NUMERICAL MODELS | Chemistry Models

NUMERICAL MODELS | Chemistry Models

NUMERICAL MODELS Contents Chemistry Models Coupled Ocean-Atmosphere Models: Physical Processes General Circulation Models Methods Model Physics Param...

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Contents Chemistry Models Coupled Ocean-Atmosphere Models: Physical Processes General Circulation Models Methods Model Physics Parameterization Parameter Estimation Parameterization of Physical Processes: Clouds Parameterization of Physical Processes: Gravity Wave Fluxes Parameterization of Physical Processes: Turbulence and Mixing Spectral Models Mesoscale Atmospheric Modeling Cloud-System Resolving Modeling and Aerosols Large-Eddy Simulation Regional Prediction Models Convective Storm Modeling

Chemistry Models MP Chipperfield and SR Arnold, University of Leeds, Leeds, UK Ó 2015 Elsevier Ltd. All rights reserved. This article is a revision of the previous edition article by M P Chipperfield, volume 4, pp 1414–1423, Ó 2003, Elsevier Ltd.

Synopsis The formulation of chemical models is discussed. This article summarizes the component modules of chemical models (gas phase chemistry, heterogeneous chemistry, photolysis, deposition). The chemical continuity equation is described. Trajectory, one-dimensional, two-dimensional, and three-dimensional models are discussed. Example applications in the stratosphere and troposphere are given.

Introduction Chemical models are used to test our understanding of atmospheric chemistry, and for predictions of the future state of the atmosphere. A model will contain different modules to treat processes such as gas phase chemistry, aqueous phase chemistry, heterogeneous chemistry, photolysis reactions, and emission and deposition of species. The formulation of the model will depend on the problem being studied. The core of a model is the chemical continuity equation, which is an expression of the rate of change of a chemical species. Integrating this continuity equation permits the model to step forward in time. In general, the large computational cost of calculating atmospheric chemistry leads to a number of

Encyclopedia of Atmospheric Sciences 2nd Edition, Volume 4

approximations in models, such as using grouping species into families and assuming that some species are in steady state. Models that are used to study atmospheric chemistry range from zero-dimensional ‘box’ models, which may contain very detailed chemistry schemes (e.g., 3000 species), to global three-dimensional models, which may contain around 50.

Use of Models Numerical models are a mathematical summary of our current understanding of atmospheric chemistry. A good model should contain a representation of all of the important species,



Numerical Models j Chemistry Models

reactions, and processes relevant for the particular system that is being studied. A numerical model can then be used for: l

testing our understanding of atmospheric chemistry by comparison between model calculations and observations; l investigating the effect of a newly discovered reaction or process on other species; and l predicting the future state of the atmosphere based on a series of assumptions. The components of the model (physical and chemical processes considered, number of chemical species and reactions) will depend on the problem being addressed (e.g., spatial and temporal scale). The model needs to have an appropriate domain (e.g., global, regional), resolution (e.g., size of grid boxes), and time step. The model must contain all of the necessary processes, but other factors (usually computer resources) often constrain those that can be included.

Components of a Chemical Model

kðTÞ ¼ Ae expð  Ea =RTÞ


Other expressions are used to fit certain bimolecular reactions, which show a pressure dependency (e.g., HNO3 þ OH) and termolecular (three-body) reactions. Expert panels regularly review the body of chemical kinetics literature and produce reports of recommended rate constants for use in atmospheric models. As well as providing modelers with an expert analysis of photochemical data, their common use permits model results to be referenced and for predictions from different models to be more easily compared.

Liquid Phase Reactions

In order to calculate the time-dependent concentrations of chemical species in the atmosphere, a model must contain a representation of the important chemical and physical processes. Figure 1 illustrates these components and how they are used in each model time step to calculate the chemical concentrations.

Gas Phase Reactions All models of atmospheric chemistry will deal with gas phase reactions between species (see Chemistry of the Atmosphere: Chemical Kinetics). Laboratory measurements provide data,

Heterogeneous/ aqueous chemistry

which can be used to calculate the rate constants for gas phase reactions in models. For bimolecular reactions, the rate constant usually depends only on temperature and can be calculated from the Arrhenius equation (eqn [1]), where k is the rate constant at a temperature T, Ae is the Arrhenius factor, Ea is the activation energy, and R is the gas constant. The values for Ae and Ea are provided by laboratory data.

