Intraseasonal and interdecadal jet shifts in the
Northern Hemisphere: the role of warm pool
tropical convection and sea ice
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Steven B. Feldstein1
Department of Meteorology, The Pennsylvania State University
University Park, PA 16802
Sukyoung Lee
Department of Meteorology, The Pennsylvania State University
University Park, PA 16802
School of Earth and Environmental Sciences, Seoul National University,
Seoul, South Korea
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Corresponding author address: Steven Feldstein, Department of Meteorology, The Pennsylvania State
University, 503 Walker Building, University Park, PA 16802
E-mail: [email protected]
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. This study uses cluster analysis to investigate the inter-decadal poleward shift of the
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subtropical and eddy-driven jets and its relationship to intraseasonal teleconnections. For
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this purpose, self-organizing map (SOM) analysis is applied to the ERA-Interim zonal-
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mean zonal wind. The resulting SOM patterns have time scales of 4.8-5.7 days, and
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undergo notable inter-decadal trends in their frequency of occurrence. The sum of these
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trends closely resembles the observed inter-decadal trend of the subtropical and eddy-
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driven jets, indicating that much of the interdecadal climate forcing is manifested through
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changes in the frequency of intraseasonal teleconnection patterns.
Abstract
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Two classes of jet cluster patterns are identified,. The first class of SOM pattern is
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preceded by anomalies in convection over the warm pool followed by changes in the
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poleward wave activity flux. The second class of patterns is preceded by sea ice and
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stratospheric polar vortex anomalies; when the Arctic sea-ice area is reduced, the
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subsequent planetary wave anomalies destructively interfere with the climatological
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stationary waves. This is followed by a decrease in the vertical wave activity flux, and a
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strengthening of the stratospheric polar vortex. An increase in sea-ice area leads to the
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opposite chain of events. Our analysis suggests that the positive trend in the Arctic
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Oscillation (AO) up until the early 1990s might be attributed to increased warm pool
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tropical convection, while the subsequent reversal in its trend may be due to the influence
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of tropical convection being overshadowed by the accelerated loss of Arctic sea ice.
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1. Introduction
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The jet streams in the Northern Hemisphere (NH) exhibit fluctuations in both their
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strength and latitude. On the inter-decadal time scale, previous studies have found that
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increased greenhouse gas (GHG) driving coincides with a poleward shift of both the
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subtropical jet (e.g., Archer and Caldeira 2008; Chen et al. 2008; Fu et al. 2006; Fu and
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Lin 2011; Hu and Fu 2007; Lu et al. 2008; Seager et al. 2003) and the midlatitude eddy-
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driven jet (e.g., Yin 2005; Lorenz and DeWeaver 2007; Lu et al. 2008; Kidston et al.
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2011). This poleward shift of the eddy-driven jet also corresponds to a trend toward the
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positive phase of the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO)
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teleconnection patterns2 (Thompson and Wallace 2000).
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Beginning in the early 1990s, after a 20-year upward trend toward its largest
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positive value, the five-year running mean winter NAO/AO index began to decline,
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becoming
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(http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/JFM_season_ao_
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index.shtml). During the same time period, Arctic sea-ice area was observed to undergo a
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steep decline in the summer and autumn (Comiso et al. 2008). Anomalously low sea-ice
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area has been linked to the negative phase of the NAO/AO (Seierstad and Bader 2009;
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Deser et al. 2010; Francis et al. 2009; Jaiser et al. 2012; Liu et al. 2012), alluding to the
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possibility that the recent trend toward the negative phase of the NAO/AO is driven by the
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declining sea ice (Jaiser et al. 2012).
negative
at
about
2010
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As will be shown in this study, the interdecadal poleward jet shift can be expressed
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in terms of the change in the frequency of occurrence of intraseasonal time scale
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As discussed by Thompson and Wallace (2000), the NAO can be regarded as the North Atlantic regional
contribution to the hemispheric scale AO.
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teleconnection patterns, as identified with cluster analysis (Self Organizing Map analysis,
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see section 2). Mathematically, this can be written as
K
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ΔU(θ , p) ≈ ∑ ΔfiUi (θ , p) ,
(1)
i=1
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where ΔU(θ , p) is the trend of the zonal-mean zonal wind at latitude θ and pressure p,
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the Ui (θ , p) are the i=1,K cluster patterns, and the Δfi are the trends in the frequency of
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the cluster patterns. (For brevity, rather than frequency of occurrence, we use the shorter
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term frequency.) For the data to be examined in this study, the cluster patterns can be
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interpreted as varying at the intraseaseasonal time scale, since as we will see, each of the
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Ui has an e-folding time scale between 4.8 and 5.7 days. The inter-decadal trend in
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variables such as the zonal-mean zonal wind, ΔU, typically has contributions from inter-
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decadal driving, possibly from GHG driving and changes in Arctic sea ice, and from
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climate noise (Feldstein 2002). Thus, to the extent that (1) accurately describes the inter-
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decadal trend ΔU, and if climate noise makes a relatively small contribution to ΔU, the
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form of (1) suggests that the interdecadal driving of ΔU is primarily manifested through
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inter-decadal changes in the frequency of the intraseasonal cluster patterns.
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The choice to use cluster patterns for this attribution study is motivated by the
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continuum property of atmospheric teleconnection patterns (Feldstein and Franzke 2005;
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Johnson et al. 2008; Johnson and Feldstein 2010). This cluster analysis approach was used
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by Lee and Feldstein (2013) to examine the inter-decadal poleward shift of the midlatitude
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jet in the Southern Hemisphere (SH). They showed that the SH inter-decadal jet shift can
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be accurately represented by an inter-decadal trend in the frequency of intraseasonal time
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scale cluster patterns as described by (1). This linkage between between interdecadal
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variability and intraseasonal teleconnection patterns suggests that it can often be helpful to
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decompose inter-decadal variability into two separate but related questions. These are: (1)
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What dynamical processes drive the intraseasonal teleconnection patterns that contribute
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to the inter-decadal variability? (2) What are the inter-decadal processes that can account
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for the changes in the frequency of the intraseasonal teleconnnection patterns? In this
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study, we focus on the former question. After showing that the inter-decadal poleward jet
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shifts can be accurately described by changes in the frequency of four intraseasonal cluster
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patterns, we proceed to examine the intraseasonal dynamical processes that drive these
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four cluster patterns. One advantage to this approach is that for the relatively short
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intraseasonal time scale, causal relationships can be revealed more readily than with
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steady state or long-term averaged responses. While it may be counter-intuitive that inter-
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decadal variability has such short time scale linkages, as will be discussed later, there are
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physical arguments as to how intraseasonal time scale processes can play an important
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role in the inter-decadal time scale changes of the atmospheric circulation.
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2. Data and methodology
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For this study, the European Center for Medium-Range Weather Forecasts ERA-
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Interim reanalysis dataset (Dee et al. 2011) for the years 1979 to 2008 is used. We also use
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daily National Oceanic and Atmospheric Administration outgoing longwave radiation
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(OLR) data as a proxy for tropical convection, and daily and monthly Arctic sea-ice data
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obtained from the National Snow and Ice Data Center (http://nsidc.org/).
