A low bias in carbon monoxide (CO) at northern high and mid-latitudes is a
common feature of chemistry climate models (CCMs) that may indicate or
contribute to a high bias in simulated OH and corresponding low bias in
methane lifetime. We use simulations with CO tagged by source type to
investigate the sensitivity of the CO bias to CO emissions, transport,
global mean OH, and the hemispheric asymmetry of OH. We also investigate how
each of these possible contributors to the CO bias affects the methane
lifetime. We find that the use of specified meteorology alters the
distribution of CO compared to a free-running CCM simulation, improving the
comparison with surface observations in summer. Our results also show that
reducing the hemispheric asymmetry of OH improves the agreement of simulated
CO with observations. We use simulations with parameterized OH to quantify
the impact of known model biases on simulated OH. Removing biases in ozone
and water vapor as well as reducing Northern Hemisphere NO
Carbon monoxide (CO) is an ozone precursor and a major sink of the hydroxyl
radical (OH) in the troposphere (Logan et al., 1981; Spivakovsky et al.,
2000). Consequently, CO indirectly impacts climate by increasing
tropospheric ozone where sufficient NO
While a large number of modeling studies have investigated the sources,
transport, and distribution of CO, global models often show major biases
compared to observations. A study of 26 atmospheric chemistry models found
that the simulated CO was biased low in the extratropical Northern Hemisphere
(NH; Shindell et al., 2006) compared to satellite observations from
MOPITT (Measurements of Pollution in the Troposphere; Emmons et al., 2004) and surface observations, especially during
spring. Shindell et al. (2006) attribute this bias primarily to an
underestimate in NH CO emissions, particularly from
Asia. Monks et al. (2015) found that Arctic CO is biased low in the
multi-model POLARCAT Model Intercomparison Project (POLMIP) and identified
differences in global OH concentrations as a major driver of the inter-model
differences in Arctic CO. The multi-model mean of the Atmospheric Chemistry
and Climate Model Intercomparison Project (ACCMIP) simulations also shows a
negative bias compared to both MOPITT and surface observations in the
northern extratropics (Naik et al., 2013). Naik et al. (2013) found that
ACCMIP models underestimate the methane and methyl chloroform lifetimes
compared to the observation-based estimates of Prinn et al. (2005) and
Prather et al. (2012) and produce a high bias in the NH to Southern Hemisphere
(SH) ratio of OH, consistent with the underestimate of NH CO. A
recent comparison of simulated and observed methyl chloroform levels also
indicates that the NH
Previous studies used models to examine the consistency of CO emission estimates with surface and satellite observations of CO concentration. A modeling study by Duncan et al. (2007a) showed that their model compared well to observations in the NH extratropics, but they point out that a low bias in their model emissions may have been compensated for by an assumption made in their simplified chemical scheme that non-methane hydrocarbons (NMHCs) oxidize to CO immediately upon release; this assumption is valid during summer months for short-lived NMHCs (e.g., alkenes, isoprene) but is not valid in winter and spring for longer-lived NMHCS (e.g., alkanes). Stein et al. (2014) found that a combination of higher winter traffic emissions from North America and Europe and reduced dry deposition during boreal winter improved the agreement between simulated and observed CO. These findings are consistent with inversions of MOPITT CO data that show that including greater winter emissions from the NH reduces the negative bias in springtime CO at northern latitudes (Petron et al., 2004).
