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  • View in gallery

    Relative frequency distributions of WPA (m s−1) showing the intensity during the RWP lifetime (black) and its maximum lifetime (red) of all significant RWPs in the NH (thick) and SH (thin).

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    The probability of significant (shaded) and extreme (contoured every 0.5% beginning at 1.0%, in black) RWPs (those exceeding 30 m s−1) at any time. For future reference, the three major regions of the NH are also marked and labeled.

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    Annual average number of RWP observations within 10 great circle degrees observed in this study for (a) all significant RWP formation points, (b) all significant RWP dissipation points, and (c) all formation points for extreme RWPs.

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    Number of significant (blue) and extreme (red) RWPs per year forming in each calendar month for (a) the entire NH, (b) the entire SH, and the (c) North Atlantic and (d) North Pacific regions.

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    Monthly average RWP activity [RWP activity; m s−1 (6 h)−1] for (a) January, (b) April, (c) July, and (d) October in the NH, obtained by calculating the WPA at each grid point and each time step in the common satellite era in excess of the minimum tracking threshold (14 m s−1), with all lesser values counting for zero and dividing by the number of time steps in each month.

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    As in Fig. 5, but for the SH.

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    (a) Zonal group velocities of significant RWP centroids obtained using an 18-h running average of their zonal movements and averaging all centroids within 10° of a given grid point, masking out all grid points for which fewer than 100 observations can be found and (b) annual average 300-hPa zonal wind (both in m s−1) as a point of comparison (with regions favoring easterly winds masked out).

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    Scatterplots (with linear regression lines) relating (a) RWP size (107 km2) to RWP amplitude (m s−1), (b) RWP propagation (degrees longitude) to RWP duration (6 h), (c) duration to maximum RWP amplitude, and (d) zonal group velocity (m s−1) to RWP amplitude. The r values shown in each plot are Spearman correlation coefficients.

  • View in gallery

    Significant (red) and extreme (blue) RWP formations by winter-centered year (NH: from July to June, SH: from January to December), listed as the year of the final month for (a) the entire NH, (b) the entire SH, and the (c) North Pacific and (d) North Atlantic regions.

  • View in gallery

    Interannual variability in RWP activity volume, where (a) activity volume (in 1011 km2 m s−1) is summed over the entire winter-centered year for each of four regions and (b) activity volume (in 1010 km2 m s−1) is summed over just the months of December–February.

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    A comparison of average RWP activity [m s−1 (6 h)−1] in varying states of ENSO as measured by the MEI. (a) The RWP activity is shown for ENSO neutral winter (DJF) seasons. The difference is shown between RWP activity in (b) El Niño and (c) La Niña seasons. El Niño is defined as any year in which the winter mean MEI is greater than 0.5, and a La Niña is defined as any year in which the winter mean MEI is less than −0.5. Shading is every 0.5 m s−1 (6 h)−1 in (a) and every 0.2 m s−1 (6 h)−1 in (b),(c).

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    Single-point correlation analysis relating WPA anomaly (the difference between the WPA values at each grid point and the climatological average for the time being measured) with the smoothed daily AO index value (shaded every 0.02). In each figure, the data were randomly drawn from all cool season (September–April, SONDJFMA) days such that the total sample being analyzed was th the size of the available dataset to avoid interdependence, and the correlation coefficients that were not statistically significant with a p value of >0.95 were masked out.

  • View in gallery

    Representative 300-hPa geopotential height (contoured every 24 dam and obtained by adding the composite height anomaly to the DJF mean height), probability of WPA exceeding 14 m s−1 (shaded every 0.1), and regions where the differences between the probability of exceedance and the DJF mean probability are statistically significant (p < 0.05), with positive anomalies in red and negative anomalies in blue. The composite includes 59 cases where the 5-day-smoothed AO index began above 1.0, fell at least 3, and ended below −1, centered on the day where the AO dropped below zero. Shown are (a) 5 days prior to center date, (b) 3 days prior, (c) 1 day prior, and (d) 2 days after.

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    As in Fig. 12, but for those days in which the 5-day average AO index (a) decreases from positive to negative or (b) increases from negative to positive.

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    (a) The rate of change in winter (DJF) seasonal RWP activity throughout the climatology (1979/80–2009/10) expressed as the slope of the single-point least squares regression line fitting a time series of mean RWP amplitude values for each point and (b) a single-point correlation analysis for each point in the NH using the same data with insignificant Spearman correlation coefficients (p > 0.05) masked out.

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The Climatology and Characteristics of Rossby Wave Packets Using a Feature-Based Tracking Technique

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  • 1 School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York
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Abstract

This paper describes an objective, track-based climatology of Rossby wave packets (RWPs). NCEP–NCAR reanalysis wind and geopotential height data at 300 hPa every 6 h were spectrally filtered using a Hilbert transform technique under the assumption that RWPs propagate along a waveguide defined by the 14-day running average of the 300-hPa wind. Track data and feature-based descriptive statistics, including area, average intensity, intensity volume (intensity multiplied by area), intensity-weighted centroid position, and velocity, were gathered to describe the interannual, annual, seasonal, and regime-based climatology of RWPs. RWPs have a more pronounced seasonal cycle in the Northern Hemisphere (NH) than the Southern Hemisphere (SH). RWPs are nearly nonexistent in the summer months (June–August; JJA) in the NH, while there is nearly continuous RWP activity downstream of South Africa during austral summer (December–February; DJF). Interannual variability in RWP frequency and intensity in the Northern Hemisphere is found to be strongly connected with the large-scale flow regimes such as El Niño–Southern Oscillation and the Arctic Oscillation. Enhanced RWP activity is also found to coherently propagate from the Pacific into the Atlantic on average when the Arctic Oscillation switches from a positive to a negative phase. No significant long-term (~30 yr) trend in RWP frequency, activity, or amplitude is found.

Corresponding author address: Dr. Brian A. Colle, School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794-5000. E-mail: brian.colle@stonybrook.edu

Abstract

This paper describes an objective, track-based climatology of Rossby wave packets (RWPs). NCEP–NCAR reanalysis wind and geopotential height data at 300 hPa every 6 h were spectrally filtered using a Hilbert transform technique under the assumption that RWPs propagate along a waveguide defined by the 14-day running average of the 300-hPa wind. Track data and feature-based descriptive statistics, including area, average intensity, intensity volume (intensity multiplied by area), intensity-weighted centroid position, and velocity, were gathered to describe the interannual, annual, seasonal, and regime-based climatology of RWPs. RWPs have a more pronounced seasonal cycle in the Northern Hemisphere (NH) than the Southern Hemisphere (SH). RWPs are nearly nonexistent in the summer months (June–August; JJA) in the NH, while there is nearly continuous RWP activity downstream of South Africa during austral summer (December–February; DJF). Interannual variability in RWP frequency and intensity in the Northern Hemisphere is found to be strongly connected with the large-scale flow regimes such as El Niño–Southern Oscillation and the Arctic Oscillation. Enhanced RWP activity is also found to coherently propagate from the Pacific into the Atlantic on average when the Arctic Oscillation switches from a positive to a negative phase. No significant long-term (~30 yr) trend in RWP frequency, activity, or amplitude is found.

