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3-D chemistry-transport model Polair: numerical issues, validation and automatic-differentiation strategy

08 Mar 2004-Atmospheric Chemistry and Physics (Copernicus GmbH)-Vol. 4, Iss: 2, pp 1371-1392

AbstractWe briefly present in this short paper some issues related to the development and the validation of the three-dimensional chemistry-transport model Polair. Numerical studies have been performed in order to let Polair be an efficient and robust solver. This paper summarizes and comments choices that were made in this respect. Simulations of relevant photochemical episodes were led to assess the validity of the model. The results can be considered as a validation, which allows next studies to focus on fine modeling issues. A major feature of Polair is the availability of a tangent linear mode and an adjoint mode entirely generated by automatic differentiation. Tangent linear and adjoint modes grant the opportunity to perform detailed sensitivity analyses and data assimilation. This paper shows how inverse modeling is achieved with Polair.

Summary (2 min read)

1. Introduction

  • Several 3-D chemistry-transport models (hereafter CTM) are now available and have15 proven to be efficient in many applications from passive-transport simulations to data assimilation with highly non-linear models.
  • The authors focus on photo- ACPD 4, 1371–1392, 2004 3-D chemistry-transport model Polair V. Mallet and B. Sportisse Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Print Version Interactive Discussion © EGU 2004 chemistry.
  • Beyond academic simulations coming along with code validation and basic modeling work, validations were undertaken through comparisons with other models and with measurements provided by dedicated campaigns.
  • Then the authors report results from validations against measurements from the ESQUIF campaign (regional scale) and measurements collected for four months in 2001 over Europe.

2. Model overview

  • Di is the deposition term (dry deposition and wet deposition – scavenging), Ei stands for the emissions (surface and volumic emissions).
  • Horizontal diffusion coefficients Kxx and Kyy are not well known and are assumed constant in time and space.
  • The authors usually use the parameterization proposed in Louis (1979).
  • Chemical reactions are modeled by χi which depends on species concentrations.

3. Some numerical issues

  • Numerical issues have received a special attention because of computational requirements (specially for data assimilation).
  • To avoid prohibitive computations, the equation is split mainly into three parts: advection, diffusion and chemistry.
  • It is well known that equations to be solved (see Eq. 1) introduce a wide range of characteristic timescales since chemical reactions have strongly different reaction rates.
  • According to their tests, they can be neglected.
  • Moreover indices of non-zero entries are known a priori.

4. Validations

  • Two validations were conducted in order to assess model capabilities in non-academic cases.
  • Figure 2b – station Ram- ACPD 4, 1371–1392, 2004 3-D chemistry-transport model Polair V. Mallet and B. Sportisse Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Print Version Interactive Discussion © EGU 2004 bouillet – demonstrates a good behavior for the two last days.
  • The first day is not well simulated because of initial conditions.
  • At continental scale, a simulation over more than four months (from May to August 2001) demonstrates the validity of the model.

5. Inverse-modeling strategy

  • Polair was designed to be well suited for data assimilation, notably thanks to the avail-5 ability of a tangent linear mode and an adjoint mode.
  • If one can get derivatives of Po-10 lair output fields with respect to input parameters, one can get sensitivities of output concentrations to input parameters and one can build inverse modeling experiments or data assimilation experiments involving variational methods.
  • Notice that absolutely all processes are differentiated: chemistry, diffusion, advection, deposition, etc.
  • The computational cost of the differentiated code is more or less a function of the15 forward code cost.
  • Nevertheless if several studies are performed in different situations, a very detailed analysis can be led and can provide results that25 encompass enough cases to describe global sensitivities.

6. Conclusions

  • The authors have summarized the main features of the chemistry-transport model Polair.
  • Two simulations at regional and continental scales have shown a reasonable agreement with measurements.
  • Thanks to those validations and the availability of differentiated versions of Polair, sensitivity studies and data assimilation can be performed.

