ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-4641-2016Using beryllium-7 to assess cross-tropopause transport in global modelsLiuHongyuhongyu.liu-1@nasa.govhttps://orcid.org/0000-0002-2164-6383ConsidineDavid B.HorowitzLarry W.CrawfordJames H.RodriguezJose M.StrahanSusan E.https://orcid.org/0000-0002-7511-4577DamonMegan R.SteenrodStephen D.XuXiaojingKouatchouJulesCarougeClaireYantoscaRobert M.https://orcid.org/0000-0003-3781-1870National Institute of Aerospace, Hampton, VA, USANASA Langley Research Center, Hampton, VA, USANOAA Geophysical Fluid and Dynamics Laboratory, Princeton, NJ, USANASA Goddard Space Flight Center, Greenbelt, MD, USAUniversities Space Research Association, Columbia, MD, USAScience Systems and Applications, Inc., Lanham, MD, USAScience Systems and Applications, Inc., Hampton, VA, USAJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USAnow at: NASA Headquarters, Washington, D.C., USAnow at: ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, AustraliaHongyu Liu (hongyu.liu-1@nasa.gov)14April20161674641465926July201525September201516March201623March2016This 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/16/4641/2016/acp-16-4641-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/4641/2016/acp-16-4641-2016.pdf
We use the Global Modeling Initiative (GMI) modeling framework to
assess the utility of cosmogenic beryllium-7 (7Be), a natural aerosol
tracer, for evaluating cross-tropopause transport in global models. The GMI
chemical transport model (CTM) was used to simulate atmospheric 7Be
distributions using four different meteorological data sets (GEOS1-STRAT
DAS, GISS II′ GCM, fvGCM, and GEOS4-DAS), featuring significantly different
stratosphere–troposphere exchange (STE) characteristics. The simulations
were compared with the upper troposphere and/or lower stratosphere (UT/LS)
7Be climatology constructed from ∼ 25 years of aircraft
and balloon data, as well as climatological records of surface
concentrations and deposition fluxes. Comparison of the fraction of surface
air of stratospheric origin estimated from the 7Be simulations with
observationally derived estimates indicates excessive cross-tropopause
transport at mid-latitudes in simulations using GEOS1-STRAT and at high
latitudes using GISS II′ meteorological data. These simulations also
overestimate 7Be deposition fluxes at mid-latitudes (GEOS1-STRAT) and
at high latitudes (GISS II′), respectively. We show that excessive
cross-tropopause transport of 7Be corresponds to overestimated
stratospheric contribution to tropospheric ozone. Our perspectives on STE in
these meteorological fields based on 7Be simulations are consistent
with previous modeling studies of tropospheric ozone using the same
meteorological fields. We conclude that the observational constraints for
7Be and observed 7Be total deposition fluxes can be used routinely
as a first-order assessment of cross-tropopause transport in global models.
Introduction
Stratosphere–troposphere exchange (STE) of air masses and chemical species
occurs at small, synoptic and global scales. It is typically associated with
the occurrences of tropopause folding and cutoff cyclones and, more
importantly, the global circulation of the atmosphere (Holton et al., 1995).
While stratosphere-to-troposphere transport removes many chemical species
from the stratosphere, it represents a significant source of ozone and other
reactive species for the tropospheric chemical system (Stohl et al., 2003).
Ozone is an important greenhouse gas, especially in the upper troposphere. It
is a harmful pollutant near the surface where stratospheric ozone intrusions
may make significant contributions (e.g., Lin et al., 2012, 2015; Langford et
al., 2015). It is also the main precursor of
hydroxyl radicals (OH) and thus plays an essential role in the oxidizing
capacity of the troposphere. In a warmer climate, the stratosphere may
increase its contribution to tropospheric ozone levels due to a stronger
residual circulation (Collins et al., 2003).
Quantitative understanding and prediction of anthropogenic (vs. natural)
perturbations to tropospheric ozone require the use of global 3-D models;
correctly representing the STE flux in these models is therefore critical.
However, current models show large (30 %) uncertainty in predicted STE
fluxes of ozone (Stevenson et al., 2006). Here we use the Global Modeling
Initiative (GMI) modeling framework (Douglass et al., 1999; Rotman et al.,
2001) to assess the utility of the aerosol tracer beryllium-7 (7Be) for
evaluating cross-tropopause transport in global models.
Beryllium-7 has a half-life of 53.3 days and is produced by cosmic ray
spallation reactions in the stratosphere and upper troposphere. After
production, it attaches immediately to ubiquitous submicron aerosols in the
ambient air. The fate of 7Be then becomes that of those aerosols, which
move with the air until scavenged by precipitation or deposited to the
surface. 7Be is a useful aerosol tracer for testing wet deposition
processes in a global 3-D model and is often used in conjunction with the
terrigenic 210Pb aerosol tracer, as wet deposition is its principal
sink and its sources are relatively well known (e.g., Brost et al., 1991;
Koch et al., 1996; Liu et al., 2001). On the other hand, because of its
source at high altitudes and the large concentration vertical gradient,
simulation of 7Be tests the model's capability to describe
stratosphere-to-troposphere transport and subsidence in the troposphere
(e.g., Liu et al., 2001; Allen et al., 2003).
Beryllium-7 has long been recognized as a tracer of downward transport from
the stratosphere to the troposphere (e.g., Husain et al., 1977; Viezee and
Singh, 1980; Sanak et al., 1985; Dibb et al., 1992, 1994; Rehfeld and
Heimann, 1995). Husain et al. (1977) reported that pulses of high 7Be
concentrations were often associated with air masses of stratospheric origin,
as indicated by large potential vorticity. Viezee and Singh (1980) showed
that the 7Be concentrations over North America show strong positive
correlations with the occurrence of tropopause folding events over several
latitude belts. 7Be has also been combined with other radionuclides
(e.g., 10Be, 90Sr) as an indicator of transport of stratospheric
air to the troposphere (Raisbeck et al., 1981; Rehfeld and Heimann, 1995;
Koch and Rind, 1998; Dibb et al., 1994; Jordan et al., 2003; Zanis et al.,
2003; Heikkilä et al., 2008a, b). Dutkiewicz and Husain (1985, hereafter
referred to as DH85) analyzed 7Be and 90Sr concentrations measured
simultaneously in samples from NASA's Global Atmospheric Sampling Program
(GASP) and showed that on an annual basis the stratosphere contributed
∼ 25 % of the observed 7Be concentration at the northern
mid-latitude surface (∼ 40 % during late spring but only 10 %
during fall).
Beryllium-7 is also a useful tracer for vertical mixing and subsidence in the
troposphere. Feely et al. (1989) examined the factors that contribute to
seasonal variations in 7Be concentrations in surface air. They found
that the influences of variations both in the STE rate and in the
tropospheric vertical mixing rate are evident in concentrations at most sites
in mid-latitudes. Convective transport carries surface air upward and brings
down the 7Be at higher altitudes to the surface layer. This is also
reflected by the 7Be /210Pb ratio that peaks at the surface in
summer when convective activity is at its maximum (Koch et al., 1996). On the
other hand, despite the UT/LS source of 7Be and the continental surface
source of 222Rn (precursor of 210Pb), 7Be concentrations have
been reported to be positively correlated with 210Pb concentrations,
reflecting mixing of subsiding middle- and upper-tropospheric air with
continental lower-tropospheric air (Li et al., 2002; Dibb, 2007).
A number of observational studies have demonstrated the feasibility of using
7Be to infer the contribution of ozone-rich stratospheric air to ozone
concentrations at ground level (e.g., Husain et al., 1977; Tsutsumi et al.,
1998; Helmig et al., 2007) and in the free troposphere (e.g., Johnson and
Viezee, 1981; Prospero et al., 1995; Graustein and Turekian, 1996; Kritz et
al., 1991; Dibb et al., 2003). These studies are usually based on the
correlations between concurrent measurements of ozone and 7Be (as well
as other tracers such as water vapor and calculated potential vorticity),
with positive 7Be-ozone correlations indicating the presence of the
upper-tropospheric or stratospheric air. For instance, Helmig et al. (2007)
showed a year-round correlation of ozone with 7Be at Summit, Greenland
and concluded that surface-layer photochemical ozone production does not
appear to have a noticeable influence on surface ozone levels. However, it
is important to note that under some circumstances the observed positive
correlations of surface ozone with 7Be may simply reflect the common
vertical trends of tropospheric 7Be and ozone and does not necessarily
indicate the influence of stratospheric air (Li et al., 2002). Recent global
modeling studies showed the models' capability to reproduce the observed
7Be-ozone relationships, providing useful constraints on the
stratospheric (vs. photochemical) contribution to tropospheric ozone in
the model (Li et al., 2002; Allen et al., 2003; Liu et al., 2004).
