# Evaluation of Different Types of Aerosols From Wildfires in South America Using Ground-Based Observations, Reanalysis and CMIP6 Models

Fernando P Forgioni, Gabriela V Müller, Damaris Kirsch Pinheiro, Mariela N Uhrig, Hassan Bencherif

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Fernando P Forgioni, Gabriela V Müller, Damaris Kirsch Pinheiro, Mariela N Uhrig, Hassan Bencherif. Evaluation of Different Types of Aerosols From Wildfires in South America Using Ground-Based Observations, Reanalysis and CMIP6 Models. International Journal of Climatology, 2025, 10.1002/joc.70149. hal-05330857

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RESEARCH ARTICLE

OPEN ACCESS

# Evaluation of Different Types of Aerosols From Wildfires in South America Using Ground-Based Observations, Reanalysis and CMIP6 Models

Fernando P. Forgioni<fnref n="1" /> ID | Gabriela V. Müller²,³ ID | Damaris Kirsch Pinheiro<fnref n="1" /> | Mariela N. Uhrig⁴,⁵ | Hassan Bencherif⁶

**Correspondence:** Fernando P. Forgioni (fernando.forgioni@acad.ufsm.br)

**Received:** 25 February 2025 | **Revised:** 25 September 2025 | **Accepted:** 30 September 2025

**Funding:** This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.

**Keywords:** AERONET | aerosol optical depth (AOD) | biomass burning | fire emissions | model evaluation | reanalysis

## ABSTRACT

The validation of aerosol optical depth (AOD) data is crucial for understanding the impact of wildfires on climate, particularly in regions such as South America, where this phenomenon is recurrent. This study assesses the performance of two global re-analysis products (MERRA-2 and CAMS) against the AERONET observation network for 2003–2023 and, for the overlapping period 2003–2014, also compares both reanalyses and CMIP6 global climate models. Results show that both reanalyses capture the spatiotemporal variability of AOD, with CAMS performing better in representing fire-derived aerosol types, including Black Carbon AOD (BC AOD) and Organic Carbon AOD (OC AOD). Seasonal patterns indicate peak AOD during the dry season (August–October), coinciding with increased fire activity, while systematic overestimation occurs in regions with complex topography. CMIP6 models reproduce the seasonal cycle of total AOD but underestimate BC AOD and OC AOD concentrations when compared with CAMS. These results underline the need for improved model parameterizations and for expanding and maintaining ground-based observation networks such as AERONET to enhance the reliability of aerosol simulations over South America.

## 1 | Introduction

Aerosols are mixtures of small solid and liquid particles suspended in the atmosphere, with typical radii ranging from 0.001 to 100 µm. They are of great interest to the scientific community due to their impact on global climate, radiative balance, air quality, human health, cloud microphysics, the hydrological cycle, ecosystems, and agriculture (Charlson et al. 1992; Xia et al. 2006; Yu and Luo 2009; Wang et al. 2015; You et al. 2015; Ali et al. 2022). While global research has advanced our understanding of aerosol properties and impacts, much less attention

has been devoted to assessing how well current reanalyses and climate models capture wildfire-related aerosols, particularly in regions with sparse observational coverage such as South America.

In this region, biomass burning episodes, which are quite common in the central Amazon during the dry season (between August and October), are one of the main sources of carbonaceous aerosols (Artaxo et al. 2002; Hoelzemann et al. 2009; Alvim et al. 2017; Vara-Vela et al. 2021). These events can release large amounts of Black Carbon (BC) and Organic Carbon

<footnote n="1">¹Departamento de Física, Universidade Federal de Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil | ²Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina | ³Centro de Estudios de Variabilidad y Cambio Climático (CEVARCAM), Facultad de Ingeniería y Ciencias Hídricas (FICH), Universidad Nacional del Litoral (UNL), Ciudad Universitaria, Santa Fe, Argentina | ⁴Centro de Investigación Científica y de Transferencia Tecnológica a la Producción (CICYTTP-CONICET-PROV. E. R.-UADER), Diamante, Entre Ríos, Argentina | ⁵Facultad de Ciencia y Tecnología (FCYT), Universidad Autónoma de Entre Ríos (UADER), Oro Verde, Entre Ríos, Argentina | ⁶Laboratoire de l'Atmosphère et des Cyclones (LACy, UMR 8105 CNRS, Université de la Réunion, Météo-France), Université de La Réunion, La Réunion, Saint Denis, France</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). *International Journal of Climatology* published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.</footnote>
Aerosols (OC) into the atmosphere, affecting regional radiative balance, cloud formation processes, and air quality over vast downwind areas. The seasonal recurrence and spatial extent of such emissions make this region a unique and challenging environment for testing the performance of atmospheric reanalyses and climate models.

Most studies characterising aerosols rely on measurements from sun photometers, such as those from the Aerosol Robotic Network (AERONET) of the National Aeronautics and Space Administration (NASA) (Holben et al. 1998). This global network provides high-quality, long-term records of aerosol optical depth (AOD) and related optical properties and has been widely used to evaluate reanalysis products and climate models in different regions of the world (Dubovik et al. 2002; Eck et al. 2003; Gueymard and Yang 2020; Isaza et al. 2021). However, its spatial and temporal coverage remains very limited in South America, with large gaps across key biomass-burning regions, which pose a challenge for robust validation of atmospheric models and reanalyses (Hoelzemann et al. 2009; Bencherif et al. 2020; Gueymard and Yang 2020). This scarcity of observations limits the ability to evaluate how well atmospheric reanalyses and climate models reproduce aerosol variability and extremes. Integrating AERONET measurements with reanalysis products and model simulations offers a more comprehensive framework for assessing the representation of wildfire-related aerosols in the region.

