Narrative description of the GEOS-Chem Model

We give here a narrative description of the current standard version of the GEOS-Chem model, with two purposes:

To provide you with a quick overview of GEOS-Chem components and capabilities;
To assist you in correctly citing relevant model components in your publications.

We strongly encourage you to be generous in citations—this not only recognizes the developer's work but also increases the traceability of your paper. Offering co-authorship to developers is encouraged for new developments flagged in this narrative if they are important for your work. It may also be appropriate to offer co-authorship for older model developments if they were new when you started your work. See the New GEOS-Chem Developments page for more specific information on the developer(s) to be credited, and contact the Model Scientist or appropriate Working Group chair if you need guidance.

The narrative below is reviewed and updated by the GEOS-Chem Steering Committee at every new X.Y model version release.

Citing GEOS-Chem

GEOS-Chem should be referenced by its version number X.Y.Z and corresponding DOI. See the history of model versions and their DOIs. The website http://www.geos-chem.org is also a useful reference. We strongly encourage you to cite GEOS-Chem journal publications, both for your general use of GEOS-Chem and for your specific applications. Consult the narrative below for referencing specific components. For questions on citations please contact the relevant Working Group Chair or Model Scientist.

Model name

The name "GEOS-Chem" was coined in 2001 and is first referred to in Bey et al. [2001]. It is not an acronym - there is nothing to spell out. GEOS stands for Goddard Earth Observing System and Chem stands for Chemistry but calling it the "Goddard Earth Observing System - Chemistry" model would be inappropriate because the GEOS Earth System Model can use other chemical modules besides GEOS-Chem, and GEOS-Chem can use other meteorological drivers besides GEOS.

If an abbreviated name for GEOS-Chem needs to be used, such as in a Figure or other context where space is limited, then 'GC' is acceptable and in fact frequently used for informal communication within the GEOS-Chem community. No other abbreviation is acceptable. In particular, 'GEOS' should not be used because of confusion with the GEOS Earth System Model.

Original/historical references

Bey et al. [2001] is the first reference to GEOS-Chem that includes a detailed model description. It is suitable and widely used as an original reference for the model. It only describes a model for gas-phase tropospheric oxidant chemistry. Other original references are:

Park et al. [2004] for aerosol chemistry;
Y.X. Wang et al. [2004] for the nested model;
Henze et al. [2007] for the model adjoint;
Selin et al. [2007] for the mercury simulation;
Trivitiyanurak et al. [2008] for TOMAS aerosol microphysics;
Yu and Luo [2009] for APM aerosol microphysics;
Eastham et al. [2014] for stratospheric chemistry;
Keller et al. [2014] for HEMCO;
Eastham et al. [2018] for the high-performance GEOS-Chem (GCHP);
Long et al. [2015] and Hu et al. [2018] for GEOS-Chem within the GEOS ESM (GEOS-GC);
Lin et al. [2020] for GEOS-Chem within WRF (WRF-GC);
Zhuang et al. [2019, 2020] for the implementations of GEOS-Chem Classic and GCHP on the cloud;
Bindle et al. [2021] for the stretched-grid capability in GCHP;
Murray et al. [2021] for GEOS-Chem driven by GISS GCM fields (GCAP 2.0)

Configurations

GEOS-Chem is a grid-independent model. It operates on 1-D columns with default or user-specified horizontal gridpoints, vertical gridpoints, and timesteps. The GEOS-Chem chemical module updates column concentrations for the effects of emissions, chemistry, aerosol microphysics, and deposition at each time step. This chemical module can be implemented in three different configurations:

GEOS-Chem Classic (sometimes abbreviated GCC). This uses archived GEOS meteorological data on a rectilinear latitude-longitude grid to compute horizontal and vertical transport. Parallelization is through an Open-MP shared-memory architecture and scales efficiently up to about 30 CPUs.
GEOS-Chem High Performance (GCHP). This uses archived GEOS meteorological data on their original cubed-sphere grid to compute horizontal and vertical transport. Parallelization is through an MPI distributed memory architecture and scales efficiently on thousands of CPUs. GCHP is described by Eastham et al. [2018].
GEOS-Chem in on-line applications. This uses the GEOS-Chem chemical module coupled with an independent simulation of atmospheric dynamics from a meteorological model, where the meteorological model handles the transport of chemicals together with that of the dynamical variables. The off-line transport component of GEOS-Chem is either totally disabled or limited to fast vertical transport (convective and boundary layer mixing). In this way GEOS-Chem can serve as an on-line atmospheric chemistry module for Earth System Models. Coupling of GEOS-Chem with the GEOS Earth System Model (GEOS-GC) is described by Hu et al. [2018], and coupling to the Weather Forecasting Model (WRF-GC) is described by Lin et al. [2020] and Feng et al. [2021].

Meteorological fields and grid resolution

GEOS-Chem in off-line mode (Classic or GCHP) is driven by assimilated meteorological data from the Goddard Earth Observation System (GEOS) of the NASA Global Modeling and Assimilation Office (GMAO). The two GEOS data archives used by GEOS-Chem are:

the operational data stream starting in 2012 from the GEOS Forward Processing (GEOS-FP) (native resolution 0.25° x 0.3125°, 72 levels)
the consistent MERRA-2 reanalysis for 1979-present (native resolution 0.5° x 0.625°, 72 levels).

These archives have 3-hour temporal resolution for 3-D fields and 1-hour resolution for 2-D fields. GEOS-Chem simulations can be conducted at the native spatial resolution of the GEOS fields or at coarser resolutions. GEOS-Chem Classic simulations can also be conducted in nested mode (see Nesting below), while GCHP simulations can be conducted in stretched-grid mode [Bindle et al., 2021]. The default timesteps are optimized to balance accuracy and speed as described by Philip et al. [2016].

GEOS-Chem can also use off-line meteorological fields from the GISS GCM for future climates and paleoclimates. These implementations are referred to as the GCAP and ICECAP models. GCAP 2.0 is described by Murray et al. [2021] and is a new development in version 13.1.0

The GEOS-Chem chemical module can be used in on-line applications on any grid of the parent meteorological model:

On-line coupling with the GEOS ESM is described by Hu et al. [2018] and is called GEOS-GC
On-line coupling with the Beijing Climate Center (BCC) climate model is described by Lu et al. [2020] and is called BCC-GC
On-line coupling with the Weather Research Forecast (WRF) model is described by Lin et al. [2020] and Feng et al. [2021] and is called WRF-GC.

Nesting

The nested capability for GEOS-Chem was originally developed by Y. X. Wang et al. [2004]. It allows simulations at the native-grid horizontal resolution of the GEOS data over a user-selected regional domain with dynamic boundary conditions from a coarser global simulation. The nesting can either be 1-way, with no influence from the nested domain on the global domain, or 2-way where the two domains interact with each other. The 2-way nesting capability with multiple nests is described by Yan et al. [2014] and on this wiki page.

The current nested version of GEOS-Chem Classic uses GEOS-FP data with 0.25° x 0.3125° resolution or MERRA-2 data with 0.5° x 0.625° resolution within the nested domain. The capability to operate at 0.25° x 0.3125°resolution with full aerosol-oxidant chemistry was originally developed by Zhang et al. [2015] for East Asia and Kim et al. [2015] for North America. FlexGrid allows users to define any nested domain at runtime, with no pre-processing of meteorological or other data files, requiring only the generation of boundary condition files at the global model resolution (new development in version 12.4.0).

GCHP cannot use nested grids but can instead use the stretched-grid confguration of Bindle et al. [2021] to provide high resolution over regions of interest This is a new development in version 13.0.0.

Transport and deposition

GEOS-Chem Classic uses the TPCORE advection algorithm of Lin and Rood [1996] on the latitude-longitude grid of the archived GEOS meteorological data. GCHP uses the FV3 advection algorithm of Putnam and Lin [2007] on a cubed sphere grid after remapping the archived GEOS meteorological data on that grid. Convective transport in GEOS-Chem is computed from the convective mass fluxes in the meteorological archive as described by Wu et al. [2007]. Boundary layer mixing in GEOS-Chem uses either the non-local scheme implemented by Lin and McElroy [2010] or full mixing up to the GEOS-diagnosed mixing depth.

