GEM-AQ Evaluation

J. W. Kaminski, L. Neary, J. Struzewska, J. C. McConnell, A. Lupu, J. Jarosz, K. Toyota, S. L. Gong, J. Côté, X. Liu, K. Chance, and A. Richter Centre for Research in Earth and Space Science, Atmospheric Modelling and Data Assimilation Laboratory, York University, Toronto, Canada Institute of Environmental Engineering Systems, Warsaw University of Technology, Warsaw, Poland Science and Technology Branch, Environment Canada, Toronto, Canada Science and Technology Branch, Environment Canada, Montréal, Canada Smithsonian Astrophysical Observatory, Cambridge, MA, USA Institute of Environmental Physics, University of Bremen, Germany now at: Goddard Earth Sciences and Technology, University of Maryland Baltimore County, Baltimore, MD, USA

//gaw.kishou.go.jp/wdcgg.html). Modelled nitrogen dioxide is compared with SCIA-MACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) (Burrows et al., 1995;Bovensmann et al., 1999) and aircraft campaigns such as TRACE-A (Transport and Atmospheric Chemistry near the Equator-Atlantic) (Fishman et al., 1996) are used to evaluate other species such as nitric acid, hydrogen per- The gas-phase chemistry mechanism currently used in the GEM-AQ model is based on a modification of version two of the Acid Deposition and Oxidants Model (ADOM) (Venkatram et al., 1988), derived from the condensed mechanism of Lurmann et al. (1986). The ADOM-II mechanism comprises 47 species, 98 chemical reactions and 16 photolysis reactions. In order to account for background tropospheric chemistry, 4 10 species (CH 3 OOH, CH 3 OH, CH 3 O 2 and CH 3 CO 3 H) and 22 reactions were added. All species are solved using a mass-conserving implicit time stepping discretization, with the solution obtained using Newton's method. Heterogeneous hydrolysis of N 2 O 5 is calculated using the on-line distribution of aerosol. The list of chemical and photolysis reactions is given in Table A2 and Table A3, respectively. 15 Although the model meteorology is calculated to 10 hPa, the focus of the chemistry is in the troposphere where all species are transported throughout the domain. To avoid the overhead of stratospheric chemistry in this version (a combined stratospheric/tropospheric chemical scheme is currently being developed) we replaced both the ozone and NO y fields with climatologies above 100 hPa after each transport time 20 step. This ensures a reasonable upper boundary to the troposphere, while ensuring that the transport of ozone and NO y fields to the troposphere is well characterised by the model dynamics. For ozone we used the HALOE (Halogen Occultation Experiment) climatology (e.g. Hervig et al., 1993), while NO y fields are taken from the CMAM (Canadian Middle Atmosphere Model) (e.g. de Grandpré et al., 2000). Photolysis rates 25 (J values) are calculated on-line every chemical time step using the method developed by Landgraf and Crutzen (1998). In this method, radiative transfer calculations are done using a delta-two stream approximation for 8 spectral intervals in the UV and 14900

Aerosol package
The current version of GEM-AQ has 5 size-resolved aerosols types, viz. sea salt, sulphate, black carbon, organic carbon, and dust. The microphysical processes which describe formation and transformation of aerosols are calculated by a sectional aerosol module (Gong et al., 2003). The particle mass is distributed into 12 logarithmically 10 spaced bins from 0.005 to 10.24 microns radius. This size distribution leads to an additional 60 advected tracers. The following aerosol processes are accounted for in the aerosol module: nucleation, condensation, coagulation, sedimentation and dry deposition, in-cloud oxidation of SO 2 , in-cloud scavenging, and below-cloud scavenging by rain and snow. 15

Gas-phase removal processes
The effects of dry deposition are included as a flux boundary condition in the vertical diffusion equation. Dry deposition velocities are calculated from a 'big leaf' multiple resistance model (Wesely, 1989;Zhang et al., 2002) with aerodynamic, quasi-laminar layer, and surface resistances acting in series. The process assumes 15 land-use 20 types and takes snow cover into account. GEM-AQ only has a simplified aqueous phase reaction module for oxidation of SO 2 to sulphate. Thus, for the gas phase species, wet deposition processes are treated in a simplified way. Only below-cloud scavenging of gas phase species is considered in the model. The efficiency of the rainout is assumed to be proportional to the pre-25 cipitation rate and a species-specific scavenging coefficient. The coefficients applied 14901 EGU are the same as those used in the MATCH model (Multiscale Atmospheric Transport and Chemistry Model) used by the Swedish Meteorological and Hydrological Institute (SMHI) (Langner et al., 1998).

