Abstract

The Kuroshio–Oyashio Extension (KOE) is a region of energetic oceanic mesoscale eddies and vigorous air–sea interaction that can influence climate variability over the northwest Pacific and East Asia. General circulation models (GCMs) exhibit considerable differences in their simulated climatology around the KOE region. Specifically, there are substantial intermodel spreads in both sea surface temperature (SST) and the upper-level westerly jet. In this study, the cause for such large spreads is studied by analyzing 21 pairs of coupled and atmospheric GCMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5).

It is found that the intermodel spread of the climatological westerly jet among coupled GCMs is largely inherited from their atmospheric models rather than being due to their SST difference as previously thought. An anomalous equatorward shift in the simulated westerly jet can give rise to a cold SST bias around the KOE region as follows. The equatorward jet shift induces cyclonic surface wind anomalies over the North Pacific, which not only enhance the turbulent heat fluxes out of the ocean south of the KOE but also drive an anomalous cyclonic ocean circulation that brings colder (warmer) water into the north (south) of the KOE. The KOE region is consequently cooled due to both the atmospheric and oceanic effects. Such processes are demonstrated through idealized perturbation experiments using an ocean model.

The results herein point to reducing atmospheric model errors in the westerly jet as the way forward to improve the coupled simulations around the KOE region.

1. Introduction

The Kuroshio–Oyashio Extension (KOE) is a region of energetic oceanic mesoscale eddies and vigorous air–sea interaction that can influence the large-scale climate variability over the northwest Pacific and East Asia through processes such as the Pacific–Japan mode (e.g., Nitta 1987), the subtropical anticyclone (e.g., Zhang et al. 1996; Wang et al. 2000; Xie et al. 2009), and the convective jump (e.g., Ueda et al. 1995; Lu et al. 2007; Zhou et al. 2016). The large-scale condition around the KOE region also has a significant impact on the track and intensity of typhoons that potentially achieve landfall at the East Asian coast (e.g., Emanuel 1999; Du et al. 2011; Mei et al. 2015). Moreover, perturbations from the midlatitudes are strongly affected by the upper-level westerly jet over East Asia and the KOE. A slight change in the jet latitude or intensity can significantly influence the evolution of the East Asian monsoon (e.g., Lin and Lu 2005). In addition, the KOE region has commercially important fisheries that are sensitive to ecosystem changes driven by its variability.

An accurate simulation of the KOE climatology is the basis for precisely predicting the climate variability over the northwest Pacific and East Asia. However, current general circulation models (GCMs), our main tool for understanding the large-scale climate system, still exhibit considerable differences in their simulated climatology around the KOE region. Particularly, there are substantial intermodel spreads in both sea surface temperature (SST) and the upper-level westerly jet. Physical insights into the cause of these large intermodel spreads are desired to reduce errors in GCMs, but a consensus is still lacking. The spread in the upper-level westerly jet has been attributed to SST forcing either around the KOE region (Ma et al. 2015) or over the tropics (Delcambre et al. 2013) while the SST spread has been attributed to either ocean model resolution (Ma et al. 2015) or the remote SST (Wang et al. 2014; Zhang and Zhao 2015). Particularly, Delcambre et al. (2013) find that the differences in the winter upper-level zonal wind among CMIP3 models are related to an El Niño–Southern Oscillation (ENSO)-like SST anomaly pattern in the tropics. Ma et al. (2015) instead show that the upper-level westerly jet latitude in CMIP5 is highly correlated with SST around the KOE region. They argue that ocean dynamics may contribute to the SST difference, which then changes the upper-level westerly jet. From a global perspective, Wang et al. (2014) show that the SST bias around the KOE region is remotely correlated with that over the North Atlantic. Zhang and Zhao (2015) further illustrate that cold SST anomalies imposed over the North Atlantic can induce a response around the KOE region through an annular mode–like atmospheric teleconnection.