Several studies have evaluated AOD from global reanalyses, such as MERRA-2 (NASA's Global Modelling and Assimilation Office) and CAMS (Copernicus Atmosphere Monitoring Service, ECMWF), against ground-based and satellite observations, while also comparing climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) with these reanalyses. At the global scale, Gueymard and Yang (2020) validated MERRA-2 and CAMS using 15 years of AERONET and MODIS data (2003–2017), reporting that MERRA-2 generally outperforms CAMS in reproducing AOD, although both products exhibit regional and seasonal biases. Su et al. (2023) extended this approach by assessing MERRA-2 AOD and related aerosol properties worldwide, including diurnal variation, against AERONET and multiple satellite sensors for the year 2018, identifying regions and conditions where reanalysis performance degrades. Xian et al. (2024) performed a multi-reanalysis intercomparison (NAAPS-RA, MERRA-2, JRAero, CAMS) versus AERONET and MODIS AOD for the 2011–2019 period, showing that combining reanalyses can reduce RMSE and improve correlation in several regions.

On a regional scale, in the Eastern Mediterranean, Tuna Tuygun and Elbir (2024) analysed CAMS AOD for the 2003–2021 period, using AERONET stations, finding generally good agreement in seasonal and spatial patterns, but with site-dependent biases. In Australia, Isaza et al. (2021) evaluated MERRA-2, CAMS, and 16 CMIP6 models against AERONET for the 2003–2018 period, reporting that MERRA-2 tends to overestimate AOD and struggles with low concentrations, while CAMS performs better under such conditions. CMIP6 models captured the annual aerosol cycle but tended to overestimate dust and underestimate biomass-burning aerosols such as BC and OC. Over China, Ali et al. (2022) compared MODIS Dark Target (DT) and Deep Blue (DB) AOD with MERRA-2 and an ensemble of 15 CMIP6

models for the 2000–2014 period, finding stronger correlations and lower errors between MERRA-2 and MODIS DB relative to CMIP6 and MODIS DT across most regions, both annually and seasonally. In Southeast Asia, Zhao et al. (2024) assessed CAMS and MERRA-2 AOD against AERONET and satellite observations for the 2007–2019 period, showing that CAMS generally outperforms MERRA-2 in representing seasonal variability, though biases persist in high-aerosol-load periods. In the Southern Hemisphere, Bencherif et al. (2020) investigated the long-range transport of biomass burning aerosols from South America to Southern Africa in August 2019, using ground-based and satellite observations together with reanalysis data, highlighting both the capability of reanalyses to capture large-scale transport and the limitations imposed by the sparse AERONET network over South America.

Although AOD from MERRA-2, CAMS, and CMIP6 models has been evaluated in various regions worldwide, no study has yet provided a comprehensive assessment specifically focused on South America, particularly one addressing wildfire-related aerosol species such as BC AOD and OC AOD. To address this gap, we present a multi-dataset evaluation that integrates ground-based AERONET observations, two global reanalysis products (CAMS and MERRA-2), and CMIP6 climate model simulations within a consistent framework. Our analysis examines seasonal and spatial variability, evaluates model and reanalysis performance under high-AOD conditions, and applies an objective model-selection criterion to construct CMIP6 multi-model ensembles.

This paper is organised as follows: Section 2 provides a description of the datasets used, including AERONET ground-based observations, MODIS active fire detections, reanalysis products (MERRA-2 and CAMS), and CMIP6 climate model simulations. Section 3 details the methods applied for data processing, calculation of climatology, and the evaluation metrics. Section 4 presents the results, including the wildfire climatology for South America, the performance assessment of reanalysis products against AERONET, the intercomparison of aerosol species, and the evaluation of CMIP6 models. Section 5 summarises the main findings and discusses their implications.

## 2 | Materials and Methods

### 2.1 | Data

Satellite-based fire detections from the MODIS Collection 6 Active Fire Product (MCD14DL; Giglio et al. 2016) were used to identify the occurrence of wildfires in South America. The product provides daily hot spot detections at a nominal spatial resolution of 1 km at Nadir. Only observations flagged as high-confidence active fires were retained for the analysis. This dataset was used to characterise the temporal and spatial distribution of fire activity during the 2003–2023 period.

Ground-based total AOD at 500 nm observations were obtained from the NASA AERONET network (Holben et al. 1998; Giles et al. 2019). The global reanalysis AOD datasets used were MERRA-2 from NASA (Randles et al. 2017) and CAMS from ECMWF (Flemming et al. 2017). For both reanalyses and the
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Location of the 27 AERONET stations over the continent used in this study and this observation period. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

CMIP6 global climate models (Eyring et al. 2016), we evaluated three AOD variables at 550 nm: total AOD, BC AOD, and OC AOD, as described in the following subsection.

### 2.1.1 | Modis

We used active fire detections from the MODIS Collection 6 Active Fire Product (MCD14DL; Giglio et al. 2016) obtained through NASA's Fire Information for Resource Management System (FIRMS) portal. This dataset provides daily hot spot detections at a nominal spatial resolution of 1 km at Nadir. Only detections with a confidence level greater than 80% were retained, following Giglio et al. (2016), to ensure high-confidence events.

### 2.1.2 | Ground-Based AERONET Measurements

AERONET is a global network of ground-based sun photometers operated by NASA (Holben et al. 1998). In this study, data from 27 stations across South America (Figure 1), managed by various national agencies, were used. We used Version 3, Level 2.0 products, which include instrument calibration and quality assurance procedures, ensuring the highest reliability for scientific analysis (Giles et al. 2019). The total AOD measurements at 500 nm, with daily resolution, were selected to ensure high-quality and temporally consistent observations. Daily mean values are computed by AERONET from all valid sub-daily measurements; if only a single valid observation is available for a given day, that value is used as the daily mean,

following AERONET data usage guidelines. To compare observational data with reanalysis products and CMIP6 models, AOD values at 500 nm must be extrapolated to 550 nm. This is done using the Ångström coefficient between 440 and 870 nm (α) to derive AOD at 550 nm, as described in Equation (1) (Ångström 1929).

$$AOD_{550nm} = AOD_{500nm}(550/500)^{-\alpha} \quad (1)$$

Observations provide information on total AOD at specific locations, while the reanalyses and CMIP6 models described below offer wildfire-related AOD species, including BC AOD, OC AOD, and sulphate at 550 nm (Rickly et al. 2022). Although sulphate is also emitted from fires, it was not included in our analysis as its characterization is not the focus of this study.

