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  • View in gallery

    (a),(b) Climatology (contour intervals of 5 K) and standard deviation of the daily SST (shading, with interval of 0.5 K) in the North Atlantic and climatologies of the (c),(d) 10-day high-frequency 850-hPa eddy heat flux (shading, with interval of 5 m s−1 K) and eddy kinetic energy at 300 hPa (contours; m2 s−2) and (e),(f) 10-day low-frequency eddy heat flux for (left) SON and (right) DJF.

  • View in gallery

    Lagged regressions of Z1000 (contour intervals of 8 m) and SST (shading, with interval of 0.05 K) onto the normalized daily NAO index in autumn. Values that are significant at the 95% confidence level using a two-tailed t test are highlighted with black dots.

  • View in gallery

    (a) Autocorrelation of the NAO and two SST indices, and (b),(c) their lagged correlations in autumn. Negative or positive lags denote that SST leads or lags NAO, respectively. The dashed lines denote the corresponding values of 95% significance level, which are estimated from the autocorrelations of each time series.

  • View in gallery

    (a)–(e) Composites of Z1000 (contour intervals of 8 m) and SST (shading, with interval of 0.05 K) anomalies for the positive phase of the normalized NAH-like SST mode index in autumn, with negative or positive lags denoting that Z1000/SST anomalies are leading or lagging NAT-like SST index, respectively. (f)–(j) as in (a)–(e) but for Z1000 (contour interval of 8 m) and Z300 (shading, with interval of 16 m). Values that are significant at the 95% confidence level are highlighted with black dots. The black-outlined boxes in (c) denote three key SST regions, including the areas off the western coast of northern Europe (label N; 55°–70°N, 20°W–10°E), the Gulf Stream region (label G; 40°–55°N, 60°–30°W), and to the south of the Gulf Stream region (label S; 25°–35°N, 60°–30°W).

  • View in gallery

    As in Fig. 4, but for the composites of (a)–(e) sensible (contour intervals of 4 W m−2) and latent (shading, with interval of 4 W m−2) heat fluxes, (f)–(j) longwave radiative flux (shading, with interval of 4 W m−2), and (k)–(o) SST tendency (shading, with interval of 0.01 K day−1). The sign is defined as positive in the downward direction for heat and radiative fluxes.

  • View in gallery

    As in Fig. 4, but for the composites of (a)–(e) surface air temperature (shading, with interval of 0.4 K) and (f)–(j) 850-hPa Eady growth rate (shading, with interval of 0.05 day−1).

  • View in gallery

    Composites of anomalous 10-day high-frequency (a),(b) eddy meridional heat flux at 850 hPa (shading, with interval of 1 ms−1 K), (c),(d) eddy kinetic energy at 300 hPa (shading, with interval of 10 m2 s−2), and (e),(f) transient E-vector fluxes at 300 hPa (gray arrows, with significant fluxes at the 80% confidence level overplotted with black; m2 s−2) and 1000–100-hPa vertically averaged zonal wind (shading, with interval of 1 ms−1) for the normalized NAH-like SST index in autumn when the atmospheric anomalies lag NAH-like SST index by (top) 10 and (bottom) 20 days. Values that are significant at the 95% confidence level are highlighted with black dots.

  • View in gallery

    As in Fig. 7, but for the composites of (a),(b) 10-day low-frequency eddy heat flux at 850 hPa (shading, with interval of 1 m s−1 K) and (c),(d) the frequency of unusually large and persistent anticyclonic wave activities at 300 hPa (shading, with interval of 0.02).

  • View in gallery

    Composites of Z1000 (contour intervals of 8 m) and Z300 (shading, with interval of 16 m) anomalies with normalized SST index for the (a),(d) N box, (b),(e) −G box, and (c),(f) S box when the atmosphere leads the SST indices by (top) 10 and (bottom) 20 days. For ease of comparison, the SST index in the G box is multiplied by −1.

  • View in gallery

    Lagged regression of Z1000 (contour interval of 8 m) and SST (shading, with interval of 0.05 K) onto the normalized daily NAO index in winter, with Z1000/SST anomalies (a) lagging NAO index by 10 days and (b) leading NAO index by 20 days. Also shown is the (c) cross correlation between the NAO index and NAT SST index during winter. Negative lags denote that the SST index leads the NAO index.

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The Role of Extratropical Air–Sea Interaction in the Autumn Subseasonal Variability of the North Atlantic Oscillation

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  • 1 Laboratory for Climate Studies and China Meteorological Administration–Nanjing University Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing, China
  • | 2 Laboratory for Climate Studies and China Meteorological Administration–Nanjing University Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing, and Department of Atmospheric Science, School of Environmental Studies, China University of Geoscience, Wuhan, China
  • | 3 China Meteorological Administration–Nanjing University Joint Laboratory for Climate Prediction Studies, Institute for Climate and Global Change Research, School of Atmospheric Science, Nanjing University, Nanjing, China
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Abstract