Though correct representation of STE is essential for simulating 7Be,
ozone and other trace species in the troposphere (e.g., Riese et al., 2012),
large variations exist among models. Stevenson et al. (2006) reported the
average STE flux of ozone from 26 models of 552 ± 168 Tg year-1.
Observation-based estimates of STE fluxes of ozone into the troposphere are
typically in the range of 400–600 Tg year-1 (Murphy and Fahey, 1994).
Some global models are able to produce STE fluxes of ozone in this range
(e.g., Olsen et al., 2004; Hsu et al., 2005; Hsu and Prather, 2009; Lin et
al., 2012; Young et al., 2013; Škerlak et al., 2014). For those models
with too fast (or rarely, too slow) cross-tropopause transport of ozone, one
way to overcome the difficulty is to use the Synoz (synthetic ozone) method
(McLinden et al., 2000). The Synoz method involves constraining the global
mean cross-tropopause ozone flux to match a prescribed value consistent with
observations (e.g., Bey et al., 2001). But this method yields an unrealistic
stratospheric ozone field and therefore does not allow for on-line
calculations of total ozone columns and photolysis rates and/or heating rates
(McLinden et al., 2000). By contrast, the other simple model for
stratospheric ozone (linearized ozone or Linoz) developed by McLinden et
al. (2000) enables these on-line calculations
by linearizing the ozone tendency about the local ozone mixing ratio,
temperature, and the overhead column ozone density. Linoz is computationally
efficient and can be readily incorporated in climate models for long-term
integrations. Nevertheless, using Linoz (or full stratospheric chemistry) in
global CTMs or chemistry-climate models that focus on the troposphere
requires a realistic model representation of net cross-tropopause total mass
fluxes. In this context, 7Be tracer simulations may provide a simple way
of evaluating cross-tropopause transport in these models.
The intermodel differences in the estimated intensity and frequency of STE
have been attributed to different meteorological fields used to drive the
models as well as different transport algorithms and chemistry processes
(Cristofanelli et al., 2003). The GMI modeling framework facilitates the
reduction of uncertainties of this kind. It is a modular CTM with the
ability to incorporate different inputs and components (e.g., meteorological
fields, emission inventories, chemical and microphysical mechanisms, and
numerical schemes) that represent the different approaches of current
models. One of the distinct features of the GMI CTM is the ability to be
driven by different meteorological data sets (e.g., Douglass et al., 1999;
Considine et al., 2005; Liu et al., 2007) while maintaining the same
algorithms for transport, deposition, emission, chemistry and other
pertinent processes. This allows us to isolate the uncertainties in the
model simulations due to differences in the meteorological data sets alone.
The number of factors that may contribute to differences in the simulations
is thus reduced, as we previously showed using the GMI simulated 222Rn
and 210Pb radionuclide tracers (Considine et al., 2005).
In this paper, we present simulations of atmospheric 7Be distributions
with the GMI CTM driven by four different meteorological data sets,
including output from GEOS1-STRAT, GISS II′ GCM, fvGCM, and GEOS4-DAS, each
featuring significantly different STE characteristics. The reader is
referred to Table 1 for a list of acronyms of models and their driving
meteorological data sets. We use here not only the meteorological fields
that are well known to have reasonably good representations of STE (e.g.,
fvGCM), but also those with poor representations (e.g., GEOS1-STRAT). The
variability in simulated STE allows us to examine and assess the utility of
7Be for evaluating STE in these (and other) global meteorological
fields. We will illustrate the consequences of incorrect STE in terms of the
simulation of tropospheric 7Be and show that 7Be concentrations
and deposition fluxes may be used routinely as a first-order assessment for
cross-tropopause transport in global models. We will discuss how the
constraints on STE from 7Be are consistent with previous modeling
studies of tropospheric ozone using the same meteorological fields.
The remainder of this paper is organized as follows. Section 2 gives a brief
description of the GMI model, 7Be source and cross-tropopause flux, and
7Be and ozone observational data sets used for evaluating the model.
Section 3 evaluates model results with UT/LS and surface 7Be data.
Section 4 assesses cross-tropopause transport of 7Be in different
meteorological fields. Section 5 compares the results with previous modeling
studies. Section 6 discusses the implications for the impact of STE on
tropospheric ozone, followed by the summary and conclusions in Sect. 7.
Acronyms of the model and driving meteorological data sets.
Model/Data Set Acronym GMI CTM Global Modeling Initiative Chemical Transport Model GEOS1-STRAT DASGoddard Earth Observing System Data Assimilation System – version 1in support of the Stratospheric Tracers of Atmospheric Transport missionGISS II′ GCMGoddard Institute for Space Studies General Circulation Model – version II′fvGCMGlobal Modeling and Assimilation Office (GMAO) finite-volume GCMGEOS4-DASGEOS Data Assimilation System – version 4Model and dataGMI CTM
The GMI (http://gmi.gsfc.nasa.gov) CTM is a global 3-D
composition model that includes a full treatment of both stratospheric and
tropospheric photochemical and physical processes. It uses a 114-species
chemical mechanism that combines the stratospheric mechanism of Douglass et
al. (2004) with the tropospheric mechanism of Bey et al. (2001). The
chemical mechanism includes both stratospheric and tropospheric
heterogeneous reactions. Tropospheric aerosol (sulfate, dust, sea salt,
organic carbon, and black carbon) fields are taken from the Goddard
Chemistry, Aerosol, Radiation and Transport model (GOCART). Details of the
model are described in Duncan et al. (2007, 2008), Strahan et al. (2007),
and Considine et al. (2008). There is also a tropospheric version of the
model that includes only tropospheric chemistry processes and uses the Synoz
(synthetic ozone) scheme (McLinden et al., 2000) to ensure a given value for
the total flux of ozone into the troposphere. The latter adopts a
cross-tropopause ozone flux of about 530–590 Tg year-1 (Stevenson et al.,
2006). In this study, we simulate 7Be using the GMI CTM without
chemistry, similar to the Considine et al. (2005) study that simulated the
radionuclides 222Rn and 210Pb. We use both the full-chemistry CTM
and the tropospheric version of the model for ozone simulations.
The simulations presented in this paper differ only in the meteorological
data used to drive the model. The four input meteorological data sets are
from (1) the Goddard Space Flight Center Data Assimilation Office (now Global
Modeling and Assimilation Office or GMAO) GEOS1-STRAT data assimilation
system (GEOS1-STRAT DAS, March 1997–February 1998), (2) GISS II′ GCM (Rind
and Lerner, 1996), (3) the GMAO finite-volume GCM (fvGCM), and (4) GEOS4-DAS
(February 2004–January 2005). The GISS II′ GCM data set is used for
7Be simulations only. The two GCM data sets are intended to represent
not any particular year but the contemporary climatological state of the
Earth's atmosphere. Note that these data sets do not reflect the
state of the art, especially the first two. However, the
choices are purposely made in order to see how a meteorological input with a
poor representation of cross-tropopause transport affects the simulated
tropospheric 7Be. Vertical levels, top pressure, near-tropopause
resolution, and bottom layer depth for each data set are listed in Table 2.
The simulations presented here use a degraded horizontal resolution
(4∘× 5∘) for computational expediency. Degraded
horizontal resolution slightly increases cross-tropopause transport (Liu et
al., 2001). Nevertheless, our objective is to assess cross-tropopause
transport in meteorological data sets at the resolution used to drive the
model, not necessarily at the original or finer resolution.
