APM aerosol microphysics

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NOTE: APM was completely re-integrated into GEOS-Chem 12.6.0, which was released on 18 Oct 2019.

This page describes the APM (Advanced Particle Microphysics) option in GEOS-Chem. APM is one of two microphysics packages being incorporated into GEOS-Chem, the other being TOMAS.


The Advanced Particle Microphysics (APM) package was developed for implementation into GEOS-Chem at State University of New York (SUNY) at Albany (Yu and Luo, 2009). The APM model is optimized to accurately simulate secondary particle (SP, composed of sulfate, nitrate, ammonium, and SOA) formation and their growth to CCN sizes, with a higher size resolution for the size range of importance (1.2 – 120 nm: 30 bins, 10 additional bins for 120 nm -12 um). The present version of the APM employs 20 bins for sea salt to cover the dry diameter size range of 0.012 μm to 12 μm, and 15 bins for dust particles to cover size range of 0.03 μm to 50 μm. Because of the large differences in the median sizes of black carbon (BC) and primary organic carbon (POC) from fossil fuel combustion and biomass burning, we employ two log-normal modes (one for fossil fuel and another for biomass burning) to represent hydrophobic BC and two other log-normal modes for hydrophilic BC. Similarly, 4 log-normal modes are used to represent hydrophobic and hydrophilic POC. The growth of nucleated particles through the condensation of sulfuric acid vapor and equilibrium uptake of nitrate, ammonium, and secondary organic aerosol is explicitly simulated, along with the scavenging of secondary particles by primary particles (dust, black carbon, organic carbon, and sea salt). The amounts of secondary species coated on primary particles (through condensation, coagulation, equilibrium uptake, and aqueous chemistry) are tracked.

Recent implementations of the aerosol optical properties look up table (Yu et al., 2012) and radiation transfer (RF) model (Ma et al., 2012; Yu et al., 2013) enable GEOS-Chem/APM to derive aerosol direct radiative forcing and first indirect radiative forcing.

Authors and collaborators

  • Fangqun Yu (SUNY Albany) -- Principal Investigator
  • Gan Luo (SUNY Albany)

APM User Groups

User Group Personnel Projects
SUNY Albany Fangqun Yu, Gan Luo, Xiaoyan Ma, Kevin Bartlett ...

Computational Information

The APM model contains a number of computationally efficient schemes:

  1. Usage of pre-calculated lookup tables for nucleation rates and coagulation kernels;
  2. Variable size ranges for particles of different types;
  3. Variable bin resolution;
  4. Variable and optimized time steps for the coagulation calculations;
  5. The coating of primary particles by sulfate is tracked using one tracer (sulfate mass) for each type of primary particles;
  6. Nitrate, ammonium, and SOAs asso-ciated with sulfate are calculated based on the equilibrium partition.

The above schemes enable the APM model to capture the main properties of atmospheric particles important for their direct and indirect radiative forcing while keeping the computational costs quite low.

In the study reported in Yu and Luo [2009], all simulations are running on 8-CPU Linux workstations with the 2.2 Ghz Dual Quad-Core AMD Opteron Processor 2354. The model system is compiled using OpenMP for running in parallel. The original GEOS-Chem code has 54 tracers, and it takes 24.23 hours for one year full-chemistry simulations at 4x5 horizontal resolutions and 47 layers (GEOS-5 data). The GEOS-Chem with APM model incorporated has 127 tracers (73 additional tracers: 40 for sulfate, 20 for sea salt, one for H2SO4 gas, 4 tracers for BC/OC from fossil fuel, 4 tracers for BC/OC from biomass/bio-fuel, and 4 for sulfate attached to dust, BC, primary OC, and sea salt particles). With full size-resolved microphysics (nucleation, condensation, coagulation, deposition, and scavenging) and chemistry, it takes the model (127 tracers) 52.35 hours for the same year simulations on the same machine. In other words, the efficient schemes allow the increase in the computing cost per 100% increase in number of tracers (associated with particle size information) to (52.35/24.23-1)/(127/54-1) = 86%. Such a relatively small increase in the computing cost associated with full size-resolved microphysics is desirable and makes the future coupling of APM model with global climate model feasible.