Corresponding author address: Dr. Brian A. Colle, School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794-5000. E-mail: brian.colle@stonybrook.edu

1. Introduction

a. Background

A Rossby wave packet (RWP) is a region of high-amplitude meridional flow in the upper troposphere associated with downstream development at scales larger than a single synoptic eddy (Hakim 2003; Lee and Held 1993). RWPs have been linked to the downstream propagation of model errors and uncertainties (Zheng et al. 2013; Hakim 2005), large-scale regime changes (Archambault et al. 2013; Li and Lau 2012), and extreme weather events (Martius et al. 2008). Several studies have investigated the structure and evolution of RWPs (Chang 2001; Chang 2005; Glatt and Wirth 2014; Hakim 2003). For example, RWPs in the North Pacific commonly form over or just east of Japan and Siberia and they are slightly less intense and initiate farther east in the midwinter (i.e., January) than during the transition seasons (Hakim 2003). Upstream short-wave troughs and their baroclinic amplification are usually the trigger for RWP formation (Hakim 2003). RWP movement is largely explained by the divergence of the fluxes of local eddy kinetic energy and ageostrophic geopotential (Chang 2001). RWPs are important to the maintenance of the eddy-driven jets (Chang 2005). RWPs require a waveguide to reduce meridional dispersion—a tight meridional gradient in potential vorticity (PV) and/or tropopause height (Martius et al. 2010). Therefore, the stronger PV gradient during winter favors more prevalent and longer-lived RWPs than during summer (Chang 1999). Also, since an RWP begins with an initial meridional perturbation (usually a baroclinic eddy), RWPs follow the storm tracks (Blackmon et al. 1984), and changes in their movement or intensity reflect changes in the storm tracks (Chang 1999).

The genesis of RWPs has also been connected to tropical cyclones (TCs) undergoing extratropical transition (ET) and downstream development. Case studies have suggested that a strong waveguide is necessary in order to develop an RWP downstream of a recurving tropical cyclone (Harr and Dea 2009; Anwender et al. 2008). Anwender et al. (2008) demonstrated that RWPs associated with an extratropical transition were often linked to relatively large model forecast uncertainty over the NH. Also, Archambault et al. (2013) found that the tendency for TCs undergoing ET to amplify the downstream flow is seasonally dependent—more common in boreal autumn than boreal summer—implying that the background flow regime is very important in the formation of an RWP from an ET.

RWPs have been linked to large-scale flow regime changes. Chang (2005) suggests that much of the forcing of the mean flow by eddy heat and momentum fluxes occurs within RWPs. RWPs have also been shown to be important in the formation of flow-blocking events (Altenhoff et al. 2008). Martius et al. (2007) classified Rossby wave–breaking events as those regions where the meridional gradient in absolute vorticity became negative. Their observations suggest that both poleward-breaking and equatorward-breaking waves are frequently associated with strong RWPs. It has been suggested that the North Atlantic Oscillation (NAO) may be the result of variability in the frequency of wave-breaking events in the North Atlantic and likewise in the Pacific for the western Pacific pattern (Woollings et al. 2008). It is even suggested that cyclonic (anticyclonic) wave breaks in the North Atlantic are responsible for negative (positive) NAO conditions (Riviere and Orlanski 2007). Since the NAO and the Arctic Oscillation (AO) are frequently linked to each other (Feldstein and Franzke 2006), and wave breaking may be linked to RWPs, RWPs may play a role in changes in the AO. It was found that the frequency of wave breaking has a statistically significant correlation with the magnitude of the AO (Strong and Magnusdottir 2008).

Similarly, changes in the large-scale seasonal climate have been linked to changes in the storm tracks and in cyclone behavior. The El Niño–Southern Oscillation (ENSO) has long been the most important tool for seasonal forecasters as it is very strongly featured in the predictability of storm tracks (Compo and Sardeshmukh 2004). The different flavors of ENSO have been linked to variations in the storm track (Trenberth and Smith 2009). ENSO and the Indian Ocean dipole have been linked to large-scale changes in SH storm tracks (Ashok et al. 2007).

It is important to produce a climatology of the genesis and distribution of RWPs given their influence on forecast uncertainty in the medium range (3–10 day) (Anwender et al. 2008; Majumdar et al. 2010) and the potential future changes in the storm track through the twenty-first century (Chang 2013). Recently, Glatt and Wirth (2014) constructed a climatology of RWPs using a Hovmöller diagram approach. They identified annual peaks in RWP frequency over the western Pacific and U.S. East Coast, with the East Coast more favored during the summer. Long-lived RWPs tended to originate over the western Pacific. Using Hovmöller diagrams to track RWPs can be problematic when more than one active waveguide exists along a single longitude, and there are sensitivities with the parameters used to define the objects. As a result, Souders et al. (2014) developed a tracking algorithm for RWPs that includes both a point-based object detection scheme and feature-based tracking rules. They verified RWPs manually over several months and found a probability of detection of ~93% and a false alarm probability of around 20%. About 75% of the false alarm events occurred during the merging and splitting of RWPs, which are difficult to track. Although Souders et al.’s (2014) tracker has some uncertainty, it is robust enough to produce a climatology of RWPs based on spatial tracking as has been done with surface cyclones, and so the results can be compared with the Hovmöller-based approach. It is also hypothesized that spatial tracking of RWPs will provide additional information about regime changes, storm track variability, and longer-term climate change than will the Hovmöller tracking approach.

b. Motivational questions

RWPs are important in weather prediction, since they have been shown to be related to downstream extreme weather events as well as forecast error development. They are also important in our understanding of atmospheric dynamics, since they serve as a dynamical link between individual Rossby waves and the storm tracks. RWPs are likely important in understanding storm-track variability from the decadal to the intraseasonal time scales. Tracking RWPs may also extend our understanding of changes in the large-scale flow regime. Therefore, this paper describes an objective climatology based on the automated tracking of RWPs for the common satellite era (1979–2010), which will help to address the following questions:

  • What are the basic characteristics of RWPs? Where do they preferentially form and dissipate? How long do they last? How far do they travel? What differences are there between this spatial climatology and that developed following the Hovmöller approach used by Glatt and Wirth (2014)?
  • How does the RWP climatology relate to the spatial and temporal changes in the seasonal hemispheric storm tracks in the NH and SH? Are these changes related to common large-scale flow patterns such as ENSO and the AO?
  • What role do large terrain features such as the Tibetan Plateau and the North American Rockies have on RWPs?
  • Do RWPs play a role in sudden changes in the large-scale flow regime, as has been suggested by previous studies of wave-breaking events and flow-blocking events?
  • Have there been any significant changes in the intensity, frequency, or position of RWPs during the common satellite era?