The model uses the flux form semi-Lagrangian advection scheme (FFSL) of Lin
and Rood (1996) and a convective transport algorithm adapted from the
CONVTRAN routine in the NCAR CCM3 physics package. The wet deposition scheme
is that of Liu et al. (2001) and includes scavenging in wet convective
updrafts, and first-order rainout and washout from both convective anvils
and large-scale precipitation. The gravitational settling effect of cloud
ice particles included in Liu et al. (2001) is not considered here. Dry
deposition of 7Be aerosols is computed using the resistance-in-series
approach. The model tracks the bulk 7Be aerosol mass. For 7Be
simulations, each simulation was run for 6 years, recycling the
meteorological data for each year of the simulation; we use the sixth year
output for analysis. For ozone simulations, the model was spun up for 10 years to remove the effect of initial conditions. Interannual variability in
STE of 7Be is not shown in this paper. However, model simulations
driven by multi-year outputs from fvGCM (1994–1998) indicate that such
interannual variability is much smaller than the differences due to using
different meteorological data sets and does not affect the conclusions of
this study.
Characteristics of meteorological data sets used to drive the GMI
CTM.
a The hybrid vertical coordinate
consists of sigma (σ) levels
below the interface pressure and constant pressure (P) levels above.b The total number of vertical levels and top level pressure in
the original meteorological data set.
The GMI CTM has been used previously to study the sensitivities of model
simulations to different sets of meteorological input. Douglass et al. (1999) used chemical tracers in the GMI framework to assess three
meteorological data sets, i.e., the NCAR Community Climate Model (CCM2),
GEOS1-STRAT, and GISS II′ GCM. They concluded that overall, CCM2 provides the
best representation of the stratosphere. Considine et al. (2005) used the
GMI model to simulate the radionuclides 222Rn and 210Pb using
three different sets of meteorological inputs (GEOS1-STRAT, GISS II′, and
CCM3) to characterize the variability occurring in their simulations.
Overall no simulation was found to be superior to the others when compared
with the climatological observations of these radionuclides. The role played
by convective transport and scavenging was found to differ substantially
among the three meteorological data sets. Liu et al. (2007) analyzed and
quantified the differences and uncertainties in GMI aerosol simulations
solely due to different meteorological fields (GEOS1-STRAT, GISS II′ GCM, and
fvGCM). They suggested that the differences in the precipitation, convective
mass flux, and horizontal advection from the three meteorological data sets
explain much of the large discrepancies in the model-calculated aerosol
concentrations.
Annual mean total precipitation (mm day-1) at the surface in
the GEOS1-STRAT, GISS II′, fvGCM, and GEOS4-DAS meteorological data sets and
in the observational data set from the Global Precipitation Climatology
Project (GPCP, 1979–2009). Also shown is the annual zonal mean precipitation
(bottom right panel).
Comparison of cloud and precipitation fields between meteorological data
sets
Clouds and precipitation play a critical role in the transport and scavenging
of 7Be aerosols and thus in determining the lifetime, burden, and
distribution of 7Be in the troposphere. Figures 1 and 2 compare the
annual surface total precipitation and convective mass fluxes in the
GEOS1-STRAT, GISS II′ GCM, fvGCM and GEOS4-DAS meteorological data sets,
respectively, following Liu et al. (2007). Also shown in Fig. 1 is the
satellite climatology of surface total precipitation (1979–2009) from the
Global Precipitation Climatology Project (GPCP) (Adler, 2003). The global mean precipitation rates are 1.9, 2.2, 2.6, 2.3
and 2.2 mm day-1 for GEOS1-STRAT, GISS II′ GCM, fvGCM, GEOS4-DAS and
GPCP, respectively, with lightest precipitation in GEOS1-STRAT and heaviest
in fvGCM. Compared to GPCP, GEOS1-STRAT and GEOS4-DAS significantly
underestimate the precipitation in the mid-latitude storm track regions,
while GISS II′ GCM, fvGCM and GEOS4-DAS largely overestimate the observations
in the tropics or subtropics. GISS II′ GCM also underestimates the
precipitation south of 50∘ S and north of 40∘ N. There are
significant differences in the convective mass fluxes among the four
meteorological data sets (Fig. 2). Consistent with the precipitation,
GEOS1-STRAT shows the weakest convection except in the tropical middle and
upper troposphere, whereas fvGCM features the strongest convection in the
boundary layer at 30–60∘ S. The effects of the above differences in
convection and precipitation between meteorological data sets on the results
of this study will be examined through model sensitivity experiments.
Annual zonal mean convective mass fluxes (kg m-2 s-1) in
the GEOS1-STRAT, GISS II′ GCM, fvGCM, and GEOS4-DAS meteorological data sets.
7Be source
There is a large discrepancy in the published estimates of 7Be
production rates (Lal and Peters, 1967, referred to as LP67 hereafter;
O'Brien et al., 1991; Masarik and Reedy, 1995; Masarik and Beer, 1999;
Usoskin and Kovaltsov, 2008). Global mean column production rates over an
average solar cycle range from 0.035 atoms cm-2 s-1 (Masarik and
Beer, 1999), 0.063 atoms cm-2 s-1 (O'Brien et al., 1991), to
0.081 atoms cm-2 s-1 (LP67). The Masarik and Beer (1999)
production function is smaller than other estimates by a factor of 2 or more.
It may have underestimated the rate of 7Be production and slightly
overestimated changes in the production rate due to variations in geomagnetic
and solar magnetic field strength (Koch et al., 2006; Field et al., 2006). We
use in the model the LP67 source for 1958 (solar maximum year) since it leads
to the best simulation of aircraft 7Be observations in the stratosphere
where 7Be concentrations are mainly determined by a balance between
production and radioactive decay (Koch et al., 1996; Liu et al., 2001). Koch
et al. (1996) previously found that the O'Brien et al. (1991) source yields model 7Be concentrations near
the surface and in the stratosphere that are much lower than observed. The
rates of 7Be production reported more recently by Usoskin and
Kovaltsov (2008) broadly agree with those of LP67 with slightly (about
25 %) lower global production rate and will be tested in a separate model
study. The LP67 source is represented as a function of latitude and altitude
(pressure) and does not vary with season (see Fig. 1 of Koch et al., 1996).
About 2/3 of atmospheric 7Be is generated in the stratosphere and 1/3 in
the troposphere. The 7Be production rate correlates inversely with solar
activity. At higher solar activity, cosmic rays are deflected away from the
solar system and the 7Be production rate is thus lower.
Constraint on stratospheric contribution to 7Be at the
surface
Cross-tropopause transport is important for simulating 7Be in the
troposphere. A useful constraint on the stratospheric contribution to
tropospheric 7Be is DH85's analysis of the observed
7Be /90Sr ratio in the stratosphere and 90Sr
concentrations at the surface. The presence of fissiogenic 90Sr in the
troposphere is due entirely to downward transport from the stratosphere,
except for a few weeks right after a nuclear detonation. Both 7Be and
90Sr are associated with submicron particles; their fates during
transport from the stratosphere are expected to be similar (no differential
removal is expected). The stratospheric 7Be component in surface air can
therefore be determined as the product of the stratospheric
7Be /90Sr ratio and the surface 90Sr concentration
(DH85). By this procedure, DH85 showed that annually 23–27 % (or about
25 % on average) of the 7Be in surface air at northern mid-latitudes
is of stratospheric origin. To use this constraint, we diagnose stratospheric
contribution to 7Be concentrations in the troposphere by transporting
separately in the model the 7Be produced in the stratosphere, as we
previously applied in GEOS-Chem with GEOS1-DAS meteorological data (Liu et
al., 2001). Since wet deposition removes both the stratospheric and
tropospheric components of 7Be at the same rate within each model
gridbox, the diagnosed stratospheric fraction of 7Be concentrations in
the troposphere does not significantly depend on the rate of wet removal.
In the stratosphere, the production of 7Be (source) is balanced by
radioactive decay and net STE fluxes of 7Be into the troposphere
(sinks), i.e.,
source(7Be)=decay (7Be)+STE(7Be).
