the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Apr 2026
ORACLE-lite (v3.0): A reduced-complexity module for simulating organic aerosol formation and evolution in long term chemistry-climate simulations
Abstract. The representation of organic aerosol (OA) in global chemistry–climate models remains computationally challenging due to the large number of volatility-resolved tracers required to simulate gas–particle partitioning and aging. We present ORACLE-lite (v3.0), a reduced-complexity version of the ORACLE module implemented within the ECHAM/MESSy Atmospheric Chemistry (EMAC) model, specifically designed for multi-decadal simulations. ORACLE-lite preserves the core mechanisms governing OA formation while employing the minimum number of surrogate tracers required to represent organic compounds across the principal volatility classes, including low-volatility (LVOC), semi-volatile (SVOC), intermediate-volatility (IVOC), and volatile organic compounds (VOC). This structured reduction lowers the computational cost per model time step by a statistically robust speed-up of 13.9 ± 1.1 %, enabling efficient multi-decadal simulations while maintaining a dynamic representation of volatility evolution. ORACLE-lite is evaluated in a 21-year global simulation (2000–2020) and compared against the standard ORACLE configuration. The simplified volatility basis set modifies gas–particle partitioning, leading to enhanced primary organic aerosol (POA) concentrations of up to 5 µg m-3 over major biomass-burning and industrial regions, while secondary organic aerosol (SOA) concentrations decrease over biomass-burning regions and increase over anthropogenic source regions due to differences in precursor allocation among volatility bins. Model performance is assessed against long-term aerosol mass spectrometer (AMS) and aerosol chemical speciation monitor (ACSM) observations across North America, Europe, and Eastern Asia. Simulated total OA agrees well with observations over North America (normalized mean bias, NMB = −4 %) and Eastern Asia (NMB = −29 %), while larger seasonal biases occur in Europe, particularly in winter. Over tropical and subtropical regions, the model shows an overall underestimation (NMB ≈ −39 %) with substantial regional variability. Across all regions, the model reproduces the observed spatial distribution and seasonal variability of OA mass and its primary and secondary components within a factor of two for the majority of sites. These results demonstrate that ORACLE-lite provides a computationally efficient and physically grounded framework capable of reproducing the key features of global OA variability, making it suitable for long-term chemistry–climate simulations.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2026-1709', Anonymous Referee #1, 24 Apr 2026
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AC1: 'Reply on RC1', Alexandra Tsimpidi, 22 Sep 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-1709/egusphere-2026-1709-AC1-supplement.pdf
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AC1: 'Reply on RC1', Alexandra Tsimpidi, 22 Sep 2026
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CEC1: 'Comment on egusphere-2026-1709 - No compliance with the policy of the journal', Juan Antonio Añel, 22 May 2026
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlFirst, although we are aware that you are not in a position to make pubic the MESSy code, this not necessarily applies to new developed modules. In this regard, we expect that you release publicly the new ORACLE-lite (v3.0) module. If there is a reason that explains that you can not publish new developed code, please, reply with it to this comment. If such reason does not exist, please, publish the ORACLE-lite code in an appropriate repository according to our policy, and reply to this comment with its link and permanent handler (e.g. DOI).
Also, you must publish the data used for comparison purposes, and state in the Code and Data Availability section how to access them. Currently you do not do it.
Therefore, you have to reply to this comment in a prompt manner with the information for the repositories containing all the above requested information. The reply must include the link and permanent identifier (e.g. DOI). Also, any future version of your manuscript must include the modified section with the new information.
I must note that if you do not fix this problem, we cannot continue with the peer-review process or accept your manuscript for publication in GMD.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/egusphere-2026-1709-CEC1 -
AC3: 'Reply on CEC1', Alexandra Tsimpidi, 22 Sep 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-1709/egusphere-2026-1709-AC3-supplement.pdf
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AC3: 'Reply on CEC1', Alexandra Tsimpidi, 22 Sep 2026
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RC2: 'Comment on egusphere-2026-1709', Anonymous Referee #2, 28 Aug 2026
The present study introduces the reduced organic aerosol module ORACLE-lite, which utilizes a simplified volatility basis set to provide a computationally less expensive solution for global chemistry-climate models. The authors demonstrate that the new module yields reasonable results and address its limitations. Publication is recommended, subject to the following minor remarks:
p.4 l.144: The relationship among the ORACLE family models is unclear. Is ORACLE-IVOC derived from ORACLE-2d or ORACLE-base?
Figure 1: ‘Short Term’ should replace ‘Sort Term’. In the ORACLE-lite box, the term ‘Identify’ implies an analysis of trade-offs between computational cost and accuracy. If sensitivity studies were not conducted to determine the current setup, consider revising the wording.
Figure 2: A more detailed figure description would be beneficial. The SOA classes remain unclear.
p.6 ll.188 ff.: Please specify the underlying emission inventories. Is there a risk of double counting IVOC emissions?
Table 1: Does each VOC group include two associated product classes? Consider adding the product classes to the table. Additionally, is 'SOA yield' the most accurate term, or would 'stoichiometric yield' be more appropriate, given that it does not describe the gas-to-particle partitioning?
Reaction R5 and R6: Do these reactions apply exclusively to anthropogenic species, or are biogenic species also included? Additionally, the meaning of 'a' is not defined.