Kopacz et al. (2010) inverted CO observations from multiple satellites and
concluded that northern midlatitude CO sources were underestimated in
winter, and that implementing large seasonal variations in emissions
improved model agreement with observations. The inversion study of
Fortems-Cheiney et al. (2011) also found that the posterior CO emissions had
large seasonality in the NH, with the maximum occurring in spring. However,
the source strengths estimated by inversions are influenced by factors such
as model transport (Arellano and Hess, 2006) and the concentrations of other
species that interact with CO through OH chemistry (Pison et al., 2009;
Muller and Stavrakou, 2005; Jones et al., 2009). Given the complexities of
the nonlinear CO–OH–CH
Uncertainty in the tropospheric burden and distribution of OH leads to
further uncertainty in the CO budget. Hooghiemstra et al. (2011) found that
higher NH OH concentrations led to higher anthropogenic CO emissions in
their inversion study, while lower OH concentrations over tropical land
masses and the SH led to lower biomass burning CO emissions and less CO from
NMHCs. Duncan et al. (2007a) found that reducing OH by 20 % globally
improved the comparison of their simulated CO with surface observations in
some locations but degraded the comparison at other locations. Patra et al. (2014) suggested that top-down emission estimates from models with much
higher OH in the NH than SH likely overestimate NH countries' emissions of
CO and other reactive species. Mao et al. (2013) found that including
conversion of HO
Accurate simulation of tropospheric OH requires models to represent multiple drivers of atmospheric OH concentrations. Holmes et al. (2013) found that temperature, water vapor, stratospheric ozone, and emissions from biomass burning and lightning could together explain most of the interannual variability in methane lifetime against OH. Duncan and Logan (2008) found that changes in the ozone column were a major driver of OH variability over 1988–1997. Murray et al. (2013) found that lightning was more important for OH variability over 1998–2006, when there was less variability in the overhead ozone column. Murray et al. (2014) found that, on glacial–interglacial timescales, OH concentrations were proportional to the tropospheric ozone photolysis rate, specific humidity, and reactive nitrogen emissions, and inversely proportional to CO emissions. The present study investigates the sensitivity of simulated CO and methane lifetime to biases in some of these processes.
Understanding the causes and implications of CO bias in CCMs is important for climate prediction as it may indicate or contribute to biases in methane and ozone and their respective radiative forcing contributions. The goal of this study is to quantify the relationship of the extratropical NH CO bias seen in CCMs with bias in oxidant concentrations and methane lifetime. Our focus is primarily on NH spring and summer, when the NH CO bias is large. We use the GEOS-5 Chemistry Climate Model (GEOSCCM) to investigate how attributing a CCM's CO bias to CO emissions versus OH chemistry impacts ozone and methane lifetime. We also quantify the contribution of model biases in other constituents such as ozone and water vapor to the simulated OH and CO distributions and methane lifetime.
Our primary constraint on the model CO distribution comes from surface observations from the NOAA Global Modeling Division (GMD) network (Novelli and Masarie, 2014). We use the monthly mean data. The MOPITT instrument on the Terra satellite provides additional constraints on the CO distribution. MOPITT provides almost global coverage every 3 days from March 2000 to present (Edwards et al., 2004). We use the level 3 CO column data from the MOPITT version 5 thermal infrared (TIR) product (Deeter et al., 2011, 2013).
Observations of tropospheric ozone are important for constraining the source of OH. Ziemke et al. (2011) created a climatology of tropospheric column ozone (TCO) based on the difference between the stratospheric column ozone (SCO) from the Microwave Limb Sounder (MLS) and total ozone column data from the Ozone Monitoring Instrument (OMI). The observations are cloud-filtered, so there is sensitivity throughout the troposphere, although there is some reduction in retrieval efficiency in the lower troposphere. We use the TCO product for 2004–2010 to constrain the tropospheric ozone column. Stratospheric ozone is also important for constraining the OH source due to its effect on photolysis (Rohrer and Berresheim, 2006). We use the Global Modeling and Assimilation Office (GMAO) ozone assimilation product for 2005–2010 to constrain stratospheric ozone concentrations. The GMAO assimilated ozone product, described in Ziemke et al. (2014) and Wargan et al. (2015), is a gridded product that was created by ingesting MLS ozone profiles and OMI total column ozone into the GEOS-5 assimilation system.
Water vapor is another important influence on OH concentrations. We use specific humidity from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) (Rienecker et al., 2011).
