Stratospheric water vapor (SWV) plays important roles in the radiation budget
and ozone chemistry and is a valuable tracer for understanding stratospheric
transport. Meteorological reanalyses provide variables necessary for
simulating this transport; however, even recent reanalyses are subject to
substantial uncertainties, especially in the stratosphere. It is therefore
necessary to evaluate the consistency among SWV distributions simulated using
different input reanalysis products. In this study, we evaluate the
representation of SWV and its variations on multiple timescales using
simulations over the period 1980–2013. Our simulations are based on the
Chemical Lagrangian Model of the Stratosphere (CLaMS) driven by horizontal
winds and diabatic heating rates from three recent reanalyses: ERA-Interim,
JRA-55 and MERRA-2. We present an intercomparison among these model results
and observationally based estimates using a multiple linear regression method
to study the annual cycle (AC), the quasi-biennial oscillation (QBO), and
longer-term variability in monthly zonal-mean H2O mixing ratios
forced by variations in the El Niño–Southern Oscillation (ENSO) and the
volcanic aerosol burden. We find reasonable consistency among simulations of
the distribution and variability in SWV with respect to the AC and QBO.
However, the amplitudes of both signals are systematically weaker in the
lower and middle stratosphere when CLaMS is driven by MERRA-2 than when it is
driven by ERA-Interim or JRA-55. This difference is primarily attributable to
relatively slow tropical upwelling in the lower stratosphere in simulations
based on MERRA-2. Two possible contributors to the slow tropical upwelling in
the lower stratosphere are suggested to be the large long-wave cloud
radiative effect and the unique assimilation process in MERRA-2. The impacts
of ENSO and volcanic aerosol on H2O entry variability are
qualitatively consistent among the three simulations despite differences of
50 %–100 % in the magnitudes. Trends show larger discrepancies among the
three simulations. CLaMS driven by ERA-Interim produces a neutral to slightly
positive trend in H2O entry values over 1980–2013
(+0.01 ppmv decade-1), while both CLaMS driven by JRA-55 and CLaMS
driven by MERRA-2 produce negative trends but with significantly different
magnitudes (-0.22 and -0.08 ppmv decade-1, respectively).
Introduction
Water vapor is one of the most influential greenhouse
gases , modulating not only the surface radiative
forcing but also stratospheric
ozone loss e.g.,. The extreme dryness of the
stratosphere results from “freeze-drying” of air entering the stratosphere,
as initially explained in relation to the Brewer–Dobson
circulation BDC;. Mixing ratios of water vapor in the
lower stratosphere are extremely low as a result, but nonetheless vary
substantially in time and space.
Substantial inconsistencies among observational estimates of stratospheric
water vapor (SWV) remains, including both balloon-based and satellite-based
instruments e.g.,. Artificial
jumps introduced by changes in the observing system influence the reanalysis
temperatures through the assimilation, which affect the reanalysis water vapor. The inconsistencies are
even more pronounced among atmospheric reanalyses and independent
observations . Uncertainties in balloon- or aircraft-based
observations arise primarily due to insufficient spatiotemporal
coverage and the unreliability of operational sondes at
stratospheric altitudes e.g.,. Inconsistencies in
satellite observations of SWV reflect limited observational periods and short
overlap times , which make it difficult to control for
platform-specific biases or differences in temporal or spatial sampling
patterns . Observations of SWV are rarely
assimilated in reanalysis systems, for which estimates of SWV are effectively
model products, in some cases nudged to climatologies .
Differences in H2O observations in the upper troposphere and
stratosphere and the unreliability of reanalysis estimates of SWV have
motivated several efforts to merge observational records from different
satellites . Such homogenized
records facilitate analysis of long-term changes in SWV across the most
recent 2–3 decades.
Chemical transport models (CTMs) provide another approach to understanding
the multitimescale variability and global distribution of
SWV e.g.,. Water vapor values entering the
stratosphere are determined primarily by the lowest temperature along their
advective transport pathway in the tropical tropopause layer
(TTL) . Lagrangian approaches
provide accurate records of the temperature histories of air parcels that
Eulerian models cannot provide, and therefore provide more reliable
representations of entry mixing ratios in
SWV e.g.,. This distinction is
particularly relevant around the tropopause, where temperature gradients are
both large and highly variable . In addition to
transport across the tropical tropopause, photochemical oxidation of methane
(CH4) is an important source of SWV, especially in the middle and
upper stratosphere. Previous studies have concluded that recent increases in
CH4 have substantially contributed to long-term variability in
H2O in the stratosphere e.g.,. In this study, we use a forward Lagrangian transport model with
implanted methane chemistry to study the climatological features of SWV, and
compare the results against observational estimates from the Stratospheric
Water and OzOne Satellite Homogenized (SWOOSH) dataset version
2.5 and the Aura Microwave Limb Sounder (MLS) version
4 .
Key aspects affecting the performance of CTMs with respect to SWV are the
meteorological fields selected to drive the transport and freeze-drying in
TTL. Modern meteorological reanalyses, as “best estimates” of the
historical evolution of the atmospheric state , are
commonly used to provide the necessary meteorological variables for CTMs.
Current widely used reanalysis products include the European Centre for
Medium-Range Weather Forecasts (ECMWF) Interim
Reanalysis ERA-I;, the Japanese 55 year
Reanalysis JRA-55;, the Modern-Era Retrospective
Analysis for Research and Applications version
2 MERRA-2;, and the Climate Forecast System
Reanalysis CFSR;. Substantial uncertainties among these
reanalyses have been identified by previous studies, including significant
differences in temperature and wind structures e.g.,, diabatic heating rates in the tropical upper
troposphere and lower stratosphere (UTLS) e.g.,, and representations of the BDC e.g.,. Such
differences among reanalysis products are critical for simulations of
atmospheric composition but have rarely been discussed in this context.
evaluated SWV simulated using the same trajectory model
with meteorological fields taken from MERRA the predecessor of
MERRA-2;, ERA-I and CFSR. The trajectory model based on
ERA-I produced a low bias of H2O in the lower stratosphere and a
“tape-recorder” signal that was 30 % too fast in the
17–22 km layer. By contrast, use of MERRA resulted in reasonable
H2O entry values but a tape-recorder signal that was 15 % too slow
within a similar altitude range, while use of CFSR resulted in a wet bias in
H2O entry values but a reasonable propagation of the tape-recorder
signal. An earlier study by reported similar biases in
simulated water vapor entering the stratosphere via the Asian monsoon,
with trajectories based on MERRA indicating systematically larger entry
mixing ratios relative to trajectories based on ERA-I. Further evaluation of
how uncertainties in current reanalysis products impact simulations of
H2O in the stratosphere is lacking, particularly with respect to the
more recently released JRA-55 and MERRA-2 products. The former is currently
the most recent full-input reanalysis (which assimilate the full observing
system) to provide coverage of the pre-satellite era, while the model used
for the latter has been reported to have a more realistic spontaneous quasi-biennial
oscillation (QBO)
than its predecessor MERRA .
In this study, we provide an intercomparison of SWV produced using a
Lagrangian transport model driven by three recent reanalysis products: ERA-I,
MERRA-2 and JRA-55. We also present comparisons against SWV estimates from
satellite observations. Our evaluations of simulated SWV focus on the
climatological annual mean, the annual cycle (AC) and the QBO. We also discuss some key sources of variability, including
the El Niño–Southern Oscillation (ENSO) and variations in volcanic
aerosol, as well as long-term linear trends in H2O entry mixing
ratios. The objective of this work is to evaluate the sensitivity of
simulated H2O variability to uncertainties in the driving
meteorological fields among recent reanalysis products. The study also sheds
light on the quality of reanalysis products in the stratosphere, especially
their representations of dynamical fields (e.g., winds, heating rates and
temperatures). Few independent observational datasets are available to
evaluate these dynamical variables in the upper troposphere and stratosphere,
especially with respect to their influences on SWV.
Data and methodsCLaMS simulations
Our simulations of SWV cover the period 1980–2013 using the Chemical
Lagrangian Model of the Stratosphere CLaMS;. Modeled water vapor mixing ratios entering the
stratosphere are based on a simplified dehydration scheme as described in
detail by and . The saturation
volume mixing ratio is calculated for each trajectory, where the saturation
pressure (ps) is given by
ps=10-2663.5/T+12.537. If
saturation occurs along a CLaMS air parcel trajectory, the amount of water
vapor in excess of the critical saturation mixing ratio (100 % with respect
to ice) is instantaneously transformed to the ice phase. The sedimentation
fall speed is calculated by assuming a mean ice particle radius. Ice
sedimentation is then determined by comparison of the sedimentation length
over the model time step against a characteristic length
(∼300 m) . Furthermore, if ice exists in
a subsaturated parcel, this ice is instantaneously evaporated until
saturation is reached.
