Narrative description (and how to cite GEOS-Chem)
Updated November 21, 2018 (version 12-1-0)
Original reference | Configurations | Met fields & grids | Nesting | Transport & deposition | Radiation
Emissions | Chemistry | Gas-phase tropospheric chemistry | Stratospheric chemistry
Aerosols | Carbon gases | Mercury | POPs | Other species | Model adjoint | References
We give here a narrative description of the current standard version 12.1.0 of the GEOS-Chem model, with two purposes:
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 is encouraged for new version 12.1 and v11-02 developments, as flagged in this narrative and if they are important for your work; see the New GEOS-Chem Developments page for more specific information on the developer(s) to be credited.
The narrative below is reviewed and updated by the GEOS-Chem Steering Committee at every new model version release. Questions and comments regarding GEOS-Chem literature should be directed to the relevant Working Group Chair or Model Scientist.
The original reference for GEOS-Chem is Bey et al. . The acronym stands for Goddard Earth Observing System (GEOS)–Chemistry but we don't recommend spelling it out because the GEOS Earth System Model can use other chemical modules besides GEOS-Chem, and GEOS-Chem can use other meteorological drivers besides GEOS. As is often the case, the acronym has outlived the full name.
The core of GEOS-Chem is a chemical module that computes the local changes in atmospheric concentrations due to emissions, chemistry, and deposition. The computation is done in individual vertical columns for user-specified horizontal locations, vertical grids, and time steps. This chemical module can be implemented in three different configurations, which all share the same GEOS-Chem chemical module:
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.  and is a new development in v11-02.
GEOS-Chem for on-line applications. This couples the GEOS-Chem chemical module with an independent simulation of atmospheric dynamics, such as from an Earth System Model (ESM), and including chemical transport. 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. This capability is described by Long et al. .
GEOS-Chem in off-line mode is driven by assimilated meteorological data from the Goddard Earth Observation System (GEOS) of the NASA Global Modeling and Assimilation Office (GMAO). The two main data archives used by GEOS-Chem are:
the operational data stream from the GEOS Forward Processing (GEOS-FP) (native resolution 0.25° x 0.3125° or c360 cubed-sphere, 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 resolution of the GEOS fields or at coarser resolution. They can be conducted at native resolution over a nested domain with dynamic 1-way or 2-way boundary conditions from a coarser global simulation (see Nesting below). The operator splitting time step in GEOS-Chem is optimized to achieve high accuracy and this is described by Philip et al.  (new development in v11-01).
The GEOS-Chem chemical module can be used in on-line applications on any grid. It is compatible with the Earth System Modeling Framework (ESMF). On-line coupling with the GEOS-5 ESM is mature and has been applied to a c720 (≈12 km) simulation [Hu et al. 2018]. Couplings with the Beijing Climate Center (BCC) ESM and with the NCAR CESM2 are presently underway.
The GEOS-Chem chemical module can be used in on-line applications on any grid. It is compatible with the Earth System Modeling Framework (ESMF). On-line coupling with the GEOS-5 ESM is mature and has been applied to a c720 (≈12 km) simulation [Hu et al. 2018]. Couplings with the Beijing Climate Center (BCC) ESM and with the NCAR CESM2 are presently underway.
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 resolution of the GEOS fields or at coarser resolution. They can be conducted at native resolution over a nested domain with dynamic 1-way or 2-way boundary conditions from a coarser global simulation (see Nesting below). The operator splitting time step in GEOS-Chem is optimized to achieve high accuracy and this is described by Philip et al. .
The GEOS-Chem chemical module can be used in on-line applications on any grid. It is compatible with the Earth System Modeling Framework (ESMF). On-line coupling with the GEOS-5 ESM is described by Hu et al. .
The nested version of GEOS-Chem, originally described by Y. X. Wang et al. , allows continental-scale simulations at the native-grid horizontal resolution of the GEOS data 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.  and on this wiki page.
The current nested version of GEOS-Chem 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 that resolution with full aerosol-oxidant chemistry was developed by Zhang et al.  for East Asia and Kim et al.  for North America. A software tool developed by the GEOS-Chem Support Team is available to process the original GEOS meteorological data to any user-selected window for application of the nested model.
GEOS-Chem Classic uses the TPCORE advection algorithm of Lin and Rood  on the rectilinear grid. GCHP uses the FV3 advection algorithm of Putnam and Lin  on the cubed sphere. Convective transport in GEOS-Chem is computed from the convective mass fluxes in the meteorological archive as described by Wu et al. . Boundary layer mixing in GEOS-Chem uses either the non-local scheme implemented by Lin and McElroy  or full mixing up to the GEOS-diagnosed mixing depth.
