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    Seasonal evolution of the lead–lag regression between the KOEI and GPH anomalies based on the NCEP–NCAR reanalysis over the AL region (30°–60°N, 150°E–150°W) for (a) 850 and (b) 250 mb (shaded area exceeds 95% confidence level based on an F test). Ordinate indicates the calendar month taken for the KOEI lead–lag regressed against the time series of GPH anomalies for a particular lag indicated on the abscissa.

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    Statistical estimation of GPH response to the KOE SST variability using Eq. (2) with lag 2 derived from the NCEP–NCAR reanalysis over the period from 1959 to 2008 for (a),(c) 850- and (b),(d) 250-mb GPH (contour interval 10 m K−1; negative contours are dashed) for (top) early winter (NDJ) and (bottom) late winter (FMA). Shaded area exceeds the 90% confidence level.

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    Statistical estimation of GPH response to the KOE SST variability using Eq. (2) with lag 2 derived from the 20CRv2. (a) Temporal evolution of the GPH response (unit: m K−1) area averaged over the AL region at 850 (squares) and 250 mb (triangles) in early winter (NDJ) based on a 50-yr running window. Solid symbols exceed the 90% confidence level based on the Monte Carlo test, and the abscissa indicates the beginning year of the running window. Spatial pattern of the GPH response at (b),(d) 850 and (c),(e) 250 mb in NDJ (contour interval 10 m K−1; negative contours are dashed) for (b),(c) 1900–49 and (d),(e) 1959–2008. Shaded area exceeds the 90% confidence level.

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    GPH response to the KOE SST variability at (a),(c) 850 and (b),(d) 250 mb in late winter (FMA) derived from the 20CRv2 (contour interval 20 m K−1; negative contours are dashed) for (top) 1900–49 and (bottom) 1959–2008. Shaded area exceeds the 90% confidence level.

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    Statistical estimation of GPH response to the KOE SST variability using Eq. (3) at (a),(c) 850 and (b),(d) 250 mb in early winter (NDJ) derived from (a),(b) 300-yr control and (c),(d) 2CO2 experiments of FOAM (contour interval of 5 m K−1 for 850 mb and 10 m K−1 for 250 mb; negative contours are dashed). Shaded area exceeds the 90% confidence level based on the Monte Carlo test. Lead–lag regression between the KOEI and 250-mb GPH anomalies over the AL region for the (e) control and (f) 2CO2 experiment of FOAM (shaded area exceeds the 95% confidence level based on an F test). Ordinate indicates the calendar month taken for the KOEI lead–lag regressed against the time series of GPH anomalies for a particular lag indicated on the abscissa.

  • View in gallery

    Statistical estimation of 500-mb GPH weighted response to the KOE SST variability using Eq. (3) in early winter (NDJ) derived from the preindustrial and the climate change simulations projected as A1B scenario simulations of 14 IPCC models. Correlation coefficient between the atmospheric response of preindustrial simulation for an individual model and the counterpart in the NCEP–NCAR reanalysis is shown in the bracket, which is significant at the 95% confidence level based on the Student’s t test. Shaded area exceeds the 90% confidence level based on the Monte Carlo test, and negative contours are dashed.

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    Northern Hemisphere storm tracks, defined as the bandpass-filtered (2–8 days) 300-mb meridional wind velocity variance (m2 s−2), in DJF for the period of (a) 1900–49 and (b) 1959–2008 derived from the 20CRv2.

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Modulation of Atmospheric Response to North Pacific SST Anomalies under Global Warming: A Statistical Assessment

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  • 1 Physical Oceanography Laboratory, Ocean University of China, Qingdao, China
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Abstract

In this study the modulation of ocean-to-atmosphere feedback over the North Pacific in early winter from global warming is investigated based on both the observations and multiple climate model simulations from a statistical perspective. It is demonstrated that the basin-scale atmospheric circulation displays an equivalent barotropic ridge in response to warm SST anomalies in the Kuroshio–Oyashio Extension (KOE) region. This warm SST–ridge response in early winter can be enhanced significantly by global warming, indicating a strengthening of air–sea coupling over the North Pacific. This enhancement is likely associated with the intensification of storm tracks and, in turn, the amplification of atmospheric transient eddy feedback in a warm climate, although the secular trend of enhanced storm-track activity over the North Pacific is suggested to be biased in reanalysis product.