Considerable progress has been made in understanding the internal eddy–mean flow feedback in the subseasonal variability of the North Atlantic Oscillation (NAO) during winter. Using daily atmospheric and oceanic reanalysis data, this study highlights the role of extratropical air–sea interaction in the NAO variability during autumn when the daily sea surface temperature (SST) variability is more active and eddy–mean flow interactions are still relevant. Our analysis shows that a horseshoe-like SST tripolar pattern in the North Atlantic Ocean, marked by a cold anomaly in the Gulf Stream and two warm anomalies to the south of the Gulf Stream and off the western coast of northern Europe, can induce a quasi-barotropic NAO-like atmospheric response through eddy-mediated processes. An initial southwest–northeast tripolar geopotential anomaly in the North Atlantic forces this horseshoe-like SST anomaly tripole. Then the SST anomalies, through surface heat flux exchange, alter the spatial patterns of the lower-tropospheric temperature and thus baroclinicity anomalies, which are manifested as the midlatitude baroclinicity shifted poleward and reduced baroclinicity poleward of 70°N. In response to such changes of the lower-level baroclinicity, anomalous synoptic eddy generation, eddy kinetic energy, and eddy momentum forcing in the midlatitudes all shift poleward. Meanwhile, the 10–30-day low-frequency anticyclonic wave activities in the high latitudes decrease significantly. We illustrate that both the latitudinal displacement of midlatitude synoptic eddy activities and intensity variation of high-latitude low-frequency wave activities contribute to inducing the NAO-like anomalies.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hong-Li Ren, renhl@cma.gov.cn

Abstract

Considerable progress has been made in understanding the internal eddy–mean flow feedback in the subseasonal variability of the North Atlantic Oscillation (NAO) during winter. Using daily atmospheric and oceanic reanalysis data, this study highlights the role of extratropical air–sea interaction in the NAO variability during autumn when the daily sea surface temperature (SST) variability is more active and eddy–mean flow interactions are still relevant. Our analysis shows that a horseshoe-like SST tripolar pattern in the North Atlantic Ocean, marked by a cold anomaly in the Gulf Stream and two warm anomalies to the south of the Gulf Stream and off the western coast of northern Europe, can induce a quasi-barotropic NAO-like atmospheric response through eddy-mediated processes. An initial southwest–northeast tripolar geopotential anomaly in the North Atlantic forces this horseshoe-like SST anomaly tripole. Then the SST anomalies, through surface heat flux exchange, alter the spatial patterns of the lower-tropospheric temperature and thus baroclinicity anomalies, which are manifested as the midlatitude baroclinicity shifted poleward and reduced baroclinicity poleward of 70°N. In response to such changes of the lower-level baroclinicity, anomalous synoptic eddy generation, eddy kinetic energy, and eddy momentum forcing in the midlatitudes all shift poleward. Meanwhile, the 10–30-day low-frequency anticyclonic wave activities in the high latitudes decrease significantly. We illustrate that both the latitudinal displacement of midlatitude synoptic eddy activities and intensity variation of high-latitude low-frequency wave activities contribute to inducing the NAO-like anomalies.

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Corresponding author: Hong-Li Ren, renhl@cma.gov.cn

1. Introduction

As the dominant mode of the subseasonal atmospheric variability in the North Atlantic Ocean, the North Atlantic Oscillation (NAO) describes the oscillation of the sea level pressure (SLP) between the Arctic and Atlantic subtropics (Hurrell 1995). The variation of NAO anomalies is often accompanied by changes in storminess, wind speeds, surface air temperature, and precipitation across the North Atlantic as well as over Europe and eastern America (Hurrell et al. 2013). Observational studies have suggested that persistence of the NAO is primarily maintained by the nonlinear positive feedback between the synoptic eddy forcing and the mean flow anomalies (Feldstein and Lee 1998; Feldstein 2003; Vallis and Gerber 2008; Barnes and Hartmann 2010), and phase transition of the NAO is often associated with wave-breaking processes (Benedict et al. 2004; Strong and Magnusdottir 2008; Woollings et al. 2008).

Besides internal eddy–mean flow feedback and wave-breaking processes, extratropical air–sea interactions may also play a role in the subseasonal variability of NAO (Lau 1997; Kushnir et al. 2002). Many previous studies have suggested that the extratropical air–sea interactions in the North Atlantic are dominated by the atmospheric driving effect (Cayan 1992; Deser and Timlin 1997; Deser et al. 2010). The positive NAO anomalies, via Ekman heat transport and surface turbulent heat fluxes, can drive significant tripolar sea surface temperature (SST) anomaly pattern (Frankignoul and Hasselmann 1977; Czaja and Frankignoul 2002) marked by zonally oriented cold anomalies in the subpolar and subtropical North Atlantic, and warm anomalies in the midlatitudes. However, it remains elusive how the underlying SST anomalies affect the atmospheric variability in the North Atlantic, especially on the subseasonal time scale. Because of the large heat capacity of the well-mixed layer of upper ocean, an oceanic thermal signal can persist for months, which allows for a persistent thermal forcing of the overlying atmosphere (e.g., impact of North Atlantic SST anomalies on the NAO; Czaja and Frankignoul 2002). Modeling studies have suggested that the SST anomaly with strong meridional gradient in the oceanic frontal zones can affect the eddy-driven jet and annular mode variability (Rodwell et al. 1999; Nakamura et al. 2008; Sampe et al. 2013; O’Reilly et al. 2017) through either the surface energy fluxes and Ekman currents (Frankignoul 1985; Minobe et al. 2008, 2010) or the eddy-mediated processes (Deser et al. 2004; Ferreira and Frankignoul 2005). However, the numerical studies show diverse atmospheric response to the given SST anomalies, probably due to different experiment designs, initial atmospheric conditions, background states (Peng et al. 1997; Brayshaw et al. 2008), or even different eddy–mean flow interactions (Kushnir et al. 2002). Suffering from the length of available observational high-frequency data, the impact of the SST on the overlying atmosphere lacks observational evidence. Using weekly atmospheric and oceanic reanalysis data, Ciasto and Thompson (2004) have first argued that the SST anomalies over the Gulf Stream region may play an important role in exciting the winter NAO-like atmospheric response on the subseasonal time scale. Wills et al. (2016) further used daily-mean reanalysis data and supported the results of Ciasto and Thompson (2004) by showing that the poleward and upward advection of anomalous warm air from the Gulf Stream SST front are the key processes in generating the winter NAO anomalies.

