## 1. Introduction

The air–sea interaction over western boundary currents and their extension is substantially stronger than in other regions. The large amounts of heat and moisture that are released from warm ocean currents to the overlying atmosphere during winter play a key role in Earth’s energy transport and climate variability (Kelly and Dong 2004). The impacts of western boundary currents on the atmosphere range from frontal to basin scales. At the basin scale, Nakamura et al. (2004) proposed the role of oceanic fronts in modulating downstream storm tracks and westerly jets. Storm-track and jet responses are revealed when sharp oceanic fronts are resolved in models (e.g., Taguchi et al. 2009).

Frontal and regional atmospheric responses to western boundary currents have been observed within the boundary layer [see the review by Small et al. (2008), and references therein]. Observational evidence of the deep-tropospheric response is provided by several studies (Liu et al. 2007; Minobe et al. 2008; Tokinaga et al. 2009). The local imprints of ocean currents, including surface wind, precipitation, and cloud formation, are well represented in satellite data from over a short period of just a few years. Two mechanisms, the vertical mixing mechanism and the pressure adjustment mechanism, have been proposed to explain the processes behind the ocean forcing on the overlying boundary layer at frontal scales. The vertical mixing mechanism attributes the correspondence of the SST and surface wind speed to changes in vertical momentum mixing, which strongly depends on atmospheric instability (e.g., Wallace et al. 1989). The pressure adjustment mechanism proposes the in-phase spatial coherence between SST and surface wind convergence, which results from SST-driven surface pressure gradient anomalies (e.g., Lindzen and Nigam 1987).

While many studies have highlighted the atmospheric response to the mean-state western boundary currents, several studies emphasize the atmospheric response to the temporal changes in the location and strength of western boundary currents, which are particularly significant on interannual and decadal time scales (e.g., Kwon et al. 2010). Frankignoul et al. (2011) investigated the correspondence of large-scale circulation with the shifts of the Kuroshio Extension (KE) and Oyashio Extension (OE). O’Reilly and Czaja (2015) focused on the North Pacific circulation response to KE variability from the perspective of eddy–mean flow interaction. The relationship between synoptic atmospheric variability and KE and Gulf Stream shifts at frontal scales was revealed by Joyce et al. (2009). Smirnov et al. (2015) investigated the dynamics behind the local atmospheric response to realistic forcing of the OE shift using reanalysis and atmospheric general circulation models with different spatial resolutions. However, in spite of the longer periods of satellite data that are currently available, little attention has been given to exploring local atmospheric responses to the low-frequency variability of oceanic fronts using satellite observations.

The goal of this study is to understand the low-frequency relationship between local variations in the atmosphere and ocean currents, not atmospheric synoptic variability, based on observations. This study focuses on atmospheric responses in boreal winter [December–February (DJF)], when the atmosphere–ocean heat exchange is most pronounced and atmospheric dynamics is active in this season. The KE, which separates from the coast of Japan to the open ocean near 35°N and is characterized by two quasi-stationary meanders, is of particular interest. KE variability has been observed to oscillate between stable and unstable states on decadal time scales (e.g., Qiu and Chen 2005; Qiu et al. 2014). Under the stable state, the KE jet strengthens the eastward transport and migrates northward, while under the unstable state, the KE jet weakens the eastward transport and migrates southward. Satellite data, which maintain high-resolution signals in time and space and are less influenced by the limitations of models such as imperfect parameterizations (e.g., Cintineo et al. 2014), can illustrate the air–sea interaction well at small scales. The results here can be compared with other observations and models to help evaluate previous findings.

This study begins with a survey of the satellite and reanalysis data in section 2 and is followed by an introduction to the climate index for the quantification of the KE variability in section 3. The analysis methods are described in section 4, and the spatial correspondence of SST to the climate index is presented in section 5. In section 6, the atmospheric response in terms of horizontal and vertical patterns is documented based on different climate variables. The comparison of the results between the satellite observations and the reanalysis data is discussed in section 7, and the last section presents a summary and a discussion.