Corresponding author address: Dr. Lixin Wu, Physical Oceanography Laboratory, Ocean University of China, 5 Yushan Road, Qingdao 266003, China. E-mail: lxwu@ouc.edu.cn

Abstract

In this study the modulation of ocean-to-atmosphere feedback over the North Pacific in early winter from global warming is investigated based on both the observations and multiple climate model simulations from a statistical perspective. It is demonstrated that the basin-scale atmospheric circulation displays an equivalent barotropic ridge in response to warm SST anomalies in the Kuroshio–Oyashio Extension (KOE) region. This warm SST–ridge response in early winter can be enhanced significantly by global warming, indicating a strengthening of air–sea coupling over the North Pacific. This enhancement is likely associated with the intensification of storm tracks and, in turn, the amplification of atmospheric transient eddy feedback in a warm climate, although the secular trend of enhanced storm-track activity over the North Pacific is suggested to be biased in reanalysis product.

Corresponding author address: Dr. Lixin Wu, Physical Oceanography Laboratory, Ocean University of China, 5 Yushan Road, Qingdao 266003, China. E-mail: lxwu@ouc.edu.cn

1. Introduction

Climate models generally indicate that the ocean–atmosphere interaction in the western North Pacific, especially the Kuroshio–Oyashio Extension (KOE), plays a critical role in generating decadal climate variability in the North Pacific, including the Pacific decadal oscillation and the North Pacific gyre oscillation (e.g., Mantua et al. 1997; Di Lorenzo et al. 2008). The KOE region is a vital region where the oceanic variability can potentially imprint on the atmosphere through the intense exchange of air–sea heat flux. The differential heat supply across this region is suggestive of maintaining the surface baroclinicity to sustain the development of storm tracks (e.g., Nakamura et al. 2004). A number of studies have suggested that the atmospheric response to the North Pacific SST anomalies (SSTAs) acts as a key process to sustain decadal SST anomalies (e.g., Latif and Barnett 1994, 1996; Barnett et al. 1999; Pierce et al. 2001; Seager et al. 2001; Schneider et al. 2002; Wu et al. 2003; Kwon and Deser 2007). However, it remains not well understood how the atmosphere responds to the midlatitude oceanic variability due to the presence of the strong atmospheric internal variability [see a review by Kushnir et al. (2002)].

Observational analysis indicated that the extratropical SSTAs may exert a significant influence on the basin-scale atmospheric circulation in the North Pacific (e.g., Frankignoul and Sennéchael 2007; Frankignoul et al. 2011; Taguchi et al. 2012) and in the North Atlantic (e.g., Czaja and Frankignoul 2002) at both interannual and decadal time scales. As an example, Taguchi et al. (2012) found that over the North Pacific, a coherent decadal-scale signal in the atmospheric circulation responded to the SSTAs in the subarctic frontal zone (SAFZ) generated by its decadal-scale meridional shifts can develop into an equivalent barotropic anomaly pattern in January. This evolution is also found for the Oyashio Extension and the Kuroshio Extension during winter (Frankignoul et al. 2011).

Recently, a number of modeling studies have documented the ocean-to-atmosphere feedback in the midlatitudes through various approaches, including atmospheric general circulation model (AGCM) experiments with prescribed SSTAs or anomalous surface heat flux or ocean–atmosphere coupled GCM experiments with prescribed subsurface temperature anomalies (e.g., Peng et al. 1997; Peng and Whitaker 1999; Kushnir et al. 2002; Liu and Wu 2004). Although the atmospheric response pattern is somewhat model dependent, the response appears to be mainly generated by the nonlinear feedback of transient eddy forcing and to depend on the mean background flow (e.g., Peng et al. 1997; Peng and Whitaker 1999; Liu et al. 2007). In addition, it has been also suggested that the response may be considered as a compromise of the SST forcing favoring a warm SST–ridge response (i.e., an equivalent barotropic high response associated with a warm SST anomaly) and the heat flux forcing favoring a warm SST–trough response (i.e., a baroclinic response with a surface low pressure associated with a warm SST anomaly) (Liu and Wu 2004).