Many of these observational and modeling studies on extratropical air–sea interactions have focused primarily on the winter season when the atmosphere driving effect on the SST variability is most vigorous and midlatitude eddy–mean flow interactions are strongest. Less is known about the pattern of air–sea interaction during the rest of the year. In particular, during the transition season (e.g., autumn), the daily SST variability is more active than that in winter (Deser et al. 2010). Meanwhile, the atmospheric internal variability is reduced but the eddy–mean flow interactions are still relevant (Peixoto and Oort 1992). As suggested by Kushnir et al. (2002), the knowledge of the SST in autumn is most likely to help in extended-range prediction. Thus, examining the physical processes on how the daily SST variation over North Atlantic in autumn influences the overlying atmospheric circulation is of great importance for understanding the NAO variability as well as enhancing the skill of extended-range climate prediction.

Using daily atmospheric and oceanic reanalysis data, this study highlights the role of extratropical air–sea interaction in the subseasonal variability of the NAO during autumn. We show that a particular horseshoe-like SST pattern can give rise to a significant NAO-like atmospheric response through the eddy-mediated processes. The structure of this paper is assigned as below. Section 2 is a description of the dataset and diagnostic method. The characteristics of the climatological SST variability and eddy statistics are reviewed in section 3. The observed SST–NAO interactions on the subseasonal time scale during autumn are investigated in section 4. The detailed processes through which the daily SST anomalies affect the NAO during autumn are elucidated in section 5. A summary and discussion of the results are presented in section 6.

2. Data and diagnostic method

In this study, the atmospheric data consist of 35 years (1982–2016) of 1.5° × 1.5° latitude–longitude gridded daily (1200 UTC) wind, temperature, geopotential height at constant pressure levels, 10-m wind components and surface heat fluxes from the ERA-Interim dataset produced by European Centre for Medium-Range Weather Forecasts (Dee et al. 2011). The daily SST data with a horizontal resolution of 1° × 1° from the NOAA Optimum Interpolation (OI) SST, version 2 (V2), dataset (Reynolds et al. 2002) are also employed. The ocean surface current data are from the NCEP Global Ocean Data Assimilation System (GODAS). Mixed layer depth data are estimated from the Ocean Mixed Layer Depth Climatology Dataset (De Boyer Montegut et al. 2004). The surface heat fluxes from the Woods Hole Oceanographic Institution Objectively Analyzed Air–Sea Fluxes (OAFlux) dataset (Yu and Weller 2007) are also tested to guarantee the robustness of the results.

To analyze the subseasonal variability of both the atmosphere and ocean, daily anomaly data of all fields are used throughout this paper by removing the mean seasonal cycle, which is defined as the annual average plus the first four Fourier harmonics of the daily climatology. To investigate the roles of synoptic and low-frequency eddies, the anomalous fields are split into high- and low-frequency components, representing variability on time scales less than and greater than 10 days, respectively. This frequency division uses a 10-day-cutoff Lanczos filter with 41 weights (Hamming 1989) applied to the eddy component of the wind and temperature of all seasons over 35 years. The September–November (SON) and December–February (DJF) fields are retained after such a filtering was performed on the entire period.

The spatial pattern of NAO in this work is represented by the leading empirical orthogonal function (EOF) of the monthly-mean geopotential height at 1000 hPa (Z1000) for the North Atlantic sector (90°W–40°E, 20°–90°N) as in the method used in Barnes and Hartmann (2010). The results in this work are insensitive to the definition of NAO pattern based on different lower-tropospheric circulation variables (e.g., SLP or zonal wind). For the EOF analysis, the data fields are properly weighted to account for the decrease of area toward the North Pole (North et al. 1982). The daily NAO indices are calculated by projecting the daily Z1000 anomalies onto the NAO pattern.

To investigate the two-way interaction between the ocean and atmosphere, two SST indices are generated by projecting the daily SST anomalies onto the two typical NAO-associated SST anomaly patterns, with one preceding the NAO peak by 20 days and the other following the NAO peak by 3 days. The interactions are thus investigated through conducting the lead–lag regressions and composites of key atmospheric and oceanic fields against these SST indices. For the composite analysis, the robust positive phase of SST index is defined as the period when the normalized SST index is greater than one. In this study, the SST and NAO indices are always centered on the SON period, whereas the lagged regressions and composites are based on 11 August–20 December.

The baroclinicity in this study is represented by the maximum Eady growth rate at 850 hPa σBI, which is calculated as in Hoskins and Valdes (1990): σBI = 0.31(f/N)(∂U/∂z), where f is the Coriolis parameter, N is the Brunt–Vӓisӓlӓ frequency, U is the horizontal wind velocity, and z is the geometric height. To examine the evolution of the anomalous wave activities associated with the underlying SST anomaly, the transient E vectors in Hoskins et al. (1983) and the local finite-amplitude wave activity (LWA) diagnostics as in Huang and Nakamura (2016) and Chen et al. (2015) are also employed in this study. The transient E vector, which is computed as E = (υ2u2, −uυ′), corresponds to the horizontal components of the high-frequency E vector defined by Hoskins et al. (1983). The vector may be interpreted as an effective momentum flux by synoptic eddies, and its horizontal divergence can force the background zonal flow. The LWA measures the waviness of a dynamical field (300-hPa potential vorticity in this study) and gives the longitudinal distribution of the finite-amplitude wave activity. It is an objective diagnostic of midlatitude atmospheric blocking and can capture well the features of the blocking high (Chen et al. 2015; Nakamura and Huang 2018). In this study, we detect blocking if the LWA is unusually larger than its climatological median value at each longitude and is persistent for at least 5 days as in Martineau et al. (2017).

