1. Introduction
Atmospheric storm tracks are very important for climate dynamics. They indicate regions of maximum transient poleward energy transport and zonal momentum transport (Chang et al. 2002) and play an important role in setting the dynamical response of the midlatitudes to global warming through their radiative forcing (Voigt and Shaw 2015). Storm tracks are generally calculated as the standard deviation of atmospheric data that has been filtered in the time domain to isolate synoptic variability (Blackmon 1976). Typical variables used to calculate storm tracks are meridional wind, eddy kinetic energy, or geopotential height, at a fixed vertical level. This metric represents the climatology of baroclinic wave activity (i.e., high and low pressure systems), but for historical reasons has been termed “storm track” [see Wallace et al. (1988) for more discussion]. Following Chang et al. (2002), we consider each ocean basin as having its own storm track. Storm tracks offer a reasonable proxy for climatological activity of extratropical cyclones (Hoskins and Hodges 2002), and their maxima occur over the oceans, in the vicinity of ocean western boundary currents (WBCs) and their extensions (e.g., Fig. 1b).
WBCs are unique regions of air–sea coupling: ocean currents in these regions generate strong ocean heat flux convergence, which can dictate spatial and temporal variability in air–sea fluxes [see reviews by Kwon et al. (2010) and Kelly et al. (2010)]. The North Atlantic and North Pacific WBCs, the Gulf Stream, and Kuroshio–Oyashio Extension (KOE) influence the atmosphere through the entire troposphere during spring and summer (Minobe et al. 2008, 2010; Xu et al. 2011; Sasaki et al. 2012) and modify low-level atmospheric baroclinicity, shifting the free-tropospheric storm track and altering the poleward heat and moisture transport (e.g., Tokinaga et al. 2009; Frankignoul et al. 2011; Ogawa et al. 2012; Taguchi et al. 2012; Kwon and Joyce 2013; O’Reilly and Czaja 2015). In the Southern Ocean, south of the Indian Ocean, the Agulhas Return Current (ARC) helps to anchor the climatological location of the free-tropospheric storm track (Nakamura et al. 2004). This causes the region to have a consistent storm track throughout the year, which, for the Southern Ocean storm track, is a trait that is unique to the ARC region.
These examples of the oceans influencing the storm tracks primarily focus on the free-tropospheric storm tracks (e.g., the filtered geopotential at 500 hPa or the filtered meridional winds at 850 hPa). However, one can also analyze the surface storm tracks based on meridional winds at 10 m. Booth et al. (2010) show that the spatial patterns of storm tracks at 10 m differ from the free-tropospheric storm tracks due to the influence of ocean WBCs. Booth et al. (2010) used physical arguments proposed by Sweet et al. (1981) to suggest that the warm water in WBC creates regions with stronger atmospheric instability during cold air outbreaks associated with extratropical cyclones. The greater instability on the warm side of the WBC increases vertical mixing of momentum in these unstable regions creating stronger surface winds (a so-called momentum-mixing mechanism; see also Wallace et al. 1989). This preferential vertical mixing of momentum causes surface storm tracks to have a maximum in a region that differs from the maximum aloft. In addition, Joyce et al. (2009) showed that the surface storm tracks covary with the WBC at the interannual-to-decadal time scale.
The momentum-mixing mechanism is one element of forcing at the WBC. It is also known that an atmospheric pressure gradient force created by strong ocean fronts can accelerate winds blowing from the cold to the warm side of the sea surface temperature (SST) front (Lindzen and Nigam 1987; Chelton et al. 2004). Thus, in the regions of surface storm tracks, it is possible that the spatial gradient in momentum mixing and the pressure gradient force, both associated with WBCs, could influence the surface winds. In the Gulf Stream region, both mechanisms have been shown to play some role in at least one general circulation model (GCM) (Brachet et al. 2012). However, other work suggests that the pressure gradient mechanism, which was created for the tropics, may not be very strong in high-wind regimes of the storm tracks (Spall 2007; Small et al. 2008; Schneider and Qiu 2015). Additionally, recent analysis by Liu et al. (2013) shows that the momentum-mixing mechanism tends to dominate on shorter time scales, such as those captured by the storm tracks.