The intermodel spread among coupled GCMs can be induced by both the direct effect from their different atmospheric models and their different SSTs as the ocean responds to the atmospheric forcing. All these previous studies have only focused on coupled GCMs. It is thus hard to distinguish the contributions from the atmospheric model and SST. In this study, we pair coupled GCMs with their atmospheric models to evaluate the effect from the atmospheric model biases. In contrast to previous studies, we find that the SST difference around the KOE region among coupled GCMs is a response to the upper-level westerly jet difference that originates from their atmospheric models. We first show that the intermodel spread in the westerly jet among coupled models is not induced by their SST difference but instead arises from their different atmospheric models. We then show that an anomalous equatorward jet shift gives rise to a cold SST bias around the KOE region by enhancing the surface heat flux out of the ocean and reducing the energy flux convergence within the ocean mixed layer. Our results suggest that understanding the error of the westerly jet in atmospheric models is essential for reducing the coupled simulation biases in the westerly jet and the large-scale ocean circulation and thus SST around the KOE region.

The paper is organized as follows: Section 2 describes the datasets and model used in this study. Section 3 elucidates the cause-and-effect relationship between the biases of SST and the westerly jet. We illustrate the physical processes by which an equatorward jet shift induces a cold bias around the KOE region in section 4 through the surface energy balance analysis of GCMs and in section 5 through idealized perturbation experiments using a numerical ocean model. Section 6 offers a summary with discussion.

2. Datasets and model

a. Datasets

Datasets used in this study include outputs from GCMs as well as the observational and reanalyzed datasets (for simplicity referred to as observations thereafter). All datasets have been regridded to a common 1° latitude × 1° longitude grid before any further analysis. The climatology is obtained by averaging over 1979 to 2005.

Monthly-mean outputs including winds, SST, surface fluxes and wind stresses, surface air pressure, temperature and humidity, and oceanic velocity are obtained from the historical experiments of 21 pairs of atmospheric and coupled GCMs in phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012). We refer to the group of atmospheric models as AMIP and the group of coupled models as CMIP in this paper. Table 1 shows the model names, modeling centers, and their labels, which are used in figures.

Table 1.

List of CMIP5 GCMs used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)

List of CMIP5 GCMs used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)
List of CMIP5 GCMs used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)

Observations are used to validate the GCM simulations: SST from the NOAA Extended Reconstruction Sea Surface Temperature version 3b (ERSST v3b; Smith et al. 2008) and winds data from the 1979–present European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-Interim; Dee et al. 2011).

A few indices are used in the paper for correlation analysis. The latitude of the upper-level westerly jet over the KOE is defined as the annual-mean and longitude-mean latitude of the maximum zonal wind at 200 hPa averaged from 130° to 170°E. The mean SST around the KOE region is defined as the average over the region 25°–45°N, 130°–200°E and where the intermodel spread of SST is largest.

b. Model

Perturbation experiments are conducted using the Modular Ocean Model version 5 (MOM5) to verify the response of the ocean circulation and SST to the anomalous atmospheric forcing caused by the equatorward jet shift. A control experiment is achieved under the “normal year forcing” from Large and Yeager (2004), as part of the Coordinated Ocean-ice Reference Experiments (COREs; Griffies et al. 2009) which explore the behavior of global ocean-ice models under common atmospheric forcing. We then perturb the atmospheric forcing that drives the ocean model to investigate the effect of the combination of both surface fluxes and wind stress anomalies, wind stress anomaly only, and surface fluxes anomaly only.

MOM5 is a numerical ocean model developed by scientists at NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL). MOM5 has been used in GFDL’s CM2 (Delworth et al. 2006) and CM3 (Griffies et al. 2011) global coupled climate models. The horizontal resolution is 1° in latitude and longitude, with the meridional resolution becoming finer equatorward of 30° and reaching 1/3° at the equator. There are 50 vertical levels, with 22 levels in the top 220 m. The model formulation and physical parameterizations are based on the Modular Ocean Model version 4 code (MOM4; Griffies et al. 2004). More details are described in Griffies et al. (2005) and Gnanadesikan et al. (2006).