### 2.1.3 | MERRA-2

The MERRA-2 reanalysis employs the Goddard Earth Observing System (GEOS-5) model coupled with the Atmospheric Data Assimilation System (ADAS). It provides data at a spatial resolution of 0.5°×0.625°. This product assimilates information from the AERONET network, MODIS aboard the Aqua and Terra satellites (Kaufmann et al. 1998), the AVHRR (Advanced Very High-Resolution Radiometer), and the MISR (Multi-Angle Imaging Spectroradiometer) (Randles et al. 2017). In this study, we use 3-hourly total AOD at 550 nm, along with 3-hourly AOD for wildfire-related aerosol species, including BC AOD and OC AOD, over the 2003–2023 period (see Table 1).
<table><caption>TABLE 1 | Main characteristics of the MERRA-2 and CAMS reanalyses.</caption><thead><tr><th>Reanalysis</th><th>Product</th><th>Variables</th><th>Temporal resolution</th><th>Spatial resolution</th></tr></thead><tbody><tr><td>MERRA-2/ NASA</td><td>MERRA-2 inst3_2d_gas_Nx<br>MERRA-2<br>tavgM_2d_aer_Nx</td><td>Total AOD<br>Total AOD, Black<br>Carbon, Organic<br>Aerosol</td><td>3h<br>Monthly</td><td>0.5° × 0.625°</td></tr><tr><td>CAMS/ECMWF</td><td>CAMS global reanalysis<br>(EAC4)</td><td>Total AOD<br>Total AOD, Black<br>Carbon, Organic<br>Aerosol</td><td>3h</td><td>0.75° × 0.75°</td></tr></tbody></table>

### 2.1.4 | CAMS

The CAMS reanalysis employs the Integrated Forecast System (IFS) 4D-Var data assimilation to incorporate MODIS data from the Aqua and Terra satellites (Kaufmann et al. 1998), as well as data from the Advanced Along-Track Scanning Radiometer (AATSR) (Flemming et al. 2017). It operates on a 0.75° × 0.75° grid. In this study, we use 3-hourly total AOD at 550 nm, along with 3-hourly AOD for individual wildfire-related aerosol species, including BC AOD and OC AOD, for the 2003–2023 period (see Table 1).

### 2.1.5 | CMIP6 Simulations

The Coupled Model Intercomparison Project (CMIP) is an initiative of the World Climate Research Programme (WCRP) that provides a framework for coordinating and comparing the work of various climate modelling centres worldwide (Eyring et al. 2016). It defines a set of standardised climate change experiments aimed at understanding the past, present, and future response of the climate system to natural and anthropogenic forcings. Past and future projections from all participating models are available in a standardised format.

In this study, we analysed monthly AOD results from the historical CMIP6 experiment (1850–2014), which incorporates climatic, volcanic, solar, and anthropogenic forcings. To ensure consistency across variables, we restricted the analysis to the subset of models providing total AOD, BC AOD, and OC AOD at 550 nm. This filtering resulted in a single set of 9 models, each including all three variables, as listed in Table 2.

## 3 | Methods

### 3.1 | Occurrence of Wildfires

Active fire detections from MODIS were used to characterise the temporal and spatial occurrence of wildfires in South America. Daily counts were aggregated to monthly and seasonal means to match the temporal resolution of the other datasets. The > 80% confidence filter was chosen to minimise commission errors, as recommended by Giglio et al. (2016). MODIS fire detection is limited under cloudy conditions,

which may cause short-term underestimations; however, given the multi-year period analysed (2003–2023), such limitations are expected to have a negligible effect on the long-term spatial and temporal patterns.

### 3.2 | Data Selection and Processing

Daily mean AOD values from AERONET (Section 2.1.1) were used to match the temporal resolution of the other datasets and subsequently extrapolated to 550 nm as described in Section 2.1.2. Similarly, sub-daily MERRA-2 and CAMS data were averaged to obtain daily AOD values, which were extracted from the grid cell closest to each AERONET station for the period January 2003 to December 2023. For validation against AERONET, both datasets were used at their native spatial resolution (see Table 1).

To compare different aerosol species in Section 4.3, MERRA-2 data at a monthly scale were used because the 3-hourly product contains only total AOD. CAMS 3-hourly data were averaged to monthly values for consistency. Both MERRA-2 and CMIP6 outputs were bilinearly interpolated to a common 0.75° × 0.75° grid for comparison. This gridding resulted in 423,936 pixels across South America, each approximately 83 km × 83 km in size. The continental mean was calculated by averaging all pixels for the period January 2003 to December 2022.

In Section 4.4, the monthly CAMS AOD series (including total AOD, BC AOD, and OC AOD) is compared with results from the CMIP6 historical experiment. Two types of multi-model ensembles were constructed for each variable: (i) an unweighted multi-model ensemble (MM Ensemble) including all available models (Equation 2), and (ii) a performance-weighted multi-model ensemble (PW-MM Ensemble) (Equations 3 and 4), where the weight of each model was proportional to the square of its correlation ($r^2$) with CAMS, following Weigel et al. (2010), Knutti et al. (2017), and Lorenz et al. (2018). Models with $r<0$ were assigned a weight of zero, but no fixed minimum threshold (e.g., 0.7) was imposed. Correlations were calculated from monthly continental mean values for the 2003–2014 period, obtained by averaging all grid cells over South America for each month. This approach follows the premise that multi-model means generally outperform
<table><caption>**TABLE 2** | List of CMIP6 models evaluated for total, BC, OC (AOD) at 550 nm.</caption><thead><tr><th>Model</th><th>Institute and country</th><th>Resolution (°lat ×°lon)</th><th>Number of family members</th><th>Interactive aerosol diagram</th><th>Aerosol scheme</th></tr></thead><tbody><tr><td>CNRM-CM6-1</td><td>CNRM-CERFAC, France<br>CNRM, CERFAC France</td><td>1.4 × 1.4</td><td>26</td><td>No</td><td>Prescribed monthly fields computed by TACTIC_v2</td></tr><tr><td>CNRM-ESM2-1</td><td>CNRM-CERFAC, France<br>CNRM, CERFAC France</td><td>1.5 × 1.5</td><td>9</td><td>No</td><td>Prescribed monthly fields computed by TACTIC_v2</td></tr><tr><td>EC-Earth3-AerChem</td><td>EC-Earth-<br>Consortium,<br>Europe</td><td>2.0 × 3.0</td><td>4</td><td>Yes</td><td>TM5</td></tr><tr><td>GISS-E2-1-H</td><td>USA NASA/GISS, USA</td><td>2.0 × 2.5</td><td>19</td><td>Yes</td><td>Varies with physicsversion (p==1 none, p==3 OMA, p==4 TOMAS, p==5 MATRIX)</td></tr><tr><td>GISS-E2-2-G</td><td>USA NASA/GISS, USA</td><td>2.0 × 2.5</td><td>15</td><td>Yes</td><td>Varies with physicsversion (p==1 none, p==3 OMA, p==4 TOMAS, p==5 MATRIX)</td></tr><tr><td>INM-CM5-0</td><td>INM, Russia</td><td>1.5 × 2.0</td><td>1</td><td>Yes</td><td>INM-AER1</td></tr><tr><td>IPSL-CM6A-LR-INCA</td><td>IPSL, France</td><td>1.3 × 2.5</td><td>1</td><td>No</td><td>—</td></tr><tr><td>MIROC6</td><td>MIROC, Japan</td><td>1.4 × 1.4</td><td>10</td><td>Yes</td><td>SPINTARS6.0</td></tr><tr><td>MRI-ESM2-0</td><td>MRI, Japan</td><td>1.121 × 1.125</td><td>12</td><td>Yes</td><td>MASINGAR mk2r4</td></tr></tbody></table>

