Implications of model bias in carbon monoxide for methane lifetime

Introduction Conclusions References

tions. A study of 26 atmospheric chemistry models found that the simulated CO was biased low in the extratropical Northern Hemisphere (Shindell et al., 2006) compared to satellite observations from MOPITT  and surface observations, especially during spring. Shindell et al. (2006) attribute this bias primarily to an underestimate in Northern Hemisphere (NH) CO emissions, particularly from Asia. Monks 10 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 (AC-CMIP) simulations also shows a negative bias compared to both MOPITT and surface 15 observations in the northern extratropics .  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 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).
Uncertainty in the tropospheric burden and distribution of OH leads to further uncer- 15 tainty 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 ob-20 servations in some locations, but degraded the comparison at other locations. Patra et al. (2014) suggest 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 2 to H 2 O on aerosols reduced OH concentrations in a global model, correcting much of the negative 25 model bias in the extratropical NH with the largest CO increase occurring in spring.
Understanding the causes and implications of CO bias in chemistry climate models 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 Introduction is to quantify the relationship of the extratropical NH CO bias seen in chemistry climate models with bias in oxidant concentrations and methane lifetime. We use the GEOS-5 Chemistry Climate Model (GEOSCCM) to compare the impact of increasing CO emissions vs. reducing OH concentrations on the CO distribution. We also quantify the contribution of model biases in other constituents such as ozone and water vapor 5 to the simulated OH and CO distributions and methane lifetime.

Constituent observations and assimilated fields
Our primary constraint on the model CO distribution comes from surface observations of CO from the NOAA Global Modeling Division (GMD) network (Novelli and Masarie, 10 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 of CO every three days from March 2000 to present . We use the level 3 CO column data from the MOPITT version 5 thermal infrared (TIR) product (Deeter et al., 2011(Deeter et al., , 2013. 15 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). We use the TCO product for 2004-2010 to constrain the tropospheric 20 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 Introduction ingests 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).

Model and methodology
Our analysis uses several chemistry options within the GEOSCCM framework 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 re-10 sults over all years of the time slice. All simulations use the Fortuna version of GEOS-5 (Molod et al., 2012) and have 2 • latitude by 2.5 • longitude horizontal resolution and 72 vertical levels. We describe each chemistry option and associated experiments below and in Table 1. 15 GEOSCCM integrates the chemistry mechanism of the Global Modeling Initiative (GMI) CTM (Duncan et al., 2007b;Strahan et al., 2007) into the GEOS-5 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 timeslice simulation conducted 20 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 . Consequently, our analyses of bias in the GEOSCCM are likely applicable to other chemistry climate models as well.

CO-only option
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 GEOSCCM ACCMIP simulation, and the CO sources from methane and isoprene oxidation are calculated using monthly methane and isoprene fields archived from the ACCMIP simulation as well. Emissions and other forcings are chosen to parallel the ACCMIP simulation; however, the COonly 15 option includes an amplification factor for CO emissions to account for the absence of co-emitted NMHCs (Duncan et al., 2007a). Section 3.2 describes CO-only sensitivity studies.
The GEOSCCM includes an option to constrain the meteorology with MERRA or any GMAO assimilation product. The simulation in pulled towards the MERRA anal-Introduction

CO-OH option
A third chemistry option within the GEOSCCM is the CO-OH parameterization of Duncan et al. (2000Duncan et al. ( , 2007a. This chemistry option is of intermediate complexity between the GMI and Tagged-CO options, and was designed to represent an older version of the chemistry scheme used in the GMI chemistry option. This option is similar to the CO- 5 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 OH parameterization such as ozone, NO x , CH 4 , and NMHC concentrations are prescribed from the archived monthly output of the ACCMIP simulation. The temperature, water vapor, and irradiance-related variables input to the parameterization are calculated within the CCM. Since the parameterization was only designed to provide tropospheric OH values, we use the results of the parameterization from the surface to 300 hPa, and archived OH fields taken from the GMI-chemistry simulation above 300 hPa. The CO emission amplification factors are adjusted slightly downward compared to the COonly simulation to bring the RefCO-OH simulation into 15 better agreement with the RefGMI simulation. We use the CO-OH option to examine how biases in a particular model field such as ozone affect OH and CO concentrations. Section 3.4 describes sensitivity studies conducted with this option. 20 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/SH OH ratio is tropospheric OH of 11.2 ± 1.3 years. The lifetimes of methyl chloroform and methane against tropospheric OH in the RefGMI simulation are 5.9 and 9.6 years, respectively ( Table 2). The lifetimes of methyl chloroform and methane against tropospheric OH in the RefCO-OH simulation are 6.4 and 10 years, respectively, within the uncertainty of the observation-based estimates. The seasonal cycles are similar in both simulations, 5 but the difference in NH OH is larger in the first half of the year than the second half ( Fig. 1d). Consequently, when using the CO-OH parameterization to investigate OH and CO, we present the changes due to a given factor rather than the absolute values of CO, OH, and methane lifetime for easier comparison with the other simulations.

