ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-3217-2015Seasonal changes in the tropospheric carbon monoxide profile over the
remote Southern Hemisphere evaluated using multi-model simulations and aircraft observationsFisherJ. A.jennyf@uow.edu.auhttps://orcid.org/0000-0002-2921-1691WilsonS. R.https://orcid.org/0000-0003-4546-2527ZengG.https://orcid.org/0000-0002-9356-5021WilliamsJ. E.EmmonsL. K.https://orcid.org/0000-0003-2325-6212LangenfeldsR. L.KrummelP. B.https://orcid.org/0000-0002-4884-3678SteeleL. P.University of Wollongong, Wollongong, New South Wales, AustraliaNational Institute of Water and Atmospheric Research, Lauder, New ZealandRoyal Netherlands Meteorological Institute, De Bilt, the NetherlandsNational Center for Atmospheric Research, Boulder, Colorado, USACSIRO Oceans and Atmosphere Flagship, Aspendale, Victoria, AustraliaJ. A. Fisher (jennyf@uow.edu.au)23March2015156321732397October20143November20147February201524February2015This 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/15/3217/2015/acp-15-3217-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/3217/2015/acp-15-3217-2015.pdf
The combination of low anthropogenic emissions and large biogenic
sources that characterizes the Southern Hemisphere (SH) leads to significant
differences in atmospheric composition relative to the better studied Northern
Hemisphere. This unique balance of sources poses significant challenges
for global models. Carbon monoxide (CO) in particular is difficult to simulate
in the SH due to the increased importance of secondary chemical production
associated with the much more limited primary emissions.
Here, we use aircraft observations from the 1991–2000 Cape Grim
Overflight Program (CGOP) and the 2009–2011 HIAPER (High-performance
Instrumented Airborne Platform for Environmental Research)
Pole-to-Pole Observations (HIPPO), together with model output from the SH
Model Intercomparison Project, to elucidate the drivers of
CO vertical structure in the remote SH. Observed CO vertical profiles from Cape Grim are
remarkably consistent with those observed over
the southern mid-latitudes Pacific 10–20 years later, despite major
differences in time periods, flight locations, and sampling
strategies between the two data sets. These similarities suggest
the processes driving observed vertical gradients are
coherent across much of the remote SH and have not changed
significantly over the past 2 decades. Model ability to simulate CO profiles
reflects the interplay between biogenic emission sources, the chemical
mechanisms that drive CO production from these sources,
and the transport that redistributes this CO throughout the SH. The four chemistry-climate
and chemical transport models included in the intercomparison show large variability in their abilities
to reproduce the observed CO profiles. In particular, two of the four models significantly
underestimate vertical gradients in austral summer and autumn, which we find
are driven by long-range transport of CO produced from oxidation of biogenic compounds.
Comparisons between the models show that more complex chemical mechanisms
do not necessarily provide more accurate simulation of CO vertical gradients
due to the convolved impacts of emissions, chemistry, and transport.
Our results imply a large sensitivity of the
remote SH troposphere to biogenic emissions and
chemistry, both of which remain key uncertainties in global modeling.
We suggest that the CO vertical gradient
can be used as a metric for future model evaluation as it provides
a sensitive test of the processes that define
the chemical state of the background atmosphere.
Introduction
Carbon monoxide (CO) plays multiple fundamental roles in
tropospheric chemistry, in particular serving as a major reactant of
the hydroxyl radical OH and as an
indirect greenhouse gas . A product of incomplete
combustion, CO has primary sources from fossil fuel and biomass
burning (BB) as well as secondary sources from oxidation of methane
(CH4) and non-methane volatile organic compounds (NMVOCs),
with a typical tropospheric lifetime of 1–2 months. In the Southern
Hemisphere (SH), the distribution of CO is strongly impacted by
emissions from BB
and biogenic sources , while
anthropogenic emissions play only a minor role due to an
inter-hemispheric transport barrier caused by the Inter-Tropical
Convergence Zone . Much of the SH is
characterized by very low CO emissions, and in these remote regions CO
is largely controlled by the balance between long-range transport, production from methane oxidation, and
chemical removal via reaction with OH. Seasonal variability in CO
sources, transport pathways, and loss processes leads to a complex
seasonal cycle that is different in the free troposphere than at the
surface . The ability of large-scale global
atmospheric models to represent the processes driving this seasonality
has been difficult to evaluate due to a paucity of measurements in the
SH free troposphere. Particularly rare are observations of the CO
vertical profile in the SH, despite the importance of such measurements for
testing model processes including source attribution and vertical
transport . Here, we use
simulations from four global chemical transport and chemistry-climate
models conducted for the Southern Hemisphere Model Intercomparison
Project (SHMIP) to interpret a unique 9-year record of airborne CO vertical
profiles in the remote SH from the Cape Grim Overflight Program (CGOP) .
Evaluation of CO distributions in atmospheric models has largely
focused on the Northern Hemisphere where observations are more widely
available, with some limited evaluation in the SH as part of global
comparisons e.g.,. The SHMIP was devised to
provide a more focused evaluation of current large-scale atmospheric
chemistry models in the SH. A central goal of SHMIP is to quantify
model ability to represent the seasonal and spatial distributions of
trace gases including CO. An overview of SH CO distributions in the
four SHMIP models is provided by , who compare
simulated CO to observations from surface in situ and ground-based
total column measurements at selected SH sites. They show that using
different biogenic emission inventories leads to marked differences in
modeled CO at these sites and that accurate representation of biogenic
emissions is critical to reproducing observed SH background CO. They
also find that the underlying chemical and transport characteristics
of each model greatly impact model ability to reproduce background SH
CO. In some cases, the inter-model differences are larger than those
associated with uncertainties in biogenic emissions, especially for
locations further from tropical biogenic and BB sources. Detailed
analyses of these uncertainties are addressed by Zeng et al. (2015) using
column and surface observations; here we expand on this analysis using
in situ observations from the remote free troposphere.
As in the SHMIP model evaluation of , previous model
comparison to observations in the SH has generally been limited to in
situ surface data e.g.,
and ground- or satellite-based remotely sensed total column data
e.g.,. Total column comparisons
provide an advantage over in situ surface comparisons for model
validation in the free troposphere
. However, neither surface nor total column
data are able to constrain the vertical structure of CO, which is
still poorly understood in the SH mid-latitudes. For example,
showed that a 26-model ensemble mean
was able to reproduce mid-tropospheric CO measurements from the
MOPITT satellite instrument in the extra-tropical SH, but the same models
uniformly overestimated the upper-to-lower troposphere ratio seen by
the satellite (as well as the seasonal cycle of the ratio). This
comparison relied on qualitative differences between MOPITT upper and
lower tropospheric retrievals , as
MOPITT sensitivity was different at these two altitudes in the version
3 data used (the newer versions 5 and 6 provide more sensitivity to
the lower troposphere). More generally, remote-sensing instruments
typically display different sensitivities at different altitudes,
making it difficult to use these data to study vertical structure. For
quantitative evaluation of vertical gradients, independent in situ
data from the free troposphere are essential.
To date, in situ observations of CO in the SH remote free troposphere
are sparse. Aircraft campaigns carried out in the SH over the last 2
decades have largely taken place near major emission sources (e.g.,
BARCA and GABRIEL in South America, SAFARI in southern Africa,
ACTIVE/SCOUT in northern Australia) or their outflow regions (e.g.,
TRACE-A in the South Atlantic). Ongoing programs such as IAGOS/MOZAIC
that conduct measurements from aboard commercial aircraft have
been limited in the SH, with most concentrated over the African
outflow region of the equatorial Atlantic. Neither of these programs
included flights over the Pacific or Indian oceans; however, IAGOS
flights to Australia began in late 2013 and will likely provide a valuable
additional SH data set in the future. More extensive remote sampling of SH CO
occurred over the South Pacific during NASA's PEM-Tropics A (1996) and
B (1999) campaigns . These campaigns provided detailed
characterization of free tropospheric distributions during austral
spring and autumn but were temporally limited and unable to capture
a full annual cycle. More recently, the HIAPER (High-performance
Instrumented Airborne Platform for Environmental Research)
Pole-to-Pole Observations (HIPPO) traversed the South Pacific during
multiple seasons over the period 2009–2011 ,
offering a previously inaccessible view of seasonal variability in the
remote SH free troposphere. However, with only one set of flights in
each season (including 4–6 individual flights in the SH), it remains
difficult to quantify the seasonal and interannual representativeness
of these data, complicating their interpretation.
