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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2018

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry is a high-dimensionality, large-data problem and thus may be suited to machine-learning algorithms. We show here the potential of a random forest regression algorithm to replace the gas-phase chemistry solver in the GEOS-Chem chemistry model. In this proof-of-concept study, we used one month of model output to train random forest regression models to predict the concentrations of each long-lived chemical species after integration based upon the physical and chemical conditions before the chemical integration. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for very long-lived species and the absolute concentration for shorter lived species. The skill of the machine learning algorithm is further improved by using a family approach for NO and NO2 rather than treating them independently.By replacing the numerical integrator with the random forest algorithm and running this model for one month, we find that the model is able to reproduce many of the features of the reference chemistry simulation. Replacing the integration methodology with a machine learning algorithm has the potential to be substantially faster. There are a wide range of applications for such an approach, e.g. to generate boundary conditions, for use in air quality forecasts or chemical data assimilation systems, etc.

Keller, Christoph A.↗
NASA NTRS2021

Grid-Stretching Capability for the GEOS-Chem 13.0.0 Atmospheric Chemistry Model

Modeling atmospheric chemistry at fine resolution globally is computationally expensive; the capability to focus on specific geographic regions using a multi scale grid is desirable. Here, we develop, validate, and demonstrate stretched grids in the GEOS-Chem atmospheric chemistry model in its high-performance implementation (GCHP). These multiscale grids are specified at runtime by four parameters that offer users nimble control of the region that is refined and the resolution of the refinement. We validate the stretched-grid simulation versus global cubed-sphere simulations. We demonstrate the operation and flexibility of stretched-grid simulations with two case studies that compare simulated tropospheric NO2column densities from stretched-grid and cubed-sphere simulations to retrieved column densities from the TROPOspheric Monitoring Instrument (TROPOMI). The first case study uses a stretched grid with a broad refinement covering the contiguous US to produces imulated columns that perform similarly to a C180 (∼50 km) cubed-sphere simulation at less than one-ninth the computational expense. The second case study experiments with a large stretch-factor for a global stretched-grid simulation with a highly localized refinement with∼10 km resolution for California. We find that the refinement improves spatial agreement with TROPOMI columns compared to a C90 cubed-sphere simulation of comparable computational demands. Overall we find that stretched grids in GEOS-Chem are a practical tool for fine resolution regional- or continental-scale simulations of atmospheric chemistry. Stretched grids are available in GEOS-Chem version 13.0.0

GEOS-Chem↗
NASA NTRS2016

Non-OH Chemistry in Oxidation Flow Reactors for the Study of Atmospheric Chemistry Systematically Examined by Modeling

Oxidation flow reactors (OFRs) using low-pressure Hg lamp emission at 185 and 254 nm produce OH radicals efficiently and are widely used in atmospheric chemistry and other fields. However, knowledge of detailed OFR chemistry is limited, allowing speculation in the literature about whether some non-OH reactants, including several not relevant for tropospheric chemistry, may play an important role in these OFRs. These non-OH reactants are UV radiation, O(1D), O(3P), and O3. In this study, we investigate the relative importance of other reactants to OH for the fate of reactant species in OFR under a wide range of conditions via box modeling. The relative importance of non-OH species is less sensitive to UV light intensity than to relative humidity (RH) and external OH reactivity (OHRext), as both non-OH reactants and OH scale roughly proportional to UV intensity. We show that for field studies in forested regions and also the urban area of Los Angeles, reactants of atmospheric interest are predominantly consumed by OH. We find that O(1D), O(3P), and O3 have relative contributions to VOC consumption that are similar or lower than in the troposphere. The impact of O atoms can be neglected under most conditions in both OFR and troposphere. Under pathological OFR conditions of low RH and/or high OHRext, the importance of non-OH reactants is enhanced because OH is suppressed. Some biogenics can have substantial destructions by O3, and photolysis at non-tropospheric wavelengths (185 and 254 nm) may also play a significant role in the degradation of some aromatics under pathological conditions. Working under low O2 with the OFR185 mode allows OH to completely dominate over O3 reactions even for the biogenic species most reactive with O3. Non-tropospheric VOC photolysis may have been a problem in some laboratory and source studies, but can be avoided or lessened in future studies by diluting source emissions and working at lower precursor concentrations in lab studies, and by humidification. SOA photolysis is shown to be insignificant for most functional groups, except for nitrates and especially aromatics, which may be photolyzed at high UV flux settings. Our work further establishes the OFR's usefulness as a tool to study atmospheric chemistry and enables better experiment design and interpretation, as well as improved future reactor design.