10
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. Section 3.1 compares the CO distribution simulated by the three options to observations and discusses the consistencies and differences between simulations. We analyze the impacts of increasing emissions, decreasing OH, and changing model transport on the 15 CO distribution in Sect. 3.2, and examine how changing CO emissions affects ozone and OH in Sect. 3.3. Section 3.4 investigates the contribution of known model biases to the simulated OH and methane lifetime. Table 1 summarizes the key results of each of the sensitivity studies. 20 This section presents a comparison of CO and OH distributions from the RefGMI, RefCO-only, and RefCO-OH simulations to each other and to observations. Figure 2 compares the annual cycle of CO in simulations to observations from six GMD sites selected to represent a range of latitudes. The RefGMI simulation (purple) is biased low at the NH sites, with the bias most prominent in the first half of the year, especially Introduction 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 Re-fGMI, 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.

5
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 parameter- 10 ization 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 RefCO-OH OH (Fig. 1). 15 Adjusting the strength of mid-latitude 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 10,20,50,20 100, 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 ACPD 15,2015 Implications of model bias in carbon monoxide for methane lifetime ported 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).

Sensitivity of CO-only simulations to sources and OH
We examine the impact of reducing OH concentrations globally or only in the NH in 5 our COonly simulation and find that a large decrease in NH OH is effective in reducing the high latitude CO bias.  found that the ACCMIP multi-model mean underestimated tropospheric OH by 5-10 % globally. The NH/SH OH ratios in the ACCMIP multi-model mean and GEOSCCM ACCMIP simulation are 1.28 and 1.18, respectively , compared to the (Patra et al., 2014) estimate of 10 0.97 ± 0.12. Figure 3c shows that reducing OH by 5-10 % leads to a small increase in CO across all latitudes, yielding a small reduction in the bias compared to GMD observations. Decreasing OH by 20 % in the NH only leads to a large improvement in high latitude CO compared to observations, with only a small effect in the Southern Hemisphere.

Impact of sources and OH on global mean and IHG bias in CO
We find that achieving zero bias in both the 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, inter-hemispheric gradient (IHG) bias, and correlation (r 2 ) of the simulated CO com-20 pared to the GMD observations for March through May. Increasing anthropogenic CO from Asia (COaa), Europe (COea), or North America (COnaa), or increasing Russian biomass burning (COrubb) reduces the bias in the IHG as well as the global mean. Increasing CO from tropical biomass burning (COtrbb) leads to a smaller reduction in the IHG bias, while increasing CO from biogenic emissions (CObio) reduces the mean bias with little effect on the IHG (Fig. 4a) 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 5 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.
We exclude increases in COnaa and CObio in the rest of our study. While increases in COaa, COea, COnaa, and COrubb show similar slopes for IHG vs. global mean bias, increasing COnaa reduces the correlation with observations, where as increasing the 10 other sources yields a slight improvement in correlation (Figs. 4b and 5b). Increases in both COtrbb and CObio reduce the correlation with observations in summer, but the effect is also present for CObio in spring.
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 15 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. 20 Based on the tagged tracer results (Figs. 4 and 5), we conduct a new simulation, called HiEmis, using the GMI chemistry with increased emissions from Asian and European anthropogenic sources as well as tropical and Russian biomass burning. All other aspects of the simulation are identical to the standard simulation. We adjust the emissions for each season based on the biases and tagged tracer sensitivities for the 25 season. We choose the adjustment factors by solving for the linear combination of COaa + COea + COrubb and COtrbb that minimizes the error in both the IHG and the global mean bias. Table 2 shows the emissions adjustments for each season. Figure 6a 20316 Introduction and b shows the simulated surface CO for April 2007 in the RefGMI and GMI-HiEmis simulations compared to GMD observations. The HiEmis simulation shows less negative bias at many of the high latitude sites, but shows a positive bias over Europe, one of the locations where the emissions were increased. This suggests that our adjustment of COea is too large, which is unsurprising given that European emissions are 5 relatively well known.

