# A global-scale analysis of the MISR Level-3 aerosol optical depth (AOD) product: Comparison with multi-platform AOD data sources

Ke Gui, Huizheng Che, Yaqiang Wang, Xiangao Xia, Brent N. Holben, Philippe Goloub, Emilio Cuevas-Agullo, Wenrui Yao, Yu Zheng, Hujia Zhao, et al.

► To cite this version:

Ke Gui, Huizheng Che, Yaqiang Wang, Xiangao Xia, Brent N. Holben, et al.. A global-scale analysis of the MISR Level-3 aerosol optical depth (AOD) product: Comparison with multi-platform AOD data sources. Atmospheric Pollution Research, 2022, Atmospheric Pollution Research, 12, 10.1016/j.apr.2021.101238. hal-04452134

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1 A global-scale analysis of the MISR Level-3 aerosol optical depth (AOD)

2 product: comparison with multi-platform AOD data sources

3 Ke Gui¹, Huizheng Che¹*, Yaqiang Wang¹, Xiangao Xia², Brent N. Holben³, Philippe

4 Goloub⁴, Emilio Cuevas-Agulló⁵, Wenrui Yao¹, Yu Zheng¹, Hujia Zhao⁶, Lei Li¹, Xiaoye

5 Zhang¹

6 1 State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric

7 Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China

8 2 Key Laboratory for Middle Atmosphere and Global Environment Observation (LAGEO),

9 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

10 3 NASA Goddard Space Flight Center, Greenbelt, MD, USA

11 4 Laboratoire d'Optique Amosphérique, Université des Sciences et Technologies de Lille,

12 59655, Villeneuve d'Ascq, France

13 5 Centro de Investigación Atmosférica de Izaña, AEMET, 38001 Santa Cruz de Tenerife, Spain

14 6 Institute of Atmospheric Environment, China Meteorological Administration, Shenyang,

15 110166, China

16

17 Corresponding author: Huizheng Che (chehz@cma.gov.cn)

18

19
20 Abstract

This study analyses the applicability of the recently released Level-3 (L3) daily and monthly aerosol optical depth (AOD) products (version F15_0032) over land retrieved from the Multiangle Imaging Spectroradiometer (MISR) instrument. For this purpose, daily AOD data from 427 Aerosol Robotic Network (AERONET) sites worldwide during 2001–2018 and 39 China Aerosol Remote Sensing Network (CARSNET) sites across China during 2002–2014 were selected for comparison. Also, MISR-based size-segregated AODs were collected to compare with the coarse- and fine-mode AODs retrieved from AERONET to reveal the particles size modes that mainly contribute to the offset of MISR total AOD relative to observations. By comparing with five other monthly AOD datasets, including two MODIS (Moderate resolution Imaging Spectroradiometer) retrieval products, a multi-satellite merged product, and two aerosol reanalysis, MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications) and CAMS (Copernicus Atmosphere Monitoring Service), we then explored the applicability of MISR for characterizing the climatology, interannual variations, and long-term (2003–2017) trends in regional aerosol loadings over the 12 regions of interest. Overall, about 80% (60%) of the daily AOD values retrieved by MISR fall within expected error bounds of ± [0.05 +0.2 × AOD] (± [0.03 + 0.1 × AOD]), with a high correlation (R =0.87). Our comparison results show that although the V23 algorithm addresses several issues in L2 AOD retrieval relative to the previous version, the L3 data aggregated from L2 data tends to overestimate low AOD values and underestimate high AOD values. These offset relative to observations are mainly attributed to the overestimation of coarse-mode AOD and the underestimation of fine-mode AOD by MISR. Intercomparison of 2003–2017 AOD trends from multiple data sources indicate that MISR can capture well the increases occurring in South Asia, as well as the
decreases occurring in Eastern China, Eastern United States, and Western Europe. Our study confirmed that MISR L3 product performs reliably at regional scales, particularly in typically polluted areas, but caution is still needed when applied to areas where sparse sampling is encountered.

**Keywords: MISR; aerosol optical depth; consistency analysis; AERONET; CARSNET**

# 1. Introduction

Over the past decades, aerosol particles emitted from anthropogenic or natural sources have been proven to affect the radiation balance, cloud-precipitation generation, life cycle and dispersion, and atmospheric circulation at regional and global scales to varying degrees, depending on their absorption and scattering properties (Ramanathan and Carmichael, 2008; Lee, 2011; Liu et al., 2017; Gui et al., 2017; Jiang et al., 2018). Such significant effects undoubtedly raise the well-established demand for a more accurate description of aerosols in the evolutionary mechanisms of regional pollution processes and in global climate system and weather prediction model studies (e.g., Huang and Ding, 2021). Moreover, an accurate characterization of aerosol loading is also equally urgent in air quality applications and trace gas remote sensing (e.g., Gui et al., 2019).

Satellite remote sensing, as a complement to ground-based observations, plays a critical role in enhancing knowledge of the response of the Earth's climate system to aerosols. Satellites carrying a variety of modern aerosol sensing instruments have been launched by different agencies to detect the atmosphere with the aim of describing the optical and microphysical properties, and the three-dimensional vertical distribution of aerosols (King et al., 2003; Ahn et al., 2014; Winker et al., 2010; Gui et al., 2021a). Using continuous measurements from multiple space-borne sensors, substantial progress has been gained over the last two decades in
understanding the distribution patterns, evolution trends, and aerosol sources from regional to global scales, as well as the effects of atmospheric aerosols on the Earth's radiation balance, cloud and precipitation processes (Kaufman and Koren, 2006; Che et al., 2019a; Zhao et al., 2018; Zhao et al., 2020).

The Multiangle Imaging SpectroRadiometer (MISR) onboard Terra satellite is unique in providing information on aerosol optical properties such as aerosol content, light absorption, and particle types using its combined advantages of high-spatial resolution and wide along-orbit viewing angle (Kahn et al., 2005, 2010). Due to its advantage of multi-angle observation, MISR is capable of allocating columnar aerosol optical depth (AOD) by particle size and morphology, in addition to retrieval of total AOD, without having to rely on information from other instruments (Garay et al., 2020). The MISR standard aerosol retrieval algorithms operate in a fully automated mode (Diner et al., 2005). As aerosol products produced using different retrieval approaches have become widely adopted in several research fields, the strengths and weaknesses of these algorithms have been widely identified (Kahn et al., 2010; Garay et al., 2020). The MISR aerosol team is committed to update aerosol retrieval algorithms to address the ever-increasing demands of users. Historically, the last two substantial updates to MISR's aerosol retrieval algorithm have occurred in December 2007 and November 2017, designated as version 22 (V22) and version 23 (V23), respectively (Garay et al., 2020). In V22, two different aerosol retrieval strategies are applied over land and over dark waters to produce AOD and particle properties datasets (Kahn & Gaitley, 2015). Retrieved aerosol optical properties for V22 have been extensively verified worldwide using aerosol retrievals from ground-based observations and other satellite sensors as a reference. These evaluation studies confirmed the good performance of the V22 aerosol product, but also identified several drawbacks, such as unrealistic
quantization of the low AOD range (AOD < 0.1) (Kahn et al., 2005; Witek et al., 2013). To address these issues in MISR's aerosol retrievals, as well as to fulfill the increasing requirements for high-resolution AOD products in air pollution monitoring and environmental health, the MISR aerosol team updated the aerosol retrieval algorithm to V23. Compared to the coarser resolution of 17.6 km for V22, the horizontal resolution of the Level-2 (L2) product (swath) for V23 has been increased to 4.4 km. Of course, in addition to the increased spatial resolution, the V23 product also includes geo-location information, pixel-level error estimates, and improved cloud filtering (Garay et al., 2020). Meanwhile, by aggregating the L2 data into global grids, a Level-3 (L3) gridded aerosol product (F15_0032) at 0.5° × 0.5° spatial resolution was developed. Although a preliminary evaluation of the global performance of the V23 L2 AOD retrievals has been conducted by Garay et al. (2020) using available AOD observations from the Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN), the consistency between the L3 gridded AOD product and observations is not yet known. More importantly, L3 AOD products are often used in climate studies, and their accuracy is critical for investigating the long-term climatology, trends, and climate effects of aerosols. Therefore, a consistent analysis of the latest MISR V23 L3 atmospheric aerosol products at global and regional scales, especially in typically polluted regions, using ground-based observations and other widely used AOD sources, is essential.