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We apply the method of self-organizing maps (SOMs) (Kohonen 2001; Johnson et al.
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2008) to the zonal-mean zonal wind during the NH winter season (December through
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February; DJF). Prior to calculating the SOMs, the zonal-mean zonal winds are multiplied
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by the cosine of latitude and mass-weighted in the vertical direction. The primary
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motivation for examining the zonal-mean zonal wind, rather than a zonally-varying
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quantity, is to facilitate comparison with previous studies, such as many of the papers
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listed in section 1. The SOM calculation organizes the daily data into a much smaller
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number of m×n cluster patterns, where each cluster represents a large number of similar
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daily patterns. The SOM patterns are displayed on a two-dimensional m×n grid, where m
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is the number of rows and n the number of columns. The SOM analysis organizes the data
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so that similar patterns are assigned to a nearby location and dissimilar patterns to a distant
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location on the grid. The SOM patterns are obtained by minimizing the Euclidean distance
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between each of the SOM patterns and the observed daily field in an N-dimensional phase
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space, where N is the number of grid points within the domain.
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The choice of the size of the SOM grid is motivated by two criteria, one being that the
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number of SOM patterns not be inconveniently large, and two that the SOM patterns are
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similar to the observed daily fields. These criteria are evaluated for three different SOM
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grid sizes, 4×1, 6×1, and 8×1 (see Table 1). Columns two through five show, for each
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SOM pattern, the mean pattern correlation between the daily field and the representative
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SOM pattern for that day (i.e., the SOM pattern with the smallest Euclidean distance on
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that day). The first column shows the weighted-mean pattern correlation over all four
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SOMs, where the weighting is based on the different SOM frequencies . As can be seen,
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the mean pattern correlation increases modestly from 0.50 to 0.58 when the size of the
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SOM grid doubles. These results suggest that a 4×1 grid is sufficient for the goals of this
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study.
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To explore the physical processes associated with the SOM patterns, composites and
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correlations are utilized which are based either on the representative SOM pattern or the
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SOM frequency. In most evaluations of statistical significance, a two-sided Student’s t-
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test is performed. For the composites, the calculation is based on those days when a
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particular SOM pattern has the smallest Euclidean distance. If two days occur within 15
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days of each other, then the day with the larger Euclidean distance is discarded. This
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procedure finds 68, 70, 65, and 66 days for SOM1, SOM2, SOM3, and SOM4
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respectively. These values are used for the number of degrees of freedom in the test of
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statistical significance. For the correlations, all quantities are averaged over the DJF
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months. With the exception of correlations involving the global-mean surface air
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temperature (SAT), where the number of degrees of freedom is determined by the method
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of Oort and Yienger (1996), for all other correlations, the number of degrees of freedom is
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specified to equal 29, corresponding to the number of winter seasons in this study. In
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contrast to the above tests of statistical significance, for OLR, to which a nine-point local
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spatial smoothing is applied, a Monte Carlo approach is used. For this calculation, to
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determine the probability distribution, we performed 1000 sets of composite calculations
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with randomly chosen days and sample sizes that are the same as those in the SOM
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composites.
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3. SOM patterns and inter-decadal variability
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The linear trend in the zonal-mean zonal wind (Fig. 1a) indicates that there has
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been a poleward shift in the latitude of both the subtropical jet and the midlatitude eddy-
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driven jet, as found in many previous studies. (In Fig. 1a, the climatological subtropical jet
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can be identified by the shallow zonal wind maximum near 30oN, and the climatological
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midlatitude eddy-driven jet by its deep vertical structure and local maximum in the lower
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troposphere near 42oN). As will be seen below, this trend is reasonably well captured by
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the sum of the trends from all SOM patterns. The first SOM pattern (SOM1; first row of
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Fig. 2) corresponds primarily to an equatorward shift of the subtropical jet and a poleward
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shift of the eddy-driven jet; the second SOM pattern (SOM2; second row of Fig. 2)
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indicates a poleward displacement of both the subtropical and eddy-driven jets, along with
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a strengthening of the latter jet; the third SOM pattern (SOM3, third row of Fig. 2)
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resembles SOM1 but with the opposite sign for each anomaly, thus corresponding to a
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poleward shift of the subtropical jet and an equatorward shift of the eddy-driven jet. The
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fourth SOM pattern (SOM4, fourth row in Fig. 2) has a structure that is close to being
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opposite to that of SOM2, thus coinciding with an equatorward movement of both jets and
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a weakening of the eddy-driven jet.
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Even though all four SOM patterns exhibit distinct properties, all 4 SOM patterns
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are associated with a large, statistically significant (p < 0.05) composite AO index (from
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the National Oceanic and Atmospheric Administration/Climate Prediction Center;
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NOAA/CPC), with maximum standardized values of 1.18, 0.82, -1.10, and -0.64, for
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SOM1, SOM2, SOM3, and SOM4, respectively. These results indicate that the SOM
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patterns can identify different AO-like patterns that occur in nature.
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The DJF-mean frequency time series (the number of days in each winter season for
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which a particular SOM pattern is the representative pattern) is shown for each SOM
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pattern in the right panels of Fig. 2. Linear regression is used to determine the trend for the
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frequency time series for each SOM pattern. The corresponding trends associated with
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each SOM are calculated by multiplying the frequency trend of each SOM pattern by the
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corresponding SOM spatial pattern, as in (1), and then dividing by 90 (DJF) days. The
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results (shown in the two middle columns of Fig. 2) indicate that the SOM2 and SOM4
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frequency trends dominate the 1979-2008 time period, but that during 1990-2008 all four
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patterns have a similar amplitude in their trends. The latter time period was chosen
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because the rapid decline in summer/autumn Arctic sea ice began in the early 1990s. (Note
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that the sign of the trend patterns for SOM1 and SOM4 is opposite to that of the
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corresponding SOM patterns because of the downward trend in the SOM1 and SOM4
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frequencies.) The sum of the trends from all four SOM patterns for the 1979-2008 time
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period is shown in Fig. 1b. A comparison with the full observed trend (Fig. 1a) shows that
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a large fraction of the observed trend in the zonal-mean zonal wind can be expressed by
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the sum of the individual trends of the four SOM patterns. Analogous results were
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obtained by Lee and Feldstein (2013) for the SH.
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The statistical significance of the linear trends in the SOM frequency is also
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indicated in the right column of Fig. 2. It found that the SOM2 and SOM4 frequency
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trends are statistically significant at p < 0.10 for the full 1979-2008 time period and the
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SOM1 frequency trend is statistically significant at p < 0.05 for the shorter 1990-2008
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time period. The SOM3 frequency is found to be marginally significant at p < 0.10 for the
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shorter time period, as its trend was significant at p < 0.05 (p < 0.10) for initial years of
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1988 (1989), slightly misses p < 0.10 for 1990, and is further from the p < 0.10 threshold
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for the initial years of 1991 and 1992.