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3-D chemistry-transport model Polair: numerical issues,
validation and automatic-dierentiation strategy
Vivien Mallet, B. Sportisse
To cite this version:
Vivien Mallet, B. Sportisse. 3-D chemistry-transport model Polair: numerical issues, validation and
automatic-dierentiation strategy. Atmospheric Chemistry and Physics Discussions, European Geo-
sciences Union, 2004, 4 (2), pp.1371-1392. �hal-00327858�

ACPD
4, 1371–1392, 2004
3-D
chemistry-transport
model Polair
V. Mallet and B. Sportisse
Title Page
Abstract Introduction
Conclusions References
Tables Figures
J I
J I
Back Close
Full Screen / Esc
Print Version
Interactive Discussion
© EGU 2004
Atmos. Chem. Phys. Discuss., 4, 1371–1392, 2004
www.atmos-chem-phys.org/acpd/4/1371/
SRef-ID: 1680-7375/acpd/2004-4-1371
© European Geosciences Union 2004
Atmospheric
Chemistry
and Physics
Discussions
3-D chemistry-transport model Polair:
numerical issues, validation and
automatic-dierentiation strategy
V. Mallet and B. Sportisse
CEREA, Joint Research Laboratory,
´
Ecole Nationale des Ponts et Chauss
´
ees, EDF R&D,
France
Received: 3 October 2003 Accepted: 3 March 2004 Published: 8 March 2004
Correspondence to: V. Mallet (mallet@cerea.enpc.fr)
1371

ACPD
4, 1371–1392, 2004
3-D
chemistry-transport
model Polair
V. Mallet and B. Sportisse
Title Page
Abstract Introduction
Conclusions References
Tables Figures
J I
J I
Back Close
Full Screen / Esc
Print Version
Interactive Discussion
© EGU 2004
Abstract
We briefly present in this short paper some issues related to the development and the
validation of the three-dimensional chemistry-transport model Polair.
Numerical studies have been performed in order to let Polair be an ecient and
robust solver. This paper summarizes and comments choices that were made in this5
respect.
Simulations of relevant photochemical episodes were led to assess the validity of
the model. The results can be considered as a validation, which allows next studies to
focus on fine modeling issues.
A major feature of Polair is the availability of a tangent linear mode and an adjoint10
mode entirely generated by automatic dierentiation. Tangent linear and adjoint modes
grant the opportunity to perform detailed sensitivity analyses and data assimilation.
This paper shows how inverse modeling is achieved with Polair.
1. Introduction
Several 3-D chemistry-transport models (hereafter CTM) are now available and have15
proven to be ecient in many applications from passive-transport simulations to data
assimilation with highly non-linear models. One may cite Cmaq (Byun et al., 1998),
Chimere (Vautard et al., 2001), Eurad (Hass et al., 1995), etc. However, building a
code flexible enough to be able to deal with any of those applications still remains a
challenge.20
Polair is an Eulerian three-dimensional chemistry-transport model developed at
ENPC (
´
Ecole Nationale des Ponts et Chauss
´
ees). It is designed to handle a wide
range of applications. Thus it has been used for passive transport by Issartel et al.
(2003), for impact at European scale, for photochemistry by Sartelet et al. (2002) and
mercury chemistry by Roustan et al. (2003). Developments are led to include size25
resolved aerosols and other chemical mechanisms. In this paper, we focus on photo-
1372

ACPD
4, 1371–1392, 2004
3-D
chemistry-transport
model Polair
V. Mallet and B. Sportisse
Title Page
Abstract Introduction
Conclusions References
Tables Figures
J I
J I
Back Close
Full Screen / Esc
Print Version
Interactive Discussion
© EGU 2004
chemistry.
Beyond academic simulations coming along with code validation and basic modeling
work, validations were undertaken through comparisons with other models and with
measurements provided by dedicated campaigns. Therefore fine analyses identified
strengths and weaknesses of the model. Some analyses may be found in papers5
previously cited. This paper reports results obtained at regional scale and European
scale. At regional scale, the intensive obser vation period (hereafter IOP) # 2 of the
ESQUIF campaign (from summer 1998 to winter 2001, over
ˆ
Ile de France) enables
comparisons with measurements. At European scale, a simulation over four months
(from May to August 2001) was compared to measurements from several countries.10
A major advantage of Polair is the tangent linear mode and the adjoint mode that are
entirely generated by automatic dierentiation. The availability of those modes enables
to get sensitivities with respect to most parameters and to put into practice virtually any
data-assimilation method involving the derivative of some output variables with respect
to some input parameters. In this paper, we explain the whole process.15
The paper is organized as follows. We first describe some abilities of Polair and we
point out some numerical issues. Then we report results from validations against mea-
surements from the ESQUIF campaign (regional scale) and measurements collected
for four months in 2001 over Europe. Finally we explain the way the tangent linear and
adjoint codes are generated and used.20
2. Model overview
As an Eulerian three-dimensional chemistry-transport model, Polair handles basic
physical processes by integrating in time the following equation:
c
i
t
+ div
(
c
i
· V
)
= div
(
K · c
i
)
+ χ
i
(c) D
i
+ E
i
(1)
where i labels a chemical species, c is a vector of chemical concentrations, V is the25
wind vector, K is the diusion matrix, χ
i
combines production and loss terms of chemi-
1373

