ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-7217-2015Multi-model simulation of CO and HCHO in the Southern Hemisphere: comparison with observations and impact of biogenic emissionsZengG.guang.zeng@niwa.co.nzhttps://orcid.org/0000-0002-9356-5021WilliamsJ. E.FisherJ. A.https://orcid.org/0000-0002-2921-1691EmmonsL. K.https://orcid.org/0000-0003-2325-6212JonesN. B.MorgensternO.https://orcid.org/0000-0002-9967-9740RobinsonJ.SmaleD.Paton-WalshC.https://orcid.org/0000-0003-1156-4138GriffithD. W. T.https://orcid.org/0000-0002-7986-1924National Institute of Water and Atmospheric Research, Lauder, New ZealandRoyal Netherlands Meteorological Institute, De Bilt, the NetherlandsUniversity of Wollongong, Wollongong, New South Wales, AustraliaNational Centre for Atmospheric Research, Boulder, Colorado, USAG. Zeng (guang.zeng@niwa.co.nz)02July201515137217724511December201427January201521May201508June2015This 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/15/7217/2015/acp-15-7217-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/7217/2015/acp-15-7217-2015.pdf
We investigate the impact of biogenic emissions on carbon monoxide (CO) and
formaldehyde (HCHO) in the Southern Hemisphere (SH), with simulations using
two different biogenic emission inventories for isoprene and monoterpenes.
Results from four atmospheric chemistry models are compared to continuous
long-term ground-based CO and HCHO column measurements at the SH Network for the
Detection of Atmospheric Composition Change (NDACC) sites, the satellite
measurement of tropospheric CO columns from the Measurement of Pollution in
the Troposphere (MOPITT), and in situ surface CO measurements from across the
SH, representing a subset of the National Oceanic and Atmospheric
Administration's Global Monitoring Division (NOAA GMD) network. Simulated
mean model CO using the Model of Emissions of Gases and Aerosols from Nature (v2.1)
computed in the frame work of the Land Community Model (CLM-MEGANv2.1) inventory is in better agreement with
both column and surface observations than simulations adopting the emission
inventory generated from the LPJ-GUESS dynamical vegetation model
framework, which markedly underestimate measured column and surface CO at
most sites. Differences in biogenic emissions cause large differences in CO
in the source regions which propagate to the remote SH. Significant
inter-model differences exist in modelled column and surface CO, and
secondary production of CO dominates these inter-model differences, due
mainly to differences in the models' oxidation schemes for volatile organic
compounds, predominantly isoprene oxidation. While biogenic emissions are
a significant factor in modelling SH CO, inter-model differences pose an
additional challenge to constrain these emissions. Corresponding comparisons
of HCHO columns at two SH mid-latitude sites reveal that all models
significantly underestimate the observed values by approximately a factor of
2. There is a much smaller impact on HCHO of the significantly different
biogenic emissions in remote regions, compared to the source regions.
Decreased biogenic emissions cause decreased CO export to remote regions,
which leads to increased OH; this in turn results in increased HCHO
production through methane oxidation. In agreement with earlier studies, we
corroborate that significant HCHO sources are likely missing in the models in
the remote SH.
Introduction
Carbon monoxide (CO) is ubiquitous throughout the troposphere and is an
important ozone (O3) precursor; it originates from both primary
emission sources (fossil fuel and biomass combustion, biogenic, and oceanic
processes) and in situ chemical production. The dominant chemical source term
in the troposphere is the photo-oxidation of methane (CH4) and
non-methane volatile organic compounds (NMVOCs) e.g..
Its principal sink is the reaction with the hydroxyl radical (OH); hence, CO
plays a key role in controlling the oxidizing capacity in the atmosphere
e.g.. The oxidation of methane and NMVOCs, such as
isoprene (C5H8), monoterpenes (C10H16), acetone
(CH3COCH3) and higher aldehydes, leads to the formation of
formaldehyde (HCHO), which, through photolysis and reaction with OH, is the
major chemical source of CO . Once formed, CO has
a relatively long lifetime of around 1–2 months, and therefore it is often
used as a chemical marker for characterizing the long-range transport of air
pollutants away from important source regions
e.g..
Due to a lack of strong regional emission sources, the Southern Hemisphere
(SH) acts as a global sink for many of the polluting trace species emitted in
the tropics, where polluted plumes are transported away out over the
relatively clean ocean becoming subject to chemical processing. The
relatively low population density, and thus low anthropogenic activity, in
the SH means that direct emission sources of CO are principally limited to
biomass burning (BB) and direct biogenic processes
e.g.. Satellite and ground-based
observations of CO in the SH have been used to identify the effect of BB and
its footprint through long-range transport in the SH, which dominates the CO
seasonal cycle there
e.g.. Global
distributions of HCHO are much more inhomogeneous than CO, due to the much
shorter lifetime of HCHO (on the order of a few hours), and the concentration
of HCHO drops off sharply away from the source regions. Observations of HCHO
are commonly used to constrain isoprene emissions in high-emission regions,
because it is a high-yield product of isoprene oxidation
e.g..
Global chemical models have been extensively used to estimate the sources and
sinks of CO e.g.. However, systematic
discrepancies between modelled and observed CO still exist, with models
generally underestimating CO in the more polluted Northern Hemisphere (NH) and overestimating CO in
the SH e.g.. In the remote SH, however,
the extremely low HCHO concentrations are expected to further complicate the
comparisons of model results with observations.
In contrast to the anthropogenic emissions dominating CO sources in the more
polluted NH, biogenic volatile organic compounds (VOCs) are important sources of CO and HCHO in the SH,
and isoprene oxidation contributes significantly to the regional CO and HCHO
abundances in this region . However, large uncertainties
exist in biogenic emissions inventories, in particular for surface fluxes of
isoprene and monoterpenes . Bottom-up estimates of annual
isoprene emissions vary between 400 and 600 TgCyr-1, and the typical range of annual total isoprene emissions
implemented in global atmospheric chemistry models is
∼ 200–600 TgCyr-1. The effect of
such uncertainties in biogenic emissions on SH composition, such as CO and
HCHO, has not been adequately assessed. Moreover, the sparsity of the
ground-based CO and HCHO measurements in the SH also limits our ability to
constrain these biogenic emissions.
In this study, we perform a number of simulations using an ensemble of
chemical transport models (CTMs) and chemistry–climate models (CCMs) as part
of the Southern Hemisphere Model Intercomparison Project (SHMIP), to compare
modelled CO and HCHO to observations and to investigate the factors that
influence the distributions of CO and HCHO in the SH. Given the relatively
low anthropogenic emissions in the SH and the dominance of biogenic emissions
of VOCs (mainly isoprene), we determine the influence that different emission
inventories of isoprene and monoterpenes have regarding their effects on
modelled CO and HCHO columns in the SH. Satellite observations of SH CO
usually are in good agreement with ground-based observations; however, the
data quality of the satellite data deteriorates towards the poles
. found that CO columns exhibit
a large-scale mode of variability in the remote SH that does not exist in the
NH. For our purposes, we make use of high-precision ground-based Fourier
transform infrared spectroscopy (FTIR) measurements of CO columns from four
SH sites that have not previously been fully exploited for model evaluations,
namely Darwin (12.43∘ S, 130.89∘ E) and Wollongong
(34.41∘ S, 150.88∘ E) in Australia, Lauder
(45.04∘ S, 169.69∘ E) in New Zealand, and Arrival Heights
(77.82∘ S, 166.65∘ E) in Antarctica. We also compare the
modelled HCHO columns to those observed by the FTIR instruments at Wollongong
and Lauder. In a companion paper, have evaluated the
vertical gradients of CO from the SHMIP models in the vicinity of Cape Grim,
Australia, which is representative of SH mid-latitude background air, using
multi-year aircraft measurements available from the Cape Grim Overflight
Programme . The influence of both chemistry and
transport on the modelled vertical gradients of CO are addressed. Although
there are biases of various magnitudes across the different models, the
seasonal variability and extent of the gradients in tropospheric CO are shown
to be captured reasonably well, especially during the tropical BB season.
In this paper we address the sensitivity of CO and HCHO distributions in the
SH to biogenic emissions of isoprene and monoterpenes as provided by the
LPJ_GUESS emission inventory and the
MEGANv2.1 model across the models included in SHMIP. In
Sect. 2 we provide model descriptions, the common emission inventories used
to drive the models, and the observations used in the study. In Sect. 3 we
compare results for the period 2004–2008 and show comparisons between
modelled CO and HCHO columns and the FTIR measurements at the four SH sites
mentioned above, modelled and MOPITT CO columns, as well as comparisons
between modelled and observed surface CO. In Sect. 4 we analyse differences
in the models' abilities to reproduce SH CO and HCHO columns, and the
underlying differences in the models' chemistry and transport. In Sect. 5 we
further analyse the chemical production and loss terms to address differences
in models' NMVOC oxidation mechanisms. In Sect. 6 we assess the sensitivity
of modelled CO and HCHO to changes in biogenic emissions and the effect of
such changes on the oxidizing capacity in the clean SH. Finally, in Sect. 7
we present our conclusions.
Model simulations and observations
The SHMIP intercomparison uses four global models, including two CTMs (Tracer Model 5 (TM5),
GEOS-Chem) and two CCMs
(the Community Atmosphere Model with chemistry (CAM-chem), NIWA-UKCA – the
National Institute of Water and Atmospheric Research – UK Chemistry and
Aerosols Model). In this section we provide the description of the
simulations performed, the common emission inventories employed, a brief
description of each model, the meteorological drivers, and the observations
used for evaluating the performance of the models.
Global annual total emissions (Tgyr-1). Values in
brackets are sums of SH emissions.
M denotes CLM-MEGANv2.1 emissions; G denotes LPJ-GUESS
emissions; * Individual model values for the year 2004 are 13.3
(NIWA-UKCA), 12.9 (TM5), 12.4 (GEOS-Chem), and 7.1 (CAM-chem).
Regional emission fluxes for isoprene between 2004 and 2008 from the
CLM-MEGANv2.1 and LPJ-GUESS emission inventories.