Models dealing with tropospheric chemistry will need to account for the uptake of gases by cloud droplets, and chemical reactions within the clouds (aqueous chemistry). This gas uptake and reaction involves several steps (diffusion of gas to droplet, dissolution in droplet, diffusion through droplet, reaction in droplet, diffusion of products, and evaporation of dissolved products at the droplet surface). Treating aqueous chemistry in a model is more complicated than gas phase chemistry. First, the lifetimes of aqueous species are usually short and the system of differential equations to solve is stiff (see below). Second, when dissolved species are removed by

Gas phase chemistry

Photolysis rate calculation

Chemical continuity equation d[AB]/dt = sources – sinks

Emissions: natural/ anthropogenic

Radiation Radiative heating: longwave (IR) shortwave (UV)

Physical removal: wet/dry deposition


Dynamics Lagrangian trajectories Eulerian: advection convection:

Figure 1 Components of a chemical model. A multidimensional model will also include dynamical and radiation modules. These may be combined so that the chemistry is or is not coupled (where changes in the concentrations of chemical species feedback on the radiation and dynamics).

Numerical Models j Chemistry Models reactions they are rapidly replaced by dissolution of more species from the gas phase. Therefore, the processes of dissolution and chemical reaction need to be solved in a coupled way.

Heterogeneous Reactions Heterogeneous reactions involve the collision of a gas phase molecule with a solid or liquid particle, followed by a chemical reaction. An example is the hydrolysis of N2O5 shown by reaction [I]. N2 O5 ðgÞ þ H2 OðaqÞ/2HNO3 ðgÞ


This reaction is normally parametrized in models using a measured ‘reaction probability’ (g) that an N2O5 molecule colliding with a surface will react, and a ‘collision frequency’ calculated using kinetic theory and the known or assumed concentration of aerosols. This treatment can also be used for heterogeneous reactions involving solid particles. An alternative treatment for liquid aerosols, when both reactants are soluble, is to treat the reaction as a liquid phase reaction (as above).

Photolysis Rate Coefficients The photolysis rate coefficient (or photodissociation frequency), J is the first-order rate constant for the process shown by reaction [II], where h is the Planck’s constant, and n is the frequency of the radiation. AB þ hn/A þ B


As solar radiation is the driving force for atmospheric chemistry, the accurate calculation of J rates is an important component of models. The photolysis rate coefficient for species AB (JAB) is calculated from eqn [2], where I(l,z) is the photon flux at wavelength l and altitude z in the atmosphere, and s is the absorption cross-section. Z Iðl; zÞsAB ðlÞdl [2] JAB ðzÞ ¼ In an atmospheric model eqn [2] is solved by replacing the integration over wavelength by a summation over discrete wavelength intervals. The World Meteorological Organization gives a list of 158 wavelength intervals, covering the range 175– 850 nm, which are typically used in stratosphere–troposphere models. Fewer wavelength intervals can be used for troposphereonly models to save computer time. The photon flux at a point in the atmosphere, I(l,z), is calculated using the flux at the top of the atmosphere and the Beer–Lambert law, Itr ¼ I0 expð  εclÞ


Where I0 is the incident radiation, Itr is the transmitted radiation, ε is the absorption (or extinction) coefficient, c is the concentration of the absorber, and l is the path length. The flux at altitude z depends on attenuation by absorbing gases (mainly O2 and O3), scattering by molecules and aerosols, and reflection by the surface and clouds. Models may calculate an instantaneous value of J, or an average value over daylight or 24 h period. An instantaneous and regularly updated value of J is necessary for a model to


reproduce the diurnal cycle of short-lived species (e.g., OH). However, this approach can be expensive computationally. For simulations of long-lived species, where it is not necessary to explicitly resolve the diurnal cycle, day- or 24-h-averaged J rates can be used. As the interactive, online calculation of J rates is nevertheless expensive, some models use precalculated ‘lookup’ tables. The J for the required conditions is interpolated from the tabulated values.