ACPD
4, 1371–1392, 2004
3-D
chemistry-transport
model Polair
V. Mallet and B. Sportisse
Title Page
Abstract Introduction
Conclusions References
Tables Figures
J I
J I
Back Close
Full Screen / Esc
Print Version
Interactive Discussion
© EGU 2004
cal reactions, D
i
is the deposition term (dry deposition and wet deposition scaveng-
ing), E
i
stands for the emissions (surface and volumic emissions).
In our model, V is prescribed (e.g. by oine meteorological forecasts). The diusion
matrix is assumed to be a diagonal matrix. Horizontal diusion coecients K
xx
and
K
yy
are not well known and are assumed constant in time and space. The vertical5
diusion coecient is estimated according to a given parameterization. For instance,
we usually use the parameterization proposed in Louis (1979).
Chemical reactions are modeled by χ
i
which depends on species concentrations.
For the time being, Polair has several chemical mechanisms:
Mercury chemistry: simplified mechanism based on Petersen (1995);10
Aerosols: modal approximation for inorganic aerosols;
Photochemistry (for ozone): several mechanisms are available among which
RADM 2 (Stockwell et al., 1990), RACM (Stockwell et al., 1997), EURORADM
(Schell, 2000), MOCA (Aumont, 1994), CBM IV (Gery et al., 1989).
Dry gaseous deposition D
i
is computed as in Wesely (1989) or as in Baer et al.15
(1992), and required land use coverage data may be provided by the USGS data
base (http://edcdaac.usgs.gov/glcc/glcc.html). Wet deposition is parameterized as in
Sportisse et al. (2002).
Emissions E
i
are generated from coarse emissions of a few emitting classes and for
a few aggregated species. Speciation and aggregation stages are followed to estimate20
E
i
as in Middleton et al. (1990).
Equation (1) is relevant at many scales, which lets a CTM be relevant from regional
scale to continental scale. Since Polair can easily handle several chemical mecha-
nisms, dierent applications may reasonably be led at those scales.
The modularity of Polair has been strengthened by computing all parameteriza-25
tions in preprocessed steps. As such, Polair only is a numerical platform for solving
1374