Emissions
The emission dataset used for global simulations was compiled using EDGAR 2.0 5 (Emission Database for Global Atmospheric Research) (archived in 2000, valid for 1990) and GEIA (Global Emissions Inventory Activity) global inventories (Olivier et al., 1999;Olivier and Berdowski, 2001). The EDGAR 2.0 data was chosen for its detailed information on non-methane volatile organic compound speciation. Emission data compiled for GEM-AQ includes global fields of anthropogenic emission fluxes with The chemical initial conditions used to initiate the model for the first time were generated from several sources. Chemical fields in the upper troposphere and lower stratosphere were taken from the CMAM model (de Grandpré et al., 2000) for January. In order to create a balanced and realistic chemical state, GEM-AQ was spun up for 6 months. This initial period was not used in the analysis of model results. In addi-20 tion, a number of fields and parameters are needed to specify surface characteristics. These are obtained from analysed climatological and geophysical datasets and include surface roughness, land-sea mask, albedo, deep soil temperature, ice cover, and topography. The surface roughness length is influenced by topography, land use, snow and ice cover.
the 1980s and 1990s.
The comparison of ozonesondes with model results consistently shows good agreement with the observations, although the region between 300 and 100 hPa tends to be under-predicted in most areas of the globe for all seasons. This height range is where the ozonesonde measurements show the most variability. Interestingly, we have found 10 that use of sigma coordinates caused an excess of ozone influx in regions of high topography such as over the Himalayas and Greenland. This resulted in too much upper tropospheric ozone in the northern hemisphere. Changing to the hybrid coordinate reduced the flux. Figure 1 shows seasonally averaged vertical profiles for two stations, Churchill (59 • N, 94 • W)and Hohenpeissenberg (48 • N, 11 • E). 15 In general, the agreement with all the stations is quite similar. Some ozonesonde stations show a summer model over-prediction in the lowest levels. This may be due to the dilution of emissions over relatively larger grid squares where ozone production is more efficient at lower mixing ratios (Liu et al., 1987).
To examine the model performance in the tropics where deep convection and light- 20 ning play a role in the distribution of ozone, the SHADOZ dataset (Thompson et al., 2003a,b) was used. Figure 2 shows a comparison of seasonally averaged SHADOZ ozonesondes and temperature measurements at four stations in the South Pacific with GEM-AQ results for 2001. There is an over-prediction in this region, likely due to an incorrect diagnosis of deep convective cloud and therefore the generation of ozone from 25 lightning generated NO x is not captured correctly. The individual ozonesonde profiles (not shown here) indicate that this may be the case. Comparison with ozonesondes gives detailed vertical resolution but over a limited 14904 EGU spatial region. Another method to evaluate the model is to compare ozone data with more limited vertical resolution but much more comprehensive horizontal spatial coverage. For this aspect of the study we have compared model results with tropospheric ozone columns from GOME, a nadir viewing instrument on ESA's ERS-2 satellite. GOME tropospheric data have been validated against contemporaneous ozonesonde 5 data (Liu et al., 2005,2006). Both GOME and GEM-AQ tropospheric columns are calculated using a tropopause determined by combining the dynamic tropopause in the extratropics and the thermal tropopause near the equator (Liu et al., 2006). In order to account for the different spatial resolutions of the GOME retrievals and GEM-AQ runs, model output profiles 10 corresponding to the model grid cells overlapping each GOME pixel were interpolated onto the GOME vertical levels, then averaged using the relative surface area of the corresponding GOME pixel and the GEM-AQ cell intersection as a weight. The GOME averaging kernel was then applied to the averaged model profile, and the tropospheric column calculated by integrating the transformed profile up to the tropopause level. 15 Finally, all the column data (GOME and model) were mapped onto the model grid by the same area-weighting method, and the monthly means obtained. Figure 3 shows the GEM-AQ, GOME and GOME-GEM differences in tropospheric ozone column for April, July and October, 2001. In April, GEM-AQ under-predicts in the high northern latitudes (>30 • N) with differences of 5-10 DU. This agrees with the 20 comparison with the ozonesondes. In the tropical ocean regions, GEM-AQ has ozone columns as large as 15 DU too large compared with GOME. This is consistent with the results compared with SHADOZ. For southern latitudes GEM-AQ has differences less than 10 DU. For July the pattern is much the same, although a plume off the coast of China is not captured by the model. For October, GEM-AQ over-predicts by 5-10 DU 25 over most of the globe. Only over the southern Pacific does the disagreement reach 15 DU. This might be because GEM-AQ is not capturing the timing and distribution of NO x generation by lightning, as mentioned above. The method that we have adopted relies on the modelled deep convective cloud, which may put too much NO x over the Introduction Many of the important processes involved in the study of air quality take place near the surface. Surface data gathered from the World Data Centre for Greenhouse Gases (http://gaw.kishou.go.jp/wdcgg.html) provides an opportunity to analyse the model performance in detail. Figure 4 shows surface ozone data from two stations , Yonagunijima