individual models, while performance weighting can further reduce systematic errors and improve reliability.

$$MME(t) = \frac{1}{N} \sum_{i=1}^{N} M_i(t) \qquad (2)$$

$$w_i = \frac{\max(r_i, 0)^2}{\sum_{j=1}^{N} \max(r_j, 0)^2} \qquad (3)$$

$$PW - MME(t) = \sum_{i=1}^{N} wi * M_i(t) \qquad (4)$$

where $M_i(t)$ is the time series from model i, $r_j$ is its correlation with CAMS, and $wi$ is the assigned weight. The performance assessment and ensemble construction were carried out using Taylor diagrams (Taylor 2001), which simultaneously evaluate correlation, standard deviation, and centred root mean square error (CRMSE) between each model and CAMS. For the CMIP6 total AOD evaluation, four representative AERONET sites across South America were selected to capture distinct climatic and aerosol regimes and to ensure the highest temporal data coverage during the 2003–2023 period. This selection provided robust monthly climatology and enabled direct comparisons between AERONET with CAMS and CMIP6 simulations.

Figure 2 summarises the methodological workflow of this study. Step 1 comprises the acquisition and processing of wildfire climatology from MODIS, AERONET observations, and reanalysis datasets (CAMS and MERRA-2), along with CMIP6 model outputs. Temporal and spatial matching, as well as quality control, were applied to harmonise the datasets. Step 2 evaluates CAMS and MERRA-2 against AERONET observations, followed by Step 3, which intercompares aerosol species between reanalyses. Step 4 evaluates CMIP6 models against reanalysis data and representative data from AERONET.

## 3.3 | Validation Metrics

To compare the different datasets used in this study, three common error measures were calculated: the Pearson correlation coefficient (r) (Equation 5), the root mean square error (RMSE) (Equation 6), and the bias (BIAS) (Equation 7):

$$r = \frac{\sum (x_k - \bar{x})(y_k - \bar{y})}{\sqrt{\sum (x_k - \bar{x}) \sum (y_k - \bar{y})^2}} \qquad (5)$$

$$RMSE = \sqrt{\frac{\sum_{i=1}^{n} (\hat{y}_i - y_i)^2}{n}} \qquad (6)$$
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Schematic workflow of the study, showing datasets used (MODIS fire detections, AERONET observations, CAMS and MERRA-2 reanalyses, and CMIP6 models) and main analysis steps (data processing, intercomparison and evaluation). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

$$BIAS = \frac{\sum_{i=1}^{n} (\hat{y}_i - y_i)}{n} \qquad (7)$$

where $x_i$, $y_i$ and $\hat{y}_i$ are the modelled (or estimated) and measured AOD values, while $\bar{x}$ and $\bar{y}$ represent the averaged AOD values.

## 3.4 | OC/BC Ratio

To characterise the composition and potential sources of biomass-burning aerosols, we computed the Organic Carbon to Black Carbon (OC/BC) ratio for each month and reanalysis dataset. This ratio is a widely used tracer of aerosol sources, with higher values generally linked to biomass-burning emissions and lower values associated with fossil fuel combustion (Andreae and Merlet 2001). Although many emission inventories assume constant OC:BC ratios for biomass-burning sources, the actual atmospheric column ratio can vary considerably due to processes such as chemical ageing, secondary organic aerosol formation, removal mechanisms, and long-range transport.

Monthly mean OC and BC AOD values were extracted from each reanalysis product across the South American domain. The OC/BC ratio was then computed as:

$$\frac{\text{OC}}{\text{BC}} = \frac{\text{OC AOD}_{550 \text{ nm}}}{\text{BC AOD}_{550 \text{ nm}}}$$

To avoid unrealistically large OC/BC ratios due to near-zero denominators, BC values smaller than $1 \times 10^{-5}$ were excluded from the analysis, following the common practice of applying concentration thresholds to avoid artefacts when values approach instrumental detection limits (e.g., O'Dowd et al. 2004; Yttri et al. 2007). Regional means over South America were calculated using latitude-weighted averages, where each grid cell was

weighted by the cosine of its latitude to account for differences in grid-cell area. Finally, a monthly climatology (multi-year mean for each calendar month) was computed to analyse the spatial and seasonal patterns of the OC/BC ratio.

# 4 | Results

## 4.1 | Wildfire Climatology

Between July and December, wildfire activity intensifies across South America, reaching its highest levels from August to October, especially in Brazil, Paraguay, Bolivia, and Argentina (Bencherif et al. 2020; Vara-Vela et al. 2021).

