Forecasts of Pacific jet variability are used to predict
stratosphere-to-troposphere transport (STT) and tropical-to-extratropical
moisture export (TME) during boreal spring over the Pacific–North American
region. A retrospective analysis first documents the regionality of STT and
TME for different Pacific jet patterns. Using these results as a guide,
Pacific jet hindcasts, based on zonal-wind forecasts from the European Centre
for Medium-Range Weather Forecasting Integrated Forecasting System, are
utilized to test whether STT and TME over specific geographic regions may be
predictable for subseasonal forecast leads (3–6 weeks ahead of time). Large
anomalies in STT to the mid-troposphere over the North Pacific, TME to the
west coast of the United States, and TME over Japan are found to have the best
potential for subseasonal predictability using upper-level wind forecasts. STT
to the planetary boundary layer over the intermountain west of the United
States is also potentially predictable for subseasonal leads but likely only
in the context of shifts in the probability of extreme events. While STT and
TME forecasts match verifications quite well in terms of spatial structure and
anomaly sign, the number of anomalous transport days is underestimated
compared to observations. The underestimation of the number of anomalous
transport days exhibits a strong seasonal cycle, which becomes steadily worse
as spring progresses into summer.
Introduction
Mass transport is important to many aspects of Pacific–North American climate, including the following: stratosphere-to-troposphere transport (STT) of ozone to the
planetary boundary layer, which has negative impacts on human health (Fiore
et al., 2003; U.S. EPA, 2006; Langford et al., 2009; Lefohn et al., 2011); STT
to the free troposphere, which is needed to estimate the North American
background distribution of ozone (Fiore et al., 2014; Cooper et al., 2015;
Young et al., 2018); and water vapor transport, which contributes to
precipitation variability (Ralph and Dettinger, 2011; Mahoney et al., 2016;
Guan and Waliser, 2015; Gershunov et al., 2017). Because of these impacts,
identifying time periods when transport forecasts might be skillful on
subseasonal timescales (forecasts 3–6 weeks into the future) is recognized as
having high societal value (e.g., Lin et al., 2015; Baggett et al., 2017, and
references therein).
Skillful subseasonal transport forecasts hinge, in large part, on the skillful
prediction of atmospheric teleconnections (Baggett et al., 2017; DeFlorio
et al., 2019). Initial studies of subseasonal teleconnection variability
suggested that enhanced predictability might occur during spring when strong
El Niño–Southern Oscillation (ENSO) conditions are present (Barnston,
1994; Branković and Palmer, 1997). However,
more recent studies suggest that, overall, teleconnections (Wang and
Robertson, 2019) and transport (DeFlorio et al., 2019) on subseasonal
timescales tend to be most predictable during winter. Indeed, one reason to
expect predictability to be lower in spring is that Pacific teleconnection
patterns become increasingly sensitive to the location and scale of tropical
forcing as the Pacific jet undergoes its seasonal transition (Newman and
Sardeshmukh, 1998; Barsugli and Sardeshmukh, 2002; discussed in more detail
below).
Still, even if teleconnections and transport are more predictable during
winter on average, skillfully predicting the atmospheric circulation during
spring is important in the context of both STT and water vapor transport. For
example, STT of ozone that affects surface air quality occurs primarily during
spring (e.g., Lefohn et al., 2011; Langford et al., 2009, 2012; Olsen et al., 2013; Škerlak et al., 2014; Lin et al.,
2015). Likewise water vapor transport during spring is also important for many
regions of the Pacific basin and North America (e.g., Cayan and Roads, 1984;
Lee et al., 2014; Swain et al., 2016; Mundhenk et al., 2016). Thus, here we
seek to explore the circumstances whereby skillful transport predictions might
be possible during the important, yet potentially less predictable, spring
season.
Stratosphere-to-troposphere transport and water vapor transport occur via
distinct physical pathways. In midlatitudes, STT occurs mainly via two
mechanisms: stratospheric potential vorticity (PV) intrusions, which include
tropopause folds, PV streamers, and PV cutoffs (Reed and Danielson, 1958;
Hoerling et al., 1993; Langford and Reid, 1998; Shapiro, 1980; Sprenger
et al., 2007; Škerlak et al., 2015), and transverse circulations in jet
exit regions (Langford et al., 1998; Langford, 1999). Intense water vapor
transport events also arise via several distinct, though interrelated,
physical processes, including so-called “atmospheric rivers”, warm conveyor
belts, and tropical moisture exports (Zhu and Newell, 1998; Stohl and James,
2005; Knippertz and Martin, 2007; Knippertz and Wernli, 2010; Newman et al.,
2012; Madonna et al., 2014; Pfahl et al., 2014; Knippertz et al., 2013; Ralph
et al., 2018; Sodemann et al., 2020). In this study, we focus on spring season
STT that extends downwards to the mid-troposphere and planetary boundary layer
(PBL) and long-range tropical-to-extratropical water vapor transports,
hereafter referred to as tropical moisture export (TME; see Knippertz et al.,
2013, for a detailed discussion of TME).
Monthly average climatologies (1979–2014) of STT to the PBL (left
column), STT to 500 hPa (middle column), and TME (right column). Units for
all panels are event frequencies (events/6-hourly time step), for which each of
the relevant event types are defined in Sect. 2.2.