## 2. Data

Gridded satellite-based datasets spanning more than a decade are used for this study. The winter months (DJF) are extracted, and the length of the base period depends on the data availability. To quantify the low-frequency variability of the KE, we use the sea surface height anomaly (SSHA) data from altimeter satellite products produced by SSALTO/Developing Use of Altimetry for Climate Studies (DUACS) and distributed by AVISO, with support from CNES (http://www.aviso.altimetry.fr/duacs/). Available monthly SSHA data from 1993 to 2013 with a resolution of 0.25° latitude by 0.25° longitude are used. The monthly mean SST data for our analyses are the average of the NOAA daily Optimum Interpolation SST on a 0.25° latitude and longitude grid (Reynolds et al. 2007). The SST data are obtained from December 1993 to February 2013, which is the same period as the SSHA data.

Precipitation observations are obtained from the Tropical Rainfall Measuring Mission (TRMM) satellites from December 1998 to February 2013. Surface precipitation rate (mm day^{-1}) is derived from the 3B43 product, which comes from both satellite and gauge measurements and covers 50°S–50°N at a 0.25° resolution (Huffman et al. 2007). The vertical profile of rainwater (g m^{-3}) and latent heating rate (°C day^{−1}) is estimated from the TRMM Microwave Imager (TMI) (i.e., the 3A12 product) with a coverage from 40°S to 40°N at a 0.5° grid resolution. Although TMI provides passive measurements and indirect estimation of latent heat, the data are valuable for understanding the vertical structure of atmospheric responses (e.g., Kummerow et al. 2000). Cloud-top temperature (K), outgoing longwave radiation (W m^{−2}), air temperature, and geopotential height from 1000 to 300 hPa since 2003 are obtained from the combined product of the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit (Chahine et al. 2006). For simplicity, the combined product is referred to as the AIRS data. The 1° gridded AIRS data from the winter months between December 2003 and February 2013 are analyzed. Both the TRMM and AIRS data are provided by the NASA Goddard Earth Sciences Data and Information Services Center. To assess the satellite-derived results, we used reanalysis data from the ERA-Interim (e.g., Dee et al. 2011), which are produced by the European Centre for Medium-Range Weather Forecasts and are available since 1979 onward at a 0.75° resolution. The atmospheric variables of the ERA-Interim data include 10-m wind velocity, sea level pressure (SLP), vertical velocity, air temperature, and geopotential height on pressure surfaces. The time periods of these variables are described in the individual result sections.

## 3. KE index

The KE jet fluctuations are characterized by fast-flowing and meandering behavior particularly at decadal time scales (Qiu et al. 2014). The KE jet variability is closely tied to the strength of its southern recirculation gyre, and can be simply described by local SSHA time series (Qiu et al. 2014). To quantify the KE jet variability, we begin by averaging the SSHA data over the domain 31°–36°N, 140°–165°E, which encompasses the time-mean KE jet and its southern recirculation gyre. The index is obtained after computing the 13-month moving average, which removes time scales shorter than the annual cycle. The SSHA-derived index represents the variations of ocean integrated heat content and the KE jet. When the index is positive, the KE jet migrates northward and intensifies its eastward transport. The KE jet exhibits the opposite behavior when the index is negative. Figure 1 shows the KE index during the boreal winter months from December 1993 to February 2013. The decadal cycle is clearly shown with the peaks centered on 2002 and 2010 and the troughs centered on 1996 and 2006. The linear trend of the KE is present in Fig. 1, which does not impact the relationship between the KE index and climate variables at frontal scales (not shown).

## 4. Methods

The SST-induced effect on the regional atmospheric response becomes more evident after the mean gradients are removed (e.g., Liu et al. 2007; Liu and Xie 2008). Applying a spatial filter is the first step in distinguishing the frontal-scale responses from the large-scale responses. Following Liu and Xie (2008), the two-dimensional spatial filter smooths the field using a running window of approximately 10° longitude × 2° latitude. The weights of the filter are determined by a sinusoidal function so that the weight is 1 for the filtered point and decays to 0 for the farthest point in the window. Small-scale signals are emphasized after the large-scale signals are subtracted from the original field. For clarity, in the remainder of the paper, the term after subtracting refers to frontal-scale signals. To eliminate the impact of the variability of the tropical SST, the signals that are linearly associated with the Niño-3.4 index are removed.

