ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-1313-2017Time-varying changes in the simulated structure of the Brewer–Dobson CirculationGarfinkelChaim I.chaim.garfinkel@mail.huji.ac.ilhttps://orcid.org/0000-0001-7258-666XAquilaValentinahttps://orcid.org/0000-0003-2060-6694WaughDarryn W.https://orcid.org/0000-0001-7692-2798OmanLuke D.The Fredy and Nadine Herrmann Institute of Earth Sciences, Hebrew University, Jerusalem, IsraelDepartment of Earth and Planetary Science, Johns Hopkins University, Baltimore, MD, USAGoddard Earth Science Technology and Research, Greenbelt, MD, USANASA Goddard Space Flight Center, Greenbelt, MD, USAChaim I. Garfinkel (chaim.garfinkel@mail.huji.ac.il)30January20171721313132716June20166July201626December20164January2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/.pdf
A series of simulations using the NASA Goddard Earth Observing System
Chemistry Climate Model are analyzed in order to assess changes in the
Brewer–Dobson Circulation (BDC) over the past 55 years. When trends are
computed over the past 55 years, the BDC accelerates throughout the
stratosphere, consistent with previous modeling results. However, over the
second half of the simulations (i.e., since the late 1980s), the model
simulates structural changes in the BDC as the temporal evolution of the BDC
varies between regions in the stratosphere. In the mid-stratosphere in the
midlatitude Northern Hemisphere, the BDC does not accelerate in the ensemble
mean of our simulations despite increases in greenhouse gas concentrations
and warming sea surface temperatures, and it even decelerates in one ensemble
member. This deceleration is reminiscent of changes inferred from satellite
instruments and in situ measurements. In contrast, the BDC in the lower
stratosphere continues to accelerate. The main forcing agents for the recent
slowdown in the mid-stratosphere appear to be declining ozone-depleting
substance (ODS) concentrations and the timing of volcanic eruptions. Changes
in both mean age of air and the tropical
upwelling of the residual circulation indicate a lack of recent acceleration.
We therefore clarify that 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.
Introduction
The global circulation in the stratosphere – the Brewer–Dobson circulation
(BDC) – consists of air masses rising across the tropical tropopause, moving
poleward, and sinking into the extratropical troposphere
. Since the BDC and its changes have important
implications on both stratospheric and tropospheric climate as well as
stratospheric ozone chemistry , it is
important to assess the factors that lead to simulated BDC changes and
whether historical changes in the BDC as simulated by models are inconsistent
with available observational constraints.
The BDC has historically been deduced either from the residual circulation or
from the average time for an air parcel to travel from the tropical
troposphere to a given stratospheric sampling region (i.e., the mean age of
air or mean age). While these two diagnostics are clearly related,
differences can arise due to isentropic mixing and recirculation
. Specifically, the vertical component of the
residual circulation (w*‾) measures the instantaneous advection,
whereas mean age is an integrated measure of the total transport. It would
not be a surprise if the two metrics had a different evolution.
Chemistry–climate models robustly predict a strengthened BDC under climate
change in the middle and lower stratosphere of approximately 1–5 % per
decade the precise rate depends on the level considered and varies
among models;
. The
model used in this study, the Goddard Earth Observing System
Chemistry Climate Model, Version 2 (GEOSCCM), predicts a trend quantitatively
similar to those in other models both for the historical period and for the
future .
It has been argued that observational estimates of historical changes do not
agree with the simulated acceleration trend. Specifically, the analysis of
historical tracer data does not provide evidence for an acceleration trend in
the mid-stratosphere Northern Hemisphere (NH), where mean age actually
appears to have increased or remained unchanged
. In particular, the mean
age evolution in the figures of and
indicates aging since the late 1980s, with earlier changes less clear.
ERA-Interim reanalysis data also suggest aging of NH mid-stratosphere air
since the late 1980s . While these
observational and reanalysis-based studies disagree about the sign of changes
in other regions of the stratosphere, they all indicate aging of air in the
NH midlatitude mid-stratosphere. argue that the large
uncertainty estimates on the trends presented by are overly
conservative and that this aging trend is statistically significant even
after taking into account the nonlinear growth rates of these trace gases
i.e., the arguments of. Several of the aforementioned
studies suggest that these changes in the mean age imply a redistribution of
the BDC, and specifically a slowdown of the deep (i.e., mid-stratospheric) NH
branch of the BDC and/or less mixing of fresh tropical air into this region.
However, it is not clear what forcings (if any) could be responsible for this
redistribution and also whether models of the kind used in WMO ozone
assessments can capture such a slowdown given known
forcings.
conducted time-slice and transient simulations using a
previous version of the model used in this study, and they found a trend
towards younger mean age everywhere in the stratosphere between 1960 and
2004, though the decrease in mean age in the Southern Hemisphere (SH) is
larger due to Antarctic ozone depletion. also found that
ozone recovery would lead to a slowdown of the BDC if not for warming sea
surface temperatures (SSTs) due to increasing greenhouse gas (GHG) emissions.
recently presented differences in mean age and the residual
circulation in time-slice simulations using the ECHAM/MESSy Atmospheric
Chemistry Model (EMAC). The changes in tropical upward mass flux indicate a
strengthening of the BDC between 1960 and 2000 in the NH winter season in the
lower stratosphere and a weakening in the upper stratosphere with a change in
sign at 10 hPa. Changes in mean age show a decrease of about
0.13 year decade-1 in the lower and middle stratosphere and a slight
increase in the Arctic upper stratosphere and lower mesosphere. While there
is some hint of a structural change in the properties of the BDC, the changes
occur higher in the stratosphere and at more polar latitudes than is
suggested by available observations.