Simulations
We perform simulations covering the period of 2004 to 2008 using a 1-year
spin-up for 2003. The two CTMs are driven by the meteorological analysis for
the same period from their respective sources, whereas NIWA-UKCA uses
observed sea surface temperature and sea ice data sets. CAM-chem runs in the
specified-dynamics mode, using meteorological fields from the reanalysis
data. Two simulations are performed in all models with identical emission
inventories for the anthropogenic and BB components, but different
inventories are adopted for biogenic isoprene and monoterpene emissions. We
also include passive CO tracers in the simulation defined as having the same
global primary, surface emission sources as CO, but with one having a fixed
lifetime of 25 days and a second having the lifetime determined by OH
distribution in each respective model. These tracers allow for the
differentiation of the inter-model variability with respect to transport of
CO to the SH from the main source regions.
Although we have been careful to harmonize the emissions used across models,
differences in the chemical mechanisms which are employed result in the
aggregated emissions of the NMVOCs being somewhat different across the
models. For anthropogenic emissions, we adopt the yearly specific
Monitoring Atmospheric Composition and Climate (MACC)/CITYZEN (MACCity)
global emission estimates nested with the
Regional Emission Inventory in Asia (REASv2.1) for the East Asian region
. Interannually varying monthly mean BB emissions are taken
from the Global Fire Emissions Database version 3 (GFEDv3)
. For lightning-NOx emissions, each model adopts
individual parameterizations, which interact with the models' convection
schemes. Natural emissions of soil NOx and CO from the ocean are taken
from the Precursors of Ozone and their Effects in the Troposphere (POET) database (http://eccad.sedoo.fr). The annual total
emission fluxes for key species are listed in Table 1 for the simulation
period of 2004–2008.
Biogenic emissions for isoprene, monoterpenes, CO, methanol, and acetone are
based on MEGANv2.1 and are calculated offline using the
Community Land Model CLM4.0;, driven by Climate Research Unit-National Centers for Environmental Prediction (CRUNCEP)
reanalyses (http://dods.extra.cea.fr/data/p529viov/cruncep/readme.html)
for each year. We refer to this data set as Model of Emissions of Gases and Aerosols from Nature (v2.1) computed in the frame work of the Land Community Model (CLM-MEGANv2.1) hereafter. We then
replace the MEGANv2.1 emissions for isoprene and monoterpenes with the
“GUESS-ES” emissions that were calculated by the LPJ-GUESS model (http://eccad.sedoo.fr) in our second set of
simulations for comparison (hereafter referred to as LPJ-GUESS simulations);
this emission data set is generated using the dynamical vegetation model
LPJ-GUESS driven with Climate Research Unit Timeseries (CRU TS) 3.1 climate data .
None of the models currently include any higher terpenes. The yearly varying
annual global total emissions of isoprene from CLM-MEGANv2.1 (462–508 Tg yr-1)
are markedly larger than the LPJ-GUESS emissions (431–450 Tg yr-1), i.e.
∼5–10 % relative to CLM-MEGANv2.1 (Table 1). The
corresponding differences are much larger for the SH between these two
inventories, i.e. ∼10–20 % relative to CLM-MEGANv2.1, over the
same period. For monoterpenes, the annual total CLM-MEGANv2.1 emissions are
substantially larger than the LPJ-GUESS emissions, i.e. a factor of 4 and 6
larger globally and in the SH, respectively. The two data sets have been
generated from the respective land surface models driven by similar
meteorological fields, as specified in the above references. Here, we do not
harmonize the model meteorology to those used in generating the biogenic
emissions. Instead, we prescribe the monthly mean biogenic emissions in the
models to ensure the consistency. Figure 1 shows the SH and regional monthly
total isoprene emission fluxes from LPJ-GUESS and CLM-MEGANv2.1 for Australia
and part of Indonesia (0–44∘ S, 94–156∘ E), southern
Africa (0–37∘ S, 9–44∘ E), and South America
(0–57∘ S, 34–84∘ W), the regions with high isoprene
emissions. The largest monthly emissions occur in austral summer in both
data sets when the differences between these two data sets are also largest.
Overall, the integrated CLM-MEGANv2.1 isoprene emissions (especially the
summer maxima) are substantially higher than LPJ-GUESS emissions with the
exception of southern Africa, where LPJ-GUESS emissions are larger. Figure 2
shows the spatial distribution of the fluxes for both isoprene and
monoterpenes from the two emission inventories for January 2005. In Amazonia,
tropical Africa, and Australia emissions
are visibly larger in CLM-MEGANv2.1 than in LPJ-GUESS.
Isoprene and monoterpene emission distributions from CLM-MEGANv2.1
and LPJ-GUESS for January 2005.
ModelsNIWA-UKCA
NIWA-UKCA stands for the version of the UK Chemistry and Aerosols Model
(UKCA) that is used and further developed at the National Institute of Water
and Atmospheric Research (NIWA). The background climate model is similar to
HADGEM3-A with a horizontal resolution of 3.75∘×2.5∘ and 60 vertical levels extending from the surface to
84 km. The physical processes in NIWA-UKCA, including interactive dry
and wet deposition of the trace gases and the FAST-JX photolysis scheme, have
been described in detail by and .
Unlike the version described by , here we now apply dry
deposition following only to the bottom model layer rather
than throughout the boundary layer. The model setup used here comprises
a coupled stratosphere–troposphere chemistry scheme. The stratospheric
reactions are the same as in and include explicit
chlorine and bromine chemistry. We have updated the NIWA-UKCA chemical
mechanism from that described in to account for
emissions and degradations of ethene (C2H4), propene
(C3H6), methanol (CH3OH), isoprene, a representative
monoterpene, and a lumped species that accounts for missing NMVOCs in the
model similar to the approach taken in the IMAGES model , with
primary emission sources. In addition to the above, the NIWA-UKCA chemical
scheme includes nitrogen oxides (NOx), CO, ethane (C2H6),
propane (C3H8), HCHO, acetone (CH3COCH3), and acetaldehyde
(CH3CHO) as primarily emitted species (O'Connor et al., 2014). The
isoprene oxidation scheme is the mechanism described by Pöschl
et al. (2000), as previously used by , but with rate
coefficients of reactions between OH and isoprene nitrates and between NO and
isoprene peroxy radicals updated following . A
diurnal cycle is imposed on isoprene emissions as a function of the solar
zenith angle. We adopt a set of monoterpene oxidation reactions initiated by
OH, O3, and NO3, described by . Methane mixing ratios
are prescribed at the surface with a constant value of 1813 ppbv
north of the Equator and a constant value of 1707 ppbv south of the
Equator, and are the same for each year. Surface emissions are as specified
in Sect. 2.1. Lightning-NO emissions are based on the parameterization of
, as a function of convection. The model uses
prescribed sea surface conditions following the Atmospheric Model
Intercomparison Project (AMIP) II (http://www-pcmdi.llnl.gov). The
chemistry is run in a semi-offline mode; i.e. chemistry feedbacks to
meteorology and hydrology are deactivated. Table 2 summarizes key model
properties for all models.
Summary of model information.
NIWA-UKCATM5GEOS-Chem*CAM-chemResolution long / lat / lev3.75∘/2.5∘/ 603.0∘/ 2.0∘/ 342.5∘/ 2.0∘/ 472.5∘/ 1.9∘/ 56MeteorologyDriven by observed SSTs and sea iceERA-InterimGEOS-5Specified dynamics, MERRA reanalysisMean surface CH4**(ppbv)Global 1758, SH 1709Global 1794, SH 1739Global 1782, SH 1731Global 1758, SH 1709ChemistryTropospheric and stratospheric chemistry; 85 species Modified CB05 chemical mechanism, 60 species tropospheric chemistry, 121 species, 106 transported150 species,MOZART scheme Isoprene oxidation mechanismMainz Isoprene Mechanism with update OH and NO initiation rates CB05 ; Modified HO2 yields Caltech Isoprene Mechanism MOZART scheme
* GEOS-Chem version v9-01-03 is used in
this study. ** Surface CH4 mixing ratios shown here are for
year 2004; NIWA-UKCA uses the same values for each year and the interannual
variation is small in other models.
TM5
TM5 is a global 3-D CTM driven by meteorological fields from the European Centre for Medium-Range Weather Forecast (ECMWF)
ERA-interim re-analysis using an update frequency of 3 h.
Interpolated fields are used for the interstitial time periods. The
version used here is identical to that described by and
uses the modified CB05 chemical mechanism for describing
the chemistry which occurs in the troposphere, along with online photolysis
rates. Details relating to the convection, advection, and deposition processes
employed are given by . TM5 includes a full description of
HOx and NOx reactions, as well as explicitly treating all C1 to
C3 organic species in addition to ammonia (NH3), sulfur dioxide
(SO2), and dimethyl sulfide (DMS). For this study a horizontal
resolution of 3∘×2∘ is chosen adopting 34 vertical
layers from the surface up to 0.5 hPa. The isoprene and monoterpene
oxidation schemes are based on the mechanisms developed by ,
with modifications to both the oxidation rate of organic hydroperoxide (ROOH)
and the production efficiency of HO2 from the OH-initiated oxidation
of isoprene following recommendations by . Also in TM5, a
diurnal cycle is applied to the monthly mean isoprene emissions. Methane
emissions are included and the simulated surface concentrations are then
nudged towards a latitudinally and monthly varying climatology based on
surface observations; a detailed description of the approach is given by
. Mean surface methane mixing ratios for the year 2004 are
listed in Table 2, and their interannual variation is small.
GEOS-Chem
The GEOS-Chem global 3-D CTM (www.geos-chem.org) is driven by
meteorology from the NASA Global Monitoring and Assimilation Office (GMAO)
Goddard Earth Observing System (GEOS-5) assimilated product .