In this study, the continuous daily AOD data from 427 AERONET sites globally during 2001-2018 and 39 CARSNET (China Aerosol Remote Sensing Network) sites across China during 2002-2014 were first collected. These datasets provide us with sufficient statistics to quantify the consistency between MISR L3 AOD and observations, and to examine the regional dependence of the degree of site-to-site consistency. Additionally, five widely used monthly
AOD data sources (three from satellite and two from aerosol reanalysis) along with AERONET monthly AOD data for the overlapping period (2003–2017) were also collected in this study to explore the ability of MISR for reproducing interannual variations and trends over the 12 regions of interest (ROIs).

## 2. Materials and Methods

### 2.1. MISR L3 aerosol property product

MISR sensor observes the Earth's atmosphere and the radiance reflected from the surface with a relatively high spatial resolution (ranging from 0.275 km to 1.1 km) in four visible (446, 558, 672, and 866 nm) or near-infrared spectral bands along nine orbital viewing angles, which allow MISR to classify AODs by different particle types and provide detailed information on aerosol particle properties (Kahn et al., 2001). Spatially, MISR is designed to provide complete near-global coverage in a 9-day cycle (Diner et al., 1998). This study uses the L3 daily and monthly global aerosol products (MIL3DAE and MIL3MAEN) from the latest release version F15_0032, which is reported at 0.5° × 0.5° spatial resolution.

The most basic parameter used in this study is the columnar AOD at 550 nm. Compared to AERONET observations, the uncertainty in MISR L2 AOD data is approximately ± 0.05 or ± (0.20 × AOD) over the land and ocean (Kahn et al., 2005, 2010). Additionally, to interpret the consistency or differences between MISR-AOD and other AOD observations, we also analyzed the aerosol properties, including fine-mode AOD (AODf) and coarse-mode AOD (AODc). In the MISR aerosol retrieval algorithm, the dominant aerosol size is distinguished into three sizes: small-size (< 0.7 µm diameter), medium-size (0.7–1.4 µm diameter), and large-size (> 1.4 µm diameter). These classifications in MISR are based on predefined particle types described by a log-normal distribution and are characterized by their characteristic width parameters and radius
(Kahn et al., 2010). Therefore, in the current study, we also extracted these three parameters related to aerosol sizes from the L3 daily global aerosol product. Following Kahn and Gaitley (2015), the MISR-based small-size AOD can be treated as the AODf, whereas the medium- and large-size AODs can be grouped together and considered the AODc.

## 2.2. Other AOD datasets

For intercomparison purposes, five different AOD at 550nm datasets obtained from three satellite retrievals and two reanalysis were also used. Three satellite-retrieved AOD datasets are from two MODIS (onboard Terra and Aqua satellite) product and a multi-satellite merged product. In this study, we used the monthly gridded AOD product Collection C6.1 (MYD08_M3 and MOD08_M3 for the Aqua and Terra satellites, respectively) at 1° × 1° resolution covering the period 2003–2017, derived from the combined Dark Target and Deep Blue algorithms (Sayer et al., 2013; Levy et al., 2013; Wei et al., 2019). The multi-satellite merged product was produced by Sogacheva et al. (2020) by aggregating 12 individual AOD datasets from different satellite sensors. This product is a high-accuracy long-term AOD dataset for the period 1995–2017, with a spatial resolution of 1° × 1°. The AERONET-based evaluation indicates that the performance of the multi-satellite merged product is at least as good as that of the individual satellite AOD datasets (Sogacheva et al., 2020). For details regarding the aggregation process, methodology, and evaluation of this merged AOD product, please see the works by Sogacheva et al. (2020).

The two AOD reanalysis datasets are from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) and the Copernicus Atmosphere Monitoring Service (CAMS). MERRA-2 is a second-generation atmospheric aerosol reanalysis of the new modern satellite era (post-1980), which assimilates both meteorological observations and the
AODs from various ground- and satellite-based observations (Buchard et al., 2017). CAMS is the latest global atmospheric composition reanalysis dataset generated by the European Centre for Medium-Range Weather Forecasts (ECMWF) and consists of atmospheric composition fields including major precursors, chemical species, and aerosols (Inness et al., 2019). Monthly mean AOD reanalysis data from MERRA-2 and CAMS were used in this study. By comparing the 3-hourly MERRA-2 and CAMS AOD products with instantaneous AOD observations from 793 AERONET sites, Gueymard and Yang (2020) found that globally, MERRA-2 performs better than CAMS, achieving a root-mean-square error (RMSE) of 0.126, as compared to 0.144 for CAMS. To enable the spatial comparability of the trend values estimated from different data sources, the MISR, MERRA-2, and CAMS AOD datasets with high spatial resolutions (0.5° × 0.5°, 0.5° × 0.625°, and 0.75° × 0.75°, respectively) were bilinearly interpolated to the 1° × 1° resolution of the MODIS/Terra and the multi-satellite merged products in this study.

## 2.3. AERONET and CARSNET

AERONET is a well-established ground-based observation network of over 700 stations worldwide established by NASA (Holben et al., 1998). The sun-photometer in the AERONET sites observes the radiances from the direct-sun and diffuse-sky at different wavelengths. AERONET AOD has an uncertainty range of approximately 0.01–0.02 (Eck et al., 1999). In this study, MISR L3 daily AOD product is compared against the AERONET Version 3 daily AOD, at Level 2.0 with both cloud-screened and quality-assured data (Smirnov et al., 2002; Giles et al., 2019), collected by 427 sites globally over land during 2001–2018.