Temperatures and horizontal winds are prescribed from the ERA-I
, JRA-55 and MERRA-2
reanalyses. The cross-isentropic vertical velocity (θ˙) is
derived from the total diabatic heating rates, which includes all-sky
radiation, latent heat release and the diffusive turbulent heat transport
from each reanalysis product e.g.,. Water vapor
mixing ratios at pressures greater than ∼500 hPa are set equal to water
vapor mixing ratio products from the corresponding reanalysis. The CLaMS runs
driven by ERA-I, JRA-55 and MERRA-2 are respectively referred to as CLaMS-ERA,
CLaMS-JRA and CLaMS-MRA. Additional information regarding the structures of
these reanalysis systems has been provided by . We
exclude CFSR because the model and data assimilation system used to produce
this reanalysis changed abruptly at the beginning of
2011 . This and other stream transitions produced
substantial discontinuities in stratospheric variables based on CFSR during
the 1980–2013 analysis period .
We use the critical saturation rate of 100 % with respect to ice, although
supersaturation with respect to ice is frequently observed at these
altitudes e.g.. Within CLaMS, we have the freedom to
“optimize” supersaturation thresholds to produce better agreement with
observed H2O values. The effects of tuning the critical
supersaturation threshold in CLaMS have an effect similar to that of applying
a frost point offset to the Lagrangian dry point temperature in that an increase in the supersaturation threshold
enhances both the mean value and the amplitude of the annual cycle in
simulated H2O. Due to uncertainty in the appropriate value of the
supersaturation threshold, differences among the modeled values of
H2O entry mixing ratio or between modeled values against
observations cannot be unequivocally interpreted as errors in Lagrangian dry
point temperatures in these reanalyses. Our intercomparison among modeled
and observed H2O values is thus limited to uncertainties related to
using different reanalyses under the standard configuration of the model.
Besides the freedom in optimization of supersaturation threshold, additional
degrees of freedom are in the parameterized small-scale mixing in the model.
The modeled water vapor mixing ratios are influenced by parameterized
small-scale mixing driven by large-scale deformation . tested the sensitivity of modeled
H2O entry mixing ratio to mixing parameters in CLaMS and found that
the range of mixing strength they considered was representative of actual
uncertainty in small-scale mixing. Our study uses the reference configuration
of mixing parameters, which has been shown to produce the best agreement with
MLS observations by .
The model calculations include methane oxidation, which is a source of water
vapor mainly in the middle and upper stratosphere, with concentrations of
hydroxyl, atomic oxygen and chlorine radicals taken from model-generated
climatologies . We explicitly diagnose the fraction of
water vapor supplied by methane oxidation. Methane-supplied water vapor
(H2OCH4) at any given location is calculated as
H2OCH4=2⋅(CH4rec-CH4),
where CH4 is modeled methane accounting for loss by oxidation and
CH4rec is the passively transported methane assuming the same
source and circulation without photochemical loss. Reconstructed methane
values (CH4rec) are calculated as the mean tropospheric
CH4 prescribed at the lower boundary of the model (see
Fig. in Appendix ), lagged by the mean age
of air (Γ) in each run :
CH4rec(x,t)=CH4LB(t-Γ(x,t)).
The fractional release factor of CH4 (α) is expressed as
α=(CH4rec-CH4)/CH4rec.
Note that showed that chemical loss can increase the
time dependence of α. Our calculation of α neglects this
effect, as also suggested that this influence should
be very small for tracers with weak tropospheric trends, like CH4
(∼0.2 % yr-1–0.3 % yr-1).
Stratospheric water vapor without the contribution from methane oxidation
(H2OnCH4) is then diagnosed as
H2OnCH4=H2O-H2OCH4.
Our intercomparison of the three reanalysis-driven CLaMS runs covers the
period from January 1980 through December 2013 (hereafter referred to as the
“CLaMS period”) within the vertical range from θ=350 K to
θ=2000 K. A reference monthly-mean SWV is calculated by averaging the
three reanalysis-driven runs (hereafter referred to as the multireanalysis
mean or MRM). The MRM is used as a benchmark to more effectively highlight
differences among the three model runs.
Observational estimates
The Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database
provided by the NOAA Chemical Sciences Division (CSD) contains vertically
resolved ozone and water vapor data from the SAGE-II/III, UARS HALOE, UARS
MLS, and Aura MLS satellite instruments starting from 1984 .
We use the SWOOSH version 2.5 zonal-mean monthly-mean time series of merged
water vapor mixing ratios with 2.5∘ resolution on 21 isentropic
levels from 300 to 650 K. The homogenization process, which has been
described by , is designed to minimize artificial jumps in
time and account for intersatellite biases. The merged SWOOSH data thus
provide a long-term time series with reliable representations of interannual
to decadal variability.
We compare monthly mean water vapor from CLaMS runs with SWOOSH water vapor
on the same vertical grid (21 θ levels from 350 to 650 K). For each
latitude–θ grid location, comparison between CLaMS and SWOOSH starts
from the first month when SWOOSH has more than 12 months of available
H2O data within the following 2 years and ends in December 2013. In
the following, we refer to this period as the “SWOOSH period”. Note that
while CLaMS provides continuous temporal coverage, the SWOOSH data may
include some gaps. Additional details regarding SWOOSH data coverage are
provided in Appendix .
In addition to SWOOSH, we use Aura MLS version 4 retrievals of
H2O for comparison with CLaMS simulations during
the period 2005–2013 (the “MLS period”). MLS provides over 3000 profiles
per day, with water vapor estimates at 30 pressure levels from 316 to 1 hPa.
The vertical resolution in the stratosphere is approximately 3 km
(2.5–3.5 km). Uncertainties in the water vapor retrievals are on the order
of ∼10 % in the lower stratosphere and ∼5 % in the upper
stratosphere. The relatively high frequency of horizontal sampling and high
quality of vertical profiles allows MLS H2O to reliably represent the
zonally and monthly-averaged distribution of SWV. We interpolate MLS
H2O profiles to 26 isentropic levels, chosen to span the range
350–2000 K at a vertical resolution close to that of the original
retrievals.
The SWOOSH dataset is also based in part on Aura MLS version 4 retrievals,
particularly during the MLS period. Thus, the comparisons of CLaMS against
SWOOSH and comparisons of CLaMS against MLS during the MLS period are not
independent. Differences between the comparisons are due to the
homogenization procedure applied in SWOOSH. We did not apply the MLS
averaging kernels to CLaMS H2O simulations. It is because the
application of MLS averaging kernels to CLaMS H2O simulations can
potentially produce artifacts, especially at high latitudes more
details provided bytheir Fig. 2. Our main focus is on the
comparison among the reanalyses and to apply averaging kernels that could smear
out the differences among the CLaMS runs. However, it is worth noting that
artifacts may appear in MLS observations, especially in high latitudes, when
MLS retrievals are compared to model results without MLS weighting functions.
Extraction of variability at multiple timescales
The objective of this study is to examine and compare the climatological
features of variability in model-based and observationally based estimates of
SWV. Pronounced periodic and quasi-periodic signals in SWV include the annual
cycle (AC), the quasi-biennial oscillation (QBO) and semiannual harmonic
(SAH). The AC, QBO and SAH are therefore considered explicitly in our
regression model:
χ(t)=χ¯+χAC(t)+χSAH(t)+χQBO(t)+χres(t).
After determining the mean value calculated over the whole considered time
series (χ¯), we extract AC (χAC(t)), SAH
(χSAH(t)) and QBO (χQBO(t)) signals following
the harmonic regression method used by . In this method,
one sine and cosine pair are used for each regression (e.g., AC and SAH).
The zonal wind records at 50 and 30 hPa over Singapore provided by Freie Universität Berlin (FUB) are quasi-orthogonal, which are used for the QBO regression. The
AC, QBO and SAH variables are assessed from two aspects of periodic
variations: amplitudes (AAC and AQBO shown in the
first half of Figs. and , ASAH in top
of Fig. ) and phases (PAC and PQBO shown
in the second-half of Figs. and , PSAH
not shown). Note that (1) the
amplitude represents half the corresponding variation from maximum to
minimum, (2) PAC and PSAH are defined as the month of
the annual maximum in the regression fit and (3) PQBO is defined
as the month (within 0–28) with the largest lag-correlations between the QBO
fit and the 50 hPa Singapore wind.
The linear trend (Ctrd) is estimated by applying a least-squares
linear fit to the residuum of H2O variability (χres(t)
in Eq. ) after removing the AC, QBO and SAH signals. Note that
the linear trend for χres(t) is identical to the trend of the
original H2O time series since periodic signals such as AC have zero
long-term trend when the full cycle is taken. The quasi-periodic signal like
QBO does not show long-term trend in the H2O entry over the
considered period. Additional variability after subtracting the AC, QBO and
SAH signals may result from the influences of ENSO e.g.,, and variations in stratospheric
aerosol e.g.,. The contributions of such
variations to H2O entry mixing ratios are discussed in
Sect. .