The native GEOS-5 product with 0.667∘×0.5∘
horizontal resolution and 72 vertical levels (surface to 0.01 hPa) is
regridded for computational efficiency to 2.5∘×2∘×47 levels (with all vertical lumping in the stratosphere). Here we
use the v9-01-03 coupled O3–NOx–HOx–VOC–aerosol
simulation with the Caltech isoprene mechanism ,
which includes 57 transported species (both gas phase and aerosol) and an
additional 49 species that interact chemically but are not transported (The
detailed chemical mechanism used in this study can be found at
http://wiki.seas.harvard.edu/geos-chem/index.php/New_isoprene_scheme_prelim).
As monoterpenes are not included as an explicit chemical species, their
emissions are used to produce CO assumed 20 % molar
yield; and acetone assume a 12 % molar
yield;. CH4 mixing ratios are prescribed annually and
latitudinally based on the National Oceanic and Atmospheric Administration's Global Monitoring Division (NOAA GMD) surface observations and are listed in
Table 2. Interactive chemistry is computed in the troposphere only, with
stratosphere production and loss rates for most species taken from the NASA
Global Modeling Initiative (GMI) Combo CTM Aura4 model .
Stratospheric ozone is simulated using the Linoz linearized ozone scheme
. Lightning-NO emissions are based on the cloud top height
parameterization of with regional correction to match
lightning distributions from satellite, as described by .
Other processes in GEOS-Chem v9-01-03 including mixing and deposition are
described in detail by . The version used here was
modified from the standard v9-01-03 to include irreversible HO2
uptake by aerosols with a gamma value of 0.2 , and to include
methanol as an interactive tracer based on the offline simulation of
. In the standard GEOS-Chem simulation, biogenic emissions
are computed online using a coupled version of the MEGAN model. Here, to
ensure consistency with the other SHMIP models, we used the pre-computed
biogenic emissions described above (Sect. 2.1) and applied an imposed diurnal
variability tied to solar zenith angle.
CAM-chem
CAM-chem is a component of
the NCAR Community Earth System Model (CESM). The version used for this study
is the same as that used for the Chemistry–Climate Model Initiative (CCMI)
, and very similar to the CAM4 version described in
. For SHMIP, CAM-chem was run in the specified-dynamics mode,
using meteorological fields from the Modern-Era Retrospective Analysis For
Research And Applications (MERRA) reanalysis product
(http://gmao.gsfc.nasa.gov/merra/), regridded to the model horizontal
resolution of 1.9∘×2.5∘, using the lowest 56 levels.
In this study, the internally derived meteorological fields (temperature,
winds, surface heat, and water fluxes) are nudged at every time step
(30 min) by 1 % towards the reanalysis fields (i.e., a 50 h
Newtonian relaxation time). The chemical mechanism, based on MOZART-4
, includes both tropospheric and stratospheric chemistry,
with 150 compounds and 400 photolysis and kinetic reactions, and
a tropospheric bulk aerosol model . Heterogeneous reactions
on aerosols are included as described in , including the
uptake of HO2 with a reaction probability of 0.2 producing
H2O2. While the option of running with online MEGANv2.1 biogenic
emissions is available in CAM-chem, this was not used and all surface
emissions were based on those specified for this intercomparison, with
diurnal variation imposed for isoprene and monoterpenes. Methane surface
mixing ratios are specified for monthly zonal averages, as used for CCMI,
based on RCP6.0 . Lightning-NO emissions are determined
according to the cloud height parameterization of and
. The vertical distribution follows and the
strengths of intra-cloud and cloud–ground strikes are assumed equal, as
recommended by .
Observations of CO and HCHO in the SH
Long-term measurements of trace gases up to the upper troposphere in the
remote SH are sparse. Continuous multi-year tropospheric columns of CO are
observed at four SH sites: Darwin, Wollongong, Lauder, and Arrival Heights,
with high spectral resolution FTIR spectrometers. In addition, HCHO columns
have been retrieved at Wollongong and Lauder. The data records and retrieval
methods have largely been presented before
and therefore we only give a brief description here.
At Wollongong, Lauder, and Arrival Heights, mid-infrared (MIR) spectra from
the FTIR measurements are used to retrieve CO columns, and these stations are
part of the Network for the Detection of Atmospheric Composition Change
(NDACC; http://www.ndacc.org). The retrieval of trace gas information
from these recorded spectra was performed based on the SFIT2 profile
retrieval algorithm using the 4.7 µm band, and is similar to that
described by . At Arrival Heights, there are no
measurements during the polar nights which last 4–5 months per year. CO
total columns have been observed at Darwin since 2005 with solar remote
sensing using FTIR measurements in the near infrared (NIR), as part of the
Total Column Carbon Observing Network (TCCON) .
The spectra used for CO retrieval are analysed with the GFIT spectral fitting
algorithm for total column CO. Details of the
retrieval method and data are described in . Daily
averaged time series of CO columns from 2004 to 2008 are used for comparison
with the models. Due to the very small “smoothing” error for CO retrievals,
which indicates the difference between realistic and retrieved CO columns,
averaging kernels are not applied when comparing with the modelled CO data
. Comparisons are made against daily-mean output from
each model.
Total columns of HCHO were retrieved at Wollongong and Lauder from the
mid-infrared spectra using the SFIT2 inversion algorithm .
HCHO is a very weak absorber in the mid-infrared spectral region. Due to its
large “smoothing” errors, the averaging kernels and a priori applied in the
retrieval were also applied to the modelled data for a like-with-like
comparison between the modelled and retrieved HCHO columns following the
method described by and references therein.
In order to provide comparisons on a larger spatial scale, we also perform
multi-year comparisons for surface CO against flask measurements available
from the NOAA Global Monitoring Division network . The
selected sites are all situated in the SH and cover an extensive latitudinal
range. They are typically located away from regions which exhibit strong
local emissions of CO. The sites shown are Mahe Island (4.7∘ S,
55.5∘ E), Ascension Island (8.0∘ S, 14.4∘ W),
Pacific Ocean (30.0∘ S, 176.0∘ W), Baring Head
(41.4∘ S, 174.9∘ E), Crozet Island (46.4∘ S,
51.9∘ E), Tierra del Fuego (54.9∘ S, 68.3∘ W),
Syowa Station (69.0∘ S, 39.6∘ E), and South Pole
(90∘ S, 24.8∘ W). The locations of all sites used in this
study are displayed in Fig. S1 in the Supplement.
To illustrate how the models perform on the global scale in general, we also
show comparisons between modelled CO and observed CO columns made by the
Measurements Of Pollution in the Troposphere (MOPITT) satellite instrument
(https://www2.acd.ucar.edu/mopitt). We use the MOPITT version 6 level 3
thermal-infrared product, and the data are monthly averages. A description of
the data and the retrieval method is given by . Here
the daytime monthly mean MOPITT CO columns for January and September 2005 are
used for comparison. Model outputs are monthly averaged and have been
interpolated to the MOPITT horizontal grid of 1∘× 1∘and 10 vertical levels with a 100 hPa spacing. The MOPITT CO averaging
kernel and a priori data are applied to the calculation of the modelled CO
columns, as described by and the references therein;
such an approach is generally recommended when comparing modelled data to
data from satellite remote-sensing instruments .
Modelled and observed daily-mean FTIR CO columns at SH stations from
four models. Simulations use CLM-MEGANv2.1 biogenic emissions.
Comparison between models and observationsFTIR CO columns
Figure 3 shows the direct comparison between modelled and FTIR daily-mean CO
columns for the CLM-MEGANv2.1 simulation. Here, we use the tropospheric FTIR
partial columns, for the reasons that not all models have well-resolved
stratospheric chemistry in the model, there is a significant contribution of
CO from the mesosphere during polar spring , and that all
models lack or have deficiencies in handling the mesospheric chemistry. FTIR
CO partial columns (0–12 km) are used for comparison at Arrival
Heights, Lauder, and Wollongong instead of total columns. However, the
partial columns of CO at Darwin are not available so we use total columns for
comparison. Note that the contribution of mesospheric CO to the total column
is expected to be minimal at Darwin given its tropical location
; therefore, the differences between the partial and the
total columns are expected to be small. The model data at all locations have
been interpolated to the dates when the measurements were carried out.
Figure 3 shows that CO seasonal cycles are well reproduced by all four models
at all locations. Models accurately reproduce the total columns of CO at
Darwin with very small inter-model differences. The Darwin measurement site
is the closest to the tropical source regions; this indicates that the
emissions in this area are well represented in the models. Inter-model
differences are notably larger at other sites which are located further from
the source regions, with consistent overestimation by TM5 and underestimation
by CAM-chem at both Arrival Heights and Lauder. Such differences are possibly
associated with both differences in the oxidative capacities in these two
models and differences in transport (discussed in Sect. 4). All models
underestimate CO columns at Wollongong, especially during the peak BB season;
this may be due to its proximity to large forested areas and/or the cities of
Sydney and Wollongong whose direct emissions may be underestimated in the
MACCity inventory. Note that due to the coastal location of Wollongong, model
grid boxes may not be representative of the measurement site.
Deviations of model ensemble- and daily-mean CO columns from the
observed FTIR CO columns with CLM-MEGANv2.1 simulation (red) and with
LPJ-GUESS simulation (blue) respectively. The difference between the modelled
CO columns from these two simulations are displayed in black symbols
(COCLM-MEGANv2.1-COLPJ-GUESS).
Percentage differences between modelled and observed multi-annual
mean CO columns at Arrival Heights, Lauder, Wollongong, and Darwin from two
simulations with CLM-MEGANv2.1 (left) and LPJ-GUESS emissions (right)
respectively.
We performed a second set of simulations using LPJ-GUESS isoprene and
monoterpene emissions (see Fig. S2 in the Supplement); the models visibly
underestimate the observed FTIR tropospheric CO columns at all sites. The
deviation of model ensemble-mean CO columns from the observed FTIR columns
are shown at the four measurement sites (Fig. 4) in comparison with the
simulation using CLM-MEGANv2.1 biogenic emissions. The differences between
these two simulations are also shown (i.e. CLM-MEGANv2.1 minus LPJ-GUESS). It
appears that a larger negative bias exists when adopting the LPJ-GUESS
emissions for all of the column measurement stations (i.e. CLM-MEGANv2.1
results in better agreement with the FTIR observations). The deviations of
both simulations from the observed CO columns exhibit large seasonal
variations but seasonal and interannual variations are consistent between
these two simulations.