Our analysis uses the GEOSCCM to assess possible causes and impacts of CO
and OH bias. After spin-up, we conduct a series of time slice simulations of
1999–2009 with fixed emissions using observed sea surface temperatures
(SSTs) to drive the CCM meteorology, and then average our results over all
years of the time slice. All simulations use the Fortuna version of GEOS-5
(Molod et al., 2012) and have 2
We use a series of sensitivity studies to analyze the role of CO emissions, OH concentrations, and transport. Two methods are used to examine the sensitivity of CO concentrations to CO emissions from different sources: scaling up the CO emissions, and scaling up CO tracers tagged by source. We quantify the sensitivity of CO to OH concentrations by applying scaling factors to the OH field. We analyze the sensitivity to transport by comparing a free-running CCM simulation with a simulation that has prescribed meteorology. Several different chemistry options within the GEOSCCM framework are used to isolate specific processes. We describe each chemistry option and associated experiments below and in Table 1.
GEOSCCM integrates the chemistry mechanism of the Global Modeling Initiative (GMI) chemistry and transport model (CTM; Duncan et al., 2007b; Strahan et al., 2007) into the GEOS-5 Atmospheric GCM (AGCM). The GMI chemistry includes a comprehensive mechanism of tropospheric and stratospheric chemistry, including 117 species and over 400 reactions. The reference simulation for this study, hereafter called RefGMI, is the year 2000 time slice simulation conducted for the ACCMIP intercomparison. The configuration of the ACCMIP simulations is described in Lamarque et al. (2013). The biases in CO and methane lifetime seen in the GEOSCCM simulation are similar to those seen in the ACCMIP multi-model mean (Naik et al., 2013). Consequently, our analyses of bias in the GEOSCCM are likely applicable to other CCMs as well. We use the GMI chemistry option to quantify the impact of changes in CO emissions on methane, OH, and ozone.
GEOSCCM includes a CO-only option to “tag” CO according to source type or location (Bian et al., 2010). This simplified chemistry option allows us to separate the contributions of different CO sources and to quantify the impact of a specific change in OH. In this chemistry option, the loss of CO is calculated based on prescribed OH fields, so changes in CO do not feed back onto OH. Our reference tagged-CO simulation, called RefCOonly, uses monthly OH fields archived from the RefGMI simulation, and the CO sources from methane and isoprene oxidation are calculated using monthly methane and isoprene fields archived from RefGMI as well. Emissions and other forcings are chosen to parallel the RefGMI simulation; however, the CO-only option includes an amplification factor for anthropogenic and biomass burning CO emissions to account for the absence of co-emitted NMHCs (Duncan et al., 2007a). We use the CO-only option to calculate the influence of specific sources on CO concentrations, and to isolate the impact of specific changes in OH on CO.
Description of reference and sensitivity simulations used in this study with each chemistry option.
* Reference simulation
The GEOSCCM includes an option to constrain the meteorology with MERRA or any GMAO assimilation product. The simulation is pulled towards the MERRA analysis through application of an incremental analysis update (Bloom et al., 1996), calculated every 6 hours from comparison of the simulation with the analysis. We conduct a CO-only simulation with specified dynamics from MERRA, which we refer to as COonlySD, where “SD” stands for “specified dynamics”. The COonlySD simulation has the same emissions and OH field as RefCOonly, but the tracer transport will differ between the two simulations.
A third chemistry option within the GEOSCCM is the CO–OH option (Duncan et
al., 2000, 2007a). This chemistry option is of intermediate
complexity between the GMI and tagged-CO options. This option is similar to
the CO-only option except that OH is calculated interactively, providing a
feedback between CO and OH concentrations. In our reference simulation with
this option, RefCO–OH, chemistry inputs to the parameterization of OH such
as ozone, NO
We use the three chemistry options of the GEOSCCM to separate the contributions of emissions, chemistry, and transport to model bias in CO and methane lifetime. In Sect. 3.1, we compare the CO distribution simulated by the three options to observations and discuss the consistencies and differences between simulations. We analyze the impacts of increasing emissions, decreasing OH, and changing model transport on the CO distribution in Sect. 3.2, and we examine how changing CO emissions affects ozone and OH in Sect. 3.3. In Sect. 3.4, we investigate the contribution of known model biases to simulated OH and methane lifetimes. Table 1 summarizes the key results of each of the sensitivity studies.
