ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-14715-2018Comparison of mean age of air in five reanalyses using the BASCOE transport modelKinematic comparison of age of air in five modern reanalysesChabrillatSimonsimon.chabrillat@aeronomie.behttps://orcid.org/0000-0003-4378-1567VigourouxCorinneChristopheYveshttps://orcid.org/0000-0003-3243-5036EngelAndreashttps://orcid.org/0000-0003-0557-3935ErreraQuentinMingantiDanielehttps://orcid.org/0000-0001-6131-2794Monge-SanzBeatriz M.SegersArjoMahieuEmmanuelhttps://orcid.org/0000-0002-5251-0286Royal Belgian Institute for Space Aeronomy, BIRA-IASB, Brussels, BelgiumInstitute for Atmospheric and Environmental Science, Goethe University Frankfurt, Frankfurt, GermanyEuropean Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, UKTNO, Department of Climate, Air and Sustainability, P.O. Box 80015, Utrecht, the NetherlandsInstitute of Astrophysics and Geophysics, University of Liège, Liège, BelgiumSimon Chabrillat (simon.chabrillat@aeronomie.be)12October2018181914715147354April20187May201814September201817September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/14715/2018/acp-18-14715-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/14715/2018/acp-18-14715-2018.pdf
We present a consistent intercomparison of the mean age of air
(AoA) according to five modern reanalyses: the European Centre for
Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), the Japanese
Meteorological Agency's Japanese 55-year Reanalysis (JRA-55), the National
Centers for Environmental Prediction Climate Forecast System Reanalysis
(CFSR) and the National Aeronautics and Space Administration's Modern Era
Retrospective analysis for Research and Applications version 1 (MERRA) and
version 2 (MERRA-2). The modeling tool is a kinematic transport model driven
only by the surface pressure and wind fields. It is validated for ERA-I
through a comparison with the AoA computed by another transport model.
The five reanalyses deliver AoA which differs in the worst case by 1 year in
the tropical lower stratosphere and more than 2 years in the upper
stratosphere. At all latitudes and altitudes, MERRA-2 and MERRA provide the
oldest values (∼5–6 years in midstratosphere at midlatitudes), while
JRA-55 and CFSR provide the youngest values (∼4 years) and ERA-I
delivers intermediate results. The spread of AoA at 50 hPa is as large as
the spread obtained in a comparison of chemistry–climate models. The
differences between tropical and midlatitude AoA are in better agreement
except for MERRA-2. Compared with in situ observations, they indicate that
the upwelling is too fast in the tropical lower stratosphere. The spread
between the five simulations in the northern midlatitudes is as large as the
observational uncertainties in a multidecadal time series of balloon
observations, i.e., approximately 2 years. No global impact of the Pinatubo
eruption can be found in our simulations of AoA, contrary to a recent study
which used a diabatic transport model driven by ERA-I and JRA-55 winds and
heating rates.
The time variations are also analyzed through multiple linear regression
analyses taking into account the seasonal cycles, the quasi-biennial
oscillation and the linear trends over four time periods. The amplitudes of
AoA seasonal variations in the lower stratosphere are significantly larger
when using MERRA and MERRA-2 than with the other reanalyses. The linear
trends of AoA using ERA-I confirm those found by earlier model studies,
especially for the period 2002–2012, where the dipole structure of the
latitude–height distribution (positive in the northern midstratosphere and
negative in the southern midstratosphere) also matches trends derived from
satellite observations of SF6. Yet the linear trends vary
substantially depending on the considered period. Over 2002–2015, the ERA-I
results still show a dipole structure with positive trends in the Northern
Hemisphere reaching up to 0.3 yr dec-1. No reanalysis other than ERA-I
finds any dipole structure of AoA trends. The signs of the trends depend
strongly on the input reanalysis and on the considered period, with values
above 10 hPa varying between approximately -0.4 and 0.4 yr dec-1.
Using ERA-I and CFSR, the 2002–2015 trends are negative above 10 hPa,
but using the three other reanalyses these trends are positive.
Over the whole period (1989–2015) each reanalysis delivers opposite trends;
i.e., AoA is mostly increasing with CFSR and ERA-I but mostly decreasing with
MERRA, JRA-55 and MERRA-2.
In view of this large disagreement, we urge great caution for studies aiming
to assess AoA trends derived only from reanalysis winds. We briefly discuss
some possible causes for the dependency of AoA on the input reanalysis and
highlight the need for complementary intercomparisons using diabatic
transport models.
Introduction
The mean age of air (hereafter AoA) is an evaluation of the time necessary
for variations of long-lived (e.g., greenhouse or ozone-depleting) species to
propagate from the troposphere to various regions in the stratosphere. This
classical diagnostic provides insights on the strength and structure of the
Brewer–Dobson circulation (BDC), the polar vortex and irreversible mixing in
the midlatitudes . Due to increased greenhouse gas
forcing, chemistry–climate model (CCM) simulations of the 1990–2090 period
predict an acceleration of the BDC and a decrease of AoA at all latitudes in
the lower part of the stratosphere . The
observational detection of trends in the BDC strength turns out to be quite
difficult. They can be indirectly derived from multidecadal records of
stratospheric temperatures but these derivations are indirect and do not yet
allow a clear confirmation of the acceleration predicted by CCM, mainly due
to an insufficiently constrained accuracy of the temperature observations
.
Observation-based AoA is derived from concentration measurements of very
long-lived tracers which increase (nearly) monotonically at the surface, such
as CO2 or SF6. Multidecadal datasets were compiled from
balloon soundings or aircraft flights e.g.,and references
therein. The corresponding time series
are precise but sparse in time and space, leading to large sampling
uncertainties. Global coverage time series have been derived from satellite
observations, but the precision is lower. The SF6 retrievals from the
Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite
instrument delivered a continuously updated dataset with global coverage for
the period 2002–2012, leading to breakthrough studies about observed AoA and
its time variations during this comparatively short period
. The magnitude, distribution and
detectability of the AoA trends observed over the past years and decades are
currently a topic of intense research
e.g.,.
Reanalysis systems combine a global weather forecast model, observations and
an assimilation scheme to provide the best estimates (analyses) of past
atmospheric states including surface pressure, temperature and wind over a
long (usually multidecadal) period. While they are derived from assimilation
systems used operationally to deliver weather forecasts, they aim to achieve
more consistent variations over long timescales, e.g., avoiding spurious
discontinuities and trends
. Hence, the same model
version and assimilation scheme are used for the whole period and special
care is given to the time-varying biases between the assimilated observations
see, e.g.,. The resulting reanalysis datasets
provide a multivariate, spatially complete and coherent record of the global
atmospheric circulation.
The Stratosphere–troposphere Processes And their Role in Climate (SPARC)
Reanalysis Intercomparison Project (S-RIP) is a coordinated intercomparison
of all major global atmospheric reanalyses. Its introductory paper
provides an overview of the past and current
reanalysis systems and datasets. The present study deals with the five modern
reanalyses of surface and satellite data retained in S-RIP: the European
Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim),
the Japanese Meteorological Agency's Japanese 55-year Reanalysis (JRA-55),
the National Centers for Environmental Prediction Climate Forecast System
Reanalysis (NCEP-CFSR) and the National Aeronautics and Space
Administration's Modern Era Retrospective-analysis for Research Applications
version 1 (MERRA) and version 2 (MERRA-2).
The absolute value of AoA and its evolution over the past decades can be
derived from the surface pressure and wind fields available in such
reanalyses, using either an offline transport model see,
e.g., or a chemistry–climate model nudged to the input
reanalysis to model the transport
of inert tracers propagating from the troposphere to the stratosphere. This
approach helped to identify shortcomings in the Brewer–Dobson circulation
described by early reanalyses
and to assess the improvements in the next generation of reanalyses, e.g.,
from ERA-40 to ERA-Interim
.