Since the timescale for downward transport from the stratosphere to
troposphere (∼ 1–2 years) is much longer than that for
radioactive decay (half-life 53.3 days), the radioactive decay term is much
larger than the STE flux term. Nevertheless, the STE term would become more
important for a model atmosphere where STE is too fast. For the simulation
of tropospheric (not stratospheric) 7Be, the stratospheric influx to
the troposphere may be adjusted by artificially scaling down (in the case of
excessive STE) or up (in the case of too slow STE) the stratospheric
7Be source. The extent to which 7Be cross-tropopause transport is
excessive or too slow in the model can be indicated by a scaling factor A,
which is defined as the ratio of model to observed STE fluxes of 7Be.
We derive the scaling factor A as follows.
According to the DH85 constraint, we have for the observations
[7Be]T,G/[7Be]S,G=(1-0.25)/0.25=3,
where the left-hand side denotes the ratio of the tropospheric
([7Be]T,G) to stratospheric ([7Be]S,G) component of
annual mean 7Be concentrations in ground air at NH mid-latitudes. On
the other hand, we have for a global model
[7Be]T,G′/[7Be]S,G′=(1-F)/F,
where [7Be]T,G′ and [7Be]S,G′ are the
model tropospheric and stratospheric components of annual mean
7Be concentrations in surface air at NH mid-latitudes, respectively, and
F is the corresponding fraction of surface air of stratospheric origin in
the model. If the model reasonably represents the vertical transport and wet
scavenging processes in the troposphere, we have
[7Be]T,G′=[7Be]T,G.
Combining Eqs. (2)–(4), we obtain the scaling factor
A≈[7Be]S,G′/[7Be]S,G≈3F/(1-F).
We will discuss the sensitivity of F and A to the assumptions with respect
to convective transport and scavenging processes in Sect. 4. The validity
of Eq. (5) will also be evaluated with actual model calculations in
that section. Unless otherwise specified, 7Be cross-tropopause fluxes
in the model calculations presented in this paper are not adjusted. However,
we will use the scaling factor A as one of the metrics for comparing the STE
characteristics of different meteorological data sets.
7Be and ozone observational data7 Be
We estimate an average solar year value
simply by averaging the long-term records of 7Be observations multiplied
by 0.72 to correct to the 1958 solar maximum source (Koch et al., 1996). The
7Be deposition flux observations are from the compilation of Koch et
al. (1996) and there are about 25 northern mid-latitude sites with available
long-term 7Be observations. The 7Be surface concentration
observations are from the data archive of the US Department of Energy (DOE)
Environmental Measurements Laboratory (EML, now part of the Department of
Homeland Security) Surface Air Sampling Program (SASP) which began in the
1980s. We also use the long-term climatological data of 7Be
concentrations in the UT/LS constructed from ∼ 25 years of aircraft and
balloon observations. Between the late 1950s and the early 1980s, EML
collected tropospheric and stratospheric aircraft and balloon measurements of
numerous radionuclides as part of the DOE High Altitude Sampling Program
(HASP). The data were compiled into a database in 1997 by R. Leifer and
N. Chan of EML (Leifer and Chan, 1997), called
RAdioNuclide DAtaBase (RANDAB). The reader is referred to Considine et
al. (2005) for a brief description of the RANDAB database. This database is
available at the Oak Ridge National laboratory's Carbon Dioxide Information
Analysis Center (http://cdiac.esd.ornl.gov/ndps/db1019.html).
Ozone
We use tropospheric ozone column (TOC) determined with the tropospheric ozone
residual method by subtracting measurements of MLS stratospheric column ozone
(SCO) from OMI total column ozone (Ziemke et al., 2006;
http://acdb-ext.gsfc.nasa.gov/Data_services/cloud_slice) or using the
TOMS and Solar Backscatter Ultraviolet (SBUV) combination (Fishman et al.,
2003; http://science.larc.nasa.gov/TOR). The OMI/MLS TOCs are from
October 2004 to July 2008, and the TOMS/SBUV TOCs are from 1979 to 2005. We
use climatological monthly average ozone profiles from 23 ozonesonde stations
as constructed by Considine et al. (2008), based on Logan (1999) and Thompson
et al. (2003). The ozonesonde data record is from 1985–2000 for
extratropical stations, and from all available data prior to 2005 for
tropical stations. The number of sondes at each station is adequate for
defining monthly means used to evaluate the accuracy of the model results
(Considine et al., 2008). Surface ozone data are taken from Logan (1999).
Model evaluation with UT/LS and surface 7Be data
In this section, we present model results of 7Be simulations driven by
four meteorological archives and evaluate them against long-term measurements
at the surface and in the UT/LS. Figure 3 shows the annual zonal mean
concentrations (in units of millibequerel per standard cubic meter or
mBq SCM-1) of 7Be in the four radionuclide simulations using GMI
CTM. All four simulations overall show a similar pattern of tropospheric
distribution. The highest concentrations are seen in the dry subsiding
subtropics. Lowest 7Be concentrations in surface air are found in the
Southern Hemisphere mid-latitudes owing to scavenging by frequent large-scale
precipitation (Fig. 1). Low 7Be concentrations are also associated with
ITCZ, which is characterized by strong convergence and convective
precipitation. It appears, however, that among all four simulations the
GEOS1-STRAT simulation gives the highest concentrations in the subtropics and
the GISS simulation shows the highest concentrations in the high latitudes.
This is partly attributed to the differences in the latitudinal distribution
of total precipitations in these meteorological archives (Fig. 1).
Annual zonal mean mixing ratios (mBq SCM-1) of 7Be as a
function of latitude and pressure (altitude), as simulated by the standard
GMI CTM. The white lines indicate the annual average thermal tropopause
height.
Figure 4 compares four 7Be simulations in the upper troposphere and/or lower
stratosphere (UT/LS) with climatological distributions constructed from the
7Be data contained in the RANDAB database, following Considine et
al. (2005) who previously made a similar comparison for 210Pb. Model
output are sampled at the months, longitudes, latitudes, and altitudes of the
7Be observations. Figure 4a compares the meridional distribution of
7Be measurements made in the 12–16 km altitude range with the four GMI
simulations. Figure 4b shows the same comparison, but for the 16–20 km
altitude range. The 12–16 km (about 200–100 hPa) range lies within the
upper troposphere in the tropics and the lower stratosphere at mid to high
latitudes. The 16–20 km (about 100–50 hPa) range lies within the
stratosphere at all latitudes.
Observed and simulated latitudinal distributions of 7Be in the
(a) 12–16 km and (b) 16–20 km regions. Observed data
from the EML RANDAB database are averaged into 10∘ bins, following
Considine et al. (2005). Error bars represent ±2 times the standard error
of the averages. Model results are sampled at observation locations on a
monthly basis. Also shown as dashed lines are model zonal mean 7Be
concentrations to show the global representativeness of the averages
constructed from sampling the simulations at the observation locations.
At 12–16 km (Fig. 4a), the observations indicate comparatively low tropical
upper tropospheric values of ∼ 35 mBq SCM-1, with increasing
trends toward high latitudes. The distribution is nearly symmetric about the
equator, with more observations available in NH high latitudes. This
latitudinal distribution of 7Be concentrations reflects a larger
production of 7Be in the lower stratosphere at high latitudes and
precipitation scavenging associated with deep convection in the tropics. All
four simulations capture the observations at 12–16 km reasonably well. The
differences between the four simulated 7Be concentrations are comparable
or smaller than the error limits.
At 16–20 km (Fig. 4b), the observations show a tropical minimum of
∼ 150 mBq SCM-1, with increasing concentrations toward high
latitudes in both hemispheres. In the tropics and the SH, the four 7Be
simulations indicate small differences. In the NH, the four 7Be
simulations reveal large differences and bracket the observations. In
particular, the GMI/GEOS1-STRAT simulation gives the lowest 7Be
concentrations among the four simulations and is lower than the observations.