Emissions and Physical Removal All chemical models, except those simulating isolated air masses over short periods, require a representation of the processes that input and remove chemicals from the atmosphere. Emissions of trace gases may occur through natural (e.g., vegetation) or anthropogenic processes (e.g., industrial emissions, aircraft). Expert panels regularly review the strength of these emission sources and provide data sets for input into models (e.g., the Global Emissions Inventory Activity (GEIA) of the International Global Atmospheric Chemistry (IGAC) program). Some of these emission sources (e.g., emissions of hydrocarbons from vegetation and trace gas and aerosol emissions from wildfires) have strong spatial and temporal variability driven by climatic conditions. Biogenic emissions of trace gases such as isoprene have a strong dependence on parameters such as temperature and sunlight, and so schemes are often included in models, which represent the response of such emissions to the meteorological data being fed to the model. Chemical species may be physically removed from the atmosphere by wet or dry deposition processes. Dry deposition removes gases and particles at air–surface interfaces. Wet deposition involves the dissolution of a gas into a cloud droplet, which is then rained out.

Chemical Continuity Equation Chemical models aim to use the known concentrations of chemical species at time t, and calculated rates of change, to calculate the concentrations at the end of the chemical time step t þ Dt. The heart of the model is a ‘chemical continuity equation.’ This is an expression for the rate of change of a chemical species due to the chemical reactions that produce or destroy it. It is an expression of the conservation of mass and can be expressed as eqn [4], where P is the rate of chemical production and L is the rate of chemical loss (which usually depends on [AB]), and [] indicates a concentration. Other terms can be included in eqn [4] to account for other processes that affect [AB] (e.g., physical removal). d½AB ¼ P  L½AB dt


Table 1 lists a small subset of the 100 reactions that are important in the chemistry of the stratosphere (see Stratospheric Chemistry Topics: Overview; Halogens; Reactive Nitrogen (NOx and Noy); HOx). This subset is used here to illustrate the form of a continuity equation, but note that detailed atmospheric models will include many more species and reactions. There are three reactions in Table 1, which either produce (reaction [X]) or

Numerical Models j Chemistry Models

Table 1 chemistry

Subset of gas phase reactions important in stratospheric

Based on the reaction scheme given above, this would give eqn [6].

Cl þ O3 /ClO þ O2


d½OH ¼ 0 dt

ClO þ O/Cl þ O2


¼ 2kIX ½Oð1 DÞ½H2 O þ JXII ½HNO3 

ClO þ NO/Cl þ NO2


ClO þ NO2 þ M/ClONO2 þ M


ClONO2 þ hn/Cl þ NO3


OH þ HO2 /H2 O þ O2


Oð1 DÞ þ H2 O/2OH


OH þ NO2 þ M/HNO3 þ M


OH þ HNO3 /H2 O þ NO3


HNO3 þ hn/OH þ NO2


destroy (reactions [XI] and [XII]) HNO3. Based on this reaction set, the chemical continuity equation for the rate of change of the concentration of HNO3 contains three terms and can be represented as eqn [5], d½HNO3  ¼ kX ½OH½NO2 ½M  kXI ½OH½HNO3   JXII ½HNO3  dt [5] where kn is the rate constant for reaction n, Jn is the photolysis frequency, and the square brackets indicate a concentration. A continuity equation can be written for each chemical species contained in the model. This gives a set of coupled first-order ordinary differential equations. In all but the very simplest cases (e.g., the decay of a radioactive tracer), an analytical solution is not possible and the coupled differential equations must be solved numerically. This system of differential equations is usually ‘stiff,’ i.e., the lifetimes (or time scales) of the chemical species being solved vary by several orders of magnitude (e.g., seconds to years). Therefore, sophisticated (and computationally expensive) solvers need to be used. A chemical continuity equation similar to [4] can be written for each species contained in the reaction scheme. However, in practice a number of conceptual simplifications and numerical approximations can be made.

Photochemical Steady State For a chemical species with a very short chemical lifetime it is not necessary, or desirable, to integrate the chemical continuity equation. The short lifetime increases the stiffness of the system and would require a short chemical time step. Computer time can be saved by placing short-lived species in steady state. In the chemistry of the stratosphere and troposphere, the chemical lifetime of OH is of the order minutes or less. Therefore, the concentration of OH can be derived by placing it in the photochemical (or photostationary) steady state.


kX ½OH½NO2 ½M  kXI ½OH½HNO3  kVIII ½OH½HO2  Therefore, at each time step in the model the [OH] can be derived from the calculated concentration of other species and the appropriate rate constants and photolysis rates. The calculated concentration of OH will vary throughout the diurnal cycle (e.g., as JXII changes), although at each time step the instantaneous concentration is assumed to be constant. Note that as several interdependent species may be treated to be in steady state (e.g., both [OH] and [HO2] in the above example), the steady state concentration of these species should be derived iteratively.