Few AoA comparisons have been performed between reanalyses originating from
different reanalysis centers. This is mainly due to technical difficulties
that are not limited to file formatting issues. While all modern systems use
hybrid σ-p vertical coordinates , each
reanalysis comes with a wind field computed on a different grid with
different horizontal and vertical resolutions. Some reanalysis forecast
models use spectral dynamical cores , while
others use finite-volume dynamics see the next section for
details. A common offline transport model may have difficulties
dealing with such different grids because it is usually tailored for a
specific family of reanalyses, e.g., using an advection algorithm similar to
the dynamical core of the driving reanalysis system or climate model
.
Section describes the input reanalyses and our modeling tools
to explain how these difficulties were circumvented. It also validates our
approach with a classical set of observations and with the results of another
transport model which is tailored for ERA-I.
The main purpose of this paper is to provide a comparison of the AoA obtained
from five modern reanalyses included in the S-RIP project in order to assess
their level of agreement or to identify outliers. Its focus is not on
detailed comparisons with observations (which are deferred to a follow-on
study) but rather on a consistent intercomparison between the reanalyses
through the use of a common transport model.
Section compares the distribution of the AoA obtained from
each reanalysis for a reference period and its time evolution in the middle
latitudes. Section uses a multiple linear regression
model to characterize the time variations of AoA, including an
intercomparison of their linear trends for several periods.
Section proposes a brief overview of the possible causes
for the disagreement between the reanalyses and states the further work
required to elucidate this disagreement. Section concludes
the paper with a summary of our findings and their implications.
MethodologyDescription and set-up of the offline transport model
Depending on their vertical coordinate system and the reanalysis data used as
input, one may distinguish between kinematic and diabatic transport models
. Diabatic models use isentropic
(θ) or hybrid σ-θ vertical coordinates and calculate the
vertical transport from diabatic heating rates which may be read from the
input reanalysis or recomputed using a separate radiation scheme. Kinematic
transport models, on the other hand, need only the surface pressure
and horizontal wind fields on input. These models are usually set on a different grid
than their input reanalysis dataset. Since this prevents the direct usage of
the vertical wind component in the reanalysis, they rely on mass continuity
to derive the vertical mass fluxes corresponding to their own grid. The
present study uses the kinematic transport model developed for the Belgian
Assimilation System for Chemical ObsErvations BASCOE:
see. Its
advection module is the flux-form semi-Lagrangian (FFSL) scheme
configured to follow closely the recommendations of
.
We briefly summarize here this configuration because it has an important
impact on the simulated distribution of AoA in the stratosphere. The FFSL
advection scheme is run on a evenly spaced latitude–longitude grid with
2∘×2.5∘ increments. This grid spacing is typical for
current simulations of stratospheric chemistry and transport over several
decades . Using the FFSL algorithm,
showed that this is the minimum resolution
allowing a realistic representation of the tropical and high-latitude mixing
barriers. The FFSL algorithm does not require satisfaction of the
Courant–Friedrichs–Lewy (CFL) condition in the longitudinal direction, which
is a big computational advantage for regular longitude–latitude grids. The
time step is set to 30 min by default and automatically split into integer
fractions in order to satisfy the CFL condition in the meridional direction.
The algorithmic structure of the FFSL scheme allows multiple choices for
monotonicity constraints that have implications on the subgrid tracer
distribution used to calculate fluxes across cell edges. These choices are
made separately in the longitudinal, meridional and vertical directions.
showed that AoA calculations are very sensitive to
the choice of constraint in the vertical direction: realistic results require
a positive–definite piecewise parabolic method, where the constraint on the
subgrid distribution is only strong enough to prevent generation of negative
values but overshoots and undershoots are allowed. There is no representation
of convection in the model nor any explicit mechanism for horizontal
diffusion.
The age of air is defined as the spectrum of transit times from a source
region to a given location, with the tropical tropopause usually defining the
source region for studies of the stratosphere. In the case of an ideal tracer
which increases linearly in the source region and has no photochemical
productions or losses, one can obtain the mean of this spectrum (denoted here
as AoA) at any time and location from the corresponding mixing ratio of the
tracer: in such a case, the AoA is simply the time elapsed since the ideal
tracer had the same mixing ratio in the source region
. Here, we follow this classical approach, using for
most simulations the 100 hPa isobar between latitudes 10∘ S and
10∘ N as the source region. In one case, we have used the surface as
source region in order to enable a comparison with a long time series of
balloon observations (see Sect. ). The output AoA datasets
are interpolated from model levels to constant pressure levels using the
instantaneous and two-dimensional input surface pressures, i.e., prior to any
averaging in the longitudinal or time dimension.
Description of the input reanalyses
We compute and compare the AoA in five recent reanalyses which are described
in detail by : ERA-Interim European Centre
for Medium-Range Weather Forecasts Interim Reanalysis;,
JRA-55 Japanese 55-year Reanalysis;, MERRA
Modern Era Retrospective Analysis for
Research and Applications;, MERRA-2 and
NCEP-CFSR National Centers for Environmental Prediction – Climate
Forecast System Reanalysis;. These datasets were used
over the period January 1980 to December 2015, except for NCEP-CFSR which
originally ended in December 2010 and is extended here with the CFSv2
dataset Climate Forecast System version 2; from
January 2011 to December 2014. Hereafter, we use “ERA-I” to refer to
ERA-Interim and “CFSR” to refer to the combined NCEP-CFSR reanalyses.
Each reanalysis is available on two vertical grids: the native grid of the
underlying atmospheric model (product on “model levels”) and an output grid
of constant pressures (product interpolated to “pressure levels”). Our
simulations are run on the native model levels in order to account for the
different vertical resolution of each reanalysis system and also to avoid any
interference from the interpolation methods used to deliver the products on
constant pressure levels. All reanalysis systems use the hybrid
sigma-pressure vertical coordinate with levels extending from the surface up
to ∼0.266 hPa (∼57 km height) in CFSR, 0.1 hPa (∼64 km) in
ERA-I and JRA-55, or 0.01 hPa (∼78 km) in MERRA and MERRA-2. The
reader is referred to for a comparison of the
vertical resolutions of the reanalysis systems.
The forecast models use two different frameworks to discretize their
primitive variables on the horizontal plane: MERRA and MERRA-2 solve for mass
fluxes on a regular latitude–longitude grid , while ERA-I,
JRA-55 and CFSR use spectral dynamical cores; i.e., they solve for vorticity
and divergence expressed on a spherical harmonics basis
e.g.,. Users of the reanalyses often
download velocity fields which are derived from the primitive variables and
evaluated on varying regular grids: these may be reduced Gaussian grids
(ERA-I and JRA-55), regular Gaussian grids (CFSR) or regular
latitude–longitude grids (MERRA and MERRA-2). This preprocessing is
described in detail in the next subsection.
We use in all cases the analyses valid at 00:00, 06:00, 12:00 and
18:00 UTC, i.e., datasets
with a 6 h time resolution. The assimilation procedure for MERRA and MERRA-2
uses an iterative predictor–corrector approach, generating two separate sets
of reanalysis products designated ANA for analysis state and ASM for
assimilated state . The latter products use a
6 h corrector forecast centered on the analysis time and an incremental
analysis update to apply the previously calculated assimilation increment
gradually rather than abruptly at the analysis time .
Thanks to this procedure, the ASM products have smaller wind imbalances than
the ANA products ; hence, they are preferable for
tracer transport simulations. We used the ASM products in MERRA-2 but could
not do so with MERRA, where the ASM products are only available on constant
pressure levels. Since we aim to evaluate each reanalysis on its native
vertical grid, we had to fall back on the ANA product in the case of MERRA.
