Introduction
Stratosphere–troposphere exchange (STE) has significant impacts on the
chemical and radiative properties of the upper troposphere and lower
stratosphere UTLS;. Oxidative and
greenhouse gases can be transported across the tropopause in two directions,
typically referred to as stratosphere-to-troposphere transport (STT) and
troposphere-to-stratosphere transport (TST). STT brings ozone-rich
stratospheric air into the troposphere, and in some cases STT can extend into
the planetary boundary layer e.g.,. Per molecule, ozone radiative forcing is maximized in the
upper troposphere . Conversely, TST processes can
inject water vapor and tropospheric pollutants, such as carbon monoxide, into
the lower stratosphere, where the lifetimes of such gases can be increased.
Because water vapor is a greenhouse gas, increases in lower stratosphere (LS) water vapor from TST
lead to an increase in radiative forcing similar to that for upper troposphere (UT) ozone
.
STE is driven by dynamic processes occurring across a wide range of spatial
and temporal scales. There are several known large-scale processes that occur
in the extratropical domain, the tropical domain, and along the boundary
between them (i.e., within the subtropics). STE in the extratropics often
occurs in the vicinity of baroclinic transient eddies or extratropical
cyclones e.g.,. Transport associated with
extratropical cyclones is dominated by STT and is primarily due to clear-air
turbulence along the edges of stratospheric intrusions (or tropopause folds)
in the upper troposphere e.g.,. Stratospheric intrusions can develop apart from
extratropical cyclones along the cyclonic side of upper tropospheric jet
streams as a result of ageostrophic circulations
e.g.,. These tend to be shallow and exchange
less than stratospheric intrusions associated with extratropical cyclones
e.g.,. Extratropical
cyclones also result in TST, which is sourced by warm-conveyor-belt flows
that bring lower troposphere air to the UTLS along isentropic surfaces and by
moist convection
e.g.,.
Similar to transient extratropical cyclones, cut-off extratropical cyclones
(i.e., limited interaction with middle latitude westerlies) occur frequently
in the upper troposphere and result in STT due to eroding processes (i.e.,
turbulence and friction) acting upon the depression in the tropopause
.
STE has been found to be globally unbalanced, with net TST in the tropics and
net STT in the extratropics. This imbalance is primarily a reflection of
ubiquitous tropospheric upwelling at the tropical tropopause and downwelling
at the extratropical tropopause that is driven by the Brewer–Dobson
circulation BDC;. The BDC is a
latitudinal circulation, where tropospheric air diabatically ascends into the
tropical lower stratosphere and is dynamically “pumped” poleward and
downward into the extratropical LS by Rossby wave breaking in the
midlatitudes . Apart from the BDC, TST in
monsoon anticyclones is an important STE mechanism in the tropics. Moist
convection rapidly lofts lower troposphere air into the interior of the UT
anticyclonic circulation where it slowly ascends into the lower stratosphere.
The North American and Asian monsoon anticyclones have received a
considerable amount of recent attention and have been shown to contribute
significantly to global STE e.g.,and references
therein.
There are also known STE processes that occur primarily in the subtropics and
involve tropical UT air and extratropical LS air. One of the most well-known
subtropical STE processes is Rossby wave breaking, which is a
quasi-isentropic transport process that can be predominantly poleward (TST),
equatorward (STT), or bidirectional e.g.,. Stratospheric intrusions along the
subtropical jet stream occur outside of the tropics but can bring
extratropical lower stratosphere air into the tropical middle and upper
troposphere .
The impacts of STE are considerable on the global scale but, as a result of
limited observations, most studies have investigated STE and associated
large-scale processes over small domains and for short time periods using
numerical models and remote sensing or aircraft platforms. For example,
used a simulated stratospheric intrusion to
estimate the annual net mass exchange across the tropopause in the Northern
Hemisphere by extrapolation. Specifically, annual estimates of STT were
approximated using an annual average of stratospheric intrusions (1460 per
year) and STT quantities found in their case study, which amounted to 1.79×1017 kg per year. Although quantitative estimates from
were similar to additional studies published at
the time, they suggested that stratospheric intrusions are invariable and
exhibit similar life spans and STE quantities, which is now known to be an
incorrect assumption. Modern numerical models and computing capabilities,
however, enable climatological studies of STE for large domains and long time
periods.
Previous studies have employed multiple years of global model output to
produce a climatology of STE. analyzed
downward fluxes out of the lower stratosphere in the Northern and Southern
hemispheres over two calendar years (1992 and 1993). The study emphasized an
Eulerian approach, the “downward control principle” (i.e., mass continuity),
to estimate seasonal net flux in the extratropics, and results indicated a
peak downward flux during Northern Hemisphere spring. Annual net downward
mass flux was estimated to be 3.5×1017 and 3.3×1017 kg for the Northern and Southern hemispheres, respectively, which
is nearly twice that found by . An important
caveat in the study is that the method does
not provide spatial distributions of STE and only considers STT in the
extratropics. Another Eulerian metric used as a diagnostic for STE is the Wei
method , which allows for determination of STT and TST on
short spatiotemporal scales but suffers from conceptual problems
.
An alternative to Eulerian methods is a Lagrangian approach, which employs a
large number of three-dimensional (or 3-D) trajectories to determine STE. For
example, developed a 1-year Northern Hemisphere
climatology of STE using trajectories and identified large-scale airstreams
and the corresponding spatial and temporal variability of STE associated with
particular flows (i.e., warm conveyor belts and stratospheric intrusions).
Annual net downward mass flux estimates in the northern extratropics were
found to be larger than previous global estimates (4.4×1017 kg).
Multiyear climatologies of STE have also employed a Lagrangian approach on
global and hemispheric scales, wherein studies aim to investigate the
quantitative and qualitative characteristics of STE
e.g.,. A recent STE climatology by hereafter Š14 evaluated STE over a 33-year period using 3-D kinematic
winds from the ERA-Interim reanalysis and a Lagrangian trajectory model.
Their method requires a parcel to remain in its parent reservoir (troposphere
or stratosphere) and destination reservoir (stratosphere or troposphere) for
at least 48 h to be classified as irreversible STE (a so-called
“residence time”). Although a Lagrangian trajectory method was employed, the
annual net exchange found (0.42×1017 kg) was an order of
magnitude smaller than estimates by . It is apparent
that residence times significantly decrease the quantitative STE estimates as
a result of separating irreversible exchange from transient exchange.
As outlined above, there have been three classes of methods used to analyze
STE: (1) Eulerian-based approaches, such as the Wei method and the downward
control principle, (2) a Lagrangian trajectory-based method that does not
use a residence filter e.g.,,
and (3) a Lagrangian trajectory-based method with a residence filter
e.g.,. Despite previous efforts to determine climatological
characteristics of STE, the various models, methods, and time periods used
have led to a wide range of transport estimates.
