPER), North Atlantic SSTs are perturbed for each calendar month by adding the twenty-first-century projected SST changes from each coupled simulation to the HadISST climatological mean. To isolate the effect of North Atlantic SST changes, the perturbations are applied only poleward of 20°N in the North Atlantic. SSTs elsewhere and SIC everywhere are set to the HadISST climatological monthly seasonal cycle. Along the southern boundary, perturbations in SST are linearly tapered to 0°C departures from the HadISST climatology over a 10° latitude band from 30° to 20°N to reduce the influence of unphysical SST gradients in the subtropics. Along the northern boundary, SST perturbations are similarly tapered toward the Arctic sea ice edge detected by HadISST. GHG concentrations correspond to average values over the 1980–99 period. The SST_NATLPER results are compared to a control AGCM experiment (SST_NATLCTL) forced with the HadISST climatological seasonal cycle across the globe. Statistical significance of the differences between SST_NATLPER and SST_NATLCTL is evaluated using a Student’s t test at the 95% confidence level, using an effective sample size calculated using the method from Bretherton et al. (1999).

Table 1.

Experimental setup of AGCM experiments. Each experiment consists of a 30-member ensemble in which each ensemble is integrated over 12 months.

Experimental setup of AGCM experiments. Each experiment consists of a 30-member ensemble in which each ensemble is integrated over 12 months.
Experimental setup of AGCM experiments. Each experiment consists of a 30-member ensemble in which each ensemble is integrated over 12 months.

Figure 2 shows the projected North Atlantic SST changes, illustrated as an average of three months (December–February) applied in the SST_NATLPER experiments. In the North Atlantic, all three coupled models exhibit similar SST changes except in the subpolar region, the location of the so-called warming hole. In the CESM1(CAM5) coupled simulations, the warming hole is manifested as a broad region of cooling by several degrees. CCSM4 and NorESM1-M, however, exhibit only weak and highly localized cooling in the subpolar North Atlantic. The projected SST changes also show substantial warming along the Gulf Stream in all three models, with the strongest increases in NorESM1-M.

Fig. 2.

Winter (December–February) mean SST from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. Projected changes (shading) are defined as the difference between 20 years of the RCP8.5 (2080–99) and historical (1980–99) scenarios. Gray contours correspond to the climatological winter (December–February) mean HadISST SST for the period 1980–99 and are drawn at 3°C intervals , starting with the 0°C contour in the Labrador and Greenland Seas.

Fig. 2.

Winter (December–February) mean SST from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. Projected changes (shading) are defined as the difference between 20 years of the RCP8.5 (2080–99) and historical (1980–99) scenarios. Gray contours correspond to the climatological winter (December–February) mean HadISST SST for the period 1980–99 and are drawn at 3°C intervals , starting with the 0°C contour in the Labrador and Greenland Seas.

In the second set of AGCM experiments (SST_GLOB experiments; Table 1), the objective is to evaluate the North Atlantic storm track’s sensitivity to remote SST changes. Each SST_GLOB simulation is also a 30-member CAM5 ensemble, but the boundary conditions (SST and SIC) are derived from the coupled simulations. In the SST_GLOBHIS experiments, the SST and SIC boundary conditions correspond to the climatological monthly mean seasonal cycle from 20 years of the historical scenario (1980–99), and GHG concentrations are the average values from the same time period. In the SST_GLOBRCP experiment, SST and SIC boundary conditions correspond to the climatological monthly mean seasonal cycle from 20 years of the RCP8.5 scenario (2080–99), and GHG concentrations are the average values from the same time period. The atmospheric circulation “response” from the SST_GLOB experiments is defined as the difference between SST_GLOBRCP and SST_GLOBHIS. Statistical significance of differences between SST_GLOBRCP and SST_GLOBHIS is evaluated using the same manner used for SST_NATL.

Figure 3 shows winter (December–February) mean projected SST changes from the coupled simulations applied in the SST_GLOB experiments. A comparison of the historical mean SST from the three coupled models (contours in Fig. 3, left) reveals considerable agreement in the structure, although NorESM1-M has slightly cooler SST relative to the other two models. The projected SST changes (SST_GLOBRCP − SST_GLOBHIST; shading in Fig. 3, left) exhibit a larger degree of spread between the models. All three coupled models simulate warming across much of the globe over the twenty-first century (the exception being the subpolar North Atlantic warming hole discussed previously) with Arctic amplification of the warming, but the strength of that warming varies substantially. NorESM1-M generally simulates weaker twenty-first-century warming than CCSM4 or CESM1(CAM5), particularly in the tropics and along the Antarctic Circumpolar Current. Differences in the magnitude of projected climate changes between CCSM4 and CESM1(CAM5) are potentially related to the different climate sensitivities (Meehl et al. 2013). The weaker SST warming in NorESM1-M could be related to the different ocean component. In particular, the average maximum AMOC intensity is considerably stronger in NorESM1-M [compared to CCSM4 and CESM1(CAM5)], and thus differences in AMOC intensity could impact the GHG-induced SST changes (Iversen et al. 2013). The three models exhibit similar Arctic sea ice distributions for the historical period (green contours in Fig. 3, right), but the two models with stronger warming [CCSM4 and CESM1(CAM5)] have less Arctic sea ice than NorESM1-M (red contours in Fig. 3, right) by the end of the twenty-first century.