Carbon monoxide
Carbon monoxide has a chemical lifetime of a few months or longer in the troposphere, 15 depending on latitude and season. It can be a very useful tracer of the resolved transport, but also impacted, of course, by large scale convection and transport in the planetary boundary layer. Thus, a comparison with observations serves as a useful diagnostic of both transport and emissions in the model. In the following section we compare model results with the CO data from the MOPITT instrument (Drummond, 20 1992; Drummond and Mand, 1996) on the NASA Terra satellite. MOPITT is a nadir viewing instrument and, like GOME, has limited vertical resolution and is most sensitive at about 500 hPa. For this study we compare with the CO volume mixing ratio data obtained from the MOPITT instrument for 500 and 850 hPa. The MOPITT kernel has been applied to the GEM-AQ data.
25 Figure 5 shows the 500 hPa data for January and October, 2002 for GEM-AQ and MOPITT. For most times of the year, GEM-AQ captures the general pattern of the measured CO quite well. In January there is good agreement between GEM-AQ and 14906 EGU MOPITT data at 500 hPa, although GEM-AQ, as revealed by the mixing ratios, appears to have too high biomass burning emissions over Amazonia.
In October, Northern hemisphere CO values at 500 hPa are in general agreement with MOPITT data except over China, where GEM-AQ over-predicts CO. In the southern hemisphere the signal from biomass burning over southern Africa is too small 5 compared to MOPITT data, although the agreement over Amazonia is reasonable. However, GEM-AQ completely misses the heavy biomass burning that occurred over Indonesia (Edwards et al., 2006) since it uses climatological emissions. Perhaps as a result of the biomass burning emissions, GEM-AQ does not produce as high values in the southern subtropics: the plumes from Africa and South America only extend to 10 Australia and Africa, respectively. Figure 6 shows the MOPITT CO mixing ratios for the 850 hPa level for the same months as Fig. 5. The strengths and weaknesses of the model predictions at this level are similar to the 500 hPa comparison. For January, GEM-AQ does quite a reasonable job in both hemispheres, although biomass emissions in southwest Africa seem dis- 15 placed southward in GEM-AQ for this year. Also, GEM-AQ CO levels appear on the high side over Amazonia but appear too low over Australia.
For October, the 850 hPa GEM-AQ CO mixing ratios are too low by about 20%. In particular, CO values are low over the northeast coast of China and Indonesia and most of the southern subtropics except for Amazonia. 20 One interesting feature that is not reproduced in the model results are high values of CO over the Sahara Desert at the 850 hPa level: there is either no suitable source or the winds do not appear to transport CO across the tropics. This is a recurrent feature of the 850 hPa MOPITT data and it might represent an artefact of the retrieval process.