Motivated by an improved understanding of UTLS characteristics and processes
and the availability of output from multiple higher-resolution global models,
this study seeks to develop and contrast climatological estimates of STE from
modern global reanalyses: the European Centre for Medium-range Weather
Forecasting Interim reanalysis (ERA-Interim), the Japanese Meteorological
Agency 55-year reanalysis (JRA-55), and the National Aeronautics and Space
Administration (NASA) Modern Era Retrospective analysis for Research and
Applications (MERRA) and version 2 of this system (MERRA-2). We apply a
Lagrangian approach using 3-D kinematic wind fields from each reanalysis to
compute STE during a 15-year period: 1996–2010. STE is further separated into
three regions in an attempt to evaluate the role of well-known large-scale
transport processes on the global scale: tropical, subtropical, and
extratropical. The direction of transport (TST or STT) is also separately
evaluated for each region. While small-scale mechanisms including gravity
wave breaking and convection are also known to contribute to STE, such
processes are not investigated in this study because they are not resolved in
the reanalyses.
Data and methods
Reanalysis model output
As outlined in Sect. 1, we employ output from four reanalysis models in
this study: ERA-Interim, JRA-55, MERRA, and MERRA-2. ERA-Interim is available
from 1979 to the present on an approximately 80 km horizontal grid and at 60 vertical model levels with a model top of 0.1 hPa . JRA-55
is available from 1958 to the present on a ∼ 60 km horizontal grid
. Similar to ERA-Interim, JRA-55 has 60 vertical
model levels with a model top of 0.1 hPa. MERRA is available from 1979 to 2016
at 0.5∘ × 0.667∘ horizontal resolution and at 72 vertical model levels with a model top of 0.01 hPa .
MERRA-2 has a similar design to MERRA, but is available at a slightly finer
horizontal resolution of 0.5∘ × 0.625∘ from
1979 to the present . Although the horizontal and
vertical resolution is similar in MERRA and MERRA-2, numerical improvements
in MERRA-2 are expected to better represent UTLS processes. We use both MERRA
and MERRA-2 to show improvements with respect to UTLS processes, specifically
STE. For a more detailed discussion of reanalyses and their differences, see
.
Furthermore, we employ 3-D output at 6 h intervals from each reanalysis in
this study. All of the data are interpolated to a regular 1∘ × 1∘ latitude–longitude grid for analysis. Model output is also used to
calculate secondary variables for analysis: tropopause pressure (using the
World Meteorological Organization lapse-rate tropopause (LRT) defined by
), potential temperature (θ), and potential
vorticity (PV) on the native model levels (i.e., no vertical interpolation is
performed). It is important to note that, while the interpolation to a
regular horizontal grid acts to slightly coarsen the original data, the
interpolation has no measurable effect on the LRT altitude distribution and,
given the 6 h time resolution, is expected to have little impact on
trajectory model solutions e.g., seeand references
therein.
Tropopause definition
The tropopause definition employed in STE studies is critical to their
outcome since it represents the boundary between troposphere and stratosphere
and the location where dynamical processes and associated transport are
evaluated. Many previous STE studies have used a “dynamical” tropopause to
represent the troposphere–stratosphere boundary, for which a PV isosurface
such as 2 PVU (1 PVU = 10-6 km2 kg-1 s-1) is
used . However, several studies demonstrate that STE
estimates can be largely sensitive to small changes in the PV isosurface
used. For example, their Fig. 6 made Lagrangian
estimates of STE using multiple control surfaces, including isobaric surfaces
and potential vorticity isosurfaces, and found downward mass flux ranged from
1 to 4 ×1017 kg year-1. used 30
years of ERA-Interim to produce a climatology of Rossby wave breaking events
and associated STE in the subtropics and demonstrated that varying the PV
boundary from 2 to 4 PVU resulted in a reversal of the net transport
direction (i.e., TST or STT). Despite these known sensitivities, many studies
have continued to employ a dynamic tropopause to avoid challenging STE
calculations in the vicinity of the sharp LRT discontinuity near the
subtropical jet known as the “tropopause break”
e.g.,. Because PV is a quasi-conserved quantity in an adiabatic
and frictionless flow, it is treated as a quasi-material surface and can
provide a continuous boundary through the tropopause break. An additional
aspect of a PV-based method that is problematic for global STE studies is the
fact that PV values converge to zero at the Equator, such that an isosurface
does not coincide with the altitude of the tropical tropopause south of the
tropopause break. As a result, most studies that use a PV-based method in the
extratropics select an alternative surface to represent the tropopause in the
tropics (e.g., cold point altitude, isentropic surface).
The LRT, on the other hand, has been shown to commonly coincide with the
sharpest stability and chemical transitions between the troposphere and
stratosphere globally
e.g.,. This is due
to the fact that a single PV value does not coincide with the LRT and
chemical transition everywhere, with PV values at the tropopause ranging from
at least 1 to 6 PVU in the extratropics. As a result, some recent studies
advocate for and employ a PV gradient-based tropopause for STE studies
e.g.,, but such an approach continues
to suffer from an inability to represent the troposphere–stratosphere
transition in the tropics. One exception to the success of the LRT as a
reliable troposphere–stratosphere boundary is in the Antarctic region during
austral winter, where reduced stability in the upper troposphere leads to an
erroneously high LRT altitude . However, this issue
is limited to latitudes poleward of 60∘ S (∼ 6 % of Earth's area)
and about 3 months out of the year. For these reasons, we use the LRT to
represent the global troposphere–stratosphere boundary in this study but
leverage beneficial information from PV analyses to identify irreversible
transport. A detailed outline of this approach is provided in the following
subsection. Apart from infrequent biases in the LRT altitude, its uncertainty
is comparable to the UTLS vertical resolution of the dataset used, which
varies amongst the reanalysis models. An approximate altitude range of the
UTLS is 8–14 km in the extratropics and 13–18 km in the tropics. Vertical
resolution in MERRA and MERRA-2 is about 1 km throughout the extratropical
and tropical UTLS, whereas JRA-55 and ERA-Interim vertical resolution ranges
from about 750 m in the extratropical UTLS to about 1100 m in the tropical
UTLS. These differences in resolution may contribute to differences in STE
found between the reanalyses outlined in the remainder of the paper.
STE identification
Trajectory calculations (∼6×109) in this study are performed
using the TRAJ3D model developed by and updated in
. Parcels are initialized daily at 00:00 UTC every
1∘ in longitude and latitude and every 20 hPa at altitudes relative
to the LRT. Analogous to STE methods in previous studies, preliminary
selection of STE parcels is dependent upon whether trajectories cross the LRT
within the initial 24 h downstream. This selection is a straightforward
process and only requires the initial and final parcel pressure and
coincident tropopause pressures. For instance, if a parcel pressure is
initially lower than its coincident tropopause pressure and one day
downstream the parcel pressure is greater than its coincident tropopause
pressure, it is flagged as possible STT (regardless of the geometric
evolution of the parcel). All potential STE parcels are then advected 5 days
forward and backward in time from the initial parcel locations for further
analysis.