The 9-year record of aircraft data from the Commonwealth Scientific
and Industrial Research Organisation (CSIRO) CGOP provides a unique data set to quantify
seasonal variability at altitudes from the surface to 8 km in
the remote SH. With monthly flights over the Southern Ocean during
clean air conditions, this record contains significant information on
the seasonal and vertical structure of CO in the SH free tropospheric
background. We use this record to develop a climatological picture of
CO seasonal cycles and vertical gradients in the remote SH that can be
used to test both the temporal representativeness of other data sets
(e.g., HIPPO) and the capabilities of models in these data-poor
environments. We first describe both the models and the observations
used for constructing the SH CO climatology (Sect. 2) and examine the
ability of the models to match observed CO vertical gradients across
different seasons (Sect. 3). We then use sensitivity studies to
quantify the roles of emissions, transport, and chemistry in driving
inter-model variability and examine the sensitivity of the simulations
to the various uncertainties introduced (Sect. 4). Finally, we
evaluate model differences in chemical mechanisms and vertical
transport in terms of their impacts on model ability to match observed
CO vertical gradients in the remote SH (Sect. 5). A summary and conclusions are
presented in Sect. 6.
Observations and modelsCape Grim Overflight Program
Australia's CSIRO has had long involvement in aircraft-based sampling
of atmospheric composition above the southeastern Australian region
. Between 1972 and 1991, multiple
sampling programs were maintained at different times, involving
various sampling strategies and locations. From August 1991, upgraded
analytical equipment and techniques allowed improved sampling relative
to earlier flights, focused on obtaining regular (approximately
monthly) vertical profiles of the clean marine troposphere. The CGOP ran from August 1991 through
December 1999, with additional sampling taking place during
August–September 2000. Flights were conducted out of Melbourne,
Victoria, flying southward over the Bass Strait towards Cape Grim,
Tasmania, with spatial coverage spanning between 38.6–41.5∘ S and
142.1–146.0∘ E. Approximately 85 flights were carried out
over the life of the program, with sampling locations shown in Fig. S1
in the Supplement. The program was designed to measure background
concentrations of CO and greenhouse gases in conditions representative
of the remote SH. Flights were therefore conducted only during
anticipated clean air conditions, typically characterized by
southwestward surface winds . Vertical
profiles from 0 to 8 km were measured due west of Cape Grim on
most flights (centered around 40.5∘ S, 144.3∘ E; see
Fig. 2) but with some variation in the exact location to avoid
sampling outflow from Tasmania. Air was collected in glass flasks,
with on average 17–20 samples per flight, and subsequently analyzed
in the CSIRO Global Atmospheric Sampling Laboratory
(GASLAB). Measurements are reported in units of nanomoles of CO per
mole of dry air, which we refer to here using the shorthand ppbv. CO
was measured using a gas chromatograph with a precision of
±1% over the calibrated range of 20–400 ppbv.
HIAPER Pole-to-Pole Observations
CGOP provides multi-year temporal coverage but limited spatial
coverage. We supplement this record using observations from the HIPPO
aircraft campaign, allowing us to test the representativeness of both
airborne data sets. HIPPO consisted of five deployments across
different seasons from 2009 to 2011 and took place primarily over the
western Pacific. Flights involved repeated vertical profiles from the
surface to 8 km with 4–6 flights in the SH during each
deployment. CO was measured during HIPPO using five instruments: the
Quantum Cascade Laser System (QCLS), the GV AeroLaser VUV CO Sensor,
the Unmanned Aircraft Systems Chromatograph for Atmospheric Trace
Species (UCATS), the PAN and other Trace Hydrohalocarbon
ExpeRiment-Electron Capture Detectors (PANTHER-ECD), and the NOAA Whole Air Sampler – Measurement
of Atmospheric Gases that Influence Climate Change (NWAS-MAGICC). From
these, a 10 s merged data set for CO based on best available data
(CO.X) was constructed . Here we use CO.X from the
most recent available revision (R_20121129). We select HIPPO data
representative of clean SH extra-tropical air, defined here as all
observations over the South Pacific mid-latitudes (20–50∘ S,
160∘ E–90∘ W) except those recorded at low altitudes
near cities (mainly Christchurch). Flight dates with available CO data
meeting these criteria included 18, 20, 23, 26, 28 January 2009
(HIPPO-1); 7, 9, 11, 14 November 2009 (HIPPO-2); 2, 5, 8, 10
April 2010 (HIPPO-3); 22, 25, 28 June 2011 (HIPPO-4); and 24, 27, 29
August and 1, 3 September 2011 (HIPPO-5). Sampling locations meeting
these criteria are shown in Fig. S1 in the Supplement.
Details of model simulations used in SHMIPa.
GEOS-ChembNIWA-UKCAcTM5dCAM-chemeModel typefCTMCCMCTMCCMHorizontal resolution (long × lat)2.5∘×2∘3.75∘×2.5∘3∘×2∘2.5∘×1.9∘Vertical levels: total (<8km)g47 (26)60 (20)34 (16)56 (27)MeteorologyhGEOS-5, GEOS-4Forced by SSTs & sea iceERA-interimMERRAEmission injection heightsiSurfaceSurfaceUp to 6 km (fires) or surfaceSurfaceMethane mixing ratiosjPrescribed fromPrescribed fromSimulatedPrescribed fromobservationshemispheric gradientobservationsGlobal mean tropospheric OHk10.5×10510.6×1058.6×1059.1×105(moleccm-3)Cape Grim background regionl138.75–143.75∘ E,136.875–144.375∘ E,138.0–144.0∘ E,138.75–143.75∘ E,41.0–45.0∘ S41.24–46.25∘ S42.0–46.0∘ S41.7–45.5∘ S
a A full description of the models including references is provided in .b GEOS-Chem (www.geos-chem.org) was modified from the standard version 9-01-03 with Caltech isoprene
mechanism to include HO2 uptake
by aerosols with γ=0.2, add methanol as an interactive tracer based on the offline
simulation of , and use pre-computed biogenic emissions with imposed diurnal variability tied to solar zenith angle.c NIWA-UKCA comprises a coupled
stratosphere–troposphere chemistry scheme
. The background climate model for
NIWA-UKCA is HadGEM3-A . The updated version
used here includes C2H4, C3H6, CH3OH,
isoprene, and monoterpene in addition to those described in
. The isoprene oxidation is based on
and the monoterpene oxidation is as described in .d The TM5 version used here employs the modified CB05
mechanism using the configuration outlined in . The isoprene and monoterpene oxidation schemes are taken from subsequently modified according to .e CAM-chem is described in . Community Earth System Model (CESM)-1.1.1 is used here, with tropospheric (MOZART-4) chemistry.f Chemical transport model (CTM) or chemistry-climate model (CCM).g Altitudes are approximated from model pressure levels. The number of levels below 8 km is for the Cape Grim background region, with bounds given below.h Goddard Earth Observing System (GEOS) fields are from the NASA Global Monitoring and Assimilation Office (GMAO). GEOS-5 was used for the base simulations, and GEOS-4 was used for a 1-year sensitivity study. NIWA-UKCA sea surface temperatures (SSTs) are from the Program for Climate Model Diagnostic and Intercomparison (PCDMI). ERA-interim fields are from the European Centre for Medium-Range Weather Forecasts (ECMWF). Modern Era Retrospective-analysis for Research and Applications (MERRA) fields are from the NASA GMAO.i With the exception of the aircraft source, emissions are generally injected at the surface or in the first few model layers. Aircraft emissions are introduced throughout the troposphere depending on airport location and flight paths. In TM5, isoprene emissions between 20∘ S and 20∘ N are introduced into the first two layers of the model to represent canopy height. Also in TM5, fire emissions are distributed over different altitude regimes depending on fire type following , except in the tropics where injection heights are increased from 1 to 2 km based on recent satellite observations .j Surface observations from the NOAA Global Monitoring Division (GMD) are used to prescribe methane mixing ratios in GEOS-Chem (all altitudes) and CAM-chem (surface only). NIWA-UKCA assumes methane mixing ratios of 1812 ppbv in the Northern Hemisphere and 1707 ppbv in the Southern Hemisphere. TM5 simulates methane interactively using emissions from the Emission Database for Global Atmospheric Research (EDGARv4.1) and the Lund–Potsdam–Jena Wetland Hydrology and Methane Dynamic Global Vegetation Model
(LPJ-WhyMe).k Multi-year mean air density-weighted OH below the climatological tropopause defined as p=300-215(cos(lat))2hPa.l Cape Grim background region is the region in each model used for comparison with clean air
observations from the Cape Grim Overflight Program (CGOP), as described in the text and shown in
Fig. 2.