Considering the fact that there are only a limited number of AERONET sites in China that can provide long-term AOD observations, it is essential to use the additional AOD observations from CARSNET to check the applicability of the MISR AOD daily product across China.
CARSNET, first established by the China Meteorological Administration (CMA) in 2002, is one of the most important ground-based networks for the observation and retrieval of aerosol optical and microphysical properties in China (Che et al., 2009, 2015). CARSNET uses the same type of sun photometer (CE-318) as the AERONET, which is capable of carrying out direct-solar and sky-radiance measurements. The instruments in CARSNET are calibrated and standardized annually to ensure the accuracy and quality of the observations (Che et al., 2009, 2019a, 2019b). The CARSNET reference instruments are periodically calibrated together with the AERONET program at Izaña, Tenerife, Spain (Che et al., 2009, 2019a, 2019b). In this study, the long-term Level 2.0 daily AOD data from 39 CARSNET sites during the period 2002–2014 were used. The Level 2.0 AOD from CARSNET has a total uncertainty of about 0.01–0.02 with the same accuracy as AERONET (Che et al., 2009). However, it should be noted that neither AERONET nor CARSNET can provide AOD measurements at 550 nm, as MISR V23 retrievals do. Therefore, to ensure the feasibility of the evaluation, the AOD observations at two adjacent bands (440 nm and 675 nm) from CARSNET or AERONET were converted to 550 nm by utilizing the Ångström exponent at 440–675 nm.

For aerosol type, we examine the possible effect of MISR-based aerosol size modes on total AOD offset by comparing MISR-based daily AODc and AODf at 550nm with AERONET daily AODc and AODf at 550nm from the spectral deconvolution algorithm (SDA) (O’Neill et al., 2003), respectively. For this purpose, this study uses the AERONET Version 3 SDA daily products (Level 2.0 data) during 2001–2018. Due to the AERONET SDA only provide the AODc and AODf products at 500 nm, so the Ångström exponent (AE) products for the total AOD and AODf derived from SDA will be used to convert the SDA fine and coarse AODs from 500 to 550 nm according to the Eq. (1). It should be noted that both fine- and coarse-mode AODs
comparisons are only approximate, as there are different definitions of those aerosol modes between AERONET and MISR.

$$ AOD^{\lambda_1} = \left(\frac{\lambda_1}{\lambda_2}\right)^{-AE} \times AOD^{\lambda_2} \qquad (1) $$

To examine the consistency of MISR compared to other widely used AOD data sources in characterizing interannual variability and trends in AOD, we further use AERONET monthly AOD values as a reference. In this study, AERONET Version 3 Level 2.0 monthly AOD data over land from 2003 to 2017 were used.

## 2.4. Statistical analysis

### 2.4.1. Statistical metrics

Firstly, MISR daily AOD product (MIL3DAE) was compared against the continuous daily AOD data from 427 AERONET sites globally and 39 CARSNET sites in China from 2001 to 2018. For each AERONET and CARSNET site, we selected only those sites with more than 1 year of data to analyze the consistency between the L3 daily AOD product and observation. This process will ensure that more robust statistics are available for each site. The time series of MISR AOD daily products at site-scale is obtained by bilinear interpolation at each 0.5° × 0.5° grid box with latitude and longitude information from AERONET or CARSNET sites. A similar comparison was carried out for AODf and AODc between MISR and AERONET. To be clear, the comparison here is different from the comparison between L2 (instantaneous) satellite data and instantaneous Sun photometer measurement. Since MISR observes once per day (around 10:30 am local solar) its daily L3 product is not really a daily measure but an aggregated composite of mid-morning observations. Therefore, the L3 comparison in this study focused on elucidating the degree of consistency of the MISR L3 products compared to observations. Of
course, given the fact that aerosol loading usually has significant diurnal variability, caution is needed in interpreting their consistency.

Subsequently, using the AERONET monthly mean AOD data as a reference, we further examined the consistency of the MISR L3 monthly AOD product (MIL3MAEN) with other AOD data sources. For this purpose, MISR, MODIS/Terra, MODIS/Aqua, the multi-satellite merged product, MERRA-2, and CMAS monthly AOD data are compared against the monthly AOD data from 575 AERONET sites during 2003–2017. These 575 AERONET sites were selected based on the criterion that each site has at least 12 months of data.

With reference to previous studies on the evaluation of the L2 AOD from MISR (Kahn et al., 2010; Garay et al., 2020), here we quantified the degree of correspondence in MISR’s L3 daily and monthly AOD products using the following statistical metrics: Spearman’s rank R (Eq. (2)), root-mean-square-difference (RMSD, Eq. (3)), the mean-absolute-difference (MAD, Eq. (4)), and the relative-mean-difference (RMD, Eq. (5)). The reasons that Spearman’s rank R was chosen to be used in this study instead of the more common Pearson’s linear R include the facts that the relationship between ground-based AOD observations and satellite-based AOD may not be linear and also that Spearman’s correlation is an ordinal measure it is less affected by extreme outliers in the distributions (Sayer et al., 2018). In addition, the fraction within expected errors (EE_1, Eq. (6) and EE_2, Eq. (7)) used in the MISR L2 assessment (Kahn et al., 2010) were also included as a supplement to assess the consistency in L3 data.

$$R = 1 - \frac{6 \sum (M_i - O_i)^2}{N(N^2 - 1)} \qquad (2)$$

$$RMSD = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (M_i - O_i)^2} \qquad (3)$$
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$$ \text{MAD} = \frac{1}{N} \sum_{i=1}^{N} |M_i - O_i| \qquad (4) $$

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$$ \text{RMD} = \overline{M} / \overline{O} \qquad (5) $$

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$$ \text{EE}_1 = \pm(0.05 + 0.20 \times \text{AOD}_{\text{obs}}) \qquad (6) $$

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$$ \text{EE}_2 = \pm(0.03 + 0.10 \times \text{AOD}_{\text{obs}}) \qquad (7) $$

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where *N* is the number of matched samples between MISR-retrieved (*M*) and ground-based observed (*O*, i.e., AERONET or CARSNET) AOD values.

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## 2.4.2. Trend analysis

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The trend analysis is carried out for annual mean AOD time series from global to regional scales by using the Mann-Kendall (M-K; Mann, 1945; Kendall, 1957) tau test with Sen's slope method. The M-K trend test is a nonparametric method that has the advantage that data samples do not have to follow a particular probability distribution, and are rarely disturbed by "outliers". In this study, the Sen's slope is applied to evaluate the strength of the trend value, and then the M-K statistical test is employed to test whether these estimated trends are significant at a given significance level (Gui et al., 2021b). To ensure the robustness of the trend assessment, we required that at least 60% of the data in each annual time series be valid before the trend calculations could be performed.

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In spatial trend maps, multiple simultaneous hypothesis testing results may be overestimated or overinterpreted (Wilks, 2016). In this study, a straightforward and statistically principled approach, control of the false discovery rate (FDR) (Wilks, 2016), was applied to the *p* values of the regression estimates to address this issue. The approach sorts the *p* values from *N* local hypothesis tests in ascending order, so that *p*<sub>(1)</sub> ≤ *p*<sub>(2)</sub> ≤ ... ≤ *p*<sub>(N)</sub>, and finds a threshold for the *p*
values. Local null hypotheses are rejected if their *p* values are smaller than or equal to the threshold level *p*<sub>FDR</sub>*, which is determined using the following Eq. (8):

$$p_{FDR}^* = \max_{i=1,...,N} \left[ p_{(i)} : p_{(i)} \le \left(\frac{i}{N}\right) \alpha_{FDR} \right] \quad (8)$$

where $\alpha_{FDR}$ is the chosen control level for the FDR and *N* is the number of grid points of the trend field. Referring to the previous similar study (Hu et al., 2021), we choose $\alpha_{FDR} = 0.1$ to achieve the statistical significance of the global hypothesis tests is at 95% confidence level.