Peng, Zhe↗
NASA NTRS2016

Non-OH Chemistry in Oxidation Flow Reactors for the Study of Atmospheric Chemistry Systematically Examined by Modeling

Oxidation flow reactors (OFRs) using low-pressure Hg lamp emission at 185 and 254 nm produce OH radicals efficiently and are widely used in atmospheric chemistry and other fields. However, knowledge of detailed OFR chemistry is limited, allowing speculation in the literature about whether some non-OH reactants, including several not relevant for tropospheric chemistry, may play an important role in these OFRs. These non-OH reactants are UV radiation, O(1D), O(3P), and O3. In this study, we investigate the relative importance of other reactants to OH for the fate of reactant species in OFR under a wide range of conditions via box modeling. The relative importance of non-OH species is less sensitive to UV light intensity than to relative humidity (RH) and external OH reactivity (OHRext), as both non-OH reactants and OH scale roughly proportional to UV intensity. We show that for field studies in forested regions and also the urban area of Los Angeles, reactants of atmospheric interest are predominantly consumed by OH. We find that O(1D), O(3P), and O3 have relative contributions to VOC consumption that are similar or lower than in the troposphere. The impact of O atoms can be neglected under most conditions in both OFR and troposphere. Under pathological OFR conditions of low RH and/or high OHRext, the importance of non-OH reactants is enhanced because OH is suppressed. Some biogenics can have substantial destructions by O3, and photolysis at non-tropospheric wavelengths (185 and 254 nm) may also play a significant role in the degradation of some aromatics under pathological conditions. Working under low O2 with the OFR185 mode allows OH to completely dominate over O3 reactions even for the biogenic species most reactive with O3. Non-tropospheric VOC photolysis may have been a problem in some laboratory and source studies, but can be avoided or lessened in future studies by diluting source emissions and working at lower precursor concentrations in lab studies, and by humidification. SOA photolysis is shown to be insignificant for most functional groups, except for nitrates and especially aromatics, which may be photolyzed at high UV flux settings. Our work further establishes the OFR's usefulness as a tool to study atmospheric chemistry and enables better experiment design and interpretation, as well as improved future reactor design.

Peng, Zhe↗
NASA NTRS2013

Submillimeter Planetary Atmospheric Chemistry Exploration Sounder

Planetary Atmospheric Chemistry Exploration Sounder (SPACES), a high-sensitivity laboratory breadboard for a spectrometer targeted at orbital planetary atmospheric analysis. The frequency range is 520 to 590 GHz, with a target noise temperature sensitivity of 2,500 K for detecting water, sulfur compounds, carbon compounds, and other atmospheric constituents. SPACES is a prototype for a powerful tool for the exploration of the chemistry and dynamics of any planetary atmosphere. It is fundamentally a single-pixel receiver for spectral signals emitted by the relevant constituents, intended to be fed by a fixed or movable telescope/antenna. Its front-end sensor translates the received signal down to the 100-MHz range where it can be digitized and the data transferred to a spectrum analyzer for processing, spectrum generation, and accumulation. The individual microwave and submillimeter wave components (mixers, LO high-powered amplifiers, and multipliers) of SPACES were developed in cooperation with other programs, although with this type of instrument in mind. Compared to previous planetary and Earth science instruments, its broad bandwidth (approx. =.13%) and rapid tunability (approx. =.10 ms) are new developments only made possible recently by the advancement in submillimeter circuit design and processing at JPL.

Schlecht, Erich T.↗
NASA NTRS1992

The Chemistry of the Global Atmosphere; International Symposium of the Commission for Atmospheric Chemistry and Global Pollution of IAMAP, 7th, Chamrousse, France, Sept. 5-11, 1990, Selected Papers

Topics presented include the adsorption and reaction of trichlorofluoromethane on various particles, equilibria of the marine multiphase ammonia system, a novel ozone sensor for direct eddy flux measurements, and characterization of the carbonate content of atmospheric aerosols. Also presented are variations in heavy metals concentrations in Antarctic snows, sources of continental dust over Antarctica during the last glacial cycle, an inventory of anthropogenic emissions and air pollution in the USSR, and atmospheric nuclei in the remote free-troposphere.