Given the potential dependence of the atmospheric response on the mean background flow in the midlatitudes, it is conceivable that the atmospheric response may be modulated from global warming. Recent studies have suggested that the ocean-to-atmosphere feedback in the tropics is weakened because of an increase of the tropospheric static stability owing to moist adiabatic adjustment under global warming (e.g., Zheng et al. 2010). However, in the midlatitudes, it remains elusive because of the strong nonlinear interaction between synoptic eddies, the stationary wave, and the jet stream (Kushnir et al. 2002). In this study, we use both the observations and multiple climate model data to investigate the modulation of the atmospheric response to the North Pacific SSTAs under global warming from a statistical prospective. It is found that a warm SST-equivalent barotropic ridge response over the North Pacific, usually identified in early winter, is intensified substantially in a warm climate, which suggests a strengthening of midlatitude air–sea coupling in the context of global warming.

The rest of this paper is arranged as follows. section 2 describes the observational and multimodel data used in this study and a KOE SSTA index is defined. section 3 describes the methodology used to assess the atmospheric response to the North Pacific SSTAs, which is based on the assumption that the time scale of atmospheric response is much shorter than that of oceanic variability in the midlatitudes. In section 4 we show the observational evidence that in early winter, the atmospheric response exhibits a substantial intensification over the twentieth century and its counterpart simulated in the context of global warming from the multiple climate models. Concluding remarks and discussions are presented in section 5.

2. Data

In this study, monthly anomalies of geopotential height (GPH) and SST are used to estimate the atmospheric response to the North Pacific SSTAs based on a linear relation described in the section 3. The KOE index (KOEI), representing the midlatitude oceanic variability, is defined as the area-averaged SSTAs over 35°–45°N, 140°E–180°. Anomalies of all variables are obtained by subtracting the climatological monthly means and removing the linear trends. For observations, the SSTAs are taken from the Met Office Hadley Centre Sea Ice and Sea Surface Temperature, version 1, (HadISST1) dataset (Rayner et al. 2003), and GPH anomalies are primarily taken from two datasets: National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis project data with a horizontal resolution of 2.5° × 2.5° (Kalnay et al. 1996), and the Twentieth-Century Reanalysis dataset, version 2 (20CRv2), with a horizontal resolution of 2° × 2° (Compo et al. 2011). The temporal coverage of the monthly GPH anomalies used in this study is from 1959 to 2008 for the NCEP–NCAR reanalysis to avoid problems arising from the observational system before the early 1960s, and from 1900 to 2008 for 20CRv2. We also utilize the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data with a horizontal resolution of 2.5° × 2.5° from 1950 to 2008 to investigate the atmospheric response as complementary evidence.

The multiple climate model data obtained from the archive of coupled climate models organized by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4; Solomon et al. 2007) are used to investigate the robustness of the atmospheric response in the preindustrial and the climate change simulations. The latter is projected as the Special Report on Emissions Scenarios (SRES) A1B scenario that corresponds roughly to a doubling in equivalent CO2 at the end of the twenty-first century. Table 1 summarizes the IPCC models analyzed in this study (full model names listed in table), and we only use one ensemble member for each of the models. Furthermore, we analyze the atmospheric response in the present-day and double-CO2 simulations of FOAM, version 1.5 (FOAM 1.5) (Jacob 1997; Wu et al. 2003). This model has been widely utilized in studies of the midlatitude ocean–atmosphere interaction over the Pacific and Atlantic Oceans (e.g., Liu and Wu 2004; Wu and Li 2007; Wu et al. 2008).

Table 1.

21 IPCC AR4 models from which the SST and GPH data are available for both the preindustrial and the scenario A1B simulations, together with FOAM. Spatial correlation coefficient between the 500-mb GPH response to the KOE SST variability in early winter (NDJ) in the model and the counterpart in the NCEP–NCAR reanalysis is shown in the last column, and the domain for its calculation is the North Pacific sector. Italic correlation coefficients exceed the 95% confidence level based on the Student’s t test and the one for FOAM exceeds the 90% confidence level. Bold model names indicate the models used to investigate the atmospheric response under global warming.

Table 1.