The wet deposition scheme in GEOS-Chem is described by Liu et al. [2001] for water-soluble aerosols and by Amos et al. [2012] for gases. Henry's law constants are from the compilation by Sander [2015] including for water-soluble organics [Safieddine and Heald, 2017]. Scavenging of aerosol by snow and cold/mixed precipitation is described by Q. Wang et al. [2011, 2014]. Faster scavenging as described by Luo et al. [2019] is an option in the model (new development in version 12.7.0)

Dry deposition is based on the resistance-in-series scheme of Wesely [1989] as implemented by Y. Wang et al. [1998a]. Aerosol deposition is from Zhang et al. [2001]. Aerosol deposition to snow/ice is described by Fisher et al. [2011]. Gravitational settling is from Fairlie et al. [2007] for dust and Alexander et al. [2005] for coarse sea salt. Sea-salt deposition is from Jaegle et al. [2011]. Cold-temperature HNO3 deposition is from Jaegle et al. [2018] (new development in version 12.6.0). There is an option for dependence of stomatal conductance on CO2 levels [Franks et al., 2013] and this is a new development in version 12.6.0. Ozone deposition to the ocean is from Pound et al. [2020] and is a new development in version 12.8.0.

See the mercury section for description of air-sea-land exchange of mercury.

Radiation and photolysis

GEOS-Chem can calculate the radiative forcing from changes in atmospheric composition using the optional RRTMG module. Implementation of RRTMG in GEOS-Chem is described in Heald et al. [2014].

Photolysis frequencies for stratospheric and tropospheric chemistry are calculated with the Fast-JX code of Bian and Prather [2002] as implemented in GEOS-Chem by Mao et al. [2010] for the troposphere and Eastham et al. [2014] for the stratosphere.

The effect of aerosols on photolysis rates is described by Latimer and Martin [2019] (new development in version 12.6.0). There is an option to add absorption of UV by brown carbon [Hammer et al., 2016]. There is an option to add aerosol nitrate photolysis following Kasibhatla et al. [2018] and this is a new development in version 12.6.0.

Emissions

All GEOS-Chem emissions are configured at run-time using the HEMCO 3.0 facility described by Lin et al. [2021]; this is a new development in version 13.1.0. HEMCO allows users to mix and match inventories from the GEOS-Chem library or add their own, apply scaling factors, overlay and mask inventories, etc. without having to edit or compile the code. HEMCO also has extensions to compute emissions with meteorological dependencies and to process other input/output data in GEOS-Chem. Version 3.0 has a number of new features including greater modularity for adaptation to other models and an intermediate grid for more accurate regional masking of emissions.

Emissions of dust aerosol, lightning NOx, biogenic VOCs, soil NOx, and sea salt aerosol are dependent on the local meteorological conditions. These emissions are computed off-line at the native resolution of the GEOS meteorological data and then archived along with the GEOS data as input to GEOS-Chem. In that way, emissions in GEOS-Chem remain the same at any model resolution. Users can also choose to compute emissions on-line rather than using the off-line emission files. Off-line biogenic VOCs, soil NOx and sea salt aerosol emissions are described in Weng et al. [2020]. The default capability for off-line emissions is a new development in version 12.4.0.

Anthropogenic. Global anthropogenic emissions up to 2017 are from the CEDS inventory of McDuffie et al. [2020] and this is a new development in version 13.0.0. EDGAR v4.3.2 [Crippa et al., 2018] with trash emissions from Wiedinmyer et al. [2014] is available as an alternative option to CEDS (trash emissions are already included in CEDS). Ethane emissions from Tzompa-Sosa et al. [2016] and propane emissions from Xiao et al. [2008] overwrite the corrsponding CEDS and EDGAR v4.3.2 emissions in the default model. Diurnal and weekend/weekday vatiations of emissions are from van Donkelaar et al. [2008]. Diurnal variation of Chinese power plant emissions is from X. Liu et al. [2019] and this is a new development in version 13.1. Vertical allocation of emissions by sector follows Hemispheric CMAQ [US EPA, 2019] and this is a new development in version 13.1.0.

Future anthropogenic emissions following the RCP scenarios have been implemented into GEOS-Chem by Holmes et al. [2013].

Aircraft. Aircraft emissions are from the AEIC inventory [Stettler et al., 2011].

Ships. Global shipping emissions are from CEDS. Shipping emissions of NOx are processed by the PARANOX module of Vinken et al. [2012] to account for ozone and HNO3 production in the plume. The PARANOX module was updated by Holmes et al. [2014].

Open Fires. Emissions from open fires for individual years are from the GFED4.1s inventory with options to use instead the FINNv1.5 inventory [Wiedinmyer et al., 2011], the QFED inventory, or the GFAS inventory. BB4CMIP historical fire emissions for 1750-2014 are from van Merle et al. [2017] and this is a new development in version 12.6.0.

Lightning. Lightning NOx emissions are as described by Murray et al. [2012] to match OTD/LIS climatological observations of lightning flashes. The climatology has been updated to 2019 and this is a new development in version 12.9.0.

Biogenic VOCs. Biogenic VOC emissions in GEOS-Chem are from the MEGAN v2.1 inventory of Guenther et al. [2012] as implemented by Hu et al. [2015b]. Leaf area indices (LAIs) used in MEGAN v2.1 are from the Yuan et al. [2011] MODIS product for 2005-2016. Dependence on CO2 was added by Tai et al. [2013]. Acetaldehyde emissions are from Millet et al. (2010). Biogenic non-agricultural ammonia sources are from GEIA.

Soils. Biogenic soil NOx emissions are from Hudman et al. [2012].

Ocean. Marine emissions of DMS are from the Lana et al. [2011] dataset as implemented in GEOS-Chem by Breider et al. [2017]. Air-sea exchange of acetone assumes fixed ocean concentrations as described by Fischer et al. [2012]. Ocean acetaldehyde emissions are from Millet et al. (2010). Ammonia emissions from Arctic seabirds are from Croft et al. [2016]. Ocean ammonia emissions are from GEIA [Bouwman et al., 1997].

Volcanoes. Eruptive and non-eruptive volcanic SO2 emissions for individual years are from the AEROCOM data base. Update to 2019 is a new development in version 12.5.0.

Other. See the carbon gases section for GEOS-Chem references on emissions of CO2 and methane. See the aerosols section for GEOS-Chem references on primary aerosol emissions. See the mercury section for GEOS-Chem references on emissions of mercury. See the POPs section for GEOS-Chem references on emissions of persistent organic pollutants (POPs).

Chemistry

GEOS-Chem simulates detailed oxidant-aerosol chemistry in the troposphere and stratosphere. The chemical solver is KPP [Damian et al., 2002] as implemented in GEOS-Chem with the FlexChem interface.

Chemical kinetics

Chemical mechanism kinetics generally follow JPL/IUPAC recommendations but go beyond the recommendations for specific aspects of the mechanism including for:

PAN [Fischer et al., 2014]
Isoprene [Bates and Jacob, 2019]; this is a new development in version 12.8.0. Older versions use isoprene chemistry from Travis et al. [2016] and Fisher et al. [2016].
Methanol [Chen et al., 2019]; this is a new development in version 12.7.0
Methyl, ethyl, and propyl nitrates [Fisher et al., 2018]; this is a new development in version 12.7.0
Halogens [Wang et al., 2019]; this is a new development in version 12.9.0. Older versions used a scheme based on Sherwen et al. [2016] and Chen et al. [2017]
Criegees [Millet et al., 2015].

See the radiation section for the calculation of photolysis frequencies. Methane is prescribed as a surface boundary condition from monthly mean maps of spatially-interpolated NOAA flask data, and subsequently allowed to advect and react [Murray et al., 2016]. Water is specified from the driving meteorological fields in the troposphere but is transported as a reactive tracer in the stratosphere.