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We next discuss the time scale for each of the SOM patterns. The time scales are
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determined by calculating lagged autocorrelations of time series which are obtained by
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projecting the daily zonal-mean zonal wind field onto each SOM pattern. The
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corresponding e-folding time scales are 4.8, 5.7, 5.3 and 5.2 days for SOM1, SOM2,
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SOM3, and SOM4, respectively (see Fig. 3). The time over which the lagged
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autocorrelations decay to zero varies from 14 to 19 days for each SOM pattern. These
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findings provide the support for the statement in the introduction (see also (1)) that the
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inter-decadal trend of the NH zonal-mean zonal wind can be expressed in terms of the
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inter-decadal trend in the frequency of the intraseasonal (4.8-5.7 day) time scale SOM
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patterns. The implication of this result is that inter-decadal driving is manifested mostly
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through its impact on the frequency of the SOM patterns, and to a lesser extent through a
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slow inter-decadal modulation of the background flow by the forcing. Otherwise, the e-
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folding time scales of the SOM patterns would have to be much longer, with some SOM
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patterns persisting for an entire winter season. It is also important to state that (1) does not
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imply causality in the sense of the intraseasonal SOM patterns driving the interdecadal
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trend, or vice versa.
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interdecadal trend and the intraseasonal SOM patterns, with the linkage between the
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interdecadal trend and intraseasonal SOM patterns being realized via the trend in the SOM
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frequencies.
Equation (1) merely indicates the relationship between the
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One may question how can the primary impact of the inter-decadal forcing be to
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alter the frequency of the much shorter time scale SOM patterns, rather than to drive slow
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inter-decadal changes to the background flow. We provide an example of this type of
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relationship by considering the question addressed by Gong et al. (2010) in the context of
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the impact of the El Niño/Southern Oscillation (ENSO) on the frequency of positive and
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negative phase events of the intraseasonal Southern Annular Mode (SAM), the most
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prominent teleconnection pattern in the SH. They found that during La Niña the
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subtropical jet becomes weaker and vice versa during El Niño, with the zonal wind
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difference between La Niña and El Niño (excluding the SAM events, i.e., corresponding
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to the slow changes of the background state) being four times smaller than the SAM
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zonal-mean zonal wind anomalies driven by the short time-scale eddy momentum fluxes.
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The reason that the difference in jet strength between La Niña and El Niño is relatively
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small, being about 10 percent of the climatological value, lies in the changes to the
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meridional potential vorticity (PV) gradient which are much greater, corresponding to a 75
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percent decline on the equatorward side of the mid-latitude jet. These changes in the PV
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gradient result in a much greater frequency of anti-cyclonic wave breaking3 (for La Niña)
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together with the concomitant increase in the strength of the poleward eddy momentum
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fluxes, followed by the excitation of the positive phase of the SAM. Opposite features
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were obtained for El Niño. As a result, during La Niña, the positive phase of the SAM is
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much more frequent than the negative phase, and vice versa for El Niño. These SAM
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events were found to have an e-folding time scale of between 12 and 27 days, depending
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upon the particular season and whether or not ENSO is active. Similar time scales for the
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SAM and the closely related SH zonal index have been found by Feldstein and Lee
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(1998), Lorenz and Hartmann (2001), and Gerber et al. (2008). In the NH, the analogous
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Northern Annular Mode (NAM) has been shown to exhibit an e-folding time scale of 7 to
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15 days (e.g., Feldstein and Lee 1998; Lorenz and Hartmann 2003; Gerber et al. 2008).
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In other words, the slow forcing indeed changes the background state, but the
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lion’s share of the actual circulation change is realized through intraseasonal time scale
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Following Thorncroft et al. (1993), anti-cyclonic (cyclonic) wave breaking corresponds to Rossby waves
that break on the anti-cyclonically (cyclonically) sheared side of the jet.
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processes. This perspective can be summarized as follows: (1) inter-decadal forcing → (2)
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alters the low-frequency background flow → (3) influences high-frequency eddy driving
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of the background flow → (4) changes the frequency of the intraseasonal SOM patterns,
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which corresponds to changes in the low-frequency background flow. As indicated in the
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above ENSO/SAM example, the third step, (3) → (4), can be very large. It is beyond the
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scope of our manuscript to determine whether this type of process is taking place in
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response to GHG or sea-ice driving, but in our view this picture presents a plausible
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explanation for why the trend in the SOM frequencies is able to account for most of the
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long-term trend (Fig. 1). 271
This type of relationship between intra-seasonal processes and interdecadal
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variability is not restricted to zonal mean wind; It has been shown that a substantial
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fraction of the inter-decadal variability in the NH atmospheric sea level pressure (Johnson
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et al. 2008; Johnson and Feldstein 2010) and 250-hPa geopotential height (Lee et al. 2011)
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can be expressed in terms of inter-decadal fluctuations in the frequency of a relatively
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small number of SOM patterns that fluctuate on a 5-10 day time scale.
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4. Exploring the possible GHG-driven response
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The spatial structure of the SOM patterns and the trends in their frequencies allude to
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the possibility that SOM2 and SOM4 are linked to increased GHG driving, whereas
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SOM1 and SOM3 to the decline in Arctic sea ice. This is because the trends associated
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with SOM2 and SOM4 correspond to a poleward shift of both jets and those for SOM1
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and SOM3 to an equatorward (poleward) shift of the eddy-driven (subtropical) jet (see Fig.
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2), which match the trends associated with GHG driving and sea ice loss in modeling and
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observational studies, as discussed in the introduction. To explore this possible
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relationship with GHG driving, we correlate the DJF-mean SOM frequency time series
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with the DJF NOAA/CPC global-mean SAT, defined as the deviation from the 1901-2000
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time average. We use global-mean SAT as an indicator of the thermodynamic response of
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the atmosphere to GHG loading. However, since the global-mean SAT is also modulated
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by internal variability, most notably that by ENSO, we apply 7-year low- and high-pass
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filters to the global-mean SAT and SOM frequency time series in order to evaluate the
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relationship with ENSO.
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Amongst the unfiltered, low-pass, and high-pass correlations between the global-
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mean SAT and the SOM frequencies, the only statistically significant (p<0.10) correlation
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is found for SOM4 at high-frequencies with a value of 0.45. The unfiltered correlations
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were all very small, with absolute values less than 0.15, with the signs of the high- and
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low-frequency correlations opposing each other for each SOM pattern. The signs of these
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correlations are positive for SOM2 and negative for SOM4, with values of 0.48
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(statistically significant at p<0.15) and -0.28, respectively. Perhaps in the future, when the
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data period becomes longer, which would increase the number of degrees of freedom, the
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correlations between patterns resembling SOM2 and SOM4 and the global-mean SAT
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may be statistically significant. However, even if the correlations are statistically
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significant, it would be only one piece of evidence, and a more conclusive attribution
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requires identification of the mechanism that links the GHG warming and the poleward jet
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shift.
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The correlations between the high-pass SOM frequencies and the Niño3.4 index (the
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sea surface temperature (SST) averaged over 5◦N-5◦S, 120◦W-170◦W) yield statistically
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significant (p<0.05) correlations only for SOM2 and SOM4, with values of
-0.57 and
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0.43, respectively. Furthermore, the linear correlation between the high-frequency global-
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mean SAT and the Niño3.4 index is found to be 0.64, also a statistically significant
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(p<0.05) value. These results suggest that El Niño raises the global-mean SAT and is
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responsible for a decrease (increase) in the frequency of SOM2 (SOM4), and vice versa
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during La Niña. Since the signs of the high- and low-frequency correlations (between the
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SOMs and SAT) are opposite, this finding also suggests that in the long-term, higher
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values of the global-mean SAT may be associated with La-Nina-like atmospheric
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conditions. As mentioned above, however, a concrete evaluation of this possibility will
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have to wait for a longer data period in the future.