After separating the large-scale and frontal-scale signals and removing the tropical variability, we perform regression analysis to the KE index and the individual climate variables point by point. The KE index is normalized by its standard deviation. The regression coefficient at each point represents the changes in one specific variable as the normalized KE index increases by one unit. The light green contours in the figures enclose areas where the regression passes the Student’s *t* test at the 95% significant confidence level. Time lag is present in the ocean–atmosphere coupling at large scales (e.g., Liu and Wu 2004; Frankignoul et al. 2011; Smirnov et al. 2014). Because the response time of the atmosphere to ocean current variations is short (Liu et al. 2007), we do not consider any time lag in our analyses.

## 5. Manifestation of KE index on SST variability

To obtain a general picture of the relationship between the KE and the SST variability, we present a map of the regressions of the KE index on the SST anomalies, which are deviations from the climatology of the SST of individual months, without spatial filtering (Fig. 2). ENSO signals are removed prior to the regression analysis. When the KE index increases, the central North Pacific becomes warmer and is surrounded by cooler SSTs to its north and east. Over the KE domain (33°–39°N, 141°–157°E; see the box in Fig. 2), the positive SST anomalies have the shape of the letter M. Colder SST anomalies are located to the north of the M-shaped warm signals. The relationship between the SST and the KE index is consistent with the relationship between SSHA and the KE index over the KE region (Qiu et al. 2014). These results imply a strengthening KE jet and weaker meanders in the positive phase of the KE index and a weakening KE jet and stronger meanders in its negative phase (O’Reilly and Czaja 2015).

Map of the regression coefficients between the KE index and the SST anomalies (°C) from December 1993 to February 2013. The black box denotes the KE domain (33°–39°N, 141°–157°E). The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Map of the regression coefficients between the KE index and the SST anomalies (°C) from December 1993 to February 2013. The black box denotes the KE domain (33°–39°N, 141°–157°E). The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Map of the regression coefficients between the KE index and the SST anomalies (°C) from December 1993 to February 2013. The black box denotes the KE domain (33°–39°N, 141°–157°E). The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

The oceanic mesoscale characteristics of the meandering current are more clearly illustrated by frontal-scale signals (Fig. 3). Positive and negative SST anomalies are aligned zonally and are oriented slightly northeast from the coast of Japan to the open ocean. The positive anomalies are collocated with the ridges of the KE meanders (cf. shading and contours of Fig. 3) near 145° and 151°E and the negative anomalies are collocated with the troughs near 148° and 154°E. Although the amplitudes of the regression coefficients increase using shorter time periods, the spatial patterns are similar (not shown). Because of its insensitivity to the length of time, the SST response of this period will be referenced throughout the paper for comparison with the responses of the other climate variables. Furthermore, the spatial pattern of the SST response is consistent with the mesoscale features of the SST’s annual climatology from Liu and Xie (2008). Warm SST signals are associated with an anticyclonic current, while cold SST signals are associated with a cyclonic current (Liu et al. 2007).

Regression coefficients between the KE index and the frontal-scale SST over the KE domain in color shading (°C). The light green contours enclose areas of statistical significance at the 95% confidence level. The mean SSTs from December 1993 to February 2013 are superimposed as contours (°C).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and the frontal-scale SST over the KE domain in color shading (°C). The light green contours enclose areas of statistical significance at the 95% confidence level. The mean SSTs from December 1993 to February 2013 are superimposed as contours (°C).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and the frontal-scale SST over the KE domain in color shading (°C). The light green contours enclose areas of statistical significance at the 95% confidence level. The mean SSTs from December 1993 to February 2013 are superimposed as contours (°C).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