Simulated annual zonal mean OH concentration for the RefGMI
Monthly mean GMD observations (black) and results from the RefGMI
(purple), RefCOonly (green), and RefCO–OH (orange) simulations. Circles and
error bars represent the mean and min–max range, respectively, for
1999–2009. The GMD sites are as follows:
Latitudinal distribution of CO observations from the GMD network
(black circles) and simulated CO from the RefCOonly simulation (purple
stars) averaged over March–August. Blue–green
This section presents a comparison of CO and OH distributions from the
RefGMI, RefCOonly, and RefCO–OH simulations to each other and to
observations. The choice of chemistry option affects the CO and OH
distributions produced by the GEOSCCM. Annually averaged OH is higher in the
RefGMI simulation in both hemispheres (Table 2; Fig. 1). High OH values
extend further down in the troposphere in RefGMI than in RefCO–OH. Global
annual mean mass-weighted tropospheric OH is approximately 7 % lower in
RefCO–OH than in RefGMI, but the NH
Tropospheric OH concentrations and lifetimes against oxidation by tropospheric OH.
Figure 2 shows the annual cycle of simulated CO and observations from six GMD sites selected to represent a range of latitudes. Observations are averaged over the period from 1999 to 2009, with the exception of Trinidad Head, which is averaged over 2002–2009. The RefGMI simulation (purple) is biased low at the NH sites, with the bias most prominent during the first half of the year, especially NH spring. The simulation shows less bias at the SH sites, but some negative bias is evident in SH spring. The RefCOonly simulation (green) shows similar results to RefGMI, including a large negative bias in the NH in spring. Consequently, diagnosing the cause of bias in the RefCOonly simulation can provide insight into the bias in the RefGMI simulation.
The impact of increasing the tagged-CO tracers for Asian
anthropogenic (
As in Fig. 4 but for June through August.
The RefCO–OH simulation (orange) shows less springtime bias compared to observations than the RefGMI and RefCOonly simulations. However, its annual cycle is shifted later, resulting in more negative biases in September–December at the higher northern latitudes. The difference between the RefCO–OH and the other two reference simulations is explained by the differences in the OH field calculated by the parameterization compared to that calculated by the GMI chemistry mechanism. In April, when the greater NH bias of the RefGMI simulation is most evident (Fig. 2), the RefGMI OH is markedly higher than the OH in RefCO–OH (Fig. 1).
We conduct a series of sensitivity studies to examine possible causes of the bias in NH CO seen in the RefGMI and RefCOonly simulations during spring and summer. We focus on spring, since it is the season with the largest bias in NH OH (Fig. 2), and contrast the spring results with those from summer, since the bias persists into summer despite seasonal differences in transport and chemistry.
CO for March through May of 1999–2009 from the GMD observations
(circles) overplotted on the surface CO from
Adjusting the strength of midlatitude CO sources can reduce the bias in the interhemispheric CO gradient. We use CO-only simulations to examine the impact of increasing specific sources of CO. Following the method of Strode and Pawson (2013), we estimate the impact of increasing a particular source by increasing the tagged-CO tracer for that source and then re-computing total CO. We impose increases of 10, 20, 50, 100, and 150 % for each tagged tracer. Figure 3 shows how the increased Asian anthropogenic CO (COaa) or tropical biomass burning CO (COtrbb) alters the comparison between modeled CO and the GMD CO observations as a function of latitude taken as an average of March to August. Similar results are present for spring and summer individually. An increase in COaa of approximately 100 % removes the negative bias at high-latitude sites but has little effect on the small negative bias at low latitudes (Fig. 3a). CO transported southward from Asia encounters higher OH than that transported northward, leading to a shorter lifetime (Duncan and Logan, 2008). Increasing COtrbb improves the model agreement with observations at low latitudes but creates overestimates at some tropical sites while providing only a modest reduction in the high-latitude bias (Fig. 3b).