This appears to be due to excessive cross-tropopause transport in
GEOS1-STRAT, as further discussed below. On the other hand, as we will also
discuss later, the fvGCM and GEOS4-DAS meteorological fields have reasonable
cross-tropopause transport. In the latter case, stratospheric 7Be
concentrations are primarily determined by a balance between production and
radioactive decay in the stratosphere. Therefore the slightly overestimated
7Be at 16–20 km suggests a slightly overestimated global production
rate of 7Be in the LP67 source. The Usoskin and Kovaltsov (2008) source,
which is about 25 % lower than the LP67 source, would probably yield
better agreements with the 7Be observations in the lower stratosphere.
Figure 5a compares the simulated and observed annual average concentrations
of 7Be near the surface as a function of latitude. Observed data are
from the EML SASP database and are averaged into 10∘ latitude bins.
Observations from sites with elevation higher than 500 m are not included
because of uncertainties involved in sampling coarse-resolution models at
high elevation sites. Model results are sampled at observation locations on a
monthly basis. Figure 5b
shows the annual zonal mean surface 7Be concentrations in the model to
indicate the global representativeness of the averages over the sampling
sites. The observations indicate concentration maxima in the subtropics
associated with subsidence and minima in the tropics. The tropical minimum
reflects rapid scavenging within the ITCZ. Low 7Be concentrations are
also observed at mid-latitudes due to efficient scavenging in the
mid-latitude storm tracks. Latitudinal trends (i.e., minima and maxima) of
7Be concentrations are well simulated with all meteorological fields
except GISS II′. The GMI/GISS simulation shows too high 7Be
concentrations at high latitudes; this is because of the well-known excessive
cross-tropopause transport at high latitudes in the GISS II′ meteorological
fields (e.g., Koch and Rind, 1998; McLinden et al., 2000; Shindell et al.,
2003). The overall positive biases in all simulations are partly due to our
correction of the long-term records of 7Be observations (by a factor of
0.72) to the 1958 solar maximum source (Sect. 2.5). We find that without this
correction, the biases would be significantly reduced.
(a) Observed and simulated latitudinal distributions of
7Be concentrations (mBq SCM-1) near the surface. 7Be
cross-tropopause fluxes were not adjusted for the GMI/GEOS1-STRAT and
GMI/GISS simulations (see Sect. 3). Observed data from the EML Surface Air
Sampling Program (SASP) database are averaged into 10∘ bins. Those
sites with elevation higher than 500 m are not included. Error bars
represent ±2 times the standard error of the averages. Model results are
sampled at observation locations and month. (b) GMI simulated annual
zonal mean concentrations of 7Be (mBq SCM-1) near the surface.
(c) Observed (black) and GMI simulated (color) annual mean total
deposition fluxes (Bq m-2 yr-1) of 7Be (at 25 sites) as a
function of latitude. The data from individual sites are averaged over
4∘ latitude bins. The model is sampled at observation locations.
(d) GMI simulated annual zonal mean total deposition fluxes
(Bq m-2 yr-1) of 7Be.
Figure 5c compares the model-simulated annual mean total deposition fluxes of
7Be at 25 northern mid-latitude sites from which long-term records of
observations are available. The 7Be deposition flux observations are
from the compilation of Koch et al. (1996), previously used in Liu et
al. (2001). The data from individual sites are averaged over 4∘
latitude bins. The model is sampled at observation locations. Figure 5d shows
the annual zonal mean total deposition fluxes of 7Be in the model to
indicate the global representativeness of the sites. The observations show a
maximum (∼ 2100 Bq m-2 yr-1) in the subtropics
(∼ 30∘ N) and the fluxes fall off with increasing latitude.
The four 7Be simulations show large discrepancies especially in the
subtropics (∼ 30∘ N). Overall, the GMI/fvGCM simulation agrees
better with the magnitude of the observed fluxes while the GMI/GEOS4
simulation yields better latitudinal trends. GMI/GEOS4 simulates best the
observations at the latitudes of 45–60∘ N, but
overestimates the observations by ∼ 50 % at
20–40∘ N. The GMI/GISS simulation overestimates the
observations at higher latitudes (45–60∘ N) by a factor
of ∼ 2. The GMI/GEOS1-STRAT simulation overestimates the observed
7Be deposition fluxes at subtropical latitudes by up to a factor of 2.5
(30∘ N). As with the above model-observation comparison of surface
7Be concentrations, the overall positive biases in model total
deposition fluxes would be lower without the correction of 7Be
observations (by a factor of 0.72) to the 1958 solar maximum source (Sect. 2.5). However, we will show in the next section that these overestimated
7Be deposition fluxes are largely due to model excessive
cross-tropopause transport, especially with the GEOS1-STRAT and GISS II′
meteorological fields.
Assessment of cross-tropopause transport of 7Be in different
meteorological archives
The above results indicate different levels of success with four
meteorological fields in reproducing long-term records of surface and UT/LS
7Be concentrations as well as total deposition fluxes. In this section,
we quantify the contribution of 7Be produced in the stratosphere to
tropospheric 7Be concentrations and deposition fluxes, followed by an
assessment of cross-tropopause transport of 7Be in the meteorological
fields used.
(a) Stratospheric fraction (%) of annual zonal mean
atmospheric 7Be concentrations in the model simulations as a function of
latitude and pressure. The white lines indicate thermal tropopause height.
(b) Stratospheric fraction of annual zonal mean surface 7Be
concentrations (solid lines) and that of annual zonal mean 7Be total
deposition fluxes (dashed lines) in the model simulations as a function of
latitude.
Figure 6a shows the stratospheric fraction (%) of annual zonal mean
atmospheric 7Be concentrations (i.e., fraction of atmospheric 7Be
produced in the stratosphere) in the model simulations as a function of
latitude and pressure. The fractions of significantly less than 100 % in
the lower stratosphere in all four simulations reflect mainly the seasonal
movement of the tropopause. With GEOS1-STRAT, stratospheric contribution to
lower-tropospheric 7Be concentrations maximizes at 25–50∘ N
(35–45 %) and 25–40∘ S (30–35 %). The tropical middle and
upper troposphere show the minimum in stratospheric impact (< 30 %). With GISS II′, the stratospheric contribution to lower-tropospheric
7Be concentrations peaks (30–40 %) at southern high latitudes and
remains nearly constant (30–35 %) north of 30∘ N while it is
quite small (< ∼ 10–20 %) in the tropical middle and
upper troposphere. The strong gradients in the subtropics suggest that the
tropics are strongly isolated from the mid-latitudes in the GISS II′
meteorological field. fvGCM and GEOS4-DAS show a similar pattern of
stratospheric influence on the troposphere; both indicate maximum
contribution from stratosphere near 30–35∘ N (∼ 25 %) and 25–30∘ S (∼ 20–25 %) in the lower
troposphere. However, GEOS4-DAS shows larger contributions from the
stratosphere to the troposphere (especially the free troposphere) than fvGCM
does by a few percent, consistent with the overestimated deposition fluxes
at 20–40∘ N by GEOS4-DAS (Fig. 5c). The area of
minimal stratospheric influence in the tropics is also narrower in
GEOS4-DAS.
Figure 6b shows the stratospheric fraction (%) of annual zonal mean
surface 7Be concentrations and that of annual zonal mean 7Be total
deposition fluxes (Bq m-2 yr-1) in the model simulations as a
function of latitude. With all meteorological fields except GISS II′, maximum
stratospheric contribution to total deposition fluxes (vs. surface 7Be
concentrations) is shifted toward higher latitudes, reflecting scavenging by
frequent mid-latitude precipitation and the dry subsidence in the subtropics.
Stratospheric fractions of surface 7Be concentrations at NH mid-latitude
are about 38 % (GEOS1-STRAT), 33 % (GISS II′), and 23-24 % (fvGCM
and GEOS4-DAS). As discussed in Sect. 2.4, the observed
7Be /90Sr ratio suggests that 23–27 % of the 7Be in
surface air at northern mid-latitudes is of stratospheric origin (DH85).
According to this constraint, cross-tropopause transport of 7Be and
subsequent transport to the surface in the GEOS1-STRAT and GISS II′
meteorological fields is excessive. On the other hand, it should be noted
that the fvGCM and GEOS4-DAS simulations show results remarkably consistent
with the DH85 constraint, suggesting that stratospheric influences on surface
7Be concentrations in these two meteorological fields are reasonable.