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Abstract: Chemists routinely create models of reaction systems to understand reaction mechanisms, kinetic properties, process yields under various operating conditions, or the impact of chemicals on man and the environment. As opposed to concise physical laws, these models are attempts to mimic the system by hypothesizing, extracting, and encoding system features (e.g. a potentially relevant reaction pathway versus another plausible one), within a process that can hardly be formalized scientifically.1 The model will hopefully help to corroborate or falsify a given description of reality, e.g. by validating a reaction scheme for a photochemical process in the atmosphere, and possibly to influence it, e.g. by allowing the identification of optimal operating conditions for an industrial process or suggesting mitigating strategies for an undesired environmental impact. These models are customarily built in the presence of uncertainties of various levels, in the pathway, in the order of the kinetics associated to the pathway, in the numerical value of the kinetic and thermodynamic constants for that pathway, and so on. Propagating via the model all these uncertainties onto the model output of interest, e.g. the yield of a process, is the job of uncertainty analysis. Determining the strength of the relation between a given uncertain input and the output is the job of sensitivity analysis.2 Mathematical sensitivities (in the form of model output derivatives) are a straightforward implementation of this sensitivity concept. If the model output of interest is Y, its sensitivity to an input factor Xi is simply Y′ Xi ) ∂Y/∂Xi. This measure tells how sensitive the output is to a perturbation of the input. If a measure independent from the units used for Y and Xi is needed, SXi r ) (Xh i/Yh )(∂Y/∂Xi) can be used, where Xh i is the nominal (or central, if a range is known) value of factor Xi and Yh is the value taken by Y when all input factors are at their nominal value. If factors are uncertain within a known or hypothesized range, then the measure SXi σ ) (σXi/σY)(∂Y/∂Xi) can be of use, where the standard deviations σXi, σY are uncertainty analysis’ input and output, respectively, in the sense that σXi comes from the available knowledge on Xi, while σY must be inferred using the model. These sensitivity measures can be efficiently computed by an array of techniques, ranging from automated differentiation (where the computer program that implements the model is modified so that the sensitivities are computed with a modicum of extra execution time3) to direct methods (where the differential equations describing the model are solved directly in terms of species concentrations and their derivatives4). There is a vast amount of literature on these sensitivity measures,5-11 which shall be referred to as local in the following. The majority of sensitivity analyses met with in chemistry and physics are local and derivative-based. Local sensitivities are useful for a variety of applications, such as the solution of inverse problems, e.g. relating macroscopic observables of a system, such as kinetic constants, to the quantum mechanics properties of the system,6 or the analysis of runaway and parametric sensitivity of various types of chemical reactors.8 Contexts where local sensitivity has been widely used are as follows: (1) to understand the reaction path, mechanism, or rate-determining steps in a detailed kinetic model with a large number of elementary reactions, e.g. in photochemistry or in combustion chemistry,4,7,9 (see ref 12 for an alternative approach in this context), (2) to extract important elementary reactions from a complex kinetic model to obtain a reduced model (e.g. a minimal reaction scheme) with equivalent predictive power7 or to select important reactions for further analysis,13,14 (3) to estimate the output of a * Corresponding author. Telephone: +39 0332 789686. Fax: +39 0332 785733. E-mail: andrea.saltelli@jrc.it. 2811 Chem. Rev. 2005, 105, 2811−2827

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  • ...…(Vautard et al., 2000; Menut et al., 2000; Schmidt and Martin, 2003), IMAGES (Muller and Stavrakou, 2005; Stavrakou and Muller, 2006), Polair (Mallet and Sportisse, 2004, 2006), TM4 (Meirink et al., 2006), the California Institute of Technology urban-scale model (Martien et al., 2006;…...

    [...]

  • ...Adjoint models of other CTMs report this ratio as: STEM: 1.5, CHIMERE: 3–4, IMAGES: 4, Polair: 4.5–7, CIT: 11.75....

    [...]

  • ...These initial works have been followed more recently by similar development and application of adjoint models of several CTMs: CHIMERE (Vautard et al., 2000; Menut et al., 2000; Schmidt and Martin, 2003), IMAGES (Muller and Stavrakou, 2005; Stavrakou and Muller, 2006), Polair (Mallet and Sportisse, 2004, 2006), TM4 (Meirink et al., 2006), the California Institute of Technology urban-scale model (Martien et al., 2006; Martien and Harley, 2006), and DRAIS (Nester and Panitz, 2006)....

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Abstract: Polyphemus is an air quality modeling platform which aims at covering the scope and the abilities of modern air quality systems. It deals with applications from local scale to continental scale, using two Gaussian models and two Eulerian models. It manages passive tracers, radioactive decay, photochemistry and aerosol dynamics. The structure of the system includes four independent levels with data management, physical parameterizations, numerical solvers and high-level methods such as data assimilation. This enables sensitivity and uncertainty analysis, primarily through multimodel approaches. On top of the models, drivers implement advanced methods such as model coupling or data assimilation.

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Cites background from "3-D chemistry-transport model Polai..."

  • ...Their performances have been evaluated inQuélo et al.(2007); Mallet and Sportisse(2004); Sartelet et al.(2007) respectively....

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  • ...The tangent linear models and the adjoint models of the first two versions can be automatically generated (Mallet and Sportisse, 2004) by O∂ysśee (Faure and Papegay, 1998)....