3. Climatology of SST variability and eddy statistics

Before analyzing the subseasonal air–sea interactions over the North Atlantic, some key aspects of the climatological SST fields and eddy statistics are reviewed in this section. Figure 1a shows the climatology and the standard deviation of the daily SST field in the North Atlantic during autumn. The SSTs have their largest horizontal gradients in the Gulf Stream region. As noted extensively in previous studies, the standard deviation of midlatitude SSTs often peaks in the oceanic front region with largest horizontal temperature gradients (Deser et al. 2010). Indeed, the largest daily SST variability in the North Atlantic is located in the Gulf Stream SST front region as in Wills et al. (2016). The winter distribution of SST fields is shown in Fig. 1b for comparison. It is shown that the daily variability of SST is more active in autumn than winter, which is consistent with Deser et al. (2010).

Fig. 1.
Fig. 1.

(a),(b) Climatology (contour intervals of 5 K) and standard deviation of the daily SST (shading, with interval of 0.5 K) in the North Atlantic and climatologies of the (c),(d) 10-day high-frequency 850-hPa eddy heat flux (shading, with interval of 5 m s−1 K) and eddy kinetic energy at 300 hPa (contours; m2 s−2) and (e),(f) 10-day low-frequency eddy heat flux for (left) SON and (right) DJF.

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

The climatological distributions of the 10-day high-frequency eddy heat flux and eddy kinetic energy (EKE), and the 10-day low-frequency eddy heat fluxes are displayed in the second and third rows of Fig. 1, respectively. The high-frequency eddies are most active along the Gulf Stream SST front with strong horizontal temperature gradient. The low-frequency eddies are more active in the higher latitudes, peaking to the east of Greenland. Comparison of the eddy heat fluxes between autumn and winter further shows that the eddy activities are more vigorous in winter. The above analyses illustrate that both the daily SST variability and eddy activities are active during autumn.

4. Observed NAO–SST relationship on the subseasonal time scale

In this section, the observed characteristics of the extratropical air–sea interaction associated with the subseasonal NAO variability in autumn are examined. We first investigate the SST pattern driven by the NAO variability. Figures 2a–f show the lagged regression patterns of SST/Z1000 anomaly with respect to the daily NAO index in autumn. The NAO pattern at lag 0 (Fig. 2c) manifests a seesaw-like change of geopotential height anomalies between the subpolar and subtropical Atlantic regions. Following the peak of NAO, the SST anomaly pattern shows tripolar zonal bands (Figs. 2d–f) marked by a cold anomaly in the subpolar North Atlantic, a warm anomaly in the midlatitudes, and a cold subtropical anomaly between the equator and 30°N as in Czaja and Frankignoul (2002), indicating a strong driving effect of NAO on the tripolar SST anomalies over North Atlantic.

Fig. 2.
Fig. 2.

Lagged regressions of Z1000 (contour intervals of 8 m) and SST (shading, with interval of 0.05 K) onto the normalized daily NAO index in autumn. Values that are significant at the 95% confidence level using a two-tailed t test are highlighted with black dots.

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

Then the SST pattern that could affect the NAO variability is examined through the regression analyses when the SST anomalies lead the NAO index, as displayed in Figs. 2a and 2b. It is clear that the SST anomalies preceding the peak of NAO show a weak and more localized tripolar pattern, with cold anomalies in the Gulf Stream area, and warm anomalies off the western coast of northern Europe and to the south of Gulf Stream region, respectively. This tripolar SST pattern is similar to the North Atlantic SST horseshoe (NAH) pattern (Czaja and Frankignoul 2002), but with the subtropical anomaly displaced to the west. The above lead–lag regression analyses reveal that the anomalous SST pattern that may induce the NAO anomalies is spatially different from the SST pattern directly forced by the NAO during autumn (spatial correlation is weak).

To quantitatively represent the SST variability associated with the NAO during autumn, two SST indices are obtained by projecting the daily SST anomaly onto the typical SST pattern following the NAO peak by 3 days (Fig. 2d) and preceding the NAO peak by 20 days (Fig. 2a), respectively. In the following analyses, the zonally tripolar SST pattern in Fig. 2d refers to North Atlantic tripolar (NAT) SST mode, which represents the typical SST pattern driven by the NAO. The localized SST pattern in Fig. 2a is denoted as North Atlantic horseshoe-like (NAH-like) SST mode, which represents the SST pattern prior to the peak of NAO anomaly. The e-folding decorrelation time scales of the NAO index and the two SST indices are further examined in Fig. 3a. During autumn, the typical time scale of NAO is 9 days, which is consistent with Feldstein (2000). The time scale of SST anomalies for either of the two indices is around 30 days because of large thermal inertia of the ocean.

Fig. 3.
Fig. 3.

(a) Autocorrelation of the NAO and two SST indices, and (b),(c) their lagged correlations in autumn. Negative or positive lags denote that SST leads or lags NAO, respectively. The dashed lines denote the corresponding values of 95% significance level, which are estimated from the autocorrelations of each time series.