In addition to the momentum mixing and pressure gradient physics, the storm tracks near the WBC need to be considered because the WBCs are extratropical cyclone genesis regions in the Northern Hemisphere (Hoskins and Hodges 2002). Because the storms typically grow due to the merging of a surface and upper-level disturbance, the near-surface behavior at the WBC region may be indicative of storm genesis. Nakamura and Shimpo (2004) examined the Southern Ocean storm track and showed that the SST gradient at the ARC is important for maintaining low-level baroclinicity. Hoskins and Hodges (2005) show that the genesis region for the Indian Ocean storm track is in the Andes mountain region; however, the large amount of secondary cyclogenesis in the Southern Ocean suggests that baroclinic anchoring by the ARC would still be important for storm genesis. Booth et al. (2010) showed that for JJA in the Indian Ocean, there are two active regions in the surface storm track: one near the ARC and another near the sea ice edge. Related to this, Nakamura and Shimpo (2004) emphasize that the ARC helps maintain a strong storm track during SH summer (DJF).
Given the climatological importance of storm tracks and the role of WBCs in forcing surface storm tracks, it stands to reason that surface storm tracks in GCMs are a good variable to analyze to check model biases and better understand coupled model physics. In particular, the biases in the surface storm tracks, as compared to the biases in the free-tropospheric storm tracks, may inform on model issues regarding the WBCs and the modeled momentum mixing in the midlatitudes. It is already known that GCMs often have issues in representing the separation of the WBCs from the coastlines in the Northern Hemisphere, in particular, for the non-eddy-resolving ocean models (e.g., Gent et al. 2011; Schoonover et al. 2016). Coupled models with eddy-resolving oceans better represent the strength, width, and path of the WBCs, but can still exhibit overshooting of the path (e.g., Small et al. 2014; Griffies et al. 2015). Therefore, an analysis of the surface storm tracks in coupled GCMs tests the physics of the ocean and atmosphere as well as their coupling. With this as motivation, the present study examines free-tropospheric and surface storm tracks along with SST in the WBC regions using 12 CMIP5 models.
Previous work has analyzed the free-tropospheric storm tracks in the CMIP5 models with a focus on future projections (Chang et al. 2012). Here, we instead focus on the historical runs, to determine the model’s ability to represent the surface storm tracks. We ask the following questions: 1) Can models capture differences in the locations and amplitudes of the free-tropospheric and surface storm tracks? 2) What factors determine the strength of the surface storm tracks in the models? 3) What are the relative influences of the free troposphere and the ocean surface in determining the modeled location of the surface storm tracks? To address these questions, we examine the storm tracks and SST at the global and ocean-basin scale. The physics that we are interested in, such as momentum mixing affecting the location of the surface storm track, have already been discussed in previous papers. Here, we are attempting to use the CMIP5 models to determine if these same physical processes cause biases in the SST to be manifest as biases in the surface storm track.
2. Data and methods
a. Models and data
The variables we analyze are meridional winds at 10 m (V10), 850 hPa (V850), and 500 hPa (V500), as well as surface temperature (TS) and a rough estimate of atmospheric stability in the lower troposphere, hereafter, TDIFF, defined as TS minus 850-hPa air temperature. Note that TS is exactly equal to SST over the oceans, except in regions of sea ice. The reanalysis data utilized here are from ERA-Interim (hereinafter ERA-I; Dee et al. 2011) and have been shown to perform as well as any other recent reanalyses at capturing midlatitude storm activity (Hodges et al. 2011). We use the SST provided with the reanalysis (which is based on merged SST observations, discussed in the next paragraph) so that 1) we use the SST that reanalysis variables were driven by, and 2) all of the reanalysis variables are on the same grid. The epoch used for this study is 1979–2005, which is the overlap of ERA-I and the time period of the historical integrations according to the CMIP protocol.