Preprocessing of the reanalyses
The BASCOE transport model (hereafter BASCOE TM) is used as a tool to perform
a fair comparison of advective transport in each reanalysis dataset, using
their native vertical grids but a common, low-resolution latitude–longitude
grid. It requires on input the surface pressure and horizontal velocity on a
so-called Arakawa-C grid; i.e., the zonal wind u must be staggered in
longitude and the meridional wind v must be staggered in latitude. As
indicated by its name, the FFSL algorithm evaluates internally the
corresponding mass fluxes and derives the vertical winds (w) from mass
conservation. Hence, the reanalysis datasets must be carefully preprocessed
from spectral or high-resolution gridded fields to the low-resolution C grid.
We have paid special attention to this preprocessing of the reanalyses to
make sure that the different types of wind fields are expressed in a
consistent manner for our transport algorithm.
Due to its assimilation procedure, the early ERA-40 reanalysis contained
large dynamical imbalances which deteriorated the Brewer–Dobson circulation
through excessive upward motion in the tropics and excessive transport from
the tropics to the midlatitudes
.
described a similar issue with MERRA and proposed to use time-averaged input
wind fields in order to remove these imbalances, but this approach is
available only for MERRA and MERRA-2. To filter out such dynamical
imbalances, BASCOE uses a preprocessor which was originally developed only
for the analyses computed by the European Centre for Medium-Range Weather
Forecast (ECMWF) including ERA-I
. Using the primitive variables
of spectral dynamical cores, i.e., the vorticity and divergence expressed on
a spherical harmonics basis, this preprocessor evaluates the zonal and
meridional winds on a regular latitude–longitude grid while correcting for
the small inconsistencies in the pressure tendency compared with the
divergence fields. This correction ensures consistent mass fields even in the
presence of spurious surface pressure increments which may be caused by data
assimilation.
Our preprocessing for the five reanalysis systems is based on this
algorithm, with a preliminary derivation of the spherical harmonics
coefficients of vorticity, divergence and surface pressure for the reanalyses
other than ERA-I. In all cases, these spectral coefficients are truncated at
wavenumber 47 to avoid aliasing on the 2∘×2.5∘ target
grid Sect. 7.4.
Comparison of age of air output with another model
Figure compares the results of the BASCOE TM driven by
ERA-I with those by a reference Eulerian model, using the standard layout of
zonal means at 20 km height and at equatorial, middle and polar latitudes
e.g.,. Both models transport idealized tracers
which increase linearly at the tropical tropopause and are driven during
20 years by repeating reanalyses of the year 2000. The reference model is
TOMCAT, driven by ERA-I analyses with 6-hourly updates. At 20 km height, we
use the results published by Fig. 28, while the
vertical profiles are those published by
Fig. 1. Some observational context is
provided with in situ observations of SF6 and CO2.
Mean age of air (AoA, in years) from two model simulations using
idealized tracers advected by ERA-I for the fixed year 2000. Models shown are
BASCOE TM (blue solid lines) and TOMCAT (blue dotted lines). The modeled AoA
fields are calculated using as reference the tropical tropopause region
(10∘ S–10∘ N, 100 hPa). (a) Values at 20 km
height; (b) vertical profiles at 5∘ S;
(c) vertical profiles at 40∘ N; (d) vertical
profiles at 65∘ N. The symbols represent in situ observations
collected during the 1990s seefor
details. The legend in panel (a) applies
to all four panels.
Very good agreement is obtained between TOMCAT and BASCOE TM. At 20 km
height, the results are nearly identical except in the Southern Hemisphere,
where TOMCAT delivers a slightly weaker latitudinal gradient, resulting in a
difference of around 0.5 years above the South Pole between both models. All
three vertical profiles show that TOMCAT delivers slightly weaker vertical
gradients in the lower stratosphere than the BASCOE TM. This results in
younger midstratospheric AoA by TOMCAT, but here also the largest difference
does not exceed 0.5 years (latitude 5∘ S, height 45 km).
Intercomparison of AoA values
Time-varying distributions of AoA were derived from each reanalysis for the
whole period (1980–2015). The initial conditions were obtained from
20-year spin-up runs simulating the 1960–1980 period with repeating
reanalyses of the year 1980. The importance of the initialization procedure
was evaluated with an alternative set of transport experiments starting in
1981 from 40-year spin-up runs driven by repeating reanalyses of the year
1981. While the initial AoA could be significantly different depending on the
initialization procedure (up to 15 % difference in 1981 in the case of
CFSR), by 1989, these differences were smaller than 1 % at all latitudes and
pressure levels for each reanalysis (not shown). Hence, the five AoA datasets
are studied only over the period 1989–2015.
For the sake of convenience, the results of each simulation will be designated
by its driving reanalysis but the reader is reminded that all results
presented here are obtained indirectly through an offline and kinematic
transport model. The outcome of the intercomparison could have been different
if the AoA had been computed directly in each reanalysis system.
Mean distribution in 2002–2007
The AoA distributions are first averaged over the period 2002–2007 in order
to remove seasonal and quasi-biennial oscillations and also to allow
comparisons with the distribution most recently derived from MIPAS
observations of SF6.
The global distribution of AoA is first compared with latitude–pressure
cross-sections. The ERA-I reanalysis is taken as reference because it
delivers intermediate values and has been used in AoA studies with several
other transport models see,
e.g.,.
Figure shows the latitude–height cross-sections of AoA
for the period 2002–2007, with a noticeable hemispheric asymmetry: as
expected, the latitudinal gradient is significantly stronger in the southern
midlatitudes and polar regions than in the Northern Hemisphere, and old air
masses reach much lower altitudes above the Antarctic than above the Arctic
(e.g., the 5-year isoline starts at 50 hPa above the South Pole and ends at
20 hPa above the North Pole). This is qualitatively confirmed by AoA derived
from MIPAS observations of SF6 for the same period
Fig. 7d.
Latitude–pressure distribution of AoA in 2002–2007 from the BASCOE
simulation driven by ERA-I.
The four other reanalyses deliver noticeably different distributions of AoA
(Fig. ). One can distinguish JRA-55 and CFSR as the
“younger reanalyses”, with AoA not exceeding 5 years in the polar upper
stratosphere, MERRA as the “older reanalysis”, with maximum AoA values as
large as 6.5 years, and ERA-I with intermediate results (5.8 years in the
same regions). MERRA-2 is a special case, with upper stratospheric values
similar to those reached by ERA-I but quite different latitudinal gradients.
The hemispheric asymmetry is more pronounced with ERA-I than with any other
reanalysis, e.g., the 3- and 4-year isolines (JRA-55 and CFSR, respectively)
or the 5-year isoline (MERRA-2 and MERRA) reach nearly the same level above
the North Pole as above the South Pole. MERRA-2 stands out in the middle
stratosphere with nearly vertical isolines, i.e., very small vertical
gradients which are not supported by MIPAS observations
.
Latitude–pressure distribution of AoA (years) in 2002–2007 from
BASCOE simulations driven by all reanalyses but ERA-I (top row; same color
scale as previous figure) and relative difference with respect to the mean
AoA by the ERA-I-driven simulation for the same period (bottom row;
difference is not plotted at grid points where ERA-I AoA is smaller than
5 days). These reanalyses are, from left to right, JRA-55, CFSR, MERRA-2 and
MERRA.
While this qualitative comparison of the AoA distributions points to
different gradients in the midlatitudes and polar regions, the relative
differences with respect to ERA-I are largest in the tropical lower
stratosphere (bottom row of Fig. ). Hence, we focus on
this region and its differences with the midlatitudes.
Figure , inspired by the AoA intercomparisons in
CCMs , shows the
intercomparison of AoA zonal means at 50 hPa, at tropical and northern
midlatitudes, and the AoA difference between these two latitude bands.