Reactive uptake of NO2, NO3, and N2O5 by aerosols is as described by Holmes et al. [2019], with reactive uptake coefficients for N2O5 on sulfate-nitrate-ammonium-organic aerosol from McDuffie et al. [2018ab]. This is a new development in version 12.6.0. HO2 uptake is from Mao et al. [2013] with a reactive uptake coefficient of 0.2 for conversion to H2O. Acid uptake by dust particles from Fairlie et al. [2010] is provided as an option. Aerosol hygroscopicity for calculating surface areas is from Latimer and Martin [2019] and this is a new development in version 12.6.0. Cloudwater pH is calculated following Shah et al. [2020] and this is a new development in version 12.9.0.

Reactive uptake of nitrogen oxides by clouds accounts for entrainment in the subgrid cloudy fraction of gridboxes and this is a new development in version 12.6.0. The same treatment is also applied for halogen reactive uptake by clouds starting with version 12.9.0.

GEOS-Chem simulations can be configured to have full chemistry only in the troposphere (“troposphere-only simulation”) with simple linear representation of stratospheric chemistry following the Linoz algorithm of McLinden et al. [2000] for ozone and 2-D (latitude-altitude) monthly mean sources and loss rate constants for other gases [Murray et al., 2012].

Aerosol processes

Sulfate-nitrate-ammonium aerosol. The original SNA aerosol simulation in GEOS-Chem coupled to gas-phase chemistry was developed by Park et al. [2004]. SNA thermodynamics are computed with the ISORROPIA thermodynamic module [Fontoukis and Nenes, 2007], most recently updated to version 2.2.

Carbonaceous aerosol. Q. Wang et al. [2014] describes the current BC simulation in GEOS-Chem. Organic aerosol in the default model follows the simple, irreversible, direct yield scheme of Pai et al. [2020]. Complex SOA can be used as an option following the simplified Volatility Basis Set (VBS) scheme of Pye et al. [2010] and the aqueous-phase isoprene SOA scheme of Marais et al. [2016] coupled to the isoprene gas-phase chemistry mechanism.

Dust aerosol. The dust simulation in GEOS-Chem is described by Fairlie et al. [2007]. Dust size distributions are from Li Zhang et al. [2013]. Fine anthropogenic dust from combustion and industrial sources is from the AFCID inventory of Philip et al. [2017].

Sea salt. The sea salt aerosol simulation in GEOS-Chem is described by Jaegle et al. [2011].

Marine POA. There is an option to emit marine POA following Gantt et al. [2015].

Aerosol microphysics. Two alternate simulations of aerosol microphysics are implemented in GEOS-Chem: the TOMAS simulation [Kodros and Pierce, 2017] and the APM simulation [Yu and Luo, 2009]. TOMAS has new developments in version 12.3.0. APM has new developments in version 12.6.0

Aerosol optical depth. Aerosol optical depth affecting photolysis rates is calculated in GEOS-Chem using RH-dependent aerosol optical properties from Latimer and Martin [2019]. Dust optics are from Ridley et al. [2012]. These calculations can be performed at user-specified wavelengths from 230 nm to 56 um when using RRTMG (see the radiation section).

Aerosol-only simulation. In addition to the fully coupled gas-aerosol simulation described in the Tropospheric Chemistry section, there is an option to conduct aerosol-only simulations using fixed 3-D monthly oxidant concentrations (from a GEOS-Chem simulation of old vintage) and simple SOA. This is described by Leibensperger et al. [2012].

Carbon gases

CO2. The current form of the simulation is described by Nassar et al. [2010]. Anthropogenic emissions are from ODIAC2019 [Oda and Maksyutov, 2011; Oda et al., 2018] and this is a new development in version 13.0.0.

Methane. The current form of the simulation is described by Maasakkers et al. [2019]. Updated soil uptake from the MeMo model v1.0 [Murguia-Flores et al., 2018] is a new development in version 12.7.0. Updated emission from fuel exploitation is from Scarpelli et al. [2020a] and is a new development in version 13.0.0. Anthropogenic emissions from Mexico are from Scarpelli et al. [2020b] and this is a new development in version 13.1.

CO. Simulation of CO in GEOS-Chem can be conducted either as part of the standard full-chemistry simulation or as a separate tagged-tracer simulation that resolves CO sources from individual regions or processes, and uses archived OH fields from a full-chemistry simulation to compute the CO sink. The most recent version is described by Fisher et al. [2017].

Mercury

The original GEOS-Chem coupled atmosphere-ocean simulation of mercury was described by Selin et al. [2007] for the atmosphere and by Strode et al. [2007] for the ocean. Extension to a coupled atmosphere-ocean-land model was described by Selin et al. [2008]. The current version of the atmospheric simulation is described by Horowitz et al. [2017], and the current version of the ocean simulation is described by Soerensen et al. [2010], with updated ocean rate coefficients from Song et al. [2015]. Treatment of Arctic sea ice and rivers is as described by Fisher et al. [2012, 2013]. Gas-aerosol partitioning of Hg(II) is from Amos et al. [2012].There is an option to couple GEOS-Chem with the terrestrial mercury module developed by Smith-Downey et al. [2010].

Anthropogenic emissions are from Y. Zhang et al. [2016]. Future SRES emission scenarios have been implemented by Corbitt et al. [2011]. Options are available to use anthropogenic emissions from Streets et al. [2019] or from EDGAR v4.2 [Muntean et al., 2018], and these are new developments in version 13.0.0.

Persistent Organic Pollutants (POPs)

The model includes a simulation of PAHs as described by Friedman et al. [2014].

Model diagnostics

The model offers detailed output diagnostics in NetCDF format including species concentrations, production and loss rates, family production and loss rates, emissions, deposition fluxes and velocities, budgets and fluxes, time series at fixed locations or along selected aircraft flight tracks and satellite orbits, etc. See the GEOS-Chem wiki diagnostics page for more information. The NOAA Obspack diagnostic is available for comparison of model output to compiled global suborbital observations of greenhouse gases.

Surface ozone and HNO3 concentrations can be diagnosed below the lowest model gridpoint to take into account aerodynamic resistance to deposition [Travis and Jacob, 2019] (new development in version 12.6.0).

PM2.5 mass concentration is diagnosed in the model as described here.

Model Adjoint

See the GEOS-Chem adjoint wiki page for description and references.