## 2.5. ROIs

Regional differences in AOD are mainly related to the intensity of regional anthropogenic activities, meteorological conditions, and land cover type. Therefore, we selected the 12 ROIs with high anthropogenic or natural aerosol pollution to explore the applicability of MISR in characterizing the climatology, interannual variability, and trends of regional AOD under high aerosol loading conditions. The 12 ROIs selected for this study are located in major aerosol emission sources, including three dust dominant regions: Sahara Desert (SD), Middle East (ME), and Northwestern China (NWC), six anthropogenic aerosol dominant regions: Southern Asia (SA), Eastern China (Northern China (NC) and Southern China (SC)), the Eastern United States (EUS), Northeastern Asia (NEA), and Western Europe (WEU), and three biomass burning aerosol dominant regions: Central Southern Africa (CSA), Amazon Zone (AMZ), and Southeastern Asia (SEA). The geographic boundaries of these ROIs (Table S1) are enclosed by red rectangular boxes in Fig. S1.
## 3. Results and discussion

### 3.1. Consistency examination of MISR L3 daily AOD products with ground-based observations

#### 3.1.1. Global-scale

Number of matched samples for MISR daily AOD product against the ground-based observed AODs from AERONET and CARSNET is shown in Fig. S2. Previously, the evaluation of MISR AOD product in China was based mainly on the limited AERONET observation sites in China, and its conclusions may have been affected by insufficient matching sample sizes. Here, when the additional 39 CARSNET sites in China are incorporated, the evaluation of MISR AOD product in China will also be more statistically robust. The number of AERONET sites located in the 12 ROIs of NEA, NC, SC, SA, NWC, SA, ME, WEU, SD, CSA, EUS, and AMZ is 13, 3, 2, 15, 1, 11, 10, 75, 11, 3, 37, and 7, respectively. The number of CARSNET sites located in NC, SC, and NWC is 12, 2, and 4, respectively.

Fig. 1 presents the results of the overall global-scale comparison of MISR daily AOD product based on all daily AOD data incorporating AERONET and CARSNET. The results show that, overall, there is a good agreement between MISR AOD and ground-based observations on a global scale, as demonstrated by Spearman's rank *R* of 0.9, RMSD of 0.12, and MAD of 0.06. Meanwhile, the MISR daily AOD product has 80.4% of matched samples falling into the EE_1, while 59.9% into the EE_2. Although the V23 algorithm addresses several issues in L2 AOD retrieval relative to the previous version, the L3 data aggregated from L2 data tends to on average overestimate low AOD values and underestimate high AOD values (Fig. 1).
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Scatter plot of the daily MISR AOD against the ground-based AODs obtained from the combined of AERONET and CARSNET observations. The solid black line, the dashed blue line, and the dashed red line in the scatterplot are the 1:1 reference line, the EE_1 of ± (0.05 + 20%) line, and the EE_2 of ± (0.03 + 10%) line, respectively.</figcaption></figure>

Further comparison of the AODc and AODf derived from MISR with those from AERONET suggests that these deviations (especially the range where the high and low values are located) may be related to the fact that MISR tends to overestimate AODc and underestimate AODf (Fig. 2). According to the statistical comparison between collocated MISR retrievals and AERONET observations (Fig. 2), the degree of consistency between MISR AODf and AODc and observations is lower than that of AOD. Although the MISR-based AODf has better agreement with AERONET observations than AODc, the former exhibits a greater deviation compared to observations than the latter. Specifically, the *R* between AODc (AODf) and observations is 0.61 (0.78), MAD is 0.04 (0.05), and RMSD is 0.07 (0.11).
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Scatter plots of MISR coarse-mode AOD (AODc) and fine-mode AOD (AODf) at 550 nm and AERONET (a) AODc and (b) AODf at 550 nm, respectively. The solid black line, the dashed blue line, and the dashed red line in the scatterplot are the 1:1 reference line, the EE_1 of ± (0.05 + 20%) line, and the EE_2 of ± (0.03 + 10%) line, respectively.</figcaption></figure>

The spatial distribution of the site-to-site statistics shows that the degree of agreement (represented by *R*) between the MISR-retrieved AODf and AERONET observations is higher than that between AODc and observations in almost all regions except SD and the ME regions, which are dominated by dust aerosols (Fig. 3). However, in most high aerosol loading regions (e.g., eastern China, SA, and SEA), both the AODc and AODf from MISR are systematically biased compared to observations, with the deviation of the former being weaker than that of the latter. Nevertheless, the systematic overestimation (underestimation) of MISR-retrieved AODc (AODf) compared to observations on a global scale is widespread.
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Statistical results of the daily MISR AODc and AODf at 550 nm and AERONET (a) AODc and (b) AODf at 550 nm: Spearman's rank *R* (top), RMSD (middle), and RMD (bottom).</figcaption></figure>

Fig. 4 shows the site-scale consistency of daily MISR AOD and the collocated ground-based AOD observations. Globally, the MISR AOD displays a high degree of agreement with ground-based observations at most sites worldwide: more than 92.7%, 81.9%, 58.2%, and 12.2% of sites had an *R* larger than 0.6, 0.7, 0.8, and 0.9, respectively; 91.2%, 82.0%, and 56.9% of sites had an MAD less than 0.15, 0.10, and 0.05, respectively; 88.0%, 82.6%, 68.0%, and 27.3% of sites had an RMSD lower than 0.2, 0.15, 0.10, and 0.05, respectively; and over 54.9% and 45.1% of sites had an RMD more than 1 and lower than 1, respectively. In addition, additional statistical indicators showed that 88.8%, 79.6%, 67.4%, and 33.7% of sites globally had a percentage of the matched sample that fell within the EE of ± (0.05 + 20%) greater than 60%, 70%, 80%, and 90%, respectively. In contrast, 61.6%, 41.6%, 9.4%, and 1.1% of sites globally had a percentage
of the matched sample that fell within the EE of ± (0.03 + 10%) greater than 60%, 70%, 80%, and 90%, respectively (Fig. S3). These results indicate that the overall statistic are very good, although there are also sites for which MISR's consistency appears lacking. For those sites, rather poor consistency may be attributed to resolution issues, overlooking of diurnal variations in aerosol loading, and the chosen collocation methodology. The AOD obtained by the sun photometer usually represents the aerosol loading for a point source, whereas the daily AOD from MISR represents the average AOD of a 0.5° × 0.5° grid box (approximately 50 km × 50 km). The magnitude of the gridded AOD is usually determined by the mixing of regional or local emission sources (Schutgens et al., 2017). Previous studies also suggested that the spatial heterogeneity of aerosols may have a negative impact on the evaluation results (e.g., Witek et al., 2018). In addition, the accuracy of satellite-retrieved AODs is often constrained by the complexity of surface types (e.g., Wang et al., 2019).

<figure><img src="image_5.png" /></figure>
**Fig. 4.** Statistical results of the daily MISR AOD against the AOD observations from 427 AERONET sites and 39 CARSNET sites: (a) R, (b) RMSD and MAD, (c) RMD, (d) within the EE_1 of ± (0.05 + 20%), (e) above the EE_1 of ± (0.05 + 20%), and (f) below the EE_1 of ± (0.05 + 20%). Circle size in Fig. 4b indicates RMSD and its color indicates MAD. Histogram inserted in the lower-left of each panel shows the frequency of each statistical measure between the MISR AOD and all ground-based observed AODs, where the darker the color, the higher the frequency.