3. Biases in SST and the upper-level westerly jet

Figure 1 shows the intermodel spread of SST and the multimodel mean (MME) SST bias relative to observations. There is substantial intermodel spread in SST around the KOE region. More importantly, the ratio between the MME bias and the intermodel spread is smaller than 1 over most of the North Pacific. Consequently, reducing the intermodel spread is effective for diminishing the bias in each particular model. The mutual cold bias over the subtropical North Pacific near the date line (indicated by the dots) is also interesting but will not be focused on here.

Fig. 1.

(a) Intermodel spread, as measured by the standard deviation, of the climatological annual-mean SST among the 21 coupled GCMs in CMIP5. (b) Multimodel ensemble (MME) annual-mean SST bias relative to observations. The dots denote where the ratio between the magnitude of the MME bias and the intermodel spread is larger than 1.

Fig. 1.

(a) Intermodel spread, as measured by the standard deviation, of the climatological annual-mean SST among the 21 coupled GCMs in CMIP5. (b) Multimodel ensemble (MME) annual-mean SST bias relative to observations. The dots denote where the ratio between the magnitude of the MME bias and the intermodel spread is larger than 1.

As pointed out by Ma et al. (2015), SST around the KOE region is correlated to the upper-level westerly jet latitude. Figure 2 shows the SST anomalies in each GCM relative to the MME. The models are ranked according to their jet latitudes over the KOE (from south to north). Notably, models with a westerly jet located farther south (upper row) generally simulate a cold SST bias around the KOE region and vice versa. The joint patterns between SST and the upper-level zonal wind are extracted using the singular value decomposition (SVD) method. The SST pattern manifests a cold bias around the KOE region and warm bias over the subpolar North Pacific (Fig. 3a). The associated wind pattern shows westerly (easterly) anomalies to the south (north) of the climatological jet stream, corresponding to an anomalous equatorward jet shift (Fig. 3b). The SST and wind patterns are highly correlated (R = 0.92; Fig. 3c) and explain a large fraction of the total variance (32.2% for SST and 63.0% for the zonal wind). This means that most variance in SST and the westerly jet can be explained by such a coherent pattern: an anomalous equatorward jet shift associated with a cold SST bias around the KOE region. Indeed, the regressed SST and upper-level zonal wind onto the latitude of the westerly jet indicate consistent patterns (Figs. 3d,e). The equatorward jet shift is further associated with cyclonic wind anomalies at the surface (Fig. 3e), suggesting a nearly barotropic structure in the three-dimensional wind shift [also see Fig. 4 in Ma et al. (2015)]. In support of the SVD analysis, the westerly jet latitude and the mean SST bias around the KOE region is highly correlated (R = 0.92; Fig. 3f).

Fig. 2.

SST bias relative to the MME in each particular coupled GCM (shading) and the climatological annual-mean zonal wind at 200 hPa in coupled (black solid contours) and atmospheric (white dashed contours) GCMs. The contours are plotted starting from 32 m s−1 with an interval of 4 m s−1. The model name and its corresponding jet latitude are labeled on top. The models are ranked according to their jet latitudes over the KOE (from south to north).

Fig. 2.

SST bias relative to the MME in each particular coupled GCM (shading) and the climatological annual-mean zonal wind at 200 hPa in coupled (black solid contours) and atmospheric (white dashed contours) GCMs. The contours are plotted starting from 32 m s−1 with an interval of 4 m s−1. The model name and its corresponding jet latitude are labeled on top. The models are ranked according to their jet latitudes over the KOE (from south to north).

Fig. 3.

Spatial maps of (a) SST and (b) zonal wind at 200 hPa for the first intermodel SVD mode. The correlation between their corresponding coefficients is shown in (c). Also shown are spatial maps of the regressed (d) SST and (e) zonal wind at 200 hPa onto the jet latitude over the KOE, and (f) the correlation between the jet latitude and the mean SST bias in the KOE region (relative to observations). The black contours in (b) are the climatological zonal wind at 200 hPa (starting from 25 m s−1 with an interval of 5 m s−1). The red vectors in (e) show the regressed surface wind (at 925 hPa) to the jet latitude. The white rectangle in (d) denotes the area over which the mean SST bias around the KOE region is computed. The black triangle in (f) indicates the observed westerly jet latitude around the KOE region.