Multiple linear regression results for monthly-averaged tropical
mean (20∘ S–20∘ N) water vapor on the θ=400 K
isentropic surface from SWOOSH. The top panel (a) shows anomalies of
SWOOSH data relative to the mean value over the 1993–2013 period (red dots)
and the regression fit (black line) based on Eq. () (correlation
coefficient r=0.91). The AC, QBO and SAH components of the regression are
shown in panels b, c and d, respectively. Additional variability
beyond the three periodic and quasi-periodic components (residuum of the
regression) is shown in the bottom panel (e). The linear trend in
this time series is also shown with ±2σ uncertainty shown as gray
shading. Note the different y-axis ranges across the different panels. The
amplitudes and phases of the AC, QBO and SAH signals are listed in the
upper-right corners of the corresponding panels. The standard deviations
corresponding to each contribution are listed near the bottom of each panel,
together with correlation coefficients for each term against the original
time series. The standard deviation of the original SWOOSH time series
(σorg) is 0.73 ppmv.
Figure provides example regression results for SWV on
the 400 K isentropic surface in the tropics (20∘ S–20∘ N)
based on the SWOOSH monthly merged satellite dataset. This time series
approximates that of H2O values entering the stratosphere
(H2Oe) at the base of the “tropical pipe” , and is
characterized by a climatological annual mean of 3.83 ppmv and a negative
trend of 0.22 ppmv decade-1. The regression model explains over 80 %
of the variance in H2Oe. According to the correlation coefficient of
each variable, the periodic terms listed in descending order of influence are
AC, QBO and then SAH. The AC represents the most pronounced variation in
H2O at this level, with a correlation coefficient of 0.88, an
amplitude of AAC=0.89 ppmv and an annual maximum in
PAC= October. The QBO is also a significant factor, with an amplitude
of 0.21 ppmv and a maximum lag-correlation (correlation
coefficient =0.16) against the 50 hPa Singapore winds of
PQBO=1 month. The bottom panel of Fig.
shows other variability in H2O entry mixing ratios (the residuum of
the regression in Eq. ). This residual variability represents a
substantial component of the total variability, and contains intraseasonal
variability, interannual variability and the long-term trend. Features of
the AC, QBO and the other variability are compared and discussed in more
detail in Sects. to , respectively. Since the
contribution of SAH to the total variance is generally smaller than that of
other variables, we defer discussion of the SAH influence to
Appendix .
Climatological annual mean of stratospheric water vapor
Figure shows climatological annual-mean simulated SWV
from the three CLaMS runs, as well as the components of SWV with and without
CH4 oxidation (H2OCH4 and
H2OnCH4). The climatological mean is based on the period
1985–2013 because CH4rec cannot be diagnosed during the first few
years of the simulation. Zonal-mean H2O matches zonal-mean
H2OnCH4 well in the lower stratosphere but consists mainly of
H2OCH4 in the upper stratosphere.
Comparison of climatological annual mean SWV during the CLaMS period
(1985–2013). Top row (a1–c1): annual-mean zonal-mean simulated
H2O based on (left to right) CLaMS-ERA, CLaMS-JRA and CLaMS-MRA.
Dashed lines mark the zonal-mean locations of isobaric surfaces. Middle row
(a2–c2): as in (a1–c1) but for simulated H2O without
methane oxidation. These distributions highlight the effects of variations in
H2O entry values (tropopause temperature). Black contours show
zonal-mean temperatures of 192.5, 195 and 200 K (from thin to thick lines)
near the tropical tropopause. Bottom row (a3–c3): as in (a1–c1)
but for simulated H2O from CH4 oxidation. The distributions
reflect the effect of the BDC on water vapor produced by methane oxidation.
Black contours show the fractional CH4 oxidation ratio (α).
The driest stratosphere is simulated by CLaMS-ERA, while the wettest is
simulated by CLaMS-JRA. This difference is mainly attributable to
H2OnCH4, as shown in Fig. a2–c2, which is in
turn controlled primarily by entry mixing ratios at the tropical tropopause
(with the notable exception of dehydration in the Southern Hemisphere polar
vortex). These differences reflect differences in tropical tropopause
temperatures among the reanalyses (black contours in Fig. 2a2, b2 and c2; see also Fig. ). Tropical tropopause
temperatures differ by about 1 K between ERA-I and JRA-55, consistent with a
difference of ∼0.6 ppmv in H2O mixing ratios assuming the same
100 % saturation threshold applied in the CLaMS model.
Comparison of climatological annual-mean zonal-mean age-of-air (AoA)
from the CLaMS-ERA, CLaMS-JRA and CLaMS-MRA simulations (a1–c1),
along with corresponding cross-isentropic velocities (θ˙) based
on total diabatic heating rates for each reanalysis product
(a2–c2). Color shading in the lower panels indicates zonal-mean
diabatic upwelling rates that exceed 0.5 K d-1. The thick gray line
shows the zero-contour of diabatic heating rates. Lighter thin gray lines
show diabatic downwelling.
The amount of H2O from CH4 oxidation is related to the time
an air parcel has spent in the stratosphere. This time is measured by the
stratospheric “age-of-air” (AoA) as shown in Fig. a1, b1 and c1. Values of H2OCH4 from the
CLaMS-MRA simulation are systematically larger than those from the CLaMS-ERA
or CLaMS-JRA simulations (see Fig. a3–c3). The most
pronounced high biases in H2OCH4 and α in
CLaMS-MRA relative to the other two simulations are colocated with the
sharpest gradients in H2OCH4 and α.
Figure provides useful context for interpreting these
differences, as high values of H2OCH4 are accompanied by
systematically older stratospheric air in CLaMS-MRA. Note that consistent
climatological mean age differences within the same model framework are
presented by . The lower panels of Fig.
show differences in reanalysis heating rates, which are used to drive
vertical transport in the CLaMS model. Tropical upwelling in the shallow
branch of the BDC is weaker in MERRA-2 than in ERA-I or JRA-55, thus
producing larger gradients of AoA and H2OCH4 between the
lower and middle stratosphere. Quantitatively, relative differences in
time-mean zonal-mean diabatic heating rates at 400–500 K are as large as
50 % between MERRA-2 and ERA-I, resulting in large differences in the
vertical advection of SWV and other tracers. It is worth noting that
differences among the reanalyses in the lower stratosphere are larger in the
NH subtropics than in the deep tropics or the SH subtropics (not shown).
Although diabatic upwelling in the tropical lower stratosphere is weakest in
MERRA-2, upwelling in the tropical middle stratosphere
(θ equal to 600–1000 K) is stronger and covers a wider range of latitudes in
MERRA-2 than in ERA-I or JRA-55. Above θ=1000 K, the deep BDC as
represented in JRA-55 is noticeably weaker than that in MERRA-2 or ERA-I.
Previous studies have pointed out that the mean magnitudes of tropical
upwelling in the lower stratosphere are substantially different among
different reanalysis products .
Annual cycle
The annual cycle is the largest contributor to total
variability in SWV. Moreover, seasonal variations in SWV give essential
insight into the overall behavior of H2O as these variations reflect
the effects of seasonal changes in both the stratospheric circulation and
temperatures at the tropical tropopause.
The H2O tape recorder
Before discussing the global features of the annual cycle in SWV, we first
report the simulated climatological seasonalities of H2O entry mixing
ratios (H2Oe) from the three simulations. Values of H2Oe
are mainly controlled by “freeze-drying” around the tropical tropopause and
the upward propagation of signals imprinted by variations in the conditions
under which this freeze-drying takes place. The upward propagation of
these signals produces the well-known stratospheric tape-recorder structure
described by . In this study, we define water vapor entry
mixing ratios (H2Oe) as averages over the
20∘ S–20∘ N tropical band on the θ=400 K
isentropic surface.
Relationships between tropical tropopause temperatures and water
vapor entry mixing ratios (mean H2O mixing ratios on θ=400 K
averaged over 20∘ S–20∘ N). Here tropical tropopause
temperatures refer to the minimum in the mean temperature profile within the
tropics (20∘ S–20∘ N) between θ=360 and 420 K. The
upper set of lines shows the climatological annual cycles for tropical
tropopause temperatures, while the lower set of lines shows the
climatological annual cycles for water vapor entry mixing ratios. Note that
the model results of annual cycles is based on “CLaMS period” (1980–2013),
which is longer than “SWOOSH period” starting around 1990.
Figure summarizes the relationship between the
climatological annual cycles of tropical tropopause temperatures (upper set
of lines) and H2Oe (lower set of lines). The annual cycles in
tropical mean tropopause temperatures based on the three reanalyses (upper
set of lines) are similar in both amplitude and phase, but with ±1 K
differences in mean value. Mean H2Oe differences among the three
CLaMS runs are roughly consistent with the corresponding differences in mean
tropopause temperatures, with a 1 K difference in temperature corresponding
to a ∼0.6 ppmv difference in H2O entry mixing ratios. Although
the differences in the mean tropical tropopause temperature are consistent
with the H2O differences, it is actually the Lagrangian cold point
that controls the H2O entry values and makes the H2O
differences, which will be indicated by the next figure. Compared with
SWOOSH, simulated annual cycles of H2Oe from CLaMS-ERA and CLaMS-MRA
are relatively reasonable in terms of both absolute value and amplitude (i.e.,
generally within uncertainties). By contrast, H2Oe from CLaMS-JRA is
systematically larger than that indicated by SWOOSH or the other two runs.