References

Alexander, B., R.J. Park, D.J. Jacob, Q.B. Li, R.M. Yantosca, J. Savarino, C.C.W. Lee, and M.H. Thiemens, Sulfate formation in sea-salt aerosols: Constraints from oxygen isotopes, J. Geophys. Res., 110, D10307, 2005.
Amos, H. M., D. J. Jacob, C. D. Holmes, J. A. Fisher, Q. Wang, R. M. Yantosca, E. S. Corbitt, E. Galarneau, A. P. Rutter, M. S. Gustin, A. Steffen, J. J. Schauer, J. A. Graydon, V. L. St. Louis, R. W. Talbot, E. S. Edgerton, Y. Zhang, and E. M. Sunderland, Gas-Particle Partitioning of Atmopsheric Hg(II) and Its Effect on Global Mercury Deposition, Atmos. Chem. Phys., 12, 591-603, 2012.
Bates, K.H., and D.J. Jacob, A new model mechanism for atmospheric oxidation of isoprene: global effects on oxidants, nitrogen oxides, organic products, and secondary organic aerosol, Atmos. Chem. Phys., 19, 9613-9640, 2019.
Bey, I., D. J. Jacob, R. M. Yantosca, J. A. Logan, B. Field, A. M. Fiore, Q. Li, H. Liu, L. J. Mickley, and M. Schultz, Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res., 106, 23,073-23,096, 2001.
Bian, H. S., and M. J. Prather, Fast-J2: Accurate simulation of stratospheric photolysis in global chemical models, J. Atmos. Chem., 41, 281-296, 2002.
Bindle, L., R.V. Martin, M.J. Cooper, E.W. Lundgren, S.D. Eastham, B.M. Auer, T.L. Clune, H. Weng, J. Lin, L.T. Murray, J. Meng, C.A. Keller, S. Pawson, and D.J. Jacob, Grid-stretching capability for the GEOS-Chem 13.0.0 atmospheric chemistry model , Geophys. Model Dev. Discuss, [preprint], https://doi.org/10.5194/gmd-2020-398, in review, 2021.
Bouwman, A. F., D. S. Lee, W. A. H. Asman, F. J. Dentener, K. W. Van Der Hoek, and J. G. J. Olivier (1997), A global high-resolution emission inventory for ammonia, Global Biogeochem. Cycles, 11(4), 561-587.
Breider, T.J., L.J. Mickley, D.J. Jacob, C. Ge, J. Wang, M.P. Sulprizio, B. Croft, D.A. Ridley, J.R. McConnell, S. Sharma, L. Husain, V.A. Dutkiewicz, K. Eleftheriadis, H. Skov, and P.K. Hopke, Multi-decadal trends in aerosol radiative forcing over the Arctic: contribution of changes in anthropogenic aerosol to Arctic warming since 1980, J. Geophys. Res., 122(6), 3573-3594, doi:10.1002/2016JD025321, 2017.
Chen, Q., J.A. Schmidt, V. Shah, L. Jaegle, T. Sherwen, and B. Alexander, Sulfate production by reactive bromine: Implications for the global sulfur and reactive bromine budgets, Geophys. Res. Lett., 44, 7069-7078, 2017.
Chen, X., Millet, D. B., Singh, H. B., Wisthaler, A., Apel, E. C., Atlas, E. L., Blake, D. R., Bourgeois, I., Brown, S. S., Crounse, J. D., de Gouw, J. A., Flocke, F. M., Fried, A., Heikes, B. G., Hornbrook, R. S., Mikoviny, T., Min, K.-E., Müller, M., Neuman, J. A., O'Sullivan, D. W., Peischl, J., Pfister, G. G., Richter, D., Roberts, J. M., Ryerson, T. B., Shertz, S. R., Thompson, C. R., Treadaway, V., Veres, P. R., Walega, J., Warneke, C., Washenfelder, R. A., Weibring, P., and Yuan, B., On the sources and sinks of atmospheric VOCs: an integrated analysis of recent aircraft campaigns over North America, Atmos. Chem. Phys., 19, 9097-9123, https://doi.org/10.5194/acp-19-9097-2019, 2019.
Corbitt, E.S., D.J. Jacob, C.D. Holmes, D.G. Streets, and E.M. Sunderland, Global mercury source-receptor relationships for mercury deposition under present-day and 2050 emissions scenarios, Environ. Sci. Technol., 45, 10477-10484, 2011.
Crippa, M., et al., Gridded emissions of air pollutants for the period 1970-2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987-2013, doi:10.5194/essd-10-1987-2018, 2018.
Croft, B., G. R. Wentworth, R. V. Martin, W. R. Leaitch, J. G. Murphy, B. N. Murphy, J. K. Kodros, J. P. D. Abbatt and J. R. Pierce, Contribution of Arctic seabird-colony ammonia to atmospheric particles and cloud-albedo radiative effect, Nat. Commun., 7:13444, doi:10.1038/ncomms13444, 2016.
Damian, V., A. Sandu, M. Damian, F. Potra, and G.R. Carmichael, The Kinetic PreProcessor KPP-A software environment for solving chemical kinetics, Computers and Chem. Engr., 26(11), 1567-1579, 2002.
Eastham, S.D., Weisenstein, D.K., Barrett, S.R.H., Development and evaluation of the unified tropospheric-stratospheric chemistry extension (UCX) for the global chemistry-transport model GEOS-Chem, Atmos. Env., 89, 2014.
Eastham, S.D., M.S. Long, C.A. Keller, E. Lundgren, R.M. Yantosca, J. Zhuang, C. Li, C.J. Lee, M. Yannetti, B.M. Auer, T.L. Clune, J. Kouatchou, W.M. Putman, M.A. Thompson, A.L. Trayanov, A.M. Molod, R.V. Martin, and D.J. Jacob, GEOS-Chem High Performance (GCHP): A next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications , Geosci. Mod. Dev., 11, 2941-2953, 2018.
Fairlie, T.D., D.J. Jacob, and R.J. Park, The impact of transpacific transport of mineral dust in the United States, Atmos. Environ., 1251-1266, 2007.
Fairlie, T.D., D.J. Jacob, J.E. Dibb, B. Alexander, M.A. Avery, A. van Donkelaar, and L. Zhang, Impact of mineral dust on nitrate, sulfate, and ozone in transpacific Asian pollution plumes, Atmos. Chem. Phys., 10, 3999-4012, doi:10.5194/acp-10-3999-2010, 2010.
Feng, X., Lin, H., Fu, T.-M., Sulprizio, M. P., Zhuang, J., Jacob, D. J., Tian, H., Ma, Y., Zhang, L., Wang, X., and Chen, Q.: WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry-meteorology interactions, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-441, in review, 2021.
Fischer, E.V., D.J. Jacob, D.B. Millet, R.M. Yantosca, and J. Mao, The role of the ocean in the global atmospheric budget of acetone, Geophys. Res. Lett., 39, L01807, 2012.
Fischer, E.V., D.J. Jacob, R.M. Yantosca, M.P. Sulprizio, D.B. Millet, J. Mao, F. Paulot, H.B. Singh, A.-E. Roiger, L. Ries, R.W. Talbot, K. Dzepina, and S. Pandey Deolal, Atmospheric peroxyacetylnitrate (PAN): a global budget and source attribution, Atmos. Chem. Phys., 14, 2679-2698, 2014.
Fisher, J.A., D.J. Jacob, Q. Wang, R. Bahreini, C.C. Carouge, M.J. Cubison, J.E. Dibb, T. Diehl, J.L. Jimenez, E.M. Leibensperger, M.B.J. Meinders, H.O.T. Pye, P.K. Quinn, S. Sharma, A. van Donkelaar, and R.M. Yantosca, Sources, distribution, and acidity of sulfate-ammonium aerosol in the Arctic in winter-spring, Atmos. Environ., 45, 7301-7318, 2011.
Fisher, J.A., D.J. Jacob, A.L. Soerensen, H.M. Amos, A. Steffen, and E.M. Sunderland, Riverine source of Arctic Ocean mercury inferred from atmospheric observations, Nature Geoscience, 5, 499-504, 2012.
Fisher, J.A., D.J. Jacob, A.L. Soerensen, H.M. Amos, E.S. Corbitt, D.G. Streets, Q. Wang, R.M. Yantosca, and E.M. Sunderland, Factors driving mercury variability in the Arctic atmosphere and ocean over the past thirty years, Global Biogeochem. Cycles, 27, 1226-1235, 2013.
Fisher, J.A., D.J. Jacob, K.R. Travis, P.S. Kim, E.A. Marais, C. Chan Miller, K. Yu, L. Zhu, R.M. Yantosca, M.P. Sulprizio, J. Mao, P.O. Wennberg, J.D. Crounse, A.P. Teng, T.B. Nguyen, J.M. St. Clair, R.C. Cohen, P. Romer, B.A. Nault, P.J. Wooldridge, J.L. Jimenez, P. Campuzano-Jost, D.A. Day, P.B. Shepson, F. Xiong, D.R. Blake, A.H. Goldstein, P.K. Misztal, T.F. Hanisco, G.M. Wolfe, T.B. Ryerson, A. Wisthaler, and T. Mikoviny. Organic nitrate chemistry and its implications for nitrogen budgets in an isoprene- and monoterpene-rich atmosphere: constraints from aircraft (SEAC4RS) and ground-based (SOAS) observations in the Southeast US. Atmos. Chem. Phys., 16, 2961-2990, 2016.
Fisher, J.A., L.T. Murray, D.B.A. Jones, and N.M. Deutscher, Improved method for linear carbon monoxide simulation and source attribution in atmospheric chemistry models illustrated using GEOS-Chem v9, Geosci. Model Dev., 10, 4129-4144, 2017.