AoA (years) in 2002–2007 by the BASCOE TM driven by five reanalyses
(solid lines) versus in situ observations (symbols) with their 1σ
uncertainties (grey shading). The five reanalyses are ERA-I (blue), MERRA-2
(red), MERRA (pink), JRA-55 (purple) and CFSR (green). (a) AoA at
50 hPa with aircraft observations of CO2. (b) AoA in the tropics
(10∘ N–10∘ S) with aircraft observations
. (c) AoA in the
northern midlatitudes (35–45∘ N) with balloon observations
. (d) AoA
differences between the northern midlatitudes and tropics
. The legend in panel
(d) applies to panels (b) and (c) as well.
The intercomparison at 50 hPa (Fig. a) shows again
the important disagreement between the five model simulations. JRA-55 yields
the youngest AoA at all latitudes with values ranging from 0.8 years at the
Equator to 3.6 years at the South Pole, while MERRA and MERRA-2 yield the
oldest AoA with 1.6 years at the Equator and around 5 years at the South Pole.
CFSR and ERA-I yield intermediate results with nearly identical values in the
northern extratropics but different latitude gradients between the tropics
and Southern Hemisphere. The sole simulation to deliver a minimum AoA in the
southern tropics is driven by CFSR, which yields the minimum AoA at
6∘ S. In the other simulations, this minimum is either exactly at the
Equator (JRA-55, MERRA) or slightly north of the Equator (ERA-I, MERRA-2). In
the Southern Hemisphere, CFSR results in AoA nearly as young as JRA-55, while
ERA-I reaches larger values which are very close to the observations.
Overall, the spread between the five simulations at 50 hPa is larger than
the 1σ observational uncertainties in the tropics and nearly as large
in the extratropics. Since the reanalyses are constrained by very similar
satellite datasets, they could have been expected to deliver more similar AoA
than an intercomparison of unconstrained climate models. Yet we note that the
spread shown in Fig. a is as large as in an
intercomparison of seven CCMs Fig. 2.
The vertical profiles of AoA (Fig. b and c) confirm
that this large spread and general hierarchy of AoA (youngest with JRA-55,
oldest with MERRA and MERRA-2) are found at all stratospheric levels. In the
northern midlatitudes (35–45∘ N, Fig. c)
MERRA-2 stands out with vertical gradients which are larger in the lower
stratosphere but smaller in the upper stratosphere than in all other
reanalyses. While the intermediate values by ERA-I and CFSR agree well with
observations in the tropics (10∘ S–10∘ N,
Fig. b), this is not the case in the northern
midlatitudes, where only MERRA and MERRA-2 deliver AoA as old as the
observations.
The AoA differences between the tropics and midlatitudes are directly
related to the inverse of the tropical upwelling velocity and independent of
quasi-horizontal mixing: a smaller AoA latitudinal gradient indicates faster
tropical ascent . These “latitudinal
gradients of AoA” were used in several CCM intercomparisons
.
Figure d shows this diagnostic for the five
reanalyses, i.e., the differences between the AoA profiles in
Fig. c and b. Except for MERRA-2, the profiles of
AoA differences delivered by the four other reanalyses agree much more
closely than the AoA profiles themselves, at least during the 2002–2007
period. The spread of AoA differences between the four reanalyses reaches a
maximum of 0.2 years at 30 hPa, much tighter than the spread of 0.8 years in
the corresponding intercomparison of six CCMs
Fig. 3c. While there is good agreement
with the observation-derived AoA differences below 60 and at 10 hPa, these
four reanalyses significantly underestimate it at intermediate pressure
levels. This indicates an overestimation of the tropical upwelling obtained
with ERA-I, CFSR, JRA-55 and MERRA in the lower stratosphere. MERRA and
MERRA-2 yield larger AoA at northern midlatitudes than the three other
reanalyses. In the case of MERRA-2, this results in a profile of AoA
differences which are significantly larger than the profiles obtained with
the four other reanalyses but agrees much better with the profile derived
from the observations. Hence, MERRA-2 apparently underestimates the tropical
upwelling in the lowermost stratosphere (100–60 hPa), agrees better with
the observations at 50 hPa than the four other reanalyses and is in
accordance with them at higher levels.
Time evolution and absence of volcanic impact
The Pinatubo eruption, which started on 15 June 1991, is expected to have had
a significant impact on AoA .
The assimilation of satellite radiance measurements by the Advanced Microwave
Sounding Unit (AMSU) started in 1998 (on 1 August in ERA-I and JRA-55, and
1 November in CFSR, MERRA and MERRA-2) and was repeatedly shown to have a
important influence on their description of the stratospheric dynamics
e.g.,.
Hence, we repeat the latitudinal gradient diagnostic but for the period
1992–1997, i.e., after the Pinatubo eruption and before the ingestion of
AMSU radiances (Fig. ). The general outcome
is the same as during the later period: the tropical ascent is too fast with
all reanalyses except with MERRA-2. Yet MERRA-2 provides a better match with
the observations during this earlier period, and the four other reanalyses do
not agree as closely.
Same as Fig. d but for the period
1992–1997.
Figure shows the averaged time evolution of simulated
AoA according to the five reanalyses, from 1989 to 2015 at 50 hPa in the
midlatitudes. The results are smoothed with a 1-year running mean in order
to highlight the long-term trends. The overall hierarchy of ages shown on
previous figures for years 2002–2007 holds for the whole 1989–2015 period:
MERRA and MERRA-2 deliver the oldest AoA, JRA-55 and CFSR the youngest. While
MERRA and MERRA-2 agree well in the Southern Hemisphere, this is not the case
in the Northern Hemisphere, where MERRA-2 starts with much older values. A
rapid decrease of MERRA-2 values during the 1990s allows these two datasets
to reach better agreement after 1998, i.e., the beginning of AMSU
assimilation. The possible causes for this apparently anomalous behavior of
MERRA-2 will be discussed in Sect. . The MERRA output in
the Northern Hemisphere delivers seasonal cycles with much larger amplitudes
than those obtained from all other reanalyses. This will be investigated in
the next section.
Time evolution of AoA (years) interpolated to a pressure of 50 hPa
in the northern midlatitudes (40–50∘ N mean, a) and in
the southern midlatitudes (50–40∘ S mean, b). Thin lines
show instantaneous model output every 5 days using the five reanalyses with
color codes according the legend shown in the lower panel. Thick lines
are smoothed with a 1-year running mean. The black vertical lines highlight
the start of the Pinatubo eruption and the first assimilation of AMSU (see
text).
The Pinatubo eruption does not appear to have any impact on the simulated AoA
at 50 hPa except with MERRA-2 which shows an increase in the southern
midlatitudes. The same time series for the tropical latitude band
(30∘ S–30∘ N) does not show any impact of the Pinatubo
eruption either (not shown). This absence of volcanic impact in the other
reanalyses is even more evident in a deseasonalized time series of the
extrapolar lower stratosphere (Fig. ). This
diagnostic is inspired by , who showed a significant
impact of the Pinatubo eruption on AoA using ERA-I and JRA-55 but with
another offline transport model. Since our results contradict this finding,
this issue will also be further discussed in Sect. .
Time evolution of the globally averaged
(72∘ S–72∘ N) anomalies of AoA (years) with respect to
their mean (1989–2015) annual cycles, between 16 and 28 km, using the five
reanalyses with the same color codes as in previous figure.
Figure displays time series of AoA in the middle
stratosphere (mean values between 30 and 5 hPa). The left plot compares the
model results in the Northern Hemisphere with balloon observations that have been collected
since the 1970s , where the derivation of AoA uses the
surface as reference and the outer error bars denote the overall uncertainty
of the mean-age value including an assessment of the representativeness of a
single profile . To allow a consistent comparison, the
solid lines in Fig. show modeled AoA using the
surface as reference, i.e., AoA evaluated from a tracer which uses as boundary
condition a global constant increasing linearly with time at the surface.