## 6. Atmospheric response

### a. Surface wind convergence

Figure 4 shows the relationship between the variations of the KE index and the frontal-scale 10-m wind convergence based on the reanalysis data. The positive values indicate convergence corresponding to one unit increase in the normalized KE index and the negative values indicate divergence. The time periods include December 1998–2012 and January–February 1999–2013, which are the same as that of the vertical velocity and precipitation in the following sections. The spatial pattern of the frontal-scale response is not sensitive if the time period is extended back to 1993 (not shown). In Fig. 4, the mesoscale features are shown clearly by the alignment of the positive and negative anomalies. Generally, the response coincides with the SST response in which the convergence overlies the warmer SST and the divergence overlies the colder SST, although the surface wind convergence anomaly appears to be located slightly southeast of the corresponding SST anomaly. The displacement of the atmospheric response is likely to result from the advection effect by the background northwesterlies (arrows in Fig. 4). The eastward displacement is more obvious over the eastern part of the KE domain because the westerly component of the surface background flow is stronger. The weak eastward atmospheric response appears to be influenced by the weaker underlying SST anomaly. Based on the similar spatial patterns, the surface wind convergence in the KE domain is likely to result from anomalous surface winds that flow from the relatively higher SLP to lower SLP centers (Fig. 5). The higher and lower SLP centers are induced by the underlying cooler and warmer SST anomalies, respectively. The spatial resemblance between the SST, surface wind convergence, and SLP responses is supported by the pressure adjustment mechanism.

Regression coefficients between the KE index and the frontal-scale 10-m wind convergence based on ERA-Interim data for DJF 1998–2012 multiplied by 10^{5} (colors, s^{−1}). The areas with convergence are shown in red, while the areas with divergence are shown in blue. The contours are the regression coefficients of the SST shown in Fig. 3. The contour interval is 0.1°C with the zero value omitted. The solid contours show positive values, while the dashed contours show negative values. The mean 10-m wind vectors are shown with black arrows.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and the frontal-scale 10-m wind convergence based on ERA-Interim data for DJF 1998–2012 multiplied by 10^{5} (colors, s^{−1}). The areas with convergence are shown in red, while the areas with divergence are shown in blue. The contours are the regression coefficients of the SST shown in Fig. 3. The contour interval is 0.1°C with the zero value omitted. The solid contours show positive values, while the dashed contours show negative values. The mean 10-m wind vectors are shown with black arrows.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and the frontal-scale 10-m wind convergence based on ERA-Interim data for DJF 1998–2012 multiplied by 10^{5} (colors, s^{−1}). The areas with convergence are shown in red, while the areas with divergence are shown in blue. The contours are the regression coefficients of the SST shown in Fig. 3. The contour interval is 0.1°C with the zero value omitted. The solid contours show positive values, while the dashed contours show negative values. The mean 10-m wind vectors are shown with black arrows.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and the frontal-scale sea level pressure for DJF 1998–2012 (colors, hPa). The black arrows show regressions of the frontal-scale 10-m wind vectors (m s^{−1}).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and the frontal-scale sea level pressure for DJF 1998–2012 (colors, hPa). The black arrows show regressions of the frontal-scale 10-m wind vectors (m s^{−1}).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and the frontal-scale sea level pressure for DJF 1998–2012 (colors, hPa). The black arrows show regressions of the frontal-scale 10-m wind vectors (m s^{−1}).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Scatterometers also provide surface wind vector products over global oceans, which have been operationally assimilated into ECMWF numerical weather predictions. They measure backscatter from surface roughness, which is believed to be in local equilibrium with surface stress (e.g., Liu 2002). Stress is the turbulent transfer of momentum generated by wind shear and buoyancy. At frontal scales, buoyancy-generated stress is in spatial coherence with SST, and convergence (the maximum gradient of wind) lies between warm and cold centers (e.g., Liu and Xie 2008; O’Neill et al. 2003). Away from the confines of the surface, atmospheric forcing (e.g., pressure gradient force and convection) may change the wind distribution from those of the stress. The difference between the wind and stress distributions is discussed by Liu and Xie (2014b), and the link to atmospheric profiles aloft is discussed by Liu and Xie (2014a).

### b. Vertical velocity response

Figure 6 shows the regression coefficients of the KE index to the pressure vertical velocity multiplied by −1. Thus, positive values represent upward air motion. The regression map between the KE index and the vertical velocity at 850 hPa shows the mesoscale feature associated with the varying KE. Generally, ascending air corresponds to warm SSTs whereas descending air corresponds to cold SSTs (Fig. 6, top). Longitude–pressure cross sections of the regression coefficients along 35°N are used to show the vertical structure of the responses with statistical significance. The mesoscale vertical velocity response extends from the surface to the free atmosphere and decreases in amplitude with height (Fig. 6, bottom). The KE index accounts for at least 10% of the variance of the vertical velocity in the areas with statistical significance. Over the western part of the KE domain where the strongest regressed SST anomaly is observed, up to 40% of the variance is explained by the KE index (not shown). Consistent vertical velocity responses at different pressure levels are evidence of the vertically penetrating effect of the local oceanic forcing.