Southern Hemisphere Model Intercomparison Project
We compare the Cape Grim and HIPPO aircraft observations to output
from a suite of model runs conducted for SHMIP. A detailed overview of
the project is given in . SHMIP included two
chemical transport models (GEOS-Chem and TM5) and two
chemistry-climate models (NIWA-UKCA and CAM-chem), with different
tropospheric and tropospheric–stratospheric chemical schemes employed
across models. Aerosol effects included in the models vary in levels
of complexity. Of particular relevance is loss of HO2 on
aerosol particles, which has been shown to increase CO mixing ratios
by 4–7 ppbv in the remote SH . This
effect is included in GEOS-Chem with aerosol uptake coefficient
γ=0.2; in other models aerosol uptake of HO2 is not
included or results in HOx recycling rather than net
loss. Additional details of the model configurations and major
differences between models are given in Table 1 and described in more
detail in . Indicative global and SH budgets for 2004 are
shown in Table 2. Simulations spanned 2004–2008
(following a 1-year spin-up) using the same emissions across models
for anthropogenic, BB, and biogenic sources. Anthropogenic emissions
were taken from the REAS v2.1 inventory
between 60–150∘ E and 10∘ S–70∘ N, nested
within the global MACCity inventory . BB emissions were from the GFEDv3 inventory
. Biogenic emissions were from the MEGAN v2.1
inventory , computed offline using the
Community Land Model CLM; for each year
of simulation (referred to here as MEGAN-CLM). Figure 1 shows the mean
seasonal cycle of primary CO emissions from biomass burning, fossil fuel,
and biogenic sources as well as biogenic isoprene emissions
(a proxy for secondary CO production) in the SH tropics and
extra-tropics used in the standard SHMIP simulations. In addition,
a set of sensitivity simulations were performed using biogenic
emissions of isoprene and monoterpenes taken from LPJ-GUESS
(with
all other species from MEGANv2.1 as in the standard runs). For
methane, an important chemical loss term for OH and an indirect
chemical source of CO, different approaches were used in each model as
described in Table 1. The models also included global and regional
idealized CO-like tracers with the same emissions as CO but with
different lifetimes, as described below.
Global and southern hemispheric CO budgets (Tg yr-1) for 2004 in the
SHMIP simulations.
a Primary emissions are from REASv2.1 nested in
MACCity (anthropogenic), GFEDv3 (biomass burning), and MEGAN2.1 calculated
using CLM (biogenic), as described in the text. Direct ocean emissions of CO are from POET (Granier et al., 2005).b Tropospheric chemical production and loss terms are expressed as the
range over the SHMIP models. Values for the individual models are given in the footnotes.c 1340 (CAM-chem), 1590 (TM5), 1690 (GEOS-Chem), 1920 (NIWA-UKCA)d 578 (CAM-chem), 748 (GEOS-Chem), 744 (TM5), 821 (NIWA-UKCA)e Production of CO from methane is estimated assuming a 100 % yield of
CO from methane oxidation. Production from NMVOCs is then estimated as the difference
between total production and production from methane. These assumptions are made for
diagnostic purposes only, and are not assumed in the chemical mechanisms. These values
are only available from GEOS-Chem and NIWA-UKCA, and the value shown is their range.f 2200 (CAM-chem), 2520 (TM5), 2770 (GEOS-Chem), 2790 (NIWA-UKCA)g 848 (CAM-chem), 1020 (TM5), 1050 (NIWA-UKCA), 1120 (GEOS-Chem)h Range from NIWA-UKCA (lower limit) and TM5 (upper limit). Loss via dry
deposition was not included in GEOS-Chem and was not archived in CAM-chem.
The 2004–2008 mean primary CO emissions (black) used in the SHMIP
simulations for the southern hemispheric tropics (solid) and
extra-tropics (dashed). The bottom panel shows biogenic emissions of both primary
CO (black, left axis) and isoprene (gray, right axis), the latter used as a proxy for secondary CO production. Error bars
represent the interannual standard deviations. Emissions are from GFEDv3 for biomass
burning (top), MACCity and REASv2.1 for fossil fuels (middle), and
MEGANv2.1 computed using CLM for biogenic sources (bottom), as
described in the text.
provide a detailed analysis of SH CO distributions
simulated by the four SHMIP models as well as the models' varied
abilities to reproduce surface and total column CO observations from
selected SH sites. Here, we provide an additional test of the models'
abilities to represent vertical structure in the SH free troposphere
(and the associated inter-model differences) using observed vertical
profiles representative of SH mid-latitudes clean background
air. For comparison with observations from CGOP, which measured
only clean background air, we sample each model over the Southern
Ocean southwest of Tasmania. We reduce the influence of model spatial
variability on the comparisons by averaging each model over four
representative grid squares in this region (referred to hereafter as the
Cape Grim background region). These grid squares, shown in
Fig. 2, were chosen to minimize the influence of outflow from the Australian
continent (which we cannot filter directly as only monthly mean model output
was archived and radon was not simulated as part of SHMIP).
We tested the influence of our choice of sampling region by also
performing our analyses using either the grid square containing the CGOP profiles or the
nearest ocean-only grid square as done for TRANSCOM, e.g.,.
We found that changing
the sampling region did not significantly
impact the shape of the model profiles or the relative differences
between the models, suggesting our results are robust
to this choice. Coordinates of the grid squares in each model that define the
Cape Grim background region are given in Table 1, with minor differences stemming from
model resolution and grid spacing as shown in Fig. 2.
The 5-year (2004–2008) mean surface CO mixing ratios from the
four SHMIP models in the vicinity of Cape Grim. The circle shows the
multi-year (1991–2000) mean observed CO below 500 m from
CGOP, plotted at the location of typical vertical profiling. Black
boxes indicate the four grid squares of the clean air Cape Grim
background region sampled in each model for comparison with the
aircraft observations, as described in the text.
Because of the temporal offset between CGOP (1990s), HIPPO
(2009–2011), and the SHMIP simulations (2004–2008), we do not
compare individual flights or profiles but instead focus on average
behavior seen across multiple years in the observations and
models. Multiple studies have shown that trends in SH CO over
similar time periods are either small or insignificant , depending on the period and region analyzed,
especially when El Niño years are neglected. We evaluated long-term
CO trends specific to the Cape Grim region over the 1991–2008, 2004–2008,
and 1991–2011 time periods relevant to this work using CSIRO flask
samples collected in surface air at the Cape Grim Baseline Air Pollution
Station. Results of this analysis, shown in Table S1 in the Supplement,
indicate that CO trends at Cape Grim over these periods were not
statistically significant on an annual basis or for any individual season,
justifying our use of long-term temporal averages.
Median monthly CO observed near Cape Grim (1991–2000; black)
and simulated for 2004–2008 in the Cape Grim background region (see
Fig. 2) by TM5 (purple), GEOS-Chem (red), NIWA-UKCA (orange), and
CAM-chem (blue). Seasonal cycles are shown for 0–2 km
altitude (bottom), 2–5 km (middle), and 5–8 km
(top). Thin black vertical lines show the observed median absolute
deviation across all years of measurement. The number of observed
data points in each monthly altitude bin is given at the top of each
plot. Shown in gray are the Cape Grim observations from 1991 to 1999 only,
excluding the September 2000 SAFARI measurements from the data set.
Figure 3 shows the median observed seasonal cycle of CO at Cape Grim
averaged over 0–2, 2–5, and 5–8 km altitude bins (black
line). The observations show increasing CO mixing ratios with altitude
in all months, as previously reported by in an
analysis of 5 years of the same data set. Peak mixing ratios were
observed in austral spring during the tropical BB season. At altitudes
below 2 km, the seasonal maximum occurred in October,
as seen also in flask samples collected in surface air. This October peak
in the boundary layer appears to represent a 1 month
offset from higher altitudes, where peak CO was observed in September.
However, the September maximum above 2 km is not statistically
significant and is skewed by a large number of samples from September
2000 collected as part of the SAFARI aircraft campaign .
As no measurements were made in other months in 2000, the SAFARI
data cannot be considered indicative for the purposes of evaluating the
annual cycle. Indeed, when these data are removed, the CGOP observations
show peak CO mixing ratios in October at all altitudes, as shown in gray in
Fig. 3 (note however that September and October remain statistically
indistinguishable above 2 km). There does still appear to be a small
delay between the boundary layer and the free troposphere, which may be
indicative of slow mixing of transported BB plumes into the boundary layer.
The colored lines in Fig. 3 show the
simulated seasonal cycles in the Cape Grim background region for the
four individual SHMIP models. Despite large differences in absolute
mixing ratios (discussed below), the models are generally able to
reproduce the shape of the observed seasonal cycle especially above
2 km, as expected from previous studies
e.g.,. In the 5-year mean, the models show
peak mixing ratios in September rather than October at all altitudes, but this
timing varies from year to year. A particularly strong September peak is
simulated by all models for 2005, reflecting significantly enhanced
BB emissions in South America and southern Africa in the GFEDv3 inventory
for this year (and leading to an outsize influence on the 5-year mean). None of
the models capture the delay in peak mixing ratios in the boundary layer,
suggesting errors in model representation of vertical mixing and/or boundary
layer heights, both known issues in atmospheric transport models e.g.,. Model ability to match other aspects of the
seasonal changes in the relationship between different altitudes is
varied and is the subject of further discussion in Sect. 3.