We examine the impact of reducing OH concentrations globally or only in the
NH in our CO-only simulation and find that a large decrease in NH OH is
effective in reducing the high-latitude CO bias. Naik et al. (2013) found
that the ACCMIP multi-model mean underestimated annual mean tropospheric OH
by 5–10 % globally. The NH
We find that achieving zero bias in both the inter-hemispheric gradient
(IHG) and the global mean would require changes in multiple emission
sources. Figure 4 illustrates how changing the concentrations of several
tagged-CO tracers, as well as OH, impacts the global mean bias, IHG bias,
and correlation (
A similar analysis for June through August (Fig. 5) shows that increasing COaa, COea, COnaa, or COrubb can eliminate the majority of the bias in both the global mean and the IHG in summer. Increasing COrubb is more effective in summer than spring since boreal biomass burning emissions are larger in summer. In contrast to the spring results, increasing COtrbb in summer leads to greater bias in the IHG. This difference occurs because tropical biomass burning occurs primarily north of the Equator in March and April but shifts to the Southern Hemisphere in June, July, and August.
While increases in COaa, COea, COnaa, and COrubb show similar slopes for IHG versus global mean bias, increasing COnaa reduces the correlation with observations, whereas increasing the other sources yields a slight improvement in correlation (Figs. 4b, 5b). Increases in both COtrbb and CObio reduce the correlation with observations in summer, but the effect is also present for CObio in spring. We therefore exclude increases in COnaa and CObio in the rest of our study.
Figures 4 and 5 also show the impact of changing OH. The sensitivity of CO to changes in OH is location dependent, with higher sensitivity in regions without strong local CO sources (Holloway et al., 2000). Reducing OH globally reduces both the IHG and global mean bias. However, reducing OH by 20 % in the NH only yields a greater reduction in both biases as well as the greatest improvement in correlation. We refer to the simulation with the 20 % decrease in NH OH as COonlyLowNHOH.
We next compare the impact of increasing emissions with that of reducing NH OH by 20 %. We compare the COonlyLowNHOH scenario with a simulation called GMI-HiEmis that includes increased CO emissions, further described in Sect. 3.3. The RefCOonly simulation shows a similar surface CO distribution to RefGMI (Fig. 6a, c). The COonlyLowNHOH scenario (Fig. 6d) improves the agreement with the remote high-latitude sites compared to the RefCOonly simulation, but like the HiEmis case (Fig. 6b) it leads to an overestimate of CO concentrations over Europe and the eastern United States. Consequently, a combination such as reduced NH OH and reduced emissions over the eastern USA is likely needed to reconcile the simulated CO with observations.
Sparse surface data makes it difficult to determine from comparison with GMD observations whether the higher CO seen in GMI-HiEmis and COonlyLowNHOH is realistic (Fig. 6), so we also compare the four simulations shown in Fig. 6 to the CO column from MOPITT. Both the RefGMI and RefCOonly simulations show a large negative bias in NH CO compared to MOPITT (Fig. 7a, c). The GMI-HiEmis and COonlyLowNHOH simulations both reduce this negative bias (Fig. 7b, d), but the increased emissions of the GMI-HiEmis simulation lead to a greater overestimate of CO over east Asia and Indonesia. Furthermore, eliminating the increase in COea to reduce the high bias compared to surface observations over Europe and compensating with a larger adjustment in Asian CO would lead to an even greater bias over Asia compared to MOPITT.
March through May difference between the simulated CO columns and
MOPITT averaged over 2000–2009 for the
Transport, in addition to chemistry and emissions, plays a role in the IHG
of CO. The simulations discussed so far are free-running CCM simulations
driven by SSTs, since our goal is to understand the biases seen in CCM
studies such as ACCMIP. Consequently, the simulated tracer transport is
affected by any differences between the simulated and actual meteorology. We
examine the sensitivity of the CO bias to model transport by comparing
RefCOonly, which is a free-running CCM simulation, with the COonlySD
simulation, which has year-specific meteorology from MERRA. The largest
difference between the simulations occurs poleward of 30
The tagged tracer results presented in Sect. 3.2.2 suggest that increasing
CO emissions can improve the agreement with CO surface observations.