The wet deposition scheme in GEOS-Chem is described by Liu et al.  for water-soluble aerosols and by Amos et al.  for gases. Scavenging of aerosol by snow and cold/mixed precipitation is described by Wang et al. [2011, 2014].
Dry deposition is based on the resistance-in-series scheme of Wesely  as implemented by Wang et al. [1998a]. Aerosol deposition is from Zhang et al. . Aerosol deposition to snow/ice is described by Fisher et al. . Gravitational settling is from Fairlie et al.  for dust and Alexander et al.  for coarse sea salt. Sea-salt deposition is from Jaegle et al. . See the mercury section for description of air-sea-land exchange of mercury.
GEOS-Chem can calculate the radiative flux effects from atmospheric composition using the optional RRTMG module. Implementation of RRTMG in GEOS-Chem is described in Heald et al. .
Photolysis frequencies for stratospheric and tropospheric chemistry are calculated with the Fast-JX code of Bian and Prather  as implemented in GEOS-Chem by Mao et al.  and Eastham et al. .
Absorption of UV by brown carbon can be implemented with the optional Hammer et al.  scheme. Absorption properties of black carbon are from X. Wang et al.  (new development in v11-02).
All GEOS-Chem emissions are configured at run-time using the HEMCO module described by Keller et al. . 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.
Anthropogenic. Anthropogenic emissions of CO, NOx, and SO2 in GEOS-Chem use as default the CEDS global inventory (new development in v11-02). EDGARv4.3.1 emissions are also available as an option (new development in v11-02). Anthropogenic emissions of NMVOCs use as default the RETRO monthly global inventory for 2000 implemented as described by Hu et al. [2015a]. Ethane emissions are from Tzompa-Sosa et al.  (new development in v11-02). Trash burning emissions are from Wiedinmyer et al. . Global anthropogenic emissions for carbonaceous aerosols (BC/OC) are from Bond et al. , as implemented into GEOS-Chem by Leibensperger et al. .
All these default inventories are scaled for individual years on the basis of economic data, and are superseded by improved inventories in regions where we have better information. The basic structure of the anthropogenic emissions inventory is described by van Donkelaar et al.  including diurnal profiles and algorithms to update to individual years. Regional emission estimates (base years in parentheses) are used in particular for
Future anthropogenic emissions following the RCP scenarios have been implemented into GEOS-Chem by Holmes et al. .
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.  to account for ozone and HNO3 production in the plume. The PARANOX module was updated by Holmes et al. .
Open Fires. Emissions from open fires for individual years are from the GFED4.1 inventory with options to use instead the FINNv1.5 inventory [Wiedinmyer et al., 2011] or the QFED inventory.
Lightning. Lightning NOx emissions are as described by Murray et al.  to match OTD/LIS climatological observations of lightning flashes, with continual updates documented on the lightning wiki page.
Biogenic VOCs. Biogenic VOC emissions in GEOS-Chem are from the MEGAN v2.1 inventory of Guenther et al.  as implemented by Hu et al. [2015b]. Dependence on CO2 was added by Tai et al. . Acetaldehyde emissions are from Millet et al. (2010) (new development in v11-02). Biogenic non-agricultural ammonia sources are from GEIA (new development in v11-02).
Soils. Biogenic soil NOx emissions are from Hudman et al. .
Ocean. Marine emissions of DMS are from the Lana et al. dataset as implemented in GEOS-Chem by Breider et al. . Air-sea exchange of acetone assumes fixed ocean concentrations as described by Fischer et al. . Ocean acetaldehyde emissions are from Millet et al. (2010). Ammonia emissions from Arctic seabirds are from Croft et al. (2016) (new development in v11-02) . Ocean ammonia emissions are from GEIA (new development in v11-02).
Volcanoes. Eruptive and non-eruptive volcanic SO2 emissions for individual years are from the AEROCOM data base originally developed by Thomas Diehl and implemented into GEOS-Chem by Fisher et al. .
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).
The standard simulation in GEOS-Chem includes coupled aerosol-oxidant chemistry in the troposphere and stratosphere. Specifics are given below. The chemical solver is KPP [Damian et al., 2002] as implemented in GEOS-Chem with the FlexChem interface.
GEOS-Chem includes detailed HOx-NOx-VOC-ozone-halogen-aerosol tropospheric chemistry. The chemical mechanism in GEOS-Chem v11-2 was updated to the most recent JPL/IUPAC recommendations. PAN chemistry is as described by Fischer et al.  (new development in v11-02). Isoprene oxidation is based on Travis et al.  and Fisher et al.  (new development in v11-02). Detailed Cl-Br-I halogen chemistry is as described by Sherwen et al.  (new development in v11-02), with addition of HOBr + S(IV) (Chen et al., 2017) (new development in v11-02). Criegee chemistry was updated by Millet et al. . 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 in all photochemical mechanisms as implemented by Murray  (new development in v11-02).