Simulated values based on CLaMS-JRA are ∼0.5 ppmv larger than SWOOSH
estimates in February and ∼1 ppmv higher than SWOOSH estimates in
September. Thus, CLaMS-JRA produces ∼0.5 ppmv larger peak-to-peak
annual amplitude than other estimates. This is qualitatively consistent with
nonlinearity in the Clausius–Clapeyron equation, which mean that the effects
of temperature offsets on H2O values are larger when the average
temperature is higher . Thus, a larger
annual amplitude in entry mixing ratios is expected when the average
tropopause temperature is larger.
The annual cycle in tropical tropopause temperature leads that of
H2Oe by 1–2 months because ascent within the tropical tropopause
layer is relatively slow. Given the strong consistency among tropopause
temperature annual cycle phases as represented in the reanalyses, we find the
modeled phases of entry H2O annual cycle are in a good agreement with
only small discrepancies. CLaMS-ERA and CLaMS-JRA indicate very consistent
phases of the AC, with the annual minimum occurring in February and the
annual maximum occurring in September. CLaMS-MRA shows the annual minimum
occurring from February to March and the annual maximum in October. SWOOSH
data indicates that the annual minimum occurs in February and the maximum in
October. The maximum value of H2Oe based on both of these estimates
occurs in October, 1 month later than the maximum values based on CLaMS-ERA
and CLaMS-JRA. This phase shift is related to the fact that the ascent in the
tropical tropopause layer is slower in MERRA-2 compared to JRA-55 or
ERA-I see also comparison of MERRA and ERA-I diabatic heating rates
by. Another relevant fact is that the strength of vertical
mixing in tropical lowermost stratosphere is important for reproducing the
observed seasonality of H2Oe, especially during boreal
summer . We did not specify the strength of vertical
mixing in this study but intend to examine its impacts in future work.
Climatological mean cold point tropopause temperatures during boreal
winter (DJF, a, c, e) and boreal summer (JJA,
b, d, f) from MERRA-2 (a, b), along with
differences in ERA-I (c, d) and JRA-55 (e, f) relative to
MERRA-2.
Isolines in the middle and lower rows show absolute values of tropopause temperature.
Cold point tropopause temperatures are defined here as minimum temperatures between θ=360 K and θ=420 K.
In addition to mean tropical tropopause temperature, the spatial distribution
of climatological tropopause temperatures is also an important factor in
determining the mean entry mixing ratio. Figure shows this
spatial pattern based on MERRA-2 during boreal winter (DJF) and boreal summer
(JJA), along with differences in tropopause temperatures from ERA-I and
JRA-55 relative to those in MERRA-2. Differences in tropopause temperatures
among the three reanalysis products vary considerably with both location and
season. ERA-I shows relatively large negative differences (≤-0.5 K)
relative to MERRA-2 over the tropical Indian and Pacific oceans during DJF.
Differences are particularly notable over the tropical western Pacific, where
tropopause temperatures based on ERA-I are substantially colder than those
based on MERRA-2 during boreal winter. Tropopause temperatures in this region
are widely believed to play a key role in determining the value of
H2Oe during boreal winter .
Warmer tropopause temperatures in JRA-55 during JJA are located mainly in the
Southern Hemisphere subtropics, as well as over the Bay of Bengal and
tropical Pacific, with differences relative to MERRA-2 reaching magnitudes of
∼1 K in these regions. The latter two regions are also widely
recognized as playing influential roles in troposphere-to-stratosphere
transport and the final dehydration of air parcels entering the stratosphere
during boreal summer .
Climatological structure of the stratospheric tape-recorder signal
based on SWOOSH and the three CLaMS runs. The tape recorder is defined as
anomalies in tropical (20∘ S–20∘ N) mean H2O
relative to the climatological mean at each level (color shading). The phase
of upward propagation (solid circles connected by a line) is defined by the
largest correlation with the layer below. For ease of comparison, propagation
based on SWOOSH is marked in each panel using gray circles connected by a
gray line. Red and blue contours indicate positive and negative contributions
of CH4 to H2O anomalies (in units of ppmv, at intervals of
0.02 ppmv).
A complementary picture of H2O entry values is provided in
Fig. , which depicts the tape-recorder signal in SWV averaged
over 20∘ S–20∘ N. Upward propagation of the tape-recorder
signal between 450 and 600 K is 0.5–1.5 months faster in CLaMS-ERA and
CLaMS-JRA relative to SWOOSH, and 1–1.5 months slower in CLAMS-MRA than in
SWOOSH. Similarly, the amplitude of the tape-recorder signal is
systematically stronger than SWOOSH in CLaMS-ERA and CLaMS-JRA, but weaker
above 450 K in CLaMS-MRA. These differences are attributable in part to the
relatively slow upwelling in MERRA-2 (as shown in Fig. ).
Slower upwelling not only delays the propagation of the signal but also
allows more time for horizontal advection and mixing of the middle latitude
air into the tropics, which tend to damp the signal. We can also see the
remarkably strong contribution of CH4 oxidation in CLaMS-MRA, which
is shown by the blue and red contour lines in Fig. . The
contribution of H2OCH4 to the tape-recorder signal is
substantially larger in CLaMS-MRA than in the other two runs. This feature is
a joint effect of slower tropical upwelling and stronger in-mixing from the
extratropics, resulting in a relatively pronounced seasonal cycle in
H2OCH4 in CLaMS-MRA with a maximum amplitude of
∼0.05 ppmv near the 450 K isentrope. The amplitude of
H2OCH4 in CLaMS-MRA is twice as large as that in
CLaMS-JRA. Meanwhile, CLaMS-ERA shows virtually no anomalies in
H2OCH4 at these levels due to relatively rapid rates of
ascent in the lower branch of the BDC. Seasonal variations in
H2OCH4 are opposite in phase relative to seasonal
variations in H2Oe, and account for ∼20 % of the reduced
amplitude of the H2O tape recorder above 450 K in CLaMS-MRA.
Global features of the H2O annual cycle
We now consider differences in the representation of the annual cycle in
H2O throughout the global stratosphere. The amplitude of the
simulated annual cycle, AAC, and its phase, PAC, are
shown in Fig. . Here, we examine the CLaMS simulations in the
context of Aura MLS observations during the MLS period (2005–2013). In
general, these comparisons show good agreement between the CLaMS simulations
and MLS. Results for comparisons against SWOOSH H2O between
θ=350 K and θ=650 K are qualitatively similar to those
against MLS and are not shown here.
AC amplitudes (AAC) and corresponding phases
(PAC) during the MLS period (∼2005–2013, the first and
third rows) and the entire CLaMS period (1980–2013, the second and fourth
rows). The leftmost panels show AAC and PAC based on
Aura MLS observations (a1, a3) and the mean of the three
reanalysis-driven models (MRM, a2, a4). The corresponding
AAC values from CLaMS-ERA, CLaMS-JRA and CLaMS-MRA are shown in
the rightmost three panels of each row. Overlaid “+” and “-” symbols in
panels (b1)–(d1) and (b2)–(d2) demonstrate major differences
by more than ±30 % in simulated AAC relative to the
benchmark estimate shown in the corresponding leftmost panel. Arrows in
panels (b3)–(d3) and (b4)–(d4) indicate the major region with
phase differences by more than 1 month against the benchmark estimate of
PAC shown in the corresponding leftmost panel. The upward arrows
show phase lagging behind of the benchmark phase while the downward arrows
show phase ahead of the benchmark phase.
The enhanced amplitude of the AC in “region 1” (see Fig. a1 and
a2) reflects the tape-recorder signal in the tropical lower stratosphere,
which propagates from tropopause to the middle stratosphere and extends into
subtropics and midlatitudes. Significant differences in both the amplitude
and phase of the AC are evident in the transition layer between the tropical
lower stratosphere and the tropical middle stratosphere (θ equal to
450–600 K). These differences are again linked to discrepancies in the
strength of tropical upwelling described above (Fig. a2, b2
and c2 and corresponding discussion). Consistent with our intercomparison of
tape-recorder amplitudes shown in Sect. , the amplitude of the AC
between θ=450 K and θ=600 K is larger in CLaMS-JRA and
smaller in CLaMS-MRA relative to that inferred from MLS observations (see the
signs of “+” and “-” overlaid on Fig. b1–d1).