The motivation for studying historical changes in the BDC in free-running
climate simulations is not to form a best estimate of the actual
historical evolution; for that purpose, reanalyses and/or nudged experiments
are far better (though care must be exercised when interpreting trends).
Rather, the motivation is twofold: one, future projections of the BDC can
only be produced by free-running climate simulations, and these projections
are of limited value if a model's simulation of the past is inconsistent with
observational constraints; two, assuming the model is capable of following
the observed evolution, the forcings that caused these historical changes can
be systematically diagnosed by sequentially adding these forcings. Combined,
the goal of this study is to understand whether historical forcings could
have led to the structural changes in the BDC that have been inferred by
observational studies.
We show that over the full duration of the experiments (i.e., for a start
date in 1960) we recover the result from previous modeling studies:
anthropogenic climate change leads to acceleration of the BDC throughout the
stratosphere. However, our model can simulate statistically significant aging
of air in the midlatitude NH near 20hPa between the early 1990s and the
present as suggested by available observations. This suggests that historical
forcings have caused structural changes of the BDC since the late 1980s,
whereby the BDC accelerated in the lower stratosphere but decelerated in the
mid-stratosphere, in both the tropics and in the NH. Mean age and the
residual circulation (as measured by tropical w*‾) change in
unison. The cause of this mid-stratospheric deceleration trend is a
combination of forcings – ozone-depleting substance (ODS) recovery and the
timing of volcanic eruptions – that together has outweighed
greenhouse-gas-induced acceleration since the late 1980s. We therefore
emphasize that, if one wishes to capture observed historical changes in a model simulation,
careful attention must be paid to the forcings included and the start and end dates used
for trend calculation.
Methods
The model focused on for this study is GEOSCCM, an aerosol- and
chemistry-focused version of the GEOS-5 Earth system model. GEOSCCM couples
the GEOS-5 atmospheric general circulation model
to the comprehensive stratospheric chemistry module StratChem described in
and the Goddard Chemistry, Aerosol, Radiation, and Transport
Model (GOCART) described in . The model has 72 vertical
layers, with a model top at 0.01 hPa, and all simulations discussed here
were performed at 2∘ latitude × 2.5∘ longitude
horizontal resolution. Previous versions of GEOSCCM have been graded highly
in the two phases of the Chemistry-Climate Model Validation
Activity . Improvements to the model since then are described
in and . Note that this model version includes a
spontaneous quasi-biennial oscillation (QBO) and that the QBO phase differs among the three ensemble
members.
A series of simulations of the period from January 1960 to December 2014
have been performed in order to understand the past evolution of the
stratosphere. These simulations were presented in , where the
focus was on changes in temperatures. Here we examine changes in the BDC. We
start with an ensemble in which the only time-varying forcing is changing
SSTs and sea ice, and then sequentially add the following forcings:
greenhouse gases, ozone-depleting substances, volcanic eruptions, and solar
variability. More specifically, these simulations are grouped into the
following five experiments:
SST, which uses time-varying observed SSTs and sea ice up to November 2006
from the Met Office Hadley Centre observational dataset
and since then from and subsequent updates
(Fig. a). GHGs and ODS concentrations are fixed at
1960 levels. Volcanic
eruptions are not included in this experiment, and the solar forcing is held
constant.
SSTGHG, which includes observed SSTs and increasing GHG concentrations
(Fig. b). GHG concentrations are from observations up to 2005 and from the
Representative Concentrations Pathway 4.5 after 2005 .
SSTGHGODS, which includes observed SSTs, increasing GHGs, and changing ODS concentrations following
Fig. c.
SSTGHGODSVOL, which includes observed SSTs, increasing GHGs, changing ODS, and
volcanic eruptions, specified after from 1979 to December 2010 and
from January 2011 to December 2014
(Fig. d). The only eruption before 1979 included
is Mt. Agung in 1963.
SSTGHGODSVOLSOL (or all-forcing), which includes observed SSTs, increasing GHGs, changing ODS, volcanic
eruptions, and changes in solar flux as in and subsequent updates (Fig. e).
Note that neither nor considered volcanic forcings and solar variability.
All simulations used emissions of tropospheric aerosol and aerosol precursors
following . We focus on transient simulations as it is
difficult to compare historical tracer observations to time-slice
simulations.
Each experiment is composed of three ensemble members initialized with
different initial conditions from a 1960 time-slice simulation. Because we
have three members for each forcing combination, we can also assess at least
partially the range of internal atmospheric variability. This internal
variability is not a source of model uncertainty; rather it is an inherent
part of the climate system. If the BDC in one ensemble member, but not in the
other two, evolves consistently with observational constraints, one can
reasonably conclude that models can capture the observed trends if part of
the observed trend was due to internal variability and was not forced
cf..
Forcing applied in the simulations.
(a) 10∘ S–10∘ N average sea surface temperatures,
with a smoothed version of the curve bolded; (b) atmospheric
concentrations of CO2 following RCP4.5; (c) equivalent effective
stratospheric chlorine EESC; Eq. 1 with a=1 and α for
Bry=60, and using 3-year mean age;
(d) ensemble mean of the aerosol optical thickness from explosive
volcanic eruptions, resulting from prescribed injections of volcanic SO2;
(e) total solar irradiance.
We assume that BDC perturbations induced by each forcing agent add linearly
to the others, as previous work focusing on forcing agents for
lower-stratospheric mass flux and mid-stratospheric mean age suggests that
nonlinearities are small e.g.,. The model
version used to perform the integrations described in this paper is no longer
supported, and hence we cannot explicitly test this assumption.
The use of observed SSTs in our simulations, rather than internally
calculated by the model, produces a climate state closer to the observed one.