Nitrogen dioxide 25
Nitrogen dioxide is an important species for the generation of ozone in the troposphere. It has a relatively short lifetime (less than a week) so it is closely linked to emission sources. NO and NO 2 are closely related and the daytime ratio of NO to NO 2 rapidly 14907 EGU increases with height in the troposphere so that most of the NO 2 is concentrated in the first few kilometres. These characteristics allow the retrieval of NO tion of scattered light in the nadir direction. In the latter mode it is similar to GOME but has a higher horizontal spatial resolution (typically 60 km×30 km). In this section we compare the NO 2 tropospheric column measurements from SCIAMACHY with the GEM-AQ column NO 2 . As SCIAMACHY measures the total vertical column of NO 2 the stratospheric component must be subtracted. This is also done for the GEM-AQ 10 results as the simulation extends into the stratosphere. Using the Canadian Middle Atmosphere Model (CMAM) (de Grandpré et al., 2000) we note that the daytime longitudinal variability of NO 2 is quite large and varies by ±20% and this likely limits the accuracy of lower NO 2 columns. Also, as for GOME and MOPITT, cloud contamination can also cause problems. In Fig. 7 we present GEM-AQ and SCIAMACHY column data 15 for September 2004 and January 2005 using a logarithmic scale because of the large variability of tropospheric NO 2 . The SCIAMACHY tropospheric column was computed by subtracting the total column over a clean reference sector in the Pacific, between 180 • and 220 • . This column is assumed to be the stratospheric contribution only. For comparison with GEM-AQ, the SCIAMACHY data is shown on the same 1.5 • ×1.5 • grid. 20 For the GEM-AQ results, the same clean reference sector method was used. The tropospheric column was also computed using the thermal tropopause and was found to be about 25% higher than the clean sector method in relatively unpolluted regions and through the tropics. This may suggest the reference sector in GEM-AQ has an excess of tropospheric NO 2 , perhaps from lightning emissions.

25
In Fig. 7 EGU region, using the thermal tropopause to determine the column gives better agreement. Again, this is probably due to an excess of lightning NO x in the reference sector. In January, a low density plume can be seen from North America over the Atlantic by both the model and observations. Figure 8 presents correlation diagrams between SCIAMACHY and GEM-AQ for 5 September and January for the globe, North and South America, Europe, Africa and China. The regional boundaries are shown in Fig. 9 and are over the continental surfaces only, not including the surrounding oceans. For September and January the global picture is that SCIAMACHY NO 2 columns are relatively high compared to GEM-AQ. At the low end of mixing ratios there is more 10 variability as might be expected. A perusal of the individual regions reveals the source of the bias. For China, South America and Africa both January and September exhibit strong biases for NO 2 columns above about 1−2×10 15 molecules/cm 2 but for smaller columns there is relatively good agreement. For North America there is quite good agreement. The bias over Africa is reduced when the NO 2 tropospheric column is 15 computed using the thermal tropopause rather than the clean sector method. This is not the case over China, indicating the anthropogenic emissions used in the simulation may be too low.

Other species
Global coverage of species other than O 3 , NO 2 and CO is not as readily available. 20 However, aircraft campaigns can provide a local but comprehensive chemical picture of the troposphere. While the aircraft campaigns are for a specific weather situation not covered by our simulation, they are still useful. is able to be transported into the upper troposphere. The observed NO profile shown in Fig. 10g has the "c" shape that is associated with NO x from convection, where the model profile is not as clearly defined.

Discussion and conclusions
In this study we have focused on the large scale properties of the presented modelling 20 system. This limited comparison indicates that GEM-AQ is, in general, able to capture the spatial details of the chemical fields in the middle and lower troposphere. The comparison with GOME and SHADOZ shows the largest discrepancy over the tropical oceans. Some of the problem may be due to our treatment of deep convection and resulting lightning NO x emissions. A more detailed study of the modelled con- Overall, the comparison of carbon monoxide and nitrogen dioxide output with MO-PITT and SCIAMACHY measurements emphasizes the need for more accurate, yearspecific emissions rates for biomass burning and anthropogenic sources. 20 One of the means of characterising the general properties of an atmospheric model is via its OH oxidation capacity and for this two gases are generally useful, CH 4  EGU and OH, respectively and k i OH is the loss rate for the species with OH. Using the rate data from JPL 2003 we find τ CH 4 = 7.7 years and τ CH 3 CCI 3 = 4.6 years to be compared with 8.4 years and 5.0 years, respectively from the IPCC report (IPCC, 2001). Another important metric for a tropospheric model is the flux of ozone from the stratosphere. We find that the flux of ozone through a single model layer with an average pressure . In our simulations we have found that both the horizontal resolution and the use of the fully hybrid vertical co-ordinate system plays a significant role in the amounts of ozone coming down from the stratosphere. We found that using In the development of the model we have tried to be as internally consistent as possible when using transport information for the tracers: for example, for boundary layer transport we use the mixing coefficients from the physics module. However, for 15 large scale convective transport we are using the Kuo scheme for the dynamics while using Zang-McFarlane for the tracers. We have commenced a study where we will use a Kain-Fritsch scheme modified for large scales for the dynamics, transport and lightning generation.