We note that the horizontal resolution of the SST used to drive ERA-I has been changed three times, which can have some impact on surface winds (e.g., Chelton 2005; Masunaga et al. 2015). However, the spatial and temporal scales analyzed in those studies differ from those of interest in the present work. Additionally, we find that surface storm tracks in ERA-I are very similar in spatial pattern and intensity to that in the NCEP CFSR (Saha et al. 2010) and NASA MERRA (Rienecker et al. 2011) (not shown).
Our analysis focuses on CMIP5-type models, which were run using observed atmospheric forcing (i.e., the “historical” run in the CMIP5 protocol). The coupled models used, along with their acronyms, are detailed in Table 1. The 12 models used in this analysis were chosen based on the availability of the variables used in the analysis, with the daily (or finer temporal resolution) surface winds often being the limiting factor. Some of our analysis also examines atmosphere-only versions of the GFDL and GISS models, referred to here as GFDL AM3 and GISS AMIP, respectively. These models are also driven by historical atmospheric radiative forcing, but they have prescribed SSTs (which are based on observations); that is, they are AMIP-type models. The horizontal resolution of each of the models is given in Table 1. For each GCM listed in the table, we analyze a single ensemble member of the model.
The reanalysis and models used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)
For TS and 850-hPa air temperature, we used monthly data to calculate the climatology. Daily data were used for V10, V850, and V500, and for models for which data were not available, 3- or 6-hourly data (if available) were averaged to create a proxy for the daily value before calculating the storm tracks.
V10 was not available for CESM1 Large Ensemble (CESM1-LE) and NorESM1-M. Therefore, we use the meridional wind at the lowest model level (VBOT), which is located at 55–70 m for CESM1-LE, and at a similar height for NorESM1-M. It may be questioned whether 55–70 m is really a representative height for a “surface” storm track. Over the land, this would be a big issue, but over the oceans, especially in the unstable regions of the WBCs, the difference between 10- and 60-m winds is most likely small. In separate non-CMIP5 simulations with CESM1 [described in Small et al. (2014)], with additional output data, it was found that typical ratios of lowest model level wind to 10-m wind were from 0.9 to 0.95 in the NH winter; that is, 10-m winds were slightly stronger than bottom level, in very unstable conditions (surface ocean temperature minus 2-m air temperature was greater than 4°C). Conversely, in more stable conditions of the Southern Ocean in austral summer, the lowest model level wind was typically 1.05 times the 10-m wind. Also, we found that climatological values of V10 and VBOT differ by less than 0.4 m s−1 and the differences can be either positive or negative (not shown). Finally, spatial patterns of the 10-m storm track are very similar to model bottom level storm track, and we refer to both as “surface storm track.” The conclusions of this paper are thus not sensitive to whether we actually used bottom-level wind or 10-m wind.
Another issue regarding V10 is the question of whether the modeling centers report the “real” V10 or the neutral equivalent V10, and this was not always clear from the provided documentation. The CESM simulations of Small et al. (2014) mentioned above show that the storm track based on V10-neutral and that based on V10-real never differ by more than 2% in the WBC regions (not shown). This is because substantial differences (e.g., of 10% or more) between the actual wind and the neutral wind only occur in quite low wind speed regimes (e.g., weaker than 5 m s−1) under strongly unstable or stable conditions (Liu and Tang 1996), but the storm-track regions have strong winds (>10 m s−1). Therefore we do not need to distinguish between neutral wind and actual wind in the analysis below. To put the differences between VBOT, V10, and neutral equivalent V10 in context, results shown below (see Fig. 9) reveal most models have surface storm tracks that are 20%–30% weaker than at 850 hPa. This is a much larger difference than between the different surface-wind variables used for calculating the surface storm track.