Fisher, J.A., E.L. Atlas, B. Barletta, S. Meinardi, D.R. Blake, C.R. Thompson, T.B. Ryerson, J. Peischl, Z.A. Tzompa-Sosa, and L.T. Murray, Methyl, Ethyl, and Propyl Nitrates: Global Distribution and Impacts on Reactive Nitrogen in Remote Marine Environments, J. Geophys. Res., 123, 12,429-12,451, 2018.
Fountoukis, C., and A. Nenes, ISORROPIA II: A computationally efficient thermodynamic equilibrium model for K+-Ca2+-Mg2+-NH4+-Na+-SO42-NO3--Cl-H2O aerosols, Atmos. Chem. Phys., 7(17), 4639-4659, 2007.
Franks, P.J., et al., Sensitivity of plants to changing atmospheric CO2 concentration: from the geological past to the next century, New Phytologist, 197.4, 1077-1094, 2013.
Friedman, C.L., Y. Zhang, and N.E. Selin, Climate change and emissions impacts on atmospheric PAH transport to the Arctic, Environ. Sci. Technol., 48 (1), 429-437, 2014
Guenther, A.B., Jiang, X., Heald, C.L., Sakulyanontvittaya, T., Duhl, T., Emmons, L.K., and Wang, X., The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471-1492, doi:10.5194/gmd-5-1471-2012, 2012.
Hammer M.S., R.V. Martin, A van Donkelaar, V. Buchard, O. Torres, D.A. Ridley, and R.J.D. Spurr, Interpreting the ultraviolet aerosol index observed with the OMI satellite instrument to understand absorption by organic aerosols: Implications for atmospheric oxidation and direct radiative effects, Atmos. Chem. Phys., 16, 2507-2523, doi:10.5194/acp-16-2507-2016, 2016.
Heald, C.L., D.A. Ridley, J.H. Kroll, S.R.H. Barrett, K.E. Cady-Pereira, M.J. Alvarado, C.D. Holmes, Beyond Direct Radiative Forcing: The Case for Characterizing the Direct Radiative Effect of Aerosols, Atmos. Chem. Phys., 14, 5513-5527, doi:10.5194/acp-14-5513-2014, 2014.
Henze, D. K., A. Hakami, and J. H. Seinfeld, Development of the adjoint of GEOS-Chem, Atmos. Chem. Phys., 7, 2413-2433, 2007. . Beyond Direct Radiative Forcing: The Case for Characterizing the Direct Radiative Effect of Aerosols, Atmos. Chem. Phys., 14, 5513-5527, doi:10.5194/acp-14-5513-2014, 2014.
Holmes, C.D., M. J. Prather, O.A. Søvde, and G. Myhre, Future methane, hydroxyl, and their uncertainties: key climate and emission parameters for future predictions, Atmos. Chem. Phys., 13, 285-302, doi:10.5194/acp-13-285-2013, 2013.
Holmes, C. D., Prather, M. J., and Vinken, G. C. M., The climate impact of ship NOx emissions: an improved estimate accounting for plume chemistry, Atmos. Chem. Phys., 14, 6801-6812, doi:10.5194/acp-14-6801-2014, 2014.
Holmes, C. D., Bertram, T. H., Confer, K. L., Graham, K. A., Ronan, A. C., Wirks, C. K., & Shah, V., The role of clouds in the tropospheric NOx cycle: A new modeling approach for cloud chemistry and its global implications, Geophysical Research Letters, 46(9), 4980-4990. https://doi.org/10.1029/2019GL081990, 2019.
Horowitz, H.M., D.J. Jacob, Y. Zhang, T.S. Dibble, F. Slemr, H.M. Amos, J.A. Schmidt, E.S. Corbitt, E.A. Marais, and E.M. Sunderland, A new mechanism for atmospheric mercury redox chemistry: implications for the global mercury budget, Atmos. Chem. Phys., 17, 6353-6371, 2017.
Hu, L., D.B. Millet, M. Baasandorj, T.J. Griffis, P. Turner, D. Helmig, A.J. Curtis, and J. Hueber, Isoprene emissions and impacts over an ecological transition region in the US Upper Midwest inferred from tall tower measurements, J. Geophys. Res., 120, 3553-3571, doi: 10.1002/2014JD022732, 2015.
Hu, L., C. A. Keller, M. S. Long, T. Sherwen, B. Auer, A. Da Silva, J. E. Nielsen, S. Pawson, M. A. Thompson, A. L. Trayanov, K. R. Travis, S. K. Grange, M. J. Evans, and D. J. Jacob, Global simulation of tropospheric chemistry at 12.5 km resolution: performance and evaluation of the GEOS-Chem chemical module (v10-1) within the NASA GEOS Earth System Model (GEOS-5 ESM), Geosci. Model Dev., 11, 4603-4620, 2018
Hudman, R.C., N.E. Moore, R.V. Martin, A.R. Russell, A.K. Mebust, L.C. Valin, and R.C. Cohen, A mechanistic model of global soil nitric oxide emissions: implementation and space based-constraints, Atm. Chem. Phys., 12, 7779-7795, doi:10.5194/acp-12-7779-2012, 2012.
Jaegle, L., P.K. Quinn, T. Bates, B. Alexander, and J.-T. Lin, Global distribution of sea salt aerosols: New constraints from in situ and remote sensing observations, Atmos. Chem. Phys., 11, 3137-3157, doi:10.5194/acp-11-3137-2011, 2011.
Jaegle, L., Shah, V.,et al., Nitrogen oxides emissions, chemistry, deposition, and export over the Northeast United States during the WINTER aircraft campaign, J. Geophys. Res.: Atmospheres, 123, https://doi.org/10.1029/2018JD029133, 2018.
Kasibhatla, P., Sherwen, T., Evans, M. J., Carpenter, L. J., Alexander, B., Chen, Q., Sulprizio, M. P., Lee, J. D., Read, K. A., Bloss, W., Crilley, L. R., Keene, W. C., Pszenny, A. A. P., and Hodzic, A., Global impact of nitrate photolysis in sea-salt aerosol on NOx, OH, and O4 in the marine boundary layer, Atmos. Chem. Phys., 18, 11185-11203, https://doi.org/10.5194/acp-18-11185-2018, 2018.
Keller, C.A., M.S. Long, R.M. Yantosca, A.M. Da Silva, S. Pawson, and D.J. Jacob, HEMCO v1.0: A versatile, ESMF-compliant component for calculating emissions in atmospheric models, Geosci. Model Devel., 7, 1409-1417, 2014.
Kim, P.S., D.J. Jacob, J.A. Fisher, K. Travis, K. Yu, L. Zhu, R.M. Yantosca, M.P. Sulprizio, J.L. Jimenez, P. Campuzano-Jost, K.D. Froyd, J. Liao, J.W. Hair, M.A. Fenn, C.F. Butler, N.L. Wagner, T.D. Gordon, A. Welti, P.O. Wennberg, J.D. Crounse, J.M. St. Clair, A.P. Teng, D.B. Millet, J.P. Schwarz, M.Z. Markovic, and A.E. Perring, Sources, seasonality, and trends of Southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem model, Atmos. Chem. Phys., 15, 10,411-10,433, 2015.
Kodros, J. K., Pierce, J. R.: Important global and regional differences in cloud-albedo aerosol indirect effect estimates between simulations with and without prognostic aerosol microphysics, J. Geophys. Res., 122, doi:10.1002/2016JD025886, 2017.
Lana, A., Bell, T. G., Simó, R., Vallina, S. M., Ballabrera-Poy, J., Kettle, A. J., Dachs, J., Bopp, L., Saltzman, E. S., Stefels, J., Johnson, J. E., and Liss, P. S.: An updated climatology of surface dimethlysulfide concentrations and emission fluxes in the global ocean, Global Biogeochemical Cycles, 25, doi:10.1029/2010gb003850, 2011.
Latimer, R.N.C., and R.V. Martin, Interpretation of measured aerosol mass scattering efficiency over North America using a chemical transport model, Atmos. Chem. Phys., 19, 2635-2653, 2019.
Leibensperger, E.M., L.J. Mickley, D.J. Jacob, W.-T. Chen, J.H. Seinfeld, A. Nenes, P.J. Adams, D.G. Streets, N. Kumar, D. Rind, Climatic effects of 1950-2050 changes in US anthropogenic aerosols - Part 1: Aerosol trends and radiative forcing, Atmos. Chem. Phys., 12, 3,333-3,348, 2012.
Li, M., Zhang, Q., Streets, D.G., He, K.B., Cheng, Y.F., Emmons, L.K., Huo, H., Kang, S.C., Lu, Z., Shao, M., Su, H., Yu, X., and Zhang, Y., Mapping Asian anthropogenic emissions of non-methane volatile organic compounds to multiple chemical mechanisms, Atmos. Chem. Phys., 14, 5617-5638, doi:10.5194/acp-14-5617-2014, 2014.
Lin, H., Xu Feng, Tzung-May Fu, Heng Tian, Yaping Ma, Lijuan Zhang, Daniel J. Jacob, Robert M. Yantosca, Melissa P. Sulprizio, Elizabeth W. Lundgren, Jiawei Zhuang, Qiang Zhang, Xiao Lu, Lin Zhang, Lu Shen, Jianping Guo, Sebastian D. Eastham, and Christoph A. Keller, WRF-GC: online coupling of WRF and GEOS-Chem for regional atmospheric chemistry modeling, Part 1: description of the one-way model (v1.0), Geosci. Model Dev., 13, 3241-3265, 2020.