The inter-model and model-observation differences near Cape Grim shown
in Figs. 2 and 3 are sizeable, consistent with the detailed analysis
of the simulations by . Annual mean mixing ratios in
surface air in this region range from less than 50 ppbv in
CAM-chem to nearly 65 ppbv in TM5 (compared to 53 ppbv
observed; Fig. 2). GEOS-Chem CO is artificially enhanced as the model
does not include a CO sink from dry deposition. A sensitivity test
including dry deposition over all vegetated surfaces led to
a 1–2 ppbv decrease in GEOS-Chem CO at all altitudes
(equivalent to ∼50Tgyr-1 or 2% of the total
global CO sink) but did not substantially change the vertical,
horizontal, or seasonal distributions. The TM5 overestimate is
consistent with the high bias in surface CO identified previously
using monthly mean surface CO measurements at Cape Grim from the year
2000 . The CO differences between
models persist with similar magnitude at all altitudes (Fig. 3). These
differences in background CO are influenced by a number of factors
including grid resolution, meteorological drivers, and chemical
mechanisms as discussed in detail by . In particular,
they find that consistent inter-model differences in the SH CO background are
largely driven by differences in CO production efficiency, with an additional
contribution from differences in oxidizing capacity (especially for TM5, which
has the lowest OH of the four models as shown in Table 1). As our focus here
is on relative rather than absolute vertical and seasonal gradients,
we remove the influence of consistent differences in the CO background
from our comparisons by showing CO mixing ratios expressed as
ΔCO, the deviation (in ppbv) from a specific baseline value, as
done previously for CGOP data by . We use as
baseline value the median mixing ratio in surface air (below
1 km) in a given season, computed separately for each model
and each set of observations. Expressing the vertical gradients as
deviations rather than absolute values also allows us to compare the
CGOP and HIPPO observations, which are on different absolute scales
due to different sampling locations and strategies.
Median observed CO vertical profiles near Tasmania from the
Cape Grim Overflight Program (CGOP; 1991–2000; black) and over the
SH mid-latitude Pacific from the HIAPER Pole-to-Pole Observations
(HIPPO; 2009–2011; gray). Profiles are shown as ΔCO, the
deviation (in ppbv) from the observed value in surface air in each
season. For HIPPO, the JA season also includes two flights from
early September. Thin horizontal lines show the observed median
absolute deviations across all years of measurement.
Observed and simulated vertical gradientsCape Grim and HIPPO observations
The median climatological vertical gradients of CO from CGOP are shown
as black lines in Fig. 4. For each season, medians were computed after
binning observations from all years into 1 km altitude
ranges. Observed variability in each 1 km altitude bin was
estimated using the median absolute deviation (MAD) statistic for all
observations in the bin, shown as the thin horizontal lines. Profiles
are expressed as ΔCO, the deviation in ppbv from the observed
median value in surface air (0–1 km) for each season, as
described in Sect. 2. Observations were grouped seasonally to increase
the number of data points used to construct each profile, with
seasonal groupings selected based on inspection of seasonal cycles in
the data. In particular, observed behavior in June showed more
similarity to that in the preceding months than in July–August in
terms of both magnitude and interannual variability, especially at
higher altitudes (Fig. 3). This reflects variability in the onset of
the SH BB season, which typically occurs sometime in July or August
. We therefore grouped June data with
austral autumn (MAMJ) rather than austral winter (JA) and retained
austral spring (SON) as a definitive season.
Observed and simulated 0–8 km CO vertical gradients near
Cape Grim, in ppbvkm-1∗.
∗ Vertical gradients were calculated using
a linear regression of the median 0–8 km observed and simulated
profiles, binned in 1 km altitude bins. Simulated gradients are for
the Cape Grim background region (see text). Errors on the observed gradients
show the 95% confidence intervals calculated using the bootstrap
method with 10 000 random samplings from the original data points. Bold
values indicate the simulated vertical gradient is within the 95%
confidence interval of the observed slope from CGOP.
Figure 4 shows moderate seasonal variability in observed CO vertical
gradients of a few ppbv km-1, with larger gradients during
the winter–spring burning seasons (JA–SON) than during the rest of
the year. As reported previously by using the first
4 years of this data set, the observations show an increase in
gradient above 2 km, with the suppressed gradient at lower
altitudes indicative of mixing throughout the local boundary layer
. We quantify the observed CO vertical gradients using
a linear fit to the median profiles for each season. Calculated
gradients are given in Table 3 and show a minimum in autumn
(1.6 ppbvkm-1) and maximum in spring
(2.2 ppbvkm-1) that are significantly different from one
another.
We evaluate the large-scale spatial representativeness of the CGOP
data using independent HIPPO observations from the South
Pacific. Seasonal profiles are shown in gray in Fig. 4 and were
constructed from one HIPPO deployment each, with the exception of MAMJ
which includes both HIPPO-3 and HIPPO-4 flights. HIPPO-5 profiles for
JA also include data from the two flights in early September to
increase the data available in that season and to keep all flights
from each deployment together. The figure shows that although the
relative variability in CO (thin lines) differs somewhat between HIPPO
and CGOP, there is generally overlap in the observed ΔCO from
each data set (thick lines). Small differences between the two are likely driven
by (1) BB plumes from Africa and South America experiencing more
dilution during transport to the Pacific than to Cape Grim, and (2) sampling of Australian
BB outflow during HIPPO but not CGOP. Both of these factors should be most
influential in austral winter–spring, when burning in the SH is at its peak (also the
period when the data sets show the most variability).
The most notable difference between
data sets (although still within the observed variability of both
campaigns) is seen above 4 km in JA. We examined this
difference using regional BB tracers in the four models (tracers are
described below) and found the offset between CGOP and HIPPO in JA is
consistent with differences in transport from southern African BB
sources to the two different sampling locations. Outflow from Africa
is frequently southeastward at this time of year, passing directly
over Cape Grim. BB plumes are not well mixed by the time they arrive
at Cape Grim, resulting in large and distinct peaks in observed CO
anywhere from 4 to 8 km. In contrast, simulated
transport to the southwest Pacific is both less frequent and less
direct in JA, leading to more diffuse BB plumes and lower CO mixing
ratios. Simulated CO profiles over the Pacific (not shown) display
a broad peak of moderately enhanced CO from 2 to 8 km,
consistent in shape with airborne observations of BB-influenced air
from PEM-Tropics A .
With the exception of the mid-troposphere in JA, the observed CO
vertical gradients are very similar between CGOP and HIPPO, despite
major differences in flight locations (Southern Ocean vs. Pacific),
observation years (1990s vs. late 2000s), and sampling strategies
(number of profiles, frequency of flights). This remarkable
correspondence lends confidence to our use of vertical gradients
derived from the CGOP data as being representative of the remote SH
(except perhaps in regions of continental outflow). It also suggests
the HIPPO CO observations are representative of long-term seasonal
patterns, facilitating future interpretation of these data.
SHMIP simulations in the Cape Grim background region
Figure 5 and Table 3 compare the observed vertical gradients from CGOP
to the SHMIP simulations in the Cape Grim background region. Simulated
vertical gradients for each model are derived from monthly mean output
(and therefore not specifically selected for baseline
conditions). Modeled monthly means for each year in 2004–2008 were
averaged over the four grid squares shown in Fig. 2. From these
spatial means, a seasonal median model profile was derived by
calculating the median model value for each 1 km altitude bin
(median over all model levels in the altitude bin and all months/years
in the season). As for the observations, the model profiles are
expressed as ΔCO, the deviation from the median model value at
0–1 km in each season. Simulated vertical gradients in
Table 3 were calculated from a linear fit to the median simulated
profiles.
Median CO vertical profiles observed from 1991–2000 during
CGOP (black) and simulated for 2004–2008 by TM5 (purple), GEOS-Chem
(red), NIWA-UKCA (orange), and CAM-chem (blue) in the Cape Grim
background region (see Fig. 2). Profiles are shown as ΔCO,
the deviation (in ppbv) from the observed or modeled value in
surface air in each season. Thin horizontal lines show the observed
median absolute deviations across all years. The number of observed
data points in each seasonal altitude bin is given at the right of
each plot.