3. Estimation of the atmospheric response

To statistically estimate the atmospheric response to extratropical SSTAs, Frankignoul and Kestenare (2002) propose that this response should be derived from the SST-lead covariance with an atmospheric signal rather than the instantaneous covariance, since the latter is dominated by the forcing of atmosphere on the ocean. Following this approach, we assume a linear relation between an atmospheric variable A and the SST anomaly or associated index T as
e1
where N is a short time-scale signal controlled by the internal variability of the atmosphere and λR represents the response or feedback per unit SSTA. By eliminating the noise N with the aid of the SST-lead covariance, the response parameter λR can be estimated as
e2
where angle brackets represent the ensemble average of covariance and a positive τ indicates that the ocean leads the atmosphere, which should be longer than the decorrelation time of the atmospheric internal variability (usually assumed as 1–2 weeks). The essence of this method is that the correlation between the SSTA of a lead time τ and the atmospheric noise is negligible, that is, , when τ is much longer than the intrinsic atmospheric persistence time. As for the time scale of atmospheric response to the extratropical oceanic variability, Deser et al. (2007) suggest that it should be 2–2.5 months to reach a fully developed equivalent barotropic response state from an initial stage characterized by a local and baroclinic response to the imposed oceanic forcing (Frankignoul and Sennéchael 2007). In addition, Liu et al. (2006) suggest that with small ensembles, the longer SST leads tend to induce larger sampling errors due to the diminishing SST autocovariance in the denominator. Therefore, only a 1-month lead (τ = 1) and a 2-month lead (τ = 2) are selected to estimate the atmospheric response based on Eq. (2), separately. Following Liu et al. (2007), a weighted response is then constructed as
e3
It is noticed that the response at a lag of 2 months (lag 2) is estimated for the observations since the atmospheric forcing and the response cannot be clearly distinguished at a lag of 1 month (lag 1) because of a persistent forcing signal. In investigating the multimodels, however, the weighted response is implemented as a compromise of distinguishing between the atmospheric forcing and response and reducing sampling errors since the simulated atmospheric response to SSTAs is much faster than that in the observation (as seen in a comparison of Fig. 1b with Fig. 5e).
Fig. 1.
Fig. 1.

Seasonal evolution of the lead–lag regression between the KOEI and GPH anomalies based on the NCEP–NCAR reanalysis over the AL region (30°–60°N, 150°E–150°W) for (a) 850 and (b) 250 mb (shaded area exceeds 95% confidence level based on an F test). Ordinate indicates the calendar month taken for the KOEI lead–lag regressed against the time series of GPH anomalies for a particular lag indicated on the abscissa.

Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00493.1

Given the far-reaching impact of ENSO on the North Pacific climate (Alexander et al. 2002), the influence of ENSO is filtered out from the monthly GPH anomalies and SSTAs by using a linear regression onto the ENSO index defined as the principal component of the leading EOF of the SSTAs in the tropical Pacific between 20°S and 20°N. Taking into account the strong seasonality of ENSO, the seasonally varying regression coefficients are derived by regressing the time series of each variable against the ENSO index for each calendar month (with a 2-month lag for SST and a 0 lag for atmospheric variables) and then smoothing these coefficients through a 3-month running mean to reduce the large sampling error. To further verify the effectiveness of the removal of ENSO, we regressed the global SST anomalies onto the KOEI used in this study over the period from 1900 to 2008. It is found that the positive SSTAs are mainly confined to the KOE region with the high regression coefficients exceeding 0.6 K K−1 flanked by negative SSTAs with a relatively small magnitude along the west coast of North America. However, the tropical SSTAs associated with the KOE SST variability are quite small (not shown). Given this sharp contrast of SST over the KOE region and the tropical Ocean, the atmospheric response to the North Pacific SSTAs is dominated by the local response to the KOE SST variability.

The statistical significance of the atmospheric response is estimated using the Monte Carlo test, a nonparametric approach, in which the calculation of λR is repeated 1000 times using the original KOEI with the atmospheric time series randomly scrambled. The probability distribution function of the obtained 1000 values for λR is then constructed to rank the significance of the actual atmospheric response.