It is interesting that, although the V23 version has made a great improvement in the algorithm compared with the V22 version (Garay et al., 2020), there are still significant regional differences in the consistency of MISR daily AOD product relative to observations. From Fig. 4c, it can be seen that sites with RMD lower than 1 are usually located in areas with enhanced anthropogenic emissions, such as NC, India, and SEA, indicating that the MISR AOD is underestimated in these areas. For example, at the AERONET Beijing site, which is dominated by anthropogenic sources, MISR daily AOD usually underestimates high AOD values (Fig. 5a), as demonstrated by the RMD of 0.60. One possible factor leading to MISR's AOD underestimations might be that at high aerosol loadings the contrast in underlying surface features is diminished, causing the MISR's heterogenous surface algorithm to assign too much signal to the surface reflection and thus underestimate the signal from the atmosphere (Chen et al., 2008). Also, the complex underlying surface and aerosol types over these regions pose a challenge to the relevant assumptions in the MISR algorithm. In comparison, MISR AODs are often overestimated (RMD > 1) in the Western United States, WEU, Australia, and other regions with low anthropogenic emissions. The above results were also supported by the spatial distribution pattern of the percentage of matched samples falling above or below the EE_1 (Fig. 4e and 4f) and EE_2 (Fig. S3b and S3c). That is, the high percentages of matched samples (> 40%) falling above EEs are mainly distributed in the Western United States, while the high percentages of matched samples (> 40%) falling below EEs are mainly located in NC, SA, and
SEA. Despite these shortcomings, the retrieval accuracy of MISR in the context of some specific surface types is trustworthy. For example, the multi-angle advantage of MISR allows for more reliable AOD retrievals on highly reflective bright surfaces (e.g. dust sources) (Kahn et al., 2010). This is illustrated by the high consistency of the observed and MISR-based daily AOD time series at the AERONET Capo Verde site (Fig. 5b).

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Time series of AOD at two AERONET sites representing regions dominated by (a) polluted continental (Beijing, East Asia) and (b) dust (Capo Verde, northwest African coast). Red and black lines are the daily AOD from MISR and AERONET, respectively. Spearman's rank *R*, RMSD, and RMD are based on collocated daily data for the entire time series shown in each panel.</figcaption></figure>

### 3.1.2. Regional-scale

Different regions have different aerosol characteristics due to changes in geographic features, surface types, local climate, and human activities over land. Therefore, it is essential to
evaluate the overall applicability of the daily AOD product from MISR in the 12 ROIs selected for this study before examining the regional interannual variations in AODs. Fig. 6 and Table 1 show the overall regional consistency of MISR AOD that integrates all ground-based sites within each ROI (as defined in Fig. S1 and table S1). Note that for three ROIs (NWC, NC, and SC) in China, the AERONET- and CARSNET-based assessments are presented separately to explore whether the number of sites or the matched sample size has an impact on the assessment results. Results show that, among the 12 ROIs, the *R* between MISR and AERONET ranged from 0.67 to 0.93, with the highest *R* (0.93) locating in CSA, and the lowest (0.67) in NWC. Meanwhile, we found that the lowest MAD (0.04) and RMSD (0.06) both occur in EUS and WEU. In contrast, the highest MAD (0.20) and RMSD (0.37) appeared in NC, indicating that MISR AOD retrievals are often underestimated in this region. The RMD indicators among different ROIs suggested that MISR AOD tends to be underestimated (RMD < 1) in NEA, NC, SC, SEA, SA, SD, CSA, and AMZ, and overestimated (RMD > 1) in NWC, ME, WEU, and EUS.

Although *R* improved from 0.67 using one AERONET site to 0.81 using additional CARSNET observations (Fig. 6b3), this is likely because the AERONET observations have a rather limited AOD range at this site (<0.5) in comparison to the CARSNET observations. Other statistics, like RMSD and MAD, get considerably worse for CARSNET sites than for the AERONET site in the NWC region. For the other two regions (i.e., NC and SC) with AERONET and CARSNET sites, the differences in statistics are considerably smaller than in NWC, with a slight worsening of the statistics for CARSNET sites. Notably, MISR tends to underestimate CARSNET AOD at high-AOD ranges (AOD > 1), which suggests that the MISR performance at NWC needs to be improved, especially during extreme dust events. For NC and SC, the degree of consistency of MISR against observation seems to tend to get worse, as demonstrated by
multiple statistical indicators. For instance, the MAD (RMSD) in SC has risen from 0.09 (0.16) to 0.14 (0.19), and the MAD in NC has also risen from 0.20 to 0.22. Such significant differences between the evaluation results conducted by using AERONET and CARSNET alone also further confirm that the statistical sample size may have a negative impact on the comparison metrics and the consistency examination presented above.

A further quantitative metric, EE, showed that the percentage of matched samples falling into EE_1 and EE_2 ranged from 63.8% to 89.5% and from 35.7% to 72.8% among the 12 ROIs, with the highest percentages located in EUS (89.5% and 72.8%) and the lowest in NC (63.8%) and SA (35.7%). In contrast, the highest percentage of the data samples falling above the EE_1 and EE_2 is found in ME (25.6% and 47.6%, respectively), while the lowest values are located in NC (1.9% and 5.1%, respectively). When the observations from CARSNET sites are used as the reference data for the assessment, it is found that the percentage of matched samples falling into EE_1 (EE_2) is 48.3% (24.5%), 70.7% (47.2%), and 62.7% (39.4%) in NC, SA, and NWC, respectively.