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TL;DR: Local, derivative-based sensitivity measures can be efficiently computed by an array of techniques, ranging from automated differentiation ( where the computer program that implements the model is modified so that the sensitivities are computed with a modicum of extra execution time) to direct methods (where the differential equations describing the model are solved directly in terms of species concentrations and their derivatives).
Abstract: Chemists routinely create models of reaction systems to understand reaction mechanisms, kinetic properties, process yields under various operating conditions, or the impact of chemicals on manhumans and the environment. As opposed to concise physical laws, these models are attempts to mimic the system by hypothesizing, extracting, and encoding system features (e.g., a potentially relevant reaction pathway), within a process that can hardly be formalized scientifically. Amodelwill hopefully help to corroborate or falsify a given description of reality, e.g., by validating a reaction scheme for a photochemical process in the atmosphere, and possibly to influence reality, e.g., by allowing the identification of optimal operating conditions for an industrial process or suggesting mitigating strategies for an undesired environmental impact. These models are customarily built in the presence of uncertainties of various levels, in the pathway, in the order of the kinetics associated to the pathway, in the numerical value of the kinetic and thermodynamic constants for that pathway, and so on. Propagating via the model all these uncertainties onto the model output of interest, e.g., the yield of a process, is the job of uncertainty analysis. Determining the strength of the relation between a given uncertain input and the output is the job of sensitivity analysis. A straightforward implementation of the “sensitivity” concept is provided by model output derivatives. If the model output of interest isY, its sensitivity to an input factorXi is simplyY0Xi= ∂Y/∂Xi. This measure tells how sensitive the output is to a perturbation of the input. For discrete input factors, local sensitivities might be impossible to evaluate as wide perturbations of the input would be implied. If a measure independent from the units used for Y and Xi is needed, S r Xi = (X 0 i /Y )(∂Y/∂Xi), which denotes the socalled elasticity coefficient, can be used, where Xi is the nominal value of factor Xi and Y 0 is the value taken by Y when all input factors are at their nominal value. The nominal (or reference, or design) value Xi can be the mean (or median) value when an uncertainty distribution (either empirical or hypothesized) is available. In this latter case an alternative measure is SXi = (σXi/σY)(∂Y/∂Xi), where the standard deviations σXi, σY are uncertainty analysis’ input and output, respectively, in the sense that σXi comes from the available knowledge onXi, while σYmust be inferred using the model. Wheras SXi is a dimensionless version of the pure derivative (∂Y/∂Xi) and, hence, still a purely local measure (i.e., relative to the point where the derivative is taken), SXi depends upon the uncertain range of factor Xi, and is in this sense a more informative measure.Coeteris paribus, factors with larger standard deviations, have more chance to contribute significantly to the uncertainty in the output. Local, derivative-based sensitivity measures can be efficiently computed by an array of techniques, ranging from automated differentiation (where the computer program that implements the model is modified so that the sensitivities are computed with a modicum of extra execution time) to direct methods (where the differential equations describing the model are solved directly in terms of species concentrations and their derivatives). There is a vast amount of literature on these sensitivity measures. 10,11 The majority of sensitivity analyses met with in chemistry and physics are local and derivative-based. Local sensitivities are useful for a variety of applications, such as the solution of inverse problems, e.g., relatingmacroscopic observables of a system, such as kinetic constants, to the quantum mechanics properties of the system, or the analysis of runaway and parametric sensitivity of various types of chemical reactors. Contexts where local sensitivity has been widely used are as follows: (1) to understand the reaction path, mechanism, or rate-determining steps in a detailed

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Abstract: Methods for estimating the dry deposition velocities of atmospheric gases in the U.S. and surrounding areas have been improved and incorporated into a revised computer code module for use in numerical models of atmospheric transport and deposition of pollutants over regional scales. The key improvement is the computation of bulk surface resistances along three distinct pathways of mass transfer to sites of deposition at the upper portions of vegetative canopies or structures, the lower portions, and the ground (or water surface). This approach replaces the previous technique of providing simple look-up tables of bulk surface resistances. With the surface resistances divided explicitly into distinct pathways, the bulk surface resistances for a large number of gases in addition to those usually addressed in acid deposition models (SO2,O3, NOx and HNO3) can be computed, if estimates of the effective Henry's Law constants and appropriate measures of the chemical reactivity of the various substances are known. This has been accomplished successfully for H2O2, HCHO, CH3CHO (to represent other aldehydes), CH3O2H (to represent organic peroxides), CH3C(O)O2H, HCOOH (to represent organic acids), NH3, CH3C(O)O2NO2 and HNO2. Other factors considered include surface temperature, stomatal response to environmental parameters, the wetting of surfaces by dew and rain, and the covering of surfaces by snow. Surface emission of gases and variations of uptake characteristics by individual plant species within the landuse types are not considered explicitly.