(top) Regression coefficients between the KE index and the frontal-scale vertical velocity at 850 hPa (colors, Pa s^{−1}), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–height cross sections of the regression coefficients of vertical velocity along 35°N for DJF 1998–2012. Note that the positive values correspond to upward motion. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale vertical velocity at 850 hPa (colors, Pa s^{−1}), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–height cross sections of the regression coefficients of vertical velocity along 35°N for DJF 1998–2012. Note that the positive values correspond to upward motion. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale vertical velocity at 850 hPa (colors, Pa s^{−1}), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–height cross sections of the regression coefficients of vertical velocity along 35°N for DJF 1998–2012. Note that the positive values correspond to upward motion. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Interestingly, the vertical velocity anomalies above 500 hPa are tilted westward, which result in the anomalies at the lower levels having different signs than those in the upper levels. The tilt of the vertical velocity response to the SST variations over the eastern equatorial Pacific has been observed near the boundary layer height, which may arise from the increase in the height of the boundary layer (Hashizume et al. 2002). In contrast, our results suggest a minor role of the lifting boundary layer because the 500 hPa is higher than the typical atmosphere boundary height in the KE region, which is approximately 2 km (von Engeln and Teixeira 2013). A potential factor for this vertical tilt will be discussed at the end of the paper.

### c. Precipitation response

Figure 7 shows the regression coefficients of the KE index on the frontal-scale surface precipitation rate in the KE region. The frontal-scale signals of the surface precipitation rate are noisier than the vertical velocity response. However, the effect of the mesoscale current on the surface precipitation is still discernible from the alignment of the positive and negative anomalies. The spatial coherence between the KE index and the surface precipitation is similar to that between the KE index and SST despite the displacement, especially over the eastern part of the KE domain. The surface precipitation response from the ERA-Interim data is consistent with the TRMM surface precipitation response (not shown).

Regression coefficients between the KE index and the frontal-scale surface precipitation from the TRMM 3B43 product for DJF 1998–2012 (colors, mm day^{−1}), superimposed by the SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and the frontal-scale surface precipitation from the TRMM 3B43 product for DJF 1998–2012 (colors, mm day^{−1}), superimposed by the SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and the frontal-scale surface precipitation from the TRMM 3B43 product for DJF 1998–2012 (colors, mm day^{−1}), superimposed by the SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

The frontal-scale responses of the rainwater and latent heating rate to variations in KE are shown in Figs. 8 and 9, respectively. The top panels show the response at the 1-km layer with the relationship between the KE index and SST superimposed, and the bottom panels show height–longitude cross sections along 35°N. The correspondences of the increase in rainwater and latent heat release with the warmer SSTs are clear and are similar to the surface precipitation. The retrievals and coarser resolution of the TRMM 3A12 product compared to the TRMM 3B43 product have little effect on blurring the atmospheric response to the KE variations.

(top) Regression coefficients between the KE index and the frontal-scale rainwater from the TRMM 3A12 product at the 1-km layer (colors, g m^{−3}), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–height cross sections of the regression coefficients of rainwater along 35°N for DJF 1998–2012. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale rainwater from the TRMM 3A12 product at the 1-km layer (colors, g m^{−3}), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–height cross sections of the regression coefficients of rainwater along 35°N for DJF 1998–2012. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale rainwater from the TRMM 3A12 product at the 1-km layer (colors, g m^{−3}), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–height cross sections of the regression coefficients of rainwater along 35°N for DJF 1998–2012. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale latent heating rate from the TRMM 3A12 product at the 1-km layer (colors, °C day^{−1}), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–height cross sections of the regression coefficients of latent heating rate along 35°N for DJF 1998–2012. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale latent heating rate from the TRMM 3A12 product at the 1-km layer (colors, °C day^{−1}), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–height cross sections of the regression coefficients of latent heating rate along 35°N for DJF 1998–2012. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale latent heating rate from the TRMM 3A12 product at the 1-km layer (colors, °C day^{−1}), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–height cross sections of the regression coefficients of latent heating rate along 35°N for DJF 1998–2012. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