Each of the models and the reanalysis were generated and saved on their own grids (Table 1). However, for our analysis we use two-dimensional interpolation via a cubic spline method to project all of the data to the same grid. We choose to use the most often occurring grid from our set of models, which is 2° latitude by 2.5° longitude. All results are shown on this grid.
Throughout the analysis, we will refer to the climatological locations of the WBCs. The locations of the Gulf Stream and Kuroshio Extension have been estimated through an analysis of the observed sea surface height using the satellite altimeters [provided by K. Kelly and S. Dickinson; see Kelly et al. (2010) for details]. The ARC has been defined as the equatorward edge of the location of observed maximum SST gradient for the climatology of SST for 1981–2005, which we calculate using a 0.25° blended SST product based on satellite measurements (OISSTv2; Reynolds et al. 2007).
b. Analysis methods
Here we focus on DJF for both the Northern and Southern Hemisphere. We also carried out an analysis of JJA in the Southern Hemisphere and a discussion of those results is included. As highlighted in the introduction, previous work by Nakamura and Shimpo (2004) suggests that the influence of the ocean surface on the storm track in the Indian Ocean sector of the Southern Ocean is more apparent in DJF than JJA. This, in combination with our findings herein, has led to our decision to present results for DJF only for this region (hereafter the Indian Ocean).
There are two advantages to using the daily difference filtering method: 1) daily outputs are easier to save, and 2) the analysis can be coded into GCMs so that in future simulations monthly storm track statistics can be saved. All that is required is to keep daily averages from the day before and the accumulated storm-track value as the month proceeds. This yields a finescale temporal resolution metric that does not require copious model output. This filtering method can also be used on observations that are available only at a daily resolution (e.g., Guo et al. 2009).
For one component of the analysis, we utilize the technique of Booth et al. (2010) to calculate an estimated surface storm track defined as the region of overlap of the upper quantiles of 
Here we refine the Booth et al. (2010) method to make it more suitable to apply it to different datasets. We start by identifying the region of the strongest 
The three separate definitions of the estimated storm track are motivated by the question of which temperature-related factors might affect the offset in location of the surface storm track compared to that at 850 hPa: 1) momentum mixing, and hence TDIFF, 2) wind acceleration related to the SST gradient, or 3) the genesis of storms at the WBC regions in the presence of upper-tropospheric perturbations (e.g., Cione et al. 1993), and hence the baroclinicity. We acknowledge that the SST and |∇SST| anomalies may not be perfectly collocated with wind anomalies, due to horizontal advection, but the coarse grid resolution used in this analysis means that the lack of collocation will likely be no more than one grid cell. We also note that there is potential for indirect impacts from TDIFF and |∇SST|, since they can also contribute to stronger storms through diabatic heating in the storms and/or frontogenesis [see Booth et al. (2012) for more discussion].
After generating the estimated storm track, we compare the spatial locations of the top M* percent of 
3. Results
a. Global scale
For both the Northern and Southern Hemispheres, the storm tracks’ maxima occur over the ocean during DJF and are evident in Fig. 1 for 
(a),(c),(e) Surface and (b),(d),(f) free-tropospheric wintertime storm tracks in DJF. Color scales are the same for the surface and free troposphere. The Gulf Stream, Kuroshio Extension, and ARC locations based on observations of SSH and SST (black lines) are shown in (a),(b). The red boxes in (a) indicate the regions of focus for this remainder of the study.
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
For each ocean basin, the location of the surface storm track maximum differs from that of 
Figure 2 shows that all models analyzed capture the equatorward shift in the location of the 
Mean latitude for the top 10th percentile of the storm tracks at surface (red) and 850 hPa (blue). All panels show DJF. The legend shown in the North Pacific panel holds for all basins.