Multi-annual averaged ensemble model mean deviations (%) from
observed FTIR CO columns.
Percentage differences between ensemble model mean and MOPITT CO
columns for January and September 2005, from two simulations with
CLM-MEGANv2.1 (top) and LPJ-GUESS (bottom) biogenic emissions, respectively.
Figure 5 shows differences between the modelled and observed FTIR CO columns
at the four measurement sites from the multi-annual ensemble-mean data for
both CLM-MEGANv2.1 and LPJ-GUESS simulations. As in Fig. 4, the seasonal
variations of the biases from these two sets of simulations follow a very
similar pattern, implying that the effect of different biogenic emissions is
reflected in the differences in the background CO columns in the SH. The
biases shown in the ensemble model means from both simulations are largest
during the SH tropical BB season of September, October, and November (SON),
although at Darwin the negative biases are also high in July and increase
from October to December. For Wollongong, Lauder, and Arrival Heights the
largest negative biases are in October, November, and December, respectively;
this suggests an underestimation of SH BB sources in GFEDv3 and the
subsequent effect on CO columns at SH remote locations through long-range
transport. At Darwin, CO columns are more likely influenced by local or
nearby BB sources which may have a different seasonality. The annually
averaged biases of the model ensemble means for each site are shown in
Table 3; the lowest biases are at Arrival Heights for both simulations,
followed by those for Darwin, Lauder, and Wollongong. Note that the low
ensemble bias at Arrival Heights is largely the result of cancellation of a
positive bias in TM5 with a negative bias from CAM-chem with a similar
magnitude. The large spread between the models indicates that substantial
differences exist in other physical and/or chemical processes which are
unrelated to emissions.
The individual model biases are also shown in Fig. 5. For both simulations,
inter-model variability is notably larger during months that lie outside the
seasons when most intensive BB occurs, i.e. typically in austral summer and
autumn (covering December and January to May). Such a seasonal dependence of
inter-model variability is consistent with that described by
who compare modelled vertical CO gradients in the Cape Grim region using the
same simulations, and is due to the difference in chemistry that controls CO
chemical production and loss processes in the seasons other than the peak
biomass burning season. Inter-model variability is generally larger in
CLM-MEGANv2.1 than in LPJ-GUESS for all seasons and locations, primarily due
to the larger response of modelled CO to its higher precursor emissions.
MOPITT CO columns
MOPITT CO columns and the individual model biases for January and September
2005 are shown in Fig. S3 in the Supplement. The model data are monthly means
convolved with the MOPITT averaging kernels and a priori data. MOPITT data
exhibit a lot of gaps over the Amazon region in January and over Africa in
September, due to persistent cloud cover. All models underestimate MOPITT CO
columns in the NH (with the exception of East Asia), and overestimate CO in
the plumes, particularly over the tropical Atlantic in January and the
Pacific in September. These plumes originate in tropical Africa and South
America, respectively, indicating an overestimation of biomass burning
emissions in these regions. Comparing the models, TM5 shows higher CO columns
throughout the SH and CAM-chem the lowest, in agreement with the comparison
of FTIR CO columns. Figure 6 shows the percentage differences between the
ensemble-mean modelled and measured columns for both the CLM-MEGANv2.1 and
the LPJ-GUESS simulations. There is a general underestimation of CO columns
by both ensembles in the NH by up to ∼-25 %. Both ensembles
overestimate CO in the source regions, with up to +30 % over tropical
Africa in January and over Amazonia in September, i.e. during the months of
peak biomass burning. In the SH, away from the CO plumes, the CLM-MEGANv2.1
ensemble clearly compares better with MOPITT CO than the LPJ-GUESS ensemble,
with biases typically between -10 and +10 % in January and September,
whereas errors typically are in the range -20 to -5 % from the
LPJ-GUESS ensemble. Both ensembles also underestimate CO columns over
Australia in September, which suggests an underestimation of biomass burning
in GFEDv3. This is also reflected in the comparison between modelled and FTIR
CO columns in the four SH locations shown above, which generally show
negative biases in modelled CO. The two ensembles are fairly similar in the
NH with regard to their CO columns but exhibit significant differences in the
extratropical SH. This is consistent with a larger relative role of biogenic
emissions in the SH versus the NH.
Modelled monthly mean surface CO with CLM-MEGANv2.1 emissions
(coloured lines) and observed monthly mean surface CO at SH sites.
Observations are from the NOAA GMD network (Novelli et al., 1998):
http://www.esrl.noaa.gov/gmd/.
Surface CO
To assess the models' ability to capture both the seasonality and
interannual variability of CO at the surface over the simulation period, we
show in Fig. 7 comparisons between the CLM-MEGANv2.1 simulations and monthly
mean CO values observed at the eight surface sites listed in Sect. 2.3.
Consistent with the FTIR column comparisons, all models capture the seasonal
cycles of observed surface CO at each location. In line with Fig. 3 in
, TM5 typically exhibits a high bias and CAM-chem exhibits
a low bias of the order of 5–10 ppbv. Large variations exist in
seasonal cycles at both Mahe Island and Ascension Island, but the timing of
the peaks are different. At Mahe Island, surface CO peaks in January and
February due to the influence of anthropogenic emissions from India
, whereas at Ascension Island, the seasonal cycle is principally
driven by CO which originates from BB in southern Africa during
June–July–August e.g.. The interannual
variability and timing in peak mixing ratios is not captured well at
Ascension Island, especially for GEOS-Chem and TM5; this is possibly related
to too strong westerly transport out of southern Africa and too weak an
oxidative capacity, especially in TM5. For the more southerly oceanic sites,
the seasonal cycles and amplitudes are remarkably similar, indicating that
the variability in background CO is rather low at the surface in the SH
remote locations. In general, NIWA-UKCA and GEOS-Chem display a better
agreement with the observations in the remote SH, indicating that their
oxidative capacities are more realistic. The consistent high and low biases
in TM5 and CAM-chem, respectively, are related to the oxidizing capacity in
these models; this is discussed in Sect. 4.
Percentage differences between monthly mean modelled and observed
surface CO; solid black lines for
CLM-MEGANv2.1 ensemble and dashed black lines for LPJ-GUESS ensemble.
Individual model deviations (coloured lines) are from the CLM-MEGANv2.1
simulations only. Data are averaged over 2004–2008.
We quantify the differences between the multi-annual ensemble means for
surface CO and the corresponding values derived from the observations for
both the CLM-MEGANv2.1 and the LPJ-GUESS simulations (Fig. 8). As was seen in
the model comparisons to the FTIR and MOPITT CO data, the observed
distributions of surface CO in the SH are better reproduced by CLM-MEGANv2.1
for most of the chosen sites. A comparison of sites shows that the seasonal
biases are more variable for the tropical sites which are affected by the
interannual variability in tropical BB. For the mid- to high-latitude sites,
the CLM-MEGANv2.1 ensemble mean accurately reproduces the observations in
most cases, whereas the LPJ-GUESS ensemble is consistently biased low. The
individual model biases (shown only for the CLM-MEGANv2.1 simulation) are up
to ±20 %, and are much larger than the differences between the two
ensemble means (∼10 %). The generally better agreement between
modelled and observed surface CO, relative to the agreement between modelled
and FTIR CO columns in the remote SH, reflects that there may be some
deficiencies in the models' vertical transport of either CO and/or its
precursors. This generally underestimation of observed vertical gradients of
CO by the models in the remote SH was shown by for the Cape
Grim region.
Modelled and observed daily-mean FTIR HCHO columns at Lauder and
Wollongong. Simulations use CLM-MEGANv2.1 biogenic emissions.
HCHO columns
Here we examine the models' ability to reproduce observed HCHO columns at the
SH mid-latitude sites Lauder and Wollongong. Figure 9 shows comparisons
between modelled daily-mean HCHO columns (from the CLM-MEGANv2.1 simulation)
convolved with FTIR a priori data and averaging kernels, and observed daily-mean HCHO columns from the FTIR measurements. The seasonal cycles are
generally well reproduced across the entire model ensemble, with the seasonal
maxima in austral summer and the minima in winter, but all models
significantly underestimate observed columns in all seasons. Inter-model
differences in modelled HCHO columns are larger at Lauder than at Wollongong;
the highest HCHO columns are produced in GEOS-Chem, whereas the lowest are
from TM5. Such variations between the models indicate that the differences in
the models' chemistry are the driving factor, in particular at the sites that
are further away from the emission sources. Significant and persistent low
bias across all models cannot be reconciled by considering the diurnal cycle
in HCHO; for testing purposes, we also calculated HCHO columns by replacing
daily-mean HCHO data shown in Fig. 9 with the daily maximum of the 3 hourly
data from one of the ensemble members (CAM-chem). This resulted in small
overall changes, with ∼10–15 % increases that occur in some
summer months, and the increases were not sufficient to close the gap between
the models and the observations. Therefore, we are confident that using daily-mean modelled HCHO columns for comparing to columns from FTIR observations
that occur during the daylight is satisfactory. Figure 10 shows the
multi-annual monthly mean FTIR HCHO columns and model ensemble means averaged
for the same years with both CLM-MEGANv2.1 and LPJ-GUESS emissions for
isoprene and monoterpenes. Overall, the models underestimate the observed
HCHO columns by approximately 50 %. Differences in biogenic emissions do
not appreciably affect this.
Observed (black symbols) FTIR multi-annual monthly-mean HCHO columns and
corresponding multi-model mean from CLM-MEGANv2.1 (red) and LPJ-GUESS (blue)
simulations. Measurement errors are shown by vertical bars (black). Model
ranges from the CLM-MEGANv2.1 simulations are also given (coloured vertical
bars).
In the case of Wollongong, proximity to Sydney and the influence of episodic
BB events in the vicinity could introduce local direct
and indirect sources of HCHO and chemical precursors which are unaccounted
for and might have contributed to the low bias simulated in the models,
particularly for the seasonal peaks. However, at Lauder there are no known
significant local sources of HCHO. We therefore assume that the
underestimation of observed FTIR HCHO columns by the models is very likely
related to missing emissions of precursors.