Impact of adjusting sources vs. NH OH on comparison to observations
We next compare the impact of increasing emissions with that of reducing NH OH by 20 %. The RefCOonly simulation shows a similar surface CO distribution to RefGMI ( Fig. 6a and 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, it leads to an overestimate of CO concentrations over Europe and eastern United States. Consequently, a combination such as reduced NH OH and reduced emissions over the eastern US is likely needed to reconcile the simulated CO with observations.

15
Comparison to MOPITT data suggests that the spring increase in Asian emissions used to reduce the NH bias in the GMI-HiEmis simulation is unrealistic (Fig. 7). Sparse surface data in this region makes it difficult to determine from comparison with GMD observations whether this higher CO seen in GMI-HiEmis is realistic (Fig. 6), so we also compare the four simulations shown in Fig. 6 to the CO column from MOPITT. Both the 20 RefGMI and RefCOonly simulations show a large negative bias in NH CO compared to MOPITT (Fig. 7a and c). The GMI-HiEmis and COonlyLowNHOH simulations both reduce this negative bias ( Fig. 7b and 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 Introduction

CO sensitivity to transport
Transport, in addition to chemistry and emissions, plays a role in the IHG of CO. 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 yearspecific meteorology from MERRA. The largest difference between the simulations 5 occurs poleward of 30 • N during July through October (Fig. 8a). The use of specified meteorology makes only a small difference in the global mean and IHG CO biases compared to surface observations in March-May (Fig. 4), but leads to a large reduction in bias as well as improved spatial correlation with the GMD observations in June-August (Fig. 5). The biases in global mean CO and the CO IHG are −3.5 and −6.6 %, respec-10 tively, in COonlySD, compared to −11 and −26 %, respectively, in RefCOonly. More CO from Northern Hemisphere anthropogenic and boreal biomass burning sources remains in the lower troposphere and reaches the high latitudes in COonlySD, where as more is transported to the upper troposphere in RefCOonly (Fig. 8b). 15 The increased CO sources considered in Sect. 3.2 will impact ozone production and the concentrations of OH and other constituents. We quantify these effects by comparing the GMI-HiEmis simulation to the RefGMI simulation. Including larger CO emissions increases the loss of OH, reducing OH concentrations. The mass-weighted global mean tropospheric OH in the GMI-HiEmis simula-20 tion is 1.11 × 10 6 molec cm −3 , a 3 % decrease compared to the RefGMI simulation ( Table 2). The methane lifetime against tropospheric OH also increases slightly, from 9.6 years in the RefGMI simulation to 9.9 years in the HiEmis simulation. This slightly improves the agreement with the observation-based estimate of 11.2 ± 1.3 years of Prather et al. (2012). Since the decrease in OH occurs primarily in the Northern Hemi-Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | tion to 1.16 in the GMI-HiEmis simulation, but the large hemispheric asymmetry in OH remains.
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 the ACCMIP multi-5 model mean (Young et al., 2013), indicating that these biases are a common feature in chemistry climate models. 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.

Sensitivity of OH to model biases
Given the sensitivity of the CO biases to  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 15 impact of several known model biases on OH and CO distributions.
The primary source of OH in the troposphere is ozone photolysis followed by reaction of O 1 D with water vapor, and secondary production of OH occurs through reaction of HO 2 with NO or O 3 . Consequently, simulated OH concentrations are sensitive to errors in NO x and ozone concentrations, water vapor, and 20 factors that influence photolysis such as overhead ozone column and clouds. 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 the tropospheric ozone photolysis rate, specific humidity, and reactive nitrogen emissions, and inversely proportional to CO emissions. Here, we examine the sensitivity of OH to model biases in some of these factors.