To ensure that transient (intermittent tropopause-crossing) STE parcels are
not erroneously counted and represented as irreversible exchange, a filtering
method is applied. Two criteria are necessary to identify irreversible
transport: (i) a residence time (τ) and (ii) a parcel PV change
occurring during the 10-day trajectory period. Based on a sensitivity study
by , a long residence time, longer than 24 h,
can decrease estimates of irreversible transport and change the direction of
the annual net STE. Also, a 4-day residence time allows STE processes with
longer transport timescales to be identified. Here, we chose a strict
residence time criteria, τ, of 96 h. The second filtering criteria
require an absolute PV change of 0.5 PVU from the initial parcel value to
that 5 days downstream. The PV criteria represent a dynamic change in a
parcel's characteristics from the influence of diabatic or frictional effects
(i.e., mixing). Parcels that meet the required criteria are retained as
irreversible exchange.
To examine irreversible STE mass flux, we compute the mass of each parcel
based on the following equation:
M=1ga02cosϕΔλΔϕΔp,
where Earth's gravitational acceleration and radius are denoted by g and
a0, respectively, and ϕ, λ, and p represent latitude,
longitude, and pressure scales of each parcel. Since the parcel resolution is
constant, parcel mass decreases from the Equator to the poles. Therefore, a greater
number of transported parcels are required in the extratropical and polar
latitudes to achieve equivalent STE to that in the tropics and subtropics. We
bin STE parcels on a global grid with a longitude–latitude resolution of
2∘ for analysis. For this purpose, the 1-day downstream parcel
locations are used in an attempt to better represent the locations where STE
occurred. Slight differences in the locations of STE in the comparisons with
Š14 in Sect. (below) may be due to this choice,
since Š14 use their initial parcel locations for binning.
Categorizing STE
In an attempt to evaluate the role of individual large-scale STE processes
globally, we separate transport into three regional categories: tropics,
subtropics (i.e., between the tropical UT and extratropical LS), and
extratropics. STT and TST occur in each of the three regions and are counted
separately. For large-scale STE processes, exchanges in the subtropics are
known to correspond primarily with Rossby wave breaking, which is a
quasi-horizontal (or isentropic) process. Exchanges in the extratropics and
tropics, however, are associated with other processes, such as extratropical
cyclones, stratospheric intrusions, downwelling in the extratropics and
upwelling in the tropics.
Hemispheric schematic of the STE identification method employed in
this study. STT and TST are shown by orange and blue arrows, respectively.
Bold and wavy arrows represent the Brewer–Dobson circulation and
quasi-isentropic mixing processes, respectively. Potential temperature
surfaces are given by the cyan lines, the lapse-rate tropopause by the black
lines, and the 2 PVU potential vorticity (PV) isosurface by the green line.
The solid gray line, coincident with the tropopause break (dashed black
line), represents the boundary between the tropics and extratropics.
Subtropical and polar jet streams are shown by solid purple
isotachs.
We classify regions as tropical if they lie equatorward of the “tropopause
break” and extratropical if they lie poleward of the break. Similar to
previous studies, the tropopause break is defined as the LRT altitude
frequency minimum between tropical (15–17 km or < 150 hPa) and
extratropical (8–12 km or > 150 hPa) modes in global and hemispheric
distributions e.g.,and Fig.
here. Analysis of tropopause altitudes
in each reanalysis model reveals that, despite slight differences in LRT
altitudes, the tropopause break can be routinely identified in each using a
tropopause pressure threshold of 150 hPa. Therefore, we set trajectories with
STE as tropical if the tropopause pressure at the initial parcel location is
less than 150 hPa, and extratropical otherwise.
Similarly, classifying subtropical STE involves evaluating both its
tropopause break-relative location and tropopause-relative altitude during
advection. In particular, if a parcel is initially located below the tropical
tropopause and located above the extratropical tropopause 5 days downstream,
it will be flagged as subtropical
TST (i.e., exchanged poleward thorough the tropopause break). Alternatively,
if a parcel is initially located above the extratropical tropopause and
located below the tropical tropopause 5 days downstream, it is flagged as
subtropical STT (i.e., exchanged equatorward through the tropopause break).
Remaining parcels are flagged as either exchange occurring only in the
extratropics or tropics. There is one important exception that must be
accounted for: stratospheric intrusions below the subtropical jet. In order
to identify these parcels as STE in the extratropics and not in the
subtropics, we require an additional condition for STT in the subtropics:
both initial and final parcel pressures must be less than (above) the initial
extratropical tropopause pressure. See Fig. for a detailed
schematic of the identification method.
December, January, and February (DJF, top row) and June, July, and
August (JJA, bottom row) mean STT mass fluxes for 1996–2010 using the
lapse-rate tropopause method (left) and for 1979–2011 using the dynamic
tropopause method (right; from ).
The same as in Fig. but for TST.
STE method comparison
As previously mentioned, there can be significant variability in
climatological STE estimates based on the tropopause definition chosen to
analyze exchange. Here, we use our STE geographic distributions from
ERA-Interim to briefly illustrate some of the differences and similarities
between our approach (i.e., using the LRT) and the recent STE climatology
presented by Š14 that employs a dynamic tropopause (i.e., a PV isosurface
of ±2 PVU) in the extratropics and the 380 K isentrope in the tropics.
In Fig. , the global summertime and wintertime geographic
distributions of STT are shown for each approach. During both seasons, the
locations of STT maxima and minima are largely similar between the methods,
but the magnitude of STT mass flux is significantly larger using the PV-based
tropopause definition, particularly in the extratropics.
On the other hand, TST geographic distributions (Fig. ) are
shown to be quantitatively and qualitatively different. Similar to the STT
comparisons, PV-based TST is larger than that found with the LRT method in
most places. However, there is a unique latitudinal dependence of the
differences, with the largest differences found in the polar regions of both
hemispheres. For the LRT method, global TST maxima are found within the
tropics (i.e., monsoon anticyclones and ubiquitous tropical upwelling), while
TST in the extratropics and especially the polar regions is found to be
comparatively weak. The opposite relationship is found using a dynamic
tropopause. According to our knowledge of large-scale processes associated
with TST, the geographic distributions from the LRT-based climatology (left
column Fig. ) agree more closely with known transport
mechanisms e.g.,.
