the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Mar 2026
Two Decades of Aerosol Optical Depth evolution from CAMS Reanalysis
Abstract. Aerosol optical depth (AOD) is a key indicator for evaluating global climate change, and its long-term trends provide critical insights into the evolving climatic impact of atmospheric aerosols. In this study, we analyse the long-term (2003–2024) AOD record from the fourth-generation ECMWF Atmospheric Composition Reanalysis (EAC4) to investigate global and regional variability and long-term trends over the past two decades. EAC4 is evaluated against independent AERONET observations from 178 stations selected using strict data-availability criteria. The impact of satellite assimilation is explicitly quantified through comparison with a free-running control simulation (CTRL). EAC4 reproduces observed global AOD variability with high skill (R = 0.84, RMSE = 0.12, IOA = 0.90; >1.6 million collocations) and shows strong agreement with AERONET-derived trends at stations exhibiting statistically significant changes (R = 0.89), correctly capturing the trend sign at 96.7 % of sites. Negative trends are generally well represented, while positive trends are more frequently underestimated. Trend analyses across 18 regions of interest reveal significant AOD declines over eastern China, North Africa, Central Europe, and the eastern United States, alongside persistent increases over South Asia and the Middle East. Decadal analyses identify pronounced transitions, including a reversal from increasing to decreasing trends over eastern China and shifts in several dust- and biomass-burning-dominated regions. Sliding-window trend diagnostics further highlight temporal variability and regime changes. By integrating long-term reanalysis data, independent validation, and component-resolved diagnostics, this study provides a consistent assessment of recent global AOD evolution.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1107', Anonymous Referee #1, 15 May 2026
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RC2: 'Comment on egusphere-2026-1107', Anonymous Referee #2, 08 Nov 2026
Summary
This paper shows the impact of satellite data assimilation in the accuracy of the AOD 550 nm product of CAMS reanalysis dataset. The authors discuss differences in the biases which arise when comparing the EAC4 dataset (includes satellite data assimilation), and the control dataset CTRL (does not include satellite data assimilation) with ground-based data from the AERONET network. In addition, a trend analysis on regional and global scale is conducted.
My background is ground-based aerosol remote sensing, satellite data validation, and climatological analysis rather than global aerosol emissions and transportation. For this reason, I focused my review on the methodology and the comparison between the EAC4 and the CTRL dataset.
The paper is carefully constructed and clearly written. I would recommend it for publication after some minor revisions.
The methodology is solid. The authors use a variety of well-documented statistical tests to evaluate statistical differences between the performance of the EAC4 and CTRL dataset. Strong points of the methodology include the box-whisker plots in addition to the bulk comparisons which indicate features hidden in the noise, de-seasonalising the timeseries before generating trends, and investigating whether using the nearest-neighbor grid-box when comparing model simulations with ground-based measurements has an impact in their analysis.
I agree with Reviewer 1 that the paper is rather long and this can break the focus and interest of the readers. I would also recommend to shorten it or break it into two papers, if the authors are willing.
General comments:
Section 2.2: Are the temporal criteria used for monthly averages purely empirical? A way to test whether they result in a representative monthly mean value is “bootstrapping”. For example, monthly mean values can be resampled repeatedly by randomly excluding each time a small number of the values used to generate the mean. This will indicate whether the mean value is sensitive to the number of points and will show if the current sampling criteria need to be revised.
Specific comments:
Lines 190-191: Is the small global AOD bias related directly to the lack of AATSR data that were used for assimilation?
Lines 197-199: This paragraph can be written in a more clear way. The important points are that the main product analyzed is the AOD and that the focus is the impact of satellite data assimilation to the accuracy of the modeled AOD. The impact of satellite data assimilation in the mixing ratio of aerosol species if of secondary importance.
Lines 256-258: This is a good point. I was wondering if using coastal and/or island stations could lead to increased biases when comparing ground-based observations with CAMS simulation. Because grid-cells are 80x80 km wide sometimes in such regions aerosols above land and aerosols above water would be averaged together. However, this seems to not be the case as the authors already tried using the information from adjacent grid-points.
Figure 2: Please indicate the wavelength of the AOD on the plot
Figure 6 and 7: Consider providing those figures in better resolution. Because of the small letter and simple size, the reader has to zoom in, and then the resolution limits start to show.
Figure 7: On a first glance it is not clear what the purpose of the colorbar is because the lime outline dominates over the faded colors of sites which have mild significant trends. Please add some more explanations about the symbols in this graph. Please also add the trend period on the graph or caption.
