the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 May 2026
A Revised Framework for Classifying Organic Aerosols using Wavelength-dependent Absorption Properties
Abstract. The representation of organic aerosols (OA) in global climate models fails to account for the wide range of species found in the atmosphere. Previous studies have observed that the optical parameters of OA species vary depending on the source from which they are emitted, as well as on their physical and chemical characteristics. However, accounting for all OA species in climate models is not practical. Hence, we have grouped OA species according to their optical parameters and physico-chemical characteristics. We classified OA as strongly absorbing brown carbon (S-BrC), moderately absorbing brown carbon (M-BrC), weakly absorbing brown carbon (W-BrC) and very weakly absorbing brown carbon (VW-BrC). We defined thresholds based on the imaginary refractive index (IRI) for a broad wavelength range from 300 to 550 nm. The classification demonstrates clear optical separation at 350–500 nm, with mass absorption coefficient (MAC) values spanning two orders of magnitude from VW-BrC (0.004 m2/g) to S-BrC (1 m2/g) at 400 nm. Representative species from each category were suggested as surrogates. This choice of species includes both absorbing and scattering OA and enables more accurate representation of OA in climate models and satellite retrievals, improving aerosol radiative forcing estimates.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1917', Anonymous Referee #1, 15 Jun 2026
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CC1: 'Comment on egusphere-2026-1917', Jing Li, 17 Jul 2026
This manuscript presents a timely and scientifically valuable framework that addresses a long-standing deficiency in global climate models: the oversimplification of organic aerosol optical properties. By synthesizing literature data across the 300 to 550 nm shortwave spectral region, the authors categorize OA into four distinct brown carbon (BrC) classes based on imaginary refractive index (IRI) thresholds: strongly absorbing (S-BrC), moderately absorbing (M-BrC), weakly absorbing (W-BrC), and very weakly absorbing (VW-BrC). The translation of these IRI thresholds into mass absorption coefficient (MAC) and single scattering albedo (SSA) spaces using Mie theory, along with the identification of specific surrogate species (e.g., tarballs, SRFA, toluene- and limonene-derived SOAs) for the upcoming Table of Aerosol Optics (TAO) database, provides a practical roadmap for modelers. The manuscript is logically structured and clearly highlights the severe limitations of legacy databases like OPAC. However, to maximize the scientific rigor and atmospheric applicability of this framework, the authors should address several methodological and conceptual issues before publication.
Major Comments
- The authors define specific IRI and MAC thresholds to separate the four BrC classes across different wavelengths (e.g., S-BrC defined as IRI > 0.03 at 550 nm). However, the manuscript lacks a quantitative explanation of how these precise numerical boundaries were established.
- Please clarify whether these cutoffs were derived from statistical clustering (e.g., k-means or quantile distributions) of the compiled literature data or if they correspond to specific physicochemical transitions (e.g., changes in volatility, O:C ratios, or aromaticity).
- At 300 nm, the MAC classification boundaries converge tightly (0.8–1.84 m²/g for W-BrC, 1.84–2.38 m²/g for M-BrC, and 2.38–3.5 m²/g for S-BrC), which the authors rightly note limits discriminating power. A sensitivity analysis demonstrating how slight shifts in these boundary thresholds impact top-of-atmosphere direct radiative forcing calculations would significantly strengthen the justification for these specific groupings.
- The introduction and methodology correctly acknowledge that OA optical properties evolve due to atmospheric processing and oxidation. In the real atmosphere, BrC undergoes rapid photochemical aging, where strong absorbers can photobleach into weak absorbers over hours to days, or where secondary chromophores form via NOx-facilitated pathways (as noted for toluene SOA).
- The current framework classifies discrete, static surrogates into fixed categories. How do the authors envision 3D Eulerian climate models implementing dynamic transitions between these classes during atmospheric transport?
- Providing a conceptual discussion or a recommended parameterization timescale for how an S-BrC or M-BrC particle might chemically transition into a W-BrC or VW-BrC particle during aging would greatly enhance the framework's utility for chemical transport modeling.
Minor and Technical Comments
- In Section 3.3, the authors note that while IRI values show four distinct categories at 300 nm, the computed MAC values converge and show minimal separation. Because MAC depends explicitly on the lognormal size distribution parameters and particle density applied in Equations (1) through (6), please clarify how much of this MAC convergence at 300 nm is driven by the intrinsic optical properties versus variations in the adopted median radii and geometric standard deviations listed in Table 1.