The vertical profiles of rainwater and latent heating rate provided by the 3A12 product indicates the depth of influence of the KE variations in the atmosphere. As shown in Fig. 8 (bottom panel), significant changes in rainwater are greatest near the surface and reach up to 4 km. The strongest latent heat response is centered aloft near 2–3 km and penetrates the troposphere to a greater depth, even to the upper troposphere (Fig. 9, bottom). The latent heat release below 4 km appears to be relevant to rain formation. In the absence of a rain response, the latent heat response above 4 km is likely tied to cloud development (Minobe et al. 2010).

Previous analyses address frontal-scale responses of precipitation and air motion to variations in the KE index. The next step is to investigate if consistent frontal-scale responses are observed in clouds and radiation. Figure 10 shows regression maps of cloud-top temperature (top panel) and outgoing longwave radiation (bottom panel) based on the AIRS data. As expected, cloud-top temperature shows mesoscale characteristics but has the opposite sign to SST. The frontal-scale response of the outgoing longwave radiation is negatively correlated with the SST anomalies, although the displacement between the outgoing longwave radiation and SST responses is present. The opposite relationship originates from the KE-induced SST anomalies, in which warm SSTs induce surface convergence, deep convection, high cloud formation, and thus lower cloud-top temperature and outgoing longwave radiation (Minobe et al. 2008). The consistent spatial coherence between SST, surface wind convergence, pressure vertical velocity, and precipitation variables suggests the contribution of the pressure adjustment mechanism to the vertical penetration of the effect of KE variability.

Regression coefficients between the KE index and (top) the frontal-scale cloud-top temperature from the AIRS data (color, K) and (bottom) the frontal-scale outgoing longwave radiation (colors, W m^{−2}) for DJF 2003–2012. The contours show the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and (top) the frontal-scale cloud-top temperature from the AIRS data (color, K) and (bottom) the frontal-scale outgoing longwave radiation (colors, W m^{−2}) for DJF 2003–2012. The contours show the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

Regression coefficients between the KE index and (top) the frontal-scale cloud-top temperature from the AIRS data (color, K) and (bottom) the frontal-scale outgoing longwave radiation (colors, W m^{−2}) for DJF 2003–2012. The contours show the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3).

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

### d. Air temperature and geopotential height response

In addition to the variables described above, the frontal-scale responses of air temperature and geopotential height are detectable in the free atmosphere based on the AIRS data (Figs. 11 and 12). Because the AIRS data are less reliable above 300 hPa (Fetzer et al. 2008), only the results below 300 hPa are presented. In addition, the infrared instrument of AIRS has problems with retrieving temperature and humidity near and below clouds in the presence of optically thick clouds. Because of cloud effects, there are fewer observations over the KE domain; there are fewer observations at lower altitudes (Tian et al. 2013). Thus, the vertical profiles of air temperature and geopotential height response at lower altitudes reflect the effect of KE variations under the observing conditions when cloud fraction is about below 70% (Susskind et al. 2006).

(top) Regression coefficients between the KE index and the frontal-scale air temperature at 850 hPa based on the AIRS data (colors, K), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of air temperature along 35.5°N for DJF 2003–12. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale air temperature at 850 hPa based on the AIRS data (colors, K), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of air temperature along 35.5°N for DJF 2003–12. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale air temperature at 850 hPa based on the AIRS data (colors, K), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of air temperature along 35.5°N for DJF 2003–12. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale geopotential height at 850 hPa (colors, m) based on the AIRS data, superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of geopotential height along 35.5°N for DJF 2003–12. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale geopotential height at 850 hPa (colors, m) based on the AIRS data, superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of geopotential height along 35.5°N for DJF 2003–12. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale geopotential height at 850 hPa (colors, m) based on the AIRS data, superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of geopotential height along 35.5°N for DJF 2003–12. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

The positive relationship between air temperature at 850 hPa and SST is shown in Fig. 11 (top); the exception occurs west of 144°E where the SST and low-level air temperature are out of phase. The vertical structure of the air temperature response shows frontal-scale signals with greater horizontal spreads and less robustness than the variables discussed previously. The air temperature response beyond the boundary layer implies the direct penetrating effect by diabatic heating associated with ocean current variations. The air temperature response above 500 hPa is displaced to the west of that below 500 hPa, which leads to the opposite vertical response. This spatial pattern and the height of the transition layer near 500 hPa are consistent with that of the vertical velocity response.