## 4.2 | Total AOD Assessment From Reanalysis Data Against AERONET

To assess the ability of reanalysis products to represent wildfire-related total AOD, we compared daily and monthly values against AERONET observations, which are widely regarded as a reliable reference (Isaza et al. 2021; Wang et al. 2015). The length of the observational record varied considerably among sites, from less than 2 years to more than a decade. All available stations were included to maximize spatial coverage, although shorter records may introduce greater uncertainty in the statistical metrics for some locations.

For daily data (Figure 3a,b), CAMS shows a slightly higher correlation coefficient (r) than MERRA-2 at most stations. In both datasets, some stations in the north-western sector of South America (Ecuador and Colombia) display low r values (0.4 and 0.3). This poor performance can be largely attributed to the limited length of the observational records. Several sites have less than 10% of the maximum possible coverage during the study period (7669 days), such as “UNC-Palmira” (455 days)
<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Correlation (a, b), RMSE (c, d), and bias (e, f) between daily Total AOD from (a, c, e) CAMS and (b, d, f) MERRA-2 against AERONET stations across South America for the 2003–2023 period. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

and “Quito-USFQ” (1621 days), which increases dispersion in the correlations and reduces their representativeness. In addition, some stations in the southernmost part of South America, such as “CEILAP-RG” (Río Gallegos, Argentina) and “CEILAP-Bariloche”, also display low correlations despite having long observational records. This suggests that factors other than record length, such as local aerosol characteristics, model representation of emission sources, and long-range transport processes may affect performance (Parrish et al. 2004; Lee et al. 2022). Although no strict minimum length was imposed for the observational record, this factor is acknowledged as a potential limitation when comparing sites. The correlation coefficient (r) for both reanalyses exceeds 0.7 at 15 of the 27 stations, while 11 stations fall within the 0.6–0.7 range. When aggregated on a monthly scale (Figure 4a,b), correlations improve markedly, often exceeding 0.9 at many sites, reflecting the reduced influence of day-to-day variability and random errors when using longer temporal averages. This improvement is largely driven by the dominance of the seasonal cycle of biomass burning in South America, which reanalyses capture more robustly than short-term variability (Giglio et al. 2016; Van Der Werf et al. 2017). In contrast, daily correlations are more sensitive to localised aerosol events, rapid meteorological changes, and emission sources that may be poorly represented or missing in the reanalysis products (Kinne et al. 2013; Randles et al. 2017; Benedetti et al. 2018), increasing dispersion in the comparisons.

RMSE values are generally low for most stations in both reanalysis datasets (Figure 3c,d). However, they are notably high at 7 of the 27 stations, primarily located in the central region of

Brazil and an Amazonian area spanning parts of Bolivia and Brazil. These regions are characterised by intense and highly variable biomass-burning emissions, which can fluctuate considerably from year to year depending on climate variability and land management practices (Aragão et al. 2018; Van Der Werf et al. 2017; Marengo et al. 2018). Such variability, combined with the challenges in accurately representing local emission sources and transport processes in reanalysis models, can contribute to the higher RMSE observed in our results. On a broader scale, similar disparities in RMSE have been reported in global validation studies, with values ranging from 0.017 to 0.232 for MERRA-2 and from 0.031 to 0.268 for CAMS when compared to over 793 AERONET stations worldwide during 2003–2017 (Gueymard and Yang 2020). These findings suggest that the higher RMSE values observed here are not anomalous but rather consistent with patterns documented in other regions, although local emission and transport characteristics likely amplify them in specific areas of South America.

Regarding BIAS (Figures 3e,f and 4e,f), both reanalyses tend to overestimate total AOD at most stations. MERRA-2 exhibits the largest positive biases, with absolute differences of up to 0.20 in AOD at La Paz (Bolivia) and Huancayo (Peru), which are greater than those from CAMS. Conversely, CAMS underestimates AOD by up to 0.20 at Arica (Chile). The spatial distribution of positive and negative biases likely reflects differences in how each reanalysis represents aerosol emission sources, transport pathways, and removal processes. For example, MERRA-2 assimilates MODIS and MISR AOD and applies the GOCART aerosol module, which can overestimate biomass-burning emissions in high-altitude tropical regions due to the choice of emission
inventories and plume injection heights (Buchard et al. 2017). In contrast, CAMS assimilates MODIS AOD but uses the MACC aerosol scheme, which may underestimate AOD in arid and coastal regions such as northern Chile due to a weaker representation of dust and sea-salt emissions (Rémy et al. 2019).

## 4.3 | Assessment of High Total AOD Events

To assess the ability of reanalyses to capture extreme Total AOD over the region, we compared the 95th percentile (P95) of daily data from AERONET stations with corresponding values from MERRA-2 and CAMS. Figure 5a shows the annual spatial

distribution of P95 derived from AERONET. The highest values (> 0.8) are concentrated in central and southern Amazonia, northern Bolivia, and Paraguay, reflecting the intense biomass burning in these regions during the peak fire season (August–October).

Differences between reanalyses and observations are expressed as percentage deviations from AERONET (Figure 5b,c). MERRA-2 tends to overestimate P95 at most stations, with particularly large positive differences (> 80%) in southern South America and parts of the Amazon Basin. CAMS shows a more mixed pattern, with underestimations of up to -60% in arid/coastal regions of Chile and overestimations (> 40%) in southern Argentina and Uruguay.

<figure><img src="image_7.png" /><figcaption>FIGURE 4 | Same as Figure 3, but for monthly Total AOD data. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>FIGURE 5 | 95th percentile (P95) of daily Total AOD at 550 nm from AERONET stations (a) and differences between AERONET and (b) MERRA-2 and (c) CAMS across South America (2003–2023). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**FIGURE 6** | Monthly climatology of Total AOD at 550 nm from CAMS and MERRA-2 across South America (2003–2023). Symbols indicate differences between AERONET observations and model values at each site: Green plus signs (+) denote AERONET > model, red minus signs (−) denote AERONET < model, and black equals signs (=) denote |ΔI| < 0.005. The colour shading represents the model AOD fields, while the symbols summarise the agreement at the ground-based stations. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

These discrepancies likely reflect the representation of extreme biomass burning plumes and transport events in each reanalysis system. MERRA-2's assimilation of MODIS and MISR AOD, combined with the GOCART module, can lead to an overestimate of emissions in high-biomass-burning years (Buchard et al. 2017). In contrast, CAMS, which uses the MACC aerosol scheme, may underestimate extremes in dust- and sea-salt-dominated regions (Rémy et al. 2019).