This boundary condition is propagated to the free troposphere through
vertical diffusion with a coefficient Kzz decreasing from an arbitrary
value of 10 m2 s-1 at the surface to zero at the pressure level
halfway between the surface and the tropopause.
Figure b compares the resulting time series in the
tropics with the usual calculation of AoA using the tropical tropopause as
reference (dashed lines). The differences between the two calculations
represent the transit times from the surface to the tropical tropopause, are
nearly independent of the simulated year and range between 3 months (with
ERA-I or JRA-55) and 6 months (with MERRA). These values are close to the
longest transit times reported in a recent intercomparison of global models
indicating slow transport from the surface to the
tropical tropopause which we attribute to the omission of deep convective
transport in our model. While the surface-based model AoA (solid lines in
Fig. ) may be slightly overestimated, these biases
have no significant interannual variations and do not hinder the
intercomparison between reanalyses.
The spread between the five simulations is as large as the observational
uncertainties, highlighting again the importance of the disagreement between
the five reanalyses. In the northern midlatitudes
(Fig. a), no reanalysis delivers any change larger
than half a year over the whole period (1989–2015) except for MERRA-2 which
indicates a large decrease of 0.8 years, but this decrease starts from values
much larger than the observations and happens mostly before 2000. ERA-I
delivers a weakly positive trend over the period 1989–2015, and we will
assess in Sect. that this trend in the model results is
significant. While the overall trend simulated with ERA-I is in agreement
with the balloon observations, this comparison should be considered with
great caution because the sign of the AoA trend is not significant in the
observations and modeled trends
over periods as long as 30 years are often not significant when the ideal
tracer is sampled like the available observations of stratospheric tracers
.
The intercomparison in the Southern Hemisphere
(Fig. c) also shows large disagreement between the
long-term trends among the five reanalyses. MERRA and MERRA-2 values decrease
quickly until 1995 and increase after 2007, while ERA-I values follow an
opposite pattern. The long-term evolution of AoA in this region is completely
different from JRA-55 (gradual decrease until 2002 followed by a stabilization) and differs yet
again from CFSR (no apparent trend before 1997 and rapid increase during
1997–2003). The thin lines allow a qualitative comparison of faster
variations. The seasonal signal dominates in all cases, with similar phases:
AoA is oldest in fall and youngest in spring. The seasonal amplitudes are
very dependent on the input reanalysis and on the considered year, so their
detailed analysis is deferred to the next section. Yet we note already that
some reanalyses exhibit a strong modulation of the seasonal cycle by the
quasi-biennial oscillation QBO; for a general review,
see, while others do not. This can be seen very
clearly during the period 2005–2009 when the seasonal amplitudes of AoA by
ERA-I and MERRA are approximately twice as small during the easterly phase of
the QBO (i.e., in 2006 and 2008) than during the westerly phase (i.e., in
2005, 2007, 2009). This modulation of the seasonal variations is weaker in
the MERRA-2 and JRA-55 datasets and absent from the CFSR dataset.
Analysis of temporal variations
We now perform a quantitative investigation of the temporal variations in
order to derive the amplitudes of periodic variations and the linear trends
of AoA at all latitudes and pressure levels, including their uncertainties.
Methodology
used a multiple linear regression model to study
the trends of ozone total columns and vertical distribution at several
ground-based stations. Here, we apply the same tool to A(t), the monthly
zonal means of AoA as a function of time, latitude and pressure (after
interpolation to a constant log-pressure grid with 2 km increments). The
multiple linear regression model is expressed as
A(t)=A0+A1⋅t+S(t)+Q(t)+ϵ(t),
where t is time, A0 is the baseline value, A1 is the annual trend of AoA, and ϵ(t) represents the residuals.
The term S(t) describes the seasonal variations in A(t):
S(t)=S1⋅cos(2πt/12)+S2⋅sin(2πt/12)+S3⋅cos(4πt/12)+S4⋅sin(4πt/12),
where the coefficients S1 to S4 describe the seasonal cycle.
The term Q(t) describes the variations due to the QBO and its seasonal modulations:
Q(t)=Q10(t)⋅[Q1+Q2⋅cos(2πt/12)+Q3⋅sin(2πt/12)+Q4⋅cos(4πt/12)+Q5⋅sin(4πt/12)]+Q30(t)⋅[Q6+Q7⋅cos(2πt/12)+Q8⋅sin(2πt/12)+Q9⋅cos(4πt/12)+Q10⋅sin(4πt/12)],
where the explanatory variables Q10(t) and Q30(t) are the zonal winds
observed above Singapore at 10 and 30 hPa (data from the FU Berlin:
http://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html,
last access: 10 October 2018), and Q1 to Q10 are the coefficients
associated with these two proxies, including their seasonal dependence.
The uncertainties arising from the fit are calculated for the 95 %
confidence interval and corrected for autocorrelation in the residuals
Eqs. 3, 4 and 6 in. Preliminary tests also
included additional terms to account for the El Niño–Southern Oscillation
(ENSO), the 11-year solar cycle and volcanic forcings but it was found that
these terms do not impact significantly the linear trends nor the amplitudes
of seasonal and quasi-biennial oscillations. Hence, they were removed from the
regression model in order to avoid any overfitting of the data and to ease
the interpretation of the results.
An important goal of this analysis is the determination of linear trends. As
seen in Figs. and , such
trends depend closely on the considered time period. Hence, the regression
model was applied not only to the whole simulation period (1989–2015) but
also to an “early period” (1989–2001) and a “recent period” (2002–2015)
which start after the assimilation of AMSU and on the same year as the MIPAS
mission .
Amplitudes of the seasonal cycle and quasi-biennial oscillation
The amplitude of the seasonal variations is approximated by the difference
between the maximum and minimum values reached by the term S(t) in the
linear regression model. Figure shows the dependence of
this approximated amplitude with respect to pressure in five latitude bands
for the period 2002–2015 (the results are similar for the period
1989–2001). The results with ERA-I are in agreement with an earlier modeling
study Fig. 9. The vertical structure agrees
broadly across all five reanalyses in the extratropics with maximum
amplitudes in the lower stratosphere (around 100 hPa), except above the
South Pole, where the amplitudes are maximum in the middle stratosphere
(10–30 hPa). MERRA and MERRA-2 stand out with larger amplitudes in the
lower stratosphere, resulting above the South Pole in a secondary maximum
which is not found by the three other reanalyses. One may argue that the
larger seasonal amplitudes of MERRA and MERRA-2 are a direct consequence of
their larger annual means (see Fig. ) but this is not
supported by the agreement of JRA-55 and CFSR with ERA-I despite their
significantly younger annual means. In the tropics, ERA-I stands out with
larger amplitudes in the upper stratosphere (around 5 hPa) and MERRA-2 with
larger amplitudes in the lower stratosphere (around 50 hPa), while the three
other reanalyses are in good agreement.
Time evolution of AoA (years) averaged from 30 to 5 hPa
(approximately 24 to 36 km). Thick lines show model output smoothed with a
1-year running mean and line color codes as in previous figure.
(a) Mean for the northern midlatitudes (40–50∘ N), where
the symbols represent values derived from balloon observations of SF6
(circles) and CO2 (triangles) with color code showing the latitude of
the measurements (according to the inset color bar) and outer error bars
including sampling uncertainties . (b) Mean
for the tropical latitudes (30∘ S–30∘ N), where the dashed
lines show AoA using the tropical tropopause as reference. (c) Mean
for the southern midlatitudes (50–40∘ S), where the thin lines show
instantaneous model output every 5 days. Except for the dashed lines in panel
(b), all AoA values in this figure use the surface as reference.