The differences between the two methods may be rooted in the altitude
placement of the tropopause definition used. The ±2 PVU surface (dynamic
tropopause used in Š14), while at times may reside at similar altitudes
compared to the LRT, often resides at lower altitudes with respect to the LRT
e.g.,. Therefore, UT stirring or mixing may lead to erroneously
flagged STT or TST parcels when using the dynamic tropopause.
Annual mean geographic distributions of total (sum of tropical,
subtropical, extratropical) STE mass flux for ERA-Interim (a, b),
JRA-55 (c, d), MERRA-2 (e, f), and MERRA (g, h).
STE is separated into STT (left) and TST (right) for each reanalysis.
Results
Throughout the following section, comparisons of STE among the four reanalysis
systems are shown using various metrics. Here, we seek to reveal important
similarities and differences in STE and the associated subcategories of
exchange between the reanalyses.
STE geographic distribution
Global spatial distributions of annually averaged STT and TST mass fluxes are
shown in the right and left columns of Fig. ,
respectively. All of the models are similar regarding peak STT regions, with
the largest differences found in contrasting the magnitudes of downward
transport. Total STT mass fluxes are maximized along the Northern Hemisphere
(NH) Atlantic and Pacific extratropical storm tracks in each reanalysis
model. Among the four reanalyses, ERA-Interim and JRA-55 STT mass fluxes are
largest (Fig. a and c) with a
maximum of ∼ 300 kg s-1 km-2 in the core of NH cyclone tracks.
In the Southern Hemisphere (SH) the STT mass fluxes are largest within the
subtropical latitudes along the western coasts of the continents. The largest
SH STT maximum in each reanalysis is located along the subtropical coast of
Chile. This is a confined but dominant region of STT mass flux and is
greatest in JRA-55 (∼ 350 kg s-1 km-2) and weakest in MERRA
(∼ 200 kg s-1 km-2). Another common region of elevated STT in
the SH occurs over a broad area in the extratropics along the west coast of
Antarctica. A noteworthy difference among reanalyses regarding STT mass
fluxes is found within the tropics, where the location of peak STT mass flux
varies considerably. Peak regions of tropical STT are found along the Equator
across the Indian Ocean in JRA-55 and ERA-Interim, but are found to be
displaced south of the Equator in both MERRA reanalyses.
The same as in Fig. but for STE in the extratropics.
Annually averaged geographic distributions of TST mass fluxes show
considerably larger magnitudes and broader maxima within the tropical
latitudes compared to STT (right column Fig. ).
Spatially, maxima in tropical TST mass fluxes coincide with minima in STT
mass flux. Distinct maxima of tropical TST mass fluxes are evident in the
South China Sea, the west Pacific, the Caribbean Sea, and southeast Asia.
Within the subtropics, a narrow band of TST extends from China into the east
Pacific and is consistent among the reanalyses. Comparison of TST among the
reanalyses reveals two modes: regionally (or latitudinally) symmetric in
ERA-Interim and JRA-55, and regionally asymmetric in MERRA and MERRA-2. The
regional asymmetry in MERRA and MERRA-2 is due to significantly enhanced TST
mass fluxes in the extratropics compared to the remaining reanalyses, with
extratropical TST near ∼ 400 kg s-1 km-2 globally in MERRA and
MERRA-2 and near ∼ 200 kg s-1 km-2 in ERA-Interim and JRA-55.
Another inconsistency among the reanalyses is the location of peak TST in the
tropical western Pacific. The peak tropical TST location in JRA-55 and
ERA-Interim is highest in the NH but extends across the Equator, whereas
MERRA and MERRA-2 show a peak located mostly in the NH.
The same as in Fig. but for the STE in the tropics.
To better understand the differences and similarities of STT and TST
geographic distributions, we can decompose both transport directions into
separate regions of exchange to determine their contributions to the
differences observed in total STE. The extratropical spatial distributions of
STT and TST are shown in Fig. . One significant
characteristic shown is the dominance of STT over TST in the extratropics
indicated by ERA-Interim, JRA-55, and MERRA-2, especially over the NH and SH
storm tracks. MERRA, however, shows the opposite behavior. As in the global
STT distributions, the largest STT mass fluxes in the extratropics coincide
with the NH cyclone tracks. In the SH, the STT mass fluxes are largest
poleward of 60∘ S. Also, STT maxima across the subtropical SH,
particularly the Chilean coast, are detected but magnitudes are weaker than
those in total STT. More generally, STT mass fluxes in the extratropics are
largest in JRA-55 and weakest in MERRA. TST in the extratropics, similar to
STT, is found to be spatially consistent amongst the reanalyses, with the
only apparent differences being the localized maxima near 60∘ N in
MERRA and MERRA-2 that are not observed in ERA-Interim or JRA-55. In
addition, TST in the extratropics in MERRA is generally larger than the
remaining reanalyses.
The same as in Fig. but for STE in the subtropics.
STT and TST mass fluxes and their geographic distributions within the tropics
are shown in Fig. . While both TST and STT mass
fluxes are similar in magnitude between the reanalyses, there are slight
offsets in the location and width of the identified maxima. For example, no
two models agree on the precise location, zonal extent, or meridional extent
of the TST maxima in the western Pacific. The offsets in STT and TST maxima
outlined in the discussion of Fig. above are also
evident in the maps of STT in the tropics.
Lastly, Fig. shows geographic distributions of STT
and TST in the subtropics (i.e., which occur between the tropics and extratropics across the tropopause break).
These maps reveal that transport in the subtropics is (1) generally weaker
than transport in the tropics and extratropics in each model, (2) dominated
by poleward transport through the tropopause break in each hemisphere (i.e.,
TST), and (3) preferentially distributed over the ocean basins and extends
poleward downstream of the ocean basins in agreement with known
evolution of Rossby wave breaking events; e.g., see Fig. 9
from. In reference to the total STE geographic
distributions, it is evident that the influence of poleward and equatorward
transport in the subtropics directly corresponds to the maxima in total STT
and TST, which is expected due to its association with the tropopause break.
STE totals
In an effort to quantitatively summarize some of the apparent differences
identified in the analysis of geographic distributions, globally integrated
and annually averaged STE mass fluxes from each reanalysis are provided in
Table . Total STT mass flux is similar among JRA-55,
ERA-Interim, and MERRA-2, while the STT mass flux in MERRA is at least 25 %
lower. In the other direction, TST mass flux totals are significantly higher
in MERRA (∼ 51 %) and MERRA-2 (∼ 29 %) compared to those from
ERA-Interim and JRA-55.
Net STE is expected to be near zero, or balanced, over a long time period as
a result of mass continuity. However, all reanalyses result in a net exchange
that is either positive or negative (i.e., net TST or STT, respectively). Net
mass fluxes in JRA-55 and ERA-Interim are both negative (STT dominant), but
the net flux amounts to only ∼ 4 % of the total flux
(TST plus STT). However, net mass fluxes in MERRA and MERRA-2 are both
positive and amount to about 33 and 12 % of their total fluxes,
respectively.