Fig. 3.

Spatial maps of (a) SST and (b) zonal wind at 200 hPa for the first intermodel SVD mode. The correlation between their corresponding coefficients is shown in (c). Also shown are spatial maps of the regressed (d) SST and (e) zonal wind at 200 hPa onto the jet latitude over the KOE, and (f) the correlation between the jet latitude and the mean SST bias in the KOE region (relative to observations). The black contours in (b) are the climatological zonal wind at 200 hPa (starting from 25 m s−1 with an interval of 5 m s−1). The red vectors in (e) show the regressed surface wind (at 925 hPa) to the jet latitude. The white rectangle in (d) denotes the area over which the mean SST bias around the KOE region is computed. The black triangle in (f) indicates the observed westerly jet latitude around the KOE region.

The tight correlation implies a cause-and-effect relationship between the westerly jet and SST. Does the westerly jet difference lead to the SST difference or is it the other way around? Figure 4 shows that the latitudes of the westerly jet over the KOE in both CMIP and AMIP. Despite a general equatorward shift, the jet latitudes of CMIP are found to be highly correlated with those in AMIP over the four seasons, resulting in a high correlation of 0.89 for the annual mean. This is corroborated by Fig. 2, showing that the climatological westerly jets are very close in each pair of atmospheric and coupled models. Since SST is specified and identical in AMIP, the jet difference among AMIP models cannot be induced by the SST difference. Given that the jet variation in CMIP is largely inherited from AMIP, this implies that the jet difference induces the SST difference. Different from Delcambre et al. (2013), we find no significant correlation between the upper-level zonal wind and the SST in the tropics (not shown). The differences between CMIP3 and CMIP5 results may be due to the reduced SST variation in the tropics among GCMs in CMIP5 with improved cold pool SSTs.

Fig. 4.

Correlation of the westerly jet latitude over the KOE in the atmospheric (AMIP) and coupled (CMIP) GCMs (a)–(d) over the four seasons and (e) for the annual mean. The number index here corresponds to the model as listed in Table 1. GCMs with a farther northwesterly jet are specified with warmer colors.

Fig. 4.

Correlation of the westerly jet latitude over the KOE in the atmospheric (AMIP) and coupled (CMIP) GCMs (a)–(d) over the four seasons and (e) for the annual mean. The number index here corresponds to the model as listed in Table 1. GCMs with a farther northwesterly jet are specified with warmer colors.

The question then arises: how can an equatorward jet shift lead to a cold SST bias around the KOE region? To answer this question, we conduct both surface energy budget analysis of GCMs in section 4 and idealized perturbation experiments using the numerical ocean model in section 5.

4. Surface energy budget analysis of GCMs

The surface energy balance equation in GCMs can be written as follows:

 
formula

where denotes the net surface energy flux from the atmosphere into the ocean and stands for the oceanic energy flux convergence within the mixed layer. As shown in Fig. 3e, the westerly jet shift will affect the surface winds, which could perturb both and . The energy balance requires the sum of the perturbation in and to be zero, that is,

 
formula

with here representing the deviation from the control.

The term can be decomposed into two parts: 1) the atmospheric response through the changes in the near-surface atmospheric condition (without the change in SST) and 2) the ocean response through the change in SST, that is,

 
formula

The dependency of on has been parameterized by a feedback parameter k. Substituting Eq. (3) into Eq. (2) yields an estimate of the change in SST as

 
formula

The above equation states that the change in SST is proportional to the sum of the change in the net surface flux without the SST response, , and the change in the energy flux convergence within the mixed layer, .