Amplitude (in years) of the seasonal variation in the 2002–2015
linear regression fit of AoA, as a function of pressure and averaged in five
latitude bands, from left to right: North Pole (70–90∘ N);
northern midlatitudes (40–50∘ N); tropics
(30∘ S–30∘ N); southern midlatitudes (50–40∘ S);
South Pole (90–70∘ S). The same color codes are used as in previous figures.
We now investigate the differences in the QBO among all reanalyses.
have compared the monthly mean zonal wind in the
equatorial stratosphere among reanalyses and found that their degree of
disagreement depends on latitude, longitude, height and the phase of the
QBO. They also noted a tendency for the agreement to be best near the
longitude of Singapore, suggesting that the Singapore observations act as a
strong constraint on all the reanalyses.
Here, we perform an intercomparison of the amplitude of the QBO signal (in
years) in each reanalysis. We approximate it again as the difference between
the maximum and minimum values reached by the term Q(t) in the linear
regression model. Our results for ERA-I show that the QBO amplitude is
largest in the subtropics around 30 hPa (not shown), which confirms again
the results of . Figure
compares the results at this pressure level. Except for CFSR, the latitudinal
dependence is similar in all reanalyses: the approximated QBO amplitude
reaches maximum values around 15∘ latitude in both hemispheres and
presents a marked minimum around the Equator. Outside of the equatorial
region, the QBO amplitudes by JRA-55 are significantly smaller than by ERA-I,
MERRA and MERRA-2. The amplitudes computed from CFSR show no clear structure
in the Southern Hemisphere and reach large values at the North Pole.
Linear trends
It is difficult to infer changes in the BDC on the basis of AoA trends over
periods shorter than several decades. Even in models where an ideal, linearly
increasing artificial tracer is used, one has to rely on zonal-mean results
over long periods to obtain trends that are clearly statistically significant
. The statement that is often made that climate
models simulate a decreasing age throughout the stratosphere only applies
over long time periods and is not necessarily the case for the past 25 years,
when most tracer measurements were taken . For
example, the analysis of a 1700-year simulation showed that it takes around
30 years for a modeled BDC trend to emerge from the noise of natural climate
variability assuming a 2 % dec-1 trend in the BDC;
.
While linear trends of AoA over shorter periods may represent transient
changes due to climate variability, such changes over timescales which are
intermediate between the QBO and the multidecadal scales are still relevant
to the study of stratospheric dynamics. Current research on AoA trends has
largely focused on a dipole-like latitudinal structure for the period
2002–2012, which was first derived from satellite observation of SF6
by the MIPAS instrument . This structure of trends
shows AoA decreasing in the Southern Hemisphere but increasing in the
Northern Hemisphere, which was used to explain a recent increase of
stratospheric HCl in the Northern Hemisphere
and interpreted as the consequence of a southward shift of the subtropical
transport barriers .
The ERA-I reanalysis supports a dipole-like latitudinal structure of AoA
trends, at least since 2002. hereafter H2015
derived AoA trends from the distribution of SF6 over the period
2002–2012, using MIPAS observations and a CCM nudged towards ERA-I below
1 hPa and found a good agreement for the signs, range and latitudinal
structure of AoA trends (see Figs. 6 and 10 in H2015). Here, we aim to verify
our methodology through a comparison of our results with H2015, to check the
consistency of AoA trends derived from the four other reanalyses and to
explore the latitudinal structure of AoA trends for periods starting earlier
than 2002.
The linear trend is represented by A1 in the multiple regression linear
model (Eq. ). It is expressed in years per decade
(yr dec-1) and is deemed significant at a given grid point if its
absolute value is larger than its uncertainty (as defined in
Sect. ). Figure presents the ERA-I trends
during the period 2002–2012 in order to compare with H2015. In the polar
regions, H2015 showed large and positive trends, while they are insignificant
according to our model (Fig. ). This disagreement can be
attributed to different approaches: here, we study the true age of air using
a theoretical tracer with no losses, while H2015 evaluated the apparent mean
age of air taking into account the mesospheric sink of SF6 which has
the largest impact in the polar regions . Outside
of the polar regions, Fig. shows good agreement with both
observational and modeling results in H2015, including with respect to the
significance of the trends: in the 30–60 hPa (approximately 25–20 km)
layer the trends are significant at all extratropical latitudes, negative in
the Southern Hemisphere and positive in the Northern Hemisphere. They reach
-0.6 yr dec-1 in the southern tropics and close to
0.5 yr dec-1 in the northern tropics. Our results also agree well with
those obtained by a diabatic model driven by ERA-I over the same period
.
Figure compares the latitude–pressure distributions
of AoA trends across all five reanalyses and for the early (1989–2001),
recent (2002–2015) and overall periods (1989–2015). It is important to note
that the trends over the early and overall periods should be considered with
caution since there were little data to constrain the stratospheric winds
until 1998 (see the discussion in the next section). The AoA trends derived
from ERA-I wind fields during the early period (Fig. ,
upper left) grow in both hemispheres except for the northern lowermost
stratosphere. During the recent period, the dipole structure derived from
ERA-I (Fig. , upper middle) is similar than over the
slightly shorter period 2002–2012 (Fig. ). The increases
in the Northern Hemisphere become weaker but remain significant at all
latitudes, although at fewer grid points. The maximum trend is located at
24∘ N and 25 hPa, where it slightly exceeds
0.3±0.2 yr dec-1. The extension of this trend analysis for the
overall period (Fig. , upper right) shows a dipole
structure with negative but mostly insignificant trends in the Southern
Hemisphere, positive trends in the northern middle stratosphere which mostly
corresponds to the region with positive trends during the 1989–2001 period,
and significantly negative trends in the lowermost stratosphere at all
extrapolar latitudes. The same plot also shows that the positive trend which
had been inferred visually for the northern midlatitudes of the middle
stratosphere (Fig. a) is significant. Our ERA-I
results for the overall period partly contradict those obtained by diabatic
models which use not only the wind fields from ERA-I but also its heating
rates . Looking at slightly
shorter periods of two decades (1989–2010 for the former and 1990–2013 for
the latter), these papers reported negative AoA trends for both hemispheres
below 28 km altitude. also looked at the middle
stratosphere, where positive trends were found at all latitudes, suggesting
that the shallow and deep Brewer–Dobson circulations may evolve in opposite
directions.
Amplitude (in years) of the QBO variation in the 2002–2015 linear
regression fit of AoA, as a function of latitude at pressure 30 hPa. Same
color codes as in previous figures.
Latitude–pressure distribution of AoA trends (in years per decade)
using the ERA-Interim reanalysis over 2002–2012. White crosses indicate grid
points where the sign of the trend is not significant; i.e., its absolute
value is smaller than the uncertainty delivered by the regression analysis at
the 95 % confidence level. The color scale is the same as in
Figs. 6 and 10 with darker blues indicating more
negative trends and darker reds more positive trends.
Latitude–pressure distributions of AoA trends (in years per decade)
over 1989–2001 (left column), 2002–2015 (middle column) and 1989–2015
(right column) using the five reanalyses (from top to bottom: ERA-I, CFSR,
JRA-55, MERRA, MERRA-2). White crosses and colors have the same meaning as
in the previous figure, but note the different scale (top of figure).
Comparing the results obtained with ERA-I with those from other reanalyses,
one notes immediately general agreement between ERA-I and CFSR on one hand
(Fig. , first and second row) and opposite trends in
JRA-55, MERRA and MERRA-2 (third to fifth row). The agreement between
multidecadal trends in ERA-I and CFSR may be related to their closeness in
AoA distribution and spatial gradients (Sect. ). For all
reanalyses except ERA-I, the trends for the overall period (1989–2015;
Fig. , right column) appear dominated by the results
from the early period which are subject to caution.