Buat-Menard, P.↗
NASA NTRS2015

Development of a Grid-Independent Geos-Chem Chemical Transport Model (v9-02) as an Atmospheric Chemistry Module for Earth System Models

The GEOS-Chem global chemical transport model (CTM), used by a large atmospheric chemistry research community, has been re-engineered to also serve as an atmospheric chemistry module for Earth system models (ESMs). This was done using an Earth System Modeling Framework (ESMF) interface that operates independently of the GEOSChem scientific code, permitting the exact same GEOSChem code to be used as an ESM module or as a standalone CTM. In this manner, the continual stream of updates contributed by the CTM user community is automatically passed on to the ESM module, which remains state of science and referenced to the latest version of the standard GEOS-Chem CTM. A major step in this re-engineering was to make GEOS-Chem grid independent, i.e., capable of using any geophysical grid specified at run time. GEOS-Chem data sockets were also created for communication between modules and with external ESM code. The grid-independent, ESMF-compatible GEOS-Chem is now the standard version of the GEOS-Chem CTM. It has been implemented as an atmospheric chemistry module into the NASA GEOS- 5 ESM. The coupled GEOS-5-GEOS-Chem system was tested for scalability and performance with a tropospheric oxidant-aerosol simulation (120 coupled species, 66 transported tracers) using 48-240 cores and message-passing interface (MPI) distributed-memory parallelization. Numerical experiments demonstrate that the GEOS-Chem chemistry module scales efficiently for the number of cores tested, with no degradation as the number of cores increases. Although inclusion of atmospheric chemistry in ESMs is computationally expensive, the excellent scalability of the chemistry module means that the relative cost goes down with increasing number of cores in a massively parallel environment.

CTM↗
NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS1982

Heterogeneous atmospheric chemistry

The present conference on heterogeneous atmospheric chemistry considers such topics concerning clusters, particles and microparticles as common problems in nucleation and growth, chemical kinetics, and catalysis, chemical reactions with aerosols, electron beam studies of natural and anthropogenic microparticles, and structural studies employing molecular beam techniques, as well as such gas-solid interaction topics as photoassisted reactions, catalyzed photolysis, and heterogeneous catalysis. Also discussed are sulfur dioxide absorption, oxidation, and oxidation inhibition in falling drops, sulfur dioxide/water equilibria, the evidence for heterogeneous catalysis in the atmosphere, the importance of heterogeneous processes to tropospheric chemistry, soot-catalyzed atmospheric reactions, and the concentrations and mechanisms of formation of sulfate in the atmospheric boundary layer.

Schryer, D. R.↗
NASA NTRS2013

Tropospheric Ozone Changes, Radiative Forcing and Attribution to Emissions in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)

Ozone (O3) from 17 atmospheric chemistry models taking part in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) has been used to calculate tropospheric ozone radiative forcings (RFs). All models applied a common set of anthropogenic emissions, which are better constrained for the present-day than the past. Future anthropogenic emissions follow the four Representative Concentration Pathway (RCP) scenarios, which define a relatively narrow range of possible air pollution emissions. We calculate a value for the pre-industrial (1750) to present-day (2010) tropospheric ozone RF of 410 mW m−2. The model range of pre-industrial to present-day changes in O3 produces a spread (+/-1 standard deviation) in RFs of +/-17%. Three different radiation schemes were used - we find differences in RFs between schemes (for the same ozone fields) of +/-10 percent. Applying two different tropopause definitions gives differences in RFs of +/-3 percent. Given additional (unquantified) uncertainties associated with emissions, climate-chemistry interactions and land-use change, we estimate an overall uncertainty of +/-30 percent for the tropospheric ozone RF. Experiments carried out by a subset of six models attribute tropospheric ozone RF to increased emissions of methane (44+/-12 percent), nitrogen oxides (31 +/- 9 percent), carbon monoxide (15 +/- 3 percent) and non-methane volatile organic compounds (9 +/- 2 percent); earlier studies attributed more of the tropospheric ozone RF to methane and less to nitrogen oxides. Normalising RFs to changes in tropospheric column ozone, we find a global mean normalised RF of 42 mW m(−2) DU(−1), a value similar to previous work. Using normalised RFs and future tropospheric column ozone projections we calculate future tropospheric ozone RFs (mW m(−2); relative to 1750) for the four future scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) of 350, 420, 370 and 460 (in 2030), and 200, 300, 280 and 600 (in 2100). Models show some coherent responses of ozone to climate change: decreases in the tropical lower troposphere, associated with increases in water vapour; and increases in the sub-tropical to mid-latitude upper troposphere, associated with increases in lightning and stratosphere-to-troposphere transport. Climate change has relatively small impacts on global mean tropospheric ozone RF.