The response of geopotential height has similar characteristics to the air temperature response (Fig. 12). An alignment of the positive and negative signals is interpreted over the KE domain at 850 hPa, which shows some spatial resemblance with the frontal-scale response of SST (Fig. 12, top). Although the centers of some of the geopotential height anomalies lie at higher altitudes, their longitudes correspond well to those of the air temperature anomalies (Fig. 12, bottom). These results suggest that the strong midlevel geopotential height response is closely tied to anomalous temperature at both lower and upper levels. In the lower troposphere, warm (cold) temperature anomalies lift (lower) the overlying geopotential height. The upper-level temperature response, which is out of phase with its lower-level counterpart, intensifies the geopotential height response from above, which leads to a strong response in the midlevel troposphere. Moisture changes are another factor that can modulate the geopotential height response (Sasaki et al. 2012).

## 7. Differences in the atmospheric responses between the AIRS and ERA-Interim data

For comparison purposes, we conduct the spatial filtering and regression analysis on air temperature and geopotential height at pressure levels using the ERA-Interim data from the same period as the AIRS data (December 2003–12 and January–February 2004–13). Using the longer period of ERA-Interim influences the regression coefficients slightly but does not change the results below (not shown). Although ERA-Interim assimilates the radiance measurements from the AIRS under clear-sky conditions (Dee et al. 2011), discrepancies in front-scale response between the AIRS and ERA-Interim data are notable. Despite the broadly consistent correspondence with SST at frontal scales, the amplitude of ERA-Interim air temperature at 850 hPa is less than that of the AIRS data (cf. top panels of Fig. 13 and Fig. 11). The penetrating effect based on the ERA-Interim data is shallower and confined to 800 hPa below (Fig. 13, bottom). A very weak negative air temperature response is observed almost everywhere above 800 hPa. The mesoscale oceanic features of the geopotential height are below 900 hPa, which are the opposite phase to its near-surface air temperature counterpart (Fig. 14). The ERA-Interim-derived geopotential height response above 900 hPa exhibits the opposite sign to that near the surface and does not have a noticeable relationship with the air temperature response at the same altitudes.

(top) Regression coefficients between the KE index and the frontal-scale air temperature at 850 hPa based on the ERA-Interim data (colors, K), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of air temperature along 35°N. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale air temperature at 850 hPa based on the ERA-Interim data (colors, K), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of air temperature along 35°N. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale air temperature at 850 hPa based on the ERA-Interim data (colors, K), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of air temperature along 35°N. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale geopotential height at 850 hPa based on the ERA-Interim data (colors, m), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of geopotential height along 35°N. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale geopotential height at 850 hPa based on the ERA-Interim data (colors, m), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of geopotential height along 35°N. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

(top) Regression coefficients between the KE index and the frontal-scale geopotential height at 850 hPa based on the ERA-Interim data (colors, m), superimposed by the frontal-scale SST response (contour interval 0.1°C with the zero value omitted; see Fig. 3). (bottom) Longitude–pressure cross sections of the regression coefficients of geopotential height along 35°N. The light green contours enclose areas of statistical significance at the 95% confidence level.

Citation: Journal of Climate 28, 23; 10.1175/JCLI-D-14-00829.1

There are several reasons for the differences between the AIRS and ERA-Interim data. As was mentioned previously, the AIRS data provide vertical profiles of temperature and humidity under the observing conditions when thick clouds are absent, whereas ERA-Interim provides data under all conditions. Unlike ERA-Interim, which generates data at 6-h time intervals, AIRS measurements cannot capture the complete diurnal cycle (e.g., Tian et al. 2013); this is believed to play a minor role because the monthly mean of the daily mean data used for this study should average out the effect of the diurnal cycle. In addition, the ERA-Interim data have greater uncertainty in the upper atmosphere because there are fewer observations to constrain modeling outputs. The ERA-Interim atmospheric variables within the boundary layer are subject to the SSTs prescribed in the atmospheric forecasting model (Masunaga et al. 2015). Because the vertical penetration of the local oceanic forcing is revealed by different atmospheric variables, the limited oceanic impact and the contrast between air temperature and geopotential height near the surface suggest possible biases in the model from the prescribed SSTs. The interpretation of the physics of frontal-scale oceanic processes is missing from the ERA-Interim assimilation system. Both the AIRS and ERA-Interim data have issues that can lead to deviations from the true atmospheric response. Our intent is not to determine which dataset gives the correct atmospheric response, but rather to reveal possible shortcomings of the ERA-Interim data, which should be further investigated and improved.