Lin, H., D.J. Jacob, E.W. Lundgren, M.P. Sulprizio, C.A. Keller, T.M. Fritz, S.D. Eastham, L.K. Emmons, P.C. Campbell, B. Baker, R.D. Saylor, and R. Montuoro, Harmonized Emissions Component (HEMCO) 3.0 as a versatile emissions component for atmospheric models: application in the GEOS-Chem, NASA GEOS, WRF-GC, CESM2, NOAA GEFS-Aerosol, and NOAA UFS models , Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-130, in review, 2021.

Lin, J.-T., and M. McElroy, Impacts of boundary layer mixing on pollutant vertical profiles in the lower troposphere: Implications to satellite remote sensing, Atmospheric Environment, 44(14), 1726-1739, doi:10.1016/j.atmosenv.2010.02.009, 2010.
Lin, S.-J., and R.B. Rood, 1996: Multidimensional flux form semi-Lagrangian transport schemes, Mon. Wea. Rev., 124, 2046-2070.
Liu, H., D.J. Jacob, I. Bey, and R.M. Yantosca, Constraints from 210Pb and 7Be on wet deposition and transporting a global threee-dimensional chemical tracer model driven by asimilated meteorological fields, J. Geophys. Res., 106, 12,109-12,128, 2001.
Long, M.S., R. Yantosca, J.E. Nielsen, C.A. Keller, A. da Silva, M.P. Sulprizio., S. Pawson, D.J. Jacob, Development of a grid-independent GEOS-Chem chemical transport model (v9-02) as an atmospheric chemistry module for Earth System Models, Geosci. Model. Dev., 8, 595-602, 2015.
Xiao Liu, Xing Gao, Xinbin Wu, Weilin Yu, Lulu Chen, Ruijing Ni, Yu Zhao, Hongwei Duan, Fuming Zhao, Lilin Chen, Shengming Gao, Ke Xu, Jintai Lin, and Anthony Y. Ku, Updated Hourly Emissions Factors for Chinese Power Plants Showing the Impact of Widespread Ultralow Emissions Technology Deployment, Environ. Sci. Technol., 53, 2570-2578, 2019.
Xiao Lu, Lin Zhang, Tongwen Wu, Michael S. Long, Jun Wang, Daniel J. Jacob, Fang Zhang, Jie Zhang, Sebastian D. Eastham, Lu Hu, Lei Zhu, Xiong Liu, and Min Wei, Development of the global atmospheric general circulation-chemistry model BCC-GEOS-Chem v1.0: model description and evaluation, Geosci. Model Dev., 13, 3817–3838, 2020.
G. Luo, F. Yu, J. Schwab, Revised treatment of wet scavenging processes dramatically improves GEOS-Chem 12.0.0 simulations of nitric acid, nitrate, and ammonium over the United States, Geosci. Model Dev., 12, 3439-3447, 2019.
Maasakkers, J.D., D.J. Jacob, M.P. Sulprizio, T. Scarpelli, H. Nesser, J.-X. Sheng, Y. Zhang, M. Hersher, A.A. Bloom, K.W. Bowman, J.R. Worden, G. Janssens-Maenhout, and R.J. Parker, Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010-2015, Atmos. Chem. Phys., 19, 7859-7881, 2019.
Mao, J., D.J. Jacob, M.J. Evans, J.R. Olson, X. Ren, W.H. Brune, J.M. St. Clair, J.D. Crounse, K.M. Spencer, M.R. Beaver, P.O. Wennberg, M.J. Cubison, J.L. Jimenez, A. Fried, P. Weibring, J.G. Walega, S.R. Hall, A.J. Weinheimer, R.C. Cohen, G. Chen, J.H. Crawford, L. Jaeglé, J.A. Fisher, R.M. Yantosca, P. Le Sager, and C. Carouge, Chemistry of hydrogen oxide radicals (HOx) in the Arctic troposphere in spring, Atmos. Chem. Phys., 10, 5823-5838, 2010.
Mao, J., S. Fan, D.J. Jacob, K.R. Travis, Radical loss in the atmosphere from Cu-Fe redox coupling in aerosols, Atmos. Chem. Phys, 13,509-519, 2013.
Marais, E. and C. Wiedinmyer, Air quality impact of Diffuse and Inefficient Combustion Emissions in Africa (DICE-Africa), Environ. Sci. Technol., 50(19), 10739-10745, doi:10.1021/acs.est.6b02602, 2016.
Marais, E. A., D. J. Jacob, J. L. Jimenez, P. Campuzano-Jost, D. A. Day, W. Hu, J. Krechmer, L. Zhu, P. S. Kim, C. C. Miller, J. A. Fisher, K. Travis, K. Yu, T. F. Hanisco, G. M. Wolfe, H. L. Arkinson, H. O. T. Pye, K. D. Froyd, J. Liao, V. F. McNeill, Aqueous-phase mechanism for secondary organic aerosol formation from isoprene: application to the southeast United States and co-benefit of SO2 emission controls, Atmos. Chem. Phys., 16, 1603-1618, 2016.
McDuffie, E. E., Fibiger, D. L., Dubé, W. P., Lopez-Hilfiker, F., Lee, B. H., Jaeglé, L., et al., ClNO2 yields from aircraft measurements during the 2015 WINTER campaign and critical evaluation of the current parameterization,J. Geophys. Res., 123(22), 12,994-13,015. https://doi.org/10.1029/2018JD029358, 2018a.
McDuffie, E. E., Fibiger, D. L., Dubé, W. P., Lopez-Hilfiker, F., Lee, B. H., Thornton, J. A., et al., Heterogeneous N2O5 Uptake During Winter: Aircraft Measurements During the 2015 WINTER Campaign and Critical Evaluation of Current Parameterizations, J. Geophys. Res., 123(8), 4345–4372. https://doi.org/10.1002/2018JD028336, 2018b
McDuffie, E. E., S. J. Smith, P. O'Rourke, K. Tibrewal, C. Venkataraman, E. A. Marais, B. Zheng, M. Crippa, M. Brauer, R. V. Martin, A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel- specific sources (1970- 2017): An application of the Community Emissions Data System (CEDS), Earth System Science Data, 12, 3413-3442, 2020.
McLinden, S.A., et al., Stratospheric ozone in 3-D models: a simple chemistry and the cross-tropopause flux, J. Geophys. Res., 105, 14653-14665, 2000.
Millet, D.B., et al., Global atmospheric budget of acetaldehyde: 3D model analysis and constraints from in-situ and satellite observations, Atmos. Chem. Phys., 10, 3405-3425, 2010.
Millet, D.B., M. Baasandorj, D.K. Farmer, J.A. Thornton, K. Baumann, P. Brophy, S. Chaliyakunnel, J.A. de Gouw, M. Graus, L. Hu, A. Koss, B.H. Lee, F.D. Lopez-Hilfiker, J.A. Neuman, F. Paulot, J. Peischl, I.B. Pollack, T.B. Ryerson, C. Warneke, B.J. Williams, and J. Xu, A large and ubiquitous source of atmospheric formic acid, Atmos. Chem. Phys., 15, 6283-6304, doi:10.5194/acp-15-6283-2015, 2015.
Muntean M, Janssens-Maenhout G, Song S, Giang A, Selin NE, Zhong H, Zhao Y, Olivier JG, Guizzardi D, Crippa M, Schaaf E. Evaluating EDGARv4.tox2 speciated mercury emissions ex-post scenarios and their impacts on modelled global and regional wet deposition patterns. Atmospheric Environment. 2018; 184:56-68.
Murguia-Flores, F., Arndt, S., Ganesan, A. L., Murray-Tortarolo, G., and Hornibrook, E. R. C., Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil, Geosci. Model Dev., 11, 2009-2032, https://doi.org/10.5194/gmd-11-2009-2018, 2018.
Murray, L.T., D.J. Jacob, J.A. Logan, R.C. Hudman, and W.J. Koshak, Optimized regional and interannual variability of lightning in a global chemical transport model constrained by LIS/OTD satellite data, , J. Geophus. Res., 117, D20307, 2012.