Globally integrated and annually averaged STE mass fluxes for each
reanalysis model. STT, TST, net (TST minus STT), and gross (TST plus STT) mass fluxes
are given for each transport region (i.e., total/global, extratropical,
tropical, and subtropical). All mass flux units are 1010 kg s-1.
Total (global) STE
Reanalyses
STT
TST
Net
Gross
JRA-55
5.83
5.29
-0.54
11.12
ERA-Interim
5.56
5.10
-0.47
10.66
MERRA-2
5.16
6.57
1.41
11.73
MERRA
3.86
7.74
3.88
11.60
Extratropical STE
Reanalyses
STT
TST
Net
Gross
JRA-55
4.53
2.30
-2.23
6.82
ERA-Interim
4.29
2.17
-2.12
6.47
MERRA-2
3.94
2.97
-0.97
6.92
MERRA
2.65
3.91
1.26
6.57
Tropical STE
Reanalyses
STT
TST
Net
Gross
JRA-55
0.77
1.81
1.04
2.58
ERA-Interim
0.72
1.69
0.96
2.41
MERRA-2
0.75
1.71
0.96
2.45
MERRA
0.74
1.64
0.90
2.38
Subtropical STE
Reanalyses
STT
TST
Net
Gross
JRA-55
0.54
1.19
0.65
1.73
ERA-Interim
0.55
1.24
0.69
1.79
MERRA-2
0.47
1.89
1.42
2.36
MERRA
0.47
2.19
1.72
2.66
Table also includes the integrated mass fluxes of the three
transport subregions. Net tropical STE fluxes are positive and of similar
magnitude in all four reanalyses, while extratropical STE fluxes are negative
(STT dominant) in JRA-55 and ERA-Interim, marginally negative in MERRA-2, and
positive (TST dominant) in MERRA. Similar to tropical STE, net subtropical
fluxes are all positive (TST dominant), with magnitudes in MERRA-2 and MERRA
2–3 times as large as those in ERA-Interim and JRA-55.
STE meridional distributions
Annually and zonally integrated latitudinal distributions of STE (TST, STT,
and net) are shown in Fig. . Meridional distributions,
similar to geographic distributions, demonstrate the latitudinal dependence
of STE and offer a more quantitative comparison of the regional differences.
Once again, we show both total STE and that separated by the regions of
transport in the meridional distributions (with tropical and extratropical
STE combined in this case).
Total STE meridional distributions (Fig. a) show
similar latitudinal variations in TST and STT in each model, but differences
in the magnitude of each transport direction lead to large differences in net
STE, especially in the extratropics. The meridional distributions of STE in
the extratropics and tropics combined (Fig. b) and in
the subtropics (Fig. c) demonstrate that the majority
of these differences can be attributed to extratropical and tropical STE,
though TST plays an important role in the subtropics and is clearly much
larger in the MERRA and MERRA-2 reanalyses (especially in the NH).
Annually and zonally averaged meridional distributions of STE from
each reanalysis as a function of latitude and for (a) total STE,
(b) extratropical and tropical STE combined, and
(c) subtropical STE. STT is shown as the dotted lines (negative),
TST as the dashed lines (positive), and the net transport is given by the
solid lines in each panel. STE from JRA-55 is shown by the purple lines,
ERA-Interim by the blue lines, MERRA-2 by the light red lines, and MERRA by
the dark red lines.
Annual cycles of (top row) normalized STT and (bottom row)
normalized TST for the (left) extratropical Northern Hemisphere and (right)
extratropical Southern Hemisphere from each reanalysis model. In each plot,
the solid colored lines are the mean annual cycles and the colored error bars
are plus/minus 1 standard deviation from the mean. STE from JRA-55 is shown
by the purple lines, ERA-Interim by the blue lines, MERRA-2 by the light red
lines, and MERRA by the dark red lines. Note that NH and SH annual cycles are
offset by 6 months.
Annual cycles of (a) normalized TST and
(b) normalized STT for the tropics from each reanalysis model. As in
Fig. , the solid colored lines are the mean
annual cycles and the colored error bars are plus/minus 1 standard
deviation from the mean. STE from JRA-55 is shown by the purple lines,
ERA-Interim by the blue lines, MERRA-2 by the light red lines, and MERRA by
the dark red lines.
Annual cycles
In addition to understanding regional differences in STE, examining
differences in the seasonality of transport can be important for determining
the significance of STE on UTLS composition throughout the year. Annual
cycles of normalized STT and TST in the extratropics are presented in Fig. and separated by hemisphere, while normalized
annual cycles for the tropics are shown in Fig. . Mass fluxes of STT and TST are normalized
using the maximum and minimum monthly means over the 15-year period:
Ni=month‾i-MINmonth‾MAXmonth‾-MINmonth‾,
where month‾i is the monthly mean for each month (i=1–12) and Ni is now the ith normalized monthly mean mass flux.
Normalized STT mass fluxes are similar among the models in the NH and SH
extratropics, but the hemispheres differ in the timing of annual minimum and
maximum STT. Annual STT is at maximum and minimum in late winter (DJF) and
late summer (JJA) in the NH, while the maximum and minimum STT occur during
early autumn (MAM) and spring (SON) in the SH. One unique difference in STT
is apparent in the SH during austral summer, where MERRA-2 normalized mass
fluxes are considerably smaller during those months compared to the other
reanalyses. Although the reanalyses show similar seasonality for STT, there
are some differences in monthly variability. While there are no uniform
differences in variability within the hemispheres or among the reanalyses,
the monthly variabilities are considerably larger in the SH. Annual cycles of
extratropical TST (bottom row of Fig. ) have
similar modes and hemispheric differences in monthly variability to that of
STT, especially in the NH. However, there is an apparent 1- to 2-month offset
in the minima and maxima of TST and STT in each hemisphere.
There are more apparent differences in the normalized annual cycles of TST
and STT in the tropics (Fig. ). In particular,
normalized TST in the tropics reveals two preferred seasonal cycles. In
JRA-55 and ERA-Interim, TST is weakly bimodal, with local maxima occurring
during the NH winter (DJF) and summer (JJA). MERRA and MERRA-2, on the other
hand, are broadly unimodal and reach a maximum during the NH summer and
minimum in NH fall (SON). STT seasonality in the tropics is more consistent
amongst the reanalyses, with general agreement of STT peaking in the late NH
summer and early fall.
The same as in Fig. but for non-normalized
total net STE (top row), combined extratropical and tropical net STE (middle
row), and subtropical net STE (bottom row).