Fig. 3.

Winter (December–February) mean (left) SST and (right) sea ice concentration (%) from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. Projected changes (shading) are defined as the difference between 20 years of the RCP8.5 (2080–99) and historical (1980–99) scenarios. Gray contours in the left panels correspond to the climatological winter (December–February) mean CMIP5 historical SST for the period 1980–99 and are drawn at 2.5°C intervals, starting with the thickened 0°C contour closest to the poles and increasing, with every 10°C contour thickened. In the right panels the 20% SIC contours from the historical and CMIP5 scenarios are represented by green and red contours, respectively.

Fig. 3.

Winter (December–February) mean (left) SST and (right) sea ice concentration (%) from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. Projected changes (shading) are defined as the difference between 20 years of the RCP8.5 (2080–99) and historical (1980–99) scenarios. Gray contours in the left panels correspond to the climatological winter (December–February) mean CMIP5 historical SST for the period 1980–99 and are drawn at 2.5°C intervals, starting with the thickened 0°C contour closest to the poles and increasing, with every 10°C contour thickened. In the right panels the 20% SIC contours from the historical and CMIP5 scenarios are represented by green and red contours, respectively.

To summarize, the design of the SST_NATL and SST_GLOB experiments focuses on different aspects of the North Atlantic storm-track response to projected SST changes. The SST_NATL experiments examine the storm-track sensitivity to SST changes in the North Atlantic alone. The SST_GLOB experiments examine storm-track sensitivity to the projected changes in global SST as well as SIC and GHG concentrations. A comparison of the coupled simulations and uncoupled experiments can provide insight on which aspects of future storm-track changes can be explained by local SST (SST_NATL) or remote (SST_GLOB) SST and other changes.

3. Projected changes in the North Atlantic storm track and jet from coupled simulations

The analysis first identifies the key characteristics of the projected twenty-first-century upper-level circulation changes obtained from the three CMIP5 coupled simulations and provides a basis for comparison with results from the uncoupled AGCM experiments. Over the last 20 years of the twentieth century from the historical scenario, the winter (December–February) climatological mean upper-level storm tracks (vv200) are broadly similar in all three coupled models over the North Atlantic (contours in Fig. 4, left). There is little intermodel spread in the geographical distribution of vv200, though the CCSM4 storm track exhibits considerably larger climatological amplitudes and a more zonal orientation relative to CESM1(CAM5) and NorESM1-M.

Fig. 4.

Winter (December–February) mean (left) vv200 and (right) u200 from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. Projected changes (shading, upper color bar for left panels and lower color bar for right panels) are defined as the difference between 20 years of the RCP8.5 (2080–99) and historical (1980–99) scenarios. Contours correspond to the climatological winter (December–February) mean values for the period 1980–99. The vv200 contour intervals are drawn every 25 m2 s−2, starting at 0 m2 s−2, and the u200 contour intervals are drawn every 10 m s−1, starting at 20 m s−1. White stippling corresponds to regions where the mean change is less than two standard deviations of the internal variability (see section 2 for details).

Fig. 4.

Winter (December–February) mean (left) vv200 and (right) u200 from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. Projected changes (shading, upper color bar for left panels and lower color bar for right panels) are defined as the difference between 20 years of the RCP8.5 (2080–99) and historical (1980–99) scenarios. Contours correspond to the climatological winter (December–February) mean values for the period 1980–99. The vv200 contour intervals are drawn every 25 m2 s−2, starting at 0 m2 s−2, and the u200 contour intervals are drawn every 10 m s−1, starting at 20 m s−1. White stippling corresponds to regions where the mean change is less than two standard deviations of the internal variability (see section 2 for details).

Considerably more intermodel spread exists between the projected twenty-first-century vv200 changes (shading in Fig. 4, left), particularly with regard to how each model represents the eastward extension of the North Atlantic storm track. The CCSM4 is the only model to simulate a clear poleward shift of the storm track over the North Atlantic. Enhanced eddy activity is also evident on the eastern end of the storm track over Europe, making the overall vv200 changes projected by CCSM4 consistent with the CMIP5 multimodel ensemble mean (cf. Fig. 4 in Chang et al. 2012). CESM1(CAM5) does not simulate a poleward shift in the storm track but does exhibit an intensification and eastward extension of the storm track over Europe. In contrast, NorESM1-M simulates no poleward shift and only a hint of an eastward extension of the storm track, but also an enhancement of eddy activity aloft upstream over North America. In general, the projected NorESM1-M vv200 changes are quite different from those in the CMIP5 multimodel ensemble mean (Chang et al. 2012).