F.Yu, 2013.05.07:

Updated computational Information for recent version of GEOS-Chem/APM with RRTMG (for 1 yr simulation on 24-core workstation: 2x2.5, 47 layers):

Original GEOS-Chem model, 59 tracers: ~ 5 day of wall clock time

With APM, 59+94 = 153 tracers: no radiative forcing calculation: ~ 11 days; with radiative forcing (RRTMG) calculation: ~ 13.5 days

Model Details

Particle Types and Representation

 Mixing State -- Semi-externally mixed: 
    Secondary particles (SP): sulfate, plus nitrate/ammonium/SOA in equilibrium
    Primary particles:  black carbon (BC), primary organic carbon (POC), dust, and sea salt 
                        + coated SP species on each type of primary particles.

 Size structures: 
    SP: 40 bins (1.2 nm - 12 μm)
    BC: two log-normal modes for hydrophobic BC and two log-normal modes for hydrophilic BC
    POC: two log-normal modes for hydrophobic OC and two log-normal modes for hydrophilic OC
    Dust: 15 bins (30 nm - 50 μm)
    Sea salt: 20 bins (12 nm – 12 μm)



Nucleation (or new particle formation) is one of the key processes connecting gas-phase chemistry to aerosol microphysics and controlling number concentrations (and size distributions) of atmospheric particles. The APM model employs ion-mediated nucleation (IMN) (Yu, JGR [2010]) and binary homogeneous nucleation (BHN) (Yu, JGR [2008]), in term of look-up tables. Other nucleation schemes can also be included.

Based on IMN mechanism, sulfuric acid vapor concentration ([H2SO4]), temperature (T), relative humidity (RH), ionization rate (Q), and surface area of pre-existing particles (S) have profound and non-linear impacts on nucleation rates. The sensitivities of nucleation rates to the changes in these key parameters may imply important physical feedback mechanisms involving climate and emission changes, chemistry, solar variations, nucleation, aerosol number abundance, and aerosol indirect radiative forcing (Yu, JGR [2010]).


H2SO4 vapor concentration is a tracer and the condensation of H2SO4 on all particles is explicitly simulated. Many field measurements indicate significant contribution of secondary organic gases (SOGs)to the growth of secondary particles. A scheme to consider the oxidation aging of SOGs and explicit condensation of low volatile SOGs has been developed (Yu, ACP [2011]).


Coagulation is a process in which particles of various sizes and compositions collide with each other and coalesce to form larger particles. In the atmosphere, coagulation is an important process scavenging small particles and turning externally mixed particles into internally mixed particles. In the present model, the mass conserving semi-implicit numerical scheme is employed to solve the self coagulation of size-resolved sulfate and sea salt particles, as well as the scavenging of sulfate particles by sea salt, dust, BC, and POC particles.

Coagulation is the most time-consuming process among various size-resolved microphysical processes (nucleation, growth, coagulation, and deposition). The reason is that coagulation involves particles of different sizes and thus adds two additional dimensions (size of particle A and size of particle B) into 3-dimensional spatial grid system. For example, for 40 bins of sulfate, coagulation among sulfate particles is equivalent to solving 40x40 = 1600 reaction equations. To reduce the computing cost of 3-D sectional aerosol microphysics model, it is critical to optimize the number of bins and coagulation calculation.

Deposition and Scavenging

To be added.

Aerosol optical properties

The key particle optical properties needed for radiative transfer calculation include extinction efficiency (Qext), single scattering albedo (ω), and asymmetry parameter (g). The absorption extinction efficiency (Qabs) can be calculated from Qext and ω as Qabs = Qext × (1- ω). The values of Qext, ω, and g depend on wavelength (λ), core diameter (dcore), shell diameter (dshell), and real (kr) as well as imaginary (ki) components of refractive index (k=kr - ki i) for both core and shell, and can be calculated with widely used Mie theory. The core-shell model of Ackerman and Toon (1981), which can use either the volume averaged refractive indices or the shell/core configuration, is employed in our study.