However, partitioning trends into an SST-driven component and a component
from other radiative or chemical forcings is somewhat artificial, as the
prescribed SST changes occur in response to and in tandem with the changing
direct atmospheric forcing; however such a partitioning is an effective tool
for disentangling the physical mechanisms leading to changes in the
atmospheric circulation . Specifically, found
that the mean tropospheric warming associated with rising SSTs had a bigger
impact on mean age than the direct radiative impacts of CO2. Note that
interannual and decadal variability in SSTs drives changes in the BDC that
likely have nothing to do with climate change, and hence we include a
smoothed version of the SST variations in Fig. a
(all experiments were forced with the full time-evolving SST fields).
A passive tracer is used to derive the mean age. The mixing ratio of this
tracer increases linearly with time, and the time lag in tracer
concentrations between a certain grid point in the stratosphere and a
reference point in the troposphere provides an estimation of mean age at this
stratospheric grid point . We adopt a reference point of 200 hPa
at the Equator. Mean age was first initialized in a 10-year time-slice
control run with 1960 conditions before we branched off for each experiment,
and so mean age can be defined at the beginning of the experiments. Note that
diagnostic output necessary to compute the full age spectrum was not saved
for these model experiments, and hence we are limited in our ability to
quantify mixing changes.
The aging of NH mid-stratospheric air in observations is pronounced mainly
after the late 1980s see figures in, and several
of the reanalysis-based studies begin their analysis in the late 1980s as
they need to initialize trajectories for 10 years before computing mean age.
Hence, in our presentation and discussion of the results, we consider trends
and variability both over the full period of integration and also since 1988.
Motivated by our results, we also discuss trends for a start date of 1992, as
the recent slowdown of the deep branch of the BDC is most pronounced (and
locally statistically significant) for this start date due to the eruption of
Mt. Pinatubo. Finally, global mean age profiles as retrieved by satellites
are only available from 2002 onward , and hence we show
trends since 2002 separately as well.
Trends in annual averaged BDC from 1960 to 2014 in the ensemble mean
of the three all-forcing integrations. Mean age trends are indicated by
contours with a contour interval of 6 days decade-1, and the residual
circulation trends are indicated with streamlines. The thickness of the
streamline is proportional to the magnitude of the wind
speed.
The trends are calculated with a linear least-squares fit. Statistical
significance of the trends in individual ensemble members of GEOSCCM is
computed using a two-tailed Student's t test, and the reduction in degrees of
freedom due to autocorrelation of the residuals is taken into account with
the formula N(1-r1)(1+r1)-1, where N is the number of years and
r1 is the lag-1 autocorrelation Eq. 6 of. In computing
the ensemble mean response, we first averaged the mean age among the three
ensemble members for each season/year (in order to damp the internal,
unforced, atmospheric variability) and then computed the trend based on the
ensemble mean mean age.
Trend through 2014 in annual averaged mean age in the NH (left),
tropics (middle), and SH (right) for start dates starting in 1960 and ending
in 2002. Each line is for one member of the all-forcing ensemble or for the
all-forcing ensemble mean. Changes in the mid-stratosphere (deep branch) are
shown in (a–c), and changes in the lower stratosphere (shallow
branch) are shown in (d–f). 95 % confidence intervals on the
calculated trends are indicated; note that in the SH mid-stratosphere the
correction of for the number of degrees of freedom leads to
very few degrees of freedom for the trend and therefore to high
uncertainty.
Results
We now examine how the BDC as simulated by GEOSCCM has changed in structure
over the past 55 years. We show that BDC changes (or “trends”) vary with
period considered and location. These structural changes are associated with
several distinct forcings, and these forcings transiently drive changes in
the BDC. When combined with internal variability, it is possible that these
forcings can drive, over the 27-year period of 1988–2014, a deceleration
trend in the NH mid-stratosphere.
Changes in the all-forcing ensemble
We begin with changes in the BDC for the all-forcing ensemble. We first
consider changes in the residual circulation and in mean age as a function of
latitude and pressure over the full duration of the experiment in the
ensemble mean (Fig. ). The BDC accelerates throughout
the stratosphere as mean age decreases and the residual circulation
accelerates. Hence, changes over the full integration period in our
experiments are consistent with previous work e.g.,.
Statistical significance of the trends are considered in
Figs. and , which show
the 95 % confidence bounds on the trends for a range of start dates for
mean age in the selected regions and for tropical mean upwelling. The changes
in both metrics of the BDC are statistically significant at the 95 %
level throughout the stratosphere for a trend start date of 1960, and all
three ensemble members indicate quantitatively similar acceleration trends.
However, the ensemble mean acceleration trend weakens (and even reverses
locally) and its robustness goes away as we consider more recent periods. To
demonstrate this, we start by showing trends since 1988 in
Fig. . In the lower stratosphere (i.e., the shallow
branch of the BDC), the BDC continues to accelerate in all three ensemble
members, and this change is statistically significant at the 95 % level
in each ensemble member individually and in the ensemble mean (e.g.,
Figs. d, e, f and b).
In the mid- and upper stratosphere, however, trends are not robust across
the various ensemble members. One of the three ensemble members shows
decreasing mean age and an accelerated residual circulation
(Fig. a and gray lines in
Figs. a, b, c and a),
while another shows the opposite (Fig. e and red lines
in Figs. a, b, c and
a). In this ensemble member with aging air,
upwelling decreases throughout the tropics, such that both the residual
circulation and mean age diagnostics suggest deceleration of the BDC. None of
the mean age trends in the mid- and upper stratosphere in
Fig. a, c, and e are statistically significant at the
95 % level (e.g., Fig. a, b, c). Note that if
the start date for the trend is advanced to 1992 then none of the three
ensemble members indicates acceleration of the BDC in the NH midlatitude
mid-stratosphere (Fig. a), but we prefer to
demonstrate that, even for a start date of 1988, aging can be simulated given
the large amount of internal atmospheric variability.