The NH extratropical jets (u200) are also qualitatively similar over the historical period in the three models (contours in Fig. 4, right), with slight differences in the tilt and strength of the North Atlantic jet, but the projected changes differ noticeably (shading in Fig. 4, right). Over the North Atlantic, the CCSM4 and CESM1(CAM5) simulations indicate an eastward extension of the jet and also a strengthening on the southern flank. The NorESM1-M simulations exhibit a different signature than CCSM4 or CESM1(CAM5) and suggest more of a merging of the subtropical and eddy-driven jets. Over the North Pacific, the projected changes are even more varied. The CCSM4 simulates an overall intensification; the CESM1(CAM5) simulates only weak, localized changes on the eastern flank of the jet; and the NorESM1-M simulates a poleward deflection of the jet exit region.

The intermodel spread described in this section is modest compared to the full CMIP5 spread in projected twenty-first-century storm-track and jet changes (Chang et al. 2012). Nevertheless, the amplitudes of projected u200 changes are significantly distinct from the range of internal variability using the metric described in section 2 (note the significance of vv200 was not assessed because of a lack of daily data availability in the preindustrial control simulations). Furthermore, the differences in Fig. 4 exemplify key characteristics of the intermodel spread in the full CMIP5 ensemble: most notably, the uncertainty in the strength and extent of projected changes for both the storm track and jet over Europe. In the next two sections, uncoupled AGCM experiments are used to examine the responses of the upper-level storm track and jets to different CMIP5 realizations of SST changes and other boundary conditions.

4. Atmospheric response to changes in local North Atlantic SST

In this section, the uncoupled SST_NATL experiments are used to examine the sensitivity of the upper-level atmospheric response over the North Atlantic to projected twenty-first century changes in local SST. The SST_NATL experiments are shown in Fig. 5, with contours representing the climatological winter (December–February) mean storm track (left panels) and jet (right panels) in SST_NATLCTL. The vv200 and u200 from SST_NATLCTL are broadly similar to the climatological vv200 from the historical coupled simulations (contours in Fig. 4).

Fig. 5.

Winter (December–February) mean response of (left) vv200 and (right) u200 from the SST_NATL experiments forced with SST boundary conditions (BCs) obtained from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. The response (shading, upper color bar for left panels and lower color bar for right panels) is defined as the difference between the SST_NATLPER and SST_NATLCTL experiments. Contours correspond to the climatological winter (December–February) mean values from SST_NATLCTL. The vv200 contour intervals are drawn every 25 m2 s−2, starting at 0 m2 s−2, and the u200 contours are drawn every 10 m s−1, starting at 20 m s−1. White stippling corresponds to regions where the differences between SST_NATLPER and SST_NATLCTL fail to exceed the 95% confidence level using a Student’s t test.

Fig. 5.

Winter (December–February) mean response of (left) vv200 and (right) u200 from the SST_NATL experiments forced with SST boundary conditions (BCs) obtained from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. The response (shading, upper color bar for left panels and lower color bar for right panels) is defined as the difference between the SST_NATLPER and SST_NATLCTL experiments. Contours correspond to the climatological winter (December–February) mean values from SST_NATLCTL. The vv200 contour intervals are drawn every 25 m2 s−2, starting at 0 m2 s−2, and the u200 contours are drawn every 10 m s−1, starting at 20 m s−1. White stippling corresponds to regions where the differences between SST_NATLPER and SST_NATLCTL fail to exceed the 95% confidence level using a Student’s t test.

The winter storm-track response forced only with projected North Atlantic (local) SST changes (i.e., SST_NATLPER − SST_NATLCTL; shading in Fig. 5, left) exhibit considerable differences compared to the projected changes in the coupled simulations (shading in Fig. 4, left) in terms of the magnitude and structure of the response. The poleward shift in the coupled CCSM4 simulations is not evident in the response to local CCSM4 SST changes (Fig. 5, top left). The storm-track response to the local CESM1(CAM5) SST changes is characterized by a weak poleward shift but no eastward extension, as seen in the coupled simulations. The response to the local NorESM1-M SST forcing shows a distinct poleward intensification of the storm track, whereas the coupled NorESM1-M simulates an enhancement on the eastern and western ends. While the projected changes from the coupled simulations all have some eastward extension of the storm track, this extension is not as clearly seen in the SST_NATL experiments, suggesting that local SST changes are not the primary cause.