4. Atmospheric response to KOE SST variability

a. Observational evidence

Previous studies have indicated that the atmospheric response to North Pacific Ocean variability exhibits a distinct seasonal evolution, in which it is dominated by the early-winter response with a warm SST-equivalent barotropic ridge pattern and then weakens significantly in late winter (Peng and Whitaker 1999; Liu et al. 2007; Taguchi et al. 2012). Therefore, we first examine the seasonal dependence of the midlatitude ocean–atmosphere interaction over the North Pacific, which can be inferred from the lead–lag regression between SSTAs associated with the KOEI and atmospheric GPH anomalies (Fig. 1). For a particular calendar month of the KOEI, the regressions exhibit a strong asymmetry with positive regression dominating when the GPH leads the SST by 1–2 months (as seen in the negative lags in Figs. 1a,b), suggesting that in the midlatitudes the monthly SST variability is forced predominantly by the atmosphere (Frankignoul et al. 1998). The atmosphere tends to force a warmer SST corresponding to a higher GPH with a maximum in December and January, indicating that the weakening of the Aleutian low (AL) warms the ocean through reducing the thermal damping effect on the SST and southward Ekman advection. This is consistent with the results of Taguchi et al. (2012), who have shown the same lead–lag relationship between the monthly SAFZ–SST index and the North Pacific index representing the AL variability both in the observations and in a climate model simulation. In contrast, the regression when SST leads (positive lags) is much weaker. Nevertheless, there are significant regressions in the cold seasons, indicating a possible response of the atmosphere to SST variability. This response has a strong seasonal dependence with significant positive regressions occurring in early winter [November–January (NDJ)], as seen in the positive lags in Figs. 1a,b, indicating an equivalent barotropic ridge response pattern associated with positive SSTAs. It is also noticed that a warm SST-equivalent barotropic trough response appears in late winter [February–April (FMA)], but it is less significant at 250 mb (Fig. 1b). Here we will mainly focus on the early winter.

To detect the possible atmospheric response to the North Pacific SSTAs, we investigate the spatial pattern of the response based on the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis from 1959 to 2008, which is estimated statistically using Eq. (2) with A being the time series of the 850-/250-mb GPH anomalies at each grid point in NDJ (FMA) and T being the KOEI in September–November (SON) [December–February (DJF)]. The response features a dominant equivalent barotropic ridge over the AL region associated with positive KOE SSTAs, corresponding to the weakening of the AL (Figs. 2a,b). These basin-scale anticyclone anomalies are centered at around 55°N, 180° with a magnitude of about 40 and 70 m at 850 and 250 mb, respectively. In late winter (FMA), however, there are no significant atmospheric response signals detected over the North Pacific (Figs. 2c,d). We also have examined the subseasonal evolution of atmospheric response to the KOE SST variability from month to month; it is found that the response is matured and strongest with a warm SST–ridge pattern in January, but it switches to a warm SST–trough in February and weakens in the following months. This is consistent with the results found in observational and modeling studies (Peng et al. 1997; Peng and Whitaker 1999; Taguchi et al. 2012). Taguchi et al. (2012) suggest that this distinct January–February response difference is attributed to the sharp contrast in anomalous near-surface baroclinicity induced by the corresponding contrast in anomalous oceanic thermal forcing, resulting in the different transient eddy feedback under the anomalous storm-track activity. It is this eddy feedback that plays a critical role in controlling the atmospheric response to the extratropical SST variability, suggested by Peng and Whitaker (1999).

Fig. 2.
Fig. 2.

Statistical estimation of GPH response to the KOE SST variability using Eq. (2) with lag 2 derived from the NCEP–NCAR reanalysis over the period from 1959 to 2008 for (a),(c) 850- and (b),(d) 250-mb GPH (contour interval 10 m K−1; negative contours are dashed) for (top) early winter (NDJ) and (bottom) late winter (FMA). Shaded area exceeds the 90% confidence level.

Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00493.1

To assess the potential modulation under global warming, we take advantage of the long temporal coverage of the recently developed Twentieth-Century Reanalysis dataset (Compo et al. 2011). We use a 50-yr running window to assess the 850-/250-mb GPH response area averaged over the AL region (30°–60°N, 150°E–150°W) sequentially. A remarkable intensification trend of the North Pacific ocean-to-atmosphere feedback over the twentieth century can be clearly detected, although the atmospheric response to the North Pacific SSTAs is weak and statistically insignificant in the early part of the twentieth century (Fig. 3a). The significant atmospheric response emerges from the mid-twentieth century, with a magnitude of about 45 and 20 m at 250 and 850 mb, respectively. To further exploit the differences in the atmospheric responses between the early and the late twentieth centuries, we analyze the spatial pattern of the responses in two periods: 1900–49 and 1959–2008. In the early part (1900–49), while an equivalent barotropic low response is detected over the AL region, it is statistically insignificant except over the KOE region, where the magnitude of the GPH response is about 20 m at 850 mb (Fig. 3b). In a sharp contrast, the atmospheric response in the late part (1959–2008) displays a dominant equivalent barotropic ridge over the AL region associated with positive SSTAs off the east coast of Japan, strikingly resembling that estimated based on the NCEP–NCAR reanalysis (ERA-40), with a high pattern correlation coefficient of 0.95 (0.92) and 0.93 (0.89) for 850 and 250 mb, respectively (Figs. 3d,e). The response has a magnitude of about 40 and 80 m at 850 and 250 mb, respectively. In the upper troposphere, the magnitude of the response is slightly larger than that for the NCEP–NCAR reanalysis by 14% but the same as that for ERA-40. It is noticed that the 250-mb GPH response in the late twentieth century resembles the Pacific–North American (PNA) pattern (Wallace and Gutzler 1981), as found in previous studies (Frankignoul and Sennéchael 2007; Liu et al. 2007; Taguchi et al. 2012). In late winter (FMA), however, the atmospheric response to the North pacific SSTAs is weakened substantially over the twentieth century, as seen in the change of response signals from significant ones in a warm SST-equivalent barotropic trough structure detected over the AL region in the early twentieth century (Figs. 4a,b) to insignificant ones over the North Pacific basin in the late twentieth century (Figs. 4c,d). The spatial pattern of this response derived from the 20CRv2 for the period of 1959–2008 is highly correlated with that from the NCEP–NCAR reanalysis (ERA-40), with a high pattern correlation coefficient of 0.90 (0.87) and 0.80 (0.74) for 850 and 25mb, respectively. Given the long-term trend of the atmospheric response over the twentieth century, we speculate that the intensification of response detected in the observations is attributed to global warming.

Fig. 3.
Fig. 3.

Statistical estimation of GPH response to the KOE SST variability using Eq. (2) with lag 2 derived from the 20CRv2. (a) Temporal evolution of the GPH response (unit: m K−1) area averaged over the AL region at 850 (squares) and 250 mb (triangles) in early winter (NDJ) based on a 50-yr running window. Solid symbols exceed the 90% confidence level based on the Monte Carlo test, and the abscissa indicates the beginning year of the running window. Spatial pattern of the GPH response at (b),(d) 850 and (c),(e) 250 mb in NDJ (contour interval 10 m K−1; negative contours are dashed) for (b),(c) 1900–49 and (d),(e) 1959–2008. Shaded area exceeds the 90% confidence level.

Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00493.1

Fig. 4.
Fig. 4.

GPH response to the KOE SST variability at (a),(c) 850 and (b),(d) 250 mb in late winter (FMA) derived from the 20CRv2 (contour interval 20 m K−1; negative contours are dashed) for (top) 1900–49 and (bottom) 1959–2008. Shaded area exceeds the 90% confidence level.

Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00493.1

b. Modeling results

To test the above-mentioned hypothesis, we first examine the atmospheric response to the KOE SST variability, estimated using Eq. (3) with the time series of GPH anomalies fixed in early winter (NDJ), in a 300-yr control (CO2 at 360 ppm) and 2CO2 (CO2 at 720 ppm) experiment of FOAM. In early winter, the GPH weighted response in the control simulation exhibits a warm SST-equivalent barotropic ridge pattern over the AL region (Figs. 5a,b), with wave train responses upstream and downstream. While in the lower troposphere, the atmospheric response in FOAM displays little resemblance to the one detected in the observations, it is still consistent with the observations in the middle and upper troposphere (as seen in Table 1 for the pattern correlations). In the 2CO2 experiment, however, this response is intensified significantly at both the lower and upper troposphere, with the magnitude nearly treble (Figs. 5c,d). This basin-scale anticyclonic response is centered at around 55°N, 160°W, with a magnitude of 35 and 100 m for 850 and 250 mb, respectively. In the upper troposphere, the North Pacific high pressure anomalies in the 2CO2 simulation are shifted eastward by 10° relative to the control simulation. This intensification of ocean-to-atmosphere feedback over the North Pacific is also demonstrated by the lead–lag regression between the KOEI and 250-mb GPH anomalies (Figs. 5e,f). The model control simulation captures the major features of the observed seasonal dependence of the atmospheric response as shown in the NCEP–NCAR reanalysis (Fig. 1b). Maximum positive regression occurs in March at zero lag and significant positive regressions occur in the cold seasons when the SST leads the GPH (positive lags) by 1–2 months (Fig. 5e). The model fails to simulate the substantial positive regressions at long SST leads (+3 to +6 month) detected in the observation. The absence of this large regression at long SST leads is probably caused by a shallower winter mixed layer in the model, which leads to a shorter persistence time of the simulated SST and thus a weaker atmospheric response, suggested by Liu and Wu (2004). In the 2CO2 experiment, however, significant regressions appear at long SST leads that are absent in the control simulation, as seen in the positive regressions for both the October and November calendar months of SST leading the GPH by +2 to +4 months (Fig. 5f). This is indicative of a strengthening of North Pacific air–sea coupling in early winter under global warming.

