Jenni Kontkanen
This data set contains output data from the Atmospheric Cluster Dynamics Code (ACDC) model, which simulates the clustering of atmospheric vapours and the growth of these clusters by further molecular and cluster-cluster collisions.
The data can be used for predictions of the production of new secondary particles from clustering and condensation of atmospheric vapours. It can be used for e.g. benchmarking or evaluating parameterizations of new particle formation (NPF) processes.
The data is output of a computational process model, and hence does not represent a specific time period or location. Simulation sets are calculated for a two-component system containing a quasi-unary inorganic compound corresponding to a mixture of sulfuric acid (SA) and ammonia (NH₃) or dimethylamine (DMA) and an organic compound. Data are provided for 18 simulations.
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Citation
Jenni Kontkanen (2022) Atmospheric molecular cluster growth data. Dataset version 1. Bolin Centre Database. https://doi.org/10.17043/kontkanen-2022-cluster-growth-1
References
Kontkanen J, Olenius T, Kulmala M, Riipinen I (2018) Exploring the potential of nano-Köhler theory to describe the growth of atmospheric molecular clusters by organic vapors using cluster kinetics simulations. Atmos. Chem. Phys. 18:13733 – 13754. https://doi.org/10.5194/acp-18-13733-2018
Olenius T, Riipinen I (2017) Molecular-resolution simulations of new particle formation: Evaluation of common assumptions made in describing nucleation in aerosol dynamics models. Aerosol Sci. Tech. 51:397 – 408. https://doi.org/10.1080/02786826.2016.1262530
McGrath MJ et al. (2012) Atmospheric Cluster Dynamics Code: a flexible method for solution of the birth-death equations. Atmos. Chem. Phys. 12:2345 – 2355. https://doi.org/10.5194/acp-12-2345-2012
Data description
The data is in the form of text files. The provided 18 datafiles (total compressed size ~44GB) correspond to one simulation each with the Atmospheric Cluster Dynamics Code (ACDC) model. Simulation sets are identified in Table 3 of Kontkanen et al. (2018), and are calculated for a system containing sulfuric acid (SA), ammonia (NH₃), dimethylamine (DMA) and organic compounds.
The table provided below is mapping each file name to the simulation sets of Table 3 in Kontkanen et al. (2018). The retrievable files are compressed (tar gzip, the total uncompressed size is ~329GB) and contain the data in ASCII format. For the interpretation of the output of the ACDC model used by Kontkanen et al. (2018) the interested user is referred to McGrath et al. (2012) and Olenius and Riipinen (2017).
| simulation set (corr. Table 3) |
inorganic compound |
organic compound (pSAT [Pa]) |
organic compound mass [amu] |
vapor concentration profiles |
growth rate study |
activation size study |
file name |
filesize [GB] |
deflated size [GB] |
| 1 |
SA-DMA |
1.00E-12 |
300 |
const. concentrations |
no |
no |
AE_SADMA_ELmono_1e12.tar.gz |
19 |
2.5 |
| 1 |
SA-DMA |
1.00E-10 |
300 |
const. concentrations |
no |
no |
AE_SADMA_ELmono_1e10.tar.gz |
23 |
3 |
| 1 |
SA-DMA |
1.00E-09 |
300 |
const. concentrations |
no |
no |
AE_SADMA_ELmono_1e9.tar.gz |
26 |
3.4 |
| 1 |
SA-DMA |
1.00E-08 |
300 |
const. concentrations |
no |
no |
AE_SADMA_ELmono_1e8.tar.gz |
31 |
4.1 |
| 1 |
SA-DMA |
1.00E-08 |
300 |
const. concentrations |
no |
yes |
AE_SADMA_ELmono_1e8_act.tar.gz |
11 |
1.1 |
| 1 |
SA-DMA |
1.00E-08 |
300 |
const. source rates |
yes |
no |
AE_SADMA_ELmono_1e8_GR.tar.gz |
2.5 |
0.83 |
| 1 |
SA-DMA |
1.00E-07 |
300 |
const. concentrations |
no |
no |
AE_SADMA_ELmono_1e7.tar.gz |
37 |
5 |
| 1 |
SA-DMA |
1.00E-07 |
300 |
const. concentrations |
no |
yes |
AE_SADMA_ELmono_1e7_act.tar.gz |
11 |
1.1 |
| 1 |
SA-DMA |
1.00E-07 |
300 |
const. source rates |
yes |
no |
AE_SADMA_ELmono_1e7_GR.tar.gz |
0.77 |
0.26 |
| 1 |
SA-DMA |
1.00E-06 |
300 |
const. concentrations |
no |
no |
AE_SADMA_ELmono_1e6.tar.gz |
36 |
4.8 |
| 2 |
SA-DMA |
1.00E-11 |
300 |
const. concentrations |
no |
no |
AE_SADMA_ELmono_1e11.tar.gz |
20 |
2.7 |
| 2 |
SA-DMA |
1.00E-11 |
300 |
const. source rates |
yes |
no |
AE_SADMA_ELmono_1e11_GR.tar.gz |
0.81 |
0.27 |
| 3 |
SA-NH3 |
1.00E-08 |
300 |
const. concentrations |
no |
no |
AE_SANH3_ELmono_1e8.tar.gz |
8.8 |
1.4 |
| 3 |
SA-NH3 |
1.00E-08 |
300 |
const. source rates |
yes |
no |
AE_SANH3_ELmono_1e8_GR.tar.gz |
1.8 |
0.59 |
| 4 |
SA-DMA |
1.00E-08 |
600 |
const. concentrations |
no |
no |
AE_SADMA_ELmono_1e8_large.tar.gz |
11 |
1.1 |
| 4 |
SA-DMA |
1.00E-08 |
600 |
const. source rates |
yes |
no |
AE_SADMA_ELmono_1e8_large_GR.tar.gz |
1.9 |
0.48 |
| 5 |
SA-DMA |
1.00E-08 |
300 |
sinusoidal source rates |
no |
no |
AE_SADMA_ELmono_1e8_sin.tar.gz |
22 |
2.7 |
| 5 |
SA-DMA |
1.00E-08 |
300 |
sinusoidal and const. source rates |
no |
no |
AE_SADMA_ELmono_1e8_sin_s.tar.gz |
65 |
8.3 |
Comments
Please, cite the article by Kontkanen et al. (2018) when using this dataset.
GCMD science keywords
Earth science > Atmosphere > Aerosols > Organic particles
GCMD location
Vertical location > Troposphere
Project
This study was funded by the European Research Council (grant no. 742206), the Academy of Finland Center of Excellence program (grant no. 307331), the Swedish Research Council Formas (grant no. 2015-749) and the Knut and Alice Wallenberg Foundation (academy fellowship AtmoRemove).
Publisher
Bolin Centre Database
DOI
10.17043/kontkanen-2022-cluster-growth-1
Published
2022-03-03 14:11:33