Murray, L. T., Lightning NOx and Impacts on Air Quality, Curr. Poll. Rep., 2(2), 115-133, 2016.
Murray, L. T., Leibensperger, E. M., Orbe, C., Mickley, L. J., and Sulprizio, M.: GCAP 2.0: A global 3-D chemical-transport model framework for past, present, and future climate scenarios, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-144, in review, 2021.
Nassar, R, D.B.A. Jones, P. Suntharalingam, J.M. Chen, R. J. Andres, K.J. Wecht, R.M. Yantosca, S.S. Kulawik, K.W. Bowman, J.R. Worden, T. Machida, and H. Matsueda, Modeling global atmospheric CO2 with improved emission inventories and CO2 production from the oxidation of other carbon species, GeoSci. Model Develop., 3, 689-716, 2010.
Oda, T. and Maksyutov, S.: A very high-resolution (1 km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights, Atmos. Chem. Phys., 11, 543-556, doi:10.5194/acp-11-543-2011, 2011.
Oda, T., Maksyutov, S., and Andres, R. J.: The Open-source Data Inventory for Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil fuel CO2 gridded emissions data product for tracer transport simulations and surface flux inversions, Earth Syst. Sci. Data, doi:10.5194/essd-10-87-2018, 2018.
Pai, S. J., Heald, C. L., Pierce, J. R., Farina, S. C., Marais, E. A., Jimenez, J. L., Campuzano-Jost, P., Nault, B. A., Middlebrook, A. M., Coe, H., Shilling, J. E., Bahreini, R., Dingle, J. H., and Vu, K. (2020), An evaluation of global organic aerosol schemes using airborne observations, Atmos. Chem. Phys., 20, 2637-2665, https://doi.org/10.5194/acp-20-2637-2020.
Park, R.J., D.J. Jacob, B.D. Field, R.M. Yantosca, and M. Chin, Natural and transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United States: implications for policy, J. Geophys. Res., 109, D15204, 10.1029/2003JD004473, 2004.
Philip, S., R.V. Martin, and C.A. Keller, Sensitivity of chemistry-transport model simulations to the duration of chemical and transport operators: a case study with GEOS-Chem v10-01, Geosci. Model Dev., 9, 1683-1695, doi:10.5194/gmd-9-1683-2016, 2016.
Philip, S., R.V. Martin, G. Snider, C. Weagle, A. van Donkelaar, M. Brauer, D. Henze, Z. Klimont, C. Venkataraman, S. Guttikunda, and Q. Zhang, Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models, Environ. Res. Lett., 12, 044018, 2017.
Pound, R.J., T. Sherwen, D. Helmig, L.J. Carpenter, and M.J. Evans, Influences of oceanic ozone deposition on tropospheric photochemistry, Atmos. Chem. Phys., 20, 4227-4239, 2020.
Putnam, W.M., and S.-J. Lin, Finite-volume transport on various cubed-sphere grids, J. Comput. Phys., 227, 55-78, 2007.
Pye, H.O.T., Chan, A. W.H., Barkley, M.P., and Seinfeld, J.H., Global modeling of organic aerosol: the importance of reactive nitrogen (NOx and NO3), Atmos. Chem. Phys., 10, 11261-11276, doi:10.5194/acp-10-11261-2010, 2010.
Ridley, D.A., C.L. Heald, and B.J. Ford, North African dust export and deposition: A satellite and model perspective, J. Geophys. Res., 117, D02202, doi:10.1029/2011JD016794, 2012.
Safieddine, S.A., and C.L. Heald, A global assessment of dissolved organic carbon in precipitation, Geophys. Res. Lett., 44, 11,672-11,681, 2017.
Sander, R., Compilation of Henry's law constants (version 4.0) for water as solvent, Atmos. Chem. Phys., 15, 4399-4981, 2015.
Scarpelli, T.R., D.J. Jacob, J.D. Maasakkers, M.P. Sulprizio, J.-X. Sheng, K. Rose, L. Romeo, J.R. Worden, and G. Janssens-Maenhout, A global gridded (0.1o x 0.1o) inventory of methane emissions from fuel exploitation based on national reports to the United Nations Framework Convention on Climate Change, Earth System Sci. Data, 12, 563-575, 2020a.
Scarpelli, T.R., D.J. Jacob, C.A. Octaviano Villasana, I.F. Ramirez Hernandez, P.R. Cardenas Moreno, E.A. Cortes Alfaro, M.A. Garcia Garcia, and D. Zavala-Araiza, A gridded inventory of anthropogenic methane emissions from Mexico based on Mexico's National Inventory of Greenhouse Gases and Compounds, Environ. Res. Lett., 15, 105015, 2020b.
Selin, N.E., D.J. Jacob, R.J. Park, R.M. Yantosca, S. Strode, L. Jaeglé, and D. Jaffe, Chemical cycling and deposition of atmospheric mercury: Global constraints from observations, J. Geophys. Res., 112, D02308, doi:10.1029/2006JD007450, 2007.
Selin, N.E., D.J. Jacob, R.M. Yantosca, S. Strode, L. Jaeglé, and E.M. Sunderland, Global 3-D land-ocean-atmosphere model for mercury: present-day vs. pre-industrial cycles and anthropogenic enrichment factors for deposition, Glob. Biogeochem. Cycles, 22, GB2011, 2008.
Shah, V., D.J. Jacob, J.M. Moch, X. Wang, and S. Zhai, Global modeling of cloudwater acidity, rainwater acidity, and acid inputs to ecosystems, Atmos. Chem. Phys., 20, 12223-12245, 2020.
Sherwen, T.,J.A. Schmidt, M.J. Evans, L.J. Carpenter, K. Grossmann, S.D. Eastham, D.J. Jacob, B. Dix, T.K. Koenig, R. Sinreich, I. Ortega, R. Volkamer, A. Saiz-Lopez, C. Prados-Roman, A.S. Mahajan, and C. Ordonez, Global impacts of tropospheric halogens (Cl, Br, I) on oxidants and composition in GEOS-Chem, Atmos. Chem. Phys., 16, 12239-12271, 2016.
Smith-Downey, N.V., Sunderland, E.M., and Jacob, D.J., Anthropogenic impacts on global storage and emissions of mercury from terrestrial soils: insights from a new global model , J. Geophys. Res., 115, G03008, 2010.
Song, S., N.E. Selin, A.L. Soerensen, H. Angot, R. Artz, S. Brooks, E.-G. Brunke, G. Conley, A. Dommergue, R. Ebinghaus, T.M. Holsen, D.A. Jaffe, S. Kang, P. Kelley, W.T. Luke, O. Magand, K. Marumoto, K.A. Pfaffhuber, X. Ren, G.-R. Sheu, F. Slemr, T. Warneke, A. Weigelt, P. Weiss-Penzias, D.C. Wip, and Q. Zhang, Top-down constraints on atmospheric mercury emissions and implications for global biogeochemical cycling, Atmos. Chem. Phys., 15, 7103-7125, doi:10.5194/acp-15-7103-2015, 2015.
Soerensen, A.L., E.M. Sunderland, C.D. Holmes, D.J. Jacob, R.M. Yantosca, H. Skov, J.H. Christensen, and R.P. Mason, An improved global model for air-sea exchange of mercury: High concentrations over the North Atlantic, Environ. Sci. Technol., 44, 8574-8580, 2010.
Stettler, M.E.J., S. Eastham, S.R.H. Barrett, Air quality and public health impacts of UK airports. Part I: Emissions, Atmos. Environ., 45, 5415-5424, 2011.