The underestimation of measured HCHO by the models at the remote SH locations
had been shown in some previous studies, and in those studies various
assumptions about missing processes have been explored
e.g.. used a box
model to simulate the measured surface HCHO at Cape Grim and were unable to
capture the magnitude of the observed mixing ratios of HCHO by including a
set of standard methane oxidation reactions in the model. Among the major
HCHO production channels, assumed a 100 % yield of
CH3OOH from CH3O2+HO2. then
experimented with an alternative oxidation pathway that involved the direct
production of HCHO (40 %) from CH3O2+HO2, which
resulted in a much improved comparison. We have not applied such high direct
yield of HCHO in our models. However, following the recent recommendation of
the International Union of Pure and Applied Chemistry (IUPAC)
, a 10 % direct yield of HCHO has been adopted by
NIWA-UKCA but no direct yield has been applied in the other three models. The
recent IUPAC recommendations assume
a temperature-dependent branching ratio for the direct HCHO production
channel (i.e. 0.09 to 0.29 for temperatures ranging from 298 to
218 K). Adopting this recommendation, an additional test was
performed in TM5, showing some modest increases in HCHO in the extratropics
of up to ∼10 %. However, this is not sufficient to explain the large
bias shown here. Another hypothesis suggested by is the
possibility of a small marine biological source of isoprene
e.g.. Recently, found relatively
abundant HCHO precursors (dicarbonyls) in two regions of the southwest
Pacific, corroborating the hypothesis that marine biological activity might
be responsible for the measured HCHO abundance. However, spatial sampling and
understanding of the underlying biological processes remain poor.
The HCHO column data set we use here is an extension of the 1992–2005 data set
described by , retrieved using the same algorithm. They also
derived HCHO mixing ratios at a coarse vertical resolution.
performed a box model simulation based on subsets of the Master Chemical Mechanism (MCM) including the isoprene oxidation scheme of
the MCM. They found that high-HCHO mixing ratios retrieved at Lauder cannot
be explained by methane oxidation alone and that additional local sources,
possibly isoprene, are needed to explain the observed near-surface HCHO
mixing ratios at Lauder. A recent study by compared
modelled and observed FTIR HCHO columns at Réunion Island, using the global
chemical transport model IMAGESv2; they also underestimate the observed FTIR
HCHO columns albeit with a smaller magnitude than that shown in this study.
The time series shown by are for August to November 2004
and for May to November 2007, respectively, and the differences between
modelled and observed HCHO columns are around 30 and 25 %, respectively.
The isoprene mechanism used by is based on the MCM and is
described by . The isoprene emissions used by
are from the MEGAN-ECMWF inventory , and
the yearly totals averaged over 2004 to 2006 are around 10 % lower than the
CLM-MEGANv2.1 inventory used here. To investigate the possible causes for the
low bias in modelled HCHO, include methane oxidation by
tropospheric chlorine, but the impact of this process on HCHO columns is only
about 1–2 % and therefore cannot explain the underestimation. They also
experimented using a different OH climatology; this increase of OH abundance
results in better agreement between observed and modelled HCHO columns but
cannot fully reconcile the substantial differences, and a more probable
explanation is an underestimation of HCHO precursors transported from
Madagascar to Réunion Island. This finding, together with our finding here,
suggests that the underestimation of HCHO columns is persistent throughout
the SH. Observations of HCHO in the remote SH regions are extremely sparse,
and it is impossible to fully constrain modelled HCHO. Note that in both
studies (; ), FTIR HCHO columns compare
well with satellite measurements, and with both satellite and MAX-DOAS
measurements, respectively. This again suggests that the FTIR HCHO retrieval
is robust at all sites, and that the likely cause for model-observation
differences is missing sources of HCHO and/or its precursors in the models.
Tropospheric CO columns from the four models for January (left) and
September (right) 2005, for the CLM-MEGANv2.1 simulations.
Model differences in chemistry and transport
Although the four models are constrained by the same emissions, there are
significant differences in the models' abilities to reproduce observed CO
columns and surface CO in the remote SH, as shown above. Here we explore the
underlying factors driving these differences. To diagnose the extent of
differences in transport between the models, we examine the two passive CO
tracers defined in Sect. 2: one with a fixed lifetime of 25 days (referred to
as CO25) and the second with first-order loss via model calculated
OH (referred to as COOH). Both tracers are subjected to
the same surface emissions as the full simulations, but not subjected to any
secondary production of CO from methane and NMVOC oxidation. Dry deposition
of CO is not included for either of the additional CO tracers as it is
considered a minor loss channel for the SH.
Tropospheric CO25 tracer columns from four models for January
(left) and September (right) 2005, for the CLM-MEGANv2.1 simulations.
The global tropospheric CO columns from all models for January and September
are shown in Fig. 11. January and September represent the seasonal maxima of
biogenic and biomass burning emissions in the SH, respectively. Here, we
define the tropospheric columns as the columns below the chemical tropopause
marked by the 150 ppbvO3 isopleth in each model (monthly
mean O3 used here is averaged over 2004–2008). Although here we
focus on the SH, we note that the inter-model differences apparent in the SH
are consistent with those occurring in the NH, namely, the lowest CO columns
occur in CAM-chem, followed by NIWA-UKCA, with higher CO columns from
GEOS-Chem and TM5 for both hemispheres, indicating systematic differences
between the models. Comparing the seasonal variations, CO columns are
generally higher in September than in January in the SH, primarily due to the
timing of the most intensive tropical BB events in austral spring. Of the
four models, CAM-chem simulates the lowest CO columns in both the source
regions and in the remote mid- to high latitudes. Examining the distributions
of the tropospheric columns of CO25 shown in Fig. 12,
CO25 exhibits similar distributions among the models for both
seasons in source regions as those shown in Fig. 11. The differences become
more obvious in the extratropics, with NIWA-UKCA showing slightly weaker
transport towards the poles, whereas GEOS-Chem shows somewhat stronger export
of CO25 out of the source regions and towards the poles. Overall,
despite some differences, the magnitude and distribution of CO25
are very similar among the models. However, such differences and similarities
in transport among the models are not reflected in the differences in CO
columns shown in Fig. 11 in which TM5 simulates the highest CO columns and
CAM-chem the lowest in both the source and remote regions. COOH
(Fig. S4 in the Supplement) is a more realistic proxy of CO, reflecting the
influences of the models' variable OH concentrations. Like CO25,
the magnitudes and distributions of COOH are similar across the
models; hence, the main driver of the model differences in total CO cannot be
attributed to primary emissions.
Monthly mean CO columns (top) and the ratio of CO25 to CO,
COOH to CO, and COsec to CO columns
averaged over three SH regions (0–30, 30–60, and 60–90∘ S).
Data are for the year 2005.
The ratio of individual models to the ensemble-mean columns averaged
over three SH regions (0–30, 30–60, and 60–90∘ S) for CO,
CO25, COOH, and COsec. Data
are for the year 2005.
Monthly mean mixing ratios averaged over three SH regions (0–30,
30–60, and 60–90∘ S) for CO, HCHO, O3, and OH. Data are
for January 2005.
Next, we quantify the roles of transport and chemistry in determining the
inter-model variability in CO columns in the SH. We examine three zonal bands
defined as 0–30, 30–60, and 60–90∘ S. These latitude bands
capture the main tropical source regions and mid- and high latitudes,
respectively. Figure 13 shows the monthly mean tropospheric columns of CO as
well as ratios of CO25/CO and
COOH/CO columns, averaged across each of these
zones for each model. COsec=CO-COOH is an estimate of the fraction of CO that is produced
by oxidation of CH4 and NMVOCs; the ratio of COsec
to CO is also shown in Fig. 13. These ratios define the contributions of
CO25, COOH, and COsec to the
total CO columns in each model. Figure 13 shows that CO columns decrease
towards the high latitudes and the seasonal maxima are during the
September/October BB season in all zones. Although CO25 is an
idealized tracer designed to diagnose differences in the long-range transport
simulated in each model, COOH should be a more realistic
measure of how much primary emissions of CO contribute to the CO columns
because COOH reflects the locally varying lifetime of CO
due to the spatial variability of OH. The ratio of CO25 to CO drops
sharply from the tropics to the pole for all models (from ∼20 to ∼5 % in the annual average), as would be expected from the hemispheric
distribution of emissions and the timescales for meridional transport. By
contrast COOH/CO reduces only from ∼30 to
∼25 % in the yearly average. This reflects that the lifetime of CO
is considerably longer outside of the source region due to lower background
O3 levels (and therefore lower OH levels) in the more pristine
environment away from strong NOx sources.
COsec/CO shows a moderate increase from 70 to
75 % from the tropical zone to the high latitudes. Overall,
primarily emitted CO makes up ∼25–45 % of total tropospheric CO in
the source region and ∼20–40 % in the polar region, depending on
season, while the secondary CO makes up the remainder of the tropospheric
columns, i.e. ∼55–75 % in the source region and ∼60–80 % in the polar region. Regarding seasonal variability,
CO25 and COOH have proportionally larger
contributions in austral spring when BB dominates the CO emissions, whereas
COsec shows larger contributions in austral summer/autumn.
Of all the models, NIWA-UKCA shows the smallest contribution from primary CO
to the columns and the largest contribution from the secondary CO, relative
to the other three models.
Inter-model differences in CO columns and the additional CO tracers are
expressed as the ratio of individual model columns vs. the multi-model mean
columns for each zone, shown in Fig. 14. For CO columns, the inter-model
differences are smallest in the tropical zone and gradually increase towards
the pole, with the highest CO columns from TM5 and the lowest from CAM-chem,
in agreement with the FTIR comparisons and the surface comparisons shown
earlier. Examining the inter-model differences in CO25, the model
spread increases substantially towards the polar zone, and is characterized
by the strongest transport out of the source region from GEOS-Chem and the
weakest from NIWA-UKCA (also shown in Fig. 12). Note that this behaviour is
not reflected in the model spread of CO columns (i.e. the highest CO occurs
in TM5 and the lowest in CAM-chem). By contrast, the patterns of model spread
in COsec and to a lesser degree in COOH
are consistent with that seen in the CO columns, indicating that the
inter-model differences in modelled CO columns are strongly influenced by the
differences in COsec, which is dependent on the oxidizing
capacity in the model that also drives the loss of primary-emitted CO by OH.