Examination of the phase of the AC likewise confirms that AC-related signals
propagate faster in CLaMS-ERA and CLaMS-JRA and slower in CLaMS-MRA than
indicated by MLS observations (see arrows overlaid on
Fig. b3–d3). Although we use different methods to estimate the
AC amplitude in this section relative to Sect. , both approaches
produce similar results with respect to differences among the simulated AC
signals in “region 1”.
MLS observations further indicate a hemispheric asymmetry in the AC amplitude
in “region 1” (NH > SH), possibly related to the transport of
relatively moist air from the Asian summer monsoon (ASM) anticyclone to the
stratosphere during boreal summer e.g.,.
This feature is also evident in all three model-based estimates of
H2O. However, all three CLaMS runs overestimate the amplitude of the
AC in the SH subtropics (around 380–450 K). One potential reason is that
water vapor in the Southern Hemisphere subtropical lower stratosphere is
highly sensitive to small-scale mixing processes that must be parameterized
in the model .
Additional local maxima in the AC amplitude are found in the subtropical
upper stratosphere of both hemispheres, marked as “region 2” in
Fig. . The AC phase in region 2 indicates that this feature
propagates quasi-meridionally. Both the enhanced amplitude of the AC and the
hemispheric asymmetry (SH > NH) in this part of the stratosphere are
related to seasonal variations in the deep branch of the
BDC . The amplitude of the feature in region 2 is
largest in CLaMS-JRA, followed by CLaMS-ERA and CLaMS-MRA. The amplitude
implied by the MLS observations falls between those produced by CLaMS-JRA and
CLaMS-ERA and is approximately double the amplitude produced by CLaMS-MRA.
Thus, the relative strength of this AC signal in the subtropical upper
stratosphere is consistent with the respective strength of diabatic upwelling
in the tropical lower and middle stratosphere (see Fig. ),
which is strongest in CLaMS-JRA and weakest in CLaMS-MRA. Weaker upwelling in
the lower part of the BDC ascending branch in CLaMS-MRA produces strong
gradients in tracer concentrations (including both H2O and
CH4) in the lower stratosphere and weak gradients in the upper
stratosphere (see also Fig. ).
The quasi-biennial oscillation
We now shift our focus to another quasi-periodic oscillation in SWV: the
quasi-biennial oscillation (QBO). Zonal-mean distributions of the
climatological amplitudes and phases of the QBO are shown in
Fig. . As above, metrics based on MLS observations are provided
in the first and third rows and metrics based on the MRM are provided in the second and fourth
rows. Two regions show clear peaks in AQBO: the
tropical lower stratosphere (region 1) and the tropical upper stratosphere
(region 2). A peak in the tropical middle stratosphere (region 3) is evident
in CLaMS-ERA and CLaMS-JRA but not in MLS or CLaMS-MRA.
Same as Fig. but for QBO amplitudes and phases. Note
that the scale of color bars are different from that in Fig. .
Overlaid “+” and “-” symbols in panels (b1)–(d1) and (b2)–(d2) demonstrate major differences
by more than ±30 % in simulated QBO amplitudes relative to the
benchmark estimate shown in the corresponding leftmost panel. The large and
small arrows show the major region with phase differences by more than 1 and
3 months, respectively, against the benchmark PQBO shown in the
corresponding leftmost panel. The upward arrows show phase lagging behind of
the benchmark phase while the downward arrows show phase ahead of the
benchmark phase.
The large values of AQBO in region 1 are due to QBO effects on
tropical tropopause temperature. Since the tropopause is colder (warmer)
during the easterly (westerly) phase of the QBO (referring here to the
50 hPa wind) , the phase of the QBO effect at the tropical
tropopause shown in Fig. is related to the phase of the 50 hPa
QBO wind. This water vapor signal then propagates upward at a speed similar
to that of the canonical tape-recorder signal. Further details about the QBO
modulation of the tape recorder were suggested by .
Similar to what we found for AC, CLaMS-ERA and CLaMS-JRA both overestimate
AQBO and show upward phase propagation ∼1 month faster than
MLS at isentropic levels between 450 and 550 K. The QBO amplitude in region
1 based on CLaMS-MRA compares better with AQBO based on MLS
observations. These amplitude differences among CLaMS runs evokes similar
differences in AAC in the tropical lower stratosphere. Both
biases can be traced back to strong upward transport in the lower to middle
stratosphere in ERA-I and JRA-55 (Fig. ), which results in
weaker damping in the amplitudes of tropopause-originating signals (such as
AAC in Fig. region 1 and AQBO in
Fig. region 1) in the vertical direction by mixing.
The large values of AQBO in region 2 are mainly linked to
QBO-related modulation of the stratospheric circulation and
references therein. The corresponding phase is effectively
simultaneous with the 50 hPa QBO wind, with very little spatial shift.
Satellite-derived estimates of this QBO signal indicate that amplitudes are
in the range of 0.2 to 0.6 ppmv . MLS H2O is on
the lower end of this range with an amplitude of ∼0.2 ppmv. However,
CLaMS runs during the MLS period produce even smaller amplitudes of 0.1 to
0.2 ppmv, below the range implied by satellite measurements. The smallest
amplitude is produced by CLaMS-ERA. This discrepancy may be related to lower
values of H2OCH4 and α in the upper stratosphere
as shown in Fig. a3, which result from a systematically
faster stratospheric circulation in ERA-I.
The amplitude and phase of the QBO signal in SWV show pronounced
discrepancies in the middle stratosphere among the CLaMS simulations driven
by different reanalyses (region 3 in Fig. ). This region
coincides with the strongest QBO signal in zonal wind. During the MLS period
(2005–2013), both CLaMS-ERA and CLaMS-JRA overestimate AQBO in
this region, particularly within the 0–20∘ N latitude band. These
overestimates can be attributed to strong upward transport of H2Oe
from the lower stratosphere. Although CLaMS-MRA underestimates
AQBO in this region, the spatial pattern produced by CLaMS-MRA in
the middle stratosphere is more consistent with that indicated by MLS. The
estimate of phase in this region from CLaMS-MRA shows ∼6 months
difference when years before MLS period are included or excluded (see
Fig. d3–d4, where phase shifts are indicated by arrows).
Uncertainties in QBO-related H2O anomalies arise not only from
different circulation responses but also from disagreements in zonal wind at
the Equator among the reanalyses. Equatorial winds at 10 hPa
(∼800–900 K) in MERRA-2 are clearly different from the
FUB records, ERA-I and JRA-55 during the 1980s and early 1990s, when
the assimilated observations are less able to pull the forecast model away
from its own internally generated QBO signal .
Considering the consistency in QBO wind between MERRA-2 during MLS period and
the FUB records, the QBO phase for CLaMS-MRA (d3) is more
reliable than that during the whole CLaMS period (d4).
Trends and residual sources of natural variability
As mentioned in Sect. and observed in the SWOOSH record (see Fig. e), residual variability beyond the AC, QBO
and SAH (χres(t) in Eq. ) also contributes
substantially to variations in H2O entry mixing ratios.
Figure a shows that the three CLaMS simulations produce
similar interannual variability in the χres(t) term. Within
the SWOOSH period (1993–2013), representations of interannual variability
among the simulations and the SWOOSH analysis are more consistent during the
Aura MLS period (2004–2013) than the pre-MLS period (1993–2004), likely due
to significant improvement in SWOOSH data once MLS observations were
incorporated as well as the contemporaneous improvements in the quantity and
quality of satellite observations assimilated by the reanalysis
systems . However, significant differences are evident in
the simulated H2O entry mixing ratio trends, as discussed later in
this section.
Comparison of residual variability after removing the AC, QBO and
SAH from H2O entry values (mean H2O mixing ratios on
θ=400 K averaged over 20∘ S–20∘ N) based on CLaMS
simulations and SWOOSH. Panel (a) shows the residual term
(χres) after applying a 3-month running mean. The long-term
linear fit is plotted as a solid line with ±2σ uncertainties.
Panel (b) shows additional components of the multiple linear
regression, including the influences of variations in volcanic aerosol (upper
set of lines) and ENSO (lower set of lines). Panel (c) shows the
residuum of (a) after subtracting all contributors shown in
(b), along with the corresponding trend.
have discussed the causes of interannual variability in
SWV using a regression model, which considered variability in the QBO, BDC
and tropical tropospheric temperature. Although this regression model is
successfully captures most of the variability, it is difficult to disentangle
the effects from each regressor. We intend to explore the processes that
ultimately control the H2O variability, i.e., the processes that might
influence SWV by modulating BDC or tropical tropospheric temperatures. Among
the potential controlling processes, ENSO and stratospheric volcanic aerosols
have been shown to modulate both the tropical ascending branch of the
BDC e.g., and tropical
tropopause temperatures e.g.,.
Consequently, variations in ENSO and stratospheric volcanic aerosols have
significant influences on H2O entry mixing
ratios e.g.,.