STT and TME have very different seasonal cycles in terms of timing and
geography, which is readily observed in monthly mean climatologies (Fig. 1;
see Sect. 2 for a detailed description of STT and TME, which are both taken
from the database of Sprenger et al., 2017). Over western North America, STT
of mass (and ozone) that reaches the PBL peaks in spring (Fig. 1, left column;
see also, Škerlak et al., 2014; Albers et al., 2018, and references
therein). Despite the strong storm track located over the North Pacific, deep
STT into the PBL is limited over the ocean due to a shallow marine boundary
layer. In contrast, STT of mass extending downwards into the middle
troposphere (500 hPa) peaks during January and February and then
slowly decreases thereafter (Fig. 1, middle column). TME also undergoes a
seemingly smooth transition during winter and spring, with an initial peak
extending from Hawaii to the western United States during February, followed by a slow
recession of transport westward, whereby a secondary peak occurs near Japan
during May (Fig. 1, third column; see also Knippertz and Wernli, 2010;
Mundhenk et al., 2016; Gershunov et al., 2017). The different regional and
temporal characteristics of the STT and TME seasonal cycles shown in Fig. 1
are in part a reflection of the different physical processes that govern them,
as outlined above. However, at least a portion of STT and TME seasonality and
variability are linked by one important commonality: they are both directly
modulated by large-scale Rossby waves (e.g., Ryoo et al., 2013; Albers et al.,
2018), which themselves owe their propagation and breaking patterns to the
strength and location of the subtropical and polar front jets (Hoskins and
Ambrizzi, 1993; Scott and Cammas, 2002; Abatzoglou and Magnusdottir, 2006;
Hitchman and Huesmann, 2007; Mundhenk et al., 2016; Olsen et al., 2019). For
example, high TME is often observed on the western edge of blocking
anticyclones in the North Pacific, where air is rising (Mundhenk et al.,
2016), while STT occurs east of the block, where sinking air and PV intrusions
frequently develop (Sprenger et al., 2007). This means that the variability
and, as we will show, the predictability of both types of transport are
dependent on the seasonal cycle of the Pacific jet.
Sometime between early March and late April, the Pacific jet undergoes a
transition – which typically occurs very abruptly – from being strong and
largely zonally contiguous between Asia and North America to being weak, with
a discontinuity in the jet that spans most of the Pacific basin (Nakamura,
1992; Newman and Sardeshmukh, 1998; Hoskins and Hodges, 2019; Breeden et al.,
2021). The characteristics of this transition, and its relationship to forms
of low-frequency variability that might be predictable on subseasonal
timescales (e.g., ENSO), have been explored in the context of the STT of mass and
ozone. For example, Breeden et al. (2021) demonstrated that early-season jet
transitions (mid-to-late March), which are more common during La Niña
conditions, are characterized by enhanced mass transport to the PBL (see also,
Lin et al., 2015, and references therein). Conversely, late transitions
(mid-to-late April) have weaker transport to the PBL, although the association
to El Niño is somewhat weaker. However, these analyses are retrospective,
and it remains unclear whether forcings such as ENSO – and the resulting
teleconnections – are actually forecast well enough to be useful when making
subseasonal transport predictions.
While the predictability of mass transport on daily timescales is typically
limited to less than 2 weeks (Lavers et al., 2016; DeFlorio et al., 2018),
weekly averages of dynamical variables can occasionally have skill out to
3–6 weeks (e.g., Wang and Robertson, 2019; Buizza and Leutbecher, 2015;
Albers and Newman, 2019). This evokes the possibility that forecasts of
atmospheric transport, which may be harder for models to explicitly predict on
subseasonal timescales, might be successfully inferred from forecasts of more
predictable or better constrained dynamical variables. Indeed, similar ideas
have been successfully applied to assess the predictability of atmospheric
blocking on seasonal timescales (Pavan et al., 2000) and precipitation on
daily timescales (Lavers et al., 2014, 2016). Here we assess the potential
predictability of transport during spring based on the predictability of zonal
wind variance associated with the Pacific jet. We do so by considering a very
simple conditional probability: if 200 hPa zonal winds have a high
(positive or negative) loading on a particular 200 hPa Pacific basin
zonal wind pattern, then what will the corresponding shift in the probability
of STT or TME be during those time periods? We first answer this question in
the context of a retrospective analysis (1979–2016), which allows us to
understand the regionality of STT and TME for different jet patterns. Then,
using the retrospective results as a guide, we utilize Subseasonal-to-Seasonal
Prediction Project database (Vitart et al., 2017) zonal wind hindcasts
(1997–2016) from the European Centre for Medium-Range Weather Forecasts (ECMWF) to
test whether STT and TME over specific geographic regions may be predictable
for subseasonal forecast leads (weeks 3–6). For both the retrospective and
hindcast analyses, STT and TME are taken from the ETH-Zürich feature-based
climatology database (available for years 1979–2016; Sprenger et al., 2017),
which allows us to apply a single, self-consistent measure of transport for
both the retrospective (1979–2016) and hindcast (1997–2016) analysis
periods.
Pacific jet and transport dataJet variability
Jet variability over the Pacific–North American region is represented via
empirical orthogonal functions (EOFs), which are based on ERA-Interim (Dee et
al., 2011) monthly mean (March–May, MAM) anomalies of 200 hPa zonal
wind (cosine latitude weighted 10–70∘ N and 125–270∘ E)
for the 1979–2016 period. Anomalies were created by removing the first four
annual harmonics of the 1979–2016 daily climatology. Using monthly averages
instead of daily or weekly values is motivated in part by the suggestion of
Newman et al. (2012) that a large fraction of ocean-to-continent transport
arises from low-frequency variability rather than individual synoptic
events. Using monthly values also significantly boosts the variance explained
by the leading three EOFs to nearly 60 % vs. <20%
for daily values (e.g., Feldstein, 2000). We use a bootstrap method to test
for EOF degeneracy (North et al., 1982) and find that the first three EOFs
(Fig. A1), which represent 25 %, 21 %, and
11 % of the total MAM monthly mean wind variance, are reasonably
well-separated and have robust spatial patterns (see Appendix for
details). Hereafter we refer to the first three EOFs (and their corresponding
principal component, PC, time series) as EOF1 (PC1), EOF2 (PC2), and EOF3 (PC3).
Correlations between MAM monthly average PC time series and various
climate indices with p values in parentheses. The West Pacific pattern (WP), Pacific–North American pattern (PNA), and NOAA Oceanic Niño Index (ONI)
are taken from NOAA Center for Weather and Climate Prediction (NOAA CPC).
While EOFs 1–3 are significantly correlated with several commonly used
climate indices (Table 1), we make no inference that the EOF patterns
represent dynamical or physical “modes” of the climate system (Monahan
et al., 2009). Indeed, the significant correlations between each of our PC
time series and multiple teleconnection indices indicates that the variance of
our EOFs almost certainly results from a convolution of external forcing and
internal variability across multiple timescales (e.g., Straus and Shukla,
2002). Evidence for this assertion can be found by noting that while EOF1 is
essentially uncorrelated with the NOAA Oceanic Niño Index (ONI)
(correlation of 0.16 and not significant), EOF1 is 1-month lag correlated
with EOF2 (correlation 0.66, significance level >95%), which is
itself highly correlated with the ONI index (correlation 0.78, significance
level >95%). Thus, with one exception (considered in the
Discussion) we simply use the EOFs as a data compression tool that helps to
isolate the largest-scale flow patterns that we anticipate will have the best
chance for prediction.