These differences in trends since 1988 among ensemble members can be
reconciled with the changes in the wave forcing of the BDC. Previous modeling
and theoretical work have demonstrated that changes in the wave forcing
directly force changes in the residual circulation .
Specifically, enhanced wave convergence (i.e., deceleration of the mean flow)
leads to enhanced upwelling on the tropical flank of the enhanced wave
convergence and downwelling on the poleward flank . As both
resolved waves and gravity waves are important for the total wave driving, we
evaluate their combined impact in common units of acceleration of the mean
flow (m s-1 day-1 decade-1); positive values indicate less
wave convergence and deceleration of the residual circulation, while negative
values indicates enhanced wave convergence and acceleration of the residual
circulation. The right column of Fig. demonstrates
that the difference between the ensemble member with a weakened BDC and the
one with an accelerated BDC is related to differences in the SH wave driving
and subsequent changes in the SH residual circulation. For the ensemble
member with a weakened residual circulation in the SH (and thus aging of air
in the mid-stratosphere), there is less wave flux converging in the SH
(Fig. f). In contrast, for the member with an
accelerated residual circulation in the SH (and thus decreasing mean age in
the NH mid-stratosphere), there is enhanced wave flux converging in the SH
(Fig. b). Note that a difference in wave fluxes in the
SH can influence mean age in the NH because mean age is an integral measure
of transport, and thus changes in the residual circulation in the tropics due
to wave flux changes in the SH can impact the transport pathway into the NH.
Hence, the difference between aging and freshening of mid-stratospheric air
is associated with the internal atmospheric variability associated with wave
fluxes. The Supplement discusses implications of this intra-ensemble
difference for trends in temperature and zonal wind. There is reduced wave
convergence in the NH upper stratosphere
in all three ensemble members, and this effect is most pronounced in boreal
winter (not shown). The cause of the decrease in NH stratospheric wave
convergence is discussed in and in the next section.
Overall, our model simulations indicate that known forcings could have led to
a slowdown of the deep branch of BDC since 1988 given the large amount of
internal variability in wave fluxes.
As in Fig. but for the upwelling mass
flux in the tropics.
(left) As in Fig. but for trends in mean age
from 1988 to 2014 in the three ensemble members. Note that
(a) corresponds to the ensemble member with decreasing mean age and
(e) to the ensemble member with aging air in the NH midlatitude
stratosphere. The right column shows the changes in total wave forcing of the
BDC (EP flux divergence plus gravity wave drag).
Time series of annual averaged mean age in the NH (left), tropics
(middle), and SH (right). Each line is for one ensemble mean. Blue is all
forcing, red is SST only, and intermediate colors span the other three
experiments performed. Changes in the mid-stratosphere (deep branch) are
shown in (a–c), and changes in the lower stratosphere (shallow
branch) are shown in (d–f).
We now consider the time evolution of changes in mean age in various regions
in Fig. . As the eruption of Mt. Pinatubo had a
pronounced impact on the BDC in these simulations, we separately consider the
evolution before and after its eruption. Changes at NH midlatitudes in the
mid-stratosphere are considered in Fig. a. Note that
this is the region where available observations suggest that air has aged
over the past several decades. In the all-forcing ensemble mean (blue line),
mean age decreases by 0.45 year between 1960 and 1992 (i.e., 0.14 year per
decade, blue in Fig. a) but then ages by
0.12 year after 1992 (i.e., 0.05 year per decade, blue in
Fig. b). The aging trend since 1992 is
statistically significant at the 95 % level in two of the three ensemble
members and in the ensemble mean (Fig. a).
Figure considers the evolution of each of the three
ensemble members individually. The ensemble spread in mean age in any given
year can exceed 5 %. One ensemble member simulates anomalously old air
after 2010, and for this member aging trends are simulated for a start date
of the trend calculation of either 1992 or 1988. Note that a different
ensemble member simulates anomalously younger air relative to the other two
after El Chichón, highlighting the large role of internal atmospheric
variability in masking the response to climate forcings.
In the tropical and SH mid-stratosphere, mean age also increases from 1992
through the end of the simulation (blue curves in
Figs. b, c and b, c). The
large uncertainty in the trends in the SH arise due to the correction of
for the effective number of degrees of freedom associated
with residuals with large autocorrelation. In contrast, in the lower
stratosphere, mean age continues to decline in the tropics and in the NH,
though not in the SH (blue curves in Fig. d, e, f).
The decline in mean age is statistically significant at the 95 % level in
two of three ensemble members and in the ensemble mean in the NH lower
stratosphere (Fig. d). Hence, there are time- and
space-varying variations in recent mean age trends, and only in some regions
of the stratosphere has mean age continued to decline.
Similar structural changes are evident for the tropical upwelling mass flux
between the turnaround latitudes (∫w*=0SHw*=0NHρw*‾dA, where ρ is the density and A is the
area of a given zonal band; Figs. and
). In the all-forcing experiment, tropical upwelling
accelerated until 1992 in both the mid-stratosphere and lower stratosphere
(i.e., blue line rises in Fig. , blue in
Fig. a, c, e, g), but since 1992 it has decreased at
all levels above 70 hPa (e.g., Figs. a and
b, d, f, h). These changes are reminiscent of those
proposed by in order to explain how NH midlatitude
air in the mid-stratosphere anomalously ages. Specifically,
concluded that for a tropical leaky-pipe model, “the best quantitative
agreement with the observed mean age and ozone trends over the past three
decades is found assuming a small strengthening of the mean circulation in
the lower stratosphere, [and] a moderate weakening of the mean circulation in
the middle and upper stratosphere”, as simulated by GEOSCCM. Overall, it is
clear that GEOSCCM can simulate structural changes in the BDC that resemble
those inferred from observations.