Section 2 will briefly describe the data and tracking methods used in the creation of the climatology of RWPs. Section 3 presents the climatology of RWPs and section 4 summarizes the conclusions drawn from this research.

2. Data and methods

To track the RWPs, wind and geopotential height data every 6 h at 300 hPa from the 2.5° National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset (Kalnay et al. 1996) from 1979–2010 were obtained, since at the start of this study the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) was only available to 1989, and the Climate Forecast System Reanalysis was not yet available. Wave packet amplitude (WPA) was calculated using the method prescribed by Zimin et al. (2006), in which the 300-hPa meridional wind was filtered using a Hilbert transform technique, and synoptic wavenumbers 3–11 were isolated along 28-day-mean streamlines (using meridional and zonal winds). Unlike Zimin et al. (2006), a 28-day running mean was used to calculate the streamflow along which RWPs traveled rather than a 14-day running mean in order to better capture the periodicity of changes in long-wave flow regimes. Some smoothing of the data was required to track signals at the RWP scale defined by Hakim (2003). A T21 Cholesky filter (Hodges 1999) was applied to the WPA data in order to filter out wavelengths too small to be associated with RWPs. A temporal smoothing (running mean) using a 24-h duration was also applied to the data in order to help track the progression of local maxima in RWPs (Souders et al. 2014).

Souders et al. (2014) describe the details of the RWP tracking approach so only a brief overview is provided below. The method uses a hybrid approach to track RWPs, by combining a point-based object identification similar to the cyclone-tracking scheme of Charles and Colle (2009), and an feature-based tracking approach similar to methods used in the tracking of tropical convective clouds (Arnaud et al. 1992). A minimum WPA threshold (14 m s−1), a minimum object size (40 grid points from 2.5°-resolution data), and a search range for track continuity (20°N/S, 30°W, and 90°E) were chosen. Sensitivity of the tracking results to changes in these parameters is discussed in Souders et al. (2014). The algorithm identifies unique RWPs by selecting significant local maxima in WPA (more detail on the meaning of significance follows), attributing the object space inside the minimum tracking threshold to each local maximum, and then tracking the objects by searching for significant overlap across a single time interval.

The first step for the hybrid tracking was feature selection. Objects were defined using the minimum-tracking threshold (WPA = 14 m s−1). If a closed contour at the tracking threshold could be drawn that included just one local maximum, all grid points within that object were attributed to its local maximum. If more than one local maximum existed within a parent object, each maximum was tested for significance. If a WPA maximum could be encircled by a contour at 95% of its magnitude without capturing another more intense feature, it was retained as significant for tracking. If more than one maximum was found within the borders of a unique WPA object, a k-nearest-neighbor technique (Wilks 2011) was used to attribute grid points to their nearest significant tracking feature, thus creating “subobjects” within the entire object space that possessed unique track identifications (IDs). These subobjects were then tracked in time.

Once RWP objects were attributed to representative local maxima in WPA, the next step was object tracking using the 50% overlap technique of Arnaud et al. (1992). Any two objects in consecutive time steps were given the same track ID if they had local maxima within the predefined search range and had an overlap region that covers at least 50% of the area in either object. The current snapshot was used to search for candidates to merge with ongoing tracks in a previous time. When multiple subobjects claimed membership in an existing track, the existing track ID was given to the largest current object, or in the case of a tie in object size, the most intense object was given the existing ID, while in either case the smaller/weaker subobject was given a new track ID. To prevent multiple separate parent objects from being awarded the same track ID, the strongest WPA maximum with the same track ID in the current time was selected and a contour in WPA halfway between the minimum tracking threshold and the maximum WPA was drawn encircling this most intense feature. If any features with the same track ID failed to be enclosed within this contour, those weaker objects were renamed.

A final consideration was to determine which RWPs were significant enough for the climatology. A minimum propagation distance (40° longitude) and duration (2 days) were regarded as significant RWPs (Souders et al. 2014), and thus tracks not meeting these criteria were discarded. The end result was a database of RWP tracks and object parameters, such as RWP size (in square kilometers), zonal group velocity, maximum and area-averaged RWP amplitude (meters per second), duration (6-hourly time steps), and total eastward propagation (degrees longitude). Glatt and Wirth (2014) conducted a study with similar goals; however, they used the zonal approximation to calculate WPA as in Zimin et al. (2003), averaged WPA over a latitude band defined by the storm track, and followed RWPs using only longitude and time. As discussed in Souders et al. (2014) and Glatt et al. (2011), this method is not effective in split-flow regimes, suffers from the problem of nonzonal flows yielding imprecise WPA values, and misses opportunities to capture other information about RWPs besides intensity, zonal group velocity, and duration. Since the methodology used in this study differs significantly from that employed by Glatt and Wirth (2014), comparisons between results of the two studies can reveal how robust these results are.

3. Climatology of Rossby wave packets

a. RWP amplitudes and spatial distribution

This section describes RWP amplitudes using WPA and the spatial distribution of RWPs in the NH and SH. RWPs are very common, with ~6000 cases observed globally from 1979 to 2010 (~200 yr−1). Figure 1 shows the maximum RWP amplitudes found in the lifetimes of all significant RWPs by hemisphere compared with all local maxima found within significant RWPs by hemisphere (binned by 1 m s−1 intervals). The weakest RWPs are bounded by the minimum tracking threshold, while the most extreme RWPs have a WPA of 48–50 m s−1. These strongest RWPs (exceeding 45 m s−1) only occur roughly once every other year globally, and are more likely in the NH (13 of 19 RWPs with >45 m s−1 in the NH). The average RWP has a mean (median) peak amplitude of 27.1 (23.8) m s−1; however, the mean (median) amplitude of RWPs throughout their life cycles is only 22.3 (20.6) m s−1. RWP amplitude using all times is skewed toward larger values, while RWP amplitude is less skewed for the most intense time of the RWPs. The 95th percentile in RWP amplitude is 29.7 m s−1 and those WPA values above 30 m s−1 will be referred to below as the “extreme” RWP events. After bootstrapping the RWP amplitude values in each hemisphere (Wilks 2011), the mean probability of RWP amplitude greater than 30 m s−1 is 0.065 in the NH and 0.041 in the SH, with no overlap in the 90% confidence intervals (not shown). There are also statistically significantly more RWPs with RWP amplitude <20 m s−1 in the NH, while RWPs of moderate intensity (20–30 m s−1) are more likely in the SH.

Fig. 1.
Fig. 1.

Relative frequency distributions of WPA (m s−1) showing the intensity during the RWP lifetime (black) and its maximum lifetime (red) of all significant RWPs in the NH (thick) and SH (thin).