While the normalized annual cycles of TST and STT demonstrate the seasonality
of STE, they do not represent amplitudes (i.e.,
MAX(month‾)-MIN(month‾)) of seasonality among the
reanalyses. Table provides annual cycle amplitudes for
each transport direction, transport region, and hemisphere. Annual cycle
amplitudes are similar among the reanalyses for extratropical STT,
subtropical TST, and tropical TST, while largely different for extratropical
TST (especially in the NH), subtropical STT, and tropical STT.
Annual cycles of net STE are shown in the top row of Fig. and left non-normalized to show the combined
effects of differences in seasonality and in dominance of STE pathway. MERRA
and MERRA-2 show a positive net cross-tropopause mass flux (TST) throughout
their annual cycles in the NH. Alternatively, JRA-55 and ERA-Interim exhibit
a NH seasonal cycle that is STT dominant in the winter and early spring and
TST dominant in the summer. In the SH, JRA-55 and ERA-Interim again show
consistent seasonality and are STT dominant through most of the year and
only briefly positive during the summertime (DJF). While similar in shape,
MERRA-2 exhibits positive net exchange that spans all seasons but winter
(JJA). MERRA, as in the NH, exhibits only positive net STE and a weaker
annual cycle compared to the other reanalyses. The largest monthly net STE
mass flux variability is found in the annual cycles of MERRA.
STT and TST annual cycle amplitudes in the extratropics, subtropics,
and the tropics from each reanalysis model. All amplitudes are in units of
109 kg s-1 and annual cycles for the extratropics and subtropics
are separated by hemisphere.
STTEx
TSTEx
Reanalysis
NH
SH
NH
SH
JRA-55
13.18
4.91
6.13
12.92
ERA-Interim
14.19
3.90
6.23
10.68
MERRA-2
14.88
5.94
11.4
12.92
MERRA
10.64
6.37
13.61
10.47
STTSub
TSTSub
Reanalysis
NH
SH
NH
SH
JRA-55
1.11
3.49
2.70
6.07
ERA-Interim
1.36
3.76
2.89
5.71
MERRA-2
0.67
7.01
2.85
4.21
MERRA
0.96
8.19
2.88
5.50
Reanalysis
STTTropic
TSTTropic
JRA-55
5.05
5.92
ERA-Interim
4.67
5.91
MERRA-2
2.06
4.23
MERRA
2.35
5.03
The same as in Fig. but for the
(a) tropics, (b) NH extratropics, and (c) SH
extratropics.
Annual cycles of the transport categorized by region also reveal important
differences in the seasonality of STE and the contribution of individual
processes to the total annual cycles. Net STE annual cycles in the
extratropics and tropics (combined) and in the subtropics are given in Fig. for both hemispheres. Generally, the seasonality
of net mass flux in the extratropics and tropics is similar in shape to the
net annual cycle of total STE in each hemisphere, with a few important
differences. Specifically, the combined extratropics and tropics net STE
annual cycle of MERRA-2 is shown to be STT dominant during the NH wintertime,
whereas the total net STE indicates positive net exchange through all
seasons. The SH combined extratropics and tropics net STE annual cycles are
not significantly different from those represented by total net STE, aside
from a negative shift (i.e., a greater influence of STT). Figure shows tropical exchanges removed from the
extratropics and reveals that annual cycles of STE in the tropics are similar
among the reanalyses. This demonstrates that the separation in STE annual
cycles in the combined hemispheric analysis (middle row of Fig. ) is due to STE in the extratropics.
For each reanalysis, time series of globally integrated (top) total,
(bottom) subtropical, and (middle) combined extratropical and tropical STT
(left) and TST (right) mass fluxes that are with respect to the 15-year study
period mean (1996–2010) are shown. The thin lines represent the monthly mean mass
fluxes, while the bold lines are the result of applying a high-pass filter to
a Fourier transform of each time series (power at timescales
less than or equal to 12 months is removed). STE from JRA-55 is shown by the purple lines,
ERA-Interim by the blue lines, MERRA-2 by the light red lines, and MERRA by
the dark red lines.
For annual cycles of transport in the subtropics, all four reanalyses show
similar seasonal behavior, with a minimum in late spring and early summer and
a maximum during the late fall and early winter, and are TST dominant in both
hemispheres. Annual cycles of subtropical transport in MERRA and MERRA-2 have
a slightly larger amplitude than those in ERA-Interim and JRA-55 and are
displaced at higher net positive fluxes, which is consistent with the
analyses presented in Sect. and
above.
STE time series
Time series of STE are examined over the 15-year period to further evaluate
similarities and differences between the reanalysis models. In Fig. , global time series of STT anomalies and TST anomalies are
shown with respect to their mean mass fluxes (i.e., 15-year means are removed).
STT throughout the period shows two modes of long-term changes in the
reanalyses: increasing mass fluxes over time in JRA-55 and ERA-Interim and
decreasing mass fluxes over time in MERRA and MERRA-2. Similar long-term
increases in TST can be seen in the ERA-Interim and JRA-55 time series. In
the MERRA reanalyses, however, long-term changes in TST are shown to be
largely increasing in MERRA and near zero in MERRA-2.
The same as in Fig. but for STE in the (top) tropics and
(bottom) extratropics.
STE time series separated by region (combined extratropical and tropical, and
subtropical) are shown in the middle and bottom rows of Fig. . Combined extratropical and tropical STT shows similar
patterns and variability to that from total STT for each reanalysis. Mainly,
there are decreasing combined extratropical and tropical STT mass fluxes in
MERRA and MERRA-2 and increasing fluxes in JRA-55 and ERA-Interim. In a
similar manner, combined extratropical and tropical TST is increasing over
the period in ERA-Interim, JRA-55, slightly decreasing in MERRA-2, and
strongly increasing in MERRA. Subtropical STE time series for all reanalyses
show little to no long-term variability. There is one exception, however,
with subtropical TST in MERRA showing a potential long-term increase in mass
flux.
The long-term increases and decreases in STE identified in Fig. are associated primarily with extratropical and tropical
STE and are considerably large relative to the mean, especially given the
relatively short time period analyzed. In order to better understand the
source of these changes we also analyzed time series of extratropical and
tropical STE separately (Fig. ). Based on the analyses
presented thus far and conventional STE knowledge, transport in the tropics
is primarily upward (TST), while it is primarily downward (STT) in the
extratropics (with the exception of MERRA). These geographically separated
upward STE modes are largely the result of the BDC. Thus, we expect long-term
changes in TST in the tropics and STT in the extratropics to be consistent.
Figure demonstrates this well, with negligible
long-term changes in STT in the tropics and apparent STT changes in the
extratropics, and the opposite behavior for TST (except for MERRA).
Specifically, JRA-55 and ERA-Interim show increasing STT in the extratropics
and TST in tropics, whereas MERRA-2 shows decreasing mass fluxes over the
15-year period.