Similar evaluations of AOD extremes in other regions have shown that reanalyses can capture the spatial patterns of high-aerosol-load events, though often misrepresent their intensity—due partly to limitations in emission inventories, plume-rise parameterization, and spatial resolution of models (e.g., Gueymard and Yang 2020; Guo et al. 2017).

**4.4 | Comparison of AOD Species Between Reanalyses**

**4.4.1 | Total AOD**

Figure 6 presents the climatology of total AOD from reanalyses, together with the corresponding monthly means from selected AERONET stations. Only sites and months with sufficient valid observations were considered, retaining months with at least 66% of daily values available and stations covering most of the seasonal cycle, to ensure a robust and meaningful comparison between reanalysis and ground-based datasets. The symbols plotted over each station indicate whether the reanalysis overestimates (+) or underestimates (−) AERONET AOD for that month. Differences smaller than ±0.02 are marked as “=”, following the expected AERONET retrieval uncertainty reported by Holben et al. (1998) and adopted in recent evaluation

studies (e.g., Wei et al. 2019; Schutgens et al. 2017). Both reanalyses consistently depict the highest total AOD values between August and October, coinciding with the peak biomass-burning season in South America. From January to April, total AOD values remain low across most of the continent, with slightly higher concentrations near the equator in the northern region. Concentrations rise from August, peak in September, and decline thereafter. Spatially, CAMS generally produces higher total AOD than MERRA-2 during the fire season, particularly over the southern Amazon and central South America, indicating a smaller underestimation relative to AERONET in those areas.

**4.4.2 | Black Carbon (BC AOD)**

Figure 7 shows the monthly distribution of BC AOD across South America. Similar to total AOD, BC AOD exhibits a marked seasonal cycle. From January to April, concentrations remain very low over most of the continent, with slightly higher values in northern South America, likely associated with widespread fire hotspots in that region. These low concentrations are consistent with the typically humid conditions prevailing during this season, as increased atmospheric moisture and precipitation enhance wet scavenging processes, thereby reducing the atmospheric residence time of aerosols (Cheng et al. 2014).

From August onwards, BC AOD concentrations rise sharply, peaking during the core fire season (August–September–October). This increase is especially pronounced in central–northern South America, where the dry season fosters frequent and intense wildfire episodes (Reddington et al. 2015). In September, BC AOD levels reach their maximum, reflecting the intensification of wildfires and agricultural burning that emit large quantities of aerosols and
BC AOD. The dry season in this region is marked by reduced pre-cipitation and low relative humidity, which limit wet deposition and extend the atmospheric residence time of aerosols (Artaxo et al. 2013; Reddington et al. 2015).

During the last months of the year (October to December), BC AOD concentrations show a decreasing trend, although moderate levels persist in some areas. This decline is consistent with reduced biomass-burning activity, likely driven by the onset of wetter atmospheric conditions, characterised by more frequent precipitation and higher relative humidity, which limit fire

spread, enhance wet deposition, and shorten the atmospheric lifetime of aerosols (Aragão et al. 2018; Van Der Werf et al. 2017).

When comparing both datasets, CAMS exhibits a smaller underestimation of BC concentrations compared to MERRA-2, particularly during the peak fire season. These differences may stem from variations in emission inventories, the temporal allocation of biomass-burning emissions, and differences in aerosol removal and transport parameterizations between the reanalysis systems (Buchard et al. 2017; Rémy et al. 2019; Inness et al. 2019). Despite these differences, both datasets

<figure><img src="image_10.png" /><figcaption>**FIGURE 7** | Monthly climatology of BC AOD at 550 nm from CAMS and MERRA-2 across South America (2003–2023). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**FIGURE 8** | Monthly climatology of OC AOD at 550 nm from CAMS and MERRA-2 across South America (2003–2023). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
successfully capture the seasonal cycle of BC AOD across South America.

### 4.4.3 | Organic Carbon (OC AOD)

Figure 8 illustrates the monthly distribution of OC AOD across South America. MERRA-2 consistently underestimates OC AOD throughout the year. Peak values occur in September, like the pattern observed for total AOD and BC AOD (Figures 6 and 7). Similar to total AOD and BC AOD, these elevated values coincide with the dry season, when biomass burning activity is most intense, indicating that the OC AOD increase is closely linked to fire emissions in the region (Artaxo et al. 2013; Reddington et al. 2015). While a portion of these fires results from natural seasonal drying, many ignition sources are anthropogenic, including agricultural burning and deforestation activities (Van Der Werf et al. 2017).

For CAMS, OC AOD levels remain low during the first months of the year, but the increase from May onwards is more pronounced than in MERRA-2. The largest differences occur in August and September, when CAMS simulates substantially higher OC AOD concentrations across large areas of central-northern South America. This behaviour may be linked to factors like those discussed for BC AOD, including differences in emission inventories, the temporal allocation of biomass-burning emissions, and the parameterization of aerosol transport and removal processes in the models (Buchard et al. 2017; Rémy et al. 2019; Reddington et al. 2015). Seasonal contrasts in OC AOD are consistent with those described for BC AOD, largely reflecting the influence of wet scavenging, with wet-season conditions favouring aerosol removal and dry-season conditions allowing for longer atmospheric residence times (Cheng et al. 2014).

When comparing both datasets, CAMS tends to slightly overestimate OC AOD concentrations during the dry season, particularly in August and September, whereas MERRA-2 underestimates them. Despite these biases, both reanalyses capture the strong seasonal influence of biomass burning on OC AOD variability across South America.

### 4.4.4 | OC/BC Ratio

Monthly regional means for OC/BC ratio (Figure 9a) indicate that CAMS exhibits a marked seasonal cycle, with maximum values (~12–13) in May–June and minimum values (~7–8) in September. In contrast, MERRA-2 maintains nearly constant ratios (~4.5–5.5) throughout the year.