To evaluate the potential predictability of Pacific jet variability, we use
hindcasts (1997–2016) of 200 hPa zonal wind from the European Centre
for Medium-Range Weather Forecasting Integrated Forecasting System (ECMWF IFS
CY43R1/R3, model operational in 2017) which were obtained from the
Subseasonal-to-Seasonal Prediction Project database (Vitart et al., 2017).
Hindcasts are “coarse-grained” in time via a 7 d running-mean and in
space via regridding to a fixed 2.5∘ latitude/longitude
grid. Anomalies are computed by removing the lead dependent climatology, which
also serves as a mean bias correction (e.g., Buizza and Leutbecher, 2015;
Monhart et al., 2018). Hindcasts are computed as 3-week averages for
weeks 3–5 (i.e., days 15–35). The 3-week averages are then projected onto
the EOF patterns described above. We also computed results for other averaging
periods including weeks 3–4 and 3–6, as well as individual week 3, 4, and 5
forecasts, but settled on weeks 3–5 because we found that this window
provided the most skillful transport forecasts. Specifically, averaging
several weeks together increased skill (i.e., an extension of the “forecast
skill horizon”; see, for example, Younas and Tang, 2013; Buizza and
Leutbecher, 2015), while extending the forecast window out all the way to week
6 degraded forecast skill because the forecast zonal wind anomaly amplitudes
become very small compared to the verification anomaly amplitudes. The IFS
hindcast PC time series are verified against ERA-Interim-based PC time series
prepared in an identical manner.
To help verify that the zonal wind EOF patterns are highlighting Pacific jet
variability (in Sect. 3.1), we compare the EOFs to a upper tropospheric jet
stream climatology (Koch et al., 2006; Sprenger et al., 2017), which is itself
based on ERA-Interim. The jet climatology (1979–2014) is based upon the vertical
averaging of zonal and meridional winds between 100–500 hPa at every
horizontal grid point, where a “jet event” at each grid point is detected
when the vertically averaged wind exceeds 30 ms-1. This procedure
yields a frequency of upper tropospheric jet events at each grid point.
Transport composites
To examine stratosphere-to-troposphere mass transport and
tropical-to-extratropical water vapor transport, we use six ETH-Zürich
feature-based ERA-Interim climatologies (Sprenger et al., 2017):
stratosphere-to-troposphere mass transport to 500 hPa (STT500),
which provides an estimation of transport into the free troposphere,
stratosphere-to-troposphere transport to the planetary boundary layer
(STTPBL) (Sprenger et al., 2003; Škerlak et al., 2014), and
a climatology of tropical-to-extratropical moisture export (TME), (Knippertz and Wernli, 2010). The STT climatologies (1979–2016) are
based on Lagrangian parcel trajectories calculated using the LAGRANTO
Lagrangian transport model (Wernli and Davies, 1997; Sprenger and Wernli,
2015), in which stratosphere-to-troposphere mass trajectories are considered as
exchange “events” if they have 48 h stratospheric, followed by
48 h tropospheric, residence times. We use both monthly mean and daily
mean climatologies of STT500 and STTPBL, all of which have
units of number of mass exchange events per 6-hourly time step. TME
climatologies (1979–2016) are calculated via LAGRANTO water mass trajectories
that originate in the tropics and reach at least 35∘ N with a water
mass flux greater than 100 gkg-1ms-1; we use monthly mean
and daily mean TME climatologies in which units are given as the number of TME
events per 6-hourly time step.
For our retrospective transport analysis, we composite STT and TME for months
when the zonal-wind PC time series were larger than 1 SD. For the
hindcasts, we use a slightly weaker 0.8 SD threshold in order to
boost the number of samples given the relatively short length of the
subseasonal-to-seasonal hindcast database (1997–2016). We chose to keep the
SD threshold as high as possible though because higher amplitude anomalies
likely correspond to periods of higher forecast skill (Compo and Sardeshmukh
2004; Van den Dool and Toth 1991; Johansson 2007). Importantly, the choice of
threshold does not qualitatively change our results. Hindcast transport
composites are based on time periods when weekly average forecasts of
zonal-wind PC time series were predicted to exceed 0.8 SD. For
hindcast verification composites, the composites are based on periods when the
verification weekly average zonal-wind PC time series exceeded
0.8 SD. This procedure typically means that the verification
composites include more samples because, as we will show, the weeks 3–5 IFS
forecasts systematically underestimate the amplitude of the zonal wind PC time
series and thus do not exceed the SD threshold as often as is observed.
We also briefly discuss the connection between STT and climatologies of
tropopause folds (Sprenger et al., 2003; Škerlak et al., 2014), PV streamers
(Wernli and Sprenger, 2007), and PV cutoffs (Wernli and Sprenger, 2007).
Tropopause folds are defined as regions where a vertical profile contains
three crossings of the dynamical tropopause, with additional criteria
applied to ensure that the folded air mass is “stratospheric” (e.g., enclosed
air mass must have PV>2PV units and cannot be of diabatic
origin). Shallow, medium, and deep tropopause folds were considered, but
only shallow and medium depth folds were found to be relevant. PV streamers
(thin filaments of stratospheric air) are identified using a geometric
contour searching algorithm, while PV cutoffs are identified as
stratospheric air masses (PV>2PV units) that are isolated and
fully embedded within the troposphere. PV streamers and cutoffs were
considered on isentropic surfaces between 305–340 K, but only the most
relevant surfaces are shown. Units for folds, streamers, and cutoffs are
events per 6-hourly time step.
Units and significance testing
While the original units of all of the ETH-Zürich climatologies are
frequencies (jet frequency, STT, tropopause folds, PV streamers, PV cutoffs,
and TME), all of our figures, except for the climatologies (Fig. 1), are
presented in units of standard deviations. That is, for every variable, we
calculate anomalies from climatology and then divide by the anomaly standard
deviation (z scoring). Thus, a unit of “1 SD” equates to a 1 standard deviation anomaly, for which the standard deviation is calculated
individually for each specific time period considered (e.g., the SD
normalization for a March monthly mean is different from the SD normalization
used for a 3-week forecast period in March).