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5. A possible mechanism for the simulataneous poleward shift of
both jets
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We next examine possible driving mechanisms for SOM2 and SOM4. The link
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between ENSO and the SOM2 and SOM4 frequencies suggests that these patterns are
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driven in part by anomalies in tropical convection. Since the extratropical response to
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tropical convection takes place on a time scale of 5-10 days (Hoskins and Karoly 1981),
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we examine daily lagged composites of anomalous OLR for the SOM patterns (see Fig. 4).
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For this calculation, the OLR anomalies are shown as a function of longitude averaged
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between 10◦S and 10◦N. Consistent with the above relationship with ENSO, SOM2 is
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associated with enhanced (reduced) convection over the Maritime continent (central
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tropical Pacific Ocean), i.e., a La Niña-like OLR pattern, with SOM4 showing the
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opposite relationship. Although not related to ENSO, the pattern of the OLR anomalies
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associated with SOM1 resemble those for SOM2, except that they are opposite in sign.
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Moreover, the anomalies in tropical convection are seen to lead the SOM patterns (except
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for SOM3), suggesting that intraseasonal tropical convection may excite the SOM patterns
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and perhaps play an important role in the inter-decadal modulation of the frequencies of
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the SOM patterns.
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This connection between intra-seasonal time scale tropical convection and the
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SOM patterns may be understood from the findings of Moon and Feldstein (2009) and
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Park and Lee (2013). These studies showed that poleward propagating wave trains excited
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by tropical convection can have a large impact on the zonal-mean flow, as the eddy
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momentum flux associated with the wave trains alters the zonal-mean flow in a manner
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that the ensuing synoptic-scale eddy momentum fluxes drive the midlatitude jet poleward.
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We investigate if this mechanism operates in the excitation of SOM2 by examining
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wave activity (Eliassen-Palm; EP) fluxes. Figure 5 presents lagged composites of the
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anomalous EP flux vectors and their divergence, with the planetary-scale (zonal
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wavenumbers 1 and 2; right column) and synoptic-scale4 (zonal wavenumbers greater than
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or equal to 3; left column) contributions shown separately. (We will not show lagged EP
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flux composites for SOM4, as the EP flux vectors and their divergence are found to be
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opposite in sign to those for SOM2.) As can be seen, between lag -30 and lag -20 days,
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there is poleward planetary-scale wave activity propagation in the upper-troposphere from
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the deep tropics to about 25◦N, which is consistent with the expected response from the
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enhanced warm pool tropical convection associated with SOM2. During the same time
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Synoptic-scale is typically regarded as excluding zonal wavenumbers 3 and perhaps 4. However, since the
EP fluxes associated with all zonal wavenumbers greater than or equal to 3 is dominated by its synopticscale contribution, for brevity, we use the term synoptic-scale to refer to all wavenumbers excluding 1 and 2.
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interval, confined mostly to midlatitudes, there is equatorward synoptic-scale wave
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activity propagation.
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As evidenced from the EP flux divergence for SOM2 (Fig. 5), the primary impact
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of this wave activity propagation from both the planetary and synoptic scales is a
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deceleration of the zonal-mean zonal wind throughout much of the troposphere at lag -20
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days near 20◦N (Fig. 6). Between lag -20 and lag -10 days, the planetary-scale EP flux
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(right panels in Fig. 5) is dominant in the subtropics while the synoptic-scale EP flux (left
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panels in Fig. 5) is stronger in midlatitudes, resulting in a weakening of the subtropical jet
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and a slight strengthening of the midlatitude jet at lag -10 days. These changes to the zonal
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wind structure correspond to an increase in the anticyclonic shear between the two jets.
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Such changes to the subtropical and midlatitude jet structure typically results in a
363
strengthened equatorward synoptic-scale wave activity flux, and a poleward shift of the
364
midlatitude jet (Gong et al. 2010, and references therein). Both of these features are seen
365
between both lag -10 and lag -5 days, and again between lag -5 and lag 0 days. The short
366
5.7-day e-folding time scale for SOM2 is consistent with the eddy driving and zonal-mean
367
flow changes being largest in the latter time interval. These results are consistent with
368
those of Moon and Feldstein (2009) and Park and Lee (2013) and therefore suggest that
369
increased warm pool tropical convection and the subsequent poleward Rossby wave
370
propagation alters the zonal-mean flow in a manner which leads to a strong equatorward
371
synoptic-scale wave activity flux that excites the SOM2 pattern.
372
373
374
6. Identifying the Arctic sea-ice driven response
15
375
Recent studies have shown that anomalies in summer and autumn Arctic sea-ice
376
area5 were followed in the winter by anomalies in the strength and latitude of the eddy-
377
driven jet that project onto the NAO/AO spatial pattern, along with widespread changes in
378
midlatitude surface air temperature, storm track location, stationary wave location,
379
blocking frequency, cold-air outbreaks, and snow cover (e.g., Singarayer et al. 2006;
380
Honda et al. 2009; Petoukhov and Semenov 2010; Deser et al. 2010; Overland and Wang
381
2010; Liu et al. 2012). This relationship has been most noticeable over the past several
382
years, when anomalously low sea-ice area in the autumn has been followed by severely
383
cold winters over large parts of the middle latitudes in the NH (e.g., Petoukhov and
384
Semenov 2010).
385
We examine lagged correlations between the detrended DJF SOM frequencies and
386
anomalous Arctic sea-ice area, with the months chosen for the sea-ice anomalies ranging
387
from 12 months prior to 12 months following the SOM frequency anomalies. Unlike for
388
the global-mean SAT, the Arctic sea-ice correlations are not split into low- and high-
389
frequency components because Arctic sea ice is not correlated with ENSO (Fig. 7f). As
390
can be seen in Figs. 7a and 7c, the frequencies of SOM1 and SOM3 are strongly linked to
391
Arctic sea-ice area over a wide range of negative lags. These correlations imply that
392
SOM1 occurs more frequently during the DJF winter following a period of enhanced sea
393
ice, and vice versa for SOM3. Most strikingly, for SOM1 and SOM3, statistically
394
significant correlations (p < 0.05) are found as far back as 12 months before the start of
395
the winter season. Also, because SOM1 (SOM3) has a large positive (negative) projection
5
Sea-ice area is defined as the area of the region that is covered by sea ice. The contribution to the sea ice
area from an individual grid cell comes from the portion of the grid cell that is covered in sea ice. For more
information, see http://nsidc.org/cryosphere/seaice/data/terminology.html.
16
396
onto the AO, not surprisingly, positive (negative) AO DJF winters are preceded by
397
positive (negative) sea-ice area anomalies (Fig. 7e).
398
The above correlations are also consistent with the inter-decadal decline in Arctic
399
sea ice, because the SOM1 (SOM3) frequency shows a downward (upward) trend (see Fig.