The upper-level jet response to local SST changes is generally more uniform across the different SST forcings than the storm-track response (shading in Fig. 5, right). The response is dominated by a weakening of the jet stream over the North Pacific and a poleward shift over the North Atlantic, except perhaps in the experiment driven by CCSM4 SST forcing. The global pattern is qualitatively consistent between each experiment despite the fact that the North Atlantic SST forcings are quite different in each experiment (cf. the figures in the left panels of Fig. 5). The broad consistency suggests that the jet response is more sensitive to the overall warming of North Atlantic SST than to any detailed differences in the three SST forcings used as boundary conditions in the SST_NATL experiments. Overall, the jet responses are weak compared to the projected changes in the coupled simulations and show no evidence of the eastward extension seen at least for CCSM4 and CESM1(CAM5) in the right panels of Fig. 4.

GHG concentrations are held fixed at average 1980–99 values in SST_NATL, so one might wonder if the lack of a direct radiative response to increasing GHG concentrations is responsible for the weak atmospheric response to local SST changes. The experimental design of SST_NATL introduces a slight inconsistency in this regard: the projected SST changes used as boundary conditions for the atmospheric model include the effect of increasing GHG concentrations, but the prescription of late historical GHG concentrations eliminates the direct atmospheric response to GHG-induced radiative forcing.

To assess the direct and immediate influence of increasing GHG concentrations, two additional sets of experiments are considered. The first is a set of experiments that test the combined influence of increased GHG concentrations and projected SST changes (SST_NATL_GHGPER; Table 1). These experiments are identical to the SST_NATL experiments, but GHG concentrations are prescribed to values averaged over the period 2080–99 from the RCP8.5 scenario rather than averaged 1980–99 values. The upper-level atmospheric response to the SST_NATL_GHGPER (not shown) is qualitatively similar to the response in the SST_NATL experiments with 1980–99 GHG concentrations. The second is a CAM4 AGCM experiment designed to isolate the direct and immediate impact of GHG-induced radiative forcing on the atmospheric circulation. This experiment, performed as part of CMIP5 (sstClim4xCO2), was run with climatological SST and sea ice and was forced by an abrupt CO2 perturbation to roughly 4 times the preindustrial concentration. Neither the North Atlantic storm track nor the jet responses in the 4 × CO2 experiments (Fig. 6) resemble the projected changes in the coupled CCSM4 or other coupled simulations (Fig. 4). Together, these experiments suggest that the weak and spatially nonuniform storm-track response in the SST_NATL experiments (Fig. 5) is not due to the lack of a direct and immediate response to increasing GHG concentrations in the experiment, consistent with previous results (Grise and Polvani 2014).

Fig. 6.

Winter (December–February) mean response of (left) vv200 and (right) u200 to abrupt CO2 increases to roughly 4 times their preindustrial values in the sstClim4xCO2 simulation from CCSM4. The simulation uses the CAM4 model and 30-yr climatological mean SST and SIC BCs from the CCSM4 preindustrial control simulation. The response (shading, upper color bar for left and lower color bar for right) is defined as the difference between the sstClim4xCO2 and sstClim simulations. Contours correspond to the climatological winter (December–February) mean values from the sstClim simulations. The vv200 contour intervals are drawn every 25 m2 s−2, starting at 0 m2 s−2, and the u200 contour intervals are drawn every 10 m s−1, starting at 20 m s−1.

Fig. 6.

Winter (December–February) mean response of (left) vv200 and (right) u200 to abrupt CO2 increases to roughly 4 times their preindustrial values in the sstClim4xCO2 simulation from CCSM4. The simulation uses the CAM4 model and 30-yr climatological mean SST and SIC BCs from the CCSM4 preindustrial control simulation. The response (shading, upper color bar for left and lower color bar for right) is defined as the difference between the sstClim4xCO2 and sstClim simulations. Contours correspond to the climatological winter (December–February) mean values from the sstClim simulations. The vv200 contour intervals are drawn every 25 m2 s−2, starting at 0 m2 s−2, and the u200 contour intervals are drawn every 10 m s−1, starting at 20 m s−1.

5. Atmospheric response to remote changes in SST and other boundary conditions

The SST_NATL experiments described in the previous section and Fig. 5 suggest that projected changes in local North Atlantic SST are not responsible for the projected changes in the North Atlantic storm track from the coupled simulations (Fig. 4). The inconsistent storm-track responses between the coupled simulations and uncoupled experiments could, in part, arise from a lack of interannual SST variability or from missing two-way air–sea interactions in AGCM experiments. However, it is also possible that the North Atlantic atmospheric circulation is more sensitive to remote SST changes or other nonlocal influences. A separate set of AGCM experiments (SST_GLOB experiments; Table 1) is performed in which the CAM5 is forced with global SST and SIC boundary conditions obtained from the three coupled simulations for the historical and CMIP5 RCP8.5 scenarios and with commensurate GHG concentrations.