## 8. Summary and discussion

We use multiyear space-based observations to study the regional atmospheric response to the decadal variability of the KE jet, which is characterized by its fast-flowing and meandering behavior. Monthly mean data are utilized to understand the low-frequency atmospheric response. Although the available period of the satellite data is limited to a maximum of two cycles of the KE fluctuation, the atmospheric association with the KE is pronounced during boreal winter after the large-scale features are removed. Linear regression analysis captures the association on decadal time scales while the time average of the frontal-scale responses over the same time period has difficulty in presenting a clear atmosphere–ocean coupling relationship, especially in the free atmosphere (not shown). The relationship in the time-averaged results is likely to be obscured by the coverage of different phases of decadal variability and/or the processes at shorter time scales.

Our results show that the varying KE jet leaves imprints on the SSTs, which show warm anomalies collocated with the ridges of the KE meanders and cold anomalies collocated with the troughs. The atmospheric responses of different variables display similar frontal-scale signals and are generally in phase with the SST anomalies. The in-phase correspondence is clear over the western part of the KE domain where the SST anomaly is strong, which suggests the importance of the pressure adjustment mechanism. According to the pressure adjustment mechanism, SST-induced changes in air temperature and hydrostatic pressure gradient yield surface wind convergence above the warm SSTs and surface wind divergence above the cold SSTs, which leads to vertical air motion and precipitation response. It is noted that the in-phase spatial correspondence is not perfect over the eastern part of the KE domain, which may be linked to the advection effect of background circulation.

The frontal-scale response of vertical air motion based on the ERA-Interim data reflects the mesoscale oceanic signals to the deep troposphere, which is consistent with space-based observations. However, there are remarkable differences between the space-based observations and the reanalysis data in terms of the air temperature and geopotential height responses. When the ERA-Interim data are used, the penetrating effect of the ocean on air temperature is too shallow, and the relationship between near-surface air temperature and geopotential height is barely detected in the free atmosphere and is anticorrelated below the boundary layer. The results are suggestive of inadequate small-scale thermodynamic processes in the models of the reanalysis data assimilation. Additional research is needed to improve the understanding of the processes causing this difference.

It is interestingly noted that as the influence of the KE variations reaches the free atmosphere, its imprints on pressure vertical velocity and air temperature are tilted westward above 500 hPa. It is possible that this vertical structure of the atmospheric response is associated with the background vertical velocity. During boreal winter, cold and descending air following the cold high pressure system over the continent is roughly located above 500 hPa, while warm and ascending air triggered by the relatively warm KE is located below 500 hPa. Over the KE domain, air subsidence aloft may act like a cap to inhibit the vertical development that leads to midlevel horizontal divergence (convergence) located above the surface convergence (divergence) (not shown), and anomalous vertical air motion showing opposite signals near surface to the upper-level troposphere. The cooler air temperature response from the upper levels to midlevels may be a consequence of the downward advection of diabatic cooling by descending air in the upper levels, while the warmer temperature response from the midlevels to upper levels may be a consequence of diabatic warming advected by ascending air.

## Acknowledgments

This work was supported by an appointment to the National Aeronautics and Space Administration (NASA) Postdoctoral Program at the Jet Propulsion Laboratory, California Institute of Techonology, administered by Oak Ridge Associated Universities through a contract with the NASA. Dr. W. T. Liu was supported jointly by the NASA Physical Oceanography Program, Precipitation Measuring Mission, and Energy and Water Cycle Studies. Dr. Xiaosu Xie kindly provided information on filtering and data access. Dr. Jui-Lin (Frank) Li shared useful comments on the results.

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