400
2). In contrast, there is no indication that SOM2 and SOM4 are preceded by anomalies in
401
the sea-ice area during the preceding months (Figs. 7b and 7d).
402
403
404
7. Possible mechanism for the impact of Arctic sea ice on
meridional jet displacements
405
406
In the previous section, a linkage between Arctic sea ice area and the SOM1 and
407
SOM3 frequencies on interannual time scales was found. That result motivates us to
408
examine whether anomalies in Arctic sea ice contribute toward the driving of these two
409
SOM patterns on the intraseasonal time scale. To investigate this possible relationship, we
410
first perform lagged composites of the 15-day running mean Arctic sea-ice area, where the
411
sea ice is averaged over 60◦N-90◦N, 30◦W-120◦E, a region that is centered on the Barents
412
and Kara Seas (Fig. 8). (This domain is chosen because, as we will see, SOM1 and SOM3
413
are most closely linked to sea ice concentration anomalies in this region.) As can be seen,
414
SOM1 is preceded by positive statistically significant sea ice area anomalies (p<0.05) over
415
a wide range of negative lags. (Lagged composites for the entire Arctic Ocean showed
416
similar results, except that the number of days with statistically significant values was
417
slightly less.) Although the other SOM pattern composites are not found to be statistical
418
significant (p<0.05), SOM3 is an exception, as it exhibits statistically significant (p<0.15)
17
419
negative composite values at negative lags. Because SOM1 and SOM3 both show a
420
strong connection to Arctic sea-ice area at interannual time scales (Fig. 7), in spite of the
421
connection between Arctic sea ice and SOM3 being weak at intraseasonal time scales, in
422
this section, we examine the possible relationship between Arctic sea ice and both SOM1
423
and SOM3 at intraseasonal time scales.
424
To investigate the possible mechanisms by which Arctic sea ice drives SOM1 and
425
SOM3, we again examine the anomalous EP flux vectors and zonal-mean zonal wind
426
(Figs. 9, 10, and 11). Figure 11 indicates that SOM1 is associated with a persistent and
427
strengthened stratospheric polar vortex and vice versa for SOM3. For both SOM1 and
428
SOM3, the presence of large amplitude stratospheric polar vortex anomalies at negative
429
lags suggests that the atmospheric response to sea ice is first imprinted upon the
430
stratosphere, which in turn alters the frequency of occurrence of these two SOM patterns.
431
In contrast, the stratospheric anomalies associated with SOM2 and SOM4 are much
432
weaker and shorter-lived (Fig. 6 for SOM2, not shown for SOM4).
433
a. stratospheric polar vortex and wave activity flux
434
We first investigate the relationship between the strength of the stratospheric polar
435
vortex and the occurrence of SOM1 and SOM3. Beginning with SOM1, it can be seen
436
that the zonal-mean zonal wind anomalies are relatively small at lag -60 days (Fig. 11).
437
An anomalous downward planetary-scale EP flux in the lower and middle stratosphere
438
near 60◦N results in an EP flux divergence (Fig. 9) and corresponding strengthening of the
439
stratospheric polar vortex by lag -45 days. Continuation of this downward (and also
440
equatorward) anomalous planetary-scale EP flux further accelerates the stratospheric polar
441
vortex within the lag -45 to lag -30 day and lag -30 to lag -10 day periods. During this
18
442
time interval, an anomalous equatorward synoptic-scale EP flux leads to the acceleration
443
of the midlatitude jet and deceleration of the subtropical jet in the troposphere. The
444
occurrence of these particular synoptic-scale EP fluxes is consistent with the findings of
445
Garfinkel et al. (2013), who show with a general circulation model that an accelerated
446
stratospheric polar vortex coincides with a synoptic-scale eddy feedback in the
447
troposphere that shifts the midlatitude tropospheric jet poleward. (Consistently, modeling
448
studies as Polvani and Kushner (2002), Kushner and Polvani (2004), Song and Robinson
449
(2004), and Simpson et al. (2009) show that changes in the stratosphere influence the
450
troposphere through downward control and an eddy feedback.) From lag -10 to lag 0 days,
451
both the planetary-scale and synoptic-scale eddies in the troposphere undergo a substantial
452
strengthening which drives the anomalous zonal winds toward a pattern that closely
453
resembles SOM1 both in spatial structure and amplitude (see Fig. 2).
454
acceleration of the zonal wind field is consistent with the short 4.8-day e-folding time
455
scale of SOM1.
This rapid
456
SOM3 exhibits similar features in its anomalous EP flux and zonal-mean zonal
457
wind (Figs. 10 and 11) as that for SOM1, except for a change in sign. For example, from
458
lag -60 to lag -10 days, the anomalous planetary-scale EP fluxes are upward and poleward.
459
This results in a substantial weakening of the stratospheric polar vortex by lag -10 days.
460
Also, the anomalous synoptic-scale EP fluxes are poleward in the troposphere (again
461
consistent with the synoptic-scale eddy feedback discussed in Garfinkel et al. (2013)),
462
which results in a deceleration and equatorward shift of the midlatitude jet and
463
acceleration and poleward shift of the subtropical jet. Between lag -10 and lag 0 days, the
464
anomalous tropospheric fluxes attain their largest amplitude and the SOM3 pattern is
19
465
excited. Again, the rapid increase in the strength of the EP fluxes during this time interval
466
is consistent with the short 5.3-day e-folding time scale of SOM3.
467
468
b. planetary-wave interference and the stratospheric polar vortex
469
For SOM1 and SOM3, the question remains as to what process can change the
470
strength of the stratospheric polar vortex. As shown in Garfinkel et al. (2010), the strength
471
of the stratospheric polar vortex can be altered by interference between the planetary wave
472
contributions to the anomalous circulation and the climatological stationary eddy fields in
473
the troposphere. Their study showed that constructive interference leads to a strengthening
474
of planetary-scale vertical wave activity propagation hence to a deceleration the
475
stratospheric polar vortex, and vice versa for destructive interference. Other studies which
476
have linked interference in the troposphere with changes in the strength of the
477
stratospheric polar vortex include Smith et al. (2011), Garfinkel et al. (2012), and Jiang et
478
al. (2014).
479
To investigate whether interference is playing a role in the changes to the strength
480
of the stratospheric polar vortex, we show in Fig. 12 the anomalous (shading) and the
481
climatological (contours) planetary-scale streamfunction at 300 hPa, both with their zonal-
482
mean values subtracted. As can be seen, for SOM1, during the lag -60 to lag -45 and lag -
483
30 to lag -10 day intervals, the positive anomaly overlaps with the negative climatological
484
eddy streamfunction over eastern Asia and the North Pacific, and vice versa over northern
485
Europe. From lag -45 to lag -30 and from lag -10 to lag 0 days, this overlap occurs
486
primarily over eastern Asia and the North Pacific. Therefore, destructive interference may
487
indeed account for the acceleration of the stratospheric polar vortex for SOM1. Analogous
20
488
interference features, but of opposite sign, can be seen for SOM3 (Fig. 12). Unlike for
489
SOM1, however, for the lag -60 to lag -45 and lag -45 to lag -30 day intervals, there is no
490
clear pattern of interference, and constructive interference becomes apparent only at later
491
periods (lag -30 to lag -10 and lag -10 to lag 0 days) over the North Pacific/eastern Asia
492
and northern Europe.