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

Figure 2 shows the annual probabilities of exceeding the 14 and 30 m s−1 WPA thresholds per grid cell within all significant RWPs. As expected, the RWP frequency maximum is closely tied to the midlatitude storm tracks of the North Pacific, western Atlantic, and in the southern Indian Ocean. The climatology suggests a high level of interaction between the North Pacific and Atlantic storm tracks, as suggested by Hoskins and Hodges (2002), and less horizontal variations in RWP frequency in the SH than the NH, consistent with the results shown in Trenberth (1991). Although the southern Indian Ocean storm track belt is the most active globally when considering all significant RWP activity, with annual significant RWP probabilities approaching 50%, extreme RWPs occurred more commonly over the North Atlantic, where there is a 2.5%–3% chance for an extreme RWP to occur when averaged over the course of the year. Meanwhile, over the North Pacific and southern Indian Ocean storm tracks, extreme events occur 2% and 1.5% of the time, respectively. This may be due to the North Atlantic receiving significant wave activity from the North Pacific storm track, as shown by the studies of Orlanski and Sheldon (1995) and Hakim (2003). With another region of active cyclogenesis over the western Atlantic, upstream Pacific RWPs may serve as the seeds for extreme Atlantic RWPs. The SH does not have as many topographic barriers and thus its storm track is relatively continuous; therefore, the SH also lacks distinct regions of climatologically favored RWPs other than in the southern Indian Ocean.

Fig. 2.
Fig. 2.

The probability of significant (shaded) and extreme (contoured every 0.5% beginning at 1.0%, in black) RWPs (those exceeding 30 m s−1) at any time. For future reference, the three major regions of the NH are also marked and labeled.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

Figure 3 shows the number of RWP formation and dissipation points, as well as the formation points for extreme RWPs during the common satellite era. Formation occurs at the first intensity-weighted RWP center (centroid) listed in a track and dissipation occurs at the last centroid position. The most common formation regions for RWPs in the NH are at 140°E–170°W and 80°–60°W (Fig. 3a). In contrast, Glatt and Wirth (2014) found that RWPs most commonly formed at 115°–145°E and 100°–80°W in the NH (a shift of 15°–45° upstream from our results). The primary reason for this difference is the manner in which starting points are chosen. Our study uses the first RWP centroid as the point of formation, whereas Glatt and Wirth (2014) used the first longitude showing WPA above the tracking threshold. The difference between a centroid and the western edge of an RWP can be as much as 30°–60° in longitude. Similarly, Glatt and Wirth (2014) found that RWPs tended to dissipate near the prime meridian (10°W–10°E) and at 110°–130°W, whereas our study finds RWPs dissipating at 60°W–20°E and 120°–150°W (Fig. 3b). Glatt and Wirth (2014) used the last longitude showing significant WPA, whereas we use the last centroid location.

Fig. 3.
Fig. 3.

Annual average number of RWP observations within 10 great circle degrees observed in this study for (a) all significant RWP formation points, (b) all significant RWP dissipation points, and (c) all formation points for extreme RWPs.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

As discussed in Souders et al. (2014), an RWP can form in four distinct ways: it may begin independently from a synoptic eddy, it may form when upstream energy “seeds” the entrance region of a storm track with a strong waveguide in place, or it may split off from a parent RWP either at its leading or trailing edges. Figure 3c highlights the genesis locations of extreme RWPs. Extreme RWPs rarely form in the North Atlantic, but rather originate in the North Pacific more than 70% of the time. Although extreme RWPs in the NH largely form in the Pacific, they often do not achieve their maximum intensity until they reach the western Atlantic, as inferred from Fig. 2, showing extreme RWPs to be much more common in the North Atlantic than the North Pacific. It is also worth noting that RWPs form much more commonly in the Pacific than the Atlantic basins in the NH, and that the distribution of RWP formations is significantly different from the distribution of cyclone formations found by previous studies such as that of Hoskins and Hodges (2002) (not shown). Hoskins and Hodges (2002) found distinct cyclogenesis regions such as the central North Pacific, central North America, and central Europe that are not identifiable in Fig. 3a.

RWPs exhibit a significant seasonal cycle. Figure 4 shows the frequency of significant and extreme RWP formation by month in the NH and SH, as well as in the North Atlantic (10°–80°N, 120°W–0°) and North Pacific (10°–80°N, 120°E–120°W) basins separately. While both hemispheres have a notable minimum in RWP activity in their respective summer months, the reduction during the NH summer [June–August (JJA); an 80% loss in total formations in Fig. 4a] is more pronounced than during the SH summer [December–February (DJF); a ~40% drop in formations in Fig. 4b]. Our choice of 300 hPa as the level at which to track RWPs may contribute somewhat to the loss of activity in the summer months, since the tropopause lifts to 200–250 hPa during the NH warm season; however, meridional winds at 250 hPa are only 5%–10% stronger than those at 300 hPa, and this is unlikely to significantly alter the overall distribution. Meanwhile, extreme RWPs do not occur in the NH summer, while there are extreme events in the SH throughout the year, with little variation in their frequency. These differences are likely related to the continued presence of strong baroclinicity in the vicinity of the circumpolar jet near Antarctica, which favors a relatively active SH storm track and RWPs even during the austral summer. The North Atlantic (Fig. 4c) and North Pacific (Fig. 4d) regions are compared to the NH as a whole (Fig. 4a). Both the North Atlantic and North Pacific have higher formation frequency during the cool season months, and neither location shows a clearly identifiable peak between October and April.

Fig. 4.
Fig. 4.

Number of significant (blue) and extreme (red) RWPs per year forming in each calendar month for (a) the entire NH, (b) the entire SH, and the (c) North Atlantic and (d) North Pacific regions.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

Figure 5 shows the mean NH RWP activity, which is defined as the mean of the difference between gridded WPA and the 14 m s−1 minimum tracking threshold (with all values below the threshold set to zero), by month (January, April, July, and October). This index is chosen to represent the meridional wind magnitudes above the normal background velocities experienced outside of RWPs using the minimum tracking threshold to separate RWP signals from background noise. There is increasing RWP activity in boreal winter (Fig. 5a) and nearly zero RWP activity in summer (Fig. 5c); RWP activity also decreases in the North Pacific during the midwinter months (January shown in Fig. 5a), with heightened RWP activity in the fall (Fig. 5d) and spring (Fig. 5b). This suggests that the so-called midwinter suppression of the North Pacific storm track (Nakamura 1992) is linked to RWP amplitude, rather than frequency, since the total activity decreases while formation frequency remains nearly constant (as seen in Fig. 4d).

Fig. 5.
Fig. 5.

