| 04 Sep 2026
Propagation of CAMS Aerosol Uncertainty to Clear-sky Solar Irradiance Estimates
Abstract. Atmospheric aerosols strongly influence surface solar radiation (SSR) through scattering and absorption processes, introducing substantial uncertainties into SSR estimates. Despite the increasing use of atmospheric reanalysis products for solar resource assessment, the propagation of aerosol-related uncertainty into SSR calculations remains insufficiently quantified. In this study, we derive aerosol optical depth (AOD) uncertainties for the fourth-generation ECMWF Atmospheric Composition Reanalysis 4 (EAC4) and investigate their propagation into clear-sky estimates of global horizontal irradiance (GHI) and direct normal irradiance (DNI) over the 60° S–60° N domain for the 2003–2024 period. Prognostic AOD uncertainties are calculated using collocated EAC4 and Aerosol Robotic Network (AERONET) observations, while the resulting global mean relative AOD uncertainty is approximately 47 %. Radiative kernels are subsequently used to quantify the sensitivity of clear-sky SSR to AOD perturbations, enabling the propagation of AOD uncertainty into GHI and DNI estimates. The results reveal pronounced spatial and seasonal variability, with the largest propagated uncertainties occurring over regions affected by persistent anthropogenic pollution, biomass burning, and desert dust. Relative GHI uncertainties generally remain below 5–6 %, whereas relative DNI uncertainties frequently exceed 18 % over major aerosol source regions and reach 25–35 % seasonally under elevated aerosol loading. Overall, the proposed framework provides a computationally efficient methodology for generating uncertainty-aware solar radiation products and supports future investigations of aerosol-driven dimming and brightening, as well as long-term solar energy resource assessments under changing atmospheric conditions.
This work presents an approach for estimating the uncertainty of the aerosol optical depth (AOD) provided by the ECMWF Atmospheric Composition Reanalysis 4 (EAC4) and for assessing its impact on clear-sky solar irradiance. The approach relies on a linear parameterization of the AOD estimation uncertainty as a function of the EAC4 AOD, followed by the propagation of this uncertainty into solar irradiance estimates using a radiative-kernel approach.
The topic is highly relevant for a wide range of applications in which the accuracy of clear-sky solar irradiance estimates is important, particularly for solar resource assessment. The proposed framework is interesting and provides a potentially useful contribution to the quantification of aerosol-related uncertainties in surface solar radiation estimates. Overall, I find the approach promising and the topic relevant to the scope of the journal. However, several aspects of the methodology and their underlying assumptions would benefit from further clarification and discussion, as detailed in the comments below.
General comments:
The proposed approach relies on several assumptions regarding the spatial and temporal “stationarity” of the AOD uncertainty. In particular, the linear relationship between AOD and its estimation error is assumed to be spatially homogeneous and to remain valid for periods other than the one used to derive the regression.
This assumption may be particularly important under extreme or unusual aerosol conditions, for which the actual AOD uncertainty could differ substantially from that predicted by the model. It would therefore be useful to clarify more explicitly what type of uncertainty is being quantified by the proposed approach. As currently formulated, the resulting uncertainty appears to represent a statistical or climatological characterization of the AOD estimation error, rather than an uncertainty that dynamically responds to the specific atmospheric conditions at a given time and location.
This distinction is important for interpreting the resulting uncertainty in the propagated solar irradiance estimates and should be discussed more explicitly in the manuscript.
The evaluation of the proposed uncertainty model remains limited. In particular, the evaluation using BSRN appears to focus on the accuracy of the EAC4 clear-sky irradiance itself, rather than on the ability of the proposed uncertainty model to characterize the actual errors.
A comparison between the observed EAC4 clear-sky irradiance errors and the uncertainty estimated by the proposed approach would therefore be highly relevant. Such an analysis would help assess whether the estimated uncertainties are consistent with the actual distribution of errors and, in particular, whether they provide an adequate characterization of the uncertainty under different atmospheric conditions.
Specific comments :
L11-13 : “Despite the increasing use of atmospheric reanalysis products for solar resource assessment, the propagation of aerosol-related uncertainty into SSR calculations remains insufficiently quantified”
The logical connection introduced by “Despite” is not entirely clear. The increasing use of atmospheric reanalysis products for solar resource assessment does not, by itself, seem to contrast with the insufficient quantification of aerosol-related uncertainty in SSR calculations. Please clarify the intended link between these two statements, or consider rephrasing the sentence.
L15-17 : « Prognostic AOD uncertainties are calculated using collocated EAC4 and Aerosol Robotic Network (AERONET) observations, while the resulting global mean relative AOD uncertainty is approximately 47%. »
The use of “while” is unclear here, as the two clauses do not seem to express a contrast. If the second clause is intended to report the result of the uncertainty calculation described in the first clause, “resulting in” or “yielding” might be more appropriate.
L110: “automatedcloud” a space is missing
Fig1: you represents the surface albedo at 550nm which is not consistent with the text that mention the use of a broadband albedo (line 150).
L148-149: you decided to use monthly average values for TCWV and TCO, which is an important methodological choice. Knowing that these quantities are available in EAC4, can you explain the motivations and the consequences of this choice?
Eq.4 This equation presumes that a+b AOD is less than 0.01. This is guaranteed by the value of a and the fact that AOD>0. Maybe add a sentence?