However, DH85 did not provide constraints on latitudinal variation of
stratospheric influence on surface 7Be. Of the four meteorological
fields, GEOS1-STRAT, fvGCM and GEOS4-DAS show very similar latitudinal
distribution of stratospheric influence at the surface (i.e., peak in the
subtropics and valley in the tropics or polar regions). By contrast, GISS II′
shows the largest impact of the stratosphere at high latitudes.
Similarly, as shown above, the model overestimates the long-term records of
7Be deposition flux observations at mid-latitudes (and subtropics) with
GEOS1-STRAT and at high latitudes with GISS II′ (Fig. 5c). Interestingly,
the fvGCM (and to a lesser extent GEOS4-DAS) simulation yields 7Be
deposition fluxes close to the observations. This suggests that the DH85
constraint and observed 7Be deposition fluxes are two complementary
constraints on cross-tropopause transport of 7Be. We therefore use the
DH85 constraint to assess the cross-tropopause transport of 7Be in the
meteorological fields.
Same as Fig. 6, except that 7Be cross-tropopause fluxes have
been adjusted for GMI/GEOS1-STRAT and GMI/GISS.
Annual average global budget of 7Be in the model troposphere.
The GMI model was driven by the GEOS1-STRAT, GISS II′, fvGCM, and GEOS4-DAS
meteorological data sets, respectively.
GEOS1-STRATGISSfvGCMGEOS4-DASBurden, g 4.95 (3.86)b4.00 (3.64)b4.314.05Residence time, daysa31 (29)31 (30)3531Sources, g day-10.22 (0.18)0.18 (0.17)0.180.19STE0.08 (0.04)0.05 (0.04)0.040.05troposphere0.14 (0.14)0.13 (0.13)0.140.14Sinks, g day-10.22 (0.18)0.18 (0.17)0.180.19dry deposition0.01 (0.01)0.01 (0.01)0.010.02wet deposition0.15 (0.12)0.12 (0.11)0.110.12radioactive decay0.06 (0.05)0.05 (0.05)0.060.05
a Against deposition only. The tropopause was
determined in the model using a criterion of 2 ∘C km-1 lapse
rate as defined by World Meteorological Organization. The diagnosed
tropopause model layer was
included as part of the troposphere.b The numbers in the brackets indicate the values when 7Be
cross-tropopause fluxes were adjusted for GMI/GEOS1-STRAT and GMI/GISS. See
text for details.
Using the approach described in Sect. 2.4 (i.e., reduced cross-tropopause
transport flux by artificially scaling down the stratospheric 7Be
source in the simulation of tropospheric 7Be), we determine the scaling
factors for GEOS1-STRAT and GISS to be 1.92 and 1.35, respectively. With the
adjustment of 7Be cross-tropopause fluxes for GEOS1-STRAT and GISS, the
model calculated stratospheric fraction of 7Be concentrations in
surface air at NH mid-latitudes is indeed close to 25 % (i.e., agree with
the DH85 constraint) (Fig. 7), thus supporting the validity of Eq. (5). With the adjustment, some simulations also simulate better surface
7Be concentrations and total deposition fluxes at the subtropics
(GEOS1-STRAT) and at high latitudes (GISS II′) (Fig. 8 vs. Fig. 5). The
improvement is clearer for total deposition fluxes than for surface
concentrations. As discussed below, on a global scale total deposition
fluxes are sensitive to STE fluxes of 7Be into the troposphere, while
surface concentrations are principally dependent on the overall wet removal
rate.
Same as Fig. 5, except that 7Be cross-tropopause fluxes have
been adjusted for GMI/GEOS1-STRAT and GMI/GISS.
Table 3 shows the annual average global budgets of tropospheric 7Be in
the four GMI simulations. With an adjustment of 7Be cross-tropopause
fluxes, the global burdens and residence times of tropospheric 7Be in
GMI/GEOS1-STRAT and GMI/GISS are reduced. In GMI/GEOS1-STRAT the source and
sink terms become much closer to that in fvGCM and GEOS4-DAS. A reduction of
global 7Be STE fluxes of 0.04 g day-1 results in a decrease of total
deposition fluxes of 0.03 g day-1 and radioactive decay of 0.01 g day-1. In GMI/GISS the changes in the budget terms are relatively small
due to the smaller adjustment of 7Be cross-tropopause fluxes.
Nevertheless, a reduction of global 7Be STE fluxes of 0.01 g day-1
results in a decrease of total deposition fluxes of 0.01 g day-1. These
calculations indicate that globally the 7Be total deposition fluxes are
sensitive to STE fluxes of 7Be into the troposphere.
The model calculated stratospheric fraction of 7Be in the troposphere
may be sensitive to the model diagnosed location of the tropopause, for which
there is some uncertainty. For instance, Stajner et al. (2008) used four
different definitions of the tropopause on the basis of temperature lapse
rate (World Meteorological Organization or WMO definition), potential
vorticity (PV), and isentropic surfaces or ozone surfaces. They found that
the WMO tropopause was about 0.7–1 km (in the northern mid-latitude) or
0.5–1 km (in the tropics) higher than the ozone or PV determined tropopause.
We examine the sensitivity of model diagnosed stratospheric fraction of
tropospheric 7Be concentrations to the location of tropopause (not
shown) by lowering tropopause height by one model level (approximately 1.2, 1.7, 1.1, and 1.1 km for GEOS1-STRAT, GISS II′, fvGCM and
GEOS4-DAS, respectively). Results indicate that stratospheric fractions of
surface 7Be concentrations increase by 5–10 %, thus requiring larger
adjustments of cross-tropopause transport of 7Be in the meteorological
fields in order to meet the DH85 constraint. This also suggests that using
the DH85 constraint requires relatively high vertical resolution near
tropopause in the model.
While the model diagnosed stratospheric fraction of tropospheric 7Be
concentrations is mainly determined by the STE processes in the UT/LS, it
may also be sensitive to precipitation scavenging and convective transport
in the troposphere. Figure 9 shows the latitude-pressure cross sections of
the differences in the stratospheric fraction (%) of annual zonal mean
tropospheric 7Be concentrations between the standard simulation and a
simulation where precipitation scavenging is turned off. Also shown are the
corresponding differences near the surface. The stratospheric fraction of
tropospheric 7Be is found to be only weakly dependent on precipitation
scavenging, with < 5 % change in most of the troposphere and
< 2.5 % change near the mid-latitude surface. Figure 10 shows a
similar plot, except that convective transport and scavenging are turned off
in the sensitivity simulation. Similarly, the stratospheric fraction of
tropospheric 7Be is not sensitive to convective transport and
scavenging processes, with < 1 % changes near the mid-latitude
surface.
Same as Fig. 6a, b, except for the differences in the stratospheric
fraction (%) of zonal mean atmospheric 7Be concentrations between the
standard simulation and a simulation where wet scavenging is turned off.
Comparison with previous modeling studies
In this section we compare the GMI CTM results for cross-tropopause
transport of 7Be with previous modeling studies based on the same or
similar meteorological fields.
Same as Fig. 6a, b, except for the differences in the stratospheric
fraction (%) of zonal mean atmospheric 7Be concentrations between the
standard simulation and a simulation where convective transport and
scavenging are turned off.
Liu et al. (2001) found that STE flux of 7Be was overestimated with the
GEOS1-STRAT fields in the GEOS-Chem model, consistent with this study using
GMI CTM. However, Liu et al. (2001) found that the reduction required to
match the DH85 constraint is a factor of 3.5 for the GEOS1-STRAT archive
with 4∘× 5∘ resolution, compared
to a factor of 1.92 in the present study. The larger reduction in the former
reflects the inclusion of ice particle gravitational settling effect, which
results in increased transport from the upper to lower troposphere, as well
as the inclusion of the diagnosed tropopause model layer as part of the
stratosphere (vs. the troposphere). Interestingly, when specifying ozone
concentrations in the lower stratosphere (70 hPa) and letting the model
(GEOS-Chem) transport this ozone as an inert tracer into the troposphere,
Bey et al. (2001) found a similar overestimate in an ozone simulation with
the GEOS-1 data, as diagnosed by the simulation of tropospheric ozone
concentrations at high latitudes in winter where transport from the
stratosphere is a major source. This indicates that the simulation's
deficiency in cross-tropopause transport as diagnosed using 7Be tracers
has similar consequences for cross-tropopause transport of ozone.
