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 as 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. 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Original/historical references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.

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. This capability is described by Long et al. [2015].

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). The default timesteps are optimized to balance accuracy and speed as described by Philip et al. [2016].

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. [2019] and is called BCC-GC
On-line coupling with the Weather Research Forecast (WRF) model is described by Lin et al. [2020] 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 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 necessary ( new development in version 12.4.0).

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. 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.

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 module described by Keller et al. [2014]. 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.

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. The default capability for off-line emissions is a new development in version 12.4.0.

Anthropogenic. Anthropogenic emissions use as default the CEDS global inventory. EDGAR v4.3.2 [Crippa et al., 2018] with trash emissions from Wiedinmyer et al. [2014] is available as an alternate option to CEDS (trash emissions are already included in CEDS). Ethane emissions are from Tzompa-Sosa et al. [2016]. Diurnal and weekend/weekday vatiations are from van Donkelaar et al. [2008]. The global default inventories are superseded by improved inventories in regions where we have better information:

the US (NEI11v1) from EPA [2014], as implemented by Travis et al. [2016]
Canada (CAC) implemented by van Donkelaar et al. [2008] with updates
East Asia (MIX inventory of M. Li et al. [2014])
Africa for 2006 and 2013 (DICE-Africa inventory of Marais and Wiedinmyer [2016])

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. The GFAS inventory is a new development in version 12.2.0). 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.

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. 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.

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

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

Gas-phase tropospheric chemistry

GEOS-Chem includes detailed HOx-NOx-VOC-ozone-halogen-aerosol tropospheric chemistry. The chemical mechanism generally follows JPL/IUPAC recommendations. Specific mechanisms are used for:

PAN [Fischer et al., 2014]
Isoprene [Travis et al., 2016; 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 [Sherwen et al., 2016; 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].

Reactive uptake of NO2, NO3, and N2O5 by aerosols and clouds 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.

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 Y. Wang et al. [1998c].

Stratospheric chemistry

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. [2014]. 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. [2000] for ozone and monthly mean sources and loss rate constants for other gases [Murray et al., 2012].

Aerosols

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 (new development in version 12.3.0). Cloudwater pH for in-cloud sulfate formation is as given by Alexander et al. [2012]. HOBr has been added by Chen et al. (2017) as an in-cloud S(IV) oxidant. .

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 Kim et al. [2015]. 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] (new development in version 12.1).

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

Marine POA. Marine POA is emitted following the optional Gantt et al. [2015] scheme.

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 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 simulation is described by Nassar et al. [2010]. Anthropogenic emissions are updated from Nassar et al. [2013].

Methane. The current 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.

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].

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. Species budgets diagnostics is a new development in version 12.1.0. The Obspack diagnostic for comparison of model output to compiled surface, tower, ship, and aircraft observations of greenhouse gases is a new development in version 12.2.0.

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.
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.
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.
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.
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.
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