Monthly average RWP activity [RWP activity; m s−1 (6 h)−1] for (a) January, (b) April, (c) July, and (d) October in the NH, obtained by calculating the WPA at each grid point and each time step in the common satellite era in excess of the minimum tracking threshold (14 m s−1), with all lesser values counting for zero and dividing by the number of time steps in each month.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

During the midwinter months, there is a split in the storm track near the Tibetan Plateau, with some RWP activity south of Tibet in January that is not present during other months. This is likely related to the general equatorward drift of the jet stream during winter, with the subtropical jet providing a waveguide that allows the coherent propagation of RWPs across southern Asia (Chang and Yu 1999). A comparison between October and April reveals some major differences. In the fall, the Atlantic and Pacific storm tracks are very well connected by a belt of elevated RWP activity running across North America; whereas, in the spring, the Pacific and Atlantic RWPs evince a significant split over the United States and Canada and the central Pacific shows much higher average RWP activity than does the Atlantic.

Figure 6 examines the monthly climatology of mean RWP activity for the SH for the same months as Fig. 5 (and in the same order). Unlike the NH, the SH remains relatively active all year and does not exhibit an equatorward shift of the entire storm track during the winter months (though some equatorward shifting does occur in the South Pacific). For each month, the most active area for RWP genesis and overall activity is over the southern Indian Ocean and south of South Africa. The RWPs are most active around the SH during austral spring (Fig. 6a), but extreme RWPs are most likely during austral fall (April; not shown). Trenberth (1991) found that the SH storm track became latitudinally constrained and intense, with large baroclinicity in austral fall, and that those gradients decreased during winter, as variances in 300-hPa meridional winds increased equatorward while remaining elevated along 50°S. As a result, the only change to the pattern in austral winter is a split in RWP activity in the South Pacific basin just south of Australia at around 120°E (Fig. 6d), which is likely related to the climatologically favored subtropical jet that develops downstream of Australia in the winter months (Trenberth 1991).

Fig. 6.
Fig. 6.

As in Fig. 5, but for the SH.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

b. Climatology of RWP characteristics

RWP duration and propagation were evaluated and the resulting climatological statistics broken down by hemisphere are shown in Table 1, which reveals some significant differences between the hemispheres. To determine the significance of these differences, a bootstrap statistical test (Wilks 2011) comparison of NH and SH RWPs was performed using a random sampling of 500 RWPs from each hemisphere and resampling 10 000 times. Because RWPs have such a large range of duration times and propagation distances, the inherent variances in samples drawn from this dataset were very large, especially since the results are so extremely skew toward larger values. As such, we have chosen a 90% confidence interval to test for significant differences. At this threshold, our analysis yielded a significant difference in the average propagation distance, but not the duration. A similar pattern emerges when we simply look at the shortest- and longest-lived RWPs found in this study. About 70% of all RWPs last for fewer than 8 days, but longer-lived RWPs survived more than 25 days (hereafter, long-lived RWPs). Obtained by hand tracking all RWPs found by two versions of the hybrid tracking scheme in Souders et al. (2014), the NH had 13 long-lived RWPs between 1979 and 2010 and the SH had 42. Despite the difference in the frequency of long-lived RWPs, in both hemispheres, the maximum durations were similar. However, RWPs making at least one complete circumnavigation of the globe (hereafter, long-tracked RWPs) were far more common in the SH, and the longest-tracked RWPs made four complete circumnavigations in the SH rather than three in the NH. Not shown in Table 1, every long-tracked RWP in the NH began in the western North Pacific, but there is no favored formation location in the SH. Also, while long-lived and long-tracked RWPs only occur during the cool season in the NH, they are a year-round phenomenon in the SH.

Table 1.

Climatological statistics for RWP duration and propagation obtained between 1979 and 2010.

Table 1.

It is important to note a few differences between this study and a recent climatology of RWPs tracked using a Hovmöller diagram approach (Glatt and Wirth 2014). Glatt and Wirth (2014) found the longest RWP durations between 1957 and 2002 were roughly 30 days, but occurred on the order of once every 100 months, and RWPs with lifetimes of as little as 10 days were still uncommon (one every 5 months). Our study finds 10-day RWPs occurring 2–3 times per month, and RWPs that last 30 days occurring about once every 12 months. This large discrepancy in the observed frequencies of long-lived RWPs is further confirmed by Souders et al. (2014), who determined using hand-tracking verification that the hybrid tracking technique used in this climatology was reliable, with false detection of RWPs of duration greater than 25 days being ~10%. This difference is likely caused by Glatt and Wirth (2014) using the Zimin et al. (2003) approach rather than that of Zimin et al. (2006) to calculate WPA. The approach of Zimin et al. (2003) assumed that all RWPs would propagate zonally, while their 2006 updated method created streamlines along which the Hilbert transform is conducted using a 28-day mean of the 300-hPa winds. As a result, Glatt and Wirth (2014) noted that areas of large WPA often erroneously split in the presence of broad ridges. Tracking WPA using 28-day running mean streamlines corrects for this problem, as shown by Zimin et al. (2006), and Souders et al. (2014) noted that using a 14-day running mean also worked well.

Figure 7 compares the average zonal group velocities of RWPs based on their observed centroid positions, located within 10 great circle degrees of a point and only for those points containing at least 100 observed RWP centroids to avoid values heavily biased by a few extreme cases, as compared with the annual average mean zonal wind speeds at 300 hPa. Zonal group velocities were calculated by finding the zonal displacement of each RWP in the previous 18 h and calculating the zonal velocity as in Hodges (1999) in order to smooth out variations in speed. The regions of highest RWP zonal velocity (25–40 m s−1) are areas just upwind of climatologically favored 300-hPa zonal jets, though the overlap is not perfect. These storm track entrance regions are frequently associated with the pulselike eastward movement in these RWPs, as described in Souders et al. (2014). This occurs when an existing RWP encounters regions favoring rapid cyclogenesis, with the RWP subsequently strengthening at its leading edge and spreading eastward rapidly, thereby creating an apparent increase in zonal group velocity. In general, RWPs move eastward at 15–25 m s−1 in regions collocated with climatologically favored positions of the strongest jets, and slower (10–15 m s−1) at the ends of storm tracks, over large mountain ranges, and in areas that favor blocked flow regimes (e.g., south of Alaska, Greenland, and Australia; over the Rocky Mountains; and just upstream of the Tibetan Plateau). Overall, RWPs move at about the same speed as the climatological zonal jets. Previous studies of individual RWPs have suggested faster speeds in some cases, but Glatt and Wirth (2014) found the average zonal group velocity of RWPs to be only marginally faster (25–30 m s−1).

Fig. 7.
Fig. 7.

(a) Zonal group velocities of significant RWP centroids obtained using an 18-h running average of their zonal movements and averaging all centroids within 10° of a given grid point, masking out all grid points for which fewer than 100 observations can be found and (b) annual average 300-hPa zonal wind (both in m s−1) as a point of comparison (with regions favoring easterly winds masked out).