To reduce computation cost for 3-D online calculation, we have designed and generated lookup tables so that Qext, ω, and g values can be determined efficiently. According to the properties of aerosols resolved by APM, three lookup tables have been developed: the first for particles without solid absorbing cores (i.e., secondary particles, coated sea salt, and coated POC), the second for coated BC, and the third for coated dust. For coated BC and dust particles, the core-shell model assumes that BC and dust have a spherical core, surrounded by a spherical shell composed of all the other non- or less- absorbing secondary species and water. For hydrated (i.e., wet) secondary particles, coated sea salt particles, and coated primary organic particles, we set the core size to zero and use the volume-average of refractive index to calculate the optical properties of particles of given wet sizes. For refractive index of BC core, we use the value recommended by Bond et al. (2006) which is 1.85 – 0.71i. For dust core, a wavelength dependent parameterization of refractive index presented in Balkanski et al. (2007) is adapted. The volume-averaged refractive indices for species other than BC and dust are calculated based on the composition predicted by APM.

Details can be found in Yu et al. (ACP, 2012).

Radiative transfer (RF)

Radiative transfer model is needed for aerosol radiative forcing calculation. Ma et al. (2012) integrated the radiation transfer (RF) model of the Canadian Center for Climate Modeling and Analysis (CCCma) with GEOS-Chem/APM and used the model to study aerosol direct RF (DRF).

A more recent comparison of DRF values (clear sky vs. all sky) based on GEOS-Chem/APM with those of other AeroCom models (discussion version of Myhre et al., 2013) indicates that CCCMa RF code may have underestimated the impacts of clouds on radiation. We found out that the underestimation is likely associated with the cloud overlapping assumption. The version of CCCMa RF code we integrated into GEOS-Chem does not contain the widely used McICA (Monte-Carlo Independent Column Approximation) scheme ─ a fast, flexible, approximate technique for computing radiative transfer in an inhomogeneous cloud field.

To properly take into account the impacts of clouds on aerosol DRF and more importantly to study aerosol first indirect RF (IRF), Yu et al. (2013) integrated the widely used Rapid Radiative Transfer Model for GCMs (RRTMG) (Mlawer et al., 1997; Iacono et al., 2003, 2008) with GEOS-Chem/APM.

RRTMG, which contains the McICA scheme, is a broadband k-distribution radiation model (Mlawer et al., 1997; Iacono et al., 2003, 2008) that has been widely used in community models (such as WRF, CAM5, etc.). The RRTMG for shortwave (SW) (used in this study) can calculate fluxes and heating rates over 14 contiguous shortwave bands (820-50000 cm-1, or 0.2-12.20 microns). The individual band ranges (in wavenumbers, cm-1) are: 2600-3250, 3250-4000, 4000-4650, 4650-5150, 5150-6150, 6150-7700, 7700-8050, 8050-12850, 12850-16000, 16000-22650, 22650-29000, 29000-38000, 38000-50000, and 820-2600, with the last band coded out of sequence to preserve spectral continuity with the longwave bands.

GEOS-Chem/APM-RRTMG results have been included into the final manuscript on radiative forcing of the direct aerosol effect from AeroCom Phase II simulations (Myhre et al., 2013) and another manuscript on host model uncertainties in aerosol radiative forcing estimates (Stier et al., 2013). GEOS-Chem/APM-RRTMG has also been employed to study the first aerosol indirect radiative forcing (Yu et al., 2013).

Dust Particle Size Distribution

Click here for plot

Model-simulated annual mean dust particle mass size distributions in Beijing, normalized to total dust mass concentrations. The distribution peaks in diameter of ~3-5 μm. Based on the size distributions, about 90% of DST4 (diameter range 6 - 12 μm) is in PM10, and 30% of DST2 (diameter range 2 - 3.6 μm) is in PM2.5.