As seen in Fig. 5, the models generally provide a good simulation of
0–8 km CO vertical gradients in austral winter (JA) and
spring (SON). With the exception of TM5 below 3 km in SON,
simulated gradients are within the large variability of the
observations in these seasons. At this time of year, the dominant
influence on SH CO is the intense BB that takes place across the
tropics and in the SH extra-tropics. Burning peaks in
August–September in southern Africa, September–October in South
America, and October–November in Australia
. Fire emissions
have been shown to influence Australia and the Cape Grim region via
long-range transport in the mid-upper
troposphere (UT)
, driving the enhanced gradient above the
surface in these months. Simulated tracers of regional influence (CO25,
described in Sect. 4.1) show peak contributions from southern African BB
at 4–7 km and from South American BB at 6–10 km.
The ability of the models to capture the observed BB
enhancement indicates that the models (all using GFEDv3 emissions) are
successfully capturing the long-range transport of BB sources. The main
exception is the positive gradient simulated from 7 to 8 km in SON (versus
the observed decrease over this altitude range in CGOP). The cause of the discrepancy
is unclear. In the models the increase above 7 km reflects a larger contribution from South
American than African BB at these altitudes, primarily in October. BB plumes are likely very dispersed at
these altitudes following long-range transport, and this dispersion complicates simulation of the gradient.
The otherwise good agreement between observed and simulated JA–SON
gradients suggests that there has
not been significant change in the major SH burning
source regions that contribute to background CO in the Cape Grim region since
the 1990s (when the observations were collected). This is consistent
with a number of recent studies (and with our own analysis of Cape
Grim surface flask data, Sect. 2.3) showing observed trends in SH CO
are much smaller than interannual variability . Significant peaks in BB have been observed for
individual years in both periods (in particular, the 1997 and 2006 El
Niño years), and these are reflected in the large interannual
variability shown for these seasons in Fig. 5 (horizontal lines).
Outside of the burning season, model ability to match observed
vertical gradients deteriorates, as does inter-model agreement
(Fig. 5). GEOS-Chem and CAM-chem in particular show a sharp drop in
gradient from spring to summer/autumn that is unmatched by the
observations; the change in NIWA-UKCA and TM5 is more gradual but
still too large (Table 3). Across all models, the overall decrease in
the vertical gradient from spring to autumn is between
1 and 2 ppbvkm-1, larger than the observed change from CGOP
of ∼0.5ppbvkm-1. In the following section, we
evaluate possible reasons for the model-CGOP and inter-model
discrepancies in the summer–autumn CO vertical gradients.
Drivers of inter-model variability
Model-observation differences can result from model errors in
emissions, chemistry, meteorology/transport, or a mix of these. As all
SHMIP models used identical emissions (except for parameterized
lightning, soil, and volcanic sources with limited impact on CO), the
inter-model differences seen here should result primarily from
differences in chemistry and meteorology/transport (resolution may
also play a small role). Here, we investigate the role of model
differences in transport and chemistry on differences in simulated
vertical gradients using a set of sensitivity simulations, run for
a 2-year period (2004–2005) to reduce the influence of interannual
variability on the results. Figure 6a shows the simulated CO vertical
gradients from the standard simulations in 2004–2005 as a point of
reference for the sensitivity simulations. As seen in the figure, simulated
profiles during the 2004–2005 test period are generally similar to those
for the full SHMIP period (Fig. 5).
Transport
The first sensitivity simulation uses an idealized CO-like tracer
(CO25) designed to quantify the impact of model transport,
independent of the influence of model chemistry. The CO25
tracer used the same emissions as CO globally with a fixed 25-day
lifetime and was not subject to chemical production or chemical
loss. In remote regions, the CO25 mixing ratio therefore
represents the balance between primary emission and long-range
transport, with differences between models caused exclusively by
differences in transport over the 25-day tracer lifetime. Vertical
gradients of the CO25 tracer are shown in Fig. 6b.
In DJF and MAMJ, all models
display a greatly diminished ability to match observed gradients when
chemistry is neglected, indicating transported primary emissions play
only a small role in driving CO vertical gradients at this time of
year. In winter–spring, CO25 vertical gradients are only
slightly shallower than those of total CO and are within the observed
interannual variability, consistent with the gradients being driven by
primary BB emissions that are well represented in all four models.
Median CO profiles from CGOP observations (black) compared to
model simulations for 2004–2005 using (a) the standard simulation;
(b) a global CO-like tracer with a 25-day lifetime (CO25;
see text); (c) a global CO-like tracer with OH-driven loss but no
secondary production (COOH; see text); and (d) LPJ-GUESS
isoprene and monoterpene emissions. Solid colored lines represent
the standard simulations and dashed lines the sensitivity
simulations for GEOS-Chem (red), NIWA-UKCA (orange), TM5 (purple),
and CAM-chem (blue; no OH-loss tracer). Profiles are shown as
ΔCO, the deviation (in ppbv) from the observed or modeled
value in surface air in each season, with the surface value
calculated separately for each sensitivity test.
The differences in CO25 between models are much smaller than
differences in total CO, especially in summer–autumn. This is consistent
with results from , who examine CO25 columns over
the entire SH and find both the magnitude and distribution to be similar
across the four models. They also show that the small inter-model
differences in CO25 columns are not reflected in the distributions of total
CO columns, indicating a limited role for horizontal transport differences as a source of
inter-model variability, at least over the 25-day lifetime of the tracer.
As seen in Fig. 6b, all four models show similarly shallow CO25
vertical gradients in DJF and MAMJ. These similarities could reflect similar transport
of primary emissions to the Cape Grim region and/or similarly rapid vertical mixing
relative to the 25-day tracer lifetime, which would obscure the role of
transport differences in driving inter-model variability. Given the lack of
primary CO sources near the Cape Grim region, the latter is unlikely
to have a major impact on primary CO gradients but may be important for
inter-model differences in secondary CO. We explore this effect further in
Sect. 5.
We further investigate the impacts of inter-model transport
differences using regional CO25 tracers. Figure 7 shows the
contribution from six regions (Australia, South America, southern
Africa, Southeast Asia, East Asia, and all other sources) to total
CO25 at Cape Grim in three altitude ranges. Total
CO25 amounts are highest in GEOS-Chem, indicating more rapid
transport to this region than in the other models, and are typically
lowest in NIWA-UKCA. In summer, the relative contributions of
different sources are largely consistent across the models, with
a slight dominance from Australia below 2 km and a slight
dominance from South America above. The other contribution shown
in gray in Fig. 7 represents the difference between the global
CO25 tracer and the sum of the regional CO25
tracers and mainly reflects the contribution from northern
Africa. Inter-model differences are larger for this contribution (other)
than for any of the regional CO25 tracers, with in
particular NIWA-UKCA showing less influence than the other
models. This contribution peaks in austral summer, likely driven by
the seasonal source from NH African burning, which is at its annual
maximum in DJF . In summer–autumn,
differences between models are largely constant with altitude and
result in very similar vertical gradients, as seen previously in
Fig. 6b.
Contributions to CO25 in the Cape Grim background
region from sources in Australia (111–156∘ E,
11–44∘ S), South America (34–84∘ W,
57∘ S–1∘ N), southern Africa (9–44∘ E,
5–37∘ S), Southeast Asia (94–156∘ E,
11∘ S–7∘ N), East Asia (91–144∘ E,
7–44∘ N), and elsewhere, as simulated by GEOS-Chem (G.C.),
NIWA-UKCA (N.U.), CAM-chem (C.C.), and TM5 for different altitude
bands and seasons in 2004–2005. Note the difference in scale
between DJF–MAMJ and JA–SON.
During the tropical BB seasons (JA–SON), inter-model differences in
the CO25 sources at Cape Grim are larger, as shown in
Fig. 7. In JA, the contribution from southern Africa is dominant and
also varies most, responsible for 50–60 % of total CO25
in NIWA-UKCA compared to only 20–30 % in CAM-chem, with the other
models falling between these values. Absolute differences in this
source of up to 7 ppbv at 8 km can explain much of the
difference in the JA CO25 gradient shown in Fig. 6b,
suggesting that long-range transport of African BB emissions
contributes to inter-model variability during the early BB season. In
SON, the South American contribution dominates, reflecting a 1-month
offset in peak emissions from these regions in 2004–2005 in the
GFEDv3 inventory. The southern African contribution is also more
consistent across models in SON, with inter-model differences of
similar magnitude to those from the South American source
(2–3 ppbv).