Chemical Families The number of continuity equations to be solved (and computational time) can be reduced by grouping closely coupled chemical species together in a family. As well as needing to solve only one continuity equation, the photochemical lifetime of the family is generally longer than the lifetimes of the individual members, producing a less stiff system (Figure 2). Finally, using chemical families has advantages in multidimensional models. Generally, it is not desirable to transport short-lived species separately as they have strong gradients (e.g., near the terminator), which can cause numerical problems (undershoots and overshoots) in advection schemes. A chemical family will generally have a smoother distribution and pose fewer problems for the advection scheme. In stratospheric models a ClOx family is often defined as [ClOx] ¼ [ClO] þ [Cl]. This is justified because Cl is in rapid


50 Altitude (km)



30 Cl ClO ClOx


10 10





10 Lifetime (s)





Figure 2 Photochemical lifetimes (defined as 1/(first-order loss rate)) of Cl, ClO, and ClOx (¼ Cl þ ClO). The ClOx family has a much longer lifetime than the shortest lived family member, resulting in a less stiff system of equations to solve.

Numerical Models j Chemistry Models photochemical equilibrium with ClO, and change in the concentration of ClO will also affect Cl through the reactions, which interconvert the two. When a chemical family is used in a model, a single chemical continuity equation is written for the overall rate of change of the family. Based on the reactions given in Table 1, the continuity equation for ClOx can be expressed by eqn [7], where M represents any air molecule. d½ClOx ¼ 0 dt ¼ kVI ½ClO½NO2 ½M þ JVII ½ClONO2 


Note that reaction [III] for example, which simply interconverts Cl and ClO has no net effect of ClOx and does not appear in eqn [7]. The concentration of the total family must be divided among the n individual members. This is achieved by writing n  1 steady state expressions for n  1 members. In the case of the ClOx family, by placing Cl in steady state (d[ClO]/dt ¼ 0) we can derive eqn [8] for the ratio of [Cl]/[ClO]. ½Cl ¼ ½ClO

kIV ½O þ kV ½NO þ JVII kIII ½O3 

½ClONO2  ½ClO


Although this equation is derived by assuming Cl is in steady state, the concentration of Cl (and ClO) will vary over the model time step as ClOx changes. However, eqn [7] effectively fixes the ratio of Cl:ClO over this time step. Care is needed when deriving these expressions for the partitioning of family members. Most of the terms in eqn [7] can be identified with reactions [III], [IV], and [V], which directly interconvert Cl and ClO. However, there is also a term involving [ClONO2]/[ClO], which is related to the two-step interconversion of ClO and Cl via the formation and photolysis of ClONO2. It is very important to include these indirect terms as they are often associated with catalytic cycles that destroy stratospheric O3 via the reaction [III]. In order for the model to correctly determine the O3 loss, the calculated [Cl] must be accurate. Another chemical family commonly used in atmospheric models is ‘odd oxygen,’ which is defined as Ox ¼ O(3P) þ O(1D) þ O3. This family provides a very convenient way of calculating the atmospheric abundances of O3, O(3P), and O(1D) below about 70 km. Above this altitude the photochemical lifetime of O becomes long (due to the low air density) and so, O and O3 can no longer be assumed to be in photochemical equilibrium.

Mechanism Reduction The number of species and reactions involved in chemical reactions of organic species in the polluted (urban) troposphere is huge. For example, the University of Leeds Master Chemical Mechanism model contains around 3800 species and 10 000 reactions. For many practical purposes the number of reactions needs to be reduced. The methods used for reducing the number of species in urban photochemical models are: (1) the carbonbond lumping method (when organic species are separated into a few common bond groups), (2) the surrogate species method (where species with similar reactivity are grouped together and solved as one species), and (3) the lumped species


method (where species are grouped together but the reaction rate constants for the lumped group is a mole fraction weighted average of the rate constants for the individual species).

Types of Models A range of chemical models exists appropriate for studying different problems. In all cases the model consists essentially of a chemical ‘box’ model (with the description of the required chemistry) either used alone, or within an array of grid boxes in a multidimensional model.