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"3-D chemistry-transport model Polai..." refers background or methods in this paper

  • ...Dry gaseous deposition Di is computed as in Wesely (1989) or as in Baer et al.15 (1992), and required land use coverage data may be provided by the USGS data base (http://edcdaac.usgs.gov/glcc/glcc.html)....

    [...]

  • ...Meteorological data could be processed in a better way, deposition velocities should be computed thanks to Wesely (1989) or Baer et al. (1992) which are now available in the code, biogenic emissions should improve results specially outside the plume....

    [...]

  • ...Deposition velocities are computed according to15 Wesely (1989)....

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Abstract: A state-of-the-art gas phase chemical mechanism for modeling atmospheric chemistry on a regional scale is presented. The second generation Regional Acid Deposition Model (RADM2) gas phase chemical mechanism, like its predecessor RADM1, is highly nonlinear, since predicted ozone, sulfate, nitric acid and hydrogen peroxide concentrations are complicated functions of NO{sub x} and nonmethane hydrocarbon concentrations. The RADM2 chemical mechanism is an upgrade of RADM1 in that (1) three classes of higher alkanes are used instead of one, (2) a more detailed treatment of aromatic chemistry is used, (3) the two higher alkene classes now represent internal and terminal alkenes, (4) ketones and dicarbonyl species are treated as classes distinct from aldehydes, (5) isoprene is now included as an explicit species, and (6) there is a more detailed treatment of peroxy radical-peroxy radical reactions. As a result of these improvements the RADM2 mechanism simulates the concentrations of peroxyacetyl nitrate, HNO3, and H{sub 2}O{sub 2} under a wide variety of environmental conditions. Comparisons of RADM2 mechanism with the RADM1 mechanism predictions and selected environmental chamber experimental results indicate that for typical atmospheric conditions, both mechanisms reliably predict O{sub 3}, sulfate and nitric acid concentrations. The RADM2 mechanism gives lower and presumably moremore » realistic predictions of H{sub 2}O{sub 2} because of its more detailed treatment of peroxy radical-peroxy radical reactions.« less

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"3-D chemistry-transport model Polai..." refers background in this paper

  • ...…based on Petersen (1995);10 – Aerosols: modal approximation for inorganic aerosols; – Photochemistry (for ozone): several mechanisms are available among which RADM 2 (Stockwell et al., 1990), RACM (Stockwell et al., 1997), EURORADM (Schell, 2000), MOCA (Aumont, 1994), CBM IV (Gery et al., 1989)....

    [...]

  • ...– Aerosols: modal approximation for inorganic aerosols; – Photochemistry (for ozone): several mechanisms are available among which RADM 2 (Stockwell et al., 1990), RACM (Stockwell et al....

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  • ...The main chemical mechanism was RADM 2 (see Stockwell et al., 1990), with 6115 species and 157 reactions....

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Abstract: A new gas-phase chemical mechanism for the modeling of regional atmospheric chemistry, the “Regional Atmospheric Chemistry Mechanism” (RACM) is presented. The mechanism is intended to be valid for remote to polluted conditions and from the Earth's surface through the upper troposphere. The RACM mechanism is based upon the earlier Regional Acid Deposition Model, version 2 (RADM2) mechanism [Stockwell et al., 1990] and the more detailed Euro-RADM mechanism [Stockwell and Kley, 1994]. The RACM mechanism includes rate constants and product yields from the most recent laboratory measurements, and it has been tested against environmental chamber data. A new condensed reaction mechanism is included for biogenic compounds: isoprene, α-pinene, and d-limonene. The branching ratios for alkane decay were reevaluated, and in the revised mechanism the aldehyde to ketone ratios were significantly reduced. The relatively large amounts of nitrates resulting from the reactions of unbranched alkenes with NO3 are now included, and the production of HO from the ozonolysis of alkenes has a much greater yield. The aromatic chemistry has been revised through the use of new laboratory data. The yield of cresol production from aromatics was reduced, while the reactions of HO, NO3, and O3 with unsaturated dicarbonyl species and unsaturated peroxynitrate are now included in the RACM mechanism. The peroxyacetyl nitrate chemistry and the organic peroxy radical-peroxy radical reactions were revised, and organic peroxy radical +NO3 reactions were added.