Chemical loss
Fixed-lifetime tracers do not account for model differences in
chemistry, which for CO include differences in both OH-driven loss and
secondary chemical production. We isolate the impact of the former
using a second set of idealized CO-like tracers (COOH). In
this case, the COOH tracers again have the same primary
emissions as CO but with tracer loss driven by each model's OH fields
and CO+OH rate constant. Differences in the rate constant at standard
temperature and pressure are on the order of 10 % (e.g., between the
IUPAC recommendation used in NIWA-UKCA and the JPL recommendation used
in GEOS-Chem). Differences in OH mixing ratio are on the order of 5–20 %
for the global tropospheric mean (Table 1) but can be much larger
regionally. Like CO25, COOH tracers are subject to
differences in model transport, with differences between
CO25 and COOH indicative of the impacts of OH-driven
chemical loss. The COOH lifetime varies spatially and seasonally
(due to OH variability), and in winter–spring can be significantly longer than
25 days. As described by , the COOH mixing
ratios therefore provide a more realistic metric than CO25 for evaluating the combined
impacts of transport and loss of primary CO.
Figure 6c shows the vertical gradients of the global COOH
tracer in the Cape Grim region as simulated by GEOS-Chem, NIWA-UKCA,
and TM5 (CAM-chem did not include a global COOH
tracer). Both the relative vertical gradients and the regional
contributions are generally similar between the two idealized tracers
(regional contributions are shown in Fig. S2 in the Supplement). In
DJF, tropospheric OH production leads to a small decrease in
mid-tropospheric COOH relative to surface values in all
three models. As for CO25, COOH gradients in
DJF–MAMJ are greatly reduced relative to those of total CO (Fig. 6a),
suggesting both transport and chemical loss of primary emissions are insufficient in these
seasons to explain the large inter-model variability, which instead
must be driven by secondary CO production.
Five-year mean DJF mixing ratios in near-surface air
(< 1 km) of (a) CO, (b) isoprene,
(c)CH2O, (d) OH, and (e)HO2 as simulated by GEOS-Chem (left) and NIWA-UKCA
(right). (h) The bottom row shows the difference between
rates of CO chemical production (PCO) and CO chemical loss
(LCO) in molecules cm-3s-1 for DJF 2004
only. Regions where production outweighs loss are shown in red and
the inverse in blue.
Chemical production
The difference between COOH and total CO for each model
represents the contribution from in situ chemical production,
estimated to account for roughly half of the total CO source globally
and an even larger
proportion in the SH . Comparing
Fig. 6a and c show that chemical production plays a dominant role
in controlling the simulated CO vertical gradient in DJF and MAMJ but
has much less influence during the tropical BB seasons when primary
emissions dominate. Chemical production also appears to be the major
source of inter-model variability in DJF and MAMJ, and uncertainties
in this term may help explain the large underestimates of the observed
summer gradient seen in particular by GEOS-Chem and CAM-chem (Fig. 5).
Chemical production of CO originates from oxidation of both methane and
NMVOCs, and inter-model variability in the vertical gradients may reflect
contributions from both. In remote regions, the methane source dominates
the CO burden while the NMVOC source dominates the variability
. Differences in the methane mixing ratios in
the four models (Table 1) are thus more likely to affect overall concentration
differences (e.g., Fig. 3) than differences in the vertical gradient. However,
the methane contribution cannot be quantified from the archived SHMIP output.
Instead, we perform a final sensitivity test to evaluate the role of the NMVOC
source in driving the simulated CO vertical gradients. Figure 6d shows the
result of replacing MEGAN-CLM biogenic emissions with LPJ-GUESS for
isoprene and monoterpenes. Methane, OH, and other emissions remain
unchanged from the standard simulation.
Since emissions are the same across models, they cannot
explain inter-model variability; however, they can help attribute
sources of model-observation bias as well as provide insight into the
dependence of the simulated vertical gradients on biogenic NMVOC
sources. The figure shows that relative to the standard simulation,
the LPJ-GUESS emissions reduce the simulated CO vertical gradient in
summer–autumn in all models. In winter–spring, the differences are negligible.
The small increases in gradient from Fig. 6c
to d reflect both methane and NMVOC contributions (which are smaller
but still significant in LPJ-GUESS). These results present
a picture consistent with the previous sensitivity tests; namely, that
observed vertical gradients are driven in winter–spring
by primary BB emissions and in summer–autumn by
secondary CO, largely of biogenic NMVOC origin.
Biogenic source regions are located far upwind of Cape Grim, so model
error in the Cape Grim background region can result from errors in
both model chemistry and the transport of secondary CO. Distinguishing
between these factors is not straightforward. Using GEOS-Chem, we
performed an additional 1-year sensitivity test for 2004 designed to
partially discriminate between these terms by replacing the standard
GEOS-5 meteorology with GEOS-4. The latter has been shown to have more
rapid vertical uplift over tropical source regions
, where biogenic emissions
are also large . The same chemical
mechanism was used in both simulations, and the CO+OH reaction rate
changed by less than 2 % from differences in temperature and
pressure, so simulated differences at Cape Grim can be attributed to
model transport. Results from this sensitivity simulation (not shown)
indicated virtually no impact on the CO vertical gradient in
summer–autumn, implying a dominant influence from the chemistry
controlling secondary CO production.
Chemistry and transport of biogenic-sourced secondary CO
In preceding sections, we have shown that inter-model differences in
the vertical distribution of CO in the remote SH are largest in
austral summer–autumn, and that these differences cannot be explained
by the transport or chemical loss of primary emitted CO; instead, they
are clearly driven by differences in CO produced chemically from
biogenic NMVOC emissions. Here we evaluate model differences in the
chemistry and transport of secondary CO from biogenic source regions
in the context of their impacts on SH background CO in summer (DJF),
when inter-model variability is largest. We focus our analysis in this
section on GEOS-Chem and NIWA-UKCA, the two models that best reproduce
absolute CO mixing ratios at Cape Grim (Fig. 3) but with significant
differences in the simulated vertical gradient (Fig. 5).
Isoprene oxidation mechanisms in SHMIP models.
ModelIsoprene oxidation scheme and referencesGEOS-ChemCaltech isoprene mechanism v9-01-03; NIWA-UKCAMainz Isoprene Mechanism; with updated rates for secondaryreactions; TM5CB05; with modified HO2 yield; CAM-chemMOZART;
Chemical mechanisms differ substantially across the SHMIP models
, and differences are difficult to interpret due to varying
levels of complexity, especially for NMVOC speciation and oxidation. Of
particular importance here are differences in the oxidation of isoprene,
summarized for all models in Table 4, and monoterpenes. The GEOS-Chem SHMIP
simulations use the Caltech isoprene mechanism as implemented in v9-01-03
(http://wiki.seas.harvard.edu/geos-chem/index.php/New_isoprene_scheme_prelim),
which includes formation of first and second generation isoprene nitrates
under high-NOx conditions and formation of
isoprene hydroperoxides and subsequently epoxydiols under low-NOx
conditions . Isoprene oxidation in NIWA-UKCA is
from the original Mainz Isoprene Mechanism
MIM; but with updated rate coefficients for
reactions between OH and isoprene nitrates and between NO and isoprene peroxy
radicals from ; still, the
NIWA-UKCA mechanism contains a limited number of species and predates many
recent advances in isoprene chemistry available in newer mechanisms like the
Caltech scheme or MIM2 . Monoterpene oxidation
is not included explicitly in GEOS-Chem v9-01-03 as used here; instead,
monoterpene emissions produce CO with an assumed 20 % molar yield
. NIWA-UKCA includes simple monoterpene oxidation
reactions based on . Oxidation products of isoprene
and monoterpenes are similar, and we do not distinguish between these two
sources in either model.
Figure 8 shows mean summertime mixing ratios of CO and key related
species (isoprene, formaldehyde, OH, and HO2) in near-surface
air (<1km) as simulated by GEOS-Chem and NIWA-UKCA for the
tropics and SH extra-tropics. Similar maps for TM5 and CAM-chem can be
found in Fig. S3 in the Supplement. At the surface, CO hotspots across
the tropics show similar magnitudes in GEOS-Chem and NIWA-UKCA,
especially in Africa and Southeast Asia where primary emissions
dominate (Fig. 8a). Surface isoprene – indicative of biogenic source
regions – is also similar across models (Fig. 8b), with maximum
values of more than 10 ppbv over South America. Comparison to
observations from the October 2005 GABRIEL campaign over the northeast
Amazon shows a 40–70 % high isoprene bias in the boundary layer
modeled means of 2.9–3.4 ppbv in the 3–6∘ N,
50–60∘ W flight region vs. observed mean of 2.00±0.76ppbv from. In the free
troposphere, mean simulated isoprene ranges from
0.04 to 0.2 ppbv across models, generally within the variability
of the GABRIEL observations 0.07±0.12ppbv;. The inter-model
consistency of the surface overestimate points to a high bias in the
MEGAN-CLM emissions, which are common to all SHMIP models.