<table><caption>**Table 1.** Statistical results of the daily MISR AOD vs. ground-based AODs (i.e. AERONET and CARSNET) over the 12 regions of interest (ROIs). <sup>a</sup> Represents the statistical results for CARSNET sites; <sup>b</sup> EE_1 indicates the expected error (EE) of ± (0.05 + 20%); <sup>c</sup> EE_2 denotes the EE of ± (0.03 + 10%).</caption><thead><tr><th>ROIs</th><th>Number of sites</th><th>Number of samples</th><th>R</th><th>MAD</th><th>RMSD</th><th>RMD</th><th>Within EE_1<sup>b</sup> (%)</th><th>Above EE_1 (%)</th><th>Below EE_1 (%)</th><th>Within EE_2<sup>c</sup> (%)</th><th>Above EE_2 (%)</th><th>Below EE_2 (%)</th></tr></thead><tbody><tr><td>NEA</td><td>13</td><td>2834</td><td>0.89</td><td>0.07</td><td>0.12</td><td>0.89</td><td>83.0</td><td>5.8</td><td>11.3</td><td>61.1</td><td>12.2</td><td>26.7</td></tr><tr><td>NC</td><td>3</td><td>1368</td><td>0.91</td><td>0.20</td><td>0.37</td><td>0.64</td><td>63.8</td><td>1.9</td><td>34.3</td><td>45.2</td><td>5.1</td><td>49.6</td></tr><tr><td>SC</td><td>2</td><td>233</td><td>0.87</td><td>0.09</td><td>0.16</td><td>0.93</td><td>76.8</td><td>11.2</td><td>12.0</td><td>53.6</td><td>23.6</td><td>22.7</td></tr><tr><td>SEA</td><td>15</td><td>1544</td><td>0.84</td><td>0.12</td><td>0.26</td><td>0.82</td><td>74.2</td><td>8.7</td><td>17.0</td><td>49.0</td><td>18.8</td><td>32.3</td></tr><tr><td>NWC</td><td>1</td><td>246</td><td>0.67</td><td>0.04</td><td>0.06</td><td>1.32</td><td>79.7</td><td>20.3</td><td>0.0</td><td>59.8</td><td>37.4</td><td>2.8</td></tr><tr><td>SA</td><td>11</td><td>2360</td><td>0.89</td><td>0.12</td><td>0.18</td><td>0.86</td><td>64.9</td><td>15.0</td><td>20.1</td><td>35.7</td><td>22.8</td><td>41.5</td></tr><tr><td>ME</td><td>10</td><td>2277</td><td>0.90</td><td>0.09</td><td>0.11</td><td>1.15</td><td>72.5</td><td>25.6</td><td>1.9</td><td>45.0</td><td>47.6</td><td>7.4</td></tr><tr><td>WEU</td><td>75</td><td>20085</td><td>0.81</td><td>0.04</td><td>0.06</td><td>1.02</td><td>89.3</td><td>7.2</td><td>3.5</td><td>70.6</td><td>17.4</td><td>12.0</td></tr><tr><td>SD</td><td>11</td><td>4462</td><td>0.89</td><td>0.12</td><td>0.19</td><td>0.92</td><td>75.2</td><td>12.2</td><td>12.6</td><td>47.6</td><td>24.2</td><td>28.2</td></tr><tr><td>CSA</td><td>3</td><td>594</td><td>0.93</td><td>0.05</td><td>0.09</td><td>0.94</td><td>87.7</td><td>6.2</td><td>6.1</td><td>70.5</td><td>13.6</td><td>15.8</td></tr><tr><td>EUS</td><td>37</td><td>7275</td><td>0.87</td><td>0.04</td><td>0.06</td><td>1.10</td><td>89.5</td><td>8.3</td><td>2.2</td><td>72.8</td><td>20.5</td><td>6.8</td></tr><tr><td>AMZ</td><td>7</td><td>1352</td><td>0.88</td><td>0.05</td><td>0.11</td><td>0.93</td><td>86.7</td><td>7.5</td><td>5.8</td><td>69.5</td><td>16.6</td><td>13.9</td></tr></tbody></table>
<table><tr><td>NC<sup>a</sup></td><td>12</td><td>2094</td><td>0.80</td><td>0.22</td><td>0.31</td><td>0.67</td><td>48.3</td><td>3.4</td><td>48.3</td><td>24.5</td><td>5.2</td><td>70.4</td></tr><tr><td>SC<sup>a</sup></td><td>2</td><td>123</td><td>0.81</td><td>0.14</td><td>0.19</td><td>0.85</td><td>70.7</td><td>4.1</td><td>25.2</td><td>47.2</td><td>8.9</td><td>43.9</td></tr><tr><td>NWC<sup>a</sup></td><td>4</td><td>1092</td><td>0.81</td><td>0.15</td><td>0.24</td><td>0.74</td><td>62.7</td><td>6.4</td><td>30.9</td><td>39.4</td><td>13.6</td><td>47.0</td></tr></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Scatter plots of the daily MISR AOD against the ground-based observed AODs from (a1)–(a12) AERONET and (b1)–(b3) CARSNET over the 12 ROIs, as defined in Fig. S1. The solid black line, the dashed blue line, and the dashed red line in the scatterplot are the 1:1 reference line, the EE_1 of ± (0.05 + 20%) line, and the EE_2 of ± (0.03 + 10%) line, respectively.</figcaption></figure>

## 3.2. Monthly AOD products intercomparison and consistency analysis with AERONET

Previously we have conducted a statistical assessment of the degree of agreement of MISR daily AOD product at site-to-site scales relative to ground-based observations. Next, we will
examine the consistency of MISR relative to other AOD data sources on a monthly average scale. This helps us to sort out the strengths and weaknesses of MISR in characterizing long-term changes in aerosol loading at global and regional scales. Therefore, five widely used AOD datasets were introduced in this study, including two MODIS aerosol products, a multi-satellite merged AOD product, and two aerosol reanalysis products (MERRA-2 and CAMS). It should be noted that these AOD datasets are not completely independent of each other. For example, MERRA-2 assimilates bias-corrected near-real-time AOD from MODIS/Terra and MODIS/Aqua radiances using a Neural Net Retrieval algorithm and assimilates non-bias-corrected AOD from MISR over bright land surfaces only (albedo < 0.15) for the period 2000–2014 (Buchard et al., 2017), CAMS assimilates Collection 6 retrievals of total AOD at 550nm from MODIS aboard the Terra and Aqua satellites (Inness et al., 2019), and the multi-satellite merged product incorporates the latest version of the MODIS and MISR monthly average products. However, given the fact that each dataset has its own strengths and limitations in characterizing global and regional aerosol loadings due to differences in aspects such as sensor design, observational techniques, retrieval algorithms, spatial sampling frequencies, and so on. Thus, by comparing MISR with other AOD data, we expect to reveal consistencies and diversions of MISR with respect to other products.

Fig. 7 gives the overall statistical results of the consistency comparison between multi-platform monthly AOD datasets and AERONET monthly data. As expected, the optimal consistency of the multi-satellite merged AOD product with AERONET further demonstrates the advantages of merging multiple products, as demonstrated by the *R* of 0.86, MAD of 0.05, RMSD of 0.08, and 88.5% of monthly AODs within the EE_1. Such overall consistency observed for the multi-satellite merged AOD product is in line with the results from the dataset
spatial intercomparison (Fig. 8). Similarly, benefiting from the assimilation of multiple AOD data sources by the MERRA-2 AOD observing system, the observed consistency between MERRA-2 AOD and AERONET is comparable to that between the multi-satellite merged product and AERONET. However, as another available aerosol reanalysis product, the agreement between CAMS and observations is slightly lower than that of MERRA-2. The overall lower consistency of CAMS is mainly attributed to the overestimation of CAMS AOD when AERONET AOD is less than 0.3. As three individual satellite retrieval products, the agreement between MISR and observations is slightly lower than MODIS/Aqua and MODIS/Terra. This is mainly related to the fact that MISR underestimates the high values of AOD to a greater extent than MODIS. In addition, we also notice a slight difference in the degree of agreement between MODIS/Terra and MODIS/Aqua and observations. Overall, MODIS/Aqua has a better agreement with observations than MODIS/Terra: the RMSD decreases from 0.10 to 0.09, and the percent of matched samples that fall within the EE_1 increases from 74.3% to 80.4% (Fig. 7c and d).