Sensitivity to tropospheric ozone
Comparison of the CCMs that participated in the ACCMIP study to OMI/MLS TCO 5 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 leads to an overestimate in NH OH production and an underestimate in SH OH production . 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) 10 (Bowman et al., 2013). A multi-species assimilation study by Miyazaki et al. (2012) found that assimilating TES ozone increased OH concentrations in the SH. We use the CO-OH parameterization 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 for 15 each month between October 2004 and December 2010 from 60 • S-60 • N so that the tropospheric ozone column is unbiased compared to the OMI/MLS TCO for that month. We use the scaled ozone input in a sensitivity study for October 2004 to January 2010, called CO-OHSensTCO, which parallels the RefCO-OH simulation but uses the scaled tropospheric ozone values in the OH parameterization. CO-OHSensTCO 20 shows a small (2 %) decrease in global mean OH compared to RefCO-OH, with a 3 % decrease in NH OH and 1 % decrease in SH OH. The increased OH leads to a 2 % increase in both methane and methyl chloroform lifetimes against OH. The NH/SH OH ratio decreases slightly to 1.19 (Table 4), suggesting that the inter-hemispheric gradient in the tropospheric ozone bias makes only a minor contribution to the model's ACPD 15,2015

Sensitivity to stratospheric ozone
We next examine how biases in stratospheric ozone affect OH through their role in photolysis.  found that changes in stratospheric ozone and tropospheric OH in the ACCMIP models both correlated strongly with J(O 1 D). We conduct a sensitivity study for 2005 through 2009, CO-OHSensStO3, which parallels the 5 RefCO-OH simulation but replaces the simulated ozone with the GMAO ozone assimilation in the stratosphere for input into the parameterization. The change in global mean OH in CO-OHSensStO3 is nearly identical to that of CO-OHSensTCO, but CO-OHSensStO3 places more of the change in the SH, causing the NH/SH OH ratio to increase to 1.23 (Table 4). The change in OH due to stratospheric ozone bias is small in part because GEOSCCM has relatively small biases in tropical stratospheric ozone. However, the multi-model Chemistry Climate Model Validation-2 (CCMVal-2) study (Morgenstern et al., 2010) shows a large spread across models for column ozone in the tropics (Austin et al., 2010). Thus, biases in stratospheric ozone may play a larger role in tropospheric OH in models with larger stratospheric ozone biases.

Sensitivity to water vapor
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 re-20 analysis 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 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 25 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 ACPD 15,2015  intervals between 900 and 200 hPa for 60 • S to 60 • N. These scaling factors are designed to remove the mean bias in the simulated specific humidity compared to MERRA while allowing the simulated water vapor to vary in space and time in a manner consistent with the simulated meteorology. The water vapor scaling is applied only to the OH parameterization, and does not impact the general circulation of the simulation. The 5 scaling results in lower specific humidity in the middle and upper troposphere, with the largest percent reductions occurring between 300 and 500 hPa (Fig. 10). 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 concen-10 trations 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/SH OH gradient is unchanged compare to RefCO-OH.

Sensitivity to NO x
Anthropogenic NO x emissions, which contribute to secondary production of OH, are 15 located primarily in the Northern Hemisphere. Consequently, an overestimate of these emissions could contribute to the NH/SH asymmetry in simulated OH. We quantify the sensitivity of simulated OH to NH NO x with a sensitivity study, CO-OHSensNOx, which parallels RefCO-OH but includes 30 % lower NO x concentrations in the NH. This reduction in NH NO x emissions leads to a 3 % reduction in global OH (Table 4). 20 SH and NH OH are reduced by 0.3 and 6 %, respectively, reducing the NH/SH OH ratio to 1.15. While this shows that the hemispheric asymmetry in OH is affected by NO x emissions, a much larger redistribution of NO x emissions would be required to completely eliminate the asymmetry. 15,2015 Implications of model bias in carbon monoxide for methane lifetime