The models show large discrepancies in surface distributions of
formaldehyde (CH2O), with much higher surface CH2O in
NIWA-UKCA than GEOS-Chem (Fig. 8c). In non-urban continental boundary
layers, the dominant source of CH2O is atmospheric oxidation
of NMVOCs, and in particular isoprene . The
higher mixing ratios simulated by NIWA-UKCA are therefore indicative
of more rapid chemical processing following isoprene oxidation. As
inter-model differences are small for isoprene mixing ratios
(Fig. 8b), OH mixing ratios (Fig. 8d), and the rate of the initial
isoprene+OH oxidation reaction (within ∼1 % at standard
temperature and pressure), the differences in surface CH2O
shown in Fig. 8 are likely driven by the chemistry (including photolysis) of second and later
generation isoprene oxidation products. CH2O oxidation provides
a source of CO over short timescales, and the faster production of
CH2O therefore also results in more rapid production of CO in
NIWA-UKCA. This is seen in Fig. 8f, which shows that the net balance
between CO chemical production (PCO) and CO chemical loss
(LCO) is more strongly weighted towards production in
NIWA-UKCA, leading to slight enhancements in boundary layer CO over
biogenic source regions (e.g., South America, Fig. 8a). While differences in
CO loss rates are likely partially responsible, we expect that CO
production contributes more to the PCO–LCO differences
given the similarity of surface OH
between models, particularly over South America where all models show
OH titration (Fig. 8d). The near-source surface differences between the two models
are consistent with the whole troposphere budgets for the SH given in
Table 2, which show total CO production is about 10 % higher in NIWA-UKCA
than GEOS-Chem, while total loss is about 5 % lower.
Five-year mean DJF longitude–altitude cross sections averaged
over 15–45∘ S of (a) isoprene, (b) OH, (c)CH2O, and (d) CO as simulated by GEOS-Chem (left)
and NIWA-UKCA (right). Numbers in (a) correspond to
locations of continental source regions: 1 = South America, 2 = Africa,
3 = Australia. The blue lines in (d) show the location and
vertical extent of the CGOP aircraft profiles.
The implications of these chemistry differences for the broader
vertical and horizontal distributions of CO depend on subsequent
transport and chemical processing. Figure 9 shows mean summertime
longitude–altitude cross sections (averaged over 15–45∘ S)
for isoprene, OH, CH2O, and CO (see Fig. S4 for TM5 and
CAM-chem). The isoprene cross sections (Fig. 9a) show key differences
in vertical transport between models. Relative to GEOS-Chem, NIWA-UKCA
shows less deep convective injection of isoprene to the
UT over Africa and Australia but more over South
America, where isoprene mixing ratios are at their maximum. As
a result, NIWA-UKCA displays an enhancement of isoprene mixing ratios
at roughly 12 km over South America while isoprene is largely
depleted at these altitudes in GEOS-Chem. The effects of the enhanced
isoprene uplift in NIWA-UKCA are compounded by lower OH in the UT
in this region (Fig. 9b). The net result for both CH2O (Fig. 9c) and
CO (Fig. 9d) is more UT production, less UT destruction, and therefore
higher UT mixing ratios in NIWA-UKCA than GEOS-Chem. Subsequent
zonal transport distributes this additional CO across the SH
mid-latitudes UT. Because isoprene emissions are much higher in South
America than other SH source regions (Fig. 8), the differences in
vertical transport over Africa and Australia play a much more minor
role in defining SH UT CO distributions.
The mean location and vertical extent of the profiles from CGOP are
shown as the blue lines in Fig. 9d. The figure shows that the
inter-model differences in CO vertical gradient seen in Fig. 5 are
consistent with the combined effects of differences in chemistry and
transport. Slower near-source oxidation of isoprene products in GEOS-Chem leads to
a horizontal smearing effect in the lower mid-troposphere, resulting
in relatively more CH2O and CO (largely of Australian biogenic
origin) reaching Cape Grim below ∼3km in GEOS-Chem
compared to NIWA-UKCA. Meanwhile, NIWA-UKCA's rapid isoprene uplift
and subsequent CO production and UT transport combined with reduced UT
loss result in relatively
more CO (largely of South American biogenic origin) reaching Cape Grim
above ∼6km in NIWA-UKCA than in GEOS-Chem. Combined,
these two factors drive a stronger vertical gradient in NIWA-UKCA in
the Cape Grim region. Impacts are similar over the western Pacific
region sampled by HIPPO.
In austral autumn (MAMJ), inter-model differences in surface mixing
ratios and vertical uplift are similar to those shown in Figs. 8 and 9
for austral summer. We have shown previously that biogenic-derived
secondary sources continue to drive simulated CO gradients in this
season (Fig. 6). Combined, these results suggest that the inter-model
variability in autumn is caused by the same differences in model
chemistry and transport as seen for summer.
Summary and conclusions
We have used a 9-year data set of monthly airborne observations of CO
from the Cape Grim Overflight Program (CGOP) to evaluate CO
distributions in the remote southern hemispheric free troposphere
as simulated by four global 3-D atmospheric chemistry models using
identical emissions. Observations above the surface in this region are
rare and are typically limited to a single year and/or season, so
interpretation of the Cape Grim data provides a unique picture of
climatological CO seasonal cycles and vertical gradients in the remote
SH. Our analysis focused on the models' relative abilities to
reproduce observed vertical gradients of CO from the surface to
8 km in different seasons. Through model sensitivity analysis
and comparison of simulated spatial distributions, we evaluated the
importance of primary vs. secondary sources on CO vertical gradients
and diagnosed the causes of inter-model divergence.
Average CO seasonal cycle at Cape Grim, expressed as the first harmonic of the monthly median CGOP
observations∗.
∗ The seasonal cycle was constructed from a
harmonic fit of the CGOP observations in each altitude bin. Only the first harmonic term
in each fit was statistically significant at the p=0.05 level or better, and those coefficients
are shown here along with their 95 % confidence intervals. The fitted seasonal cycle in each
bin, shown in Fig. S5, can be reconstructed as [CO](t)=Asin(2π(t+ϕ))+C, where A is the amplitude of the
seasonal cycle in ppbv, ϕ is the phase offset in years, C is a constant term
representing the overall mean CO in the altitude bin, and t is the time in fractional year.
Further details of the fitting methodology are given in the Supplement.
Average seasonal CO vertical profiles at Cape Grim, expressed as polynomial terms of the CGOP observed seasonal median vertical profile∗.
∗ The vertical profiles were constructed from a polynomial
fit of the 1 km binned CGOP observations in each season. The number of polynomial terms
in each season was chosen to minimize the residual error and maximize the adjusted r2,
and the resultant coefficients are shown here along with their 95 % confidence intervals.
All fits are statistically significant at the p=0.01 level or better. The fitted vertical profile in each
season, shown in Fig. S6, can be reconstructed as [CO](z)=a0+a1z+a2z2+a3z3+a4z4, where ai
are the fit coefficients and z is the altitude in km. Further details of the fitting methodology are
given in the Supplement.
Observations from both CGOP near Tasmania (1991–2000) and the recent
HIPPO campaigns over the SH Pacific (2009–2011) showed similar
seasonality, with larger gradients during the austral winter–spring
burning seasons (JA–SON) than during the rest of the year. The close
correspondence between these two data sets despite differences in
location, time period, and sampling strategies suggests the processes
driving observed vertical gradients are coherent across much of the
remote SH and have not changed significantly over the past 2
decades. The consistency between the two data sets further suggests that
quantitative metrics derived from the CGOP observations can be used to
diagnose model performance, both for the SHMIP models used here and
more generally for future revisions of these and other models. Tables 5 and
6 provide tabulated observation-based metrics for the two salient features
of the CGOP data: the seasonal cycle at different altitudes
(represented in Table 5 by a harmonic fit), and the vertical profile in
different seasons (represented in Table 6 by a polynomial fit).
Tables S2 and S3 in the Supplement provide the equivalent parameters for the
SHMIP models as a baseline against which to test future improvements
to these models. The fitting methodologies are described in detail in the
Supplement and can be easily applied
to any atmospheric chemistry model for quick-look diagnosis of the ability
to represent the SH free tropospheric CO background.
The four SHMIP models (GEOS-Chem, NIWA-UKCA, TM5, and CAM-chem)
were all able to reproduce observed vertical gradients during
winter–spring, but observed gradients were underestimated in austral
summer (DJF) and autumn (MAMJ) by GEOS-Chem and CAM-chem. All models
overestimated the seasonal cycle of the vertical gradient to some
degree. Sensitivity analysis showed that transport of primary BB CO is the
main driver of the observed gradients in winter–spring, when models
and observations agree. Regional tracers with CO-like primary
emissions and either fixed (CO25) or OH-driven
(COOH) lifetimes suggested a dominant influence in winter–spring
from southern African BB in JA and South American BB in SON,
with the seasonal offset due to the timing of peak emissions from
these two regions. Inter-model variability was relatively small in
both seasons and could generally be attributed to variability in the
influence of the southern African source. In summer–autumn, model
ability to match observed gradients was significantly diminished when
secondary CO sources were not included. Inter-model differences in
both CO25 and COOH tracers were much smaller than
differences in total CO during non-BB seasons, suggesting that neither
transport nor loss of primary CO are sufficient to explain inter-model
variability at this time of year. Instead, simulated gradients and
inter-model variability in these gradients are driven by secondary CO
of biogenic origin, implying a strong sensitivity of tropospheric
composition in the remote SH to long-range transport of biogenic
emissions and their oxidation products.