Trends in midlatitude NH mean age in the ensemble mean of each of
the five experiments (a) from 1960 to 1992 and (b) from
1992 to 2014. The trend over the full duration of the experiments is shown in
panel (c). The horizontal line indicates the ensemble mean trend,
and the vertical line indicates the 95 % uncertainty
bounds.
Modeled annual averaged mean age in the NH mid-stratosphere between
30 and 10 hPa in each of the three all-forcing GEOSCCM integrations. Thin
lines denote individual integrations, while thick lines denote ensemble
means. For clarity, we also include the ensemble mean of the
SSTGHGODS simulation.
Forcing of the trends
We now consider the forcing mechanisms behind these structural changes in the BDC.
Mid-stratosphere
As shown above, mean age at NH midlatitudes in the mid-stratosphere decreases
by 0.45 year between 1960 and 1992 in the all-forcing ensemble but then ages
by 0.12 year after 1992 (blue line; Figs. a and
). This evolution can be broken down into its
various forcing components.
SSTs. The red line in Fig. a shows that SSTs
lead to a decrease in mean age of 0.1 year over the course of these
55 years, albeit with substantial interannual and decadal variability.
Specifically, over the last 30 years of the integrations (from 1985 to the
end), there is a weak and insignificant aging trend (e.g., red in
Fig. b). The likely cause of this is a tug-of-war
of opposing effects. On the one hand, gradual warming of the oceans in
isolation leads to an acceleration of the BDC
Fig. a and . On the other
hand, the spatial pattern of recent changes in SSTs has led to a decline in
planetary wave flux (especially wave 1) entering the NH stratosphere at
midlatitudes : the vertical component of the Eliassen–Palm
flux at 100 hPa area averaged between 40 and 80∘ N declines in all
three all forcing and in all three SST-only experiments, and the decrease is
statistically significant at the 95 % level in the ensemble mean both in
January through March the focus of and in the annual
mean. This decline in upward-propagating midlatitude planetary waves at
100 hPa impacts the deep branch more strongly . Hence,
it is not surprising that little change has occurred over the last 30 years.
Greenhouse gases. The influence of GHG can be deduced from the
difference between the red and magenta curves in
Fig. a, as the SST-only ensemble is conducted with
radiative forcings fixed at 1960 levels. The difference between the curves
exceeds 0.1 year towards the end of the integrations.
Declining ODS concentrations. The effect of increasing ODS concentrations
can be deduced from the difference between the magenta and green curves in
Fig. a, and it suggests that increasing ODS
concentrations led to a decrease in mean age of 0.25 year by the late 1990s
(Fig. a) when the ODS burden was peaking. More
recently, declining ODS concentrations (Fig. c)
lead to a recovery towards older air; that is, the gap between the magenta
and green curves decreases between the late 1990s and the present in
Fig. a. Note that also found that
ozone recovery leads to a slowdown of the BDC in a previous version of the
model we use. An ozone-induced acceleration (or deceleration)
of the residual circulation in the SH and tropics can affect mean
age in the NH due to mixing. Finally, we note the caveat that, while
declining ODS concentrations clearly impact the BDC in these integrations, a
statistically significant recovery of ozone has been detected in observations
only in the upper stratosphere at midlatitudes and in the tropics
.
Time series of annual averaged mass flux between the turnaround
latitudes at 50 hPa (top) and 100 hPa (bottom). Each line is for one
ensemble mean. Blue is all forcing, red is SST only, and green is for
SSTGHGODS. For clarity, we suppress two of the intermediate
experiments.
Volcanoes and solar. The influence of volcanic eruptions can be
deduced from the difference between the green and cyan curves in
Fig. a, and in our model simulations the eruption of
Mt. Pinatubo and El Chichón led to a decrease in mean age of 0.2 year,
which gradually decayed over 4 to 6 years. Minor volcanic eruptions in the
past 10 years may have led to an additional decrease in mean age of 0.05 to
0.1 year. The net effect is that large eruptions (or lack thereof) can
influence decadal variability in mean age in GEOSCCM. Solar influences appear
to be relatively minor (Fig. a), and we therefore
focus our attention on the other forcings in this paper.
The net effect is that, over the second half of the experiments (when
observations are more numerous), decreasing ODS concentrations and the
recovery from Pinatubo overcame the influence of rising GHG concentrations
and led to aging of 0.12 year.
As in Fig. but for tropical mean
upwelling at a variety of pressure levels.
The same forcings that led to a statistically significant aging trend in the
NH after 1992 in the ensemble mean and in two integrations also led to similar significant
aging in the tropics (blue curve in Figs. b and
b). Specifically, changes in this region are dominated
by ODS concentrations, and since ODS concentrations decrease after the late
1990s (Fig. c), mean age increases despite rising
GHG concentrations. As for the NH, the earliest start date of significant
aging trends is in the early 1990s due to the eruption of Mt. Pinatubo. ODS
concentrations also dominate the SH mid-stratospheric mean age evolution
(Fig. c), and since ODS concentrations decrease after
the late 1990s, mean age does not change significantly after 1992 in the
all-forcing ensemble as well. Note that the influence of the eruption of Mt.
Pinatubo is weaker in the SH than in the NH; Mt. Pinatubo is located at
15∘ N, and in our experiments the majority of the aerosols stay in
the NH.