To clarify the ENSO and volcanic aerosol impacts on the H2O entry
mixing ratios in each simulation, we analyze the residuum of H2O
entry values (χres(t) in Eq. ) using the following
multiple regression model:
χres(t)=a⋅Pvolcano(t-τvolcano)+b⋅PENSO(t-τenso)6+χres′(t),
where PENSO is the normalized Multivariate ENSO Index (MEI;
) and Pvolcano is the aerosol optical depth
(AOD) as recorded in satellite data . The coefficients are
the amplitude a and lag τvolcano associated with volcanic
aerosols and the amplitude b and lag τenso associated with
ENSO. We determine the parameters a, b, τvolcano and
τenso as the parameter set that minimizes the residual
(χres′) in the least-squares sense. Additional details
of the method and its application have been summarized by .
Figure b shows the response of H2O to
stratospheric volcanic aerosol (upper set in panel b) and ENSO (lower set in
panel b) based on the simulations and the observations. Beyond some
differences in magnitude, the volcanic aerosol-induced changes in H2O
entry values agree well in terms of response signs and lags. The increase in
modeled H2O associated with volcanic aerosols arises mainly from
aerosol-induced warming of the TTL . Positive H2O entry anomalies induced by the two major
volcanic eruptions during the CLaMS period are twice as large in CLaMS-JRA
and CLaMS-MRA (0.6 ppmv for El Chichón and 0.8 ppmv for Pinatubo) as in
CLaMS-ERA (0.3 ppmv for El Chichón and 0.4 ppmv for Pinatubo). Note
that among these three reanalyses, only MERRA-2 explicitly includes
perturbations to the stratospheric aerosol burden following volcanic
eruptions , although the effects of these perturbations
may also enter all of the reanalyses indirectly through the assimilation of
temperature, ozone and other affected quantities.
As with the volcanic aerosol-induced effects, ENSO impacts on H2O
entry values from CLaMS and SWOOSH agree well with respect to the main
characteristics of the response. All four estimates show positive H2O
entry anomalies during El Niño and negative anomalies during La Niña,
consistent with previous studies e.g.,. Lags (τenso) in
ENSO-induced tropical mean H2O anomalies at θ=400 K relative
to the ENSO signal (here represented by the MEI index) are likewise
consistently between 10–11 months among all three simulations and the
observations. However, there are notable quantitative differences in the
magnitude of H2O entry mixing ratio changes induced by ENSO
variability. CLaMS-JRA and CLaMS-MRA show increases in H2O entry
mixing ratios about ∼0.1 ppmv larger than CLaMS-ERA during strong
El-Niño events (1982/1983, 1986/1987 and 1997/1998). However, the response
in the latter exhibits the best agreement with SWOOSH during the 1997/1998 El
Niño.
Figure c shows the residual terms
(χres′) and the trends related to the residual terms
after removing ENSO and volcanic aerosol-induced variability. We notice that
the significant differences among the trend estimates shown in
Fig. a largely remain in the residual variability.
Further quantification of contributions to the trends is listed in
Table . We also notice a substantial quasi-decadal variability
remaining in the residuals. After removing the high-frequency variability and
the linear trends, this quasi-decadal variability agrees well among the three
simulations as well as with the SWOOSH data. Further interpretation of this
consistent quasi-decadal signal is out of the scope of this study, which aims
mainly to intercompare different reanalyses.
Trend estimates in H2O entry mixing ratios and contributions
from different sources of variability based on the three CLaMS simulations
and SWOOSH over the SWOOSH period (1993–2013) and the longer CLaMS period
(1980–2013). Trends are reported in units of parts per million by volume per decade (left of
the slash) and percent per decade (right of the slash). Statistically
significant trends are marked in bold.
Table lists the trends in H2O entry mixing ratios
along with the estimated contributions of ENSO and volcanic aerosols to
trends in the three reanalysis-driven simulations and SWOOSH over the shorter
period (1993–2013) and the longer-term period (1980–2013). Trends are
calculated by analyzing the residual of the H2O entry anomalies with
and without the ENSO and volcanic aerosol signals. The trend produced by
CLaMS-ERA is very consistent with that produced by CLaMS-MRA after 1985 (not
shown), as is the residual variability shown in Fig. c.
By contrast, CLaMS-JRA produces a strong negative trend. As nonperiodic
variability, such as ENSO variability and the volcanic aerosol burden, may
contribute to these trends, their estimated contributions are listed in
Table . ENSO variability contributes to negative trends in
both modeled and observed H2O entry mixing ratios over the 1993–2013
SWOOSH period. For the entire simulation period (1980–2013), the ENSO
contribution to trends is also negative but with a reduced magnitude. The
variability due to volcanic eruptions has a zero-trend contribution to the
H2O entry values over the longer period
(1980–2013), which is consistent with
the volcanic-aerosol-induced zero effect on the trends of mean age of air at
the tropical lower stratosphere shown in . However, it has
a positive trend contribution during the 1993–2013 SWOOSH period, likely due
to the increased frequency of minor volcanic eruptions after
2008 . Conversely, the volcanic aerosol contribution to the
trend from 1980 to 2008 (not shown) is negative due to the forcing of the two
major eruptions. This analysis suggests that the contribution of volcanic
aerosols to interannual variability in H2O entry values is highly
period dependent. Although we find near compensation over the CLaMS period
used in this work (1980–2013), trends in SWV can be substantially influenced
by periods of enhanced or reduced supplies of volcanic aerosol to the
stratosphere.
Differences among the trend estimates persist in the residual variability
even after accounting for the effects of ENSO and volcanic aerosol
(Fig. c and last row in Table ),
indicating that uncertainties in the model-based trends do not emerge from
different responses to major volcanic eruptions or strong ENSO events. The
model-based H2O trends are sensitive to the representation of the
tropical tropopause temperature from reanalyses, especially for the early
years of the satellite era (e.g., 1980–1985) .
Differences in the observations assimilated by the three reanalyses or in the
representation of the BDC may also contribute to differences in the trend
estimates. Reliable attribution of differences in simulated SWV trends will
require disentangling complex interactions among changes in tropopause
temperature, the stratospheric circulation and anthropogenic factors such as
the amount of methane entering the stratosphere at global scale.
Discussion
In the sections above, we relate differences in representations of the
climatological mean, AC and QBO signals of SWV to differences in upwelling
rates in the shallow branch of the BDC among the reanalysis products used to
drive the model. One remaining question is whether these differences in
upwelling strength are also responsible for differences in AC or QBO
variance. Analysis of the total standard deviation and its contributors in
each simulation can shed light on this question. If different upwelling
strengths are the main factor, then these different rates of vertical
advection should influence each contributor similarly to the total variance.
In other words, a larger (or smaller) magnitude of the AC or QBO variance
should be approximately in proportion to a larger (or smaller) magnitude of
the total variance. We use the standard deviation from the full H2O
time series (σχ, where χ is the H2O volume mixing
ratio) to quantify the magnitude of total variability. This metric is shown
for each of the three simulations in Fig. a–c. We then check the fraction of the total
variance attributable to the AC and QBO, respectively,
σχAC2/σχ2 and
σχQBO2/σχ2. The variance fractions
attributable to the AC and QBO components of the total signal are shown in
Fig. d–f as gray shading and purple
contours, respectively.
The standard deviation in monthly-mean H2O (σχ;
upper row) and the fraction of AC and QBO variance relative to total variance
(in %; d, e, f) for CLaMS-ERA (a, d),
CLaMS-JRA (b, e) and CLaMS-MRA (c, f). Shading in the lower
panels shows the variance fraction attributed to the annual cycle
(σχAC2/σχ2), while purple contours show
the variance fraction attributed to the QBO
(σχQBO2/σχ2).
Figure indicates that zonal-mean distributions of AC
and QBO variance fractions are qualitatively consistent across all three
simulations (Fig. d–f), although the magnitudes of
total variance differ to some extent among the three CLaMS runs
(Fig. a–c). For example, CLaMS-MRA produces the
smallest amplitudes of both the AC and QBO signals in the lower and middle
stratosphere among the three simulations. Figure. c
(corresponding to CLaMS-MRA) shows that these smaller amplitudes are in turn
associated with weaker total variance in the middle stratosphere. By
contrast, the fractional contributions attributed to the AC and QBO
(Fig. f) are comparable to if not larger than the
corresponding fractional contributions based on CLaMS-ERA or CLaMS-JRA. This
similarity among variance fractions implies that differences in the strength
of the tropical upwelling introduced via the prescribed reanalysis diabatic
heating rates can adequately explain differences in the amplitudes of the AC
and QBO signals produced by the CLaMS simulations.
Joint distributions of daily-mean gridded diabatic heating rates
(θ˙) in the tropical lower stratosphere (θ=380 K to
θ=460 K) against the corresponding long-wave cloud radiative effect
(LWCRE) based on ERA-I (a), JRA-55 (b) and
MERRA-2 (c) during 1980–2013. The LWCRE is calculated as clear-sky
minus all-sky outgoing long-wave radiation. Also shown are means and standard
deviations of θ˙LS composited into LWCRE bins in
intervals of 25 W m-2 starting from zero. Estimates based on ERA-I
(blue), JRA-55 (purple) and MERRA-2 (red) are shown on all three panels to
facilitate comparison. Distributions are based on variables archived on
1∘×1∘ (ERA-I) and 1.25∘×1.25∘
(JRA-55 and MERRA-2) latitude–longitude grids between 30∘ S and
30∘ N.