We compared simulated austral summer (DJF) horizontal and vertical
distributions of CO and related species between NIWA-UKCA and
GEOS-Chem (the models with the most realistic CO mixing ratios at Cape
Grim) and found significant differences driven by chemical processing
and vertical transport. While OH-driven oxidation of isoprene is
similar between the models, the ensuing chemistry of isoprene
oxidation products appears to proceed faster in NIWA-UKCA than in
GEOS-Chem, leading to more rapid production of formaldehyde and
CO. The slower chemistry in GEOS-Chem leads to a smearing effect, with
CO produced further downwind from source regions, and this effect is
particularly pronounced in the lower mid-troposphere near biogenic
sources. Inter-model chemistry differences are compounded by
differences in vertical transport. More rapid uplift over South
America in NIWA-UKCA leads to a secondary isoprene maximum at roughly
12 km that is not seen in GEOS-Chem. Subsequent oxidation
produces additional CO in the UT near biogenic source
regions, and zonal transport distributes this CO across the SH
mid-latitudes. The net effect of the differences in chemistry and
vertical transport is less CO at the surface and more at altitude in
NIWA-UKCA than GEOS-Chem, resulting in a stronger gradient that is
more consistent with CGOP observations.
It is important to note that the simulated summer–autumn CO vertical
gradients shown in Fig. 5 reflect the convolved effects of biogenic
emissions, model chemistry, and model transport, and the ability to
match the observed gradients cannot unambiguously test whether any of
these are correct (e.g., the emissions sensitivity test in
Fig. 6d). NIWA-UKCA's superior ability to match the observed DJF
gradient relative to GEOS-Chem or CAM-chem is achieved despite the
fact that its isoprene oxidation scheme (MIM with some updates) is
relatively simple and has known deficiencies
. Many recent
advances in our understanding of isoprene chemistry – including some
that are included in the other models' mechanisms – are not yet
implemented in NIWA-UKCA e.g.,,
although the mechanism does include updated reaction coefficients from
. Simulated agreement
therefore cannot be considered an endorsement of the chemical scheme
but rather an indication that the chemistry, transport, and emission
inventory are well matched to one another. This has important
implications for the use of model inversion studies to correct
emission estimates, as the strength of the correction will depend
heavily on the chemical scheme and driving meteorology used. Global,
satellite-based CO-only inversions in particular may be significantly
impacted, as constraints include observations over remote SH scenes
such as those studied here, which we have shown to be driven primarily
by secondary biogenic sources. Improved quantification of CO sources
may require combined inversion of multiple species with different
lifetimes and different contributions from biogenic vs. fuel sources,
such as CO and CH2O.
The results presented here, along with the companion analysis of the
SHMIP models presented in , point to biogenic NMVOC
emissions and chemistry as clear priorities for improving atmospheric
chemistry models in the remote SH. Isoprene and monoterpene emissions
from tropical and SH sources remain highly uncertain even in state-of-the-science
emission models like MEGAN and LPJ-GUESS .
In many data-poor parts of the world
where biogenic sources are expected to be dominant, constraints on emissions
are limited by fundamental uncertainties in the factors that cause plants to emit
isoprene and other NMVOCs . Improving the
process-based NMVOC emission models used to drive atmospheric chemistry
models will be key to improving model ability to simulate the background atmosphere.
Despite many recent advances, fundamental uncertainties also remain concerning
the chemistry of NMVOC oxidation
,
with large impacts on CO as shown here. Ongoing work to advance our
understanding of isoprene oxidation pathways, particularly in the low-NOX
environments characteristic of much of the SH
e.g.,,
should significantly improve simulation of SH CO production.
Understanding the clean background atmosphere is essential for
accurately attributing the impacts of ongoing anthropogenic and
natural global change. With relatively few primary source locations,
the remote SH serves as a large-scale test bed for quantifying
background processes. Although much of the previous work on SH
atmospheric composition has focused on the impacts of tropical
burning, we have shown here that the non-BB seasons (austral summer
and autumn) provide a more nuanced and critical test of the chemistry
of the background atmosphere. We have also shown that the vertical
gradient of CO is a particularly sensitive test of this chemistry as
it is driven by chemical production in summer and autumn. Regular
measurements of CO vertical profiles in the remote SH, such as those
conducted during the 1990s under the Cape Grim Overflight Program,
would thus provide an extremely valuable data set for probing the state
of the background atmosphere and its response to ongoing
change. Current models display varying degrees of fidelity in
reproducing observed CO gradients in a way that is consistent with
a state-of-the-science understanding of isoprene chemistry, and
increasing the complexity of the chemical mechanisms does not
necessarily improve simulation of CO gradients. Disentangling the
impacts on model biases of uncertainties in emissions from those in
chemistry and transport will necessitate broader in situ sampling
during non-burning seasons of multiple species with different chemical
lifetimes (including CO, NMVOCs, and HOx), at altitudes
throughout the tropospheric column, and in a range of SH environments
including near-source, direct outflow, and remote downwind
regions.
Abbreviated Campaign and Model NamesCGOP:Cape Grim Overflight ProgramHIAPER:High-performance Instrumented Airborne Platform for Environmental ResearchHIPPO:Pole-to-Pole ObservationsSHMIP:Southern Hemisphere Model Intercomparison ProjectMOPITT:Measurements of Pollution in the TroposphereBARCA:Regional Carbon Balance in AmazoniaGABRIEL:Guyanas Atmosphere-Biosphere exchange and Radicals Intensive Experiment with the LearjetSAFARI:Southern African Regional Science InitiativeACTIVE:Aerosol and Chemical Transport In tropical conVEctionSCOUT:Stratospheric-Climate Links with Emphasis on the Upper Troposphere and Lower StratosphereTRACE-A:Transport and Atmospheric Chemistry in the AtlanticIAGOS:In-service Aircraft for a Global Observing SystemMOZAIC:Measurement of Ozone and Water Vapor by Airbus In-Service AircraftPEM-Tropics:Pacific Exploratory Mission – TropicsREAS:Regional Emission inventory in ASiaGFED:Global Fire Emissions DatabaseMACC:Monitoring Atmospheric Composition and ClimateMACCity:MACC-CityZenMEGAN:Model of Emissions of Gases and Aerosols from NatureCLM:Community Land ModelLPJ-GUESS:Lund-Potsdam-Jena General Ecosystem SimulatorTRANSCOM:Atmospheric Tracer Transport Model Intercomparison ProjectIUPAC:International Union of Pure and Applied ChemistryJPL:Jet Propulsion LaboratoryMIM:Mainz Isoprene Mechanism
The Supplement related to this article is available online at doi:10.5194/acp-15-3217-2015-supplement.
Acknowledgements
This work was funded by a University of Wollongong Vice Chancellor's
Postdoctoral Fellowship to J. A. Fisher, with the assistance of resources
provided at the NCI National Facility systems at the Australian
National University through the National Computational Merit
Allocation Scheme supported by the Australian Government. We
gratefully acknowledge the staff of CSIRO GASLAB involved in the
collection and processing of these data and the Australian Bureau of
Meteorology/Cape Grim Baseline Air Pollution Station for funding the
Cape Grim Overflight Program for 9 years. We also thank the
entire HIPPO team and in particular Steve Wofsy, Rodrigo Jimenez,
Bruce Daube, Eric Kort, Jasna Pittman, Greg Santoni, and Teresa
Campos for providing CO measurements during the campaign. G. Zeng
acknowledges NeSI high-performance computing facilities for
NIWA-UKCA simulations and UKMO for using the UM. NZ's national
facilities are provided by the NZ eScience Infrastructure and funded
jointly by NeSI's collaborator institutions and through the MBIE's
Research Infrastructure programme. The National Center for
Atmospheric Research is operated by the University Corporation for
Atmospheric Research with funding from the National Science
Foundation. We thank Jingqiu Mao for help in implementing the
passive CO-like tracers in GEOS-Chem; Dagmar Kubistin and Clare
Paton-Walsh for helpful discussions; and D. Parrish, B. Pak, and an anonymous
reviewer for insightful comments that improved the manuscript.
Edited by: A. Pozzer
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