To summarize, the signs of the trends depend strongly on the input reanalysis
and on the considered period with values above 10 hPa varying between
approximately -0.4 and 0.4 yr dec-1. JRA-55, MERRA and MERRA-2
indicate an AoA increasing globally over 2002–2015, except in the lowermost
stratosphere, while ERA-I and CFSR indicate the opposite
(Fig. , middle column). These trends are significant
only in specific regions of the stratosphere, and the regions of significance
vary depending on the considered reanalysis. ERA-I stands out as the only
reanalysis yielding a dipole structure of AoA trends for the period
2002–2015, although one may note that, in the lower stratosphere, the AoA
growth derived for this period from MERRA and MERRA-2
(Fig. , middle column, fourth and fifth row) is faster
in the Northern Hemisphere than in the Southern Hemisphere. One notes also a
reversal of the trends between the early (1989–2001) and recent (2002–2015)
periods. This reversal is found for all five reanalyses and in all regions of
the stratosphere but it is difficult to interpret because it goes in opposite
directions in ERA-I and CFSR versus JRA-55, MERRA and MERRA-2.
Discussion and outlook
The present intercomparison reveals large disagreement between the AoA
derived from the five reanalyses, both with respect to their values and their
linear trends. The spread of AoA at 50 hPa (Fig. a)
is as large as in an intercomparison of CCMs .
An intercomparison of AoA trends during the 21st century among five CCMs
shows negative trends in the whole middle atmosphere (about
-0.05 yr dec-1) with no large hemispheric asymmetry
, while our results for 1989–2015 show faster
changes (-0.4 to 0.4 yr dec-1) with different signs depending on the
reanalysis and the stratospheric region. Since these results call for further
research, we propose here a summary overview of the possible causes for this
disagreement and some venues to attempt their identification.
Many intercomparisons of reanalyses have focused on the instantaneous values
or long-term evolution of direct output fields such as temperature or zonal
winds
.
These intercomparisons do not find large discrepancies, especially after the
introduction of new satellite instruments around the year 2000. The large
disagreement obtained here may be explained by the lack of wind observations
available for assimilation in the tropics, high latitudes and stratosphere
. This deficiency of wind information explains the
divergences between trajectories obtained with different reanalyses in the
lower stratosphere, e.g., in the equatorial region during some phases of the
QBO or above the Antarctic during the vortex
break-up season . Such divergent trajectories
could have a significant cumulative impact on the mean age of air because it
is a time-integrated diagnostic spanning several years.
Since the wind fields are weakly constrained, the causes for the disagreement
found here may lie in the differences between the underlying models which
were summarized recently in the context of S-RIP .
Let us first look at vertical resolution, which has an important impact on
the modeling of lower stratospheric dynamics .
In the lower stratosphere, the vertical resolution of CFSR is finest, while
the resolution of and ERA-I and JRA-55 is the coarsest, with the resolution
of MERRA and MERRA-2 in between . This has no
clear impact on AoA, since CFSR and JRA-55 deliver the youngest AoA, while
MERRA and MERRA-2 deliver the oldest, with ERA-I results in between. Hence,
one cannot establish a simple link between vertical resolution and AoA in
this intercomparison.
The present intercomparison cannot
establish the impact of different horizontal resolutions because it uses a
common horizontal grid with a coarse resolution of
2∘×2.5∘ (see Sect.
and ). For example, the intercomparison of AoA
distributions (Sect. ) showed that JRA-55 and CFSR yield the
weakest latitudinal gradients despite their horizontal grid spacing which is
finest among the five reanalyses studied here
seeTable 2. Another intercomparison could
yield different results if it uses the wind fields in each reanalysis at its
original resolution – but this could lead to difficulties in the handling of
horizontal diffusion .
Different parameterizations of gravity wave drag are another possible modeling
cause for the disagreement in AoA. ERA-I, JRA-55 and CFSR all neglect
non-orographic gravity wave drag (except for CFSv2, i.e., CFSR after 2010)
and each uses its own parameterization of orographic gravity wave drag. MERRA
and MERRA-2, on the other hand, use the same parameterization for orographic
gravity wave drag and both take non-orographic gravity
wave drag into account.
compared the mean-meridional circulations and
also the mixing strengths in six reanalyses – including ERA-I and JRA-55 –
and also found significant disagreement. Their diagnostics are closely
related to AoA, since a faster mean-meridional circulation evidently leads to
younger AoA and increased mixing corresponds mostly to additional aging of
air due to recirculation from the extratropics to the tropics
. For example, the disagreement of linear trends for
1989–2015 (right column in Fig. ) confirms the finding
that ERA-I and JRA-55 have opposite linear trends of tropical upward mass
flux for the period 1979–2012, with fluxes increasing at all levels in
JRA-55, while in ERA-I they increase only in a shallow layer of the lower troposphere
but decrease in the middle stratosphere
Fig. 11. Similar disagreement has also been
reported between the trends of the annual mean tropical upwelling in three
reanalyses over the period 1979–2012, with vertical residual velocities
(w‾*) increasing in MERRA and JRA-55 and decreasing in ERA-I
Fig. 11.
MERRA-2 stands out with outlying AoA values during the 1990s. A connection is
plausible with its difficulties representing correctly the QBO before 1995
. noted
on that same year a marked decrease in temperature near 1 hPa
and associated it with a change in assimilated radiance data.
describe three features which are absent from the
other reanalysis systems and could also play a role in the description of
middle atmosphere dynamics in MERRA-2, contributing to its outlying AoA. With
respect to assimilated observations, MERRA-2 is the only reanalysis to
assimilate Microwave Limb Sounder on the Aura satellite (Aura-MLS) temperatures, from 2004 onwards and above 5 hPa. While
this has an important impact on temperatures in the upper stratosphere and
lower mesosphere, it does not seem to have an impact on the AoA time series
in the middle stratosphere (Fig. ) and cannot
explain the large values obtained during the 1990s. With respect to forward
model forcings, MERRA-2 is the only reanalysis which includes a large source
of non-orographic gravity wave drag in the tropics
and realistic aerosol optical depths. This last feature most likely explains
the sensitivity of the MERRA-2 AoA at 50 hPa to the Pinatubo eruption, which
cannot be seen with any other reanalysis (Fig. ).
Yet the impact of the Pinatubo eruption on MERRA-2 AoA at 50 hPa cannot be
seen in the northern midlatitudes, and in the southern midlatitudes it is not
larger than the amplitude of seasonal variations. In Sect. ,
we could not find any influence of volcanic aerosols at the global scale
(Fig. ), contrary to recent results obtained by
using the Chemical Lagrangian Model of the
Stratosphere (CLaMS) driven by ERA-I and JRA-55. While CLaMS is a Lagrangian
transport model including a mixing parameterization
and BASCOE TM a Eulerian transport model, we suggest that these conflicting
results are better explained by the different approaches with respect to
vertical transport: BASCOE TM is a kinematic model (see
Sect. ), while CLaMS is a diabatic transport model and hence also
driven by the heating rates from the reanalysis forecast models
.
have shown that the heat budgets differ
significantly in the tropical tropopause layer among the reanalyses, with
substantial implications for representations of transport and mixing in this
region. evaluated the vertical component of the
advective BDC in ERA-I, MERRA and JRA-55, and found substantial differences
between direct (i.e., kinematic) estimates and indirect estimates derived
from the thermodynamic balance (i.e., using diabatic heating rates). These
intercomparisons of dynamical diagnostics highlight the need for another
intercomparison of AoA using a diabatic transport model, because this
approach would also reflect the differences between the diabatic heat budgets
of each reanalysis – including the temperature increments from the
assimilation of temperature radiances .
Future work will also involve the disentangling of the contributions to AoA
of the residual circulation, mixing on resolved scales and mixing on
unresolved scales (i.e., diffusion) as recently performed with ERA-I
and quantitative
comparisons with observational datasets, using both MIPAS observations of
SF6 and balloon
observations of SF6 and CO2.
