Top PDF The chemical transport model Oslo CTM3

The chemical transport model Oslo CTM3

The chemical transport model Oslo CTM3

In Figure 6b we compare the models with MLS HNO measurements (Santee et al., 2007; Livesey et al., 2011), where the CTM3 and CTM2 reproduce MLS well in summer but underestimates it at altitudes above 30 hPa in winter. The latter may be due to lack of nitrogen species transported downwards from the mesosphere (Randall et al., 2006, 2009), or the lack of in-situ NOx sources caused by energetic particle precipitation (Jack- man et al., 2008; Semeniuk et al., 2011) and conversion to HNO by e.g. ion clusters (Verronen et al., 2008, 2011). Below 30 hPa the models do fairly well, also when it comes to the standard deviation. There are no big dif- ferences between CTM2 and CTM3, although CTM3 seems to perform slightly better in summer. Again, C3_V2 and C3_30MIN produce almost identical pro- les, except the latter at SH high latitudes, where small dierences up to 5 % in HNO can be seen (not shown). Lastly, we compare compare modelled N O with MLS measurements (Lambert et al., 2007; Livesey et al., 2011), shown in Fig. 6c. CTM3 and CTM2 produce very similar N O, however, at all latitudes except high wintertime latitudes, both models underestimate N O between about 30 hPa and 1 hPa, indicating stagnant vertical transport in the meteorological data. An ear- lier cycle of meteorological data (cycle 29) shows slightly better comparison (not shown), which could indicate that cycle 36r1 has stagnant vertical transport. We will come back to this. Again there are negligible dierences between C3_NIT, C3, C3_30MIN and C3_V2.
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Evaluation of a three-dimensional chemical transport model (PMCAMx) in the European domain during the EUCAARI May 2008 campaign

Evaluation of a three-dimensional chemical transport model (PMCAMx) in the European domain during the EUCAARI May 2008 campaign

Three options are available in PMCAMx-2008 for the simulation of inorganic aerosol growth. The most com- putationally efficient approach is the bulk equilibrium ap- proach, which assumes equilibrium between the bulk in- organic aerosol and gas phase. At a given time step the amount of each species transferred between the gas and aerosol phases is determined by applying the aerosol ther- modynamic equilibrium model ISORROPIA (Nenes et al., 1998) and is then distributed over the aerosol size sections by using weighting factors for each size section based on their surface area (Pandis et al., 1993). The second approach (hybrid approach) assumes equilibrium for the fine particles (<1 µm) and solves the mass transfer differential equations for the coarse particles (Capaldo et al., 2000). The most accurate but computationally demanding method is the dy- namic approach where mass transfer is simulated explicitly for all particles (Pilinis et al., 2000). In this work we use the bulk equilibrium approach since we are focusing on the model’s performance for fine particles.
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Kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB): the influence of interfacial transport and bulk diffusion on the oxidation of oleic acid by ozone

Kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB): the influence of interfacial transport and bulk diffusion on the oxidation of oleic acid by ozone

we show how interfacial transport and bulk transport, i.e., surface accommodation, bulk accommodation and bulk diffusion, influence the kinetics of the chemical reaction. Sen- sitivity studies suggest that in fine air particulate matter oleic acid and compounds with similar reactivity against ozone (C=C double bonds) can reach chemical lifetimes of multiple hours only if they are embedded in a (semi-)solid matrix with very low diffusion

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Validation of MIKE 11 Model Simulated Data for Biochemical and Chemical Oxygen Demands Transport

Validation of MIKE 11 Model Simulated Data for Biochemical and Chemical Oxygen Demands Transport

Therefore, an increase in BOD can be observed in the river during post monsoon compared to pre-monsoon season. Chemical Oxygen Demand (COD) was found to be high on some days. The COD between 39-49 mg L −1 was observed during High Water Flow (HWF). On the other hand much lower concentrations were detected during Average Water Flow (AWF). The COD between 10-14 mg L −1 was found during this period. This is in agreement with other researchers who have found COD less than 20 mg L -1 in rivers during AWF (Rosli et al. 2010). Overall, comparison between BOD and COD in both sampling moments (average and high water flow) shows water quality deterioration during high water flow. Therefore, it can be concluded that non-point sources of pollution have significant impacts on water quality of Bertam river. This confirms that selection of “Distributed source” as boundary descriptionin previous stage was appropriate and valid choice.
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Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach

Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach

of starting from only the source composition like RMs do. In addition, with the hybrid method, secondary pollutants are apportioned to specific sources while in RMs they are aggregated together. For example, after the hybrid method refinement livestock impacts advance in rank among top contributors in Midwestern cities: Chicago, Detroit and Pittsburgh (Table 5), mostly through the secondary formation of ammonium and the