Implementation notes

We have downloaded the recently released version of GEOS-Chem v8-03-01. We are in the process of incorporating the APM model into v8-03-01. We are trying to have the GEOS-Chem and APM integrated as much as possible while minimize the modifications to GeosCore codes. A single switcher (LAPM, specified in input.geos) has been designed to turn on/off APM processes. When LAPM = F, the model should reduce to the original GeosCore simulation. We are still optimizing and testing the code. We will get the GEOS-chem + APM to the GEOS-Chem support team as soon as possible.


The plan is to work with the Support Team to update the code into v10-01 or later version.
Date Milestone
25 Aug 2010 Finally we got the APM properly incorporated into GEOS-Chem v8-03-01. The switcher LAPM appears to work very well and the results look reasonable. We are in the process of doing final clean-up and adding comments.
15 Sep 2010 A new SOA aging and condensation scheme has been developed based on v8-02-02 (Yu, ACPD [2010]). We have been trying to add new SOA scheme into version v8-03-01 which has additional anthropogenic organic species. More time is needed to make it work properly and evaluate the results. Considering that we have already been later in getting the code to GEOS-Chem support team and per Bob's suggestion, we decide to send the working code (v8-03-01 + APM without the SOA aging and condensation scheme) to the support team first and add SOA condensation scheme separately in the future.
23 Sep 2010 Following Bob's suggestion, Gan is in the process of adding and testing "#if defined( APM )" in the code to block out APM-specific sections. Will need some additional time to finish the task. Upon completion, the APM code will be ignored during compilation if we want to run the original version. If we compile the code with APM, we will still have switcher available in input.geos to turn on/off APM.
18 Oct 2010 Gan has finished adding "#if defined( APM )" to the integrated GEOS-Chem + APM. It took some additional time to re-arrange/re-organize the code in a way to facilitate the adding of "#if defined (APM)-- #endif" blocks. In addition, we spent sometime to move the APM code in the previous GEOS-Chem version to the most recent version(v8-03-02). Based on preliminary tests, the model works pretty well. The fully integrated APM model should be able to run under all the configurations that original GEOS-Chem v8-03-02 can run. Fangqun is doing the final cleanup and evaluation. We expect to deliver the integrated code to Bob shortly.
03 Nov 2010 The APM code fully integrated into GEOS-Chem-v8-3-2 has been delivered to Bob.
17 Feb 2011 Bob has finished merging APM into the most recent GEOS-Chem version under development (which will be released as v9-01-02). There were many changes between v8-03-02 and v9-01-01, so Bob decided to wait until v9-01-01 was stable before proceeding. We are in the process of evaluating the merged code.
10 Mar 2011 There were some bugs in the merged code which took us quite some time to debug and fix. We have finished 1 yr 4x5 simulation (GEOS5 met, 47 layers). Preliminary analysis indicates that the aerosol results are reasonable. There exist some differences between results of this version and those of previous versions, but these differences are expected considering the substantial changes/updates in v9-01-02. We are doing some final checks and will get the code back to Bob shortly.
11 Mar 2011 We have finished the evaluation of the GEOS-Chem v9-01-02 APM simulations and sent the final code back to Bob.
15 Mar 2011 APM is integrated into GEOS-Chem v9-01-02.
23 Aug 2011 APM is re-integrated into GEOS-Chem. 1-month benchmark simulation v9-01-02l ensured that the import of APM did not break the standard chemistry simulation.
07 Sep 2011 Fixed some outstanding minor issues in APM (bug fixes from G. Luo).
28 Feb 2012 Synched the APM code with GEOS-Chem v9-01-02f. The code compiles but we invite the APM team to repeat the validation. The wet scavenging algorithms have been modified since the last time APM was synched with the rest of GEOS-Chem, and this may require further coding changes.
3 May 2013 The updated APM code in GEOS-Chem v9-01-02 has been delivered to the Support Team. The new code includes the following new features: (1) Consideration of aging of secondary organic gases and their contribution to particle growth and SOA formation (Yu, ACP, 2009); (2) Development of scheme for online calculation of aerosol optical properties based on APM simulated particle size distributions and mixing states (Yu et al., ACP, 2012); (3) Fully integration of AER shortwave RRTMG with GEOS-Chem/APM (Yu et al., ERL, 2013); (4) Online calculation of aerosol direct radiative forcing (Ma et al., ACP, 2012); (5) Capability of APM for the simulations over nested domains (Luo et al., 2013).
2015 Recent development:

Integrated aerosol optical depth (AOD) and surface area based on APM aerosols with photo-chemistry and heterogeneous chemistry

Integrated cloud optical depth (COD) based on APM aerosols and explicit cloud droplet activation scheme with photochemistry

Added tracers and processes to predict global concentrations of amines (Yu and Luo, 2014)

Updated APM has been integrated into v9.2.0

APM works with 0.25x0.3125 nested domains

--Bob Y. 11:09, 29 February 2012 (EST)

Validation, Application, and Development

The first application of GEOS-Chem + APM focuses on predicting the number concentrations of particles in the troposphere. Significant amount of efforts have been devoted to validate the simulated global spatial distributions of particle number abundance, using a large amount of land-, ship-, and aircraft- based measurements. See following publications for details (full citations are given in the in the References section).

  1. Yu and Luo, ACP [2009]
  2. Yu et. al., JGR [2010]

The model has been applied in a number of other studies and results have been reported in the following papers:

  1. Luo and Yu, ACP [2011]
  2. Luo and Yu, ACP [2010]
  3. Yu and Luo, Atmosphere [2010]
  4. Yu et al., ACPD, [2011]

Recent Developments:

  1. Extended SOA formation model that considers oxidation aging and kinetic condensation: Yu, ACP [2011].
  2. Scheme to calculate aerosol optical properties based on size, composition, and mixing state resolved particle properties predicted by APM.
  3. APM nested domain capability. -- Done
  4. Integration of CCCMa radiative transfer module with APM to calculate online direct radiative forcing of various aerosols.
  5. Integration of AER shortwave RRTMG radiative transfer module with GEOS-Chem/APM.
  6. Implementation of schemes to calculate first aerosol indirect radiative forcing.

Validation plots:

  1. This plot shows simulated annual mean [SO2], [H2SO4], nucleation rates, and total number concentration of particles larger than 10 nm (CN10), averaged in the boundary layer. This is from a 1-year, 4x5 simulation for 2005 done by the SUNY/Albany group.

--Bob Y. 11:42, 15 March 2011 (EDT)

AEROCOM intercomparison

The AEROCOM-project is an open international initiative of scientists interested in the advancement of the understanding of the global aerosol and its impact on climate. We have made a commitment to represent the GEOS-Chem community to submit aerosol microphysics simulations based on GEOS-Chem + APM.

We submitted our size-resolved microphysics simulation results for A2-CTRL-2006 (GEOS-5, 2x2.5) to AEROCOM in January, 2011. The submitted results include OC and SOA mass concentrations which have been used in AEROCOM OA intercomparison. We also managed to submit AOD, AAOD, aerosol direct radiative forcing results to AeroCom in September 2011. The results have been or will be reported in the following papers:

Aerosol direct radiative forcing (Myhre et al., 2013)

Host model uncertainties in aerosol radiative forcing estimates (Stier et al., 2013)

Aerosol microphysics (Mann et al., 2013)

Aerosol organics (Tsigaridis et al., 2014)

APM in other community models

The same APM model initially developed for GEOS-Chem (Yu and Luo, 2009) has been incorporated into WRF-Chem (Luo and Yu, 2011). GEOS-Chem/APM results provide initial and boundary conditions for WRF-Chem/APM simulations.