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

The interactions between the NAO and two modes of SST anomalies in autumn are further investigated by calculating the lead–lag correlation between the NAO index and the two SST indices. As shown in Fig. 3b, the correlation is largest when the NAT SST mode lags the NAO by 3 days, suggesting an apparent driving effect of NAO anomaly on the NAT SST mode. At negative lags, when the NAT SST mode leads the NAO, the positive correlation is relatively weak, showing little feedback of the NAT SST mode to the NAO anomaly. For the NAH-like SST mode, as shown in Fig. 3c, the significant correlation reaches its maximum (0.3) when the SST anomalies lead the NAO by around 10–20 days, consistent with a significant delayed response of NAO to the NAH-like SST mode. This correlation analysis further validates the different roles of the NAT and NAH-like SST modes in the NAO variability during autumn.

5. Atmospheric response to the NAH-like SST mode

The atmospheric response to the NAH-like SST mode in autumn is investigated in this section. The temporal evolution of the composite SST and Z1000 anomalies for the positive NAH-like SST index is shown in the left panels of Fig. 4. By construction, the SST anomalies peak at lag 0 and decay slowly with increment of lag. At negative lags (i.e., the atmosphere leading the NAH-like SST mode), the Z1000 anomalies manifest a southwest–northeast tripolar pattern, with a negative height anomaly located northeast of the cold anomaly in the Gulf Stream SST front region and positive anomalies to its northeast and southwest. As indicated in previous studies (Deser and Timlin 1997; Magnusdottir et al. 2004), such Z1000 anomalies at negative lags are consistent with the atmospheric forcing of the SST field, which could be triggered by some remote tropical or extratropical disturbances (e.g., Cassou 2008; Scaife et al. 2017).

Fig. 4.
Fig. 4.

(a)–(e) Composites of Z1000 (contour intervals of 8 m) and SST (shading, with interval of 0.05 K) anomalies for the positive phase of the normalized NAH-like SST mode index in autumn, with negative or positive lags denoting that Z1000/SST anomalies are leading or lagging NAT-like SST index, respectively. (f)–(j) as in (a)–(e) but for Z1000 (contour interval of 8 m) and Z300 (shading, with interval of 16 m). Values that are significant at the 95% confidence level are highlighted with black dots. The black-outlined boxes in (c) denote three key SST regions, including the areas off the western coast of northern Europe (label N; 55°–70°N, 20°W–10°E), the Gulf Stream region (label G; 40°–55°N, 60°–30°W), and to the south of the Gulf Stream region (label S; 25°–35°N, 60°–30°W).

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

At positive lags, when the Z1000 anomalies lag the SST index, the Z1000 evolves to a dipolar structure resembling the NAO pattern located farther eastward, with negative anomalies to the east of Greenland and positive anomalies south of it. Note that the magnitude of the geopotential height anomalies at lag +20 day is roughly 30% of the maximum NAO variability (magnitude of geopotential height anomalies at the NAO peak day). The response of upper-level geopotential height (Z300) is further displayed in the right panels of Fig. 4 for comparison. The Z300 response shows similar structures to the Z1000 at large positive lags, indicating that eddy-mediated processes may play a role in generating such quasi-barotropic atmospheric response (Deser et al. 2004; Ring and Plumb 2007, 2008; Nie et al. 2016). The detailed processes through which the atmosphere responds to the NAH-like SST anomalies are analyzed in the following sections by conducting lagged composite analyses.

a. Response of the surface heat flux and lower-tropospheric temperature

Figure 5 shows the lagged composites of the spatial patterns of the dominant components in the heat budget of the ocean surface (i.e., sensible heat flux, latent heat flux, radiative flux) and the SST tendency with respect to the NAH-like SST index. The surface turbulent heat flux anomalies, especially the latent heat flux, as shown in Figs. 5a–e, change their spatial patterns from driving the SST anomalies at negative lags to dissipating the SST anomalies at positive lags. Prior to the peak of SST anomalies, both the surface sensible and latent heat fluxes exhibit tripolar structures, with a downward heat flux above the warm SST anomalies. This indicates the warming effect of the atmospheric circulation anomalies on the local SST pattern (positive SST tendency shown in Figs. 5k,l). However, following the peak of SST anomalies, the surface heat fluxes show opposite spatial structures, with the largest upward heat flux above the warm SST anomalies, dissipating the SST anomalies (negative SST tendency shown in Figs. 5n,o) and thus heating the atmosphere. The radiative flux anomalies, dominated by the longwave component (Figs. 5f–j), show a spatial pattern similar to those of the surface heat fluxes, but with much weaker magnitude, suggesting its minor role in affecting the atmosphere, which is consistent with the previous study of Cayan (1992). The Ekman heat transport, which is mainly induced by the surface wind anomaly, always acts to drive the SST anomaly (results thus not shown). The above results show that the NAH-like SST anomaly affects the lower troposphere mainly through the surface heat flux.

Fig. 5.
Fig. 5.

As in Fig. 4, but for the composites of (a)–(e) sensible (contour intervals of 4 W m−2) and latent (shading, with interval of 4 W m−2) heat fluxes, (f)–(j) longwave radiative flux (shading, with interval of 4 W m−2), and (k)–(o) SST tendency (shading, with interval of 0.01 K day−1). The sign is defined as positive in the downward direction for heat and radiative fluxes.