climate change↗
NASA NTRS1990

Cassini atmospheric chemistry mapper. Volume 1. Investigation and technical plan

The Cassini Atmospheric Chemistry Mapper (ACM) enables a broad range of atmospheric science investigations for Saturn and Titan by providing high spectral and spatial resolution mapping and occultation capabilities at 3 and 5 microns. ACM can directly address the major atmospheric science objectives for Saturn and for Titan, as defined by the Announcement of Opportunity, with pivotal diagnostic measurements not accessible to any other proposed Cassini instrument. ACM determines mixing ratios for atmospheric molecules from spectral line profiles for an important and extensive volume of the atmosphere of Saturn (and Jupiter). Spatial and vertical profiles of disequilibrium species abundances define Saturn's deep atmosphere, its chemistry, and its vertical transport phenomena. ACM spectral maps provide a unique means to interpret atmospheric conditions in the deep (approximately 1000 bar) atmosphere of Saturn. Deep chemistry and vertical transport is inferred from the vertical and horizontal distribution of a series of disequilibrium species. Solar occultations provide a method to bridge the altitude range in Saturn's (and Titan's) atmosphere that is not accessible to radio science, thermal infrared, and UV spectroscopy with temperature measurements to plus or minus 2K from the analysis of molecular line ratios and to attain an high sensitivity for low-abundance chemical species in the very large column densities that may be achieved during occultations for Saturn. For Titan, ACM solar occultations yield very well resolved (1/6 scale height) vertical mixing ratios column abundances for atmospheric molecular constituents. Occultations also provide for detecting abundant species very high in the upper atmosphere, while at greater depths, detecting the isotopes of C and O, constraining the production mechanisms, and/or sources for the above species. ACM measures the vertical and horizontal distribution of aerosols via their opacity at 3 microns and, particularly, at 5 microns. ACM recovers spatially-resolved atmospheric temperatures in Titan's troposphere via 3- and 5-microns spectral transitions. Together, the mixing ratio profiles and the aerosol distributions are utilized to investigate the photochemistry of the stratosphere and consequent formation processes for aerosols. Finally, ring opacities, observed during solar occultations and in reflected sunlight, provide a measurement of the particle size and distribution of ring material. ACM will be the first high spectral resolution mapping spectrometer on an outer planet mission for atmospheric studies while retaining a high resolution spatial mapping capability. ACM, thus, opens an entirely new range of orbital scientific studies of the origin, physio-chemical evolution and structure of the Saturn and Titan atmospheres. ACM provides high angular resolution spectral maps, viewing nadir and near-limb thermal radiation and reflected sunlight; sounds planetary limbs, spatially resolving vertical profiles to several atmospheric scale heights; and measures solar occultations, mapping both atmospheres and rings. ACM's high spectral and spatial resolution mapping capability is achieved with a simplified Fourier Transform spectrometer with a no-moving parts, physically compact design. ACM's simplicity guarantees an inherent stability essential for reliable performance throughout the lengthy Cassini Orbiter mission.

Smith, William Hayden↗
NASA NTRS2000

Integrated Global Observation Strategy - Ozone and Atmospheric Chemistry Project

The "Long Term Continuity of Stratospheric Ozone Measurements and Atmospheric Chemistry" project was one of six established by the Committee on Earth Observing Satellites (CEOS) in response to the Integrated Global Observing Strategy (IGOS) initiative. IGOS links satellite and ground based systems for global environmental observations. The strategy of this project is to develop a consensus of user requirements including the scientific (SPARC, IGAC, WCRP) and the applications community (WMO, UNEP) and to develop a long-term international plan for ozone and atmospheric chemistry measurements. The major components of the observing system include operational and research (meeting certain criteria) satellite platforms planned by the space faring nations which are integrated with a well supported and sustained ground, aircraft, and balloon measurements program for directed observations as well satellite validation. Highly integrated and continuous measurements of ozone, validation, and reanalysis efforts are essential to meet the international scientific and applications goals. In order to understand ozone trends, climate change, and air quality, it is essential to conduct long term measurements of certain other atmospheric species. These species include key source, radical, and reservoir constituents.