The SST_GLOB winter (December–February) storm-track responses (i.e., the difference in vv200 between SST_GLOBRCP and SST_GLOBHIS; shading in Fig. 7, left) are clearly different from the SST_NATL responses (shading in Fig. 5, left) and capture more of the characteristic features in the coupled simulations (shading in Fig. 4, left). Most notably, the SST_GLOB storm-track responses are considerably stronger than the SST_NATL responses and comparable in strength to the projected changes from the coupled simulations. Moreover, the SST_GLOB patterns are dominated by an eastward extension of the storm track, a feature that is not evident in the SST_NATL experiments but is in many CMIP5 coupled simulations, including CCSM4 and CESM1(CAM5) (Fig. 4, left). The SST_GLOB jet responses (shading in Fig. 7, right) are also clearly different from the SST_NATL responses (shading in Fig. 5, right). Over the North Pacific, the SST_NATL jet responses exhibit consistent weakening, but the SST_GLOB jet responses are more varied, similar to the coupled simulations (shading in Fig. 4, right). Over the North Atlantic, the SST_GLOB jet responses to CCSM4 and CESM1(CAM5) forcings exhibit the eastward extension also evident in their respective coupled simulations [at least in CCSM4 and CESM1(CAM5)] but not in the SST_NATL experiments.

Fig. 7.

Winter (December–February) mean response of (left) vv200 and (right) u200 from the SST_GLOB experiments forced with SST and/or SIC BCs obtained from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. The response (shading, upper color bar for left panels and lower color bar for right panels) is defined as the difference between the SST_GLOBRCP and SST_GLOBHIS experiments. Contours correspond to the climatological winter (December–February) mean values from the SST_GLOBHIS experiments. The vv200 contour intervals are drawn every 25 m2 s−2, starting at 0 m2 s−2, and the u200 contour intervals are drawn every 10 m s−1, starting at 20 m s−1. White stippling corresponds to regions where the differences between SST_GLOBRCP and SST_GLOBHIS fail to exceed the 95% confidence level using a Student’s t test.

Fig. 7.

Winter (December–February) mean response of (left) vv200 and (right) u200 from the SST_GLOB experiments forced with SST and/or SIC BCs obtained from (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M coupled simulations. The response (shading, upper color bar for left panels and lower color bar for right panels) is defined as the difference between the SST_GLOBRCP and SST_GLOBHIS experiments. Contours correspond to the climatological winter (December–February) mean values from the SST_GLOBHIS experiments. The vv200 contour intervals are drawn every 25 m2 s−2, starting at 0 m2 s−2, and the u200 contour intervals are drawn every 10 m s−1, starting at 20 m s−1. White stippling corresponds to regions where the differences between SST_GLOBRCP and SST_GLOBHIS fail to exceed the 95% confidence level using a Student’s t test.

The North Atlantic storm-track changes are better represented in SST_GLOB than in SST_NATL, suggesting an important contribution from factors beyond local North Atlantic SST changes. Because of the SST_GLOB experimental design, the differences in the atmospheric responses between SST_NATL and SST_GLOB (cf. Figs. 5 and 7) could be attributed to a combination of direct radiative forcing associated with increasing GHG concentrations, reduced SIC, and remote SST changes. The previous section demonstrated that the direct atmospheric response to increase CO2 is small. One caveat is that SST_NATL uses HadISST climatology (with SST perturbations imposed in the North Atlantic) as the background climate, whereas SST_GLOB experiments use climatologies from the coupled CMIP5 simulations. The different background climates in SST_GLOB can result in a different atmospheric response (see Kushnir et al. 2002). However, a comparison of the atmospheric responses between the climatological SST from HadISST alone (i.e., SST_NATLCTL; contours in Fig. 5) and climatological historical SST conditions (i.e., SST_GLOBHIS; contours in Fig. 7) shows relatively small differences. The linearity of the atmospheric response to North Atlantic SST perturbations has also been noted (Hand 2014), in which projected SST changes applied in a warmer climate (i.e., SST perturbations subtracted from RCP8.5 climate) result in qualitatively similar atmospheric responses to projected SST changes applied in the historical climate.

The role of SIC is not explicitly evaluated in the current analysis, but it is known that long-term SIC changes may also affect North Atlantic storm-track variability [Cohen et al. (2014), among others]. The Arctic sea ice reduction prescribed in the SST_GLOB experiments (Fig. 3, right) may therefore contribute to the strong atmospheric response in SST_GLOB (shading in Fig. 7,) relative to the SST_NATL experiments (shading in Fig. 5). However, studies have also indicated that upper-level North Atlantic circulation changes are potentially more affected by internal atmospheric variability or remote surface changes (Graversen et al. 2008) than by Arctic sea ice changes itself (Deser et al. 2010; Screen et al. 2012). Therefore, the remainder of this section explores the relationship between North Atlantic circulation changes and remote surface changes, particularly SST in the tropical Pacific, a region known to have a significant impact on high-latitude climate (Screen et al. 2012; Ding et al. 2014; Lee and Yoo 2014), including Arctic sea ice (Wettstein and Deser 2014), and thus potentially also on the atmospheric circulation over the North Atlantic.