Box Models and Trajectory Models A chemical ‘box’ model solves the chemical continuity equations in a single air mass. These models can be computationally cheap, allowing detailed reaction schemes to be included (e.g., up to 500 species) and avoiding the need for numerical approximations such as chemical families. Box models can either represent a stationary, idealized air mass, or can be combined with a calculated air mass trajectory to produce a ‘Lagrangian’ model. Several chemical trajectory models can be integrated simultaneously to create a ‘domain-filling trajectory model,’ in which the number of model boxes is sufficient to fill a region of the atmosphere so that 3D distributions can be obtained. Results from trajectory box models are generally valid over the time scale on which the approximation of no mixing into, or out of, the box is valid. This depends on the location in the atmosphere and may vary from a few days in the troposphere to a few weeks to months in the polar lower stratosphere.

Three-Dimensional Models Three-dimensional (3D) models solve the chemistry on a longitude  latitude  altitude array of grid boxes. Dynamical processes are included that transfer chemical species between these fixed grid boxes in a so-called ‘Eulerian’ model. The chemical component of a 3D model is essentially a chemical box model. However, the high computational cost of a 3D model means that the reaction schemes have to be limited (e.g., to around 40–50 species) and some careful approximations used (e.g., families). The nature of 3D models, with their arrays of chemical box models, mean that they can be written to take good advantage of high performance vector and parallel computers. Nevertheless, when included in a 3D model, the cost of chemistry normally dominates the cost of other processes (e.g., radiative and dynamical calculations). Even in Earth System Models (ESMs) atmospheric chemistry is likely to be one of the most expensive components. The chemistry and transport in a 3D model can be combined in an ‘operator split’ approach. In this method the chemical integration is separated from the dynamical integration and the advection of tracers. This decoupling of chemical and dynamical time steps is often more efficient as optimum time steps can be chosen for each process (e.g., the need for a short chemical time step does not imply an equally short dynamical time step). A multidimensional chemical model requires a module for


Numerical Models j Chemistry Models

transporting chemical species – i.e., an ‘advection scheme.’ The advection scheme should ideally be: conservative, monotonic (no undershoots and overshoots), nondiffusive (maintain tracer gradients), and nondispersive (tracer features should advect independent of their scale). The advection scheme should also advect species independent of their concentration. In practice, essentially all of the tracer advection schemes in use compromise on one or more of these criteria. Two types of 3D chemical models are commonly used. General circulation models (GCMs) are global radiative dynamical models used in numerical weather prediction and climate studies (see Numerical Models: General Circulation Models). Chemistry can be included in these models to produce a chemical GCM, allowing the calculation of the coupled effects of chemical and dynamical changes. In these coupled models, a chemically induced change in O3, for example, will affect the atmospheric heating rates, temperature, and therefore the dynamics. In turn, these dynamical changes redistribute O3. As GCMs calculate their own circulation, the results do not correspond to a specific day, but represent the typical behavior of the atmosphere. Therefore, the results of GCMs need to be compared with observations in a climatological sense. More recently, ESMs have been developed, which couple together processes that govern the evolution of the atmosphere, biosphere, oceans, and cryosphere. These models also contain atmospheric chemistry, and can be used to investigate potentially important Earth system feedback processes, for example between the biosphere and climate, driven by changes in atmospheric trace gas and aerosol abundances in response to changes in emissions from the biosphere. Offline chemical transport models (CTMs) do not calculate the atmospheric circulation. Instead the wind (and temperature, humidity, cloud) fields are read in from another source (e.g., meteorological analyses). This has a number of advantages: the model is cheaper to run compared to the full GCM and, importantly, the model dynamics are constrained to the

Table 2

‘real’ meteorological situation. This permits direct comparison between model calculations and observations. As the circulation in CTMs is fixed, they cannot be used for future predictions involving coupling of chemistry and dynamics. The meteorological analyses used to force CTMs come from weather services such as the European Centre for Medium-Range Weather Forecasts (ECMWF), U.K. Met Office (UKMO), or the National Centers for Environmental Prediction. They are produced as part of the routine weather prediction and now usually extend from the surface to the upper stratosphere. The accuracy of CTM results depends critically on the quality of these meteorological analyses and how they are used in the model. In the stratosphere the advection by the analyzed winds is usually the only transport process considered, while in the troposphere the model will usually need to parametrize ‘subgrid-scale’ transport processes such as convection and boundary layer mixing. Table 2 illustrates the chemistry, which is included in a typical stratospheric CTM while Table 3 illustrates a similar tropospheric model. The stratospheric model contains detailed halogen chemistry, while the tropospheric model contains more hydrocarbon species. Simulations of Arctic O3 depletion from the stratospheric model are shown in Figure 3. Figure 4 shows an example of CO distribution from the tropospheric model.