Spatial patterns for the months with the highest (May) and lowest (September) CAMS ratios (Figure 9b,c) show that this variability is not homogeneous across South America. In May, elevated OC/BC ratios extend across central and northern regions, suggesting a greater relative contribution from organic aerosol sources, such as biomass burning during the early dry season and biogenic emissions. Conversely, in September, lower ratios dominate most of the continent, indicating a relatively higher BC AOD contribution, likely linked to more intense combustion processes and/or differences in the representation of emission profiles between models.

These results highlight that, despite the constant biomass-burning emission factors in inventories, modelled OC/BC ratios can vary substantially in time and space due to differences in aerosol transport, ageing, and removal processes (Andreae and Merlet 2001; Akagi et al. 2011; Cubison et al. 2011).

## 4.5 | Performances of the CMIP6 Global Climate Models

Figure 10 shows the position of each model and the ensemble means in the Taylor space, with CAMS indicated as the reference point. The diagrams show the correlation coefficient, standard deviation, and CRMSE comparing the model outputs with CAMS. The PW-MME ensemble produced results similar to the unweighted MME Ensemble, but with slightly better performance for Total AOD, reflected in higher correlations and reduced standard deviation differences with respect to CAMS (left panel, Figure 10a). For BC AOD and OC AOD, differences between the two ensembles were minimal, and both performed better than most individual models (Figure 10b,c).

<figure><img src="image_12.png" /><figcaption>**FIGURE 9** | (a) Monthly OC/BC ratio from CAMS and MERRA-2 over South America (2003–2023). (b) Spatial distribution for May (maximum OC/BC ratio) and (c) September (minimum OC/BC ratio). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**FIGURE 10** | Taylor diagrams showing the performance of CMIP6 models, MM Ensemble, and PW-MM Ensemble against CAMS for (a) Total AOD, (b) BC AOD, and (c) OC AOD over South America (2003–2014). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**FIGURE 11** | RMSE (top row) and bias (bottom row) for (a, b) Total AOD, (c, d) BC AOD, and (e, f) OC AOD from the CMIP6 models and PW-MM Ensemble compared to CAMS over South America (2003–2014). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

4.5.1 | Total AOD

Figure 11a shows the RMSE of CMIP6 models simulating total AOD at 550 nm for the entire region. The performance-weighted multi-model ensemble (PW-MME) exhibits the lowest RMSE among all evaluated models, indicating better agreement with reference data than any individual model. This improvement likely results from the ensemble's ability to combine the strengths of different models, thereby reducing random errors and partially offsetting individual model biases (Weigel et al. 2010; Knutti et al. 2017; Lorenz et al. 2018). In contrast, models such as GISS-E2-1-H and INM-CM5-0 present higher RMSE values, suggesting larger discrepancies with observations.

Figure 11b presents the bias, where most models display values close to zero, indicating minimal overestimation or

underestimation relative to CAMS total AOD. Nevertheless, some models exhibit more pronounced biases with overestimation (e.g., GISS-E2-1-G and GISS-E2-1-H) and underestimation (e.g., IPSL-CM6A-LR-INCA and MRI-ESM2-0).

As CAMS exhibits a positive bias compared to AERONET observations, the lower values simulated by the CMIP6 ensemble suggest that it could be closer to the ground-based observations than CAMS itself, an aspect supported by our AERONET-based evaluation (Figure 12). In this figure, the ensemble (dashed blue line) follows a trend very similar to that of CAMS (dashed grey line), both in magnitude and in its seasonal cycle. Both datasets indicate a peak in total AOD during August, corresponding to the fire season in South America. For most of the year, the multi-model mean remains close to CAMS values, although slightly lower. However, during
<figure><img src="image_15.png" /><figcaption>**FIGURE 12** | Monthly climatology of Total AOD from AERONET (Black dashed line), CAMS (grey dashed line), and CMIP6 (blue dashed line for PW-MM Ensemble) models for four representative sites across continent: (a) CEILAP-BA, (b) Alta Floresta (c) Rio Branco and (d) CEILAP-RG. For 2003–2014 period. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

September and October, the ensemble's underestimation becomes more pronounced. While this behavior is generally consistent across the representative stations, some locations such as CEILAP-RG (d) and Rio Branco (c) exhibit larger deviations, with the PW-MME occasionally exceeding CAMS during the wet season or showing stronger underestimation during the dry-season peak, suggesting local differences in emission representation and transport processes.

Spatially, both CAMS and the ensemble identify the southern Amazon and central South America as the main Total AOD hotspots during the biomass-burning season. However, the ensemble generally reduces the positive bias seen in CAMS over these regions, while still underestimating in some peripheral areas (see annual-mean spatial difference maps in Figure 13a and SON period in Figure 13b). These patterns indicate that, although the ensemble captures the main source regions, it tends to smooth extreme values compared to CAMS, potentially yielding more realistic distributions relative to AERONET.

A direct comparison between the CMIP6 ensemble and AERONET at four representative sites (Figure 12 and Table 3) shows that the ensemble reproduces the seasonal cycle of total AOD reasonably well, with a mean bias of -0.009 and an average correlation of 0.67. Performance is stronger at Alta Floresta and Rio Branco, while CEILAP-RG shows weaker agreement, reflecting site-dependent differences. As CMIP6

outputs are only available at monthly resolution, short-term variability is not captured, which may partly explain the lower correlations at certain sites. Nevertheless, the ensemble captures the main features of AOD variability as observed by AERONET.

### 4.5.2 | Black Carbon (BC)

Figure 11c shows the RMSE for BC AOD, where the performance-weighted multi-model ensemble records one of the lowest errors, along with models such as GISS-E2-1-H and INM-CM5-0. Conversely, MRI-ESM2-0 exhibits the highest RMSE. Figure 11d presents the bias, with all models slightly underestimating BC AOD relative to CAMS. The ensemble and models like CNRM-CM6-1 and EC-Earth3-AerChem show biases closest to zero, while MRI-ESM2-0 and IPSL-CM6A-LR-INCA have the largest negative biases.