When comparing forecast and verification transport probability density
functions (PDFs), we evaluate significance via a combination of bootstrap
confidence intervals (10 000 ensembles with replacement) and two-sample
Kolmogorov–Smirnov distribution tests (KS-test; Marsaglia et al., 2003;
Hollander et al., 2013), in which the latter tests whether the shape and location
of two empirical distributions are significantly different. The PDFs
themselves are created by taking box-area means of STT or TME for a specified
geographic region at every forecast time step and using each as a
“sample”. The PDFs are then calculated via kernel density estimation based
on the collection of all samples for either the forecasts or verifications.
ResultsRetrospective analysis
The first three EOF patterns of the 200 hPa zonal wind all exhibit
anomalies that correspond to some amount of extension or retraction and/or
latitudinal shifting of the Pacific jet compared to climatology
(Fig. 2). This interpretation is confirmed by compositing ETH-Zürich
feature-based jet frequencies for time periods with high EOF loading (PC
amplitude >1SD), which yields jet frequency distributions that
correspond extremely well with each of the first three EOF wind patterns
(Fig. S1 in the Supplement). This suggests that the amount of wind variance
explained by each of the individual EOFs is sufficiently large that when the
PC magnitude is high, there are notable corresponding shifts in the location of
the Pacific jet stream. While the EOF patterns likely combine jet variability
due to both the subtropical and polar front jets (Koch et al., 2006), a strong
jet stream of either type will act as a waveguide for Rossby waves (e.g.,
Schwierz et al., 2004; Rivière, 2010, and references therein) with an
increased frequency of STT (e.g., Shapiro and Keyser, 1990) and TME (e.g.,
Higgins et al., 2000; Sprenger et al., 2017).
Spring (MAM, 1979–2014) zonal wind climatology (filled contours)
with colored contours showing the first three EOF patterns. The variance
explained by each EOF is shown in the title for each panel. Units of the
zonal wind climatology are meters per second (ms-1). The EOF zonal wind anomaly contours span
±1 to 7 in 2 ms-1 intervals.
Monthly mean (MAM, 1979–2014) frequencies (filled contours) of STT
to 500 hPa(a–c), STT to the PBL (d–f), and TME (g–i) for
time periods when PCs 1–3 are greater than 1 SD from climatology for the
negative EOF phase (units of SDs). Colored contours show the EOF patterns
associated with each composite. See Figs. S2–S4 for composites of
the positive EOF phase.
To evaluate the jet-transport connection, we consider STT and TME for time
periods with high zonal wind EOF loading (absolute value of
PCs > 1 SD). Because the patterns of the STT and TME anomaly
composites are so similar for both EOF phases, we show only the negative EOF
pattern; see Figs. S2–S4 in the Supplement for the positive
phase. STT500 maxima match the EOF wind patterns quite well (Fig. 3, top
row), with positive (negative) STT500 anomalies tending to occur along
the northern flanks of the regions of stronger (weaker) winds (Koch et al.,
2006) and hence increased (decreased) jet frequency. The correspondence of
higher STT500 with higher wind speeds suggests that transverse
circulations around the jet play a key role in transport and confirms that
the EOF-based STT500 anomalies are related to variations in the North
Pacific storm track (Škerlak et al., 2014). The STT500 anomalies are
most closely associated with shallow to medium depth tropopause folds
(Figs. S5 and S6 in the Supplement) and PV cutoffs along the 310 K
isentropic surface (Fig. S7 in the Supplement).
STTPBL, on the other hand (Fig. 3 middle row), has maxima slightly
downstream of the 500 hPa maxima, which reflects the fact that deep
STT tends to occur as maturing Rossby waves amplify and PV streamers become
increasingly stretched and filamented along isentropic surfaces that slope
equatorward and downwards towards the surface (see, for example, discussion of
Fig. 5 in Škerlak et al., 2014; see also Reed and Danielson, 1958;
Shapiro, 1980; Shapiro and Keyser, 1990; Wernli and Bourqui, 2002; Sprenger
et al., 2003; Appenzeller et al., 1996; Wernli and Sprenger, 2007). In
addition, as the PV streamers become more filamented, isolated regions of high
PV stratospheric air often become fully detached as PV cutoffs. Indeed, MAM
STTPBL appears to be closely associated with PV streamers and PV
cutoffs along the 310 and 305 K isentropic surfaces (Figs. S8 and S9
in the Supplement, respectively). In contrast to STT500,
STTPBL does not appear to be strongly associated with tropopause
folds (not shown; see also Fig. S1e in the Supplement of Škerlak et al.,
2015), though some caution should be exercised when interpreting the relative
importance of tropopause folds, PV streamers, and PV cutoffs for deep STT as
shown here because previous authors, using alternative techniques, have found
that tropopause folds play an important role in deep STT (e.g., Shapiro, 1980;
Langford et al., 2009; Breeden et al., 2021, and references therein). Anomalous
TME also corresponds well with the EOF patterns (Fig. 3, bottom row) except
that the anomalies are on the southern edge of the positive EOF wind patterns,
which is due to the tendency for strong TME to occur along the warm sector of
a breaking Rossby wave (Bao et al., 2006; Knippertz et al., 2013).
While all of the transport composites are physically consistent with the EOF
patterns, and hence jet variability, the STT500 and TME composites have a
much more robust signal compared to STTPBL. That the
STTPBL is weaker is not entirely surprising because while a high
percentage of upper level breaking waves extend downwards to the middle to upper
troposphere, subsequently causing associated STT500 and TME, only a small
subset of these waves will achieve the needed amplitude and depth to extend
all the way to the PBL. Moreover, transport to the STTPBL is also
dependent on the depth of the PBL, which tends to be relatively shallow until
late spring to early summer when convective heating begins to increase (Seidel
et al., 2012; Škerlak et al., 2014; Breeden et al., 2021). Nevertheless,
all of the composites provide a basis for the expectation that Pacific jet
variability can be used as a predictor for transport over landmasses of
interest, including the western United States, southern Alaska, and Japan.