The reason that diabatic heating rates in the tropical lower stratosphere are
smaller in MERRA-2 than in ERA-I or JRA-55 is as yet unclear. Although a full
attribution is beyond the scope of this study, we consider here two possible
contributors that may be informative for other users of these data products:
(1) the larger long-wave cloud radiative effect (LWCRE) in MERRA-2 than ERA-I
or JRA-55 and (2) the unique assimilation process used in MERRA-2.
Diabatic heating rates immediately above the tropical tropopause are
dominated by radiative heating Qe.g.,, which, under
the plane-parallel assumption applied in the reanalysis models, indicates the
net vertical convergence of energy in the form of radiation. Adopting the
Newtonian cooling approximation Q∼-α(T-Teq) (with
α the inverse of the radiative relaxation time), radiative heating
rates Q can be treated as inversely proportional to the difference between
the local temperature T and a local radiative equilibrium temperature
Teqe.g.,. The latter depends on the
vertical profile of temperature, the chemical composition, and the radiative
effects of aerosol and clouds within the atmospheric column.
Figure illustrates the relationship between daily-mean gridded
diabatic heating rates in the tropical lower stratosphere
θ˙LS (30∘ S–30∘ N; θ=380 K to
θ=460 K) and the corresponding LWCRE (defined as clear-sky minus
all-sky outgoing long-wave radiation at the nominal top of atmosphere) based
on ERA-I, JRA-55 and MERRA-2. Daily-mean diabatic heating rates and
associated upwelling in the tropical lower stratosphere are negatively
correlated with LWCRE in all three reanalyses, with r ranging from -0.18
in JRA-55 to -0.38 in ERA-I and -0.54 in MERRA-2. This negative
relationship may be explained by noting that clouds occur almost exclusively
within the troposphere, so that a larger LWCRE corresponds to a smaller
upward flux of long-wave radiation across the tropopause. All else remaining
equal, a reduced upward flux across the tropopause acts to reduce
Teq and the net convergence of radiant energy, and therefore
implies a smaller diabatic heating rate. Moreover, values of the LWCRE in
MERRA-2 (median value: 15.9 W m-2) are systematically larger than
those in ERA-I (10.0 W m-2) or JRA-55 (6.8 W m-2), especially
at the upper end of the range (cf. 90th percentile values of 82.9, 50.1, and
34.8 W m-2, respectively). It is thus unsurprising that MERRA-2
produces weaker diabatic upwelling near the base of the tropical pipe.
However, this effect does not appear to account for the entire difference as
lower stratospheric heating rates composited for the same ranges of LWCRE are
still systematically smaller in MERRA-2 than in ERA-Interim or JRA-55
(Fig. ).
Another potential contributor is the unique assimilation process used in
MERRA-2 (and its predecessor MERRA), which includes an additional
“corrector” step after the initial “3D-Var” analysis i.e., an
“iterative predictor-corrector approach”; see also. During the corrector step, all analysis
increments are applied gradually over time as additional tendency terms. This
procedure improves the internal consistency among analyzed variables and
other model variables. However, it also means that the diabatic tendency and
other physical tendency terms in MERRA-2 are archived during the corrector
step, whereas other reanalyses produce these terms during an initial forecast
analogous to the predictor step in MERRA-2. This may have the unintended
effect of systematically damping (or amplifying) the tendency terms produced
by the physical parameterizations. Again adopting the Newtonian cooling
approximation, systematic biases in T or radiatively active constituents
could result in the analysis tendency constantly acting to reduce T-Teq in the lower stratosphere, thereby damping radiative heating
rates and associated diabatic upwelling. Using “replay” simulations, which
mimic the corrector forecast using a modified version of the atmospheric
model and data assimilation system used for MERRA-2, showed
that a simulation constrained by time-averaged assimilated fields produced
approximately 30 % slower ascent in the tropical lower stratosphere than
one based on instantaneous analysis fields. Although
concluded that the slower ascent in the simulation based on time-averaged
assimilated fields produced output in better agreement with available
observations, their result nonetheless highlights the potential impact of the
predictor–corrector approach on upwelling in the tropical lower
stratosphere. Additional evidence that data assimilation may suppress lower
stratospheric upwelling in MERRA-2 is provided by comparison against the
MERRA-2 AMIP dataset , a 10-member ensemble of
free-running simulations generated using the same model and boundary
conditions as MERRA-2 but without data assimilation. Time-mean zonal-mean
diabatic heating rates in the tropical lower stratosphere are about
0.1–0.2 K d-1 smaller in MERRA-2 than in MERRA-2 AMIP over the period
1980–2017 (see Fig. in Appendix ).
Conclusions
We evaluate representations of SWV and its variations at
multiple timescales using the Chemical Lagrangian Model of the Stratosphere
(CLaMS) driven by horizontal winds and diabatic heating rates from three
recent atmospheric reanalyses: ERA-I, JRA-55 and MERRA-2. The analysis is
based on CLaMS simulations of monthly-mean zonal-mean H2O from
1980–2013. We present an intercomparison of simulated variations in
H2O in the context of observational estimates from Aura MLS and the
Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database,
focusing on the annual cycle (AC), the quasi-biennial oscillation (QBO), and
long-term variability and trends. Based on the results of this
intercomparison, we reach the following conclusions.
The climatological means of SWV, which represents a combination of H2O
entering the stratosphere through the tropical tropopause (dominant in the
lower stratosphere) and H2O supplied by CH4 oxidation
(dominant in the upper stratosphere), are in a good agreement (within
±10 % differences) among the three simulations. The “age-of-air” (AoA) and the
ratio of H2O from CH4 oxidation in the lower stratosphere are
larger in CLaMS-MRA than in the other two runs, consistent with relatively
weaker diabatic upwelling in the lower and middle tropical stratosphere in
MERRA-2 relative to ERA-I or JRA-55. Mean tropopause temperatures in ERA-I
are approximately 1 K colder than those in JRA-55, resulting in a tropical
lower stratosphere that is ∼0.6 ppmv drier in CLaMS-ERA than in
CLaMS-JRA. CLaMS-MRA produces a moderate H2O entry mixing ratio close
to observed as a result of moderate mean tropopause temperature among the
three reanalyses.
Differences in amplitudes of modeled H2O entry mixing ratio ACs can
largely be explained by differences in mean tropical tropopause temperatures.
Tuning of the dehydration scheme could possibly eliminate differences in both
mean entry values and the AC amplitude, but not the phase see
also. The spatial distribution of tropopause
temperatures is also important to understanding differences in seasonal
H2O entry mixing ratios. The relative dryness of CLaMS-ERA is most
pronounced in boreal winter, consistent with especially large negative biases
in tropopause temperatures over the western Pacific in ERA-I relative to
MERRA-2. By contrast, CLaMS-JRA produces particularly large values of
H2O entry mixing ratios during boreal summer, consistent with
systematically warmer tropopause temperatures over the Bay of Bengal and
tropical Pacific.
The AC and QBO signals in simulated SWV are robust across the three
simulations and reasonably consistent with observations. The main
discrepancies in the AC and QBO components of H2O variability are
located in the tropical lower and middle stratosphere. The observed AC and
QBO signals are generally located between those produced by CLaMS-ERA and
CLaMS-JRA (too strong) and those produced by CLaMS-MRA (too weak). That is
mainly linked to the fact that the upwelling rates in the tropical lower
stratosphere are smaller in MERRA-2 than ERA-I or JRA-55, which is
attributable to the larger long-wave cloud radiative effect and the unique
assimilation process in MERRA-2. CLaMS-MRA produces a more realistic pattern
of QBO-related signals in SWV (but a bit weaker than observation) than the
other two runs, especially in the middle stratosphere. The “tape recorder”
signal is 25 % weaker and ∼1.5 month slower (between 450 and 600 K)
in CLaMS-MRA relative to SWOOSH. By contrast, the tape-recorder signals are
both stronger and faster than observed in CLaMS-ERA (20 % stronger and
∼1 month faster than SWOOSH) and CLaMS-JRA (40 % stronger and
∼1 month faster than SWOOSH). We find that differences in the rate of
tropical upwelling among the reanalyses not only modulate the propagation of
the simulated tape-recorder signal, but also interact with CH4
photochemistry in ways that tend to amplify the differences due to
propagation rates alone (Sect. ).