One-Dimensional and Two-Dimensional Models Before computer power permitted the use of 3D chemical models one-dimensional (1D) and two-dimensional (2D) models were widely used for atmospheric studies. Onedimensional models represent variations of tracers with altitude and were the main tool in the 1970s and early 1980s. The models generally represent a global mean atmosphere at each layer and vertical motion is parametrized as a diffusion process. Clearly this is a gross approximation of the real atmosphere and these models are no longer used.

Details of the SLIMCAT 3D stratospheric chemical transport model


Coupled short-lived species

Steady state Source gases and long-lived species

Fixed Reactions

Dynamics Resolution


Meteorology Tracer advection Horizontal Vertical Domain Language

Ox (¼ O3 þ O(3P) þ O(1D)) NOx (¼ N þ NO þ NO2), NO3, N2O5, HNO3, HO2NO2 ClOx (¼ Cl þ ClO þ Cl2O2), ClONO2, HCl, HOCl, OClO BrOx (¼ Br þ BrO), BrONO2, BrCl, HBr, HOBr CH2O, CH3OOH H, OH, HO2 CH3, CH3O2, CH3O, HCO CH4, N2O, CO, H2O, CFCl3 (CFC-11), CF2Cl2 (CFC-12), C2F3Cl3 (CFC-113), CHF2Cl (HCFC-22), CH3Cl, CH3CCl3, CCl4, CH3Br, CBrClF2, CBrF3 COF2, COFCl, HF O2, N2, H2, CO2 120 Gas phase 8 Heterogeneous 36 Photodissociation From analyses (e.g., ECMWF, UKMO) Finite volume scheme Variable 10  10 –0.5  0.5 Variable: 0.5–3 km Global: surface – 60 km Fortran (parallel) Parallel/vector machines (Inc. Workstations)

Numerical Models j Chemistry Models Table 3

Details of the TOMCAT 3D tropospheric chemical transport model


Coupled short-lived species

Steady state Source gases and long-lived species Fixed Reactions

Physics Dynamics Resolution




Deposition Emissions Meteorology Tracer advection Horizontal Vertical Domain Language

Ox (¼ O3 þ O(3P) þ O(1D)) NOx (¼ NO þ NO2 þ NO3), N2O5, HNO3, HO2NO2, HONO CH2O, CH3OOH, CH3CHO, C2H6, C2H5OOH, C2H5CHO, C3H8, i-C3H7OOH, n-C3H7OOH, (CH3)2CO, CH3COCH2OOH, CH3COO2NO2, C2H5COO2NO2, CH3ONO2, C5H8 H, OH, HO2 CH3O2, CH3CO3, MeCOCH2OO, C2H5OO, i-C3H7OO, n-C3H7OO, C2H5COO2 CH4, CO, H2O O2, N2, H2, CO2 110 Gas phase 2 Heterogeneous 40 Photodissociation Dry and wet deposition Surface (natural and anthropogenic), lightning, aircraft From analyses (e.g., ECMWF, UKMO) Finite volume scheme Variable 5  5 –0.5  0.5 Variable: 0.5–3 km Global: surface – 30 km (or higher) Fortran (parallel) Parallel/vector machines (Inc. Workstations)

SLIMCAT 3D model ozone 17 March 2000 17 km



Ozone VMR (ppm)



3 2.5 2 1.5 SLIMCAT Passive ozone Ozonesondes

1 0.5


450 K

0 50 Date (day of the year 2000)

Figure 3 Example results from the SLIMCAT three-dimensional chemical transport model (Table 2) for the Arctic winter of 1999–2000. The model was run with a horizontal resolution of 2.5 latitude  3.75 longitude. (a) The distribution of O3 near 17 km on 17 March 2000. The model is in good agreement with O3 sonde observations at around 17 km (450 K potential temperature surface) at the Arctic station of Ny Alesund (78 N). (b) The difference between the model O3 and the ‘passive’ O3 indicates the chemical destruction since 1 January 1999. VMR, volume mixing ratio.