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

How the surface heat flux anomalies affect the lower-tropospheric temperature and thus baroclinicity is further examined in Fig. 6. The left panels of Fig. 6 show the composite pattern of the tropospheric 2-m temperature against NAH-like SST index. In addition to the surface heat flux exchange, the variations of surface air temperature are affected by the atmospheric dynamical processes in the boundary layer. As shown in Figs. 6a–e, the variations of 2-m temperature south of 60°N are generally consistent with those of the SST anomalies, reflecting the adjustment of the surface air temperature anomalies through the surface heat flux. At negative lags, as shown in Figs. 6a and 6b, the surface air temperature displays warm anomalies above the warm SST anomalies, which is consistent with the positive surface heat flux from atmosphere to the ocean in Figs. 5a and 5b, and implies a warming effect of the atmosphere on the SST anomalies. However, at positive lags, the surface air temperature south of 60°N over the North Atlantic displays an opposite-sign spatial pattern to the surface turbulent heat fluxes in Figs. 5d and 5e, suggesting a possible governing effect of the SST anomalies on the overlying atmosphere through the surface heat flux exchange. The high-latitude cold surface air temperature anomalies to the southeast of Greenland at positive lags are primarily affected by a northeastward cold air advection (figures not shown).

Fig. 6.
Fig. 6.

As in Fig. 4, but for the composites of (a)–(e) surface air temperature (shading, with interval of 0.4 K) and (f)–(j) 850-hPa Eady growth rate (shading, with interval of 0.05 day−1).

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

The response of the lower-tropospheric baroclinicity (i.e., 850-hPa Eady growth rate) in the right panels of Fig. 6 shows a consistent pattern with the surface air temperature anomalies. Prior to the peak of SST anomalies, the baroclinicity shows a tripolar pattern. Following the variation of NAH-like SST mode, the significant change of baroclinicity moves eastward, with a reduced temperature contrast over subtropics and Arctic but an enhanced contrast between the two regions. As suggested by Nie et al. (2016) and Xiao et al. (2016), such lower-tropospheric baroclinicity anomalies can evidently affect the eddy activities through either the eddy generation or the eddy dissipation and wave breaking, which may further give rise to the upper-tropospheric atmospheric circulation anomalies related to the NAO.

b. Response of the atmospheric eddy activities

As suggested by previous studies (e.g., Seo et al. 2017), the formation of the quasi-barotropic atmospheric response could be facilitated by two possible eddy-mediated processes: synoptic eddy feedback in midlatitude (Ren et al. 2009, 2011) and low-frequency dynamics involving wave breaking and blocking in high latitudes (Woollings et al. 2008, 2010). In this section, both the responses of the synoptic eddy activities and the low-frequency wave breaking/blocking are examined through the lagged composite analyses when the NAH-like SST anomaly leads the atmosphere.

1) Roles of the synoptic eddies

The response of the synoptic eddy activities to the NAH-like SST mode is investigated in Fig. 7. As shown in Figs. 7a and 7b, the composite synoptic eddy heat flux in the lower troposphere shows a significant dipolar structure at lag +20 days, implying that the SST anomalies could induce the poleward shift of the generation zone for the synoptic eddies. The response of the synoptic eddy kinetic energy at 300 hPa, which often manifests the storm-track activities, is then displayed in Figs. 7c and 7d. The synoptic EKE exhibits a stronger poleward displacement at 10°W–40°E, consistent with the synoptic eddy heat flux shift but moving farther eastward. The response is strongest when the SST leads by 20 days. The horizontal wave propagation properties and the associated barotropic zonal wind are further displayed in Figs. 7e and 7f. Given that the synoptic E vector can be interpreted as an effective momentum flux by synoptic eddies, and its horizontal divergence can force the background zonal flow, we composite the horizontal E vectors and the vertically averaged zonal wind for the positive phase of NAH-like SST index. The composite wind shows a clear poleward shift of the jet. This can be understood because the E vector exhibits evident anomalous divergence where the zonal wind is stronger and convergence where the zonal wind is weaker, which implies a strong synoptic eddy momentum forcing on the zonal flow. The above analyses illustrate that in response to the positive phase of NAH-like SST mode, the synoptic eddy heat flux, eddy kinetic energy, and eddy momentum forcing all shift poleward, which further result in the poleward shift of the barotropic zonal wind and thus favor a positive phase of NAO anomalies.

Fig. 7.
Fig. 7.

Composites of anomalous 10-day high-frequency (a),(b) eddy meridional heat flux at 850 hPa (shading, with interval of 1 ms−1 K), (c),(d) eddy kinetic energy at 300 hPa (shading, with interval of 10 m2 s−2), and (e),(f) transient E-vector fluxes at 300 hPa (gray arrows, with significant fluxes at the 80% confidence level overplotted with black; m2 s−2) and 1000–100-hPa vertically averaged zonal wind (shading, with interval of 1 ms−1) for the normalized NAH-like SST index in autumn when the atmospheric anomalies lag NAH-like SST index by (top) 10 and (bottom) 20 days. Values that are significant at the 95% confidence level are highlighted with black dots.