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
b. Spatial location of the surface storm tracks
Next we examine the spatial correlations between the models and reanalysis. The variables examined for spatial correlations are 
Figure 3 shows the results of the spatial correlation analysis per basin. For all basins except the Indian Ocean in DJF, the storm tracks have the strongest correlations. The higher skill for the storm tracks is partially attributable to the significant SST biases in the climate models (as discussed in the introduction), which impacts TDIFF, |∇SST|, and σBI. However, it is also the case that all of the correlations above 0.6 are statistically significant at the 95 percentile, based on the Student’s t test after a Fisher transformation of a Pearson correlation coefficient. The temperature related fields are significant despite their lower values because the degrees of freedom for those fields are greater than those for the storm tracks. This is due to the fact that the storm tracks are a spatially smooth field compared to the temperature fields, and thus have larger serial correlations.
Spatial correlations between ERA-I and each model for 
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
Next we use the spatial correlations to consider physical forcing of 
Model-to-model correlations of the spatial correlations shown in Fig. 3 between the pair variables listed. Asterisks indicate the correlations that are significant at the 95% based on the Student’s t test after a Fisher transformation of the Pearson correlation coefficient.
The results in Table 2 indicate that across the ocean basins the strongest model-to-model covariability for 
The strong covariability of the differences with ERA-I for the surface and 850 hPa implies that any surface forcing would be a secondary influence on the surface storm tracks. This secondary forcing can be seen in the CMIP5 models in the North Atlantic, where the model-to-model correlations of the surface storm track and TDIFF, and |∇SST|, and σBI are all statistically significant at the 95%. This result appears to be related to SST biases in the Gulf Stream extension and North Atlantic Current region and is discussed in detail in section 4.
Table 2 also shows that there is high model-to-model covariability between TDIFF and |∇SST| biases in all of the ocean basins. This result suggests that the SST is strongly reflected in the spatial patterns of the lower-tropospheric stability: biases in the spatial pattern of SST translate to biases in the spatial pattern of TDIFF. One might ask if the forcing is the other direction: TDIFF bias generating surface flux biases that change the SST. However, our analysis of the surface fluxes found that climatological biases in the fluxes were acting to dampen the SST biases (not shown).
The baroclinicity, despite being calculated at 850 hPa, also has a strong model-to-model covariability with TDIFF and |∇SST| in the North Pacific. As will be shown below, in the North Pacific the maxima for these three variables are located close to one another. Thus, if a model has a bias that affects one, it will most likely impact all three.
In the Indian Ocean for DJF, there a strong model-to-model covariability in the spatial correlations of σBI and the storm track, which agrees with Nakamura and Shimpo (2004). This result is much weaker in JJA (not shown), due to the additional influence of the baroclinicity and low-level stability associated with sea ice near Antarctica [discussed in Booth et al. (2010)].
The next analysis focuses on the region of the strongest storm track per basin. Figure 4 shows TDIFF, |∇SST|, and σBI for the North Atlantic (Figs. 4a–c) for ERA-I. Figures 4d–f shows the estimated surface storm track (defined in section 2) using each of the variables from Figs. 4a–c. The 
Shown are (a) TDIFF (K), (b) |∇SST| (K km−1), and (c) σBI (day−1) for the North Atlantic in ERA-I for DJF. (d)–(f) Actual (black contours) and estimated storm tracks (blue-green shading) using each variable from (a)–(c), respectively. Blue contour shows the top 10th percentile for 
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
We quantify the skill of the estimated storm tracks by calculating their overlap with 
Percent overlap of each estimated storm track and the top 10th percentile of 
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
For the North Pacific, Fig. 6 shows TDIFF, |∇SST|, and σBI from ERA-I for reference, as well as the predicted storm track for each variable. Unlike the Gulf Stream, there is no northward moving current at the terminus of the KOE, and as such the SST and the atmospheric stability above the KOE are very zonal. Figure 5b shows that the estimated storm track using TDIFF and σBI has a stronger overlap with the location of the surface storm track in comparison to 
As in Fig. 4, but for the North Pacific. The cut-off percentile used so that the estimated storm track is equal in size to the top 10th percentile is 35% for TDIFF, 20% for |∇SST|, and 29% for σBI. In (a)–(c), the dark blue contour shows the top 10th percentile, and the black line shows the KOE path.