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Comparison between MISR, Multi-satellite merged product, MODIS/Terra, MODIS/Aqua, MERRA-2, and CAMS monthly AODs and AERONET monthly AODs for each site with respect to Spearman's rank R. Data are from 2000 to 2017.</figcaption></figure>

As an extension of Fig.7, Fig.9 shows the differences between all AOD products and AERONET AOD as a function of AERONET AOD. A general tendency towards positive AOD offsets is observed for all products under background conditions (AOD < 0.1). Among all the products, CAMS has the highest positive offset, which is consistent with the overall global
comparison result (Fig.7f). Offsets close to 0.05 for background AOD are observed for the MERRA-2 product. However, the offsets for all products become negative when AOD > ~ 0.4. For most products, except MODIS/Terra, MODIS/Aqua, and CAMS, AOD offsets become negative for AOD > 0.2 with increasing amplitude towards highest AOD values. Offsets for MODIS and CAMS remain positive with a similar magnitude for AOD < 0.2 and become negative for AOD > 0.6. The multi-satellite merged product shows the lowest offsets for AOD < 0.3. For the MISR V23 monthly product, AOD is systematically underestimated for AOD > ~ 0.1, and the magnitude of underestimation increases with increasing AOD values. To conclude, AOD products (here, MERRA-2 and multi-satellite merged product) retrieved from multiple satellite data having better coverage show a better agreement with AERONET monthly aggregates than individual satellite products. Therefore, multi-source data fusion is a favorable way to improve the accuracy of gridded aerosol products.

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>
**Fig. 9.** Monthly AOD differences (satellite-based or reanalysis-based – AERONET) as a function of AERONET AOD; data are divided into 40 equally populated bins within each of the AOD regimes.

**3.3. Intercomparison of climatology, interannual variability, and trends of AOD**

In order to move towards consistency in regional and global AOD records derived from multiple satellites using different sensors and retrieval techniques, as well as from aerosol reanalysis data that assimilate multi-source aerosol observations, this section examines annual regional AOD time series obtained from the different products. We have already shown that the multi-satellite merged product is the one with the **smallest** observed offset among all five data sources **in almost all bins (especially for low AOD values)**, thus in the following, we will use the multi-satellite merged product as a reference to conduct a consistency analysis for MISR and other products in characterizing the climatology, interannual variability, and trends of AOD.

Fig. 10 shows the multi-year average AOD climatology calculated for different products during the period 2003–2017. A consistent spatial distribution pattern was observed among all products, although some regional differences were found. Compared to the multi-satellite merged product, MODIS/Terra has the largest positive offset (0.026), and MISR has the highest negative offset (–0.022) on the global average (see Fig. 11a). Note that when calculating the global average, we did not include Antarctica and Greenland, due to the unavailable satellite retrievals in the aforementioned regions (see Fig. 10a-d and Fig. 12). Over land, the sign of offset between different products relative to the multi-satellite merged product has not changed, but the degree of offset is decreasing. The negative offset for MISR relative to the multi-satellite merged product is –0.012. In contrast, the positive offsets are for MODIS (0.029 and 0.014 for Terra and Aqua, respectively).
Regional annual climatology between individual AOD products and the merged AOD product over 12 ROIs are shown in Fig. 11a. In all ROIs except NWC, SD, and CSA, MISR shows negative offsets relative to the merged product, with the largest offset occurring in NC (0.176) and the smallest in ME (0.011). In contrast, positive offsets were found for MODIS/Terra and/or MODIS/Aqua in almost all ROIs. Among all ROIs, the largest offset for MODIS occurs in NC (0.083 and 0.069 for Terra and Aqua, respectively). For MERRA-2, the ratio of the number of ROIs showing either positive or negative offsets is 50%, and the offsets all fall into the interval of -0.05 to 0.05. However, for CAMS, a prominent positive offset was found in NC and SC (0.178 and 0.128, respectively). This positive offset for CAMS in eastern China is consistent with its spatial distribution.
<figure><img src="image_16.png" /><figcaption>**Fig. 10.** Global AOD Climatology calculated from (a) MISR, (b) a multi-satellite merged product, (c) MODIS/Terra, (d) MODIS/Aqua, (e) MERRA-2, and (f) CAMS during the period 2003–2017.</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>**Fig. 11.** Intercomparison of global and regional annual AOD (a) climatology and (b) trends estimated from multiple data sources during the period 2003-2017. Note that the Antarctica and Greenland regions are excluded from all data sources when calculating AOD climatology and trends over global and land scales. Small panel inserted in the upper right corner in Fig. 14b is a zoomed-in view of global and land trends. Shaded bars indicate trend values statistically significant at the 95% confidence level.</figcaption></figure>

In terms of the year-to-year variations in regional AOD time series, MISR shows good consistency with other AOD products over all ROIs except NWC, albeit with slight differences in magnitude (Fig. 12). Specifically, among the 12 ROIs, the *R* between MISR and the multi-satellite merged product, MODIS/Terra, MODIS/Aqua, MERRA-2, and CAMS varies from 0.48 to 0.99, 0.59 to 0.98, 0.84 to 0.98, 0.36 to 0.98, and 0.20 to 0.98, respectively. The highest *R* was observed in EUS, AMZ, SEA, and ME, respectively, whereas the lowest *R* was found in NWC in all matches. The large differences in NWC may be related to the sampling and temporal
differences between different datasets, the latter being mainly reflected in differences in the
ability to capture diurnal changes in dust aerosols.

<figure><img src="image_18.png" /><figcaption>**Fig. 12.** Interannual variations in regional AOD averaged over the 12 regions of interest (ROIs). Note that the colored numbers inserted at the top of each panel represent Spearman's rank *R* between MISR and the other five datasets.</figcaption></figure>

Accurately assessing the long-term trends in aerosol loading is one of the main ways to quantify aerosol changes and is essential for assessing the effectiveness of implementing measures such as regional air quality improvements and anthropogenic aerosol reductions. Fig. 13 presents the global distribution of AOD trend calculated from different data sources during 2003–2017. Overall, the MISR-based trend estimation identifies fewer grid points with trend value pass the significance test (i.e., the number of grid boxes with black circle signs in Fig. 13) at the global scale relative to other data sources. Nevertheless, the trend results for MISR demonstrate that enhanced aerosol loading (at the 95% significance level) is mainly located in
SA, while the declines are located in WEU, eastern China, and the EUS, which are generally consistent with the other five datasets. It is worth noting that the MISR AODs do not indicate a positive trend in central Siberia, which is visible in some other datasets. This could be attributed to the sparser sampling of MISR (as shown in Fig. 14), as it actually agrees well with the only one AERONET site available in this region (see Fig. 4). Enhanced aerosol loadings in central Siberia are mainly associated with extensive fire activities induced by persistent high temperatures and dry air due to large-scale circulation anomalies in the region (Kim et al., 2020).