Hilsenrath, Ernest↗
NASA NTRS1978

Atmospheric chemistry - Response to human influence

Global atmospheric chemistry is surveyed, and the agreement of models with observed distribution of gases is considered. The influence of human perturbations due to combustion, agriculture, and chloro-carbon releases is examined with emphasis on ozone. Effects of combustion-related releases of CO on the abundances of other gases as well as possible effects of CO on tropospheric ozone are discussed. Other topics include the contribution of the chlorocarbon industry to stratospheric chloride and the recombination of nitrogen fixed by agriculture and combustion.

Logan, J. A.↗
NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗
NASA NTRS2017

An Overview of Atmospheric Chemistry and Air Quality Modeling

This presentation will include my personal research experience and an overview of atmospheric chemistry and air quality modeling to the participants of the NASA Student Airborne Research Program (SARP 2017). The presentation will also provide examples on ways to apply airborne observations for chemical transport (CTM) and air quality (AQ) model evaluation. CTM and AQ models are important tools in understanding tropospheric-stratospheric composition, atmospheric chemistry processes, meteorology, and air quality. This presentation will focus on how NASA scientist currently apply CTM and AQ models to better understand these topics. Finally, the importance of airborne observation in evaluating these topics and how in situ and remote sensing observations can be used to evaluate and improve CTM and AQ model predictions will be highlighted.

Atmospheric↗
NASA NTRS2010

Carbon Dioxide (CO2) Retrievals from Atmospheric Chemistry Experiment (ACE) Solar Occultation Measurements

The Atmospheric Chemistry Experiment ACE satellite (SCISAT-1) was launched into an inclined orbit on 12 August 2003 and is now recording high signal-to-noise 0.02 per centimeter resolution solar absorption spectra covering 750-4400 per centimeter (2.3-13 micrometers). A procedure has been developed for retrieving average dry air CO2 mole fractions (X(sub CO2)) in the altitude range 7-10 kilometers from the SCISAT-1 spectra. Using the N2 continuum absorption in a window region near 2500 per centimeter, altitude shifts are applied to the tangent heights retrieved in version 2.2 SCISAT-1 processing, while cloudy or aerosol-impacted measurements are eliminated. Monthly-mean XCO2 covering 60 S to 60 N latitude for February 2004 to March 2008 has been analyzed with consistent trends inferred in both hemispheres. The ACE XCO2 time series have been compared with previously-reported surface network measurements, predictions based on upper tropospheric aircraft measurements, and space-based measurements. The retrieved X(sub CO2) from the ACE-FTS spectra are higher on average by a factor of 1.07 plus or minus 0.025 in the northern hemisphere and by a factor of 1.09 plus or minus 0.019 on average in the southern hemisphere compared to surface station measurements covering the same time span. The ACE derived trend is approximately 0.2% per year higher than measured at surface stations during the same observation period.

Rinsland, Curtis P.↗
NASA NTRS2017

Description and Evaluation of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry Model (NMMB-MONARCH) Version 1.0: Gas-Phase Chemistry at Global Scale

This paper presents a comprehensive description and benchmark evaluation of the tropospheric gas-phase chemistry component of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMBMONARCH), formerly known as NMMB/BSC-CTM, that can be run on both regional and global domains. Here, we provide an extensive evaluation of a global annual cycle simulation using a variety of background surface stations (EMEP, WDCGG and CASTNET), ozonesondes (WOUDC, CMD and SHADOZ), aircraft data (MOZAIC and several campaigns), and satellite observations (SCIAMACHY and MOPITT).We also include an extensive discussion of our results in comparison to other state-of-the-art models. We note that in this study, we omitted aerosol processes and some natural emissions (lightning and volcano emissions). The model shows a realistic oxidative capacity across the globe. The seasonal cycle for CO is fairly well represented at different locations (correlations around 0.3-0.7 in surface concentrations), although concentrations are underestimated in spring and winter in the Northern Hemisphere, and are overestimated throughout the year at 800 and 500 hPa in the Southern Hemisphere. Nitrogen species are well represented in almost all locations, particularly NO2 in Europe (root mean square error - RMSE - below 5 ppb). The modeled vertical distributions of NOx and HNO3 are in excellent agreement with the observed values and the spatial and seasonal trends of tropospheric NO2 columns correspond well to observations from SCIAMACHY, capturing the highly polluted areas and the biomass burning cycle throughout the year. Over Asia, the model underestimates NOx from March to August, probably due to an underestimation of NOx emissions in the region. Overall, the comparison of the modeled CO and NO2 with MOPITT and SCIAMACHY observations emphasizes the need for more accurate emission rates from anthropogenic and biomass burning sources (i.e., specification of temporal variability).

atmospheric chemistry↗