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
We also note that in the North Pacific the maximum in the ocean current is not collocated with the strongest SST gradient (e.g., Yasuda 2003). This is apparent in Fig. 6b, which shows no overlap in the location of the KOE based on altimetry data (which captures the current) and the maximum in |∇SST|. Because our analysis examines the SST, we cannot comment directly on the ocean currents, but previous research has shown that coarse-resolution models like those used in this study produce a single, merged front that has both the strong ocean current and the SST front rather than having separate Kuroshio and Oyashio Extension fronts (e.g., Thompson and Kwon 2010). In the analysis presented here, the strong collocation of the TDIFF, |∇SST|, and σBI may be a result of the merged locations of the SST gradient and the ocean currents in the CMIP5 models, and for ERA-I it relates to the reduced spatial resolution we use for the analysis.
In the Indian Ocean, the maximum in TDIFF extends from the coast of South Africa toward the ARC (Fig. 7a). The maximum in |∇SST| is situated along the ARC (Fig. 7b), while the σBI maximum is located farther south (Fig. 7c), due in part to the large weakened atmospheric stability over the cold water south of the ARC. The surface storm track maximum is almost completely dislocated from the maximum for 
As in Fig. 4, but for the Indian Ocean. The cut-off percentile used so that the estimated storm track is equal in size to the top 10th percentile is 57% for TDIFF, 48% for |∇SST|, and 44% for σBI. In (a)–(c), the dark blue contour shows the top 10th percentile, and the black line shows the ARC.
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
c. Amplitude of
The global maps of the storm tracks (Fig. 1) show that GFDL CM3 and CCSM1-LE differ significantly in their representation of the strength of 
Figure 8 shows the strength of the storm track for 
Scatterplots for the storm track intensities (m s−1) at surface vs 850 hPa (black letters) and 850 vs 500 hPa (red letters) in the (a) North Pacific, (b) North Atlantic, and (c) Indian Ocean. The blue dashed lines indicate 1:1 relationship. The black lines are the least squares fit to the 850-hPa vs 10-m data, excluding models numbered 5, 6, 9, and 15 in the legend. The red lines are the least squares fit to the 500- vs 850-hPa data. Models with surface storm tracks that are too large have a black asterisk next to their number. For all of the other models, the bottom-right corner of the number corresponds to the location of the data point. Note that the correlation between 
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
A linear analysis of 
For 
Figure 8 also shows that the majority of the models are weaker than the reanalysis in their storm track maximum at 850 and 500 hPa (e.g., examine the values along the x axis vs model name in Fig. 8). This result has been shown previously by Chang et al. (2012); however, we mention it here as a contrast to the storm-track strength at the surface.
We also examined the strength of the top 10th percentile for the North Atlantic compared to the North Pacific, per model (Fig. 9). The strong linear relationship at 850 hPa suggests that the model-to-model variability in storm track strength is mostly independent of the basin (i.e., the model differences span the hemisphere). On the other hand, there is only a weak linear relationship at the surface when excluding the four outliers, which implies some influence from the ocean surface, which is likely distinct between the two basins, on the strength of 
Scatterplots for the North Pacific vs North Atlantic in terms of the storm track intensities (m s−1) at (a) surface and (b) 850 hPa. Models with surface storm tracks that are too large have a black asterisk before their number in (a).
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
4. Discussion
The model-to-model correlation analysis (i.e., Table 2) suggests that the modeled North Atlantic surface storm track was influenced by biases in the modeled SST (albeit secondarily compared to the influence of the 850-hPa storm track). Figure 10 explores this issue by analyzing the multimodel means for SST, TDIFF, and the storm tracks as compared to reanalysis. Because of the findings shown in Fig. 8 regarding the large bias in the strength of the surface storm tracks in four of the GCMs, the multimodel mean in Fig. 10 excludes those four models and the AMIP models.