The term can be estimated from the net surface flux in AMIP, which by definition isolates the SST response and undergoes nearly the same variation in their westerly jet and surface winds as CMIP. We first calculate the regression factor of to the jet latitude in AMIP and then multiply it by the minus standard deviation of the jet latitude (the minus sign here represents an equatorward shift). The result of is shown in Figs. 5a and 5b for the surface turbulent heat flux and the surface radiative flux , respectively. We can see that the change in the net surface flux is dominated by the turbulent heat flux while the radiative flux only contributes modestly. An anomalous equatorward jet shift leads to more turbulent heat flux out of the ocean around the KOE region. To the first order, the change in the turbulent heat flux can be explained by the changes in both the surface wind speed and air properties as follows:

 
formula

Here , , and are the turbulent heat flux, surface wind speed, and surface enthalpy disequilibrium in the control simulations. The terms and represent changes in and due to the jet shift. As shown in Figs. 6a and 6c, there is considerable seasonal variation in the response of the turbulent heat flux as the climatological jet latitude shifts northward from winter to summer. The approximate estimate according to Eq. (5) (contours in Figs. 6a and 6c) explains the seasonal variation of the simulated reasonably well. The cyclonic surface wind anomalies associated with the equatorward jet shift not only enhance the surface wind speed but also bring cold and dry air with low enthalpy. The enhanced is caused mainly by the cold advection for summer (Fig. 6b) and the enhanced surface wind speed for winter (Fig. 6d). This seasonal variation in the response of the turbulent heat flux leads to a slight different SST response between seasons. Particularly, there is a noticeable subtropical branch in winter (Fig. 7) due to the more equatorward turbulent heat flux response (Fig. 6c).

Fig. 5.

Regressed annual-mean (a) surface turbulent heat flux in AMIP (shading); (b) surface radiative flux in AMIP (shading; be aware of the smaller color bar range); (c) ocean energy flux convergence (shading), sea surface height (black contours with an interval of 0.05 m, negative values are dashed), and ocean circulation (integrated from the surface to 250 m, red vectors); and (d) the total atmospheric and oceanic effects (shading) and SST (black contours with an interval of 0.5 K; negative values are dashed) onto the westerly jet latitude around the KOE region. The sign convention is that positive values are into the ocean.

Fig. 5.

Regressed annual-mean (a) surface turbulent heat flux in AMIP (shading); (b) surface radiative flux in AMIP (shading; be aware of the smaller color bar range); (c) ocean energy flux convergence (shading), sea surface height (black contours with an interval of 0.05 m, negative values are dashed), and ocean circulation (integrated from the surface to 250 m, red vectors); and (d) the total atmospheric and oceanic effects (shading) and SST (black contours with an interval of 0.5 K; negative values are dashed) onto the westerly jet latitude around the KOE region. The sign convention is that positive values are into the ocean.

Fig. 6.

(a),(c) Regressed surface turbulent heat flux in AMIP (shading) onto the westerly jet latitude around the KOE region and estimated surface turbulent heat flux change from Eq. (5) (black contours with an interval of 6 W m−2; negative values are dashed) according to Eq. (5) for (a) summer and (c) winter. (b),(d) Regressed surface wind speed (shading), surface wind velocities (red vectors) and surface air enthalpy (black contours with an interval of 0.2 K) in AMIP onto the jet latitude around the KOE region for (b) summer and (d) winter. The sign convention is that positive values are into the ocean.

Fig. 6.

(a),(c) Regressed surface turbulent heat flux in AMIP (shading) onto the westerly jet latitude around the KOE region and estimated surface turbulent heat flux change from Eq. (5) (black contours with an interval of 6 W m−2; negative values are dashed) according to Eq. (5) for (a) summer and (c) winter. (b),(d) Regressed surface wind speed (shading), surface wind velocities (red vectors) and surface air enthalpy (black contours with an interval of 0.2 K) in AMIP onto the jet latitude around the KOE region for (b) summer and (d) winter. The sign convention is that positive values are into the ocean.

Fig. 7.

Spatial maps of SST for the first SVD mode in (a) summer and (b) winter.

Fig. 7.

Spatial maps of SST for the first SVD mode in (a) summer and (b) winter.