Considering also the absolute contributions of both primary CO sources and
secondary CO production to the SH CO columns (these being ∼35 and ∼65 %, respectively), we can deduce that inter-model differences in CO
columns attribute about one-third to primary and two-thirds to the secondary CO
production in the SH. Note that here we only take into account the
accumulated effects of primary and secondary contributions to CO; we do not
differentiate or individually identify the separate influences, e.g. of
transport and chemistry. For example, the large CO columns in TM5 can be the
result of combined effects of slower chemical loss of CO due to lower OH
levels in the model and a faster secondary CO production in the source
region, as reflected in higher ratios of COsec to CO shown
in Fig. 15. In contrast, GEOS-Chem CO has faster loss by OH than TM5 (but
slower than the others), but this is outweighed by a stronger transport
resulting in higher CO compared to that in NIWA-UKCA and in CAM-chem. For
CAM-chem, moderately slow transport of CO out of the source region combined
with slower secondary CO production results in the lowest CO columns. More
quantitative analyses of differences in chemistry are carried out in Sect. 5.
To further probe the differences between the models, we show vertical
profiles of modelled key species, namely CO, HCHO, O3, and OH mixing
ratios from each model in Fig. 15. We display data for January 2005 from the
CLM-MEGANv2.1 simulation, because in austral summer the chemical production
maximizes due to stronger photochemistry and higher biogenic emissions, and
absolute inter-model differences in CO columns are also larger than in other
seasons. TM5 is characterized by consistently high CO throughout the SH. The
CO values in NIWA-UKCA and in GEOS-Chem are very close in all three zones,
exhibiting differences of ∼5–10 %, although CO in NIWA-UKCA is
slightly higher than that in GEOS-Chem in the tropics but becomes lower
towards remote regions. This may reflect slower meridional transport in
NIWA-UKCA (shown in CO25) combined with larger chemical production
in the source region. The HCHO mixing ratios decrease sharply with altitude
due to the dominant chemical precursors residing in the boundary layer and
the efficient photo-dissociation, but the vertical gradient becomes smaller
away from the source region, particularly in TM5, due to depletion of the
biogenic precursor emissions in the remote SH. HCHO abundances in the four
models correlate with OH to some extent; i.e. both OH and HCHO are relatively
large in GEOS-Chem, whereas both are relatively small in TM5; this reflects
the approximate linearity between the modelled HCHO
abundance and methane oxidation via OH in the remote SH. However, there is no
simple linear relationship between HCHO and OH; OH is involved in both the
loss and the production of HCHO, and HCHO is one of the OH sources. The modelled OH profiles do not
seem to be closely related to O3 (the primary source of OH) in that
TM5 has the lowest OH but its O3 values lie in the middle of the
model range; this is likely due to differences in photolysis schemes. Water
vapour fields are very similar among the models.
Tropospheric CO budget for 2004. Units in TgCOyr-1
Global SH 0–30∘ S 30–60∘ S 60–90∘ S MGMGMGMGMGNIWA-UKCA Surface emission1010101030630628828818180.30.3Total CP18871786821742718631971035.86.2CH4 oxidation*1067108643745135336778765.05.2NMVOC oxidation**82070038429136526419270.81.0Chemical loss2790266810579868718141701571515Dry deposition10198272525232.01.60.00.0TM5 Surface emission1010101030630628828818180.30.3Total CP1650153574366365456586943.94.2CH4 oxidation86592736139028431273743.73.9NMVOC oxidation78561838227337025313200.20.3Chemical loss2516241010169418017482011801413Dry deposition115107363233292.32.10.50.5GEOS-Chem Surface emission1010101030630628828818180.30.3Total CP168616707707296466001161218.08.2CH4 oxidation1046107245547036037589905.65.7NMVOC oxidation64059831525928622527312.42.5Chemical loss27492689114210888978552242122221CAM-chem Surface emission1010101030630628828818180.30.3Total CP1263121055250446541582844.84.9CH4 oxidation86289036438328230177774.74.8NMVOC oxidation401320188121183114570.10.1Chemical loss206820218047716326121591471313
M: CLM-MEGANv2.1 emissions; G: LPJ-GUESS emissions; CP:
chemical production; units in Tgyr-1; * A
conversion factor of 1.0 from methane oxidation is assumed here for
diagnostic purposes; ** NMVOC oxidation is derived from total
chemical production and methane oxidation, i.e. CPNMVOCs=CPTotal-CPCH4.
Tropospheric HCHO budget for year 2004. Units in Tg(HCHO) yr-1.
Global SH 0–30∘ S 30–60∘ S 60–90∘ S MGMGMGMGMGNIWA-UKCA Total source183917647777236726121011056.26.6Surface emission13134.94.94.74.70.20.20.00.0Total CP182617517727186676071011036.26.6CP from CH41137116446848337839383835.35.6CP from NMVOC68958730423528921417200.91.0Total sinks183917647807266726121021076.36.7OH + HCHO50751919019317017220200.50.5HCHO + hν1248117055350247141576815.56.0Dry deposition2421109971.31.50.00.0Wet deposition58532623221844.40.10.4TM5 Total source17481647777704674593991064.14.4Surface emission13134.94.94.74.70.20.20.00.0Total CP*17351634772699670588991064.14.4CP from CH492799338741830433479802.22.3CP from NMVOC80864138528136625420251.92.1Total sinks17481647777704674593991064.14.4OH + HCHO2933171061159310113140.20.2HCHO + hν1247115657751549943075813.43.6Dry deposition34281310129110.00.0Wet deposition1741468164705310100.50.6
* Total chemical productions in TM5 are balanced by total
sinks and surface emissions.
Analysis of chemical production and loss rates
To quantify the effects of differences in model chemistry, we analyse
chemical production (CP) and chemical loss (CL) rates of CO and of HCHO, as listed in
Tables 4 and 5 for both simulations (i.e. with CLM-MEGANv2.1 and LPJ-GUESS
emissions, respectively). The budget terms displayed are for year 2004 and
for the whole globe, the SH, and the three SH latitudinal bands defined above.
The corresponding burdens of CO, HCHO, and OH are shown in Table 6. We define
the tropopause of each model as the 150 ppbv O3 isopleth in each
model, as in Sect. 4.
Tropospheric CO, HCHO, and OH burden for year 2004.
Global SH 0–30∘ S 30–60∘ S 60–90∘ S MGMGMGMGMGNIWA-UKCA CO (Tg)34131913412084744036109.5HCHO (Gg)912846471429378330859078OH (Mg)2062161331419410132336.77.1TM5 CO (Tg)3773311741431058454461513HCHO (Tg)77071435730830525450522.02.1OH (Mg)1962168596657518201.61.7GEOS-Chem CO (Tg)323307142130867746421211HCHO (Gg)1052104547345137234690931111OH (Mg)262272120126919626272.93.0CAM-chem CO (Tg)264246113100726333308.27.6HCHO (Gg)73370032029125822757594.64.7OH (Mg)20722192101697721212.32.3
Monthly CO surface emissions, chemical production and loss terms,
and the ratio of NMVOC oxidation to total chemical production in the SH. Data
are for 2004.
Examining the CO budget terms shown in Table 4, the SH CPs and CLs of CO are
under half of the global values. The main contribution to the SH CPs come
from the 0–30∘ S latitude band; production decreases sharply towards the
southern polar region. In general, chemical production and loss rates of CO
are larger in the CLM-MEGANv2.1 simulations, indicating larger biogenic
emissions leading to larger CO production. However, the CO production from
methane oxidation is generally larger in LPJ-GUESS as a result of increased
OH (shown in Table 6) due to the reduction of biogenic emissions. In all
models, the ratio of CP of CO to surface emissions is markedly larger in the
SH than the global values; this results in larger inter-model differences in
the SH due to the differences in the models' underlying chemistry. NIWA-UKCA
shows the largest total CPs for all domains, followed by GEOS-Chem, TM5, and
CAM-chem, and the differences in total CPs are dominated by the differences
in the oxidation of NMVOCs. Methane oxidation is more constrained among the
models, and the differences in methane oxidation are mainly driven by
differences in OH (shown in Table 6). Note that we do not calculate CO
production rates from NMVOC oxidation explicitly in the models; instead they
are deduced from the total CP and methane oxidation terms, assuming a
100 % yield of CO from methane oxidation; this is only for diagnostic
purposes and we do not make such assumptions in the actual mechanisms. Dry
deposition of CO is a small loss term, particularly for the SH. TM5 and
NIWA-UKCA have comparable dry deposition loss rates. Note that CO loss
through dry deposition is not included in GEOS-Chem and is not provided for
CAM-chem.
Relative differences (%) in modelled CO columns between the
LPJ-GUESS and the CLM-MEGANv2.1 simulations from four models for January (left)
and July (right). Results are expressed as “100×(COLPJ-GUESS-COCLM-MEGANv2.1)/COCLM-MEGANv2.1”. Data
are averaged over 2004–2008.
The seasonal variation of CP and CL of CO in the SH are shown in Fig. 16. The
surface emission of CO peaks in September in the SH which is dominated by
biomass burning emissions. CPs and CLs maximise in austral summer and
minimise in austral winter. CAM-chem shows much lower total CP and NMVOC
oxidation compared to the other three models, in particular during the summer
months, indicating below-average oxidizing capacity in that model. Examining
the contribution to the total CPs, methane oxidation and oxidation from
NMVOCs are nearly equal in all models except CAM-chem where NMVOC oxidation
is significantly lower than methane oxidation. Methane oxidation is largest
in GEOS-chem, followed by NIWA-UKCA reflecting the higher OH in these two
models. The peak chemical loss shown in all four models in October is in
response to the peak of surface emissions of CO. We also display the ratio of
CO production from NMVOCs to the total CP, showing that TM5 has the highest
ratio, indicating a fast conversion of NMVOCs to CO in TM5. In comparison,
CAM-chem has a substantially lower NMVOC oxidation to total CP ratio,
indicating a slower NMVOC to CO conversion; this is the primary cause for low
CO in this model. We have not explicitly quantified the CO production from
isoprene oxidation but assume that isoprene oxidation is the dominant
contributor to NMVOC oxidation in the SH . We therefore
suggest that the different isoprene oxidation schemes used in the models are
responsible for the differences in the chemical production rates of CO.