However, increasing CO emissions will lead to feedbacks on OH, CH
The results shown in Figs. 4 and 5 suggest that increasing CO from
tropical biomass burning along with Asian anthropogenic, European
anthropogenic, and Russian biomass burning CO can eliminate both the IHG
and global mean bias. We therefore adjust the emissions for winter, spring,
and summer in the GMI-HiEmis simulation based on the biases and tagged
tracer sensitivities for the season. We do not alter the September–December
emissions since the RefGMI simulation shows little NH bias in those
months (Fig. 2) and our focus is on spring and summer. We choose the
adjustment factors by solving for the linear combination of
COaa
CO emission adjustment for the high-emission simulation compared to the standard simulation.
Including larger CO emissions increases the loss of OH, reducing OH
concentrations. The mass-weighted global mean tropospheric OH in the
GMI-HiEmis simulation is
Both the RefGMI and GMI-HiEmis simulation show a large high bias in NH TCO compared to the OMI/MLS observations, as well as a low bias in the equatorial Pacific and the extratropical SH (Fig. 9). Similar biases were present in the ACCMIP multi-model mean (Young et al., 2013), indicating that these biases are a common feature in CCMs. The increased CO emissions in the GMI-HiEmis simulation slightly increase the high bias in TCO in the northern midlatitudes, but the increase is small compared to the bias in the RefGMI simulation.
Given the sensitivity of the CO biases to OH (Figs. 3, 4, and 5) and the relatively small changes in OH resulting from the increased CO emissions in the GMI-HiEmis simulation, we next examine how other biases in the RefGMI simulation may impact OH concentrations and consequently the CO distribution and methane lifetime. We conducted a series of sensitivity studies using the CO–OH parameterization option to isolate the impact of several known model biases on OH and CO distributions. We analyze the results for the entire year in order to compare our results to observation-based estimates of methane lifetime.
Annual mean tropospheric column ozone (TCO) from the RefGMI
The primary source of OH in the troposphere is ozone photolysis followed by
reaction of O
Comparison of output from the CCMs that participated in the ACCMIP study to OMI/MLS TCO reveals positive biases in simulated tropospheric ozone over the NH midlatitudes and negative biases over the tropical Pacific and SH (Young et al., 2013), which lead to an overestimate in NH OH production and an underestimate in SH OH production (Naik et al., 2013). The multi-model mean tropospheric ozone also shows a high bias in the NH and low bias in the SH compared to the Tropospheric Emission Spectrometer (TES) (Bowman et al., 2013). A multi-species assimilation study by Miyazaki et al. (2012b) found that assimilating TES ozone increased OH concentrations in the SH.
We use the CO–OH option to investigate the impact of removing the GEOSCCM's
tropospheric ozone column bias relative to the OMI/MLS observations (Fig. 9). We scale the tropospheric ozone values input to the parameterization of
OH for each month between October 2004 and December 2010 from 60
NH
We next examine how biases in stratospheric ozone affect OH through their
role in photolysis. Voulgarakis et al. (2013) found that changes in
stratospheric ozone and tropospheric OH in the ACCMIP models both correlated
strongly with J(O
We investigate the impact of model biases in water vapor on OH concentrations through its role in OH production. Inter-model differences in OH in the POLMIP study are correlated with inter-model differences in simulated water vapor (Monks et al., 2015). The GEOS-5 AGCM exhibits a high bias in specific humidity compared to the MERRA reanalysis in much of the troposphere (Molod et al., 2012), and a high bias in the midtroposphere is also seen throughout the year in GEOSCCM compared to Atmospheric Infrared Sounder (AIRS) data (Lamarque et al., 2013).