Lower stratosphere
In the tropical and NH lower stratosphere (Fig. d–e),
increasing GHG concentrations and warming SSTs drive a decrease in mean age
throughout the period of the all-forcing ensemble integrations. In agreement
with the modeling results of , warming SSTs (cf.
Fig. a) impact the shallow branch more strongly
than the deep branch. As midlatitude planetary waves are less important for
the shallow branch than for the deep branch , it
is reasonable to expect that a reduction in their strength has a relatively
smaller impact on the shallow branch. Volcanic eruptions have a weaker effect
in the lower stratosphere as compared to the middle stratosphere (compare the
difference between the blue and green curves for the years 1963/1964,
1983/1984, and 1991/1992 between Fig. a and d). A
possible explanation is that longwave and near-infrared heating due to volcanic
aerosols occurs at the level of the aerosols (not shown), and in our
integrations the aerosols are quickly lofted higher in the stratosphere
compare the volcanic influence on temperature as a function of time in
the various levels of . This allows for stronger and
longer-lasting changes mainly in the “deep” branch of the BDC. As for
volcanoes, changing ODS concentrations impact the deep branch of the BDC more
strongly (except in the SH where ozone depletion is strongest), and this
effect is consistent with the idealized modeling results of . The
colder vortex that follows ozone depletion creates a waveguide higher into
the stratosphere, raising the breaking level of Rossby waves and deepening
the BDC. Hence, it is to be expected that ozone depletion and recovery have a
disproportionate impact on the deep branch. Overall, the aging in the deep
branch since 1992 does not extend to the shallow branch because the two
factors that led to aging (recovery from the Pinatubo eruption and declining
ODS concentrations) preferentially impact the deep branch, while the two
factors that led to freshening (GHG increases and SST warming) preferentially
impact the shallow branch.
In the SH lower stratosphere (Fig. f), ODS
concentrations are the dominant forcing, but the gradual decline in ODS
concentrations is balanced out by rising GHG concentrations and mean age is
flat after 1995 in the blue all-forcing curve. It is known that
ozone-depletion-induced polar cooling can directly modulate extratropical
wave propagation down to the troposphere ,
though future work is needed in order to understand how this influence led to
an accelerated shallow branch.
Residual circulation
The same forcings led to structural changes in tropical upwelling
(Figs. and ). In the
all-forcing experiment, tropical upwelling accelerated until 1992 in both the
mid-stratosphere and lower stratosphere (i.e., blue line rises in both panels
of Fig. ), and this acceleration is driven largely
by rising greenhouse gas concentrations, warming SSTs, and ozone depletion
(Fig. a, c, e, g). Since 1992, however, the
residual circulation has decelerated at 50 and 70 hPa
(Figs. a, a, and
b, d). The recent deceleration of w*‾
at 50 hPa comes about due to competition between changing SSTs and declining
ODS concentrations: changing SSTs lead to continual though insignificant
acceleration (i.e., the red curve continues to rise in
Fig. a though trends in
Fig. b, d are not significant), but ODS recovery
leads to a slight deceleration (i.e., the gap between the red curve and green
curve gradually decreases after 2000 in Fig. a).
Because of the eruption of Pinatubo, the deceleration trend starts in 1992
in the all-forcing experiment (blue curve in Fig. a)
rather than in the late 1990s, when ODS concentrations began to decrease
(Fig. a). For a start date of 1991, the
deceleration trend at 50hPa is statistically significant at the 95 %
level in two ensemble members and in the ensemble mean
(Fig. a). At 100 hPa
(Fig. b), on the other hand, the dominant forcing is
SSTs, and w*‾ continues to increase throughout the experiment
(Fig. b). The implications of these changes for
ozone and temperature in the lower stratosphere are discussed in
. Overall, the same forcings that control the age of
mid-stratospheric air also control the residual circulation, and these
forcings can explain the lack of acceleration of the deep branch of the BDC
since 1992.
Summary of key forcing agents
In summary, from 1960 to the late 1980s (the first half of the experiments),
ozone depletion, rising GHG, and warming SSTs all led to a decrease in mean
age in all regions of the stratosphere. Over the second half of the
experiments (since the early 1990s), rising GHGs have continued to lead to
decreasing mean age, though the decrease is more prominent in the lower
stratosphere. However, declining ODS concentrations and the proximity of the
start date to the eruption of Pinatubo lead to an aging trend that is most
prominent in the mid-stratosphere. The degree of compensation between these
forcings is region-specific, and for the NH midlatitude mid-stratosphere the
volcanic effects and declining ODS concentrations dominate, while in the lower
stratosphere the SSTs and GHGs dominate. Hence, structural changes occurred
in the BDC in our simulations.
Discussion of observed changes
There are no direct measurements of historical changes in the BDC. However,
its past evolution can be deduced from trace gas measurements or from
satellite data, and here we consider whether the modeled evolution of the BDC
in GEOSCCM is consistent with these constraints.
Comparison with BDC changes inferred from in situ CO<inline-formula><mml:math id="M30" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> and SF<inline-formula><mml:math id="M31" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">6</mml:mn></mml:msub></mml:math></inline-formula> concentrations since 1975
Balloon measurements of CO2 and SF6 concentrations are available from
1975, and these data do not provide evidence for an acceleration trend in
the mid-stratosphere NH, where mean age actually
appears to have increased . In particular, the mean
age evolution in the figures of and
indicates aging since the late 1980s, with earlier changes less clear. As
discussed in Sect. , a similar evolution is present in
our simulations. In order to make the comparison more precise, we subsample
the simulated mean age on the day of each flight analyzed by
. As daily three-dimensional fields of mean age were not
archived from the simulations, we cannot map the simulated age into equivalent
latitude space as done by. We show all three GEOSCCM
members in order to estimate the internal variability in the model-simulated
mean age (see Fig. ).