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

2) Roles of low-frequency anticyclonic wave activities

The responses of the low-frequency eddy activities and atmospheric blocking to the NAH-like SST mode are examined in Fig. 8. The low-frequency eddy activity is represented by the 10-to-30-day meridional eddy heat flux, and the atmospheric blocking is diagnosed through the frequency of unusually large and persistent anticyclonic local finite-amplitude wave activities (Nakamura and Zhu 2010; Huang and Nakamura 2016; Martineau et al. 2017; Nakamura and Huang 2018). Figures 8a and 8b display the response of the low-frequency eddy heat flux. The low-frequency eddy heat flux in the lower troposphere shows a significant reduction in western northern Europe, suggesting a suppression of the low-frequency eddy generation in the high latitudes. Figures 8c and 8d then display the composite patterns of the unusually large and persistent anticyclonic LWA frequency at 300 hPa for the NAH-like SST index. The anticyclonic wave activity frequency shows a strong reduction to the southeast of Greenland, in agreement with the reduction of the low-frequency eddy heat flux in the lower troposphere. The connection between the NAO and persistent anticyclonic wave activities around southern Greenland has been demonstrated extensively as a Greenland blocking regime (Cheng and Wallace 1993; Vautard 1990; Kimoto and Ghil 1993), which suggests that a positive NAO phase can be considered as a basic, unblocked situation and a negative NAO often describes a period with frequent Greenland blocking. The large simultaneous correlation between the daily NAO index and Greenland blocking frequency shown in Woollings et al. (2008) further confirms the clear contribution of the changes in Greenland blocking occurrence to the NAO anomalies. Following these above arguments, our study further shows that the NAH-like SST anomaly pattern, through altering the low-frequency anticyclonic wave activity in the high latitude of North Atlantic, can give rise to an NAO-like atmospheric circulation anomaly during autumn.

Fig. 8.
Fig. 8.

As in Fig. 7, but for the composites of (a),(b) 10-day low-frequency eddy heat flux at 850 hPa (shading, with interval of 1 m s−1 K) and (c),(d) the frequency of unusually large and persistent anticyclonic wave activities at 300 hPa (shading, with interval of 0.02).

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

6. Atmospheric response to the variation of SST anomalies in three key regions

The above analyses have suggested that the tripolar NAH-like SST mode has a significant impact on the overlying atmosphere, particularly on the subsequent NAO-like anomalies. To further validate these results, we define three SST indices by area-averaging the SST anomalies near each center of the NAH-like SST mode, including the areas off the western coast of northern Europe (N box; 55°–70°N, 20°W–10°E), the Gulf Stream region (G box; 40°–55°N, 60°–30°W), and to the south of the Gulf Stream region (S box, 25°–35°N, 60°–30°W) as denoted in the black-outlined boxes of Fig. 4c. The forcing and response to the SST anomalies in these different boxes are delineated through composite analyses of the lower-level and upper-level geopotential height anomalies during the typical positive phase of each SST index. Prior to the peak of N-box SST anomalies by 10 days, as shown in Fig. 9a, there is a tripolar southwest–northeast geopotential height anomaly structure in North Atlantic, with largest positive anomalies located to the east of Greenland and negative anomalies to the south of Greenland, which is consistent with the atmospheric circulation pattern preceding the NAH-like SST anomalies (Fig. 4g). This suggests that the atmospheric forcing on the N-box SST anomaly is in agreement with the atmospheric forcing on the NAH-like SST anomaly. By contrast, the atmospheric forcings on the G box and S box shown in Figs. 9b and 9c are relatively local. Following the peak of SST anomalies by 20 days, as shown in Fig. 9d, the geopotential height anomalies in response to N-box SST anomalies are characterized by a positive phase of NAO-like structure, which is also consistent with the circulation anomaly response to NAH-like SST anomaly shown in Fig. 4j. This suggests that the dipolar height anomalies in response to NAH-like SST anomalies is mainly contributed by the atmospheric response to the N-box SST anomalies. The atmospheric responses to the G-box and S-box SST anomalies are relatively weak and mainly confined in the lower troposphere (Figs. 9e,f). We also carry out the composites of the surface heat fluxes and eddy activities against the SST indices of these three regions. It is found that the N-box SST anomalies are most efficient in affecting the surface heat fluxes. Meanwhile, the response of low-frequency eddies is also strongest to the N-box SST anomalies (figures not shown), probably because the climatological low-frequency eddies peak in the higher latitudes. Therefore, the above analyses imply that the SST anomalies in the higher latitudes might be more efficient in shaping the low-frequency eddies and further affect the full-tropospheric NAO pattern, whereas the SST anomalies in the middle latitudes can only affect the lower-tropospheric circulation.

Fig. 9.
Fig. 9.

Composites of Z1000 (contour intervals of 8 m) and Z300 (shading, with interval of 16 m) anomalies with normalized SST index for the (a),(d) N box, (b),(e) −G box, and (c),(f) S box when the atmosphere leads the SST indices by (top) 10 and (bottom) 20 days. For ease of comparison, the SST index in the G box is multiplied by −1.

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

7. Summary and discussion

Many progresses have been made over the past years in understanding the subseasonal variability of winter NAO with a focus on the internal eddy–mean flow interaction. This study highlights the role of extratropical air–sea interactions in the NAO variability during the transition season (autumn) when the daily SST variability is stronger. More importantly, using daily atmospheric and oceanic reanalysis data, our study illustrates the physical processes through which the daily SST variation over North Atlantic affects the NAO during autumn.

On the subseasonal time scale, the NAO anomalies in autumn can significantly force a zonally tripolar SST anomalies in the North Atlantic but with little feedback from this anomalous SST pattern. However, a distinct and localized SST anomaly preceding the NAO anomalies by 10–20 days could induce a significant and moderate NAO-like atmospheric response. This SST anomaly, marked by a cold anomaly in the Gulf Stream region and warm anomalies off the western coast of northern Europe and to the south of the Gulf Stream area (Fig. 4c), is implied to be a potential candidate for predicting the subseasonal variability of the NAO.