Technical comments:
Lines 47-50: Consider splitting this long sentence in smaller parts
Line : Please rephrase “instantaneous AERONET AOD” to e.g. AERONET AOD in the highest available temporal resolution
Citation: https://doi.org/10.5194/egusphere-2026-1107-RC2
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- 1
This manuscript analyzes the two-decadal (2003-2024) AOD variability and trends using the AOD from the 4th generation of the ECMWF atmospheric composition reanalysis (EAC4) that assimilates the AATSR and MODIS satellite AOD into the CAMS model. First, AOD from EAC4 and CTRL run without assimilation are evaluated with AERONET observations from 178 stations using seven specific metrics (R, MAE, RMSE, RMB, %MFE, %FGE, and IOA). The evaluation shows better agreement between EAC4 and AERONET than between CTRL and AERONET. Second, the global and 18 regional AOD trends over the two decades (2003-2024) as well as over the first (2003-2013) and second (2014-2024) half are examined for both EAC4 and CTRL to understand the impact of satellite data assimilation. Third, the AOD trends from individual aerosol species of dust, sulfate, organic matter, black carbon, and sea salt from EAC4 in 18 regions are assessed to gain insights in explaining the contributions of aerosol species to the total AOD trends.
I found this study is thorough and robust. It provides quantitative evaluations of the quality of AOD reanalysis and model simulations with multiple evaluation metrics and reports the 22-year AOD trends in different regions of the world. The results are appropriate for publications on AMT/ACP. However, it is a very long paper that takes quite a bit effort to go through. I would suggest either shorten it to make it more concise or break it into two papers, if the authors are willing. In addition, I do have several major comments and many specific ones that should be addressed or clarified before publication.
Major comments:
(1) It is described that in 2003-March 2012 the AOD from both AATSR and Aqua-MODIS were used to produce the EAC4 AOD reanalysis, but after that only Aqua-MODIS was used. How consistent is EAC4 AOD in the two periods (before and after March 2012)? Have you examined the differences by excluding AATSR in data assimilation for 2004-March 2012 to quantify the effects?
(2) It is not clear to me how the fractions of speciate AOD get adjusted during assimilation (e.g., the pie charts for speciated AOD fractions from CTRL and EAC4 shown in Fig. 4b and Fig. 6). Since there is no information about aerosol composition in satellite AOD data, the speciated AOD fractions are expected to be very similar between CTRL and EAC4 even though the values of total AOD are changed after assimilation.
(3) Related, it is important to clarify that speciated AOD, or AOD type as referred in the manuscript, are not reanalysis products and should not be misused.
(1) Nitrate: The CAMS aerosol omits one of the major aerosol species – nitrate. Numerous observations and modeling studies have shown that nitrate aerosol have emerged to be one of the major aerosol species especially in urban and agriculture environments where its amount is similar to, or even higher than, sulfate aerosol. I understand the complexity in incorporating nitrate aerosol in global models because chemical productions from multiple precursor gases and the thermodynamic equilibrium processes must be considered. However, the errors and implications by omitting nitrate aerosol should be at least acknowledged, estimated, and discussed, especially when the speciated AOD trends are explained.
(2) Volcanic aerosol: It is not described in the manuscript how volcanic aerosols are dealt with in the CAMS model, such as emissions of precursor gases, plume injection height, and timing for sporadically erupting and constant degassing volcanic emissions.
(3) Stratospheric aerosol: It seems, from previous publications (e.g., Benedetti et al., 2009) that the aerosols in the stratosphere are not included in the model. Although in general stratospheric aerosols are a small fraction of total AOD except in cases of large volcanic eruptions and pyroCb fires, omission of stratospheric aerosols should be acknowledged and discussed for attributing contributions of individual aerosol types.
It is nice to see the comparisons of AOD evaluations, trends, and variability for both EAC4 and CTRL in this manuscript, and the differences between EAC4 and CTRL should be solely attributed to the satellite AOD data assimilation. However, it is not elaborated what can be learned from the differences between EAC4 and CTRL in terms of physical processes in the underlying model improvements in addition to the possible over- or under-estimation of some emission sources. Afterall, data assimilation can bring the results closer to the observations without fully understanding of the drivers for the model-data differences in the process level, but it is the model that carries the burden for understanding what happened in the past and projecting the future change.
Seven metrics (R, MAE, RMSE, RMB, %MFE, %FGE, and IOA) are introduced to evaluation AOD from EAC4 and CTRL, measuring absolute, relative, directional, the time evolution agreements. Such suite of metrics is comprehensive, but on the other hand it is hard to grasp the overall performance. Is it possible to develop an overall score or reduce the number of metrics without sacrificing the quality of evaluation? In several cases in the text, only a subset of these metrics was quoted to show the agreement between EAC4 (or CTRL) and AERONET anyway.
Specific comments:
Line 522-523: Again, I don’t understand how satellite AOD assimilation modifies the fraction of individual aerosol components using Eq. 1. Although AOD changes after assimilation, the relative contribution should remain the same since no information about the composition from satellite AOD.