Narrative description (and how to cite GEOS-Chem)

Updated September 13, 2021 (version 13.020)

Citing GEOS-Chem | Name | Original references | Configurations | Met fields & grids | Nesting | Transport & deposition | Radiation
Emissions | Chemistry | Aerosols | Carbon gases | Mercury | POPs | Diagnostics | Model adjoint | References

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. In addition, we strongly encourage you to cite GEOS-Chem journal publications, both for your general use of GEOS-Chem and for your specific applications. We also encourage you to name GEOS-Chem in your Abstract (or in your title, if appropriate) so that your paper gets picked up by GEOS-Chem searches and getslisted in the GEOS-Chem publications page.. Consult the narrative below for referencing specific components of the model. 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 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 as an original reference for the model. It only describes a model for gas-phase tropospheric oxidant chemistry. Subsequent original references for major additional model features 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;
  • Long et al. [2015] for the grid-independent GEOS-Chem; 
  • Eastham et al. [2018] for the high-performance GEOS-Chem (GCHP);
  • 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 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 meteorological models. 

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. Simulations focused on the troposphere can reduce the number of vertical levels from 72 to 47 by coarsening the vertical resolution in the stratosphere and mesosphere. GEOS-Chem Classic simulations can also be conducted in nested mode (see Nesting below). 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 first implemented and described 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.

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 the non-local scheme implemented by Lin and McElroy [2010]; an option allows instead for full instantaneous 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. [2020] is an option in the model (new development in version 13.2.0). An older version [Luo et al., 2019] was implemented as a 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]. Size-dependent aerosol dry deposition is from Emerson et al. [2020] and this is a new development in version 13.3.0; older versions used aerosol dry deposition 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 this is a new development in version 12.8.0.

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

Radiation

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. HEMCO 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]. 

Anthropogenic. Global anthropogenic emissions up to 2019 are from the CEDS v2 inventory (CEDS v_2021_04_21 gridded emissions data | Datahub (pnnl.gov) as of version 13.2.0. An  older CEDS version from McDuffie et al. [2020] was 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 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 13.1.0. Vertical allocation of emissions by sector follows Hemispheric CMAQ [US EPA, 2019] and this is a new development in version 13.1.0. 

Future projections of 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 2005-2020. 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 from 1978 to present are from the AEROCOM data base. Extension of the data to May 2020 is a new development in version 13.3.0. Older versions did not extend beyond 2018.  For simulations of more recent periods when data are not available the non-eruptive volcanic emissions are set to a climatology and the eruptive emissions are set to zero. 

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:

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, 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 an option in the model. 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. However, a bug caused the limitation by entrainment not to be implemented properly until version 13.3.o and this is a new development in version 13.3.0.

GEOS-Chem simulations prior to version 13.2.0 could 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 monthly mean sources and loss rate constants for other gases [Murray et al., 2012]. This capability was disabled in version 13.2.0. 

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]. An update to include emissions from blowing snow [Huang and Jaegle, 2017] is a new development in version 13.2.0.

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

Trace metals. Simulation of 12 aerosol-borne trace metals is from Xu et al. [2019] and is a new development in version 13.2.0.

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]. 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. [2020] 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 13.1.0.

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).

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.
  • Bates, K.H., Jacob, D.J., Wang, S., Hornbrook, R.S., Apel, E.C., Kim, M.J., Millet, D.B., Wells, K.C., Chen, X., Brewer, J.F., Ray, E.A., Diskin, G.S., Commane, R., Daube, B.C. and Wofsy, S.C., The global budget of atmospheric methanol: new constraints on secondary, oceanic, and terrestrial sourceJ. Geophys. Res., 126,  e2020JD033439, 2021. 
  • Bates, K.H., D.J. Jacob, K. Li, P. Ivatt, M.J. Evans, Y. Yan, and J. Lin, Development and evaluation of a new compact mechanism for aromatic oxidation in atmospheric models [preprint], Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-605, in review, 2021.
  • 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.
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  • 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, 2020.
  • 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.
  • 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.
  • 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.
  • van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Yue, C., Kaiser, J. W., and van der Werf, G. R., Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750-2015), Geosci. Model Dev., 10, 3329-3357, https://doi.org/10.5194/gmd-10-3329-2017, 2017.
  • 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,  Atmos. Chem. Phys., 21, 13973-13996, 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.
  • Xu, J.-W., R.V. Martin, B.H. Henderson, J. Meng, Y.B. Oztaner, J.L. Hand, A. Hakami, M. Strum, and S.B. Phillips, Simulation of airborne trace metals in fine particulate matter over North America, Atmos. Environ., 214, 116883, 2019.
  • 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., L. Liu, Y. Zhao, S. Gong, Z. Zhang, D.K. Henze, S.L. Capps, T.-M. Fu, Q. Zhang, and Y. Wang (2015), Source attribution of particulate matter pollution over North China with the adjoint method, Environ. Res. Lett., 10, 084011.
  • 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.