Instead of explicitly calculating all the advection and mixing terms, the ocean energy flux convergence within the ML, , can be computed as according to the energy balance within the ML [Eq. (1)]. Similar to , we regress the surface heat flux in CMIP to the jet latitude and multiply them by the negative standard deviation of the jet latitude. The result of is shown in Fig. 5c. The ocean effect manifests a strong warming (cooling) effect north (south) of the KOE region. To help understand this pattern, we further analyze the sea surface height (SSH) and ocean velocities responses to the jet shift. Indeed, there are negative SSH anomalies centered at the KOE. This forces an anomalous cyclonic ocean circulation, which brings cold (warm) water from the east (southwest) north (south) of the KOE, leading to the warming and cooling patterns described above.

The total effect of is shown in Fig. 5d. The patterns are highly correlated with the SST pattern, as predicted by Eq. (4). For the cooling north of the KOE, the ocean effect dominates. But the atmospheric surface flux is also important to offset the warming effect from the ocean south of the KOE.

5. Idealized perturbation experiments using MOM5

The energy budget analysis above illustrates that the cold SST bias around the KOE region can be induced by the enhanced surface heat flux and the reduced energy transport into the mixed layer associated with the ocean circulation change. As illustrated in Fig. 6, the surface heat flux change can be explained by the near-surface atmospheric change. In this section, we conduct idealized perturbation experiments using the numerical ocean model, MOM5, to verify that the atmospheric forcing is able to induce the ocean circulation change shown in Fig. 5c.

Figure 8a shows the climatological SST and SSH in the control simulation forced by the normal-year atmospheric forcing. The perturbing experiment is performed by adding the anomalous atmospheric forcing, including changes in near-surface air temperature, humidity, and surface winds, associated with the equatorward jet shift (with a magnitude of one standard deviation of the intermodel spread among GCMs) to the control. As shown in Fig. 8b, this results in negative SSH anomalies centered at the KOE and cold SST around the KOE region. These patterns are nearly identical to the regressed SST (Fig. 5a) and SSH (Fig. 5c) changes to the jet shift among GCMs. This confirms the atmospheric forcing change caused by the equatorward jet shift can indeed induce the cyclonic ocean circulation anomaly and the cold SST bias seen in the intermodel spread of GCMs.

Fig. 8.

(a) Climatological annual-mean SST (shading) and SSH (contours with an interval of 0.2 m) in the control simulation under the normal-year atmospheric forcing. The SST (shading) and SSH (contours with an interval of 0.05 m) changes in perturbation experiments with (b) both surface wind stresses and surface heat/water fluxes anomalies in associated with the equatorward jet shift, (c) surface wind stresses anomaly only, and (d) surface fluxes anomaly only.

Fig. 8.

(a) Climatological annual-mean SST (shading) and SSH (contours with an interval of 0.2 m) in the control simulation under the normal-year atmospheric forcing. The SST (shading) and SSH (contours with an interval of 0.05 m) changes in perturbation experiments with (b) both surface wind stresses and surface heat/water fluxes anomalies in associated with the equatorward jet shift, (c) surface wind stresses anomaly only, and (d) surface fluxes anomaly only.

Two additional perturbation experiments are performed to further evaluate the respective contribution from the surface wind stresses and the surface heat/water fluxes. For the surface wind stresses only perturbation experiment, surface wind anomalies are added only in the computation of the surface wind stresses but not in the computation of the surface fluxes. For the surface heat/water fluxes only perturbation, the surface wind, air temperature, and humidity anomalies are added in the computation of surface heat/water fluxes but not in the computation of the surface wind stresses. We find that the total SSH response (Fig. 8b) is largely induced by the surface wind stresses (Fig. 8c). It is also confirmed that the SST change due to the surface wind stresses resembles the pattern of the regressed ocean energy transport as shown in Fig. 5c and the SST change due to the surface fluxes resembles the pattern of the regressed surface turbulent heat fluxes as shown in Fig. 5a.

6. Summary and discussion

GCMs in CMIP5 still have systematic errors in their simulated climatology, which leads to considerable biases in simulations of climate variability and uncertainty in projections of future climate change. Identifying and reducing these errors are topics of great importance in the modeling community.