493
In a recent modeling study with the Community Atmospheric Model (CAM5),
494
Peings and Magnusdottir (2014) examined the response of the atmospheric circulation to
495
sea ice anomalies. For the years 2007-2012, they found interference within the
496
troposphere to be followed by a weakening of the stratospheric polar vortex and the
497
excitation of the negative NAM, as in the present study.
498
c. sea-ice anomalies and planetary-scale waves
499
To examine the plausibility of the anomalous 300-hPa streamfunction field in Fig.
500
12 arising as a response to diabatic heating anomalies associated with changes in Arctic
501
sea ice, we compare the anomalous 300-hPa streamfunction field in Fig. 12 with the
502
atmospheric response to sea-ice concentration anomalies in climate models. From this
503
perspective, an anomaly in sea-ice concentration may excite a particular wave field that
504
either constructively (SOM1) or destructively (SOM3) interferes with the climatological
505
stationary eddy field which leads to the more frequent excitation of SOM1 or SOM3.
506
We first compare the results in Fig. 12 with the findings of Deser et al. (2007),
507
who examine the transient atmospheric response to sea-ice concentration anomalies in the
508
NCAR Community Climate Model version 3 (CCM3) for the winter season. In their
509
model calculation, they imposed a negative sea-ice concentration anomaly over the
510
Barents and Greenland Seas and a positive sea-ice concentration anomaly over the
21
511
Labrador Sea. They found that the initial adjustment exhibits a baroclinic vertical structure
512
with an anomalous low at 1000-hPa and an anomalous high at 300-hPa over the region
513
where sea ice has been removed and vice versa over the region where sea ice has been
514
added. This baroclinic local response persisted for two to three weeks. Throughout the
515
following two months, the vertical structure of the atmospheric circulation became
516
increasingly barotropic and increasingly resembled the negative phase of the NAM, i.e.,
517
SOM3. They also showed with a linear primitive equation model that the initial baroclinic
518
circulation can be understood as the forced response to diabatic heating in the lower
519
troposphere and the equivalent barotropic circulation two months later as being due to
520
driving by transient eddy vorticity and heat fluxes. Similar findings were obtained by
521
Deser et al. (2004) by separating the response to Arctic sea ice forcing into indirect
522
(projection onto the leading EOF) and a direct (obtained as a residual) responses.
523
In another more recent modeling study of the atmospheric response to changes in
524
sea ice over the Barents Sea, Liptak and Strong (2014) found a similar initial baroclinic
525
response over the Arctic Ocean that was followed two months later by an equivalent
526
barotropic response that extended into midlatitudes. When sea ice was reduced over the
527
Barents Sea, the initial baroclinic resembled that of Deser et al. (2007), and when the sea
528
ice was increased the sign of the baroclinic response was reversed.
529
For SOM3, inspection of Fig. 13 shows a reduction in sea ice over the Greenland
530
and Barents Seas with the largest amplitude anomalies occurring from lag -60 to lag -30
531
days. (Note that the sea-ice concentration anomalies in the Barents and Kara Seas in Fig.
532
13 have opposite signs, with those in the Barents Sea being stronger and more widespread.
533
This contrasts many studies where the sea-ice concentration anomalies in both seas have
22
534
the same sign.) In the upper troposphere, at 300-hPa, an anomalous high can be seen over
535
the Greenland, Barents, and Kara Seas at lag -45 to lag -10 days. This is followed by the
536
equivalent barotropic negative NAM (Fig. 11) almost two months later. These results are
537
consistent with the findings of Deser et al. (2007) discussed above. For SOM1, there is an
538
increase in sea ice mostly over the Kara Sea that is largest from lag -60 to lag -45 days
539
which then slowly declines for the rest of the time period (Fig. 13). An anomalous low at
540
300 hPa is observed over the same region and time period (Fig. 12), which is followed
541
about one month later by the equivalent barotropic positive NAM (Fig. 11). Thus, the sea-
542
ice concentration, 300-hPa streamfunction, and zonal-mean zonal wind anomalies are
543
consistent with sea ice contributing to the excitation of SOM1 and SOM3 via the wave
544
response to the diabatic heating associated with the sea ice changes, followed by
545
interference, changes to the strength of the stratospheric polar vortex and the subsequent
546
excitation of SOM1 and SOM3.
547
548
8. Discussion and conclusions
549
This study uses SOM analysis to examine the inter-decadal poleward shift of the
550
subtropical and eddy-driven jets and its relationship to intraseasonal teleconnections as
551
determined from SOM analysis. It is found that these jet shifts can be expressed in terms
552
of an inter-decadal trend in the frequency of four SOM patterns, each of which has an e-
553
folding time scale of between 4.8 and 5.7 days. The SOM analysis finds two classes of
554
zonal-mean zonal wind patterns. One class is associated with simultaneous shifts of the
555
subtropical and eddy-driven jets in the same direction and the second class with jet shifts
556
in opposite directions. The inter-decadal trend in the frequency of the first class of SOM
23
557
patterns corresponds to a poleward shift of both jets and that for the second class to a
558
poleward (equatorward) shift of the subtropical (eddy-driven jet).
559
In this study we exploit the short time scale characteristics to explore the physical
560
mechanisms that drive the jet variability represented by the SOM patterns. Amongst the
561
first class, it was found for SOM2 that the jet shifts were preceded by enhanced warm pool
562
tropical convection and then by poleward wave activity propagation (identified with EP
563
fluxes). This wave activity propagation with its attendant equatorward eddy momentum
564
flux weakens the subtropical jet, and is followed by the excitement of a synoptic-scale
565
eddy momentum flux that drives both jets poleward. The excitement of SOM4 was found
566
to exhibit the same features, but opposite in sign.
567
For the second class, the driving mechanism involves sea-ice concentration anomalies
568
over the Greenland, Barents, and Kara Seas. For SOM1, which is preceded by an increase
569
in sea-ice over the Kara Sea, an anomalous low develops in the upper troposphere over the
570
same region. (This relationship is consistent with the findings based on climate model
571
experiments when sea ice is added.) This is followed by destructive interference between
572
the anomalous wave field and the climatological stationary eddies. These changes result
573
in a weakening of the vertical flux of wave activity into the stratosphere and thus an
574
acceleration of the stratospheric polar vortex. Each of these steps is illustrated in Fig. 14a.
575
The strengthened stratospheric polar vortex is then followed by the excitation of SOM1,
576
presumably through a positive synoptic-scale eddy feedback process (Garfinkel et al.
577
2013).
578
SOM3 was found to exhibit the same features but opposite in sign. The excitation of
579
this SOM pattern was preceded by a loss of sea ice over the Greenland and Barents Seas.
24
580
This was followed by the formation of an anomalous high over these two seas (again
581
consistent with climate model experiements), constructive interference, a strengthening of
582
the vertical flux of wave activity into the stratosphere and a weakening of the stratospheric
583
polar vortex (see Fig. 14b). This is followed by the excitation of SOM3, again most likely
584
through a synoptic-scale feedback process.