Fig. 5.
Fig. 5.

Statistical estimation of GPH response to the KOE SST variability using Eq. (3) at (a),(c) 850 and (b),(d) 250 mb in early winter (NDJ) derived from (a),(b) 300-yr control and (c),(d) 2CO2 experiments of FOAM (contour interval of 5 m K−1 for 850 mb and 10 m K−1 for 250 mb; negative contours are dashed). Shaded area exceeds the 90% confidence level based on the Monte Carlo test. Lead–lag regression between the KOEI and 250-mb GPH anomalies over the AL region for the (e) control and (f) 2CO2 experiment of FOAM (shaded area exceeds the 95% confidence level based on an F test). Ordinate indicates the calendar month taken for the KOEI lead–lag regressed against the time series of GPH anomalies for a particular lag indicated on the abscissa.

Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00493.1

To further verify this result and its potential model dependence, we investigate the atmospheric response to the KOE SST variability in early winter in both the preindustrial and the climate change simulations projected as A1B emission scenario simulations of the IPCC models. Given that in early winter the atmospheric circulation displays an equivalent barotropic ridge in response to warm KOE SST anomalies, the responses in the IPCC models are estimated using Eq. (3) with the time series of 500-mb GPH anomalies fixed in NDJ and the KOEI taken in October–December (OND) (for lag 1) and SON (for lag 2). We first examine the response in the preindustrial simulation of each of the 21 IPCC models and then compare it with the counterpart in the NCEP–NCAR reanalysis, summarized in Table 1. Only these models, in which the atmospheric response pattern is significantly correlated with the observation (as seen in the italic spatial correlation coefficient in Table 1), are selected to examine the response under global warming. It turns out that 14 models capture the major features of the warm SST-equivalent barotropic ridge pattern detected in the observations over the North Pacific (Fig. 6). Among these 14 models, there are 11 models that exhibit an intensification of the response in early winter under global warming. In these 11 models, the intensification is mainly manifested in magnitude with little change in spatial extent, except for the INM and MRI models, in which the spatial scale of the warm SST–ridge response in the A1B simulation extends much larger than that in the control simulation. Furthermore, the atmospheric response in the CSIRO Mk3.0, GFDL CM2.0, INGV, HadCM3 models display the most significant change from the less significant response in the control simulation to a pronounced ridge response downstream of the KOE region in the A1B simulation. The magnitude of the response in the A1B simulation is more than 2 times larger than that in the control simulation of these models. It is noticed that in the CSIRO Mk3.0 model, the center of the warm SST–ridge response is shifted eastward by 30° in the A1B simulation relative to the control simulation, which is also found in the INGV model. In short, although the pattern and magnitude in the atmospheric response vary from model to model, the warm SST–ridge response tends to be robust and substantiated by global warming displayed in the majority of the climate models.

Fig. 6.
Fig. 6.

Statistical estimation of 500-mb GPH weighted response to the KOE SST variability using Eq. (3) in early winter (NDJ) derived from the preindustrial and the climate change simulations projected as A1B scenario simulations of 14 IPCC models. Correlation coefficient between the atmospheric response of preindustrial simulation for an individual model and the counterpart in the NCEP–NCAR reanalysis is shown in the bracket, which is significant at the 95% confidence level based on the Student’s t test. Shaded area exceeds the 90% confidence level based on the Monte Carlo test, and negative contours are dashed.

Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00493.1

5. Summary and discussion

In this study the modulation of atmospheric response to the North Pacific SSTAs associated with the KOE SST variability under global warming is investigated using both the observations and multiple climate model simulations from a statistical perspective. After removing the ENSO teleconnections by seasonally varying regression onto the first principal component (PC1) of the tropical Pacific SSTAs, it is found that in early winter the basin-scale atmospheric circulation displays an equivalent barotropic ridge in response to warm SSTAs in the KOE region. Over the twentieth century, this warm SST–ridge response exhibits a remarkable intensification trend, which is likely attributed to global warming. To further assess the potential modulation from global warming, the atmospheric response is examined in the 2CO2 experiment of FOAM and in the A1B scenario simulation of 14 IPCC models that reasonably capture the major features of this warm SST–ridge response in the control simulation. It is found that both FOAM and 11 IPCC models produce the substantial intensification of the North Pacific ocean-to-atmosphere feedback under global warming, although there are some discrepancies in pattern and magnitude of response among the models.