To examine the links between the upper-level atmospheric circulation and tropical Pacific SST, winter (December–February) u200 anomalies from the entire transient RCP8.5 simulations are regressed onto standardized indices of area-averaged SST anomalies in two regions: the eastern Pacific (EP; SST averaged over 10°N–10°S, 150°–90°W) and western Pacific (WP; SST averaged over 10°N–10°S, 130°E–170°W). Both u200 and SST anomalies are taken from fully coupled CCSM4, CESM1(CAM5), and NorESM1-M RCP8.5 simulations (2006–99). There are clear similarities between the WP and EP regression patterns in the low latitudes (Fig. 8), where easterly anomalies are evident along the equatorial Pacific, including a stronger subtropical jet over the North Pacific. These patterns are likely related to changes in the Walker and Hadley circulations (Gill 1980; Held and Hou 1980; Lee et al. 2009).

Fig. 8.

Tropical–extratropical linkages in coupled RCP8.5 (2006–99) simulations. Regressions (shading) of winter (December–February) u200 anomalies onto the standardized area-averaged SST anomalies over the (left) eastern Pacific (10°N–10°S, 150°–90°W; green box) and (right) western Pacific regions (10°N–10°S, 130°E–170°W; green box) in (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M. Contours correspond to the climatological winter (December–February) mean u200 and are drawn at 10 m s−1 intervals, starting at 20 m s−1. Anomalies are relative to the 2006–99 climatology.

Fig. 8.

Tropical–extratropical linkages in coupled RCP8.5 (2006–99) simulations. Regressions (shading) of winter (December–February) u200 anomalies onto the standardized area-averaged SST anomalies over the (left) eastern Pacific (10°N–10°S, 150°–90°W; green box) and (right) western Pacific regions (10°N–10°S, 130°E–170°W; green box) in (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M. Contours correspond to the climatological winter (December–February) mean u200 and are drawn at 10 m s−1 intervals, starting at 20 m s−1. Anomalies are relative to the 2006–99 climatology.

The u200 regression maps also show remote extratropical teleconnections associated with undetrended tropical SST variability (i.e., including the overall effects of global warming). Anomalously warm SSTs drive rising motion through deep convection, creating upper-level atmospheric divergence and generating Rossby waves that radiate from the tropics via interactions with the subtropical jet (Hoskins and Karoly 1981; Sardeshmukh and Hoskins 1988). The EP-related u200 patterns (Fig. 8, left) show alternating bands of easterly and westerly anomalies over the extratropical North Pacific but not much of a signal over the North Atlantic. In contrast, WP-related u200 anomalies (Fig. 8, right) extend not only over the North Pacific, but also across the NH into the high latitudes of the North Atlantic. Relative to the EP regression patterns, these North Atlantic anomalies in the WP regression patterns are more similar to the projected changes in both the coupled simulations (shading in Fig. 4, right) and uncoupled SST_GLOB experiments (shading in Fig. 7, right). WP SST anomalies are associated with an eastward extension of the North Atlantic jet, at least in CCSM4 and CESM1(CAM5). Regressions of zonally asymmetric z200 anomalies onto the WP SST anomalies (Fig. 9) further illustrate the extension of a Rossby wave train over the northern North Atlantic in these two models. The North Atlantic circulation pattern is considerably weaker in NorESM1-M, and the asymmetric z200 anomalies show little evidence of a planetary-scale Rossby wave train.

Fig. 9.

Tropical–extratropical linkages in coupled RCP8.5 (2006–99) simulations. Regressions (shading) of winter (December–February) z200 anomalies onto standardized, area-averaged SST anomalies over the west Pacific region (10°N–10°S, 130°E–170°W) in (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M. The zonal mean has been removed from the z200 data prior to performing the regression.

Fig. 9.

Tropical–extratropical linkages in coupled RCP8.5 (2006–99) simulations. Regressions (shading) of winter (December–February) z200 anomalies onto standardized, area-averaged SST anomalies over the west Pacific region (10°N–10°S, 130°E–170°W) in (top) CCSM4, (middle) CESM1(CAM5), and (bottom) NorESM1-M. The zonal mean has been removed from the z200 data prior to performing the regression.

The results in Figs. 8 and 9 indicate that the atmospheric circulation over the North Atlantic may be more closely related to SST changes in the western tropical Pacific than in the eastern Pacific. Ding et al. (2014) noted a similar wave train in reanalysis data associated with tropical SST variability unrelated to El Niño. However, a clear attribution of causality is difficult in our results because the regressions are performed on a fully coupled simulation responding to a strong external forcing (RCP8.5). That said, a reasonable physical link is that WP SST changes influence the character of tropically forced Rossby waves through specific convection changes. In observations, convection [often diagnosed via outgoing longwave radiation (OLR)] typically occurs over 27°–30°C water, but the SST–convection relationship is nonlinear and complicated (Graham and Barnett 1987; Zhang 1993; Taschetto et al. 2010).