Summary of OH sensitivities
In the previous sections, we examined the sensitivity of OH and its interhemispheric gradient to model biases in tropospheric and stratospheric ozone, water vapor, and NH NO x emissions. We find that water vapor has the largest impact on global mean OH, while NH NO x emissions have the largest impact on the NH/SH ratio. However, none of these biases individually explains the 20 % reduction in NH OH that would remove the interhemispheric asymmetry and which Figs. 4 and 5 suggest are necessary to remove most of the CO bias. We note, however, that the OH parameterization simulations do not account for all the chemical feedbacks between ozone, methane, OH, and other species, and may underestimate the sensitivity of the full chemistry simulation to some 10 of these biases. 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 x emissions. This simulation results in a 13 % reduction in global mean OH, with a 10 % reduction in the SH and 16 % reduction in the NH. The NH/SH OH ratio is reduced to 1.14. The lifetimes of methyl chloroform and methane in this simulation increase by 12 and 15 %, respectively. Applying this 15 % increase in methane lifetime to the RefGMI methane lifetime against 20 OH of 9.6 years would yield a lifetime of 11 years, consistent with the 11.2 ± 1.3 year estimate of Prather et al. (2012). This shows that removing the main model biases related to OH production would bring simulated global mean OH into good agreement with observation-based estimates, although some NH/SH asymmetry would remain.
The reduction in OH in the sensitivity studies compared to RefCO-OH leads to higher 25 surface concentrations of CO. The bias in the global mean March through August surface CO changes from −4 % in RefCO-OH to 3 % in CO-OHSensAll, while the bias in the interhemispheric gradient changes from −11 to −4 %. Figure 11 shows the latitudi-ACPD 15,2015 Implications of model bias in carbon monoxide for methane lifetime nal distribution of the change in CO for each sensitivity simulation vs. RefCO-OH. The sum of the changes for CO-OHSensTCO, CO-OHSensStO3, CO-OHSensQ, and CO-OHSensNOx, shown in the dotted line, is similar but slightly smaller than the change for CO-OHSensAll, indicating small non-linearities in the system. Correcting water vapor (CO-OHSensQ) makes a large contribution to the CO enhancement in both hemi-5 spheres, while adjusting NO x emissions (CO-OHSensNOx) and to a lesser extent tropospheric ozone (CO-OHSensTCO) contributes to the larger increase in the Northern Hemisphere.

Conclusions
We examined possible causes of CO model bias, such as underestimated emissions 10 or overestimated OH, in a global chemistry climate model. 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. 15 Either increasing emissions or decreasing Northern Hemisphere OH concentrations 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 concentrations in the Northern 20 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 one. 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. 25 The availability of satellite-based constraints on CO, O 3 , 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 concentrations. Removing the high bias in Northern Hemisphere tropospheric ozone, a common feature of CCMs, had only a small effect on simulated OH and CH 4 lifetime. A high bias in water vapor had a larger impact on global mean OH, but removing this 5 bias did not remove the Northern vs. Southern Hemisphere asymmetry in OH. Removing both ozone and water vapor biases, as well as decreasing Northern Hemisphere NO x , provided the desired increase in CH 4 lifetime, but was insufficient to remove the hemispheric asymmetry in OH. Thus, while a NH/SH OH ratio near one improves the simulation of CO, we cannot generate this ratio in our simulations by removing known 10 model biases in ozone and water vapor.
Our study suggests that the springtime low bias in CO at northern latitudes often seen in chemistry climate model simulations likely indicates a bias in CH 4 lifetime. Improving the model representation of water vapor and ozone, as well as reducing uncertainty in NO x emissions, could reduce these biases. However, additional research 15 is needed to understand the causes of northern vs. southern asymmetry in simulated OH. Future field missions that provide data on the latitudinal distribution of CO and oxidant sources and losses will be valuable for understanding biases in simulated CO and OH. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Frith, S., Garcia, R. R., Garny, H., Gettelman, A., Hardiman, S. C., Kinnison, D., Lamarque, J. F., Mancini, E., Marchand, M., Michou, M., Morgenstern, O., Nakamura, T., Pawson, S., Pitari, G., Pyle, J., Rozanov, E., Shepherd, T. G., Shibata, K., Teyssedre, H., Wilson, R. J., and Yamashita, Y.: Decline and recovery of total column ozone using a multimodel time series analysis, J. Geophys. Res.-Atmos., 115, D00M10, doi:10.1029/2010jd013857,  15,2015 Implications of model bias in carbon monoxide for methane lifetime ACPD 15,2015 Implications of model bias in carbon monoxide for methane lifetime