In the annual mean (Figure 13c), most of South America exhibits negative differences, with moderate underestimations concentrated over the southern Amazon and central Brazil. During SON (Figure 13d), which coincides with the peak of the fire season in the region, these underestimations intensify and expand, reaching values below -0.032 over large portions of the Amazon Basin. Positive differences are minimal in both cases, restricted to small areas in southeastern South America.
<figure><img src="image_16.png" /><figcaption>**FIGURE 13** | Differences (Δ) between PW-MM Ensemble and CAMS AOD over South America (2003–2014). Columns show Total AOD (a, b), BC AOD (c, d), and OC AOD (e, f). Top row (a–c): Annual mean; bottom row (d, f): (Aug–Oct). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<table><caption>**TABLE 3** | Validation statistics (Bias, RMSE, and correlation coefficient *r*) of CMIP6 ensemble AOD against AERONET observations at the four representative South American sites (2003–2014).</caption><thead><tr><td>Site</td><td>BIAS</td><td>RMSE</td><td>r</td></tr></thead><tbody><tr><td>Alta Floresta</td><td>-0.064</td><td>0.149</td><td>0.95</td></tr><tr><td>CEILAP-BA</td><td>0.000</td><td>0.026</td><td>0.64</td></tr><tr><td>CEILAP-RG</td><td>0.053</td><td>0.059</td><td>0.14</td></tr><tr><td>Rio Branco</td><td>-0.024</td><td>0.088</td><td>0.94</td></tr></tbody></table>

### 4.5.3 | Organic Carbon (OC)

Figure 11e shows the RMSE for OC AOD, where the performance-weighted multi-model ensemble and models such as MRI-ESM2-0, CNRM-ESM2-1, and EC-Earth3-AerChem achieve some of the lowest errors. In contrast, models like IPSL-CM6A-LR-INCA and INM-CM5-0 present higher RMSE values. Figure 11f presents the bias, with all models tending to underestimate OC AOD relative to CAMS. The ensemble, along with MRI-ESM2-0 and EC-Earth3-AerChem, shows biases closer to zero, while IPSL-CM6A-LR-INCA and MIROC6 exhibit the largest underestimations.

The results reveal a widespread underestimation across South America, with the largest negative differences concentrated over central-western Brazil and extending into Bolivia and Paraguay (Figure 13e). The underestimation intensifies during SON (Figure 13f), coinciding with the peak fire season, where

the bias exceeds -0.16 in large areas of the Amazon basin. In the annual mean, the negative bias is less intense but still widespread, particularly over biomass-burning regions.

## 5 | Discussion and Final Remarks

On the daily scale, both reanalysis products represent Total AOD concentrations with high accuracy when compared against AERONET observations, showing correlation coefficients of 0.89 for MERRA-2 and 0.91 for CAMS. Performance improves at the monthly scale, reaching 0.96 and 0.97, respectively, confirming that both datasets capture the annual variability of Total AOD concentrations well. Nonetheless, their accuracy decreases at stations located at high altitudes or in mountainous regions (e.g., Quito, Mount Chacaltaya, Bariloche), primarily because the reanalyses assimilate satellite-derived AOD retrievals, which are more uncertain over complex terrain. This limitation refers to the satellite products used in the assimilation process, not to the AERONET ground-based retrievals. Such topographic effects on satellite-based AOD have been recognised in previous studies (Gueymard and Yang 2020) and are reflected in the re-analysis products.

Regarding BC AOD and OC AOD, both reanalyses reproduce the annual variability, with peak concentrations during the wildfire season (August–October). CAMS generally shows smaller overestimations than MERRA-2, likely due to differences in emission inventories and fire detection methods (GFAS in CAMS versus QFED in MERRA-2) together with distinct aerosol transport and removal parameterizations. These methodological
contrasts explain CAMS' comparatively better performance in South America and Asia (Fu et al. 2022), although both products could benefit from bias correction and error-reduction techniques (Gueymard and Yang 2020; Su et al. 2023). This comparison also provides the context for interpreting the seasonal and spatial variability of the OC/BC ratio discussed in Section 4.4, since differences in emission estimates, transport, and removal processes can modulate the relative contributions of these carbonaceous aerosol species.

In contrast, CMIP6 models exhibit a wider range of performance in representing total AOD and wildfire aerosol species over South America. While some models approach the skill of MERRA-2 and CAMS in reproducing the seasonal cycle and spatial distribution, most present larger biases in both magnitude and variability.

## Author Contributions

**Fernando P. Forgioni:** conceptualization, investigation, writing – original draft, methodology, validation, visualization, writing – review and editing, software, data curation, formal analysis. **Gabriela V. Müller:** formal analysis, supervision, conceptualization. **Damaris Kirsch Pinheiro:** funding acquisition, formal analysis, project administration, supervision, resources, data curation, conceptualization. **Mariela N. Uhrig:** conceptualization, investigation, validation, software. **Hassan Bencherif:** formal analysis, supervision.

## Acknowledgements

The authors would like to thank the engineers Eibar Ibañez, Daniel Perez Ramirez, Luis Escalante, Hector Moreno, and Jorge Colmenares for their help in data management. The first author also thanks the Postgraduate Program in Meteorology at the Federal University of Santa Maria (PPGMET-UFSM). This research was funded by Coordenação de aperfeiçoamento de pessoal de nível superior (CAPES). The Article Processing Charge for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (ROR identifier: 00x0ma614).

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

All scripts are publicly available at: https://github.com/fforgioni/AOD-validation. MODIS fire data were obtained from the Fire Information for Resource Management System (FIRMS) archive (https://firms.modaps.eosdis.nasa.gov/download/). AERONET data are available at https://aeronet.gsfc.nasa.gov/cgi-bin/webtool_aod_v. MERRA-2 data were obtained from NASA's Goddard Earth Sciences Data Information Services Center (GES DISC) (https://disc.gsfc.nasa.gov/datasets keywo rds=merra2&page=1&measurement=Aerosol%20Optical%20Dep th%2FThickness). CAMS reanalysis data were downloaded from the ECMWF Atmospheric Data Store (ADS) (https://ads.atmosphere.coper nicus.eu/datasets/cams-global-reanalysis-eac4?tab=overview). CMIP6 model outputs were obtained from the Earth System Grid Federation (ESGF) archive (https://esgf-node.llnl.gov/projects/cmip6/).

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