Multimodel mean minus reanalysis for (a) SST (K), (b) TDIFF (K), (c) 
Citation: Journal of Climate 30, 13; 10.1175/JCLI-D-16-0228.1
Figure 10a shows that the models are too warm in the shelf water region, indicative of the Gulf Stream separation problem in the models. The models are also too cold in the North Atlantic Current (NAC) region, most likely because they do not have the proper northward warm advection generated by the NAC due to an overly zonal and southerly NAC path. Figure 10b shows that TDIFF has many of the same biases as SST. In the multimodel mean, the differences in the surface turbulent heat fluxes (compared to reanalysis) showed the surface fluxes in the models act to dampen the SST biases (not shown). Thus, the SST and TDIFF biases are related to ocean circulation issues, as highlighted in previous work (e.g., Kelly et al. 2010; Kwon et al. 2010).
Meanwhile, the difference plots for the storm tracks (Figs. 10c,d) show that the models are too weak on the poleward flank of the storm tracks and too strong in the Azores region. This difference between the storm tracks and reanalysis partially relates to a long-standing issue of the GCM storm tracks being too zonal (e.g., Ulbrich et al. 2008). However, the biases for 
These differences in the shelf water can be examined through a different perspective, by considering the error in the storm tracks normalized by the error in 
If momentum mixing is the dominant physical mechanism creating differences in the spatial location of the surface and 850-hPa storm track (as suggested in our results above), then we might expect that in regions where the models mix too much the surface storm track is biased too strong (relative to that model’s 
Given the fact that climatologically WBC regions have the largest turbulent surface heat fluxes out of the ocean, we also explore the relationship between the surface storm track, TDIFF, 10-m zonal wind (U10), and the fluxes. Turbulent heat flux includes latent and sensible heating, however, the sensible heat flux (SHF) is directly proportional to TDIFF and more likely to reflect the local surface heating that would drive boundary layer momentum mixing. With this in mind, we focus here on SHF. (However, the results do not change drastically if we use total turbulent flux.) The North Atlantic and Indian Ocean have a weak linear relationship for the top 10% in 
Separate from the flux issue, we note that the AMIP models included in the analysis were able to capture the storm track with more fidelity than the CMIP models for the North Atlantic and North Pacific (Fig. 3). For the Indian Ocean, this was true in JJA; however, in DJF the GFDL AM3 model performed worse than some of the CMIP models. The AMIP models also capture the 850-hPa baroclinicity more realistically than the coupled models (Fig. 3), which suggests that the SST has an appreciable influence on the spatial distribution of the 850-hPa baroclinicity.
Finally, our analysis found no relationship between the strength or spatial representation of the storm track and atmospheric model horizontal resolutions (e.g., Table 1). Studies focused on a single model found that the strength of the storm track increases with finer resolution (e.g., Champion et al. 2011). The horizontal resolution of CMIP5 GCMs may not be fine enough to properly capture physics within the storms (Willison et al. 2013). However, the lack of a relationship between the grid spacing and the storm tracks might also be impacted by other factors that influence storm track location, such as the stationary wave pattern (Brayshaw et al. 2009).
5. Summary
Analysis of the surface storm tracks in the CMIP5 models shows that the models capture the equatorward shift in the location of storm track maximum relative to the storm track maximum at 850 hPa. The result holds for all ocean basins in DJF, however in the Indian Ocean in JJA, the pattern is obscured by the influence of the sea ice margin on the storm track. To analyze what might generate the equatorward shift in region of the maximum values we refine the definition of the estimated storm track metric and define a skill score to quantify its relationship to the actual storm track. If the estimated storm track is generated based on the overlapping region between the 850-hPa storm track and the 850-hPa baroclinicity, then it captures the equatorward shift in location. For many of the models, an estimated storm track in which the 850-hPa baroclinicity is replaced by the temperature difference between the surface and 850-hPa is equally successful, suggesting an influence of atmospheric stability in driving this equatorial shift. Thus, the models’ surface storm tracks spatial pattern more closely resembles a combination of the 850-hPa storm track and the baroclinicity or TDIFF rather than the 850-hPa storm track alone. Replacing the baroclinicity with the SST gradient for the calculation of the estimated storm track degrades the skill of the estimated storm track. This suggests that the air–sea stability and stratification influence, rather than the influence of the SST gradient, generate the physical mechanism that shifts the surface storm tracks equatorward in the models.