Without a detailed comparison of the chemical mechanisms used in the models,
we cannot identify which processes and/or parameters that make up the
mechanisms are responsible for the differences in the models employed here,
and such tests would be more suitably done in a box model in which parameters
can be more straightforwardly controlled e.g.. Four
different isoprene oxidation mechanisms are included in the models presented
here. They vary in complexity and also in the approaches to treat degradation
products. The isoprene oxidation mechanism in NIWA-UKCA is based on a smaller
mechanism (Mainz Isoprene Mechanism (MIM); ) than those used in GEOS-Chem
and in CAM-chem . NIWA-UKCA
contains some recently updated rate coefficients of reactions between NO and
peroxy radicals from the OH-initiated isoprene oxidation reactions, and
reactions between OH and isoprene nitrate . The
isoprene oxidation scheme in TM5 is based on the CB05 chemical mechanism
with modifications made to both the oxidation rates of
peroxides and the production efficiency of HO2 from the OH-initiated
oxidation of isoprene based on recommendations by . Our
results here show that the rates of NMVOC oxidation are substantially faster
in TM5 (shown in Table 4 and Fig. 16) than in the other models, and such
faster NMVOC oxidation rates are largely driven by the isoprene oxidation
scheme in that model, which, together with the lower OH (shown in Table 6),
lead to higher CO than in the other models.
HCHO budget terms from NIWA-UKCA and TM5 are listed in Table 5 (These terms
were not saved in the other models). The surface emissions of HCHO are small
compared to the in situ chemical production and loss terms. The global total
CP in NIWA-UKCA is slightly larger than in TM5 for both simulations, but the
amounts are comparable for the SH. Methane oxidation rates are higher in
NIWA-UKCA for all regions due to the higher OH in that model, and NMVOC
oxidation rates are significantly larger in TM5. Examining the chemical
losses, HCHO loss through the reaction with OH is much higher in NIWA-UKCA;
however, HCHO losses through photolysis are comparable between these two
models. Together with the smaller burden of HCHO in TM5, this implies that
HCHO photolysis rates are larger in TM5 than in NIWA-UKCA. (This diagnostic
is not directly available for either model.) The much larger wet deposition
of HCHO in TM5 (i.e., ∼10 % of the total loss terms), compared to that
in NIWA-UKCA (∼3 %), could explain the lower HCHO burden/columns in
TM5. An additional hydration of HCHO is applied in TM5 (but not in the other
models), which further enhances the effective solubility of HCHO in aqueous
solution . This may have resulted in an additional loss of
HCHO to wet deposition in TM5 which is however still substantially smaller
than the gas-phase loss processes. The Henry's law coefficients, governing
gas- and liquid-phase partitioning of HCHO, applied in the other models are
comparable.
Same as Fig. 17, but for HCHO.
Same as Fig. 17, but for OH.
Sensitivity of modelled SH CO and HCHO to uncertainties in biogenic emissions
In Sect. 3, we showed the model deviations in CO and HCHO columns from
observed FTIR values at four remote SH sites using two different biogenic
emissions inventories (for isoprene and monoterpenes), and found that
modelled CO columns with LPJ-GUESS biogenic emissions are consistently lower
and less representative of observed values than those produced using
CLM-MEGANv2.1 emissions (Table 3). Here we further quantify the changes in CO
and HCHO columns in response to changes in biogenic emissions at the
hemispheric scale, and also highlight associated changes in the corresponding
OH columns. Figures 17–19 display the monthly mean global distributions of
relative differences in CO, HCHO, and OH columns between simulations using
CLM-MEGANv2.1 and LPJ-GUESS, respectively, for January and July (averaged
over 2004–2008). The differences calculated for all species are expressed as
the percentage change relative to the CLM-MEGANv2.1 simulation. Here, we show
results for the January and July months in order to contrast the seasonal
features in oxidizing capacity. For all models, applying LPJ-GUESS emissions
results in significant decreases in CO columns throughout the SH, with the
largest decreases in the South American and Australian source regions
(Fig. 17), in response to the smaller emission fluxes of isoprene and
monoterpenes from LPJ-GUESS (the accumulated peak isoprene emissions in
CLM-MEGANv2.1 are 25 % higher than in LPJ-GUESS during the peak season of
the austral summer months, shown in Fig. 1, and the biggest differences are
in South America). Away from these source regions, the differences are
largely homogeneous in the mid- to high latitudes. The models' responses to
changes in biogenic emissions vary considerably, with TM5 having the largest
sensitivity of CO columns change to changes in biogenic emissions, namely
∼35 % in January and ∼ 25 % in July in the source regions
and 10–15 % over the remote SH. GEOS-Chem has the lowest sensitivity
with 15–20 % changes in January and 10–15 % in July in the source
regions and less than 10 % in remote regions in response to the same
emission changes of isoprene and monoterpenes.
For corresponding changes in tropospheric HCHO columns (Fig. 18), substantial
decreases (up to ∼50–60 %) occur in the source regions of South
America and Australia in response to smaller emission fluxes in LPJ-GUESS,
relative to CLM-MEGANv2.1. These reductions in HCHO columns propagate to the
sub-tropical remote oceans where the magnitude of the decreases is greatly
reduced. There are some increases in HCHO columns over southern Africa, which
are responses to the higher isoprene emissions in LPJ-GUESS. However, there
is a consistent increase of up to 5 % over large areas of the mid- to
high latitudes which is apparently not directly caused by reduced biogenic
emissions. We find that changes in both CO and HCHO are associated with
changes in OH (Fig. 19); the tropospheric OH columns exhibit substantial
increases in the source regions as a result of reduced isoprene and
monoterpene emissions; qualitatively these effects follow the differences in
the geographical distributions of the emissions, and are of opposite sign to
both the CO and the HCHO columns changes there. OH increases in remote
regions are largely positive, and are opposite in sign to the CO changes;
i.e. reduced loss rates of CO cause increases in OH. However, increases in OH
columns away from the source regions correlate with HCHO changes; this
implies that increases in HCHO in remote regions under LPJ-GUESS emissions
are due to strengthened methane oxidation through increases in OH. The
inter-model differences in HCHO changes are generally small in remote
regions; TM5 shows the largest sensitivity over the source regions in both OH
and HCHO, due primarily to the faster isoprene oxidation processes in that
model. Note that the large relative differences in both HCHO and OH in July
at high latitudes shown in CAM-chem are not significant because the
background abundances of both species in the polar region are extremely
small.
Relative differences (%) in tropospheric CO budget terms between
CLM-MEGANv2.1 and LPJ-GUESS simulations for 2004.
Total CP CP CH4CP NMVOCs Total CL Burden NHSHNHSHNHSHNHSHNHSHCO NIWA-UKCA-2.1-9.60.83.2-6.2-24.2-2.9-6.7-3.9-10.4TM5-3.9-10.76.58.0-14.4-28.5-2.1-7.4-7.4-17.8GEOS-Chem2.7-5.31.93.34.3-17.8-0.4-4.7-2.2-8.5CAM-chem-0.7-8.71.85.2-6.6-35.6-1.1-4.1-3.3-11.5HCHO NIWA-UKCA-2.0-7.01.83.2-8.6-22.7-1.8-6.5-5.4-8.9TM5-2.9-9.56.58.0-14.9-27.5-1.6-7.8-1.7-13.7
Differences calculated as 100×(LPJ-GUESS -
CLM-MEGANv2.1)/CLM-MEGANv2.1.
In Table 7 we summarise hemispheric changes in chemical production and loss
rates of tropospheric CO and HCHO, in response to the differences in biogenic
emissions. Values expressed are percentage changes (i.e. LPJ-GUESS minus
CLM-MEGANv2.1 relative to CLM-MEGANv2.1), and are given for both hemispheres
to assess the hemispheric impact of biogenic emissions. In the SH, the
changes in all terms are negative, except for the rates of chemical
production of both CO and HCHO from methane oxidation; this is generally the
result of increased OH in the LPJ-GUESS simulation, in response to reduced
biogenic emissions in that inventory. For all models, relative reductions in
NMVOC oxidation rates (-17.8 to -35.6 %) are substantially larger than
relative increases in CP from methane oxidation (3.2 to 8.0 %), in response
to changes in biogenic emissions. Therefore, NMVOC oxidation (mainly of
isoprene) is the driving factor for model differences in in situ CO and HCHO
production. The burden changes are closely related to the changes in total
CP; i.e. TM5 has the largest changes in both burden and the CP, and GEOS-Chem
has the smallest terms for both. For all models, relative responses in the SH
are much larger than in the NH, emphasizing the importance of biogenic
emissions for CO and HCHO formation in the SH.
Complementing the comparison of columns, we here compare the seasonal
differences in vertical profiles of CO mixing ratios between CLM-MEGANv2.1
and LPJ-GUESS simulations, averaged zonally and over 2004 to 2008. Figure S5
in the Supplement shows large reductions in CO over the SH tropics in all
simulations using LPJ-GUESS emissions of isoprene and monoterpenes, relative
to those using CLM-MEGANv2.1, and these reductions propagate to the upper SH
tropical troposphere and spread throughout the middle and high latitudes.
This shows that the CO column changes in the extratropics are dominated by
the changes in the free and upper troposphere, where CO has a relatively long
lifetime. Overall, the impact of biogenic emissions on CO are more
significant in the SH than the NH. In the SH, throughout the depth of the
troposphere, the LPJ-GUESS simulations have reduced CO, which is linked to
much reduced CO in the tropics. This effect maximizes during austral winter
and spring.