GEOSCCM captures the climatological value of mean age averaged over this
period accurately: the difference between the observations and model for
these data points is 3 months. A similar 3-month offset is evident
when comparing GEOSCCM to the mean ages reported by , which
falls within the 6-month uncertainty in the observations .
The climatological value of mean age in other regions also agrees well with
satellite-based estimates presented in .
GEOSCCM mean age lies within the error bar for most measurements. While the
weak (non-significant) aging trend noted in observations since 1975 is not
present in GEOSCCM, observed and modeled trends agree within the 95 %
uncertainty level. Note that, if we use the wider uncertainties reported by
, mean age in GEOSCCM agrees with all balloon flights and
trends agree within the 90 % uncertainty level (Fig. ).
Over the recent period (since 1992), one of the three ensemble members
simulates a trend in close agreement with the observed trend (0.12±0.1 year decade-1 for GEOSCCM and 0.14±0.14 year decade-1
for observations). That being said, there is apparently less subseasonal and
QBO variability in GEOSCCM than in the observations and also the
tropical leaky-pipe model of, and the trend towards older air is
weaker in the GEOSCCM ensemble mean than in the observations. (Changes in
mixing cannot be diagnosed from these simulations, but it is conceivable that
mixing could account for some of the difference). For future work, we will
consider whether GEOSCCM is consistent with more recent tracer measurements.
Mean age estimates in the data from Fig. 7 of in
the NH mid-stratosphere between 20 and 25 km and in the three all-forcing
integrations for the same days. The trend from 1992 to 2012 is included. Note
that in computing the trends we do not include any information as to the
difference in uncertainty among the measurements
for the data because all samples in GEOSCCM
have the same uncertainty, and we prefer to use the same statistical test for
both data sources for consistency. This leads to overly conservative
estimates on the uncertainty for the
data.
Comparison with BDC changes since 2002
While extreme caution must be exercised in interpreting a trend over such a
short period due to the large stochastic variability in the atmospheric
circulation, we now assess whether BDC changes since 2002 in GEOSCCM are
consistent with observational constraints.
Vertically and latitudinally resolved changes in satellite-measured SF6
are available from 2002 onward, and infer mean age trends from
these data (their Fig. 6). They find that mean age declines in the tropical
lower and mid-stratosphere south of the Equator and increases at NH
midlatitudes and in the SH polar stratosphere. We show changes in annual
averaged mean age from January 2002 to December 2011 in
Fig. . The model simulates younger mean age in the
lower stratosphere in all three ensemble members, but changes higher in the
stratosphere are not robust among the various ensemble members. These
intra-ensemble differences highlight the fact that one should not base any
conclusions on the long-term behavior of the BDC on 10-year trends, as trends
over 1 decade are strongly influenced by unforced (internal) variability.
While not one of the ensemble members captures the interhemispheric dipole in
the trends as suggested by satellite data (though individual integrations
separately capture half of the dipole), we suggest that such a correspondence
should not necessarily be expected as the wave forcing of the BDC differs in
any realization of the atmospheric state e.g., see the discussion
in.
deduce changes in the tropical mass upwelling from changes
in ozone, and they infer a lack of acceleration in mass upwelling above
70 hPa since 2002 (no significant changes) and an acceleration below 70 hPa
of 11 % per decade. Quantitatively similar behavior is evident in
Fig. – upwelling increases at 100 hPa by
approximately 10 % between 2000 and 2014 but decreases at 50 hPa. Hence,
the trend towards younger air in the NH lower stratosphere in GEOSCCM is
consistent with observational constraints. further speculate
that this slowdown of the upwelling above 70hPa is associated with the La
Niña-like sea surface temperature trends over this period. This
conjecture is supported by our modeling results: in the SST-only experiment,
w*‾ at 100 hPa continues to increase over this period but is
largely flat at 50 hPa (and also at 70 and 30 hPa, not shown).
also noted that recent changes in SSTs (including the La
Niña-like sea surface temperature trends) lead to less planetary wave
flux entering the stratosphere
at midlatitudes, and changes in midlatitude planetary waves will impact the
deep branch more strongly.
Mean age estimates in the data from in the NH
mid-stratosphere between 30 and 5 hPa and in the three all-forcing
integrations for the same locations and months (results are similar if we use
30 to 10 hPa). The uncertainty for the observational estimates is taken from
, and the uncertainty of the model simulated mean age can be
deduced from the intra-ensemble spread. The GEOSCCM mean age is offset by
3 months, i.e., the bias in the mean age which is less than the
6-month uncertainty in the observed mean age as evaluated by
.
Response to the eruption of Mt. Pinatubo
The eruption of Mt. Pinatubo led to younger mean age throughout the
stratosphere and enhanced tropical upwelling in our model. Figure 2 of
suggests that similar behavior is present in the Whole
Atmosphere Community Climate Model (WACCM). Similar behavior is evident in
the mid-stratosphere in SOCOL (SOlar Climate Ozone Links), though not in the
lower stratosphere . also infer older mean
age in the lower stratosphere following Pinatubo using ERA-Interim data.
However, changes in the residual vertical velocity following Pinatubo differ
among reanalysis products and for varying methodologies used for computing
the residual vertical velocity , and hence the actual
response of the BDC to Pinatubo is poorly constrained by existing reanalysis
data. Specifically, use diabatic heating rates in
ERA-Interim to define w*‾, and tropical diabatic heating rates
show cooling after Pinatubo in the reanalyses as the reanalyses do not
assimilate aerosol burden; cf. Fig. 1 of but warm in GEOSCCM due
to increased shortwave heating from the aerosol plume (not shown). Future
work is needed in order to better constrain the response of the BDC to
volcanic eruptions using observations.