Figure 10 shows the SST–OLR relationship in the historical and RCP8.5 scenarios. The joint distribution of SST and OLR (left panels) indicates that the SST at which convection mostly likely occurs (defined as the SST above which 90% of the SST–OLR distribution lies) increases from the twentieth century (historical scenario) to the twenty-first century (RCP8.5 scenario). During the end of the historical scenario (1980–99), convection is most likely to occur at SST greater than about 29°C for CCSM4 and greater than about 28°C for both CESM1(CAM5) and NorESM1-M. The region over which this convective SST is reached is similar across the models, covering the western tropical Pacific (green contour in Fig. 10, right). During the last 20 years of the RCP8.5 scenario, the threshold increases to about 31.5°C in CESM1(CAM5) and CCSM4 (Fig. 10, left, brown shading), but the region over which these convective SSTs occur (brown contour in Fig. 10, right) does not change considerably because of the strong projected tropical SST warming in these simulations (Fig. 3, left, shading). In NorESM1-M, the most likely SST for deep convection is above about 31°C, but the region over which this temperature is attained is confined to a smaller region of the western tropical Pacific because of relatively weaker projected SST warming in NorESM1-M.

Fig. 10.

(left) Data distribution of winter (December–February) tropical OLR (in 10 W m−2 bins) and SST (in 0.5°C bins) from the last 20 years of the historical (green shading) and RCP8.5 (brown shading) scenarios. Shading represents the number of observations that fall within each SST–OLR bin. (right) Projected changes in winter (December–February) mean 200-hPa divergence (shading). Gray contours correspond to the winter (December–February) climatological mean divergence over the historical (1980–99) scenario and are drawn at 2 × 10−6 s−1 intervals, starting at 1.5 × 10−6 s−1.The projected changes are defined as the difference between 20 years of the RCP8.5 (2080–99) and historical (1980–99) scenarios. Green and brown contours correspond to the SST above which 90% of the historical SST–OLR distribution at left occurs in the historical and RCP8.5 scenarios, respectively.

Fig. 10.

(left) Data distribution of winter (December–February) tropical OLR (in 10 W m−2 bins) and SST (in 0.5°C bins) from the last 20 years of the historical (green shading) and RCP8.5 (brown shading) scenarios. Shading represents the number of observations that fall within each SST–OLR bin. (right) Projected changes in winter (December–February) mean 200-hPa divergence (shading). Gray contours correspond to the winter (December–February) climatological mean divergence over the historical (1980–99) scenario and are drawn at 2 × 10−6 s−1 intervals, starting at 1.5 × 10−6 s−1.The projected changes are defined as the difference between 20 years of the RCP8.5 (2080–99) and historical (1980–99) scenarios. Green and brown contours correspond to the SST above which 90% of the historical SST–OLR distribution at left occurs in the historical and RCP8.5 scenarios, respectively.

The shading in the right panels of Fig. 10 suggests that changes in the convectively prone SST may influence the projected changes in the upper-level (200 hPa) divergence, an important indicator of the potential for extratropical Rossby wave generation (Sardeshmukh and Hoskins 1988). All three models show some decrease in divergence (shading) from the historical scenario (black contours), coincident with changes in the region of convectively prone SST. However, NorESM1-M exhibits a much stronger decrease in divergence relative to CCSM4 and CESM1(CAM5) (more than double). The results in Fig. 10 suggest a hypothesis in which the decrease in NorESM1-M divergence related to reduced SST-induced convection may be strong enough to inhibit Rossby wave generation. This hypothesis would be challenging to test in an AGCM with imposed SST anomalies in the western Pacific. Any imposed SST anomalies could create unphysical SST gradients that would likely influence the Walker circulation, the subtropical jet, and remote teleconnections, so the influence of any prescribed tropical Pacific SST anomalies on extratropical teleconnections would be difficult to interpret. A future study could use a larger suite of CMIP5 models to examine the robustness of the relationship between changes in the SST-induced convection and the Rossby waves that influence North Atlantic jet and storm track.

6. Summary and discussion

This study uses a set of related models within the CMIP5 suite to assess whether future changes in SST may influence the projected poleward shift and eastward extension of the North Atlantic storm track. In particular, fully coupled simulations and uncoupled AGCM experiments are used to examine the response of the North Atlantic winter storm track to different realizations of local and remote SST changes from three models. The results shed light on the two motivating questions presented in the introduction:

  • 1) To what extent is the North Atlantic storm track sensitive to local and remote SST changes over the twenty-first century?

  • Projected changes in local SST have a weak impact on the upper-level North Atlantic storm track (SST_NATL experiments), with few regions that are statistically significant. Moreover, the magnitude and pattern of the North Atlantic circulation responses to local SST changes from the three models do not resemble the patterns in the coupled simulations.