The ocean-to-atmosphere feedback in the midlatitudes has been suggested to be associated with strong nonlinear interaction between synoptic eddies, the stationary wave, and the jet stream (e.g., Kushnir et al. 2002). The modulation of atmospheric transient eddies migrating along storm tracks on the heating-forced anomalous flow primarily controls the atmospheric equilibrium response to the extratropical SSTAs mainly through anomalous eddy vorticity forcing (e.g., Peng and Whitaker 1999; Deser et al. 2007; Taguchi et al. 2012). Furthermore, recent studies have highlighted the important role of the subarctic frontal zone in the KOE region in anchoring nearby storm tracks through the oceanic baroclinic adjustment associated with the SST front (e.g., Nakamura et al. 2004; Taguchi et al. 2009). Considering the intimate relation between the midlatitude ocean-to-atmosphere feedback and atmospheric transient eddy feedback tied to storm tracks (e.g., Chang et al. 2002), we also examine the changes of storm tracks in the twentieth century using daily data of the 20CRv2. It is found that relative to the early part of the twentieth century, the storm tracks over the North Pacific are enhanced by 75% in the late part of the century (Fig. 7). There is also a possibility that the secular trend of enhanced storm-track activity in the reanalysis product is biased due to the sparse ship observations, especially over the North Pacific (Chang 2007). The intensification of storm tracks in a warm climate, however, has been also identified in the twenty-first-century climate projections from the IPCC AR4 (Yin 2005). Such intensification may amplify the midlatitude transient eddy feedback, and thus the ocean-to-atmosphere dynamic feedback. It is also noticed that the influence of the North Pacific SSTAs on the storm tracks is proposed to be highly sensitive to the position of the anomalous SST gradient relative to that of the background subtropical jet (Brayshaw et al. 2008).

Fig. 7.
Fig. 7.

Northern Hemisphere storm tracks, defined as the bandpass-filtered (2–8 days) 300-mb meridional wind velocity variance (m2 s−2), in DJF for the period of (a) 1900–49 and (b) 1959–2008 derived from the 20CRv2.

Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00493.1

The intensification of ocean-to-atmosphere feedback in the midlatitudes under global warming forms a sharp contrast to the tropics, where the feedback is weakened in a warm climate. The latter is due to an increase of the tropospheric static stability (e.g., Zheng et al. 2010), while the former is likely associated with the intensification of nonlinear eddy feedbacks although the tropospheric static stability in the midlatitudes also increases under global warming (Frierson 2006). Future studies using both the observations and climate models are clearly needed to understand the dynamics of transient eddy feedback in a warm climate. In addition, the warm SST–ridge response tends to be robust and substantiated by global warming in early winter; however, in late winter the response with a warm SST-equivalent barotropic trough pattern over the AL region appears to be weakened significantly over the twentieth century based on the 20CRv2. In the climate models, it is hard to capture this warm SST–trough response, perhaps due to the reasonable simulation of the atmospheric background flow in February. Therefore, we mainly focus on the atmospheric response in early winter in this study and recommend further studies to investigate the robustness of this response in late winter.

It is noticed that although the climate models used in this study capture the pattern of atmospheric response to midlatitude SST anomalies, the magnitudes are generally weaker than these in the observations. Perhaps this is associated with the right representation of storm tracks in the climate models. According to Table 1, it is also worth noting that the model with relatively higher resolution exhibits a lower spatial correlation with the observation. In addition, the removal of strong seasonality of ENSO exerts more influence on the North Pacific ocean-to-atmosphere feedback with a slight reduction in magnitude, compared with a constant regression coefficient applied to filter out the influence of ENSO. Whether taking into account the lagging effect of ENSO on the North Pacific SST has no impact on the feedback.

Acknowledgments

We are grateful to the China National Natural Science Foundation Key Project (Grant 41130859) and the National Natural Science Foundation Projects (Grants 40788002, 40921004). We also appreciate three anonymous reviewers for their comments to improve the manuscript substantially. Discussions with Drs. F.-F. Jin and Shiling Peng were helpful.

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