The observed consistency in the sense of the long-term changes of TST in the
tropics and STT in the extratropics in ERA-Interim, JRA-55, and MERRA-2
suggests that changes in the BDC may be responsible for this behavior.
Specifically, the increasing fluxes for tropical TST and extratropical STT
over time in JRA-55 and ERA-Interim indicate an acceleration
of the BDC, while the decreases in
MERRA-2 indicate a deceleration of the BDC. Changes in the speed of the BDC
have been examined in previous studies. For example,
evaluated the dynamics of the BDC using ERA-Interim, JRA-55, and MERRA and
show that there is general agreement in a strengthening BDC over the period
1979–2012 by 2–5 % per decade. Observational studies show decreases in
tropical stratospheric water vapor, ozone, and temperature observed by
satellite, which also corresponds to an increase in tropical upwelling
associated with an accelerated BDC . Chemistry–climate
models have also indicated an acceleration of the BDC over time
e.g.,. These previous reanalysis,
observational, and modeling studies are consistent with the results from
ERA-Interim and JRA-55 here, while MERRA-2 is in disagreement and MERRA does
not indicate changes in the BDC over time in our analysis.
Global occurrence frequency distributions of (left) STT and (right)
TST events for (a, b) ERA-Interim, (c, d) JRA-55,
(e, f) MERRA-2, and (g, h) MERRA.
Reanalysis model evaluations
This paper is largely a comparison of STE estimates using multiple
state-of-the-art reanalysis models, but we also briefly evaluate some model
differences through various diagnostics here. The goal is to provide general
context and logical reasoning to explain some of the aforementioned STE
differences among the reanalyses.
STE occurrence geographic distributions
In order to assess quantitative and qualitative STE differences, particularly
the larger differences found among the models' representation of TST, we have
to consider whether the frequency of STE events differs among the models.
Evaluating STE occurrence frequency in each reanalysis informs us whether the
amount of STE is a result of more frequent STE or differences in the
magnitude of transport in individual events.
In Fig. , total STT and TST occurrence frequencies are
shown. There are noticeable differences between the ERA-Interim and JRA-55
pair and the MERRA reanalyses. In particular, ERA-Interim and JRA-55 show
higher occurrence frequencies globally for STT, while the MERRA and MERRA-2
occurrence frequencies are higher for TST. Taken together with the results
from the geographic distributions of STE mass flux
(Figs. –), these analyses
suggest that differences in mass flux between the reanalyses are largely the
result of differences in the frequency of exchange events.
Probability density functions (PDFs) of vertical wind (omega;
Pa s-1) at the tropopause in the (top row) NH extratropics, (middle
row) SH extratropics, and (bottom row) tropics for each season (columns from
left to right are DJF, MAM, JJA, and SON, respectively). JRA-55 is shown by
the purple lines, ERA-Interim by the blue lines, MERRA-2 by the light red
lines, and MERRA by the dark red lines. Note that the vertical wind extremes
(i.e., those beyond the limits of the abscissa) are consolidated into the
leftmost and rightmost bins of each PDF.
Other diagnostics
The differences in STE occurrence and mass flux estimates among the models,
to some extent, are due to dynamical and/or physical differences between the
models. Over a long period, these small differences may result in considerable
variations in climatological evaluations of STE. Dynamical differences may
include variability in vertical winds among the reanalyses and in the
strength of the subtropical and polar jets. Physical differences, for
example, include the altitude of the tropopause and its variability among the
reanalyses. These dynamical and physical characteristics can impact a
reanalysis model's short- and long-term representation of STE. For example,
differences in STE can be the result of higher or lower tropopause altitudes
among the models if the position and strength of the 3-D wind fields are
similar in the UTLS.
PDFs of global tropopause pressure
(hPa) over the 15-year period (1996–2010), separated by season:
(a) DJF, (b) MAM, (c) JJA, and (d) SON.
JRA-55 is shown by the purple lines, ERA-Interim by the blue lines, MERRA-2
by the light red lines, and MERRA by the dark red lines.
As the largest differences in our comparison are those associated with STE in
the extratropics, comparing the magnitudes of vertical motion at the
tropopause may reveal a dynamical source of transport differences. In Fig. , probability density functions (PDFs) of vertical
motion are shown for each season and separated into the NH extratropics, SH
extratropics, and tropics (for a single year – 2003, but note that additional
years are similar). While these PDFs demonstrate well that vertical motion is
a bit stronger (i.e., with more frequent extremes) in JRA-55 and ERA-Interim,
there are not necessarily clear differences in the skewness of these PDFs that
support the differences in net STE outlined previously. This result may
suggest that differences in quasi-isentropic exchange are a source for the
observed differences. It is important to note, however, that the strongest
vertical winds (i.e., extremes of the PDFs) have higher frequencies in the SH
extratropics, which may help to explain the larger STE variability observed
there.
Physical differences among the reanalyses are very important for STE studies,
since identifying STE in the first place requires a definition of the
troposphere–stratosphere boundary (i.e., LRT or PV isosurface). To examine
such differences, we evaluate global PDFs of tropopause pressure from each
reanalysis for the entire 15-year period analyzed in this study, separated by
season (Fig. ). These PDFs show a bimodal distribution
in each season, with a tropical mode at pressures less than 150 hPa and an
extratropical mode at pressures greater than 150 hPa (as outlined
previously). However, there are consistent differences between the models
that are clear in the extratropical mode of the distribution. In particular,
extratropical tropopause pressures are skewed to lower values (or higher
altitudes) in MERRA and MERRA-2 compared to those in JRA-55 and ERA-Interim.
These differences suggest that offsets in the altitude of the tropopause may
be an important contributor to the dichotomy in net STE observed between
these two pairs of reanalyses, which was found to be greatest in the
extratropics.
Conclusions and discussion
In this study, we examined global characteristics of STE over a 15-year
period (1996–2010) using a trajectory model and output from multiple
reanalyses: ERA-Interim, JRA-55, MERRA-2, and MERRA. STE was separated into
three regions based upon the altitude of the tropopause in an attempt to
isolate known transport processes associated with STT and TST.
Principal conclusions
In contrast to the vast majority of previous work, this study used the
lapse-rate tropopause or LRT as the troposphere–stratosphere boundary rather
than an isosurface of potential vorticity (PV) or dynamic tropopause. In
order to demonstrate the impact of this choice for STE studies, we presented
a comparison of STE estimates using the LRT method and results from a recent
study that used a dynamic tropopause . We found
that
magnitudes of STE are uniformly smaller using the LRT,
spatial placement and variability of STT are similar between methods,
and
spatial placement and variability of TST are largely different, with the most significant differences found in the polar
regions.