Summary and conclusions
We have compared modelled daily-mean CO and HCHO columns from a four-model
ensemble with the observed daily-mean FTIR columns of these two species at SH
sites including the tropical site Darwin, the mid-latitude sites Wollongong
and Lauder, and the Antarctic site Arrival Heights for CO, and Wollongong and
Lauder for HCHO. We use CLM-MEGANv2.1 biogenic emissions for the first set of
simulations; for these simulations modelled and measured CO are in reasonable
agreement, albeit with some low biases, in all models at most locations;
annually averaged deviations relative to the observations are -3.2 % at
Arrival Heights, -8.6 % at Lauder, -19.2 % at Wollongong, and
-6.9 % at Darwin for the four-model mean. The largest discrepancies
between modelled and observed CO columns occur at Wollongong which is heavily
influenced by local urban and industrial sources and episodic nearby bush
fires that are most likely unaccounted for in the emission inventories. Large
inter-model differences exist at all locations for all seasons with the
exception of austral spring at Darwin where the local biomass burning sources
dominate the CO columns. We also compare the modelled surface CO to
observations; significant inter-model differences exist although the ensemble
mean exhibits good agreement with the observed values for most sites. The
inter-model differences for modelled surface CO are markedly larger than the
differences between the ensemble mean and observed surface CO. In agreement
with previous modelling studies of HCHO in the remote SH
, the models significantly underestimate
observed HCHO columns at Wollongong and Lauder by more than a factor of 2,
and the largest discrepancy occurs during austral summer. We cannot reconcile
such significant differences between the modelled and observed HCHO columns
over the remote SH with our current understanding. We hypothesize that
missing local sources and/or missing chemical processes are the most likely
causes. The fact that model differences are much smaller than the differences
between the models and the observations indicate that the cause of such a
large discrepancy probably goes beyond what the differences in chemical
mechanisms can explain.
To determine the sensitivity of CO and HCHO distributions to biogenic
emissions, we perform a second set of simulations with emissions of isoprene
and monoterpenes from the LPJ-GUESS data set; results show that the LPJ-GUESS
simulations exhibit systematically lower CO columns and lower surface CO than
the CLM-MEGANv2.1 simulations, in response to an average of ∼9 %
reduction in isoprene emissions globally and a ∼17 % reduction in
the SH (monoterpene emissions are also substantially lower in LPJ-GUESS; see
Table 1). Annually averaged relative differences between ensemble model mean
and observed CO columns are -10.5 % at Arrival Heights, -17.1 %
at Lauder, -27.5 % at Wollongong, and -19.9 % at Darwin. The
differences in surface CO at remote monitoring sites between the two
simulations are generally smaller than 5 %. At neither Wollongong nor
Lauder do we find that differences in biogenic emissions have any significant
impact on modelled HCHO columns.
Examining the response of CO and HCHO columns to differences in biogenic
emissions of isoprene and monoterpenes on the hemispheric scale, we show that
both species exhibit large sensitivity to emissions in the source regions,
with 30–40 % reductions in CO and HCHO columns, as a direct consequence
of the mainly reduced emissions of isoprene and monoterpenes in the LPJ-GUESS
inventory, relative to CLM-MEGANv2.1 (i.e. ∼37 % reduction of
isoprene emissions in Australia and Indonesia, ∼23 % reduction in
South America, and ∼13 % overall increase in Africa, with both
increases and reductions occurring in different regions), and these
reductions in CO and HCHO are generally larger in summer than in winter. Away
from the source regions and throughout the SH, decreases in CO columns are
roughly half those occurring in the source regions, whereas there are
moderate increases in HCHO columns (∼5 %) despite the significant
decreases in and near the source regions for all models. We show that the
increases in HCHO columns in the remote SH for LPJ-GUESS, relative to
CLM-MEGANv2.1, are linked to the increases in OH columns through enhanced
methane oxidation in the remote SH (see Tables 5 and 6). There are
substantial increases in OH columns in the source regions in direct response
to the reduced isoprene and monoterpene emissions in the LPJ-GUESS inventory,
whereas the general increase (up to ∼5 % across the models) in the
remote regions is the result of reductions in CO and possibly other
longer-lived isoprene oxidation products.
Significant inter-model differences exist in modelled CO columns; we quantify
these differences in three latitudinal regions (SH tropics, mid-, and high
latitudes). The ratios of individual model columns to the ensemble-mean
columns (annually averaged and averaged across the three regions) are between
0.85 and 1.15 for the tropical region, and the range increases to between 0.7
and 1.2 at high latitudes. Using diagnostic tracers, we assess the impact of
modelled transport (by CO25), the contribution from
primarily emitted CO (by COOH), and CO produced and
transported from secondary CO production (COsec=CO-COOH). The results reveal that the differences
in transport are not sufficient to explain the differences in modelled CO
columns. The modelled range of COOH corresponds much
better to the modelled CO columns than CO25 but still cannot fully
explain the inter-model differences in modelled CO columns. The differences
in secondary CO production, i.e. COsec, however,
correspond well with those in modelled CO columns. TM5 exhibits the highest
values in both variables, followed by GEOS-Chem, NIWA-UKCA, and CAM-chem in
magnitude. We calculate that COsec contributes around
65 % to CO in the tropics and around 75 % in the polar region in each
model, and is responsible for two-thirds of the inter-model differences in
modelled CO columns overall. This suggests that the models' differences in
secondary CO production from methane and NMVOC oxidation play a major role
in their ability to reproduce the CO columns in the SH, as also noted by
.
We further quantify the models' differences in chemistry by examining the
chemical production and loss terms of CO and HCHO in the models. Results show
that large differences in chemical production between the models are largely
attributed to differences in the rates of NMVOC oxidation, which are mainly
driven by the differences in isoprene oxidation processes, which exhibit
varying degrees of complexity in the models. We show the collective effects
that different isoprene oxidation schemes have on the rates of chemical
production of CO and HCHO but are not able to individually quantify which
reactions/processes are responsible for the differences in modelled CO and
HCHO. Among the four models, NIWA-UKCA has the highest total chemical
production rates of CO, followed by GEOS-Chem, TM5, and CAM-chem which has
the lowest chemical production rates. Methane oxidation rates are mainly
driven by the OH abundance in the models with TM5 having the lowest OH hence
the lowest methane oxidation rates. The fastest conversion rates from NMVOCs
to CO occurs in TM5, and the slowest in CAM-chem, leading to respectively
high and low CO in these two models. Modelled CO in NIWA-UKCA and GEOS-Chem
both better matches the observations in general, irrespective of the
different complexities of the isoprene oxidation schemes employed in these
two models. Moreover, GEOS-Chem includes some recent advances in isoprene
oxidation mechanisms, for example, OH formation of epoxide species which
regenerate OH under low NOx conditions . Epoxides are not
included in other models. We have not specifically tested how recent
experimental evidence on isoprene oxidation mechanisms, e.g. OH regeneration
in a low-NOx environment , will impact on modelled species.
More detailed and targeted studies will be needed to clarify how individual
approaches/processes making up isoprene oxidation schemes will impact
chemical production of CO and HCHO in global models.
Production and loss terms of HCHO are assessed in NIWA-UKCA and TM5. We find
that total chemical productions are comparable in the two models, with
moderately larger chemical production and loss rates in NIWA-UKCA. Again, the
production of HCHO from the oxidation of NMVOCs is faster in TM5 although
this is partly offset, for HCHO production, by the slower methane oxidation
rates due to lower OH. The markedly lower HCHO in TM5 than in NIWA-UKCA could
be due to the substantially larger wet deposition loss rate of HCHO, and a
faster photo-dissociation rate of HCHO in TM5. Despite the differences in
rates of HCHO formation and loss, we cannot, based on these differences
alone, explain the substantial low bias in modelled HCHO in all models
compared to the observed HCHO columns at Lauder and Wollongong. We therefore
suspect that missing local sources and/or HCHO precursors might contribute to
the differences between modelled and observed HCHO.
We conclude that the uncertainty in biogenic emissions remains a significant
problem in modelling both long- and short-lived species throughout the SH.
Understanding the differences between isoprene oxidation mechanisms and the
resultant differences in modelled CO and HCHO is critical, and might result
in an improvement in these mechanisms, allowing for a more robust use of HCHO
and CO columns to constrain biogenic emissions and reduce this uncertainty.
Given that the differences between the two biogenic emissions inventories
used here are moderate compared to the much larger uncertainties existing in
the current estimates of isoprene and monoterpene emissions, the resultant
uncertainty in modelled CO could be much larger. Although the ensemble model
mean satisfactorily compares to observed CO in the SH, the large inter-model
differences add more uncertainties in modelled CO and in constraining
biogenic emissions. Note that in this paper, we do not separately quantify
the effect from changes in monoterpene emissions. The emissions from
monoterpenes are around 30 and 10 % of those of isoprenes in
CLM-MEGANv2.1 and LPJ-GUESS inventories, respectively, which could have
a significant impact on modelled CO. However, due to the large uncertainty in
emissions and the varying degrees of complexity of the monoterpene
degradation schemes included in each model, this will further complicate the
interpretation of the impact from changing monoterpene emissions.
The Supplement related to this article is available online at doi:10.5194/acp-15-7217-2015-supplement.
Acknowledgements
This work has been supported by NIWA as part of its Government-funded, core
research. We acknowledge the UK Met Office for the use of the Unified Model,
the University of Cambridge for the development of UKCA, and the contribution
of NeSI high-performance computing facilities to the results of this
research. NZ's national facilities are provided by the NZ eScience
Infrastructure and funded jointly by NeSI's collaborator institutions and
through the Ministry of Business, Innovation & Employment's Research
Infrastructure programme (https://www.nesi.org.nz). JAF was funded by
a University of Wollongong Vice Chancellor's Postdoctoral Fellowship, 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. Jingqiu Mao
assisted with implementing the CO25 tracers in GEOS-Chem. The
National Centre for Atmospheric Research is operated by the University
Corporation for Atmospheric Research with funding from the National Science
Foundation. We also acknowledge Antarctica New Zealand, and the Australian
Research Council for support (DP110101948). Edited by: A. Pozzer
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