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

Figure 8 highlights how some of these characteristics of RWPs are related to each other by correlating RWP characteristics two at a time. RWP amplitude is correlated with RWP size with a correlation coefficient r of 0.75 and a p value of <0.005 (Fig. 8a), revealing that extreme RWPs tend to be larger than ordinary RWPs. RWP duration (in 6-hourly time steps) is correlated with RWP propagation in degrees longitude (Fig. 8b), with a statistically significant positive correlation (r = 0.73) and a p value of <0.005. RWP amplitude is correlated with RWP duration (Fig. 8c) with a positive correlation (r = 0.52) and a p value < 0.005, meaning that stronger RWPs tend to last longer and travel farther (not shown). RWP amplitude is not significantly correlated with 18-h-averaged RWP centroid zonal velocity (Fig. 8d), suggesting that RWP velocities depend more on the jet wind speed, not on other RWP characteristics. There are RWP velocities <0 (westward) in Fig. 8d, but that this does not necessarily imply upstream dispersion/propagation. To obtain one position for an RWP, the centroid position was used to measure zonal group velocity. In cases where the RWP weakened substantially at its leading edge, split, losing a large portion of its leading edge, or merged with an RWP arriving from upstream, the centroid may drift or even jump westward for a time, leading to some negative zonal velocities.

Fig. 8.
Fig. 8.

Scatterplots (with linear regression lines) relating (a) RWP size (107 km2) to RWP amplitude (m s−1), (b) RWP propagation (degrees longitude) to RWP duration (6 h), (c) duration to maximum RWP amplitude, and (d) zonal group velocity (m s−1) to RWP amplitude. The r values shown in each plot are Spearman correlation coefficients.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

c. Interannual variability of RWPs

RWP frequency has relatively large interannual variability. Figure 9 shows significant and extreme RWP counts in the NH and SH by year (centered in the winter months such that a “year” in the NH runs from July to June and from January to December in the SH), as well as in the North Atlantic and North Pacific basins. There is no trend in the frequency of significant or extreme RWP formation in any region. The interannual variability in formation frequency in the NH (Fig. 9a) as measured by annual variance is 23.8 formations (the mean formation count is 82.6), with similar variances found in the SH (Fig. 9b), North Pacific (Fig. 9c), and North Atlantic (Fig. 9d) basins, respectively.

Fig. 9.
Fig. 9.

Significant (red) and extreme (blue) RWP formations by winter-centered year (NH: from July to June, SH: from January to December), listed as the year of the final month for (a) the entire NH, (b) the entire SH, and the (c) North Pacific and (d) North Atlantic regions.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

To explore the possible sources of interannual variability in RWPs, Fig. 10a highlights the annual RWP activity volume (defined by multiplying the size of each RWP with its RWP activity and attributing each packet to a basin based on its centroid position) in the NH, SH, North Pacific, and North Atlantic regions (cf. Fig. 2 for region locations), while Fig. 10b shows the boreal winter activity in the Eurasian, North Pacific, and North Atlantic regions. The SH has experienced a steady climb in net RWP activity volume from 1979 to 2010, which is a gain of roughly 15% in average annual activity volume in the 2000s versus the 1980s. However, Guo and Chang (2008) found that the SH wind data were less reliable with an increasingly negative (slow) bias in wind speeds the farther back in time one looks. It is, therefore, not clear whether this positive trend is due to the increasing reliability of NCEP–NCAR reanalysis wind speeds (Guo and Chang 2008), or whether there is some real climate shift in the SH, such as the observed trend in the SH toward the positive phase of the southern annular mode (e.g., Thompson and Solomon 2002). The lack of a similar increase in RWP activity volume in the NH, or any sign of longer-term variability, casts doubt on the veracity of the SH trend. A simple correlation between the activity volume values shown in Fig. 10b for the NH and the formation frequencies for significant RWPs during the midwinter (DJF) months was performed (not shown). There is no significant correlation (r = 0.30, p = 0.38), suggesting that some seasons may produce many weaker RWPs (and low overall activity volume), while others produce a few stronger packets (and higher RWP activity volume). No region shows any significant correlation to any other region when comparing either RWP activity volume or formation frequency, annually or in just the midwinter months (not shown). Based on the lack of clear patterns in RWP activity volume and formation frequency variations, a further examination of the sources of RWP variability is required.

Fig. 10.
Fig. 10.

Interannual variability in RWP activity volume, where (a) activity volume (in 1011 km2 m s−1) is summed over the entire winter-centered year for each of four regions and (b) activity volume (in 1010 km2 m s−1) is summed over just the months of December–February.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

Large-scale flow regimes defined by teleconnection indices might explain some portion of the variability in RWP activity, or the relationship may be reversed, with RWPs explaining changes in the flow. Since ENSO has been linked to changes in the NH storm tracks (e.g., Chang et al. 2002; Orlanski 2005), as well as the AO (Chang and Fu 2002), it is hypothesized that the primary coherent mode of RWP variability is in its geographical distribution—where they are found, rather than how strong or frequent they are—and that this variability is driven primarily by ENSO and the AO. To test these claims further, several teleconnection indices were examined from 1979 to 2010 in order to determine if there is a common pattern in RWP activity associated with a particular large-scale flow regime. The primary mode of low-frequency variability is ENSO (McCreary and Anderson 1984), and Fig. 11 shows the role of ENSO in varying the distribution of RWP activity in the NH. The 3-month-average values of the multivariate ENSO index (MEI; Wolter and Timlin 1993, 1998) were used to select El Niño and La Niña seasons (MEI exceeding 0.5 or less than −0.5, respectively). Figure 11a shows the normal boreal winter (DJF) RWP activity during near-neutral ENSO conditions (−0.5 < MEI < 0.5). Figure 11b shows the difference in RWP activity between El Niño and ENSO neutral conditions (positive values are regions where RWP activity is enhanced during El Niño years). The differences were tested for statistical significance by finding the mean WPA for each grid point spatially in each time step of a calendar year for the 32-yr climatology period, removing the seasonal cycle with a simple 14-day running-average filter and calculating the standard deviation in WPA that remains. Differences falling short of 2 times these standard deviations were then masked out for each grid point. During El Niño, there is increased activity in the southern North Pacific near and east of Hawaii, a slight increase in activity over Canada and Alaska, and a general reduction of RWP activity in the rest of the midlatitude storm tracks. Figure 11c shows the changes in activity during La Niña seasons relative to near-neutral years. The main difference appears to be a northward shift in RWP activity throughout the Pacific and a reduction of activity over much of the Atlantic basin, rather than a shift in the Atlantic track.

Fig. 11.
Fig. 11.