Streets, D.G., et al., Global and regional trends of mercury emissions and concentrations, 2010-2015, Atmos. Environ., 417-427, 2019.
Strode, S., L. Jaeglé, N.E. Selin, D.J. Jacob, R.J. Park, R.M. Yantosca, R.P. Mason, and F. Slemr, Air-sea exchange in the global mercury cycle, Glob. Biogeochem. Cycles, 21, GB1017, doi:10.1029/2006GB002766, 2007.
Travis, K.R., and D.J. Jacob, Systematic bias in evaluating chemical transport models with maximum daily 8-hour average (MDA8) surface ozone for air quality applications: a case study with GEOS-Chem v9.02, Geophys. Model Dev., 12, 3641-3648, 2019.
Travis, K. R., D. J. Jacob, J. A. Fisher, P. S. Kim, E. A. Marais, L. Zhu, K. Yu, C. C. Miller, R. M. Yantosca, M. P. Sulprizio, A. M. Thompson, P. O. Wennberg, J. D. Crounse, J. M. St. Clair, R. C. Cohen, J. L. Laughner, J. E. Dibb, S. R. Hall, K. Ullmann, G. M. Wolfe, J. A. Neuman, and X. Zhou, Why do models overestimate surface ozone in the Southeast United States, Atmos. Chem. Phys., 16, 13561-13577, doi:10.5194/acp-16-13561-2016, 2016.
Trivitayanurak, W., P. Adams, D. Spracklen, and K. Carslaw, Tropospheric aerosol microphysics simulation with assimilated meteorology: model description and intermodel comparison, Atmos. Chem. Phys., 8, 3149-3168, 2008.
Tzompa-Sosa, Z.A., E. Mahieu, B. Franco, C.A. Keller, A.J. Turner, D. Helmig, A. Fried, D. Richter, P. Weibring, J. Walega, T.I. Yacovitch, S.C. Herndon, D.R. Blake, F. Hase, J.W. Hannigan, S. Conway, K. Strong, M. Schneider, and E.V. Fischer, Revisiting global fossil fuel and biofuel emissions of ethane, J. Geophys. Res., 12, 2493-2512, 2016.
US EPA, Preparation of Emissions Inventories for the Version 7.1 2016 Hemispheric Emissions Modeling Platform. U.S. Environmental Protection Agency, Research Triangle Park, NC, 2019. https://www.epa.gov/sites/production/files/2019-12/documents/2016fe_hemispheric_tsd.pdf (see Section 2.1.4)
van Donkelaar, A., R.V. Martin, W.R. Leaitch, A.M. Macdonald, T.W. Walker, D.G. Streets, Q. Zhang, E.J. Dunlea, J.L. Jimenez, J.E. Dibb, L.G. Huey, R. Weber, and M.O. Andreae, Analysis of Aircraft and Satellite Measurements from the Intercontinental Chemical Transport Experiment (INTEX-B) to Quantify Long-Range Transport of East Asian Sulfur to Canada, Atmos. Chem. Phys., 8, 2999-3014, 2008.
Vinken, G.C.M, K.F. Boersma, D.J. Jacob, and E.W. Meijer, Accounting for non-linear chemistry of ship plumes in the GEOS-Chem global chemistry transport model, Atmos. Chem. Phys., 11, 11707-11722, 2011.
Wang, Q., D.J. Jacob, J.A. Fisher, J. Mao, E.M. Leibensperger, C.C. Carouge, P. Le Sager, Y. Kondo, J.L. Jimenez, M.J. Cubison, and S.J. Doherty, Sources of carbonaceous aerosols and deposited black carbon in the Arctic in winter-spring: implications for radiative forcing, Atmos. Chem. Phys., 11, 12,453-12,473, 2011.
Wang, Q., D.J. Jacob,J.R Spackman, A.E. Perring, J.P. Schwarz, N. Moteki, E.A. Marais, C. Ge, J. Wang, and S.R.H. Barrett, Global budget and radiative forcing of black carbon aerosol: constraints from pole-to-pole (HIPPO) observations across the Pacific, J. Geophys. Res., 119, 195-206, 2014.
Wang, X., D.J. Jacob, W. Downs, S. Zhai, L. Zhu, V. Shah, C.D. Holmes, T. Sherwen, B. Alexander, M.J. Evans, S.D. Eastham, J.A. Neuman, P. Veres, T.K Koenig, R. Volkamer, L.G. Huey, T.J. Bannan, C.J. Percival, B.H. Lee, and J.A. Thornton, Global tropospheric halogen (Cl, Br, I) chemistry and its impact on oxidants , submitted to Atmos. Chem. Phys., 2021.
Wang, Y., D.J. Jacob, and J.A. Logan, Global simulation of tropospheric O3-NOx-hydrocarbon chemistry, 1. Model formulation, J. Geophys. Res., 103/D9, 10,713-10,726, 1998a.
Wang, Y., D.J. Jacob, and J.A. Logan, Global simulation of tropospheric O3-NOx-hydrocarbon chemistry, 3. Origin of tropospheric ozone and effects of non-methane hydrocarbons, J. Geophys. Res., 103/D9, 10,757-10,768, 1998c.
Weng, H.-J., Lin, J.-T. *, Martin, R., Millet, D. B., Jaeglé, L., Ridley, D., Keller, C., Li, C., Du, M.-X., and Meng, J., Global high-resolution emissions of soil NOx, sea salt aerosols, and biogenic volatile organic compounds, Scientific Data, 7, 148, doi:10.1038/s41597-020-0488-5, 2020.
Xiao, Y., J. A. Logan. D. J. Jacob, R. C. Hudman, R. Yantosca, and D. R. Blake, The global budget of ethane and regional constraints on U.S. sources, J. Geophys. Res., 113, D21306, doi:10.1029/2007JD009415, 2008.
Yan, Y.-Y., Lin, J.-T., Kuang, Y., Yang, D.-W., and Zhang, L., Tropospheric carbon monoxide over the Pacific during HIPPO: Two-way coupled simulation of GEOS-Chem and its multiple nested models, Atmos. Chem. Phys., 14, 12649-12663, doi:10.5194/acp-14-12649-2014, 2014.
Yu, F., and G. Luo, Simulation of particle size distribution with a global aerosol model: Contribution of nucleation to aerosol and CCN number concentrations, Atmos. Chem. Phys., 9, 7,691-7,710, 2009.
Yuan, H., Dai, Y., Xiao, Z., Ji, D., Shangguan, W., Reprocessing the MODIS Leaf Area Index Products for Land Surface and Climate Modelling, Remote Sensing of Environment, 115(5), 1171-1187. doi:10.1016/j.rse.2011.01.001, 2011.
Zhang, L., Gong, S., Padro, J., and Barrie, L.: A size-segregated particle dry deposition scheme for an atmospheric aerosol module, Atmos. Environ., 35, 549–560, https://doi.org/10.1016/S1352-2310(00)00326-5, 2001.
Zhang, Y., D.J. Jacob, H.M. Horowitz, L. Chen, H.M. Amos, D.P. Krabbenhoft, F. Slemr, V. St. Louis, and E.M. Sunderland, Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions, PNAS, doi:10.1073/pnas.1516312113, 2016.
Zhuang, J., D.J. Jacob, J. Flo-Gaya, R.M. Yantosca, E.W. Lundgren, M.P. Sulprizio, and S.D. Eastham, Enabling immediate access to Earth science models through cloud computing: application to the GEOS-Chem model, Bull. Amer. Met. Soc., https://doi.org/10.1175/BAMS-D-18-0243.1, 2019.
Zhuang, J., D.J. Jacob, H. Lin, E.W. Lundgren, R.M. Yantosca, J. Flo Gaya, M.P. Sulprizio, S.D. Eastham, and K. Jorissen, Enabling high-performance cloud computing for Earth science modeling on over a thousand cores: application to the GEOS-Chem atmospheric chemistry model, JAMES, 12, e2020MS002064, 2020.