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Optimal estimation of the surface fluxes of methyl chloride using a 3-D global chemical transport model

Optimal estimation of the surface fluxes of methyl chloride using a 3-D global chemical transport model

Figure 8 shows the averaged seasonal cycle results (red lines) compared to the reference ones (blue lines), and Fig. 9 shows their corresponding uncertainties. For the tropical plant emissions, the seasonal variations differ somewhat from the reference for the three regions, with the most sig- nificant differences occurring for the tropical American re- gion which exhibits two emission peaks. One is in January (its reference value shows a peak in December) and the other one is in August. As noted earlier, the variability in tropical emissions is the net of the combined influences of the vari- ables atmospheric temperature, precipitation, and available light, and therefore does not generally reflect the annual cy- cle of one of these variables alone. While emissions from tropical plants in Africa have a maximum in December, they have a minimum in July. This is probably because at this time Africa is very dry and plant growth activity (NPP) is inhibited.
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Simulating the formation of carbonaceous aerosol in a European Megacity  (Paris) during the MEGAPOLI summer and winter campaigns

Simulating the formation of carbonaceous aerosol in a European Megacity (Paris) during the MEGAPOLI summer and winter campaigns

while it can undergo complex atmospheric chemical and physical processing (Hallquist et al., 2009). The description of all these emissions and processes in chemical transport models (CTMs) is not a trivial task. Earlier modeling ef- forts for the megacity of Paris (Sciare et al., 2010) have assumed that primary OA (POA) is non-volatile and used a single-step oxidation SOA scheme thus underestimating SOA concentrations by a factor of three. Even larger er- rors were encountered when aged air masses with high SOA levels arrived at the observation site. More recently, models taking into account the semivolatile nature of POA (Robin- son et al., 2007) have been applied over Paris. Couvidat et al. (2013) applied the Polyphemus model, which incor- porates a two-surrogate-species (hydrophilic/hydrophobic) SOA formation scheme taking into account POA volatility and chemical aging, during the MEGAPOLI July 2009 cam- paign. The model estimated a 30–38 % local contribution to OA at the city center and overpredicted morning OC concen- trations. Zhang et al. (2013) implemented the volatility ba- sis set (VBS) approach into the chemistry transport model CHIMERE and applied it to the greater Paris region for the summer MEGAPOLI campaign. Simulation of organic aerosol with the VBS approach showed the best correlation with measurements compared to other modeling approaches. They also showed that advection of SOA from outside Paris was mostly responsible for the highest OA concentration lev- els. Fountoukis et al. (2013) examined the role of horizontal grid resolution on the performance of the regional 3-D CTM PMCAMx over the Paris greater area during both summer and winter and concluded that the major reasons for the dis- crepancies between the model predictions and observations in both seasons are not due to the grid scale used, but to other problems (e.g., emissions and/or process description). Skyllakou et al. (2014), using the particulate matter source apportionment technology (PSAT) together with PMCAMx, showed that approximately 50 % of the predicted fresh POA originated from local sources and another 45 % from areas 100–500 km away from the receptor region during summer in Paris. Furthermore they found that more than 45 % of OOA was due to the oxidation of volatile organic compounds (VOCs) that were emitted 100–500 km away from the center of Paris.
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Magnitude and seasonality of wetland methane emissions from the Hudson Bay Lowlands (Canada)

Magnitude and seasonality of wetland methane emissions from the Hudson Bay Lowlands (Canada)

Figure 3 shows the observed seasonal variations at Fraserdale and Alert for 2004–2008. The observations at Alert show a July minimum due to chemical loss in the Northern Hemisphere. The model minimum lags 4–6 weeks behind, an offset that can be attributed to background error in the seasonal variation of sources, transport, or OH concen- trations. The observations at Fraserdale follow the seasonal variation at Alert in winter-spring but deviate in late May toward an August maximum, ostensibly due to emissions from the HBL. The model shows the same seasonal devia- tion at Fraserdale relative to Alert but shifted 6 weeks early. A model sensitivity simulation with no HBL emissions (also shown in Fig. 3) confirms that the seasonal deviation between Fraserdale and Alert is due primarily to HBL emissions. The model shows multiple seasonal peaks at Fraserdale (late June, late August, early November) compared to a single ob- served peak, but this fine structure reflects fluctuations in the background rather than HBL emissions as discussed below.
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Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model

Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model

Kloster, S., Koch, D., Kirkev ˚ag, A., Kristjansson, J. E., Krol, M., Lauer, A., Lamarque, J. F., Liu, X., Montanaro, V., Myhre, G., Penner, J., Pitari, G., Reddy, S., Seland, Ø., Stier, P., Takemura, T., and Tie, X.: Analysis and quantification of the diversities of aerosol life cycles within AeroCom, Atmos. Chem. Phys., 6, 1777–1813, doi:10.5194/acp-6-1777-2006, 2006. Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale

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Effect of chemical degradation on fluxes of reactive compounds – a study with a stochastic Lagrangian transport model

Effect of chemical degradation on fluxes of reactive compounds – a study with a stochastic Lagrangian transport model

While micrometeorological flux measurement techniques are commonly used to measure exchange of e.g. carbon dioxide, water vapour and methane between ecosystems and atmo- sphere, they are also applied to an increasing degree to the emissions of chemically reactive species, such as volatile or- ganic compounds (VOC), from different ecosystems (Fowler et al., 2009; Kesselmeier et al., 2009; Rinne et al., 2009). These above canopy fluxes are also used to infer functional dependencies of VOC emissions on environmental parame- ters such as temperature and solar radiation (e.g. Rinne et al., 2002; H¨ortnagl et al., 2011; Taipale et al., 2011). When inter- preting the measured fluxes of e.g. VOCs, the chemical life- time of a compound is commonly assumed to be much longer than the transport time leading to correspondence of mea- sured flux and surface exchange. However, there are com- pounds emitted by e.g. vegetation with lifetimes comparable to the transport time, such as β-caryophyllene, a sesquiter- pene. The chemical degradation of these compounds below the flux measurement level can lead to a significantly lower measured flux as compared to the actual emission from the vegetation surfaces (Ciccioli et al., 1999).
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Stratospheric O<sub>3</sub> changes during 2001&ndash;2010: the small role of solar flux variations in a chemical transport model

Stratospheric O<sub>3</sub> changes during 2001&ndash;2010: the small role of solar flux variations in a chemical transport model

The SORCE mission is described by Rottman (2005). For the SORCE fluxes used here, we combine data from two of the instruments on board SORCE: the SOLar STellar Irradiance Comparison Experiment (SOLSTICE; McClin- tock et al. 2005); and the Spectral Irradiance Monitor (SIM; Harder et al. 2009, 2010). We wish to make a direct com- parison with Haigh et al. (2010) and thus use the same data set for most of our runs. It is based on SOLSTICE (ver- sion 10) below 200 nm and on SIM intermediate-release ver- sion (J. Harder, personal communication, 2010) for wave- lengths above 200 nm. We label this data set SORCE_1. The use of SIM data below 310 nm is no longer recommended, so we also included two test runs using the currently avail- able SORCE data. These data are labelled SORCE_2 and use SOLSTICE (version 12) and SIM (version 17) data for wave- lengths below and above 310 nm, respectively.
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A global simulation of brown carbon: implications for photochemistry and  direct radiative effect

A global simulation of brown carbon: implications for photochemistry and direct radiative effect

In this study, we estimate global primary BrC emissions from open burning and biofuel use based on a reported relationship between AAE and modified combustion effi- ciency (MCE) (McMeeking, 2008). In addition to the pri- mary source above, we also consider SOC produced from aromatic oxidation as a secondary source of BrC (Hecobian et al., 2010; Jaoui et al., 2008; Lin et al., 2015; Nakayama et al., 2010; Nakayama et al., 2013; Zhong and Jang, 2011). Based on these sources, a global distribution of BrC concen- trations is explicitly simulated for the entire year of 2007 us- ing a global 3-D chemical transport model (GEOS-Chem). We evaluate the model by comparing its results with obser- vations in the United States and all over the globe. Using the best estimate of annual mean BrC concentrations, we exam- ine the global direct radiative effect (DRE) of BrC and its effect on photochemistry.
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A review of operational, regional-scale, chemical weather forecasting models in Europe

A review of operational, regional-scale, chemical weather forecasting models in Europe