- The observation in Section 3.3 that OPAC systematically underestimates absorption for water-soluble and water-insoluble organics (S-BrC and M-BrC) while overestimating absorption for biogenic SOA (VW-BrC) is a critical finding. It would be beneficial to add a brief discussion on how these competing biases might interact in global models. For example, in biomass-burning plumes where S-BrC dominates, legacy OPAC assumptions likely cause severe underestimations of shortwave atmospheric heating rates, whereas in pristine biogenic environments, OPAC may artificially inflate aerosol absorption.
- In Section 4.1, the authors rightly note that the IRI of VW-BrC above 400 nm and W-BrC above 500 nm falls below the AERONET detection limit shown in Figure 1. To increase the practical impact of this observation, briefly discuss how remote sensing retrieval algorithms might handle these low-absorption regimes without defaulting to pure scattering assumptions, which currently propagate systematic biases in aerosol radiative forcing estimates.
- In Table 6, surrogate species are assigned specific complex refractive indices at discrete 50 nm intervals. Please clarify in the text whether the upcoming TAO database will provide continuous spectral parameterizations (e.g., polynomial fits or dispersion models across the wavelength continuum) or if climate models will be expected to linearly interpolate between these discrete 50 nm wavelength steps.
This revised framework represents a substantial step forward in organizing the optical complexity of organic aerosols for the climate modeling community. By tightening the quantitative justification for the classification thresholds and explicitly addressing how models should handle atmospheric aging, this work will serve as an indispensable reference for both modelers and observationalists. To help me tailor any further recommendations for the revised manuscript, could you clarify whether your primary target audience for the TAO database implementation is global climate modelers running coarse-grid radiative transfer codes, or regional chemical transport modelers attempting to resolve high-resolution aerosol aging dynamics?
Citation: https://doi.org/10.5194/egusphere-2026-1917-CC1 - The authors define specific IRI and MAC thresholds to separate the four BrC classes across different wavelengths (e.g., S-BrC defined as IRI > 0.03 at 550 nm). However, the manuscript lacks a quantitative explanation of how these precise numerical boundaries were established.
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RC2: 'Comment on egusphere-2026-1917', Anonymous Referee #2, 27 Jul 2026
General comments
This manuscript proposes a revised framework for classifying organic aerosols using wavelength-dependent absorption properties. The authors compile literature values of complex refractive indices and related physical parameters for selected OA species, classify OA into very weakly, weakly, moderately, and strongly absorbing categories, and calculate corresponding MAC and SSA values using Mie theory. The broader motivation is to improve the representation of OA optical properties in climate models, satellite retrievals, and radiative-transfer calculations.
However, the current work is mainly a literature-based optical-parameter compilation and classification exercise. The proposed framework has some conceptual value, but it does not yet provide a clear pathway for use in real atmospheric studies or models. In particular, the manuscript does not explain how users should determine the abundance or mass fraction of each proposed OA class for a given time, region, source mixture, altitude, or aging state. This missing step severely limits the practical value of the proposed classification for radiative forcing, satellite retrieval, or climate-model applications.
I therefore believe that the manuscript would require a fundamental revision and substantial redevelopment before it could be considered for publication. If an operational abundance-assignment method and application test cannot be provided, the work may be framed explicitly as a conceptual, perspective-like, or technical/database contribution rather than as an already deployable research framework.
Specific comments
1. The most important limitation is that the manuscript provides optical properties for selected surrogate species, but not a practical method for assigning ambient OA to the proposed classes. For radiative-effect calculations, modelers and observational researchers need to know not only the RI, MAC, and SSA of S-BrC, M-BrC, W-BrC, and VW-BrC, but also the amount of OA in each class for the specific atmosphere being studied. For example, users would need to know the relative abundance of these classes in wintertime urban China, biomass-burning plumes over the Amazon, aged wildfire smoke over the free troposphere, biogenic SOA-dominated forest regions, or polluted continental outflow. The manuscript does not provide such spatiotemporally resolved class abundances, nor does it explain how they can be obtained. This is a critical gap. Without a mapping from real atmospheric OA to the proposed optical classes, the framework cannot be directly used for regional or global radiative forcing calculations. At present, the manuscript appears to provide an optical lookup table rather than a complete classification framework that can be implemented in models or applied to observations.
2. The manuscript currently gives the impression that the proposed classification and TAO database can serve as a broadly applicable tool for climate models, satellite retrievals, and field campaigns. This claim is too strong in its current form. The work may be useful as a preliminary optical-parameter compilation or as a conceptual framework for organizing OA absorption strength. However, it is not yet a complete database for atmospheric radiative applications.
I recommend that the authors substantially revise the framing. The manuscript should explicitly state that the proposed classes are optical endmembers or representative surrogate classes, not a complete description of real-world OA spatiotemporal distributions. The authors should avoid implying that the framework can already be directly applied to global radiative forcing estimates unless they demonstrate such an application.