  1. Iacono, M.J., J.S. Delamere, E.J. Mlawer, S.A. Clough: Evaluation of upper tropospheric water vapor in the NCAR community climate model (CCM3) using modeled and observed HIRS radiances. J. Geophys. Res., 108(D2), 4037, doi:10.1029/2002JD002539, 2003.
  2. Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M.W., Clough, S. A., and Collins,W. D.: Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113(D13), D13103, doi:10.1029/2008jd009944, 2008.
  3. Luo, G., and F. Yu, A numerical evaluation of global oceanic emissions of alpha-pinene and isoprene, Atmos. Chem. Phys., 10, 2007-2015, 2010. PDF
  4. Luo, G., and F. Yu, Sensitivity of global cloud condensation nuclei concentrations to primary sulfate emissions parameterizations, Atmos. Chem. Phys., 11, 1949-1959, doi:10.5194/acp-11-1949-2011, 2011. PDF
  5. Luo, G., and F. Yu, Simulation of particle formation and number concentration over the Eastern United States with the WRF-Chem + APM model, Atmos. Chem. Phys. Discuss., 11, 11281-11309,2011. PDF
  6. Ma, X., F. Yu, and G. Luo, Aerosol direct radiative forcing based on GEOS-Chem/APM and uncertainties, Atmos. Chem. Phys., 12, 5563-5581, doi:10.5194/acp-12-5563-2012, 2012.
  7. Mann, G. W., Carslaw, K. S., Reddington, C. L., Pringle, K. J., Schulz, M., Asmi, A., Spracklen, D. V., Ridley, D. A., Woodhouse, M. T., Lee, L. A., Zhang, K., Ghan, S. J., Easter, R. C., Liu, X., Stier, P., Lee, Y. H., Adams, P. J., Tost, H., Lelieveld, J., Bauer, S. E., Tsigaridis, K., van Noije, T. P. C., Strunk, A., Vignati, E., Bellouin, N., Dalvi, M., Johnson, C. E., Bergman, T., Kokkola, H., von Salzen, K., Yu, F., Luo, G., Petzold, A., Heintzenberg, J., Clarke, A., Ogren, J. A., Gras, J., Baltensperger, U., Kaminski, U., Jennings, S. G., O'Dowd, C. D., Harrison, R. M., Beddows, D. C. S., Kulmala, M., Viisanen, Y., Ulevicius, V., Mihalopoulos, N., Zdimal, V., Fiebig, M., Hansson, H.-C., Swietlicki, E., and Henzing, J. S.: Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity, Atmos. Chem. Phys., 14, 4679-4713, doi:10.5194/acp-14-4679-2014, 2014.
  8. Mlawer, E.J., S.J. Taubman, P.D. Brown, M.J. Iacono and S.A. Clough: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16,663-16,682, 1997.
  9. Myhre, G., Samset, B. H., Schulz, M., Balkanski, Y., Bauer, S., Berntsen, T. K., Bian, H., Bellouin, N., Chin, M., Diehl, T., Easter, R. C., Feichter, J., Ghan, S. J., Hauglustaine, D., Iversen, T., Kinne, S., Kirkev�g, A., Lamarque, J.-F., Lin, G., Liu, X., Lund, M. T., Luo, G., Ma, X., van Noije, T., Penner, J. E., Rasch, P. J., Ruiz, A., Seland, �., Skeie, R. B., Stier, P., Takemura, T., Tsigaridis, K., Wang, P., Wang, Z., Xu, L., Yu, H., Yu, F., Yoon, J.-H., Zhang, K., Zhang, H., and Zhou, C.: Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations, Atmos. Chem. Phys., 13, 1853-1877, doi:10.5194/acp-13-1853-2013, 2013.
  10. Stier, P., Schutgens, N. A. J., Bellouin, N., Bian, H., Boucher, O., Chin, M., Ghan, S., Huneeus, N., Kinne, S., Lin, G., Ma, X., Myhre, G., Penner, J. E., Randles, C. A., Samset, B., Schulz, M., Takemura, T., Yu, F., Yu, H., and Zhou, C.: Host model uncertainties in aerosol radiative forcing estimates: results from the AeroCom Prescribed intercomparison study, Atmos. Chem. Phys., 13, 3245-3270, doi:10.5194/acp-13-3245-2013, 2013.