Analysis of the amplitude of the storm tracks shows that the modeled 850-hPa storm track is stronger than that at the surface. However, there are four outlier models that generate a surface storm track whose strength exceeds that of the 850-hPa storm track. This bias in strength also occurs in unfiltered surface winds in the models, but it does not translate to large biases in the surface turbulent heat fluxes. No statistically significant relationship is found between the strength of the surface and 850-hPa storm tracks, even if the outlier models are excluded. However, there is a strong linear relationship across models between the strength of the storm tracks at 500 and 850 hPa, and there are no outlier models. These analyses suggest that the strength of the surface storm track maxima is controlled by more than just the strength of the free-tropospheric storm track in the majority of the CMIP5 models, and that the issues in the boundary layer or surface physics in the models most likely cause the surface storm-track biases in the outlier models.
We analyzed the spatial correlations between the models and ERA-I for the storm tracks as well as on a set of temperature-related fields that are influenced by the SST in the WBC regions. Our analysis indicates that the models capture the spatial patterns of the storm tracks with more fidelity than they do for TDIFF, |∇SST|, and σBI; however, in most of the cases, the spatial correlations were statistically significant. A subsequent study of the model-to-model covariability of the spatial correlations shows that 1) models with strong or weak biases in the spatial pattern of the 850-hPa storm tracks tend to have similar biases in the surface storm tracks, and 2) for the North Atlantic, the across-model covariability of the spatial regressions of TDIFF and/or σBI and the surface storm tracks is also strong. Thus, in the North Atlantic we find indicators suggesting that the biases in the SST create dominant biases in the surface storm track.
An analysis of the multimodel mean using only the CMIP model without the large bias in the surface storm track also shows forcing from the SST biases impact the surface storm track in the North Atlantic. Along the shelf water region, the models’ SST is too warm. This creates a weakened surface stability, which creates more momentum mixing. However, the impact that this has on the surface storm tracks is only apparent when we consider a new metric: the ratio of the surface storm track to the storm track at 850 hPa. This is because the primary forcing of the surface storm tracks is the storm track aloft. Thus, significant momentum mixing in a region with a warm SST bias will strengthen the surface storm track in a model. However if the model has a biased weak storm track at 850 hPa, the surface storm track may still appear to be bias weak.
The work here provides metrics for testing the climatology of the surface storm tracks. However, more work is needed using a perturbed physics analysis of a single model, and our group is pursuing such work. Additionally, the analysis here does not isolate individual storms, nor does it focus on the different dynamic and thermodynamic conditions within the warm and cold sectors of storms. Such studies could help in interpreting the relative influence of baroclinicity and the momentum-mixing mechanism.
Acknowledgments
We thank ECMWF for providing ERA-Interim via a public server. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (see Table 1) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. CESM1-LE data were downloaded from the Earth System Grid at NCAR. JFB was partially supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections program (Grant NA15OAR4310094). Y-OK was supported by NSF Division of Atmospheric and Geospace Science Climate and Large-scale Dynamics Program (AGS-1355339), NASA Physical Oceanography Program (NNX13AM59G), and DOE Office of Biological and Environmental Research Regional and Global Climate Modeling Program (DE-SC0014433). RJS was supported by DOE Office of Biological and Environmental Research (DE-SC0006743) and NSF Directorate for Geosciences Division of Ocean Sciences (1419584), We thank the reviewers for useful suggestions and comments that have significantly improved this manuscript.
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