  • Projected changes in remote SST have a considerably stronger impact on the upper-level North Atlantic storm track (SST_GLOB experiments). The atmospheric response to projected changes in global SST and SIC exhibits an eastward extension of the North Atlantic jet and storm track that is consistent with projected changes from the coupled simulations, particularly in CCSM4 and CESM1(CAM5).

  • The projected eastward extension of the North Atlantic jet and storm track can, in part, be linked to projected SST changes in the western Pacific (SST_GLOB experiments and coupled RCP8.5 simulations). However, these physical links cannot be decoupled from long-term trends that are ultimately driven by the strong external greenhouse forcing.

Overall, the results suggest that remote SST changes, particularly in the tropical Pacific, may contribute to upper-level storm-track changes over the North Atlantic, consistent with previous studies highlighting the links between the tropical Pacific and high-latitude climate (Ding et al. 2014; Baggett and Lee 2015). Nevertheless, some limitations of the study should be mentioned. While similar results were found using CAM4 (not shown) as well as CAM5, the relative influence of the local and remote SST on the North Atlantic changes may depend on the choice of atmospheric model. The stratosphere may play an important role (Butler and Polvani 2011; Butler et al. 2014; Manzini et al. 2014), and the relevant processes would not be fully resolved in the versions of CAM used here. It is also noted that the analysis and experiments presented here could be sensitive to the effect of imposed surface boundary conditions on the strength of the Hadley circulation and may impact the structure of tropical–extratropical connections. Finally, the results of the study highlight that remote tropical SST changes may impact changes in the upper-level North Atlantic storm-track indicators, but low-level storm-track indicators may respond differently. Indeed, a preliminary analysis of some low-level storm-track indicators suggests that the projected low-level eastward extension is linked to the projected changes in local North Atlantic SST, consistent with previous studies (Woollings et al. 2012; Harvey et al. 2014, 2015). These SST changes could be related to changes in the AMOC (Woollings et al. 2012) and would affect the equator-to-pole temperature gradient, which is known to influence the North Atlantic storm track (Harvey et al. 2014, 2015). The links between the remote–local SST changes and the lower-tropospheric storm-track signature in CCSM4, CESM1(CAM5), and NorESM1-M is the focus of ongoing work.

  • 2) To what extent is the North Atlantic storm track sensitive to different realizations of SST changes over the twenty-first century?

  • The upper-level storm-track response to different patterns of local SST forcing exhibits some differences (SST_NATL experiments), but the signals are not largely statistically significant. A poleward shift is evident in response to the CESM1(CAM5) and NorESM1-M SST forcings, whereas only a weak intensification is evident in response to the CCSM4 SST forcing in the North Atlantic.

  • The upper-level storm-track response to different patterns of global SST and/or SIC forcings is less varied (SST_GLOB experiments). An eastward extension is evident in all three experiments forced by global changes in boundary conditions. However, the magnitude and longitudinal extent of the extension varies between responses, similar to the intermodel differences from the coupled simulations.

  • It is hypothesized that intermodel differences in the North Atlantic upper-level atmospheric circulation can, in part, be linked to intermodel differences in WP SST. In CCSM4 and CESM1(CAM5), the convective SST area remains relatively constant with global warming, while in NorESM1-M it shrinks considerably. This suppresses upper-level divergence associated with deep convection and could inhibit the ability of SST variability in this region to force Rossby wave trains, leading to a different North Atlantic response in NorESM1-M than in the other two models.

The results of the analysis suggest a potential link between the intermodel spread in western Pacific SST and North Atlantic upper-level storm-track changes for three of the CMIP5 models. These three do not represent the full range of storm-track changes within the CMIP5 suite, and models with different atmospheric parameterizations could exhibit different sensitivities to the SST changes. The small number of models used here limits the ability to quantify the extent to which the intermodel spread in SST changes accounts for the intermodel spread in the North Atlantic storm-track changes. Moreover, it is likely that other factors will also influence the North Atlantic storm track, including ocean circulation changes (Woollings et al. 2012) and equator-to-pole temperature gradients (Harvey et al. 2015). However, the objective of this analysis is to identify the sources of intermodel spread for the extratropical atmospheric circulation in a particular set of closely related CMIP5 simulations. Moreover, the results highlight the tropical Pacific SST as a potential source of uncertainty in North Atlantic storm-track variability, adding support to the growing body of literature that links the tropics to North Atlantic atmospheric circulation.

Acknowledgments

We thank N. Keenlyside and T. Woollings for helpful discussions and I. Bethke for assistance with the design and setup of the AGCM experiments. This work was supported by the Research Council of Norway Projects EarthClim (207711/E10), EVA (22977), and jetSTREAM (231716). JJW acknowledges support by a donation from the G. Unger Vetlesen Foundation. We also acknowledge the Norwegian Metacenter for Computational Science and Storage Infrastructure (NOTUR and Norstore Projects NN2345k and NS2345k).

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