Koch and Rind (1998) used a 31-layer version of the GISS GCM to simulate
7Be and 10Be and used tropospheric 10Be /7Be as
indicator of STE. Based on limited observations, they suggested that leakage
into the troposphere is somewhat excessive in the model, particularly at high
latitudes. Using the GISS II′ GCM, McLinden et al. (2000) found that a large
fraction of the cross-tropopause transport of ozone occurs at the poles which
is inconsistent with the current understanding of stratosphere–troposphere
exchange, despite the fact that the global stratosphere–troposphere exchange fluxes of
ozone compare well with their best estimate of
475 ± 120 Tg year-1 based on measurements and tracer-tracer
correlation. Shindell et al. (2003) presented an updated version of the
GISS II′ climate model which still overestimates ozone in the middle
troposphere at high latitudes, likely reflecting deficiencies in the model's
downward transport of stratospheric air. Our conclusions about
cross-tropopause transport of 7Be in GISS II′ in this work are consistent
with these previous studies. Overestimated STE fluxes of 7Be as
diagnosed in GMI/GISS based on the DH85 constraint simply reflect the
incorrect latitudinal distribution of cross-tropopause transport, that is,
too fast STE at higher latitudes and too slow STE at lower latitudes. The
DH85 constraint was only applicable and applied for NH mid-latitude surface
and thus does not provide constraint on the model global STE flux of 7Be
if the latitudinal distribution of STE is incorrect.
The large-scale stratospheric transport (Brewer–Dobson circulation) in fvGCM
has been shown to be realistic (Douglass et al., 2003) and mean age of
stratospheric air is similar to observations (Strahan and Douglass, 2004;
Douglass et al., 2008; Strahan et al., 2009). This suggests credible
cross-tropopause transport of mass and ozone in fvGCM because the large-scale
exchange between the stratosphere and troposphere is largely tied to the
Brewer–Dobson circulation through the overworld wave driving (Holton et al.,
1995; Olsen et al., 2004). Based on this finding, the meteorological data
from fvGCM were used to drive GMI CTM by several authors to study tropospheric
ozone. Considine et al. (2008) evaluated near-tropopause ozone distributions
with ozonesonde data. Terao et al. (2008) examined the role of variability in
the input of stratospheric ozone on the interannual variability of
tropospheric ozone in the northern extratropics. Liang et al. (2009)
investigated the impact of stratosphere-to-troposphere transport on
tropospheric ozone and NOx chemistry over the Arctic. By contrast,
GEOS4-DAS tends to have too strong a residual circulation, and the age of
air is too young compared to observations (Schoeberl et al., 2003;
Schoeberl, 2004; Douglass et al., 2008). A GMI CTM simulation driven with the
GEOS4-DAS meteorological fields showed the model's inadequacy in simulating
upper-tropospheric ozone (Liang et al., 2009). These findings are consistent
with what we illustrated in this study from a perspective of 7Be
tracers. That is, GEOS4-DAS features a larger impact of STE on the troposphere
(especially UT) than fvGCM does, while the latter has more credible
cross-tropopause transport as constrained by observed 7Be deposition
fluxes (Fig. 5c) and the DH85 criterion (Fig. 6).
Implications for cross-tropopause transport of ozone
In this section we discuss the implications of different characteristics of
cross-tropopause transport of 7Be for stratospheric influence on
tropospheric ozone in different meteorological fields. At the time of this
study, the GMI full-chemistry model can be driven with GEOS1-STRAT, fvGCM
and GEOS4-DAS, but not GISS II′ meteorological fields. This allows us to
examine any potential relationship between the cross-tropopause transport of
7Be and ozone when these fields are used to drive the model.
Ozonesonde, surface and satellite observations provide useful constraints on
the stratospheric contribution to tropospheric ozone (e.g., Rind et al.,
2007; Lin et al., 2012). Figure 11 shows comparisons of model tropospheric
ozone profiles with annual mean ozonesonde observations for a range of
latitudes (Considine et al., 2008). These results are typical of other
stations at similar latitudes. The GMI/GEOS1-STRAT simulation produces
excessive ozone throughout the troposphere at all latitudes except in the
tropics while the GMI/fvGCM and GMI/GEOS4-DAS simulations are generally in
agreement with the observations (with slightly overpredicted ozone in the
mid-latitude upper troposphere). The GEOS1-STRAT simulation has the largest
overestimate of O3 in spring. We also compared model surface ozone
concentrations with the Logan (1999) surface ozone data set (not shown).
Among the three GMI simulations, the GMI/GEOS1-STRAT simulation shows the
largest errors in surface ozone concentrations during winter and spring when
stratospheric contribution is at its peak. These are in line with the
relative magnitudes of cross-tropopause transport efficiencies of 7Be in
the three meteorological fields (i.e., too fast STE in GEOS1-STRAT),
discussed in previous sections. Indeed, the tropospheric version of the
GMI/GEOS1-STRAT model with constrained STE flux of ozone using the Synoz
approach (about 579 Tg year-1) simulates ozonesonde observations of
tropospheric ozone reasonably well (dotted line, Fig. 11).
Comparisons of GMI simulated tropospheric ozone profiles (color
lines) with ozonesonde observations (black line) for a range of latitudes.
Values are annual averages. Solid color lines indicate the GMI simulations.
Also shown as dotted lines are tropospheric ozone profiles as simulated by
the GMI tropospheric model driven by the GEOS1-STRAT meteorological field.
The horizontal gray line indicates the approximate location of tropopause
(i.e., the pressure level corresponding to 100 ppbv ozone concentrations in
the ozonesonde observations).
Figure 12 shows GMI simulated annual zonal mean tropospheric ozone column
(TOC), in Dobson Units, compared with observed climatologies from TOMS/SBUV
(1979–2005; Fishman et al., 2003) and OMI/MLS (October 2004–July 2008;
Ziemke et al., 2006). The WMO definition of thermal tropopause is used to
calculate the model TOC. While the GMI/fvGCM and GMI/GEOS4-DAS simulations
are similar and overestimate TOC by up to ∼ 20 DU, the
GMI/GEOS1-STRAT simulation overestimates TOC by as much as ∼ 40 DU. The excessive O3 in the GMI/GEOS1-STRAT simulation with maxima
at 30∘ N and 30∘ S suggests downward
transport of ozone from the stratosphere is too fast. The tropospheric
version of the GMI/GEOS1-STRAT model with constrained STE flux of ozone
provides a much better simulation of global TOCs (red dashed line, Fig. 12), which are comparable to those from GMI/fvGCM and GMI/GEOS4-DAS
simulations. However, model TOCs are still ∼ 10–14 DU larger
than satellite observations in the subtropics and mid-latitudes. Previously,
Ziemke et al. (2006) considered uncertainties in both model and observations
and subjectively interpreted model-OMI/MLS TOC differences of 10 DU and
higher as being significant. As Stajner et al. (2008) noted, a low extratropical
tropopause used by Ziemke et al. (2006) may have played an important role in
the underestimation of OMI/MLS TOC. Yang et al. (2010) also found that their
OMI/MLS potential vorticity mapped TOCs are smaller than ozonesonde TOCs by
5.9 DU with a standard deviation of the differences of 8.4 DU. On the other
hand, the GMI/fvGCM simulation tends to overestimate ozone just below the
tropopause at mid-latitudes (Fig. 11); these biases do not appear to be
due to excessive stratospheric influence (Considine et al., 2008). Current
global models also tend to overpredict surface ozone during summer and early
fall over the eastern US and Japan (Fiore et al., 2009). Therefore the
simulated TOCs are very likely biased high.
GMI simulated annual zonal mean tropospheric ozone column (TOC in
Dobson Units) compared with observed tropospheric ozone residuals from
TOMS/SBUV (1979–2005 average) and OMI/MLS (October 2004–July 2008 average).
Also shown is the annual zonal mean TOC simulated by the tropospheric version
of the GMI model.