Potential predictability of jet shifts and transport
While subseasonal forecasts of teleconnection indices are known to exhibit
reasonable correlation-based skill (Wang and Robertson, 2019), the amplitude of
the anomalies is often quite weak compared to observations (Yamagami and
Matsueda, 2020). Thus, the relevant question here is, do forecast models
predict jet variability well enough – in terms of both correlation and
anomaly amplitude – to provide guidance for subseasonal transport
forecasting?
Correlations between MAM weekly average PC time series of IFS
hindcasts and ERA-Interim verifications. The 95th percentile confidence
intervals are shown in square brackets underneath each correlation
coefficient. All p values are less than 0.05 when all data are used in the
forecast-verification correlation calculations; however, if the correlation
calculations are repeated instead using every third forecast (to take into
account autocorrelation in the forecast time series), then PC3 has large
p values (0.12 and 0.32) at weeks 5 and 6, respectively, while all other PC
correlations at all forecast leads remain <0.05.
For weekly forecasts, the correlation between the forecasted and verified
zonal wind PCs is “skillful” (correlations >0.5–0.6; Hollingsworth
et al., 1980; Arpe et al., 1985; Murphy and Epstein, 1989) within the
deterministic timeframe (weeks 1–2) for all three EOFs (Table 2). Beyond week
2, however, the PC1 and PC3 correlations drop off rapidly, with the skill of
predicting PC3 almost completely limited to synoptic timescales. On the other
hand, PC2 retains useful skill all the way out to forecast week 6, which may
be due to its stronger relationship to ENSO (Table 1). These correlations
suggest that only the first two PCs retain enough skill to be useful on
subseasonal leads. The same result is true for the weeks 3–5 forecast window
(Fig. 4), for which forecast-verification correlations for both PC1 and PC2 are
near or above 0.5, while PC3 exhibits very low correlation-based skill. In
terms of the PC amplitudes of the weeks 3–5 forecasts, both PC1 and PC2
regularly exceed our 0.8 SD threshold, while the PC3 amplitude rarely
exceeds it. Thus, while EOF3 is related to large transport anomalies over land
regions of interest (e.g., STTPBL and TME over North America), it
is unfortunately not predictable on subseasonal timescales (similar results
are also found for EOFs 4 and higher). We therefore focus on predicting
transport via PC1 and PC2.
Time series of weeks 3–5 average zonal wind projected onto EOFs
1–3 for IFS forecasts (orange lines) and ERA-Interim verifications (black
lines). The horizontal dashed lines denote ±0.8SDs from the mean of
the verification time series. For reference, the light blue and red shading
denote the months that were included in the monthly average composites used
to create Fig. 3. Correlations between the forecasts and verifications (with
95th percentile confidence intervals) are shown in the titles of each
panel.
Number of times that a weeks 3–5 average verification or
forecast exceeded the 0.8 SD threshold for the 1997–2016 hindcast period
(i.e., the periods in Fig. 4 when the black or orange lines, respectively,
were above or below the dashed horizontal SD reference lines). The 95th
percentile bootstrap confidence intervals are shown as whiskers.
(a, b) EOF1-based composites of STT to 500 hPa for weeks 3–5
forecast periods when the verification time series (black line in Fig. 4)
was above (positive phase) or below (negative phase) the 0.8 SD threshold.
(c, d) EOF1-based composites of STT to 500 hPa for weeks 3–5 forecast
periods when the forecast time series (orange line in Fig. 4) was above
(positive phase) or below (negative phase) the 0.8 SD threshold. The black
box outlines the North Pacific subregion used for creating the transport PDF
in Fig. 10a. Units are in SDs, and pattern correlations between top and
bottom panels (cf., a vs. c and b vs. d) are shown in the
bottom row titles.
(a, b) EOF2-based composites of STT to the PBL for weeks 3–5
forecast periods when the verification time series (black line in Fig. 4)
was above (positive phase) or below (negative phase) the 0.8 SD threshold.
(c, d) EOF2-based composites of STT to the PBL for weeks 3–5 forecast
periods when the forecast time series (orange line in Fig. 4) was above
(positive phase) or below (negative phase) the 0.8 SD threshold. The black
box outlines the western to intermountain-western US subregion used for
creating the transport PDF in Fig. 10b. Units are in SDs, and pattern
correlations between top and bottom panels (cf., a vs. c and b vs. d) are shown in the bottom row titles.
(a, b) EOF1-based composites of TME for weeks 3–5 forecast
periods when the verification time series (black line in Fig. 4) was above
(positive phase) or below (negative phase) the 0.8 SD threshold. (c, d)
EOF1-based composites of TME for weeks 3–5 forecast periods when the
forecast time series (orange line in Fig. 4) was above (positive phase) or
below (negative phase) the 0.8 SD threshold. The black box outlines the
western US subregion used for creating the transport PDF in Fig. 10c. Units
are in SDs, and pattern correlations between top and bottom panels (cf., a vs. c and b vs. d) are shown in the bottom row titles.
(a, b) EOF2-based composites of TME for weeks 3–5 forecast
periods when the verification time series (black line in Fig. 4) was above
(positive phase) or below (negative phase) the 0.8 SD threshold. (c, d)
EOF2-based composites of TME for weeks 3–5 forecast periods when the
forecast time series (orange line in Fig. 4) was above (positive phase) or
below (negative phase) the 0.8 SD threshold. The black box outlines the
West Pacific subregion used for creating the transport PDF in Fig. 10d.
Units are in SDs, and pattern correlations between top and bottom panels
(cf., a vs. c and b vs. d) are shown in the bottom row titles.