We quantify the impact of this bias by conducting a sensitivity study,
CO–OHSensQ, that applies altitude- and latitude-dependent zonal mean scaling
factors for each month to the specific humidity provided to the OH
parameterization. The scaling factors are based on comparison of the RefGMI
simulation to MERRA and are applied in 100 hPa intervals between 900 and 200
hPa for 60
Simulated OH is 6 % lower in the CO–OHSensQ simulation than the RefCO–OH
simulation, and the methane and methyl chloroform lifetimes against OH are
thus 6 % longer (Table 4). Consequently, water vapor bias has a larger
impact on OH concentrations than the stratospheric or tropospheric ozone
biases in our simulations. The OH reduction is similar in both hemispheres
in the annual mean, so the NH
Anthropogenic NO
In the previous sections, we examined the sensitivity of OH and its IHG to
model biases in tropospheric and stratospheric ozone, water vapor, and NH
NO
Annual mean percent change in specific humidity imposed in the
CO–OHSensQ experiment between 900 and 200 hPa, 60
Table 4 suggests that applying multiple bias corrections simultaneously
would bring our simulation into good agreement with the observation-based
estimates of global mean OH. We conduct a final sensitivity simulation,
called CO–OHSensAll, incorporating the bias corrections for tropospheric and
stratospheric ozone, as well as the specific humidity scaling and 30 %
reduction in NH NO
The reduction in OH in the sensitivity studies compared to RefCO–OH leads to
higher surface concentrations of CO. The bias in the global mean March–August
surface CO changes from -4 % in RefCO–OH to 3 % in
CO–OHSensAll, while the bias in the IHG changes from
Zonal mean surface CO difference compared to RefCO–OH for the CO–OHSensTCO (green), CO–OHSensStO3 (cyan), CO–OHSensQ (blue), CO–OHSensNOx (pink), and CO–OHSensAll (solid black) simulations averaged over March through August of 2005 to 2009. The sum of the differences for the CO–OHSensTCO, CO–OHSensStO3, CO–OHSensQ, and CO–OHSensNOx compared to RefCO–OH is shown as the black dotted line.
We examined possible causes of CO model bias, such as underestimated emissions or overestimated OH, in a global CCM. An underestimate of CO emissions can impact the chemistry–climate simulation through the interaction of CO with methane via OH and with ozone, but we find the effects to be small. In contrast, a CO bias due to excess OH would imply biases in methane lifetime, further influencing the simulated climate.
Either increasing emissions or decreasing Northern Hemisphere OH can remove the bias in the latitudinal gradient of CO compared to surface observations. However, we find that the large increases in Asian anthropogenic emissions needed to remove the negative CO bias at remote surface sites leads to overestimates of CO over Asia compared to MOPITT. In contrast, reducing OH in the Northern Hemisphere improves the agreement between simulated and observed CO concentrations. This is consistent with the finding of Patra et al. (2014) that the ratio of Northern Hemisphere to Southern Hemisphere OH is close to 1. We note that biases in OH, CO emissions, and transport are not mutually exclusive, and the model bias in CO is likely influenced by a combination of these factors.
The availability of satellite-based constraints on CO, ozone, and water
vapor enables us to assess model biases that affect the major sources and
sinks of OH and hence CO concentrations and methane lifetime. We used a
CO–OH parameterization to explore the effect of model biases in ozone and
water vapor on simulated OH and CO. Removing the high bias in Northern Hemisphere
tropospheric ozone, a common feature of CCMs, had only a small
effect on simulated OH and methane lifetime. A high bias in water vapor had
a larger impact on global mean OH, but removing this bias did not remove the
NH–SH asymmetry in OH. Removing both ozone and
water vapor biases, as well as decreasing Northern Hemisphere NO
Our study suggests that the springtime low bias in CO at northern latitudes
often seen in CCM simulations likely indicates a bias in methane lifetime.
Improving the model representation of water vapor and ozone, as well as
reducing uncertainty in NO
Support for this work comes from NASA's Modeling, Analysis, and Prediction Program. Computing resources were provided by the NASA High-End Computing (HEC) Program. Edited by: P. Jöckel