Comparisons with long-term records of other long-lived tracers will provide further insight at multidecadal scales.
A recent study by explained that the relationship
between AoA and the fractional release of such tracers is a stronger test of
the realism of simulated transport than the simple comparisons of mean age
distributions. This approach seems very promising not only in the context of
S-RIP but also for observation-based evaluations of stratospheric transport
in global circulation–chemistry models.
Summary and conclusions
We have developed a preprocessor to feed a Eulerian and kinematic transport
model with any of the available global reanalysis datasets. This has allowed
us to compute the mean AoA in the stratosphere and its evolution
from 1985 to 2015, according to five modern reanalyses: ERA-Interim, JRA-55,
MERRA, MERRA-2 and CFSR. Our results compare well with those published
previously using other transport models driven by ERA-Interim and MERRA-2.
The five reanalyses deliver very different and diverse results. In the middle
and upper stratosphere, MERRA yields the oldest AoA (∼5–6 years at
midlatitudes) and JRA-55 the youngest one (∼3.5 years). MERRA-2
provides a different distribution of latitudinal and vertical AoA gradients
than any other reanalysis, with near-zero vertical gradients in the middle
stratosphere which are not supported by observations. CFSR and ERA-I give the
most similar AoA distributions, with the latter providing stronger gradients
vertically in the middle stratosphere and latitudinally in the Southern
Hemisphere. The relative differences between ERA-I and the four other
reanalyses are largest in the lower tropical stratosphere. Tropical ascent
rates have been compared through the difference between AoA in the northern
midlatitudes and in the tropics, showing good agreement between all
reanalyses except for MERRA-2 and an overestimation of the upwelling in the
tropical lower stratosphere.
The time variations of AoA were studied first through a qualitative analysis
of raw time series in the midlatitudes, then through a fit with a multiple
linear regression model. While the linear trends vary considerably depending
on the considered period (2002–2012, 2002–2015 or 1985–2015), the general
hierarchy of older (MERRA, MERRA-2) and younger (JRA-55, CFSR)
reanalyses holds during the whole 1985–2015 period, with ERA-I keeping
intermediate AoA values. The MERRA-2 results stand out again, with an
exceptionally large initial AoA in the Northern Hemisphere which quickly
decreases during the 1990s to reach values similar to those in MERRA. A
comparison was performed with a time series of balloon observations realized
since the 1970s in the northern midlatitudes, where the uncertainties include
an evaluation of the sampling error . The spread
between the five simulations is as large as the observational uncertainties,
highlighting again the importance of the disagreement between the five
reanalyses.
The amplitudes of seasonal variations agree broadly across all five
reanalyses but in the lower stratosphere they are larger in MERRA and MERRA-2
than in the three other reanalyses. The latitudinal dependence of QBO
amplitudes is similar in all five reanalyses except for CFSR which shows no
clear structure in the Southern Hemisphere.
The linear trends of ERA-I AoA confirm again the dipole structure of the
latitude–height distribution of AoA trends as derived from MIPAS
observations of SF6 for the 2002–2012 period
, with a decrease in the Southern Hemisphere
reaching about -0.6 yr dec-1 and an increase in the northern lower
stratosphere reaching about 0.5 yr dec-1. The increase in the Northern
Hemisphere is significant (at the 95 % confidence level) and it is not
obtained in multidecadal climate model simulations. Yet the trends derived
from ERA-I are shown to closely depend on the considered period. When it is
extended to 2002–2015, the positive trends in the Northern Hemisphere become
weaker (about 0.3 yr dec-1) and they are significant at fewer grid
points. A further extension to 1989–2015 shows that the negative trends in
the southern middle stratosphere become insignificant. For all five
reanalyses, the trends over the early period (1989–2001) have opposite signs
compared to over the recent period (2002–2015). Looking only at the recent
period which is better constrained by observations, the main outcome is again
large disagreement between the reanalyses: JRA-55, MERRA and MERRA-2 provide
increasing AoA in the middle stratosphere, while CFSR provides a decreasing
but mostly insignificant trend. To summarize, the signs of the trends depend
strongly on the input reanalysis and on the considered period with values
above 10 hPa varying between approximately -0.4 and 0.4 yr dec-1.
Independently of the considered period, no reanalysis other than ERA-I finds
any dipole structure in the latitude–height distribution of AoA trends.
Since the wind fields are weakly constrained, the causes for the
disagreement found here may lie in the differences between the underlying
models. While no obvious cause could be found, we suggest that the
parameterization of non-orographic gravity wave drag deserves further
investigation, especially in the case of MERRA-2, which has difficulties
representing correctly the QBO before 1995. No global impact of the Pinatubo
eruption can be found in our simulations of AoA, contrary to a recent study
which used ERA-I and JRA-55 to drive a diabatic transport model. This
highlights the need to repeat the present intercomparison with diabatic
transport models because they would reflect directly the significant
differences between the heating rates in the reanalyses
. Future work will also focus on quantitative
comparisons with AoA derived from MIPAS observations of SF6,
comparisons with the long-term records of other long-lived tracers to provide
further insight at multidecadal scales, and disentangling the contributions
to AoA of residual circulation, mixing on resolved scales and mixing on
unresolved scales.
The main conclusion of this study is the significant diversity in the
distribution of mean AoA which we obtain with our transport model, depending
on the input reanalysis. This casts doubt on our ability to model accurately
the time necessary for variations of greenhouse or ozone-depleting species to
propagate from the troposphere to the stratosphere. We have also found large
disagreement between the five reanalyses with respect to the long-term
trends of age of air. This suggests that with our type of offline transport
model, the wind fields in modern reanalyses are not sufficiently constrained
by observations to evaluate the actual changes of stratospheric circulation.
Yet this conclusion should not be hastily extended to other types of
transport models which also use the reanalyses of temperature and heating
rates.
The monthly zonal averages of AoA, as delivered by the
BASCOE TM experiments driven by the five input reanalyses, are distributed as
the Supplement to this article. The source code of the BASCOE TM, including
its tools to preprocess the reanalyses, is available by email request to the
corresponding author. The ERA-Interim reanalysis is
provided by the ECMWF; see
http://apps.ecmwf.int/datasets/data/interim-full-daily (last access: 10
October 2018). MERRA data and MERRA-2 data are
provided by the Global Modeling and Assimilation Office at NASA Goddard Space
Flight Center through the NASA GES DISC online archive. The CFSR
and CFSv2 reanalyses data
were obtained from NOAA. The JRA-55 reanalysis was obtained
from the NCAR Research Data Archive.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-14715-2018-supplement.
SC designed the study and wrote the paper. CV performed the multiple linear
regression analysis. YC, QE, DM, BMMS and AS contributed to data
preprocessing, model development and validation. AE provided the
observational balloon dataset. EM provided advice and insights during the
course of the study. All co-authors contributed to the interpretation of the
results and the reviews of the draft manuscripts.
The authors declare that they have no conflict of
interest.
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
We thank the reanalysis centers (ECMWF, NASA GSFC, NOAA NCEP and JMA) for
providing their support and data products. We thank Gabriele Stiller, Paul
Konopka and Bernard Legras for fruitful discussions during the preliminary
steps leading to this study and Masatomo Fujiwara for his coordination of
the S-RIP. We would also like to thank the editor and two anonymous reviewers
for their valuable comments. Yves Christophe's contribution was partly
supported by the European Commission project MACC-II under the EU Seventh
Research Framework Programme (contract no. 283576). Daniele Minganti's
contribution was financially supported by the Fonds de la Rechecherce
Fondamentale Collective through research project ACCROSS (convention
PDRT.0040.16). Emmanuel Mahieu is a research associate with the F.R.S.-FNRS.
Edited by: William Lahoz
Reviewed by: two anonymous referees
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