An initial southwest–northeast tripolar geopotential anomaly in the North Atlantic forces this localized tripolar SST anomaly. Then the SST anomaly pattern acts to alter the spatial patterns of the lower-tropospheric temperature and baroclinicity anomalies through surface heat flux exchange (Fig. 5). Changes in the lower-tropospheric baroclinicity further induce the poleward shift of synoptic eddy generation in the midlatitude and strong decrease of the low-frequency eddy generation and anticyclonic wave activity in the high latitude. On the one hand, the poleward shift of the synoptic eddy generation modulates the poleward shift of the transient eddy kinetic energy and eddy momentum forcing, which further results in the poleward shift of the barotropic zonal wind thus contributing to the positive phase of NAO (Fig. 7). This is consistent with transient eddy feedback playing an important role in the atmospheric response to the underlying SST anomalies in previous studies (Peng and Whitaker 1999; Magnusdottir et al. 2004; Deser et al. 2007; Sampe et al. 2010; Ren et al. 2009; Seo et al. 2017). On the other hand, the reduction of low-frequency high-latitude blocking also contributes to the positive phase of NAO (Fig. 8), which supports Woollings et al. (2008) that a positive NAO is envisaged as being a description of periods in which the high-latitude North Atlantic blocking is infrequent and can be considered as an unblocked situation.

Furthermore, we show that the SST anomalies in the higher latitudes might be more efficient in shaping the low-frequency eddies and anticyclonic blocking, which could further affect the barotropic NAO-like pattern (Fig. 9). In contrast, the direct impact of the SST anomaly in the middle latitudes is relatively shallow with the atmospheric response confined in the lower troposphere. This probably can be explained from the thermodynamic viewpoint that the surface heat flux into the atmosphere accompanied with the midlatitude SST anomalies are quickly balanced by the strong horizontal wind in the lower troposphere above the front region (Hoskins and Karoly 1981).

Our study contributes to a growing body of observational evidence that extratropical SST anomalies are capable of significantly influencing the large-scale atmospheric circulation on the subseasonal time scale during autumn (Xiao et al. 2016; Wills et al. 2016), which supports previous modeling studies (Rodwell et al. 1999; Nakamura et al. 2008; Sampe et al. 2013; O’Reilly et al. 2017). The impact of the SST anomalies on the overlying atmospheric circulation in winter is much weaker as a result of the less active daily SST variability. Air–sea interaction over North Atlantic during winter is dominated by the stronger atmospheric driving effect (see the appendix for details). Note that this study mainly addresses the role of extratropical SST anomalies in affecting the lower-tropospheric NAO variability. The upper-tropospheric NAO variability is different because of some remote influences from the tropical SST anomalies or the stratosphere (e.g., Cassou 2008; Scaife et al. 2017), and thus deserves further studies. Our work also demonstrates that the barotropic NAO-like response to the extratropical SST anomalies can be attributed to two eddy-mediated processes: the synoptic eddy feedbacks and high-latitude blocking variation. However, the relative importance of these two processes is difficult to evaluate simply from the statistical diagnostic analysis. Future works will be conducted to quantify these processes by setting up numerical experiments in an idealized air–sea coupled model, and further examine the impact of high-latitude SST anomalies on the formation of low-frequency eddies and the NAO persistence.

Acknowledgments

We thank Fei-Fei Jin, Adam Scaife, and Nick Dunstone for valuable discussions and the three anonymous reviewers for their constructive suggestions, which helped to improve the quality of the paper. This work is supported by NSF of China under Grants 41705043, 41775066, and 41675055.

APPENDIX

Winter Subseasonal Air–Sea Interaction in the North Atlantic

The winter subseasonal air–sea interaction over the North Atlantic is briefly discussed through the lagged SST–NAO regression analyses. Here, the SST and NAO indices are centered on the DJF period, whereas the lagged regressions are based on 11 November–20 March from lag −20 days to lag +20 days. Figures A1a and A1b display the regressions of SST anomalies against winter daily NAO index when SST anomalies lag the NAO by 10 days and lead the NAO by 20 days, respectively. As shown in Fig. A1a, the SST anomalies following the NAO peak display an NAT spatial pattern, which is similar to that in autumn but with stronger amplitude. This indicates a stronger atmospheric driving effect on the SST anomalies. The SST anomalies preceding the NAO (Fig. A1b) exhibit a similar spatial pattern to those following the NAO (Fig. A1a) but with much weaker amplitudes, suggesting an evidently weaker impact of this SST pattern onto the atmospheric circulation. To further quantify the relationship between the NAT SST anomaly pattern and NAO, Fig. A1c further shows the lagged correlation between the NAT index and NAO index during winter. The largest correlation between the NAO index and NAT SST index appears when the NAO leads by 3–10 days, implying the strong driving effect of NAO on the underlying NAT SST anomaly pattern in winter. In contrast, no significant positive correlations are observed when the SST index leads NAO, showing little feedback of SST anomalies on the NAO. These results illustrate that the winter air–sea interaction is dominated by stronger atmospheric driving effect, which supports the results of Deser and Timlin (1997) and Ciasto and Thompson (2004).

Fig. A1.
Fig. A1.

Lagged regression of Z1000 (contour interval of 8 m) and SST (shading, with interval of 0.05 K) onto the normalized daily NAO index in winter, with Z1000/SST anomalies (a) lagging NAO index by 10 days and (b) leading NAO index by 20 days. Also shown is the (c) cross correlation between the NAO index and NAT SST index during winter. Negative lags denote that the SST index leads the NAO index.

Citation: Journal of Climate 32, 22; 10.1175/JCLI-D-19-0060.1

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