Figure 3: The figure references SOAlv, which is not addressed elsewhere in the manuscript.
Figure 4: The color scheme in panel (a) is relatively dark. Consider selecting a color scheme that assigns zero to white or a brighter color.
p.12 l.379. How does the figure in the supplement support this statement?
Figure 7: Instead of plotting seasonal averages, consider including regression lines.
A general suggestion for your discussion is to include a direct comparison of ORACLE-lite and ORACLE-base with the observational data to highlight performance differences, potentially across different regions.
Citation: https://doi.org/10.5194/egusphere-2026-1709-RC2 -
AC2: 'Reply on RC2', Alexandra Tsimpidi, 22 Sep 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-1709/egusphere-2026-1709-AC2-supplement.pdf
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AC2: 'Reply on RC2', Alexandra Tsimpidi, 22 Sep 2026
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Cited
2 citations as recorded by crossref.
- Implementation of the ORACLE (v1.0) organic aerosol composition and evolution module into the EC-Earth3-AerChem model S. Kakavas et al. https://doi.org/10.5194/gmd-19-4271-2026
- Quantifying Drivers of Aerosol Acidity in Diverse Atmospheric Chemical Regimes X. Wang et al. https://doi.org/10.1021/acsestair.6c00078
This work documents a newly-developed model (ORACLE-lite) for organic aerosol with simplified representation of organic aerosol volatility and aging chemistry and embedded as a module in the global model ECHAM/MESSy, and further demonstrates the model’s ability to improve computational efficiency (in comparison to a previous version of the ORACLE model) and to capture observed OA/POA/SOA concentrations across the globe. Several model deficiencies demanding further strengthening are identified (e.g., aqueous SOA formation). Overall, this works represents a concrete step of advancement of the ECHAM/MESSy-ORACLE modeling system, which has continuously progressed over the past decade. There is no major issue from this reviewer and publication is recommended if the following comments can be addressed.
1. In Figure 1, change “Sort Term” to “Short Term”
2. In Figure 2, the meaning of SOA-sv/SOA-iv/SOA-v are not immediately clear; would be helpful to add a legend.
3. In Line 190-191, the authors used the term “emission factor”, which is confusing, because what the authors really mean is the emission ratio of I/S/LVOCs to the traditional emission factor of POA. Please revise the text here.
4. In Line 235, it is stated that “only homogeneous gas-phase aging is included”. Since the ECHEM-MESSy-ORACLE system is intended for long-term global simulations, wouldn’t heterogeneous oxidation have a substantial impact on this spatial and temporal scale (Hodzic et al., 2016)? If the authors agree, please acknowledge this as an issue for future improvement in the Conclusions Section.
5. In Figure 4a, there seems to be some hotspots of OA in Canada. Are there any justifications for these hotspots? There seems to be no discussion of it in the current text.
6. In Figure 4b, the OA prediction from ORACLE-lite differs significantly from the prediction from ORACLE-base, mainly due to a change of volatility bins used to represent POA. Since it is not definite which representation (ORACLE-lite or ORACLE-base) is better, which results should be trusted more? Please elaborate and add to the text.
7. In Section 3.2.1, there lacks a description of the modeling configuration; that is, what emission inventories and meteorological fields (if any) are used. Are the emission inventories up-to-date?
8. In Section 3.2.1, it is stated that “Overall, the model reproduces the large-scale spatial variability of OA across regions, with most locations showing agreement within a factor of two”, which is not substantiated by the data shown in Figure 6 alone. Therefore, the reviewer suggests that Figure 6 and Figure 7 be combined to form a single Figure, so that the scatter plots can be visible alongside the relevant text.
9. In Section 3.2.1, it is not clear whether the model-measurement comparison is on basis of annual average or 20-year average. Please clarify early in this section.
10. In Line 523, “key missing SOA formation pathways” is mentioned; thus, a comprehensive discussion of the missing pathways should include auto-oxidation of larger molecules (e.g., monoterpenes) to rapidly form extremely low volatility organic compounds (Bianchi et al., 2019).
11. In Line 99, the ORACLE-lite model is described as “standalone”. If so, would it be valuable for the OA model to be configured as a 0-dimension box model to simulate laboratory experiments in the literature to further validate model predictions in an isolated setting? Please consider this possibility and discuss it in the Conclusions Section.
References:
Bianchi, F., Kurtén, T., Riva, M., Mohr, C., Rissanen, M. P., Roldin, P., Berndt, T., Crounse, J. D., Wennberg, P. O., Mentel, T. F., Wildt, J., Junninen, H., Jokinen, T., Kulmala, M., Worsnop, D. R., Thornton, J. A., Donahue, N., Kjaergaard, H. G., and Ehn, M.: Highly Oxygenated Organic Molecules (HOM) from Gas-Phase Autoxidation Involving Peroxy Radicals: A Key Contributor to Atmospheric Aerosol, Chem. Rev., 119, 3472–3509, https://doi.org/10.1021/acs.chemrev.8b00395, 2019.
Hodzic, A., Kasibhatla, P. S., Jo, D. S., Cappa, C. D., Jimenez, J. L., Madronich, S., and Park, R. J.: Rethinking the global secondary organic aerosol (SOA) budget: stronger production, faster removal, shorter lifetime, Atmos. Chem. Phys., 16, 7917–7941, https://doi.org/10.5194/acp-16-7917-2016, 2016.