3. The classification into VW-BrC, W-BrC, M-BrC, and S-BrC appears to be closely related to previous absorption-strength frameworks, particularly those based on volatility and imaginary refractive index. The manuscript extends this idea across multiple wavelengths and compiles selected surrogate values, but the conceptual novelty is not sufficiently clear.
The authors should more explicitly state what is new relative to previous work. Is the novelty the wavelength-dependent thresholds? The selection of representative surrogates? The conversion from IRI to MAC and SSA? The intended inclusion in TAO? If so, the authors need to demonstrate why these additions constitute a substantial advance rather than an incremental reorganization of existing literature values.
At present, the work lacks a clear independent validation or application showing that the proposed scheme improves model performance, retrieval interpretation, or radiative forcing estimates relative to existing OA representations such as OPAC or previous BrC parameterizations.
4. The manuscript first classifies OA into BrC and scattering OA, but then assigns biogenic SOA and anthropogenic SOA to VW-BrC and W-BrC categories. This creates conceptual confusion. If a species is essentially scattering, has SSA close to 1, has very low MAC, and has an imaginary refractive index near or below detection limits at visible wavelengths, it is not clear that it should be called brown carbon in the usual atmospheric sense. The use of “VW-BrC” may be defensible as an operational optical class, but the manuscript needs to make this distinction explicit.
I recommend that the authors consider alternative terminology such as “very weakly absorbing OA” and “weakly absorbing OA,” rather than labeling all low-absorption OA as BrC. If the authors retain the BrC terminology, they should clearly explain that VW-BrC and W-BrC are operational absorption-strength classes and do not necessarily imply that all included species would be identified as brown carbon in the traditional chemical or observational sense.
5. The manuscript selects a small number of surrogate species for each class, including fresh and aged tarballs, acetone-extracted OA, SRFA, toluene-derived SOA, and limonene or α-pinene-derived SOA. These choices are partly reasonable, but the justification is not sufficient for a framework intended to serve broad atmospheric applications.
The authors should provide a more systematic rationale for surrogate selection and discuss the representativeness and limitations of each surrogate.
6. To establish practical significance, the authors should include at least one demonstration showing that the proposed framework improves something relative to existing OA representations. Possible demonstrations may include: a radiative-transfer sensitivity calculation comparing OPAC-style OA, single non-absorbing OA, and the proposed multi-class OA representation; a regional or global model sensitivity test showing the impact of plausible class fractions on direct radiative forcing; comparison with independent field measurements of multi-wavelength MAC, SSA, or AAE; application to biomass-burning, urban, and biogenic-dominated cases to show how the classification performs under different source regimes; a satellite retrieval sensitivity test showing how the different OA classes affect inferred aerosol optical properties.
Without such a demonstration, the manuscript remains largely conceptual and does not provide enough evidence that the framework has substantial practical value.
Technical corrections
- The manuscript should consistently distinguish OA from OC. The current discussion sometimes uses OA classification while Table 2 refers to “OC classification,” which may cause confusion.
- The term “strongly absorbing BrC” should be carefully distinguished from black carbon. The manuscript should make clear that high BrC absorption does not imply BC-like refractoriness, morphology, lifetime, or source behavior.
- Table 5: the W-BrC SSA range at 300 nm is written as “0.87-92”; this presumably should be “0.87-0.92”.
- Throughout: define abbreviations at first use and use them consistently, including IRI, RI, MAC, SSA, TB, HULIS, SRFA, TAO, LVOC, SVOC, ELVOC, EELS, and AERONET.
- References: correct duplicated DOI prefixes such as “https://doi.org/https://doi.org/...”, standardize journal-title capitalization, complete incomplete bibliographic entries, and check typographical problems such as “De-scription”, “sub-stances”, “Berdowslci”, “Zandyelt”, and inconsistent capitalization of journal names.
Citation: https://doi.org/10.5194/egusphere-2026-1917-RC2
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The manuscript presents a revised framework for classifying organic aerosols based on wavelength-dependent absorption properties. By compiling refractive index data from previous studies and translating the proposed IRI thresholds into MAC and SSA space, the authors attempt to provide a practical optical classification scheme for organic aerosols and brown carbon. The topic is relevant to aerosol-radiation interactions, climate-model parameterization, and satellite retrieval applications. The manuscript is generally well structured, and the effort to extend previous BrC classification concepts from a single reference wavelength to a broader spectral range is valuable. However, several issues need to be addressed before the manuscript can be considered for publication.