  11. Tsigaridis, K., Daskalakis, N., Kanakidou, M., Adams, P. J., Artaxo, P., Bahadur, R., Balkanski, Y., Bauer, S. E., Bellouin, N., Benedetti, A., Bergman, T., Berntsen, T. K., Beukes, J. P., Bian, H., Carslaw, K. S., Chin, M., Curci, G., Diehl, T., Easter, R. C., Ghan, S. J., Gong, S. L., Hodzic, A., Hoyle, C. R., Iversen, T., Jathar, S., Jimenez, J. L., Kaiser, J. W., Kirkevåg, A., Koch, D., Kokkola, H., Lee, Y. H, Lin, G., Liu, X., Luo, G., Ma, X., Mann, G. W., Mihalopoulos, N., Morcrette, J.-J., Müller, J.-F., Myhre, G., Myriokefalitakis, S., Ng, N. L., O'Donnell, D., Penner, J. E., Pozzoli, L., Pringle, K. J., Russell, L. M., Schulz, M., Sciare, J., Seland, Ø., Shindell, D. T., Sillman, S., Skeie, R. B., Spracklen, D., Stavrakou, T., Steenrod, S. D., Takemura, T., Tiitta, P., Tilmes, S., Tost, H., van Noije, T., van Zyl, P. G., von Salzen, K., Yu, F., Wang, Z., Wang, Z., Zaveri, R. A., Zhang, H., Zhang, K., Zhang, Q., and Zhang, X.: The AeroCom evaluation and intercomparison of organic aerosol in global models, Atmos. Chem. Phys., 14, 10845-10895, doi:10.5194/acp-14-10845-2014, 2014.
  12. Yu, F., Updated H2SO4-H2O binary homogeneous nucleation rate look-up tables, J. Geophy. Res., 113, D24201, doi:10.1029/2008JD010527, 2008. PDF
  13. Yu, F., Ion-mediated nucleation in the atmosphere: Key controlling parameters, implications, and look-up table, J. Geophys. Res., 115, D03206, doi:10.1029/2009JD012630, 2010. PDF
  14. Yu, F., A secondary organic aerosol formation model considering successive oxidation aging and kinetic condensation of organic compounds: global scale implications, Atmos. Chem. Phys., 11, 1083-1099, doi:10.5194/acp-11-1083-2011, 2011. PDF
  15. Yu, F., Diurnal and seasonal variations of ultrafine particle formation in anthropogenic SO2 plumes, Environmental Science & Technology, 44 (6), 2011-2015, DOI: 10.1021/es903228a, 2010. PDF
  16. 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, 7691-7710, 2009. PDF
  17. Yu, F., and G. Luo, Oceanic dimethyl sulfide emission and new particle formation around the coast of Antarctica: A modeling study of seasonal variations and comparison with measurements, Atmosphere, 1(1):34-50, 2010. PDF
  18. Yu, F., G. Luo, T. Bates, B. Anderson, A. Clarke, V. Kapustin, R. Yantosca, Y. Wang, S. Wu, Spatial distributions of particle number concentrations in the global troposphere: Simulations, observations, and implications for nucleation mechanisms, J. Geophys. Res., 115, D17205, doi:10.1029/2009JD013473, 2010. PDF
  19. Yu, F., G. Luo , R. P. Turco , J. Ogren , R. Yantosca, Decreasing particle number concentrations in a warming atmosphere and implications, Atmos. Chem. Phys. Discuss., 11, 27913-27936, doi:10.5194/acpd-11-27913-2011, 2011. PDF
  20. Yu, F., G. Luo, and X. Ma, Regional and global modelling of aerosol optical properties with a size, composition, and mixing state resolved particle microphysics model, Atmos. Chem. Phys., 12, 5719-5736, doi:10.5194/acp-12-5719-2012, 2012.
  21. Yu, F.,X. Ma, and G. Luo, Anthropogenic contribution to cloud condensation nuclei and the first aerosol indirect climate effect, Environ. Res. Lett., 8 024029 doi:10.1088/1748-9326/8/2/024029, 2013.
  22. Yu, F. and Luo, G.: Modeling of gaseous methylamines in the global atmosphere: impacts of oxidation and aerosol uptake, Atmos. Chem. Phys., 14, 12455-12464, doi:10.5194/acp-14-12455-2014, 2014.

Known issues

None at this time.