Aerosols interact with gas-phase chemistry in GEOS-Chem through the effect of aerosol extinction on photolysis rates [Martin et al., 2003], heterogeneous chemistry [Jacob, 2000], and gas-aerosol partitioning of semi-volatile compounds (see the aerosols section). N2O5 uptake by aerosols is from Evans and Jacob . HO2 uptake is from Mao et al.  with a reactive uptake coefficient of 0.2 for conversion to H2O. Acid uptake by dust particles from Fairlie et al.  is provided as an option.
Tropospheric ozone can also be simulated in GEOS-Chem as a linearized odd oxygen tracer with archived sources and loss rate constants from the full-chemistry simulation. This method was originally described by Wang et al. [1998c] and its most recent implementation in GEOS-Chem is described by Zhang et al. .
GEOS-Chem includes detailed stratospheric chemistry fully coupled with tropospheric chemistry through the Unified tropospheric-stratospheric Chemistry eXtension (UCX) as described in Eastham et al. . UCX is the standard version of the model, but an option is also available to use parameterized linear chemistry in the stratosphere (“troposphere-only simulation”) including the Linoz algorithm of McLinden et al.  for ozone and monthly mean sources and loss rate constants for other gases [Murray et al., 2012].
Sulfate-nitrate-ammonium aerosol. The original SNA aerosol simulation in GEOS-Chem coupled to gas-phase chemistry was developed by Park et al. . SNA thermodynamics are computed with the ISORROPIA II thermodynamic module [Fontoukis and Nenes, 2007], as implemented in GEOS-Chem by Pye et al. . Cloudwater pH for in-cloud sulfate formation is as given by Alexander et al. . HOBr has been added by Chen et al. (2017) as a S(IV) oxidant (new development in v11-02). In-cloud SO2 oxidation by transition metals is as described by Alexander et al. (2009) (new development in v11-02) .
Carbonaceous aerosol. Q. Wang et al.  describes the current BC simulation in GEOS-Chem. Organic aerosol in the default model follows a simple, irreversible, direct yield scheme similar to Kim et al.  (new development in v11-02) . Complex SOA can be used as an option following the simplified Volatility Basis Set (VBS) scheme of Pye et al.  and the aqueous-phase isoprene SOA scheme of Marais et al.  (new development in v11-02) coupled to the isoprene gas-phase chemistry mechanism.
Dust aerosol. The dust simulation in GEOS-Chem is described by Fairlie et al. . Dust size distributions are from Li Zhang et al. . Fine anthropogenic dust from combustion and industrial sources is from the AFCID inventory of Philip et al.  ((new development in version 12.1)
Sea salt. The sea salt aerosol simulation in GEOS-Chem is described by Jaegle et al. .
Marine POA. Marine POA is emitted following the optional Gantt et al.  scheme.
Aerosol microphysics. Two alternate simulations of aerosol microphysics are implemented in GEOS-Chem: the TOMAS simulation of Trivitiyanurak et al.  updated by Kodros and Pierce  and the APM simulation of Yu and Luo .
Aerosol optical depth. Aerosol optical depth is calculated in GEOS-Chem using RH-dependent aerosol optical properties from Martin et al. . Dust optics are from Ridley et al. . These calculations can be performed at user-specified wavelengths from 230 nm to 56 μm 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. .
CO2. The current simulation is described by Nassar et al. . Anthropogenic emissions are updated from Nassar et al.  (new development in v11-01) .
Methane. The current simulation is described by Maasakkers et al.  (new development in v11-02).
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.  (new development in v11-02).
The original GEOS-Chem coupled atmosphere-ocean simulation of mercury was described by Selin et al.  for the atmosphere and by Strode et al.  for the ocean. Extension to a coupled atmosphere-ocean-land model was described by Selin et al. . The current version of the atmospheric simulation is as described by Horowitz et al.  (new development in v11-02), and the current version of the ocean simulation is as described by Soerensen et al. , with updated ocean rate coefficients from Song et al. . 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. .There is an option to couple GEOS-Chem with the terrestrial mercury module developed by Smith-Downey et al. .
Anthropogenic emissions are from Y. Zhang et al. . Future SRES emission scenarios have been implemented by Corbitt et al. .
The model includes a simulation of PAHs as described by Friedman et al. .
Specific simulations of other species were included in the standard GEOS-Chem model at various stages in the model history but have not been revisited or maintained since. These include H2/HD from Price et al. , HCN/CH3CN from Li et al. , CH3I from Bell et al. , and acetylene from Xiao et al. . They should be considered obsolete but can provide a foundation for future development.
See the GEOS-Chem adjoint wiki page for description and 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.