These differences correspond to a change in the net transport direction in
the polar regions when using the LRT (i.e., STT dominant rather than
TST dominant, though the magnitudes in each case are small compared to the
global amounts). Such net transport at high latitudes from the LRT method is
more consistent with our established understanding of UTLS dynamics: net TST
in the tropics and net STT in the extratropics and polar regions.
The main focus of this paper was not a method comparison, but a comparison of
STE among four state-of-the-art atmospheric reanalyses. Doing so, we
separated transport into several regions in order to investigate the STE
climatologies both quantitatively and qualitatively. It was found that the
models can be grouped into two populations: STT dominant and TST dominant
(Table ). JRA-55 and ERA-Interim are STT dominant, while
MERRA and MERRA-2 are TST dominant. The net transport in the STT-dominant
reanalyses, however, is small relative to the total transport, while the
opposite is true for the TST-dominant reanalyses.
Geographic distributions and zonal mean latitudinal distributions revealed
important characteristics about the two reanalysis populations. Notably, the
largest differences in STE were found in the extratropics. Geographic
distributions of STT maxima were similar amongst all reanalyses, while the
opposite was true for TST. MERRA was typically an outlier relative to the
remaining reanalyses, but similar differences (though largely diminished)
were found between MERRA-2 and the STT-dominant reanalyses (ERA-Interim and
JRA-55). STE in the subtropics was found to be consistent geographically with
prior studies of Rossby wave breaking events along the tropopause break
e.g.,.
Although geographic placement and net transport for STE in the subtropics
were consistent among the models, the MERRA reanalyses showed roughly twice the
magnitude of net poleward transport from the tropical troposphere into the
extratropical lowermost stratosphere (i.e., TST).
Seasonality of STE amongst the reanalyses was also found to be similar for
some transport regions and directions and significantly different for others.
Similar to geographic consistencies observed, we found that STT and TST
annual cycles in the extratropics are consistent among the reanalyses and in
both hemispheres. However, seasonality of TST in the tropics was found to be
weakly bimodal in STT-dominant reanalyses and unimodal in the TST-dominant
reanalyses, while STT seasonality in the tropics showed smaller differences.
Larger differences were found for annual cycles of net STE from the
reanalyses, with MERRA showing little seasonality in each hemisphere.
Differences in net STE were shown to be associated primarily with
extratropical STE in each reanalysis.
Long-term changes were also investigated using time series analysis over the
15-year study period. These analyses indicated gradual increases and
decreases in STT and TST mass flux for the STT-dominant models and MERRA-2,
respectively. Further analyses suggested that long-term changes in total STE
are associated with either an acceleration or deceleration of the BDC.
Specifically, the BDC is apparently decelerating in MERRA-2 and accelerating
in JRA-55 and ERA-Interim from 1996 to 2010.
Finally, several diagnostics were applied to the reanalyses in order to shed
light on the sources of the STE differences. We found that differences in
transport are likely the result of differences in the frequency of
irreversible STE rather than the magnitude of individual events. We also
found the altitude of the tropopause between the STT-dominant and
TST-dominant models to differ considerably in the extratropics. An analysis
of vertical winds at the tropopause showed some differences between the
TST-dominant and STT-dominant reanalyses, but these were not necessarily
consistent with the nature of the STE differences found. Taken together,
these physical and dynamical differences may be significant sources of
variability for climatological analyses and it is likely that they contribute
to some of the STE differences observed in this study.
Discussion
The analyses in this study demonstrate that while there are some areas of
agreement in the magnitude, geographic distribution, and frequency of
large-scale STE among modern reanalyses, there are important differences that
can lead to varying conclusions of the impact of STE on UTLS composition, the
radiation budget, and climate. While this study is the first model comparison
of global STE estimates, there are some limitations that could be improved
upon in future work. First, the analysis time period could be increased. Each
reanalysis model used in this study has output available from 1979 to 2015,
roughly 2.5 times longer than that used here. Expanding the analysis period
may provide improved confidence in the statistical behavior of STE regarding
the long-term changes associated with the BDC (Sect. ).
An extended analysis period may also reduce variability in the seasonality
and regional distributions analyzed here and thus increase confidence in
those results. The primary challenges with this suggested expansion in the
analysis time period are the computational time required and the cost of data
storage (15 years of 6 h model output and trajectory model calculations from
the four reanalyses used here requires ∼ 10 TB of disk storage).
Second, while the present generation of the reanalysis models are significant
advancements for studies of UTLS dynamics and associated processes over
previous generations, model improvements can still be made in the UTLS. Given
the limited spatiotemporal observations of STE available, it is
understandable that model simulations of transport could differ considerably.
However, some of these differences are likely related to basic model choices
such as grid resolution. For example, the vertical grids are nearly
equivalent in ERA-Interim and JRA-55, which differ considerably from that
used in MERRA and MERRA-2. Notably, vertical resolution is finer at levels
below the tropical tropopause in ERA-Interim and JRA-55 but finer above the
tropical tropopause in the MERRA Reanalyses. Since the vertical grid
resolution and placement of vertical grid levels in the UTLS may have
important impacts on tropopause-relative analyses such as STE (especially
since current vertical resolution of reanalyses in the UTLS is ∼ 1 km),
it is important to understand the sensitivity of such analyses to this model
design choice.
Third, as referred to in the Introduction, large quantitative uncertainties
in STE exist from previous Eulerian and Lagrangian STE studies. In
particular, estimates for STE have often been limited to specific regions or
time periods or based on inadequate and/or incomplete methods (compared to
those possible with current methods and computational abilities). Here, we
found that mean net STE magnitudes also range considerably when an equivalent
method is applied to multiple modern reanalyses (e.g., see Table ). However, few previous studies enable direct comparison with
our estimates. In particular, the alternative PV-based approach by
is arguably the most direct, where ERA-Interim net
STE integrated globally over the 15-year period in our study is approximately
1.48×1017 kg year-1 downward (STT), while it is about 3.5 times
smaller (4.2×1016 kg year-1) in the study.
These differences do not necessarily suggest that one method is superior to
the other, but that two largely similar Lagrangian approaches can yield
substantially different results due to the employed troposphere–stratosphere
boundary (e.g., see Figs. and ).
Lastly, while this study compared STE among reanalyses and attempted to
diagnose potential sources of those differences, much more work can be done
to examine them. The clearest source of STE differences found was related to
STT and TST event frequencies (Fig. ), but differences in
the vertical winds and extratropical LRT distributions shown in Figs. and may imply that the dynamical
regimes evaluated for exchange in each reanalysis are slightly different. One
approach to further evaluate the role of these differences in dynamics and
boundary conditions may be to combine tropopause altitudes from one
reanalysis with the winds of another reanalysis for STE trajectory
calculations. Such analyses may lead to future improvements in the model
grids and numerics, especially in the UTLS.