With respect to residual variability in SWV, the consistency among the model
results and the SWOOSH analysis improves with time, including the responses
of SWV to variations in ENSO and volcanic aerosol. All simulations indicate
strong and consistent quasi-decadal variability. Trends in H2O entry
mixing ratios over the 1980–2013 period are highly sensitive to the
reanalysis used to drive the model, leading to different magnitudes and even
different signs. This sensitivity of simulated H2O trends to choice
of reanalysis cannot be fully explained by discrepancies in the response of
modeled H2O to ENSO variability or major volcanic eruptions. The
H2O trends are particularly sensitive to the quality of the
reanalyses during the first 5 years of the analysis period (1980–1985).
As best estimates of the true state of the atmosphere, meteorological
reanalyses are widely used to drive tracer transport or constrain the
meteorological state during model simulations. Our study indicates that the
ERA-I, JRA-55 and MERRA-2 reanalyses are all capable of reproducing the
seasonality and interannual variability in SWV related to QBO, ENSO and
volcanic aerosol. However, particular attention should be paid to potential
reanalysis-dependent biases when studying longer-term variability in SWV or
other atmospheric tracers. Moreover, the available observations allow no
conclusions about which reanalysis is most realistic. Therefore, multiple
reanalyses should be used whenever possible to better characterize the
effects of uncertainties in the atmospheric state.
Data availability
The CLaMS model data used for this paper may be requested
from the corresponding author (m.tao@fz-juelich.de)
SWOOSH period of H2O data used for comparison with CLaMS
In Sect. , we have clarified the procedure used to determine the
“SWOOSH period”. The start time for each latitude–θ grid location is
the first month when SWOOSH H2O has more than 12 months of available
data within the next 2 years. The end time is always December 2013. This
procedure excludes some early months of the SWOOSH record (mainly
intermittent observations from SAGE-II in the lower stratosphere), thereby
improving the continuity of the H2O time series.
Figure shows the distribution of the SWOOSH period start
years identified via this procedure. Due to data filtering applied during the
production of SWOOSH , the SWOOSH period is typically
(1) shorter at lower levels than at higher levels, (2) shorter in the tropics
and polar regions than in the midlatitudes and (3) shorter in the Southern
Hemisphere than in the Northern Hemisphere. The shortest SWOOSH periods
start from 2004 and are found in the tropical lower stratosphere and at
Southern Hemisphere high latitudes. The longest SWOOSH period starts from
1986, in the midlatitudes between θ=550 K and θ=650 K.
Although SWOOSH H2O data have some gaps from the start month (shown in
Fig. ) to the end of 2013, the full monthly and zonally mean
modeled H2O are taken into account for comparison. This allows us to
keep the modeled H2O time series as continuous as possible, which in
turn allows us to extract more reliable variance estimates. The results are
virtually unchanged when (SWOOSH) missing months are also excluded from the
CLaMS runs.
Distribution of start years for the “SWOOSH period” used to
compare against the model results. The start time for each latitude–θ
grid point is the first month for which more than 12 months of SWOOSH
H2O data are available over the following 2 years.
Tropospheric methane in CLaMS
The boundary condition for CH4 mixing ratios in CLaMS is prescribed
in the lowest model layer, corresponding to the hybrid vertical coordinate
ζ equal to 0–100 K. For the simulation period between 1985 and 2012,
CH4 measurements from the NOAA/CMDL ground-based measurement network
are used . For simulations in 2012 and later,
observations from the AIRS instrument are used. The CH4 mixing ratios
between 1980 and 1984 in Fig. B1 are just repetition of the year 1985 since
the NOAA/CMDL data are not available before 1985. As CH4 is a
long-lived tracer, we use zonal-mean mixing ratios to prescribe the lower
boundary condition. Figure shows the time series of
global-mean CH4 mixing ratios prescribed at the model lower boundary.
Since the time series before 1985 is artificial, Fig.
shows a start of the methane from the mid-1980s before leveling off in the
mid-1990s. The long-term trend in CH4 at the model lower boundary
(1980–2013) is 0.054 ppmv decade-1. In fact, there is one option to
extend the period of tropospheric methane before 1985 following
by using the Antarctic ice core data
. We will consider this option for the lower boundary
condition in future applications of CLaMS.
Temporal variations from 1980–2013 in the global mean methane
mixing ratio prescribed at the model lower boundary, i.e., the hybrid vertical
coordinate ζ equal to 0–100 K.
Semiannual variation
Figure shows the spatial distribution of the amplitude and
corresponding variance fraction of semiannual harmonic (SAH) signals in
simulated SWV. The enhanced SAH signal in region 1 is associated with the
semiannual oscillation (SAO) of the upper stratospheric circulation.
Satellite observations of the SAH feature in the tropical upper stratosphere
and the mechanisms behind this feature have been studied by
and . The enhanced SAH signals in
region 2 and region 3 are located between two strong AC signals in the polar
regions (SH > NH; see Fig. ). The mechanism behind the SAH
signals in regions 2 and 3 is related to the combined effects of seasonality
in the formation of polar stratospheric clouds (PCSs) and seasonality in vertical
transport e.g.,.
The top row shows SAH amplitudes in the same way as in the second
row of Fig. . The bottom row shows the fraction of SAH variance
relative to total variance, similar to the bottom row of
Fig. .
The SAH fraction shows that the SAH contributed more than 10 % of the total
variance in region 1 and region 3 (both located in the upper stratosphere).
The SAH amplitudes in region 1 simulated by the CLaMS runs are in good
agreement (within 0.1–0.2 ppmv), and also agree well with HALOE and MLS
observations . As with the AC and QBO
features, the SAH signal in region 1 is most pronounced in CLaMS-JRA,
followed by CLaMS-ERA and CLaMS-MRA. Similarly, the SAH fraction relative to
the total variance is again higher in CLaMS-MRA (greater than 15 %–20 %)
due to the low total variance simulated by CLaMS-MRA in the mid-upper
stratosphere.
MERRA-2 versus MERRA-2 AMIP diabatic heating rates
The MERRA-2 AMIP dataset is an ensemble of 10 Atmospheric Model
Intercomparison Project (AMIP)-type simulations conducted using the
atmospheric model used to produce MERRA-2. All simulations used identical
boundary conditions and model configurations to MERRA-2 but did not
assimilate observations . Therefore, a comparison of
MERRA-2 against that of MERRA-2 AMIP gives a rough indication of the effect
of the iterative predictor–corrector data assimilation procedure used in
MERRA-2. The comparison of the diabatic heating rates is shown in
Fig. . The climatological patterns in MERRA-2 and MERRA-2 AMIP
are qualitatively similar; however, their quantitative differences are
substantial (bottom panel). Most notably, diabatic heating rates are about
0.2–0.4 K d-1 larger in MERRA-2 than in MERRA-2 AMIP at pressures
greater than 200 hPa, while diabatic heating rates in the lower stratosphere
(pressures less than 100 hPa) are 0.1–0.2 K d-1 smaller in MERRA-2
than in MERRA-2 AMIP. The results suggest that the data assimilation
procedure suppresses upwelling in the tropical lower stratosphere by as much
as 20 %–40 %.
Comparison of time-mean zonal-mean diabatic heating rates in the
tropical (30∘ N–30∘ S) lower stratosphere between the
MERRA-2 reanalysis (a) and the MERRA-2 AMIP ensemble
mean (b) over 1980–2017. Differences between MERRA-2 and MERRA-2
AMIP are shown in (c).
Author contributions
MT carried out the analysis on the simulations and on the
reanalysis data. PK and FP contributed the code for analysis. JSW prepared
the MERRA-2 reanalysis data and provided the attribution for slow upwelling
rates in MERRA-2 for the discussion section. PK, FP and XY performed the
simulations driven by ERA-I, JRA-55 and MERRA-2, respectively. MD contributed
the regression on natural variability. SF and MR gave helpful suggestions,
especially on the dehydration scheme in the model and the interpretation of
trends. MT wrote the paper. All the co-authors provided helpful discussions
and comments on the paper.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “The SPARC Reanalysis
Intercomparison Project (S-RIP) (ACP/ESSD inter-journal SI)”. It is not
associated with a conference.
Acknowledgements
This research was supported by a joint DFG–NSFC research project with DFG
project number 392169209 and NSFC project number 20171352419. Mengchu Tao
thanks the German Helmholtz-Gemeinschaft within the Helmholtz-CAS Joint
Research Group (JRG) “Climatological impact of increasing anthropogenic
emissions over Asia”, enabling her research position in Institute of Energy
and Climate Research, Stratosphere (IEK-7), Forschungszentrum in Jülich
during which this work was carried out. We thank the Helmholtz Association
under grant number VH-NG-1128 (Helmholtz-Hochschul-Nachwuchsforschergruppe),
providing the research funding for the young investigator group in IEK-7
including the co-authors Felix Ploeger and Mohamadou Diallo. We thank
Krzysztof Wargan for assistance with the MERRA-2 AMIP data. The article processing charges for this open-access
publication were covered by a Research Centre
of the Helmholtz Association.
Review statement
This paper was edited by Farahnaz Khosrawi and reviewed by
three anonymous referees.
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