A comparison of average RWP activity [m s−1 (6 h)−1] in varying states of ENSO as measured by the MEI. (a) The RWP activity is shown for ENSO neutral winter (DJF) seasons. The difference is shown between RWP activity in (b) El Niño and (c) La Niña seasons. El Niño is defined as any year in which the winter mean MEI is greater than 0.5, and a La Niña is defined as any year in which the winter mean MEI is less than −0.5. Shading is every 0.5 m s−1 (6 h)−1 in (a) and every 0.2 m s−1 (6 h)−1 in (b),(c).

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

Since we could not find a reliable connection between the MEI and RWP formation frequency, the two remaining potential explanations for these changes are the intensity of the RWPs and their typical tracks. El Niño years have a more active southern North Pacific storm track, but the main change seems to be a reduction in RWP intensity, since big losses in activity in the storm tracks are not replaced with large gains elsewhere. El Niño favors a split-flow regime over the eastern North Pacific, with an active subtropical jet (characterized by frequent weak eddies) and a tendency toward higher than normal heights across much of the North Pacific and central and northern North America. This should lead to a decrease in baroclinicity in the eastern North Pacific and Atlantic storm tracks, with a corresponding increase in baroclinicity along the subtropical jet, yielding the reduced RWP activity found in the primary storm tracks and increased activity near Hawaii, as suggested by Orlanski (2005). Meanwhile, La Niña has greatly enhanced RWP activity over the eastern North Pacific, likely due to a stronger Pacific jet. The same active Pacific jet favors more westerly winds across North America and the Pacific, leading to less intrusion of arctic air over Canada, and thus favoring less baroclinicity over eastern North America, a weaker Atlantic storm track, and reduced RWP activity.

Another major influence in winter flow regimes and storm activity is the AO (Chang and Fu 2002). Using single-point correlation analysis, one can explore the relationship between the AO and the position and intensity of common RWP tracks, and potentially even the role of RWPs in altering the AO. Figure 12 shows the significant (p < 0.05) correlations between the WPA anomaly and the 5-day-smoothed daily AO index. The daily AO index results were provided by the University of Washington (http://jisao.washington.edu/analyses0302/; T. Mitchell 2013, personal communication 2013). The daily values are the projection of the first principle component of monthly sea level pressure (SLP) anomalies, derived from monthly data, onto daily anomaly SLP fields. To avoid the problem of oversampling covariate data, 750 dates were sampled randomly (out of a possible 7752 days in the cool season of September–April during 1979–2010). Positive regions indicate positive correlations between the smoothed AO index and the WPA anomaly (meaning that positive AO conditions favor increased WPA). When the AO is positive, there is increased activity in and to the north of the main storm track regions throughout the NH, especially in the Pacific and Eurasian regions, suggesting that not only does RWP activity increase, but that there is a northward displacement. Although the correlations are statistically significant (p < 0.05), they are rather low in magnitude, explaining 1%–5% of the variance in the WPA anomaly. When the AO is negative, these regions become less active and Greenland becomes more active.

Fig. 12.
Fig. 12.

Single-point correlation analysis relating WPA anomaly (the difference between the WPA values at each grid point and the climatological average for the time being measured) with the smoothed daily AO index value (shaded every 0.02). In each figure, the data were randomly drawn from all cool season (September–April, SONDJFMA) days such that the total sample being analyzed was th the size of the available dataset to avoid interdependence, and the correlation coefficients that were not statistically significant with a p value of >0.95 were masked out.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

The link between RWPs and changes in the AO was also explored. Figure 13 is a composite of all events between November and March where the AO index began at greater than 1.0, fell at least three standard deviations within 10 days, and by the 11th day it was below −1.0 (56 cases in all). The day where the AO index became less than zero was the central reference date, and then the WPA and probability of WPA exceeding 14 m s−1 were composited. A robust RWP signal appears in the central and eastern Pacific (day −5 from the time of AO transition; Fig. 13a) and propagates into the western and northern North Atlantic (Fig. 13b) before dissipating in a more meridional flow regime (day −1; Figs. 13c,d). While not conclusive, this does strongly suggest that RWPs may play an important role in a rapid decrease in the AO, as originally suggested by Franzke et al. (2004) for the North Atlantic Oscillation, a teleconnection frequently related to the AO. Importantly, although, as was previously discussed in the introduction to this study, linkages between the NAO–AO and wave-breaking events or changes in the storm tracks have been found, a direct link between RWPs and an AO+ to AO− transition has never been shown.

Fig. 13.
Fig. 13.

Representative 300-hPa geopotential height (contoured every 24 dam and obtained by adding the composite height anomaly to the DJF mean height), probability of WPA exceeding 14 m s−1 (shaded every 0.1), and regions where the differences between the probability of exceedance and the DJF mean probability are statistically significant (p < 0.05), with positive anomalies in red and negative anomalies in blue. The composite includes 59 cases where the 5-day-smoothed AO index began above 1.0, fell at least 3, and ended below −1, centered on the day where the AO dropped below zero. Shown are (a) 5 days prior to center date, (b) 3 days prior, (c) 1 day prior, and (d) 2 days after.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

To confirm the results of the composite, each event was viewed and the significant RWPs tracked. In 56 cases, an extreme Pacific RWP (>30 m s−1) occurred 29 times within the first 5 days of the composite (~52% of the time, compared with cool season probabilities of extreme RWPs on the order of 5%). Only nine cases failed to produce any significant RWP activity in the Pacific (meaning 84% of cases had a significant RWP, compared with a cool season probability of significant RWPs of roughly 35%), suggesting that the strongest RWPs are most important in causing a rapid decline of the AO, and that the AO is unlikely to suddenly drop without the involvement of RWP activity. A similar composite has also been produced for periods where the AO rapidly rose (not shown), and no robust RWP signal is present.

Figure 14 highlights the correlation between the probability of WPA exceeding the minimum tracking threshold with the 5-day smoothed AO index, using only the 56 cases of positive to negative AO transition from day −10 to day +10 of the date when the AO first crossed zero (Fig. 14a) and the 81 cases of negative to positive transition (Fig. 14b). In the case of a positive-to-negative transition, there is a west–east separation in the correlation field, with values as high as 0.4 over Asia during positive AO days, while there is a high probability of significant RWPs over North America and Greenland during negative AO days. This suggests the coherent movement of RWPs from west to east during the transition to negative AO, similar to what was found in the composite (Fig. 13). In the case of the AO transition to positive (Fig. 14b), the pattern is considerably less clear. There is some hint of the breakdown of a high-latitude RWP signal, with increased activity over Greenland during AO− conditions and over central and southern Europe during AO+ conditions; however, the signal is much more local in scale and of considerably lower amplitude/significance, suggesting that RWP propagation is less important during positive transitions. Figure 14 provides further evidence that the formation of a negative AO is potentially driven by RWPs while the positive transition has a different source.

Fig. 14.
Fig. 14.