Generally, the sample size can affect the significance and magnitude of the estimated regional trend to some extent. For example, in SA, the relatively sufficient number of available days (~4-5 days) within each month (Fig. 14) ensures a certain degree of consistency between MISR and other data sources. The difference in sample size (i.e., sensor swath width) between MISR and MODIS (see Fig. 14 and Fig. S4) is partially responsible for the fact that the AOD trends derived from MODIS are greater than those from MISR (Fig. 13a-c). In addition, we also observed that the diurnal variability of AOD also plays an important role in driving interannual variability in aerosol loading, as evidenced by the difference in trend between the two MODIS products (see Fig. 13c and 13d). Furthermore, a key point should be noted, that is, even though the aerosol reanalysis (e.g., MERRA-2) possesses a comparable consistency with satellite retrievals relative to observations (see Figs. 7 and 8), the spatial distribution differences in trends estimated from both (i.e., satellite and aerosol reanalysis) further confirm that aerosol diurnal variability has a non-negligible influence on AOD trends. This is because satellite observations (mainly Polar-orbiting satellites) are generally based on instantaneous observations, while assimilation reanalysis takes into account the diurnal cycle of aerosol loading.
<figure><img src="image_19.png" /><figcaption>**Fig. 13.** Annual trends in AOD calculated from (a) MISR, (b) multi-satellite merged product, (c) MODIS/Terra, (d) MODIS/Aqua, (e) MERRA-2, and (f) CAMS during the period 2003–2017. Note that the grids with black circle signs indicate *p* values small enough to satisfy the FDR criterion α<sub>FDR</sub> = 0.10 [Eq. (8)] so that the statistical significance of the global test is at 95% confidence level.</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>**Fig. 14.** Monthly sample sizes (left-hand-side panel) and available days (right-hand-side panel) averaged over 18 years (2001–2018) of MISR AOD data.</figcaption></figure>
Fig. 11b further shows the regional AOD trends during the period 2003–2017 estimated from different data sources. For the global average, MISR AOD exhibits a decrease of –0.0006 per decade globally during 2003–2017, which is comparable to MERRA-2, but opposite to multi-satellite merged product, MODIS (Terra and Aqua), and CAMS. In contrast, for the global land average, except for MODIS/Terra showing an increase, MISR, along with the other four datasets, all capture a decline in global land AOD, but the magnitude of the trend values varies considerably across datasets. Different from the global and global land averages, the regional trend values for MISR and other datasets over the 12 ROIs generally exhibit similar signs (positive or negative), albeit with differences in magnitude. Specifically, over the anthropogenic-aerosol-dominant areas, the MISR AOD captures the prevailing negative trends in the NEA, NC, SC, WEU, and EUS regions, as well as the non-negligible positive trends in the SA region. These regional trend signs have been identified in previous studies through ground- and space-based observations, and model simulations (e.g., Li et al., 2014; Klingmüller et al., 2016; David et al., 2018; Che et al., 2019a; Jin and Pryor, 2020).

As far as the magnitude of trend values calculated by MISR, there are a varying degree of differences (~0.01–0.04 per decade) in anthropogenic aerosol dominant regions (e.g., the SA, NC, EUS) compared to the other datasets (Fig. 11b). Over the dust-dominant areas, the trends (both sign and magnitude) were significantly different between the different datasets in both NWC and SD regions, except for a consistent positive trend in ME. The above results indicate that there are large uncertainties in the assessment of the long-term AOD trend among different datasets over the dust-dominant areas, and that changes in dust AOD are accompanied by strong interannual fluctuations. Over the biomass-burning-dominant areas, the overall result of MISR is in agreement with other analyzed datasets, capturing well the decrease in AOD over SEA and
AMZ and the increase in CSA, which are in good agreement with the other datasets. The above
results demonstrate that the recently released of MISR V23 L3 aerosol product performs
comparatively reliably in characterizing the interannual variations and trends of regional aerosol
loading, especially in typically polluted areas (e.g. anthropogenic-aerosol-dominant and biomass-
burning-dominant areas), but when applied to dust-dominant areas, more data sources need to be
introduced for synergistic analysis.

4. Conclusions

The applicability (i.e., daily variability, climatology, and trends) of the MISR L3 AOD
datasets from global to regional scales is systematically analyzed in this paper by comparing with
the long-term ground-based observations (i.e., AERONET and CARSNET) and with five widely
used AOD data sources.

Our consistency analysis illustrated that, although the latest release of L3 MISR daily AOD product is in accordance with the combined AOD observations from ground-based references at the global scale (R = 0.85, MAD = 0.06, RMSD = 0.12, and RMD = 0.96), there are noticeable differences in consistency at the regional scale. Overall, about four-fifths of the MISR-retrieved L3 daily AOD values fall within previously published EE bounds of ± (0.05+0.2×AOD). In addition, approximately 60% of data samples fall within the EE of ± (0.03+0.1×AOD). A more in-depth analysis showed that the MISR L3 daily data aggregated from L2 retrievals tends to on average overestimate low AOD values (dominated by coarse-mode) and underestimate high AOD values (dominated by fine-mode). Regionally, among the 12 ROIs, Rs are highest for the CSA and AMZ regions (~0.94), and lowest for dust-dominated and anthropogenic-aerosol-dominated regions (~0.72–0.79).
Subsequently, the consistency of MISR monthly product (MIL3MAEN) with the five other monthly AOD datasets (MODIS/Terra, MODIS/Aqua, the multi-satellite merged product, MERRA-2, and CAMS) at the global scale was examined and verified against a ground-based AERONET monthly mean dataset. The results suggest that AOD products retrieved from multiple satellite data having better coverage show a better agreement with AERONET monthly aggregates than individual satellite products (i.e., MISR and MODIS). Nevertheless, all monthly AOD products were observed to have negative offsets relative to observations for AOD > ~ 0.4 with increasing amplitude towards the highest AOD values.

Finally, we applied MISR's monthly product to analyze the climatology, interannual variability and trends in AOD at global and regional scales, and compared them with those from the other five AOD datasets. The results show that although a consistent spatial distribution pattern of AOD climatology is found for all products, MISR has the highest negative offset (-0.022) on the global average relative to the multi-satellite merged product. Regionally, it was found that the MISR shows good consistency with other AOD product in terms of year-to-year variation over all ROIs except NWC, albeit with slight differences in magnitude. A comparative analysis of the trend assessment among different AOD datasets shows that MISR captures well the significant increase in aerosol loading over SA and the significant decrease in the Whole of Europe, the EUS, and Eastern China during 2003–2017. Unfortunately, however, due to sampling and temporal differences, MISR AODs do not indicate a positive trend in central Siberia, which is visible in other datasets. The above results suggested that the recently released MISR V23 L3 aerosol product performs comparatively reliably in characterizing the interannual variations and trends of regional aerosol loading, especially in typically polluted areas, but caution is still needed when applied to areas where sparse sampling is encountered.
673 Acknowledgments

674 This study is supported by the National Key R & D Program Pilot Projects of China
675 (2016YFA0601901 & 2016YFC0203304) and the National Natural Science Foundation of China
676 (41825011 & 42030608 & 41590874), and the Basic Research Fund of CAMS (2021Y001).

677 Thanks to NASA's MISR team for making the MISR aerosol component product publicly
678 available and for working to improve aerosol retrieval algorithms. Thanks are also extended to
679 the PIs around the world for their efforts in maintaining AERONET networks. MISR L3 aerosol
680 product is available at https://l0dup05.larc.nasa.gov/L3Web/download. MODIS/Terra,
681 MODIS/Aqua, and MERRA-2 monthly AOD dataset are available through the Giovanni website
682 (https://giovanni.gsfc.nasa.gov/giovanni/). CAMS global monthly AOD field is available through
683 the Copernicus atmosphere data store (https://ads.atmosphere.copernicus.eu/cdsapp#!/home).

684 AOD data from AERONET sites are available through https://aeronet.gsfc.nasa.gov/. The
685 CARSNET daily AOD data used in this paper are available from the corresponding author upon
686 request.

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