Alexander, B., Park, R.J., Jacob, D.J., and Gong, S., Transition metal catalyzed oxidation of atmospheric sulfur: Global implications for the sulfur budget, J. Geophys. Res., 114, D02309, 2009.
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.
Bell, N., L. Hsu, D.J. Jacob, M.G. Schultz, D.R. Blake, J.H. Butler, D.B. King, J.M. Lobert, E. Maier-Reimer, Global budgets of oceanic and atmospheric methyl iodide: development of methyl iodide as a tracer for marine convection in atmospheric models, J. Geophys. Res., 10.1029/2001JD001151, 2002.
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.
Bond, T.C. et al, Historical emissions of black and organic carbon aerosol from energy-related combustion, 1850-2000, Global Biogeochem. Cycles, 21, GB2018, doi: 10.1029/2006GB002840, 2007.
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.
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.
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.
Evans, M.J., and D.J Jacob, Impact of new laboratory studies of N2O5 hydrolysis on global model budgets of tropospheric nitrogen oxides, ozone and OH, Geophys. Res. Lett, L09813, 2005.
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.
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, M.T. Purdy, M. Kopacz, P. Le Sager, C. Carouge, C.D. Holmes, R.M. Yantosca, R.L. Batchelor, K. Strong, G.S. Diskin, H.E. Fuelberg, J.S. Holloway, E.J. Hyer, W.W. McMillan, J. Warner, D.G. Streets, Q. Zhang, Y. Wang, and S. Wu, Source attribution and interannual variability of Arctic pollution in spring constrained by aircraft (ARCTAS, ARCPAC) and satellite (AIRS) observations of carbon monoxide, Atmos. Chem. Phys., 10, 977-996, 2010.
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.
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.
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.
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.
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, K.R. Travis, C. Tessum, J. Marshall, W.F. Reinhart, T. Mikoviny, M. Müller, A. Wisthaler, M. Graus, C. Warneke, and J. de Gouw, Emissions of C6-C8 aromatic compounds in the United States: Constraints from tall tower and aircraft measurements, J. Geophys. Res., 120, 826-842, doi:10.1002/2014JD022627, 2015a.
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, 2015b.
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.
Jacob, D.J., Heterogeneous chemistry and tropospheric ozone, Atmos. Environ., 34, 2131-2159, 2000.
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.
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.
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.
Li, Q., D.J. Jacob, R.M. Yantosca, C.L. Heald, H.B. Singh, M. Koike, Y. Zhao, G.W. Sachse, D.G. Streets, A Global 3-D Model Evaluation of the Atmospheric Budgets of HCN and CH3CN: Constraints From Aircraft and Ground Measurements, J. Geophys. Res., 108, 8827, doi:10.1029/2002JD003075, 2003.
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.
Maasakkers, J.D., et al., in preparation, 2018.
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.
Martin, R.V., D.J. Jacob, R.M. Yantosca, Mian Chin, and Paul Ginoux, Global and Regional Decreases in Tropospheric Oxidants from Photochemical Effects of Aerosols, J. Geophys. Res., 108(D3), 4097, doi:10.1029/2002JD002622, 2003.
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.
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.
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.
Nassar, R., L. Napier-Linton, K.R. Gurney, R.J. Andres, T. Oda, F.R. Vogel, and F. Deng, Improving the temporal and spatial distribution of CO2 emissions from global fossil fuel emission data sets, J. Geophys. Res. Atmos., 118, 917-933, doi:10.1029/2012JD018196, 2013.
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.
Paulot F., D.J. Jacob, R.W. Pinder, J.O. Bash, K. Travis, D.K. Henze, Ammonia emissions in the United States, Europe, and China derived by high-resolution inversion of ammonium wet deposition data: Interpretation with a new agricultural emissions inventory (MASAGE_NH3), J. Geophys. Res., 119, 4,343-4,364, 2014.
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.
Price, H., L. Jaeglé, et al., Global budget of molecular hydrogen and its deuterium content: Constraints from ground station, cruise, and aircraft observations, J. Geophys. Res., 112, D22108, 2007.
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., H. Liao, S. Wu, L.J. Mickley, D.J. Jacob, D.K. Henze, and J.H. Seinfeld, Effect of changes in climate and emissions on future sulfate-nitrate-ammonium aerosol levels in the United States, J. Geophys. Res., 114, D01205, 2009.
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.
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. Jaegle, 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.
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.
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., 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.
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., Heald, C. L., Ridley, D. A., Schwarz, J. P., Spackman, J. R., Perring, A. E., Coe, H., Liu, D., and Clarke, A. D.: Exploiting simultaneous observational constraints on mass and absorption to estimate the global direct radiative forcing of black carbon and brown carbon, Atmos. Chem. Phys., 14, 10989-11010, doi:10.5194/acp-14-10989-2014, 2014.
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.
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.