As in Fig. 12, but for those days in which the 5-day average AO index (a) decreases from positive to negative or (b) increases from negative to positive.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

It was also investigated whether there has been a significant change in RWP tracks, amplitude, or frequency related to longer-term climate shifts or decadal cycles like the Pacific decadal oscillation or the Atlantic multidecadal oscillation. Figure 15a shows the rate of change in mean WPA obtained from single-point linear regressions between DJF-mean WPA (by grid point) and year from 1979/80 to 2009/10 (Fig. 15a). The values plotted are the slope of the regression line at each grid point. A value of 0.1 means that the mean WPA has increased by 0.1 m s−1 yr−1, meaning that in the 30 years analyzed here, the mean WPA has increased by ~3 m s−1. Figure 15b shows the single-point correlations between the winter season and the RWP activity anomaly, with insignificant (p > 0.05) coefficients masked out. The southern North Pacific has seen a significant decline in RWP activity through time, consistent with a northward shift in the Pacific storm track; however, the gains in activity in the northern North Pacific have not yet increased to the level of statistical significance. There has also been a significant decline in RWP activity over Siberia, potentially related to the weakening of the East Asian winter monsoon (e.g., Wang et al. 2009). There is an important caveat that this is only 30 winter seasons’ worth of data. Figure 15 also illustrates that there has not been a noticeable increase in the intensity of any of the NH storm tracks.

Fig. 15.
Fig. 15.

(a) The rate of change in winter (DJF) seasonal RWP activity throughout the climatology (1979/80–2009/10) expressed as the slope of the single-point least squares regression line fitting a time series of mean RWP amplitude values for each point and (b) a single-point correlation analysis for each point in the NH using the same data with insignificant Spearman correlation coefficients (p > 0.05) masked out.

Citation: Monthly Weather Review 142, 10; 10.1175/MWR-D-13-00371.1

4. Summary and conclusions

Using an automated RWP tracking scheme applied to 6-h NCEP–NCAR reanalysis data from 1979 to 2010, the climatology and characteristics of RWPs were explored. RWPs were found to be tightly linked to climatologically favored storm-track belts in the North Pacific, North Atlantic, and to the east of South Africa in the SH. The NH seasonal cycle was found to be more significant than the SH seasonal cycle, as RWP activity in the NH almost vanishes during the summer months (JJA), while RWPs continue forming regularly in the SH summer (DJF) likely due to the constant presence of the circumpolar jet near Antarctica (Trenberth 1991). Extreme RWPs (>30 m s−1 maximum lifetime intensity) peak in November and January in the NH and April in the SH, with the bimodal spatial distribution of NH extreme RWPs that is likely related to peaks in the two different ocean basins. The North Pacific has a peak in RWP activity in November and March/April, with a midwinter suppression of RWP activity, possibly related to the influence of the Tibetan Plateau, while the North Atlantic reaches its peak in January. The genesis location of the extreme RWPs is centered over the western and central Pacific in the NH. In the SH, RWPs are most frequent in August, and the South Pacific storm track splits into a northern and southern branch downstream of Australia during austral summer.

The characteristics of RWPs were also studied and compared. RWPs have mean intensities of 22 m s−1 and are skewed toward stronger events, with peak amplitudes as high as 50 m s−1 in rare cases. RWPs last an average of 5.8 days in the NH and 7.8 days in the SH, traveling an average of 119° eastward (NH) and 151° (SH), respectively, and this difference in propagation distance is statistically significant. RWPs in the SH travel faster than their NH counterparts overall, with typical speeds of 20–25 m s−1 in the SH and 15–20 m s−1 in the NH. However, in both hemispheres, the fastest-moving RWPs were found just upstream of jet-entrance regions, where RWP energy can rapidly pulse eastward when an upstream region of enhanced baroclinic conversion forces the RWP to move rapidly eastward. Typical RWP zonal velocities are close to the mean zonal winds at 300 hPa. Extreme RWPs also tend to be larger than significant RWPs, long-lived RWPs tend to travel the farthest eastward, and extreme RWPs typically last the longest, and all these relationships have statistically significant positive correlations. On the other hand, no relationship between RWP zonal velocity and any of the other physical parameters was found, suggesting that RWPs move at speeds governed by the large-scale flow, not by their structure.

RWPs also have significant interannual variability, with RWP activity volume varying by as much as 25% from year to year. This variability cannot be readily explained by seasonal teleconnection patterns like ENSO, or the AO (not shown here). However, each of those teleconnection patterns does have a statistically significant correlation with spatial changes in RWP activity in the NH winter months. El Niño winters are associated with suppressed RWP activity in the primary storm tracks while not suppressing RWP formation frequency, suggesting that RWPs are simply weaker (by as much as 1 m s−1 on average, about the same as the difference between April and May RWP activity in the central North Pacific). El Niño conditions also favor a slight increase in activity near Alaska in the north and Hawaii in the south, hinting at the tendency for the Pacific jet to become split with an active, but mostly zonal southern jet, and higher than normal heights across northern North America. La Niña winters also suppress the North Atlantic storm track, especially north of 40°N; however, in the Pacific, a normal level of activity continues, with a northward displacement of the most common RWP tracks. The AO plays a key role in RWP activity in the storm tracks, with positive AO conditions favoring high activity in the main storm tracks, while negative AO conditions favor increased RWP activity over Greenland.

The role of RWPs in large-scale flow regime changes was also explored. The 56 events where the AO rapidly dropped from a high of greater than one standard deviation above normal to a low of at least one standard deviation below normal in 10 days or less were composited, centered on the date when the AO crossed the zero line. A robust (statistically significant) RWP-like enhancement in the probabilities of WPA exceeding the minimum tracking threshold was seen to propagate from the eastern Pacific (day −5) to the western Atlantic and Southeast United States (day −3) to the North Atlantic, with an apparently meridional flow regime at day −1. In fact, of 56 cases, 29 included extreme RWPs somewhere in the Pacific basin at day −5, while only 9 were devoid of any significant RWPs. This strongly suggests that a rapid drop in the AO is unlikely without the involvement of RWP activity in the Pacific, and is more likely with more extreme RWPs.

Future work will apply these tracking techniques to RWPs simulated by GCMs and forecasted by NWP models to investigate the similarities and differences between the observed RWP climatology and RWPs produced by these models. Given the importance of RWPs to the inherent predictability of cyclones, it is important to better understand the strengths and limitations of current NWP models/ensembles and GCMs in predicting RWPs. It is known, for instance, that current climate models struggle to reproduce the basic teleconnection patterns observed in the real atmosphere, and one reason could be that they are not simulating RWPs accurately. For operational forecasters, it would be similarly useful to know whether NWP models are accurately simulating RWPs.

Acknowledgments

This work is supported by NOAA-CSTAR (NA10NWS4680003). We thank the two anonymous reviewers for their thoughtful comments that helped improve the manuscript.

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