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"3-D chemistry-transport model Polai..." refers background in this paper

  • ...…based on Petersen (1995);10 – Aerosols: modal approximation for inorganic aerosols; – Photochemistry (for ozone): several mechanisms are available among which RADM 2 (Stockwell et al., 1990), RACM (Stockwell et al., 1997), EURORADM (Schell, 2000), MOCA (Aumont, 1994), CBM IV (Gery et al., 1989)....

    [...]

  • ...For the time being, Polair has several chemical mechanisms: – Mercury chemistry: simplified mechanism based on Petersen (1995);10 – Aerosols: modal approximation for inorganic aerosols; – Photochemistry (for ozone): several mechanisms are available among which RADM 2 (Stockwell et al., 1990), RACM (Stockwell et al., 1997), EURORADM (Schell, 2000), MOCA (Aumont, 1994), CBM IV (Gery et al., 1989)....

    [...]

  • ..., 1990), RACM (Stockwell et al., 1997), EURORADM (Schell, 2000), MOCA (Aumont, 1994), CBM IV (Gery et al....

    [...]

  • ...One may use RACM instead....

    [...]

  • ...The chemical mechanism is RACM....

    [...]


Journal ArticleDOI
Abstract: [1] We have developed a global three-dimensional chemical transport model called Model of Ozone and Related Chemical Tracers (MOZART), version 2. This model, which will be made available to the community, is built on the framework of the National Center for Atmospheric Research (NCAR) Model of Atmospheric Transport and Chemistry (MATCH) and can easily be driven with various meteorological inputs and model resolutions. In this work, we describe the standard configuration of the model, in which the model is driven by meteorological inputs every 3 hours from the middle atmosphere version of the NCAR Community Climate Model (MACCM3) and uses a 20-min time step and a horizontal resolution of 2.8° latitude × 2.8° longitude with 34 vertical levels extending up to approximately 40 km. The model includes a detailed chemistry scheme for tropospheric ozone, nitrogen oxides, and hydrocarbon chemistry, with 63 chemical species. Tracer advection is performed using a flux-form semi-Lagrangian scheme with a pressure fixer. Subgrid-scale convective and boundary layer parameterizations are included in the model. Surface emissions include sources from fossil fuel combustion, biofuel and biomass burning, biogenic and soil emissions, and oceanic emissions. Parameterizations of dry and wet deposition are included. Stratospheric concentrations of several long-lived species (including ozone) are constrained by relaxation toward climatological values. The distribution of tropospheric ozone is well simulated in the model, including seasonality and horizontal and vertical gradients. However, the model tends to overestimate ozone near the tropopause at high northern latitudes. Concentrations of nitrogen oxides (NOx) and nitric acid (HNO3) agree well with observed values, but peroxyacetylnitrate (PAN) is overestimated by the model in the upper troposphere at several locations. Carbon monoxide (CO) is simulated well at most locations, but the seasonal cycle is underestimated at some sites in the Northern Hemisphere. We find that in situ photochemical production and loss dominate the tropospheric ozone budget, over input from the stratosphere and dry deposition. Approximately 75% of the tropospheric production and loss of ozone occurs within the tropics, with large net production in the tropical upper troposphere. Tropospheric production and loss of ozone are three to four times greater in the northern extratropics than the southern extratropics. The global sources of CO consist of photochemical production (55%) and direct emissions (45%). The tropics dominate the chemistry of CO, accounting for about 75% of the tropospheric production and loss. The global budgets of tropospheric ozone and CO are generally consistent with the range found in recent studies. The lifetime of methane (9.5 years) and methylchloroform (5.7 years) versus oxidation by tropospheric hydroxyl radical (OH), two useful measures of the global abundance of OH, agree well with recent estimates. Concentrations of nonmethane hydrocarbons and oxygenated intermediates (carbonyls and peroxides) generally agree well with observations.

894 citations


Frequently Asked Questions (2)
Q1. What contributions have the authors mentioned in the paper "3-d chemistry-transport model polair: numerical issues, validation and automatic-differentiation strategy" ?

Mallet et al. this paper proposed the 3D chemistry-transport model Polair, which is an Eulerian three-dimensional transport model developed at ENPC ( École Nationale des Ponts et Chaussees ). 

The main10 future works will be devoted to aerosol modeling and air-quality ensemble forecast.