585
From the findings above, what can we learn about the interdecadal jet shifts? For the
586
first class, to the extent that GHG driving enhances warm pool convection (Lee et al.
587
2011; L’Heureux et al. 2013), one expects a trend toward an increased frequency of
588
convection on intra-seasonal time scales that has a La Niña-like spatial structure. This
589
would lead to more frequent excitation of planetary-scale poleward propagating wave
590
trains and the subsequent poleward jet shifts represented by SOM2 and SOM4. This study
591
cannot be conclusive about this linkage (between the frequency trends of SOM2 and
592
SOM4, and the global-mean SAT which is treated as a proxy for GHG driving) perhaps
593
because the data period is too short; at time scales longer than that of ENSO, the
594
correlations were statistically significant only at the p<0.15 level. In the future, as the
595
dataset becomes longer, the relationship between this class of patterns and the global-
596
mean SAT may exhibit a higher degree of statistical significance, which would be
597
consistent with results from climate model runs with increased CO2. On the other hand, it
598
is also possible that these interdecadal jet shifts can be explained by climate noise
599
(Feldstein 2002). For the second class, its interdecadal trend was associated with the
600
decline in Arctic sea ice. Since the poleward (equatorward) shift of the eddy-driven jet
601
corresponds to the positive (negative) phase of the NAO/AO, these findings suggests that
602
the positive trend in the Arctic Oscillation (AO) from the late 1960s through the early
25
603
1990s might possibly be attributed to the influence of GHG warming on tropical
604
convection, while the subsequent reversal in its trend since that time period is likely due to
605
the loss of Arctic sea-ice, which may also be influenced by the GHG warming.
606
Returning to the question of the mechanism that drives latitudinal jet shifts, there
607
are a number of other mechanisms that have been proposed for poleward jet shifts. These
608
include an increase in latent heat release by midlatitude storms (Son and Lee 2005), a
609
higher tropopause (Lorenz and DeWeaver 2007), an enhancement in subtropical and
610
midlatitude static stability and thus a poleward shift of both the subtropical jet and latitude
611
of strongest baroclinic instability (Lu et al. 2008), higher latitude wave breaking caused by
612
an increased midlatitude eddy phase speed, poleward displaced critical latitudes, and an
613
increased eddy length scale (Chen et al. 2008; Lu et al. 2008; Kidston et al. 2010, 2011),
614
an increase in the frequency of anticyclonic wave breaking of longer waves associated
615
with greater upper tropospheric baroclinicity (Riviere 2011), and a poleward shift in the
616
middle latitude storms track due to both tropical upper tropospheric warming and a
617
cooling of the polar stratosphere (Butler et al. 2010). The finding in this study, that the
618
poleward trend in the NH jets can be expressed through changes in the frequency of
619
occurrence of teleconnection patterns with an e-folding time scale of 4.8-5.7 days, poses
620
the constraint that these possible mechanisms also occur on a similarly short time scale.
621
Lastly, it was found that the frequencies of two SOM patterns were preceded by
622
statistically significant (p < 0.05) anomalies in Arctic sea-ice area that extend back as far
623
as 12 months. These findings suggest that Arctic sea-ice may serve as an important source
624
of predictability for the NH with lead times of many months.
26
625
Acknowledgments.
626
This study is supported by National Science Foundation grants AGS-1139970, AGS-
627
1036858, and AGS-1401220. We also thank the European Centre for Medium-Range
628
Weather Forecasts, the Climate Analysis Branch of the NOAA Earth System Research
629
Laboratory/Physical Sciences Division, and National Snow and Ice Data Center for
630
making available the ERA-Interim reanalysis data, the outgoing longwave data, and the
631
sea ice area data, respectively. We would also like to thank three anonymous reviewers
632
for their helpful comments.
633
634
635
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775
List of Tables:
776
Table 1: Time-averaged pattern correlations between the daily zonal-mean zonal wind and
777
the representative SOM pattern for each day (columns two through five), and the time-
778
averaged pattern correlation averaged over all 4 SOMs weighted by the different SOM
33
779
frequencies (first column). When there are two SOM4-like patterns, two correlations are
780
shown.
781
782
List of Figures:
783
Figure 1: The 1979-2008 DJF zonal-mean zonal wind trend in the NH: (a) total trend, and
784
(b) the sum of the four 1979-2008 SOM trends shown in the second column of Fig. 2. The
785
solid contours show the climatological zonal-mean zonal wind. The black dots in (a)
786
indicate statistical significance (p < 0.05) based on a two-sided Student’s t-test.
787
788
Figure 2: The SOM patterns of the zonal-mean zonal wind for a (4 X 1) grid. The left
789
column shows the SOM patterns, the second column is the 1979-2008 trend for each SOM,
790
the third column is the 1990-2008 trend, and the right column shows the frequency time
791
series (solid blue line) shown as the number of days for each DJF season. The solid
792
contours in the first three columns show the climatological zonal-mean zonal wind. In the
793
fourth column, the dashed (dotted) black lines are the least squares linear fit of the 1979-
794
2008 (1990-2008) frequency time series. If the linear fit is statistically significant at p <
795
0.05 (p < 0.10) based on a two-sided Student’s t-test, the black line is replaced by a red
796
(blue) line. The colorbar is for the first column. The scale for the second and third
797
columns is shown in the colorbar of Fig. 1.
798
799
Figure 3: Lagged autocorrelations of the daily projection time series for each SOM pattern
800
801
Figure 4: Lagged composite outgoing longwave radiation (OLR) based on (a) SOM1, (b)
802
SOM2, (c) SOM3, and (d) SOM4. The dots indicate statistical significance (p < 0.05)
34
803
based on a Monte Carlo test. Blue (negative) indicates enhanced convection, and red
804
(positive) suppressed convection. Prior to calculating the composites, the OLR fields are
805
spatially smoothed using NCAR Command Language function, smth9, which performs
806
nine-point local smoothing.
807
Figure 5: The composite SOM2 Eliassen-Palm flux vectors and their divergence (shading)
808
for planetary-scale (zonal wavenumbers 1 and 2; right panels) and synoptic-scale (zonal
809
wavenumbers greater than or equal to 3; left panels) eddies. Each panel shows a time
810
average for the interval indicated. Vectors shown correspond to those that have a least
811
one statistically significant (p<0.05) component.
812
Figure 6: The composite SOM2 anomalous zonal-mean zonal wind at the lag indicated.
813
Statistical significance at a location is indicated by the presence of a black dot.
814
Figure 7: Time-lag correlations between monthly mean Arctic sea-ice area/extent and the
815
DJF-mean frequency of occurrences for (a) SOM1, (b) SOM2, (c) SOM3, and (d) SOM4.
816
Panels (e) and (f) shows correlations between the monthly mean Arctic sea-ice area/extent
817
and the DJF-mean AO index and the DJF-mean Niño3.4 index, respectively.
818
correlation values shown with black dots indicate statistical significance (p < 0.05) for a
819
two-sided Student’s t-test. Lag 0 corresponds to December, and negative (positive) lags
820
correspond to sea-ice leading (lagging) the SOM frequency.