We have focused on the KOE region, which is a key area for climate variability over the northwest Pacific and East Asia but where GCMs disagree with each other substantially in both their climatological SST and upper-level zonal wind. We show that most intermodel variance in SST and zonal wind can be explained by a joint pattern between them: an anomalous equatorward jet shift associated with colder SST around the KOE region. Paired analysis of both atmospheric and coupled models shows that the westerly jet shift is caused by errors in atmospheric models and is not a result of the SST difference from coupled models. Instead, an anomalous equatorward shift in the westerly jet, accompanied by cyclonic wind anomalies at the surface, can lead to a cold SST bias around the KOE region through both atmospheric effect and oceanic adjustment. For the atmospheric effect, the cyclonic surface wind anomalies increase the surface wind speed and advect cold and dry air over the KOE region, enhancing the surface heat fluxes out of the ocean and thus cooling the KOE region. For the oceanic adjustment, the cyclonic surface wind anomalies induce a cyclonic ocean circulation anomaly, which brings colder (warmer) water into the north (south) of the KOE. The atmospheric and oceanic effects together lead to a cold SST bias around the KOE region.

Numerical simulations show that cold SST anomalies imposed over the North Atlantic can induce a cold SST bias around the KOE region (Zhang and Zhao 2015). Imposing cold SST anomalies over the North Atlantic might shift the westerly jet over the North Pacific and trigger the same process as we discussed in this study. In fact, the surface winds response as shown in Fig. 10 of Zhang and Zhao (2015) is very similar to the cyclonic surface wind anomalies shown in our Fig. 3e. It should, however, be emphasized that such cyclonic surface wind anomalies in coupled models already exist in atmospheric models (Figs. 6b,d) due to the equatorward jet shift and do not necessarily rely on the colder SST over the North Atlantic.

The dominant SST pattern for the intermodel spread among GCMs shown in Fig. 3a is reminiscent of one dominant SST mode for the decadal variability of the North Pacific (Deser and Blackmon 1995; Nakamura et al. 1997; Xie et al. 2000; Seager et al. 2001). Both of them are driven by surface wind stress anomalies. For the intermodel spread, the wind stress anomalies are barotropically associated with the upper-level westerly jet latitude whose variation originates from different atmospheric models, whereas for the decadal variability they are due to decadal changes in the Aleutian low (Deser et al. 1999; Miller et al. 1998).

Our results point to reducing atmospheric model errors in the westerly jet as the way forward to improve the coupled simulations in the westerly jet and the oceanic large-scale circulation, and thus SST around the KOE region. As shown in Fig. 3f, the observed SST and jet latitude around the KOE region (black triangle) fit into the line of the simulated intermodel spread rather well; this indicates that reducing the errors in the westerly jet will be effective for reducing the SST bias around the KOE region in each particular GCM. Future studies on the physical dynamics that control the latitude of the westerly jet in atmospheric models are desired. Both the surface drag parameterization (Chen et al. 2007; Lindvall et al. 2017; Pithan et al. 2016) and the internal wave-mean interactions (Pope and Stratton 2002; Lu et al. 2010, 2015) could affect the latitude of the westerly jet. Our study has only focused on the impact around the KOE region, but potential benefits of an improved jet latitude could also be seen in other regions, such as the northwest Atlantic and the Southern Ocean. The paired analysis of atmospheric and coupled GCMs can be readily applied to other studies as an effective method to distinguish the effects from the atmospheric model and SST.

Acknowledgments

We acknowledge the WCRP Working Group on Coupled Modeling, which is responsible for CMIP, and the climate modeling groups for producing and making available the model outputs. We thank the Model Development Lab team for supporting MOM5 and making it freely available. MOM development is led by scientists at GFDL in collaboration with scientists worldwide. Numerical simulations are conducted using the computing resources provided by GFDL. This work is supported by U.S. National Science Foundation (NSF) Grant 1305719.

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Footnotes

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