(a) Latitudinal variations of annual zonal mean 7Be
overestimate (Δ7Be) and tropospheric ozone column overestimate
(ΔTOC) as simulated by GMI/GEOS1-STRAT. Error bars represent
±2 times the standard error of the averages. (b) The correlation
between the global distributions of Δ7Be and ΔTOC. The
lines of best fit are calculated using the reduced-major-axis (RMA) method
(Hirsch and Gilroy, 1984). See text for details.
We further examine the relationship between the cross-tropopause transport
of 7Be and ozone with the GEOS1-STRAT meteorological fields, in which
case STE is known to be too fast. Figure 13a shows the latitudinal
variations of annual zonal mean tropospheric 7Be column overestimate
(Δ7Be) and TOC overestimate (ΔTOC) in the
GMI/GEOS1-STRAT simulation. Δ7Be is obtained by subtraction of
the STE-flux-adjusted simulation (Sect. 2.4) from the standard simulation.
ΔTOC is obtained by subtracting the GMI tropospheric model
simulation (with STE flux of ozone about 579 Tg year-1) from the GMI
full-chemistry model simulation. Figure 13b shows the correlation between
the global distributions of Δ7Be and ΔTOC. The lines of
best fit are calculated using the reduced-major-axis (RMA) method (Hirsch
and Gilroy, 1984). Standard errors for the intercept and the slope are
computed as described by Miller and Kahn (1962). Overall, the location of
overestimated ozone follows that of overestimated 7Be, with both maxima
near 30∘ N and 30∘ S. The strong correlation
between Δ7Be and ΔTOC implies that 7Be is a good
indicator of cross-tropopause transport of ozone. These support our
conclusion that 7Be is a useful utility for assessing cross-tropopause
transport of ozone in global models.
Summary and conclusions
We have assessed the ability of the Global Modeling Initiative (GMI)
chemical transport model (CTM) using different meteorological data sets to
simulate the atmospheric distributions of 7Be, a natural aerosol tracer
originating from the upper troposphere and/or lower stratosphere and removed from
the troposphere primarily by wet deposition. The model was driven by four
meteorological data sets (GEOS1-STRAT, GISS II′, fvGCM, GEOS4-DAS) which
feature significantly different cross-tropopause transport characteristics.
The GMI modeling framework was configured such that the variability between
the simulations mainly reflects the use of different meteorological data.
Our goal was to assess the utility of 7Be as a tracer of
cross-tropopause transport in global models and develop a methodology to
exploit such a utility. We have also discussed the implications of excessive
cross-tropopause transport as revealed by 7Be simulations for the
modeling of tropospheric ozone.
We evaluated the four simulations of 7Be with RANDAB, a unique database
of upper atmosphere radionuclide climatological observations compiled by the
DOE (now DHS) Environmental Measurement Laboratory, as well as long-term
measurements at the surface. Model simulations capture the UT/LS observations
with respect to latitudes. The GMI/GEOS1-STRAT simulation shows the lowest
7Be concentrations among the four simulations in the lower stratosphere,
and underestimates the observations. This reflects the well-known highly
overestimated cross-tropopause transport in GEOS1-STRAT DAS. At the surface,
GMI/GISS II′ reproduces the observed latitudinal trends of 7Be
concentrations, but shows too high concentrations at high latitudes. The
GMI/fvGCM simulated 7Be deposition fluxes are the closest to the
observations, while the GMI/GEOS1-STRAT overestimates the observed 7Be
deposition fluxes at subtropical latitudes by up to a factor of 2.5
(30∘ N) and the GMI/GISS simulations at high latitudes
(45–60∘ N) are a factor of 2 too high. We were able to show that
the observed 7Be deposition fluxes offer a strong constraint on
stratosphere-to-troposphere transport in global models.
We examined the observational constraint from Dutkiewicz and Husain (1985)
(DH85) on the stratospheric contribution to tropospheric 7Be using the
GMI modeling framework. DH85 analyzed the observed 7Be /90Sr
ratio, which suggests that 23–27 % of the 7Be in surface air at
northern mid-latitudes is of stratospheric origin. This constraint offers a
sensitive test of cross-tropopause transport in global models. Comparison of
the fraction of surface air of stratospheric origin estimated from the
7Be simulations with the DH85 constraint indicates excessive
cross-tropopause transport at mid-latitudes with the GEOS1-STRAT
meteorological fields and at high latitudes with the GISS II′ fields.
Interestingly, these simulations also overestimate observed 7Be
deposition fluxes at middle and high latitudes, respectively. With a
correction to cross-tropopause flux, the model simulates better surface
7Be concentrations and total deposition fluxes. By contrast, the fvGCM
meteorological data yield the most reasonable cross-tropopause transport of
7Be according to the DH85 constraint, consistent with the fact that the
GMI/fvGCM simulated 7Be deposition fluxes are closest to the
observations. These results illustrate that the GMI framework is very useful
for characterizing and helping to reduce uncertainties in the processes such as
cross-tropopause transport in the meteorological fields that are used to
drive chemical transport models. Note that since wet deposition removes both
the stratospheric and tropospheric components of 7Be
nondiscriminatively, the model diagnosed fraction of 7Be of
stratospheric origin does not significantly depend on the rate of wet
removal.
The model diagnosed stratospheric fraction of 7Be in surface air is
sensitive to the diagnosed location of tropopause, in particular when the
model vertical resolution is relatively coarse (> 1–1.5 km) near
the tropopause region. This suggests that stratospheric fraction of 7Be
is a more useful diagnostic when the model has sufficient vertical resolution
(< 1–1.5 km) so that the tropopause can be well defined. We used the
WMO definition of thermal tropopause and include the diagnosed tropopause
model layer as part of the troposphere (vs. the stratosphere). As such
our assessment of cross-tropopause transport of 7Be in the four
meteorological data sets in the GMI CTM is consistent with previous modeling
studies of stratospheric influence on tropospheric ozone.
Incorrect cross-tropopause transport of 7Be implies misrepresented
downward influx of stratospheric ozone to the troposphere in a model. We
demonstrated this by examining the relationship between the cross-tropopause
transport of 7Be and ozone as simulated by GMI CTM driven with
GEOS1-STRAT, fvGCM and GEOS4-DAS meteorological fields. We found that
excessive cross-tropopause transport of 7Be corresponds to
overestimated stratospheric contribution to tropospheric ozone, as
constrained by ozonesonde, surface and satellite observations.
In summary, the 7Be simulation, which is computationally cheap and
technically simple, in combination with the DH85 7Be observational
constraint and observed 7Be deposition fluxes may be used routinely to
assess cross-tropopause transport in global models. We recommend separate
transport of the 7Be produced in the stratosphere (7Be-strat) to
evaluate the ratio of 7Be-strat to total 7Be (i.e., beryllium-7
produced in both the stratosphere and the troposphere) in surface air against
the DH85 constraint. This can serve as a first-order assessment of
cross-tropopause transport in the meteorological fields. With improved
estimates of 7Be production rates as well as their year-to-year
variations, model multi-year 7Be simulations together with long-term
observations would provide useful constraints on the interannual variability
of STE. While this study uses 7Be alone, future modeling work will
include using 10Be /7Be, a more sensitive indicator of STE
(Rehfeld and Heimann, 1995; Koch and Rind, 1998; Jordan et al., 2003).
Data availability
A description of the data sets used in this paper and their availability
can be found in Sect. 2.5.
Acknowledgements
This work was supported by the NASA Modeling, Analysis and Prediction (MAP)
program, the Atmospheric Composition Modeling and Analysis Program (ACMAP),
and the Atmospheric Composition Campaign Data Analysis and Modeling (ACCDAM)
program. We thank Bryan Duncan for his contribution to the GMI model
development, and two anonymous reviewers for constructive comments. The GMI
core team at NASA GSFC is acknowledged for programming support. NASA Center
for Computational Sciences (NCCS) provided supercomputing resources. The
GEOS-Chem model is managed by the Atmospheric Chemistry Modeling Group at
Harvard University with support from ACMAP and MAP.
Edited by: J.-U. Grooß
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