821
Fig. 8: Lagged composites of anomalous sea-ice area averaged over 60◦N-90◦N, 30◦W-
822
120◦E for all four SOM patterns. The thick curve indicates values that are statistically
823
significant at p<0.05. Negative lags correspond to sea ice leading the SOM patterns.
824
Figure 9: As Fig. 5, except for SOM1.
825
Figure 10: As Fig. 8, except for SOM3.
35
The
826
Figure 11, As Fig. 6, except for SOM1 and SOM3.
827
Figure 12: Lagged composites of the planetary-scale (zonal wavenumbers 1 and 2) SOM1
828
and SOM3 anomalous 300-hPa streamfunction (shading) and planetary-scale 300-hPa
829
climatological streamfunction (contours) for the averaging time period indicated.
830
Figure 13: Lagged composites of the SOM1 and SOM3 anomalous sea-ice concentration
831
for the time interval indicated. Note that the shading level is reversed with that for the
832
previous figures.
833
Figure 14: A schematic depiction of the mechanism proposed by this study for the linkage
834
between Arctic sea-ice anomalies and changes in the strength of the stratospheric polar
835
vortex for (a) SOM1 and (b) SOM3. For SOM1, the picture presented suggests that the
836
cooling which arises when there is an increase is sea ice is balanced by warm thermal
837
advection.
838
destructive interference, weaker vertical wave activity propagation, and an acceleration of
839
the stratospheric polar vortex. SOM3 shows the opposite features.
The phase of the anomaly that generates this advection coincides with
840
841
842
843
844
845
846
847
848
849
36
850
851
852
853
854
855
856
857
858
859
860
861
862
863
Table 1: Time-averaged pattern correlations between the daily zonal-mean zonal wind and
the representative SOM pattern for each day (columns two through five), and the timeaveraged pattern correlation averaged over all 4 SOMs weighted by the different SOM
frequencies (first column). When there are two SOM4-like patterns, two correlations are
shown.
864
865
866
867
868
37
869
870
871
872
873
874
875
876
877
Figure 1: The 1979-2008 DJF zonal-mean zonal wind trend in the NH: (a) total trend, and
(b) the sum of the four 1979-2008 SOM trends shown in the second column of Fig. 2. The
solid contours show the climatological zonal-mean zonal wind. The black dots in (a)
indicate statistical significance (p < 0.05) based on a two-sided Student’s t-test.
878
879
38
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
Figure 2: The SOM patterns of the zonal-mean zonal wind for a (4 X 1) grid. The left
column shows the SOM patterns, the second column is the 1979-2008 trend for each
SOM, the third column is the 1990-2008 trend, and the right column shows the frequency
time series (solid blue line) shown as the number of days for each DJF season. The solid
contours in the first three columns show the climatological zonal-mean zonal wind. In the
fourth column, the dashed (dotted) black lines are the least squares linear fit of the 19792008 (1990-2008) frequency time series. If the linear fit is statistically significant at p <
0.05 (p < 0.10) based on a two-sided Student’s t-test, the black line is replaced by a red
(blue) line. The colorbar is for the first column. The scale for the second and third
columns is shown in the colorbar of Fig. 1.
896
39
897
898
899
900
Figure 3: Lagged autocorrelations of the daily projection time series for each SOM pattern.
901
902
903
904
905
906
40
907
908
909
910
911
912
913
914
915
Figure 4: Lagged composite outgoing longwave radiation (OLR) based on (a) SOM1, (b)
SOM2, (c) SOM3, and (d) SOM4. The dots indicate statistical significance (p < 0.05)
based on a Monte Carlo test. Blue (negative) indicates enhanced convection, and red
(positive) suppressed convection. Prior to calculating the composites, the OLR fields are
spatially smoothed using NCAR Command Language function, smth9, which performs
nine-point local smoothing.
41
916
917
918
919
920
921
Figure 5: The composite SOM2 Eliassen-Palm flux vectors and their divergence (shading)
for planetary-scale (zonal wavenumbers 1 and 2; right panels) and synoptic-scale (zonal
wavenumbers greater than or equal to 3; left panels) eddies. Each panel shows a time
average for the interval indicated. Vectors shown correspond to those that have a least
one statistically significant (p<0.05) component.
42
922
923
924
925
Figure 6: The composite SOM2 anomalous zonal-mean zonal wind at the lag indicated.
Statistical significance (p<0.05) at a location is indicated by the presence of a black dot.
43
926
927
928
929
930
931
932
933
934
935
936
Figure 7: Time-lag correlations between monthly mean Arctic sea-ice area/extent and the
DJF-mean frequency of occurrences for (a) SOM1, (b) SOM2, (c) SOM3, and (d) SOM4.
Panels (e) and (f) shows correlations between the monthly mean Arctic sea-ice area/extent
and the DJF-mean AO index and the DJF-mean Niño3.4 index, respectively. The
correlation values shown with black dots indicate statistical significance (p < 0.05) for a
two-sided Student’s t-test. Lag 0 corresponds to December, and negative (positive) lags
correspond to sea-ice leading (lagging) the SOM frequency.
44
937
Fig. 8: Lagged composites of anomalous sea-ice area averaged over 60◦N-90◦N,
30◦W-120◦E for all four SOM patterns. The thick curve indicates values that are
statistically significant at p<0.05. Negative lags correspond to sea ice leading the SOM
patterns.
45
938
939
940
941
942
Figure 9: As Fig. 5, except for SOM1.
46
943
944
945
Figure 10: As Fig. 8, except for SOM3.
47
946
947
Figure 11, As Fig. 6, except for SOM1 and SOM3.
48
948
949
950
951
952
953
Figure 12: Lagged composites of the planetary-scale (zonal wavenumbers 1 and 2) SOM1
and SOM3 anomalous 300-hPa streamfunction (shading) and planetary-scale 300-hPa
climatological streamfunction (contours) for the averaging time period indicated.
49
954
955
956
957
958
959
960
Figure 13: Lagged composites of the SOM1 and SOM3 anomalous sea-ice concentration
for the time interval indicated. Note that the shading level is reversed with that for the
previous figures.
50
(4)$Strong$polar$vortex$
(a)$
(3)$Weaker$ver2cal$$
wave$ac2vity$flux$into$$
the$stratosphere$
(1)$cooling$
(2)$Destruc2ve$$
interference$with$$
climatological$high$
961
(b)$
(4)$weaker$polar$vortex$
(3)$Stronger$ver3cal$$
wave$ac3vity$flux$into$$
the$stratosphere$
(1)$warming$
(2)$Construc3ve$$
interference$with$$
climatological$high$
962
963
964
965
966
967
968
969
Figure 14: A schematic depiction of the mechanism proposed by this study for the linkage
between Arctic sea-ice anomalies and changes in the strength of the stratospheric polar
vortex for (a) SOM1 and (b) SOM3. For SOM1, the picture presented suggests that the
cooling which arises when there is an increase is sea ice is balanced by warm thermal
advection. The phase of the anomaly that generates this advection coincides with
destructive interference, weaker vertical wave activity propagation, and an acceleration of
the stratospheric polar vortex. SOM3 shows the opposite features.
51