2. MICROORGANISMOS
2.1.4. Bacterias
The main way the nitrated-extended version of the TOMCAT-GLOMAP-mode coupled model (GMV4-nitrate) (Benduhn et al., 2016) differs from the TOMCAT-GLOMAP-mode couple model version 6 (GMV6) (Mann et al., 2010) is through the inclusion of the inorganic dissolution module discussed by Benduhn et al. (2016). A dissolution module is a thermodynamic module required for the partitioning of semi-volatile inorganic aerosols (Benduhn et al., 2016).
Dissolution is the combination of condensation and partial dissociation. GMV6 considers the condensation of sulfuric acid, which condenses irreversibly under tropospheric conditions.
While GMV4-nitrate considers semi-volatile species (such as water, HNO3, HCl and NH3) in addition, which may re-evaporate from the aerosol phase as a function of temperature and chemical composition in the atmosphere (Benduhn et al., 2016).
GMV4-nitrate follows the same gas-phase advected tracer setup as GMV6 (Mann et al., 2010), but with the addition of an NH3 tracer. In GMV4-nitrate a hybrid solver is utilised to tackle numerical instability and computational expense in order to allow the simulation of semi-volatile inorganic gases in to the aerosol liquid phase (Benduhn et al., 2016).
To calculate the aerosol cloud albedo effect (aCAE), aerosol direct radiative effect (aDRE) and ozone direct radiative effect (O3DRE) methodologies used in previously published literature by Spracklen et al. (2011a), Rap et al. (2013) and Richards et al. (2013) (respectively) are applied.
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Evaluation of the gas-phase chemistry within GMV4-nitrate is conducted through the comparison of model simulations without aviation emissions against ozonesonde data compiled by Tilmes et al. (2012). The 41 ozonesonde launch sites considered by Tilmes et al.
(2012) span the global domain with ozonesonde profiles obtained between 1987–2011.
Evaluation of GMV4-nitrate simulated profiles (using CMIP5 recommended emissions) against those compiled by Tilmes et al., (2012) show that the model can skilfully replicate ozone profiles, while replicating regional and altitudinal variations in ozone concentrations.
The model is able to replicate ozone profiles, demonstrate seasonal variability, with the level of agreement between model and ozonesonde profiles being dependant on location and altitude. Analysis of seasonal profiles across each site yields a global mean bias (NMB) of +4.36%, while analysis of global annual mean profiles returns a mean bias of +5.31% (with a bias of +6.98% when the model-observations comparison from Praha is considered).
Grouping model-observation scatters plots in to latitudinal bands regional trends are seen.
Partitioned in to latitudinal bands seasonal profiles indicate that GMV4-nitrate overestimates over the 90°N–60°N and 30°S–60°S bands, underestimates over the 30°N–30°S band and returns both negative and positive biases over the 60°N–30°N and 60°S–90°S bands.
Considering annual mean concentrations positive biases are found for the 90°N–60°N, 60°N–
30°N and 30°S–60°S latitudinal bands, with negative biases between 30°N–30°s and 60°S–90°S.
Evaluation of model performance over latitudinal and altitudinally resolved regions, shows that the GMV4-nitrate shows greatest global model skill between 400<hPa<700 (representing the lower troposphere) with an annual mean bias of 3.75%. The next best performing level is representative of the upper troposphere (100<hPa<400) with a mean bias of 5.14%, while the surface layer (700<hPa<1000) returns an annual mean bias of 6.11%. Ultimately greatest model skill is seen in the NH and SH mid-latitude regions (60°N–30°N and 30°S–60°S bands) within the lower troposphere and surface layer (400<hPa<700 and 700<hPa<1000 levels).
Evaluation of the aerosol-phase chemistry within GMV4-nitrate was conducted through comparison of sulfate, nitrate, ammonium and organic carbon simulated concentration profiles against the aircraft field campaigns conducted between 2001 to 2008 collated by Heald et al., (2011). GMV4-nitrate is able to replicate observational sulfate profiles while overestimating observational concentrations, but is in fair agreement with GEOS-Chem simulations from Heald et al. (2011). The model is seen to generally underestimate nitrate profiles over the majority of the global domain, but demonstrates skill in replicating European nitrate profiles. The model is capable of replicating observed ammonium profiles over the
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majority of field campaigns investigated. Additionally, the model underestimates observed organic aerosol profiles over the majority of the global domain, but is capable of replicating the shape of profiles returned by field campaigns.
Model analysis over each site indicate that GMV4-nitrate returns biases ranging between – 71.72% to +190.26% for sulfates, –99.31% to –47.62% for nitrates, –91.24% to +24.67% for ammonium and –94.42% to +215.09% for organic aerosols. Taking all campaigns in to account global mean biases of +8.90% were found for sulfates, –93.89% for nitrates, –59.05% for ammonium and –71.91% for organic aerosols; indicating that globally in comparison to observations used the model overestimates sulfates, while underestimates nitrates, ammonium and organic aerosols.
Discrepancies between simulated aerosol profiles with field campaign observations taken from literature can be partly attributed to differences between the year simulations were conducted for (2000) and the year field campaigns were conducted (2008–2001), changes in global SO2, NOX and NH3 emissions, along with previously assessed disparities in global emissions datasets (Whitburn et al.; Spracklen et al., 2011b). Additionally, discrepancies in model evaluation will arise from the varying timeframes each field campaign was conducted over, the time of year each campaign was conducted over along with the conditions each field campaign was investigating.
Overall GMV4-nitrate is able to skilfully replicate ozone and sulfate profiles with global annual mean biases of +5.31% [seasonal mean = +4.368%] and 38.92% respectively. Global annual mean biases for nitrates, ammonium and organic aerosols indicate model underestimations (–
93.89%, –59.05% and –71.91% respectively), while model mean biases for sulfates indicate model overestimations (+8.90%). Nitrate and ammonium underestimations will be due to higher model SO2 sources, while underestimations in organic aerosol are due to global underestimations anthropogenic SOA emission of ~100 Tg yr-1 (Spracklen et al., 2011b).
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