The number of observed instances (verifications) when the PC1 and PC2
amplitudes exceed 0.8 SDs exhibits a seasonal cycle (Fig. 5), though
the degree of overlap of the confidence intervals suggests that the seasonal
cycle is more pronounced for EOF2 than for EOF1. The situation is a bit more
complicated if the individual phases of each EOF are considered (see Fig. S10
in the Supplement), though the small sample sizes make conclusive inferences
difficult. Nevertheless, the slow decay of observed PC1 and PC2 exceedances
(i.e., large amplitude jet events) between March and May is qualitatively
consistent with previous studies documenting the seasonality of jet activity
and Pacific baroclinic wave amplitudes (Nakamura, 1992; Koch et al.,
2006). Unfortunately, the number of PC1 and PC2 exceedances predicted by the
IFS at 3–5 week lead times has a much stronger seasonal cycle compared to
observations, with early spring having many more exceedances than for late
spring for both phases of PC1 and PC2 (Figs. 5 and S10). This implies that the
transport anomalies outlined next are more predictable, and hence the
composites more heavily weighted, for the periods before the jet undergoes its
spring transition (Newman and Sardeshmukh, 1998; Breeden et al., 2021).
Based on the regions with the largest transport anomalies (Fig. 3) for the
more predictable PC1 and PC2 time series (Fig. 4), we chose four subregions
within the full Pacific domain to examine the potential predictability of STT
and TME: EOF1-based STT500 for the North Pacific, which includes southern
Alaska and the Russian Far East; EOF2-based STTPBL for the western
to intermountain-western United States; EOF1-based TME for the western United States; and EOF2-based
TME for the West Pacific (Japan and far eastern Asia). These subregions are
highlighted by the boxes in Figs. 6a, 7a, 8a, and 9a, respectively. To provide
context for the four subregion forecasts, we first show forecast and
verification transport anomalies for the entire Pacific domain. For each of
the four full domain figures (Figs. 6–9), the top two panels show
verification transport composites which are based on times when the
verification zonal wind PC time series amplitude is greater than
±0.8SD (black lines in Fig. 4), while the bottom two panels show
corresponding transport composites except for time periods when the
forecasted zonal wind PC time series amplitude is greater than
±0.8SD (orange lines in Fig. 4). For comparison, the months that
are included in the retrospective composites (Fig. 3) are highlighted by the
light red and blue shading in Fig. 4 (note that the time periods when the
week 3–5 time series exceed the ±0.8SD threshold do not always
match the red and blue shading regions because the shaded regions highlight
periods when the monthly mean time series exceeded the monthly 1 SD
threshold). The pattern correlations between the forecast and verification
transport composites for the full domains in Figs. 6–9 (not just for the
boxed-in areas) are included in the forecast titles for both EOF
phases. Transport predictability for the four boxed subregions is subsequently
evaluated via PDFs of transport for the forecasts and verifications
(Fig. 10). Beyond the four region- and transport-type combinations just mentioned,
STT500 and TME were found to be potentially predictable for EOFs 1 and 2
over several additional subregions of the central Pacific basin, but because
those results are similar to what we discuss below, they are not shown.
STT500 based on the EOF1 forecast is qualitatively consistent with the
verification-based composite for both positive and negative EOF phases
(Fig. 6), though the STT500 pattern is better reproduced for the negative
phase (pattern correlation of 0.49 vs. 0.75 for the positive vs. negative
phases, respectively). In addition, the verification composites show an
asymmetry between opposite EOF1 phases in the amount of STT500, which is
also accurately forecasted, with the negative EOF phase exhibiting peak values
in the 0.75–1.25 SD range vs. 0.25–0.5 SDs for the
positive EOF phase. This asymmetry is likewise reflected in the forecast and
verification PDFs of STT500 for the North Pacific subregion (Fig. 10a),
where the median for the positive EOF1 phase is weakly negative, while the
negative EOF1 phase has a greater than +0.5SD median anomaly. The
only noteworthy difference between the North Pacific forecast and verification
PDFs is that the forecast-based PDF is shifted towards more positive values
than the verification-based PDF. Regardless, the confidence intervals for the
medians of the positive vs. negative phases of the forecast-based PDFs are
very well-separated, and the underlying distributions are different according
to a KS-test, which suggests that the predicted shifts in transport are
significant. We also evaluated forecasts of STT500 for various subregions
over populated land masses (e.g., the western United States), but the resulting
verification and forecast PDFs were not significantly different, which
reflects the fact that STT500 peaks over the North Pacific portion of the
storm track (Fig. 3a).
Probability density functions (PDFs) of (a) EOF1-based STT to
500 hPa for the North Pacific subregion, (b) EOF2-based STT to the PBL for
the western to intermountain-western US subregion, (c) EOF1-based TME to the
western US subregion, and (d) EOF2-based TME to the West Pacific subregion.
IFS-based forecasts are shown in solid dark lines, and ERA-Interim-based
verifications are shown as thicker light lines; for both forecasts and
verifications, medians are shown as blue dots, and 95th percentile
bootstrap confidence intervals are shown as whiskers. Units are in SDs.
For EOF2-based STTPBL over the western United States, the verification
composite is consistent with the retrospective composites (cf., Figs. 7a and b
vs. 3e); however, the pattern is much weaker. Nevertheless, the forecast- and
verification-based STTPBL composites (Fig. 7c and d) and PDFs for
the western US subregion (Fig. 10b) do agree quite well. However, the
STTPBL distribution is notably shifted away from zero only for the
negative EOF phase and the confidence intervals for the medians overlap, which
suggests that the STTPBL forecasts are probably borderline in
their usefulness for most forecast periods. Still, the forecasted
STTPBL do represent different distributions according to a
KS-test, so the change in the shape of the tails of the distributions may be
of some practical use for the prediction of extreme STTPBL events.
There are several potential reasons why the STT500 forecast and
retrospective composite pattern amplitudes compare quite well (c.f. Figs. 3a
and 6), while the STTPBL forecast and verification patterns are
weaker than their retrospective counterparts (c.f. Figs. 3e and 7). First,
STTPBL over the Pacific–North American region tends to be largest
for two circumstances: regions with high orography and time periods when the
PBL height is particularly high. These two circumstances coincide over the
western to intermountain-western United States (Škerlak et al., 2014; Breeden et
al., 2021) during MAM, which coincides with the box area (Fig. 7a) used for
our STTPBL PDF calculations (Fig. 10b). Unfortunately, PBL heights
do not get particularly high until middle to late spring (see Fig. 5c of Breeden
et al., 2021), which is the time period when Pacific jet forecasts are the
least skillful (Fig. 5b). A second potential issue is that only a small
percentage of overall STT events are deep enough to reach the lowermost
troposphere (e.g., Škerlak et al., 2014, find that 36 % of STT events reach 500 hPa, while only 5 % reach 800 hPa;
see their Fig. 4), which may magnify sampling issues related to the much
smaller hindcast period (1997–2016) compared to the longer period
(1979–2016) used for the retrospective analysis. We attempted to address the
sampling issue by expanding the forecast averaging window from 3 weeks to
4 weeks; however, using an expanded 4 week averaging window yielded fewer
well-forecasted periods, which also resulted in a weaker STTPBL
pattern.