It is worth noting that the response to the eruption of Mt. Pinatubo is
stronger in the NH mid-stratosphere than in the SH (cf.
Fig. ). The likely cause of this is as follows: Mt.
Pinatubo is located at 15∘ N, and in our experiments the majority of
the aerosols stay in the NH. Therefore, the increased shortwave heating is
stronger in the NH. Future work is needed in order to explore sensitivity to
the details of the prescribed volcanic forcing in the model.
As in Fig. but for changes in the BDC from
January 2002 to December 2011 in the annual mean of the three all-forcing
ensembles. Note that the contour interval differs from
Fig. .
Summary of the comparison of GEOSCCM to observations
In conclusion, the evolution of the BDC in GEOSCCM is qualitatively, and by
most measures considered here quantitatively, consistent with observational
constraints. There is a transition between declining mean age throughout the
stratosphere before the late 1980s and regionally-specific changes in mean
age afterwards (including the possibility of aging in the midlatitude
mid-stratosphere in the NH). 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 the case for the past 25 years, for which we
have most tracer measurements.
Conclusions
The Brewer–Dobson Circulation and its changes have important
implications for both stratospheric and tropospheric climate as well as
stratospheric ozone chemistry . Hence, it
is crucial to understand (1) the structure of historical changes in the BDC
and (2) the factors that lead to these changes. It is also important, for
predicting future changes, to know how well models can simulate historical
changes of the BDC as given by available observational constraints.
Analysis of a series of chemistry–climate model experiments of the period
January 1960 through December 2014 yielded the following conclusions:
Over the full duration of the experiments (i.e., for a start date in 1960),
we recover the result from previous modeling studies: anthropogenic climate
change leads to acceleration of the BDC throughout the stratosphere. Ozone
depletion, rising GHG concentrations, and warming SSTs all led to declining
mean age in all regions of the stratosphere.
Since the late 1980s, structural changes have occurred in the BDC. The BDC
accelerated in the lower stratosphere in the NH and tropics but not in the
mid-stratosphere. Specifically, since 1992 in the ensemble mean, mean age has
increased by 0.12±0.09 year (95 % confidence intervals) in the
mid-stratosphere of the midlatitude NH, and tropical mass upwelling has slowed
down by 2 % (statistically significant in two ensemble members). The
trend in one ensemble member is quantitatively similar to that in available
observations. Hence, there is no inconsistency in trends between our model
and available observations.
The source of this structural change is the time-varying evolution of the
forcing factors. While warming SSTs and rising greenhouse gas concentrations
both lead to acceleration of the BDC (consistent with previous work), their
influence is stronger in the lower stratosphere. In contrast, volcanic
eruptions and ODS concentrations generally impact the deep branch more
strongly. Declining ODS concentrations and the proximity of the start of
declining ODS concentrations to the eruption of Pinatubo led to an aging
trend beginning in the early 1990s in the midlatitude NH mid-stratosphere. If
internal atmospheric variability is taken into consideration, then the
start date of an aging trend in the midlatitude NH mid-stratosphere can be
pushed back to 1988.
In light of these results, we wish to emphasize that, if one wishes to
understand the causes of observed historical changes
in the BDC, careful attention must be paid to the start and end dates used
for trend calculation and the forcings included in a model simulation. In
addition, it should not be expected that any single free-running model
experiment should capture the precise observed trend or necessarily resemble
a second integration using that same model, as the wave forcing of the BDC
differs in any realization of the atmospheric state.
Many questions as to the historical changes in the BDC are left unanswered by
this study. Diagnostic output necessary to compute the full age spectrum was
not saved for these model experiments, and hence we are limited in our ability
to quantify mixing changes, but it is conceivable that mixing changes
contributed to recent observed mean age trends .
Future work is needed in order to better constrain the response of the BDC to
volcanic eruptions using observations and to explore sensitivity to the
details of the prescribed volcanic forcing in the model. Finally, a more
quantitative comparison of model mean age from a range of modeling centers to
recent in situ observations is needed in order to more firmly diagnose areas
of agreement and disagreement between models and observations.
Data availability
Data related to this article can be found in the Supplement. Additional data
requests should be addressed to Chaim Garfinkel
(chaim.garfinkel@mail.huji.ac.ikl).
The Supplement related to this article is available online at doi:10.5194/acp-17-1313-2017-supplement.
Acknowledgements
The work of Chaim I. Garfinkel was supported by the Israel Science Foundation
(grant number 1558/14) and by a European Research Council starting grant
under the European Union's Horizon 2020 research and innovation program
(grant agreement no. 677756). The work of Darryn W. Waugh is supported, in
part, by grants of the US National Science Foundation to Johns Hopkins
University. Valentina Aquila and Luke D. Oman thank the NASA MAP program for
their support. We also thank Eric Ray for providing data from Figs. 7 and 8
of and for help in interpreting balloon data and their
uncertainties, and the four anonymous reviewers for their constructive
criticism. We also thanks those involved in model development at GSFC-GMAO
and Steven Pawson for initially suggesting the suite of GEOSCCM simulations
analyzed here. High-performance computing resources were provided by the NASA
Center for Climate Simulation (NCCS). Correspondence and requests for data
should be addressed to Chaim I. Garfinkel (email:
chaim.garfinkel@mail.huji.ac.il).Edited by: G.
Stiller Reviewed by: five anonymous referees
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