Simple parameterizations could potentially be sufficient for CWF models, especially for off-line coupled models, which have no access to the full meteorological model mi- crophysics. For a reliable short-term estimate of near-ground air-pollutant concentrations, below-cloud scavenging is ex- pected to dominate, at least in areas characterized by relevant local and regional emissions – in other words, where short- range transport dominates over long-range sources (e.g. in continental and Mediterranean Europe). Neglecting in-cloud scavenging should underestimate the mass of deposited pol- lutant, but have only a weak effect on surface concentrations. Moreover, cloud-aerosol interactions can modify precipita- tion rate and its spatial distribution, and therefore indirectly influence near-surface scavenging. However, these phenom- ena can be described only by on-line coupled CWFs that can implement cloud-pollutant interactions and can take into ac- count feedback effects of air pollution on meteorology. Wet deposition schemes vary much more than the dry deposition schemes for the operational CWF models in this article. For example, LOTOS-EUROS, MATCH, FARM and RCG use simple parameterizations of scavenging rates that are similar to those implemented in the EMEP Unified model (Simpson et al., 2003). These depend on Henry’s law constant, rain rate and cloud-water mixing ratio for gases, and, on particle size, precipitation intensity and raindrop fall speed for aerosols. The possible release of scavenged gases and aerosols due to cloud- or rain-water evaporation is not taken into account by the latter parameterization. In contrast, NAME, SILAM and THOR use scavenging coefficients depending upon cloud type (convective vs. stratiform) and precipitation type (rain vs. snow). Other models, such as CHIMERE and Enviro- HIRLAM, use more complex in-cloud and below-cloud scav- enging parametrizations, whereas LOTOS-EUROS and RCG neglect in-cloud scavenging.
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A quasi chemistry-transport model mode for EMAC

A quasi chemistry-transport model mode for EMAC

and a sensitivity simulation with sources that differ from the reference setup slightly. If the simulations include any feed- back between dynamics and chemistry, then the meteorology of the two simulations will diverge despite binary identical initialization due to the inherent chaos in the system, a prop- erty commonly referred to as butterfly effect. Such behavior is inevitable, even for the smallest chemically triggered per- turbation of the meteorological state. After a small number of time steps with perturbed conditions, the two simulations will differ completely in their meteorological patterns, com- municating the meteorological variability to mixing ratios of chemical compounds such as NO x (e.g. Unger et al., 2008).
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Late summer changes in burning conditions in the boreal regions and their implications for NO x and CO emissions from boreal fires

Late summer changes in burning conditions in the boreal regions and their implications for NO x and CO emissions from boreal fires

were developed for the 2004 North American boreal fire season. Pfister et al. [2005] used an inverse modeling approach to constrain 2004 fire CO emissions using MOPITT observations and MOZART chemical transport model simulations. They applied a weekly adjustment to their a priori emissions estimate, which resulted in more than a twofold increase in the total summer emissions. Another inventory for the 2004 fires was developed by Turquety et al. [2007], who used a bottom-up approach with emphasis on the large deduced contribution of peat burning. Although these approaches differed, both inventories resulted in an estimate of 30 Tg CO released from Alaskan and Canadian fires over 2004 summer season, with Pfister et al. [2005] reporting an uncertainty of ±5 Tg CO. We obtained a somewhat higher estimate of 37 Tg CO using BWEM. (For comparison, anthropogenic CO emissions for the entire continental U.S. during the same period were approximately 25 Tg CO [Pfister et al., 2005].) In addition, there are significant differences in the timing of these emissions. For example, while all three inventories predict large peaks in CO emissions at the end of June and throughout July, BWEM emissions stay high throughout August (Figure 1a). A decrease in burned area in August resulted in the decline in emissions in the previous inven- tories, while the higher August emissions in BWEM are the result of accounting for deeper burning of the organic soil layer in late summer.
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A multi-model assessment of pollution transport to the Arctic

A multi-model assessment of pollution transport to the Arctic

The fractional variation of sensitivity is always larger in the full chemistry analyses than in the prescribed 50 day life- time case (numerical values in Fig. 8). The relative size of the fractional variations in the prescribed lifetime and full chem- istry runs depends on the altitude analyzed and the source region. At the Arctic surface, the intermodel fractional vari- ation in the prescribed lifetime runs is 9–14%, roughly two- thirds that seen in the full chemistry runs (16–26%) for all regions (Fig. 8). This indicates that differences in modeled transport to the Arctic play an important role in CO near the surface. In the middle troposphere, transport and chemical oxidation by OH contribute a comparable amount to inter- model differences in Arctic CO, while in the upper tropo- sphere transport plays a much smaller role. At the surface and in the mid-troposphere, adding in the intermodel varia- tion in emissions (i.e. no longer normalizing by emissions) leads to larger fractional variances across the models. This is especially so for East Asia, where including the intermodel variation in emissions nearly triples the fractional variance of the Arctic response at the surface and middle troposphere across models. The effects are smaller for emissions from Europe or North America at these levels, where emissions variations add ∼5–13% to the fractional variance, a com- parable range to that from transport (8–14%) and oxidation (6–11%) variations among models. Emissions uncertainties from South Asia have an even smaller impact than those from Europe or North America, barely changing the frac- tional variance.
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Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model

Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model

Compared to GLOMAP-bin, simulated sulphate and POM burdens are slightly lower in GLOMAP-mode (−12 and − 3 %) whereas BC and sea-salt are slightly higher (+4 and + 9 %). These are reflected in the slightly shorter lifetimes for GLOMAP-mode simulated sulphate and POM, while BC and sea-salt are longer-lived with the modal approach. How- ever, for each species, the global burdens compare well, with GLOMAP-mode within about 10 % of GLOMAP-bin. To set these differences in context, we note the findings in Textor et al. (2006), who examined diversity in simulated lifetimes among the AEROCOM models, finding standard deviations among the models of 58, 43, 18, 33 and 27 % for sea-salt, dust, sulphate, BC and POM, respectively. Thus, inter-modal diversity is much larger than the difference introduced by the simplified model treatment of the evolving size distribution. The percentage removal by wet deposition illustrates that wet removal is the dominant removal process for sulphate, BC and POM, which reside mainly in sub-µm particle sizes, whereas the coarser sea-salt aerosol is influenced strongly by sedimentation. While the bin and mode schemes predict sim- ilar wet removal for sulphate, BC and POM, there is a sub- stantial difference for sea-salt, with 27.1 % of mass removal by wet deposition in GLOMAP-mode compared to 47.1 % in GLOMAP-bin. This suggests that the wet removal is acting on a larger proportion of the sea-salt particles in GLOMAP- bin than GLOMAP-mode, likely due to more highly size- resolved treatment possible in the sectional scheme, giving different removal timescales for each size bin.
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Improving the representation of secondary organic aerosol (SOA) in the MOZART-4 global chemical transport model

Improving the representation of secondary organic aerosol (SOA) in the MOZART-4 global chemical transport model

In this section, analyses from the base-case and updated MOZART-4 simulations are presented, followed by com- parisons with observations and previous modeling studies. Note that the first month of each simulation was excluded from the analysis to account for model spin-up time. There is no direct measurement of SOA as a component of total OA, thus observational data for global SOA levels are essen- tially non-existent. Previous studies (see for example, Lack et al., 2004; Heald et al., 2006; Liao et al., 2007; Farina et al., 2010; Jiang et al., 2012; and Lin et al., 2012) have com- pared modeled SOA to SOA determined indirectly from to- tal OA measurements. Some of these studies have also com- pared modeled SOA with reported SOA levels from relevant modeling studies. It is important to recognize that both of these techniques, comparing modeled levels with indirect de- terminations and/or with other modeling studies, have limi- tations. For example, most of the measurements are taken at specific locations over a short period of time that often do not capture the range of conditions including the influence of local emissions represented in a simulated grid, which is typically in the order of degrees (latitude × longitude) in a global chemical transport model. This makes the comparison of a global chemical transport model output with observa- tions quite challenging.
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A global stratospheric bromine monoxide climatology based on the BASCOE chemical transport model

A global stratospheric bromine monoxide climatology based on the BASCOE chemical transport model

Here, we report on the development of a stratospheric BrO profile climatology designed for use in the retrieval of global tropospheric BrO VCDs from space-borne nadir ob- servations. A new method for the estimation of the strato- spheric BrO content is proposed, which is able to reproduce the important spatial and temporal variations of stratospheric BrO by using dynamical and chemical indicators. In prac- tice, the climatology uses measured quantities to evaluate stratospheric BrO, which makes the approach well suited for satellite nadir retrieval since it guarantees that the sounded air masses and geophysical conditions are optimally repre- sented. In Sect. 2, we briefly review the 3-D chemical trans- port model (CTM) which is at the heart of our study. We describe the set-up of bromine species implemented in the model, in the light of our current understanding of strato- spheric bromine photochemistry and budget. The treatment of sulphate aerosols in the model is also presented, since it can have a substantial impact on the stratospheric chemistry. Comparisons between modelled results and correlative ob- servations of O 3 , NO 2 and BrO are shown in Sect. 3. The
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The impact of soil uptake on the global distribution of molecular hydrogen: chemical transport model simulation

The impact of soil uptake on the global distribution of molecular hydrogen: chemical transport model simulation

Θ sat is the maximum aerial volume per unit volume of soil. In the land process model, the solid ratio of the soil is given for each soil type in each grid and the presence of liquid water and ice limits the diffusion of the gas in the soil. The biological uptake rate will be determined not only by the degree of activity of each enzyme but also by the amount of enzyme present. However, global distributions of the H 2 -comsumption

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