The TME forecasts match the verifications very well, both for EOFs and for
both phases of each EOF, with basin-wide pattern correlations ranging from
0.57 to 0.88 (Figs. 8 and 9). In addition, the magnitude of the anomaly values
for both EOFs are notable, with both TME phases exhibiting anomalies in the
0.5–1.25 SD range over relatively large portions of the Pacific
domain. Interestingly, positive TMEs centered over Alaska are predicted very
well for the positive phase of EOF1 (Fig. 8a and c) and the negative phase of
EOF2 (Fig. 9b and d), yet it is unclear if this pattern represents a reliably
predictable form of TME because neither of the corresponding TME composites
for the longer time record retrospective analysis show anomalies over Alaska
(cf., Figs. 8 and 9 to 3g and h, respectively). In contrast, the forecasted
patterns of TME between Japan and the west coast of the United States (south of
55∘ N) are quite consistent with the jet (Figs. 1 and S1) and TME
(Fig. 3, bottom row) patterns from the retrospective analysis, which suggests
that TME over broad regions of the Pacific basin may be reasonably predictable
during spring. Indeed, the western United States and West Pacific subregion TME PDF
shifts are robust and match the verification PDFs very well (Fig. 10c and d,
respectively). This is particularly true for the West Pacific where the median
shift in TME transport is nearly ±1SD for each EOF phase, and
the PDF forecast and verification PDFs are nearly identical.
(a) El-Niño- and (b) La-Niña-based monthly mean (MAM,
1979–2014) frequencies of STT to the PBL (filled contours) and zonal winds
(contours) for time periods when the NOAA ONI was ±0.8SDs from
climatology (units of SDs). Note: for correct comparison, panel (a) here should
be compared to (d) from Fig. 4; compare also panels (a) and (b) here
to panels (d) and (c) from Fig. S3. (c) Probability density functions (PDFs)
of EOF2-based STT to the PBL for the western to intermountain-western US
subregion. (d) Time series of the NOAA ONI (blue line) and PC2 (orange
line), for which ONI has been multiplied by -1 for ease of comparison.
Discussion and conclusions
Many “modes” of climate variability are known to be associated with
anomalous atmospheric transport. For example, stratosphere-to-troposphere mass
and ozone transport to the PBL over North America is known to be influenced by
ENSO (Breeden et al., 2021; Lin et al., 2015, and references therein), while
the frequency of atmospheric rivers is thought to be modulated by a variety of
climate phenomena, including ENSO, the Madden–Julian oscillation, and the
quasi-biennial oscillation (Guan et al., 2012; Lee et al., 2014; Kim and
Alexander, 2015; Guan and Waliser, 2015; Mundhenk et al., 2016; Guirguis et al., 2019). However,
retrospectively isolating such associations, which is equivalent to conducting
a “perfect model” forecast, does not assure that current operational
forecast models can successfully predict those relationships, particularly on
subseasonal timescales (e.g., Lavers et al., 2016; Baggett et al.,
2017). Nevertheless, some teleconnection and transport patterns appear to be
potentially predictable on subseasonal timescales (e.g., Mundhenk et al.,
2018; Wang and Robertson, 2019; Pan et al., 2019; DeFlorio et al., 2019;
Yamagami and Matsueda, 2020), though these forecasts are typically found to
occur during boreal winter.
Our analyses have shown that stratosphere-to-troposphere transport (STT) to at
least 500 hPa and long-range tropical-to-extratropical moisture
export (TME) over the Pacific–North American region can potentially be
skillfully predicted on subseasonal timescales (3–5 weeks ahead of time)
during boreal spring. The transport forecasts themselves were inferred from
ECMWF-IFS-based forecasts of Pacific jet variability. IFS Pacific jet
forecasts for four Pacific–North American subregions are associated with
significant shifts in the probability of anomalous transport, including the following: STT
into the free troposphere over the North Pacific (Fig. 10a); STT into the
planetary boundary layer over the intermountain-western United States (Fig. 10b); TME
over the west coast of the United States (Fig. 10c); and TME to Japan and far eastern
Asia (Fig. 10d). While the forecasted shifts in transport probability match
verifications quite well, one deficiency is apparent: the IFS is able to
predict the sign of the zonal wind PC time series with reasonable success
(Table 2 and Fig. 4), yet it consistently struggles to maintain enough
zonal-wind PC amplitude relative to the substantial weather-related noise
(compare amplitude of forecast and verification time series in Fig. 4). This
results in an underestimation of the number of anomalous transport days
compared to observations (Fig. 5), which degrades the estimation of the
transport probabilities (Fig. 10).
The underestimation of the number of anomalous transport days exhibits a
strong seasonal dependence, which becomes quite acute during April and May
(Fig. 5). This implies that either overall teleconnection predictability
decreases as spring proceeds, or alternatively, the IFS is simply unable to
skillfully predict large amplitude jet anomalies with consistency beyond
early spring. While it is beyond the scope of the current study to explore
which one of these possibilities is responsible for the lack of consistent
late spring skill, this is clearly an important question because the first
possibility would be a fundamental feature of the climate system, while the
latter would be a model-based constraint that might theoretically be
improved. Of course, these two possibilities are not mutually exclusive
because the increasing sensitivity of Pacific–North American teleconnections
to tropical forcing at smaller spatial scales during the spring jet transition
(Newman and Sardeshmukh, 1998) may be inherently less predictable yet also
more difficult to accurately model. That said, despite the IFS underestimation
of the number of days with anomalously strong jet patterns (Fig. 5), the IFS
is still able to identify roughly 15 % (PC1) and 30 %
(PC2) of all spring days (March–May) that are anomalous, which suggests that
using upper-level winds to forecast transport may currently be possible.