Two-dimensional (latitude–height) radiative dynamical chemical models calculate the zonal mean state of the atmosphere. They were the principal computational tool in the 1980–90s and are still in use. Although 2D models cannot capture the motion of stratospheric polar vortex or the longitudinal asymmetry of tropospheric surface emissions, for example, they are computationally cheap and are useful for calculating a wide range of multiyear scenarios and for sensitivity studies.

Testing Models Given the complexities of atmospheric chemistry, and the many interactions with other processes, atmospheric chemical models can be very large programs. Much care is needed to write the code in a rigorous way to minimize the risk of errors. Generally the program should make use of all of the options available in a particular language, and on a particular machine, to test for coding errors. When a model is running it is


Numerical Models j Chemistry Models

Figure 4 Example results from the TOMCAT three-dimensional tropospheric chemical transport model (Table 3) showing total column CO for July 2008 compared with satellite observations from the MOPPITT instrument. The model was run with a horizontal resolution of 2.8 latitude  2.8 longitude. The upper panel shows direct model output while the lower panel shows the MOPPITT observations. The middle panel shows the model results sampled with the MOPPITT averaging kernels in order to give a true comparison of the model with the observations. CO is enhanced in regions of strong emission, such as the industrial regions of the northern hemisphere and in regions of tropical biomass burning. Figure courtesy of Sarah Monks, University of Leeds.

evaluated by comparison with observations to test its ability to capture processes in the real atmosphere. Periodically different atmospheric models are intercompared in international workshops to assess the uncertainties in model calculations due to differences in approach. The Stratospheric Processes and their Role in Climate program organized the chemistryclimate model (CCM) evaluation project CCMVal. This was an extensive process-based evaluation of the schemes in CCMs. All CCMs performed the same controlled experiments. Model output was compared against observations and against detailed ‘benchmark’ models. The performance of models was

graded. The overall result was an improved understanding of the models and a reduction of uncertainties in future predictions.

See also: Chemistry of the Atmosphere: Chemical Kinetics. Numerical Models: General Circulation Models; Methods. Radiation Transfer in the Atmosphere: Absorption and Thermal Emission. Tropospheric Chemistry and Composition: Cloud Chemistry; Volatile Organic Compounds Overview: Anthropogenic.

Numerical Models j Chemistry Models

Further Reading Atkinson, R., et al., 2004. IUPAC (International Union of Pure and Applied Chemistry) evaluated kinetic and photochemical data for atmospheric chemistry: volume I – gas phase reactions of Ox, HOx, NOx and SOx species. Atmospheric Chemistry and Physics 4, 1461–1738. Brasseur, G., Solomon, S., 2005. Aeronomy of the Middle Atmosphere, third ed. D. Reidel Publishing, Dordrecht, Netherlands. Brasseur, G.P., Orlando, J.J., Tyndall, G.S. (Eds.), 1999. Atmospheric Chemistry and Global Change. Oxford University Press, Oxford. Hemispheric Transport of Air Pollution (HTAP), 2010. United Nations, New York & Geneva, Dentener, F., Keating, T., Akimoto, H. (Eds.), ECE/EB.AIR/100, ISBN: 97892-1-117043-6. Jacobsen, M.Z., 2005. Fundamentals of Atmospheric Modeling, second ed. Cambridge University Press, Cambridge. Park, J., et al., 1999. Models and Measurements Intercomparison II. NASA Publication NASA/TM-1999–209554, Langley Research Center, Hampton, VA. Sander, S.P., et al., 2010. NASA/JPL, Chemical Kinetics and Photochemical Data for Use in Stratospheric Modeling. Evaluation no. 17. JPL Publication, 10–6.


SPARC, 2010. In: Eyring, V., Shepherd, T., Waugh, D. (Eds.), Chemistry-Climate Model Validation. SPARC Report Number 5. WMO, 1985. World Meteorological Organization, Atmospheric Ozone. Global Ozone Research and Monitoring Project, Report No. 16, WMO, Geneva, CH 1211, Geneva 20, Switzerland.

Relevant Websites – Community Earth System Model. – IUPAC Kinetic Data. – JPL Kinetic Data. – Leeds Master Chemical Mechanism. – SPARC. – TOMCAT/SLIMCAT 3-D CTM. – UKCA 3D CCM.