For the three types of transport that we have evaluated here, STT into the
free troposphere and TME are the most robustly predicted, at least in terms of
shifts of the average and extremes of their transport distributions
(Fig. 10). STT to the PBL over the western United States, on the other hand, mainly
exhibits a change in the shape of the tails of the transport distributions
but a rather weak shift in the median (i.e., the shifts of the medians of the
two EOF2 phases have confidence intervals that are strongly overlapping;
Fig. 10b). This has implications for the suggestion that ENSO may be used to
predict air quality related to STT of ozone during spring (e.g., Lin et al.,
2015 and Albers et al., 2018, and references therein). Similar to previous
retrospective analyses (e.g., Lin et al., 2015; Breeden et al., 2021), we find
that mass transport to the PBL is associated with ENSO (Fig. 11), where here,
we have composited STTPBL based on periods when the NOAA ONI is
greater than 0.8 SDs from the historical mean, which yields an
equivalent number of samples to our EOF2-based results. The ONI-based
(retrospective) transport composites look very similar to our earlier
EOF2-based retrospective results (cf., Figs. 11a and b to 3e and S3c,
respectively. For a proper comparison, note that PC2 and ONI are negatively
correlated). Moreover, the transport PDFs for the intermountain-western US
subregion based on PC2 vs. ONI, for both ENSO phases, are drawn from the same
distributions according to a two-sample Kolmogorov–Smirnov test
(Fig. 11c). This close correspondence is due to the high correlation between
the ONI and PC2 time series (Fig. 11d). Yet, because we have found
STTPBL predictions related to EOF2 to be significant only in terms
of shifts in the tails of the distributions (cf. Figs. 10b and 11c), our
results suggest that at best, ENSO may be harnessed to provide
STTPBL forecast guidance on subseasonal timescales for extreme
events only. Complicating matters further in the context of ozone transport to
the PBL (as opposed to simply mass transport as investigated here) is that
predictions based on ENSO will likely be even more difficult because the STT of
ozone is also modulated by the seasonal variability in the available reservoir
of ozone in the extratropical lower stratosphere (Olsen et al., 2013; Neu
et al., 2014; Albers et al., 2018). That said, because it is doubtful that
Niño-3.4-based indices like ONI capture the full dynamical scope of ENSO
variability (Penland and Matrosova, 2006; Capotondi et al., 2015), the
complete impact of ENSO on STTPBL predictability certainly
deserves further study.
To verify that EOFs 1–3 represent distinct patterns that are robust to
variations in sampling period (North et al., 1982), we conducted several
calculations. To begin, a 10 000 member bootstrap ensemble of 200 hPa zonal
wind EOFs was created (resampling with replacement), in which each bootstrap
member consisted of “N” randomly selected monthly mean 200 hPa zonal wind
anomalies for the Pacific basin domain shown in Fig. 2. The N randomly
selected anomalies are chosen from the pool of all MAM 1979–2016 monthly
means, and N=114, which is the number of months in the original EOF
calculation for MAM, 1979–2016. The resulting data were used in three
calculations.
First, the pattern correlation between each bootstrap ensemble member EOF and
the corresponding original EOF was calculated. The median pattern correlation
for all 10 000 bootstrap ensemble members was then calculated. For all three
EOFs, the median pattern correlation was near 0.9 (individual values are shown
for each of the three EOFs in the title bars of Fig. A1a–f). Next, the median
of the variance explained was calculated for each bootstrap ensemble EOF. For
all three EOFs, the variance explained for the original EOFs and for the
median of the bootstrap ensemble EOFs is within a couple percent (individual
values are shown for each of the EOFs in the title bars of Fig. A1a–f). Finally, the standard deviation of the variance explained was calculated for
each of the bootstrap ensembles (Fig. A1g). The spread (measured by the
standard deviation) is small enough that there is no overlap between each of
the first three EOFs. In combination, these calculations support the notion
that the first three 200 hPa zonal wind EOFs are not degenerate
according to the criteria outlined in North et al. (1982).
(a, c, e) 200 hPa zonal wind EOF patterns for MAM,
1979–2016, which correspond to the EOF contours shown in Figs. 2–3 and
S1–S4. (b, d, f) 200 hPa zonal wind EOF patterns for the bootstrap
ensembles corresponding to panels (a, c, e), respectively. For each
row in (a–f), the median pattern correlation between the original (left
column) and bootstrap ensembles (right column) are shown in the subtitle.
The subtitle of each panel in (a–f) also shows the variance explained
(original EOFs, left column) or the median variance explained (bootstrap
ensembles, right column) for each EOF. (g) Median variance explained for the
bootstrap ensemble (solid marker) and the spread of variance explained for
the bootstrap ensembles of each EOF, in which the spread is calculated as 1 SD
of the variance explained (shown as whiskers).
Data availability
The ERA-Interim reanalysis data used in this study are available through the
National Center for Atmospheric Research Consortium for Atmospheric Research
Data Archive: 10.5065/D6HH6H41. The STT, TME, and jet ERA-Interim
feature-based climatology data are available from
http://eraiclim.ethz.ch/ (ETH Zürich, 2020). ECMWF IFS hindcast data are available via the S2S
Prediction Project (http://s2sprediction.net/, Vitart et al., 2017).
The supplement related to this article is available online at: https://doi.org/10.5194/wcd-2-433-2021-supplement.
Author contributions
JRA wrote retrospective and hindcast analysis code, created the
figures, and wrote the manuscript. AHB, MLB, AOL, and GNK provided comments and edited the
manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank Michael Sprenger for graciously making the
6-hourly ETH-Zürich feature-based data available, which made this study possible.
The authors also wish to thank two anonymous reviewers whose comments improved
the science and clarity of the manuscript. The authors would like to thank Yan Wang for preparing the
IFS S2S data and Benjamin Moore, who helped furnish the 6-hourly TME data.
Financial support
John R. Albers and Amy H. Butler were funded in part by NSF grant #1756958. Melissa L. Breeden was funded by the NOAA Climate and Global Change Postdoctoral Fellowship Program, administered by UCAR's Cooperative Programs for the Advancement of Earth System Science (CPAESS) under award # NA18NWS4620043B.
Review statement
This paper was edited by Christian M. Grams and reviewed by two anonymous referees.
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