1. Introduction
Aeolian dust makes up the largest mass fraction of the global aerosol burden (Textor et al. 2006) and produces profound impacts on natural (Rosenfeld et al. 2001; Swap et al. 1992; Mahowald 2011; Miller et al. 2004; Ault et al. 2011; Evan et al. 2006; Strong et al. 2018) and anthropological systems (Tong et al. 2017; Griffin 2007; Shao 2008; Mani and Pillai 2010; Ai and Polenske 2008; Brown 2002). A major limiting factor in understanding the global distribution of dust, how it will change in the future, and its complex interactions with Earth’s climate, is the limited availability of measurements especially over the ocean (Prospero and Mayol-Bracero 2013). Airborne campaigns have provided measurements for select places and times (Ralph et al. 2016; Formenti et al. 2003; Chen et al. 2011; Formenti et al. 2008; Stith et al. 2009; Ryder et al. 2013; Klaver et al. 2011) and ground-based networks have provided long-term measurements for specific regions (Malm et al. 1994; Prospero and Nees 1986; Prospero 1999; Holben et al. 1998). Most satellite sensors do not isolate the dust contribution to aerosol optical depth (AOD) at the point of retrieval, so several methods have been proposed and utilized for studying spatial and temporal variations of dust using satellite measurements and models. These methods have mostly used optical properties of the aerosol column related to particle size (Ginoux et al. 2012; Kaufman 2005), color, and single scattering albedo (Ginoux et al. 2012), or have used the ultraviolet (UV) absorptive property of dust for identification (Ginoux 2003). Several studies have used satellite-based products to study dust aerosol optical depth τd over either the ocean or land in select locations. Over the Atlantic Ocean, Kaufman (2005) analyzed τd using the Moderate Imaging Spectroradiometer (MODIS) retrievals of AOD and Evan and Mukhopadhyay (2010) used a similar method to create a record of τd based on retrievals by the Advanced Very High Resolution Radiometer (AVHRR). In addition, Ginoux et al. (2012) used τd over bright land surfaces that is based on MODIS Deep Blue AOD to identify dust source regions. Monthly τd has also been used in evaluation of the dust emission schemes of global climate models (Pu and Ginoux 2018b).
More recently, studies have used a combination of satellite observations and models to investigate the distribution of dust globally and trends in dust for select regions. Ridley et al. (2016) produced a long-term mean dataset of τd between 2004 and 2008 using AOD retrievals from multiple satellite platforms and dust estimates from several global models and Chin et al. (2014) used satellite AOD from several sensors alongside the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model to investigate multidecadal regional trends in aerosol species, including dust. These merged approaches have value in allowing for elucidation of aerosol sources and the ability to distinguish anthropogenic from natural dust. However, they are susceptible to model biases in dust emission and transport. Climate models display a large diversity in dust aerosol optical depth and dust emission, mostly underestimating dust emission from major sources, and do not consistently agree with observations (Evan et al. 2014; Huneeus et al. 2011; Pu and Ginoux 2018a; Kok et al. 2017; Kok 2010; Ryder et al. 2018). Therefore, it is valuable to have complimentary extensive observational records of dust for comparison.
Here, we expand on the work of Evan and Mukhopadhyay (2010) by using the extended record of satellite retrievals to produce two nearly global τd datasets developed using MODIS (2001–18) and AVHRR (1981–2017) at daily and monthly resolution, respectively, with the former dataset extended to cover both land and ocean regions. Although dust transport happens on a temporal scale of days to weeks, the previously mentioned efforts have focused on estimating monthly mean τd. This work provides the longest record of observed daily τd to date, at a temporal resolution suitable for analysis of daily transport events (daily) and 1° × 1° spatial resolution.
In the next section we describe the data and methods used in the estimation of global τd and provide an estimation of the associated uncertainty. Our results in section 3 include identification of climatological seasonal patterns in τd, a comparison of our τd estimates as derived from two different sensors, and a comparison of the MODIS-derived dataset with preexisting ground-based measurements of dust aerosol from several locations. We then identify regional trends in τd for the period of each record.
2. Method
Two datasets of τd were created. The first, which hereinafter will be referred to as
a. Datasets used
MODIS level-3 (L3), Collection 6.1 (C061), daily Dark Target AOD is derived from the measured 500-m resolution reflectance from all visible MODIS bands by taking the average of the measured radiance over all scenes that are cloud free and glint free over the ocean within a 10-km grid box and fitting these values to a lookup table. Level-3 MODIS Dark Target AOD is calculated using the quality assurance confidence screened level-2 pixel-level data. MODIS 550-nm fine-mode fraction f is calculated from the ratio of the small-mode AOD to the total AOD. MODIS Collection 6 (C006) AOD over ocean has been shown to have an expected error of +(0.04 + 10%)/−(0.02 + 10%) (Levy et al. 2013). For dust-dominated regions, it has been shown that there is a bias of +5% (Kaufman 2005). There has been no systematic program for evaluation of MODIS f because its definition is somewhat ambiguous, making evaluations against other estimates difficult (Kleidman et al. 2005). At this time, there is no envelope of error for C061 f. However, Collection 5 (C005) f over ocean was found to agree with Aerosol Robotic Network (AERONET) retrieved sky radiance fine-mode fraction within approximately ±0.20 (Kleidman et al. 2005), and Bréon et al. (2011) found agreement between MODIS (τf,MODIS) and AERONET (τf,AERONET) fine-mode AOD, as evidenced by a correlation r value of 0.76, root-mean-square error (RMSE) of 0.08, bias of 0.01, and 53% of retrievals within the C005 envelope of error, δτ = 0.03 + 0.05τAERONET, which describes a confidence envelope containing 1 standard deviation σ (i.e.,68%) of τf,MODIS/τf,AERONET matchups. In our estimate of the known uncertainty in τd, we assume that the C061 f uncertainty has not changed from the C005 values. Although a quality-assured dataset does exist for τf,MODIS, we chose to use the standard dataset so as to retain additional coverage.
The equivalent set of estimates for
Because our τd estimate is built upon AOD, it carries with it the known limitations of the sensors from which it is derived. MODIS AOD retrievals are limited to cloud-free pixels, and errors in cloud screening will impact the AOD estimate. The MODIS aerosol cloud mask uses the standard deviation of reflectance in sets of pixels to remove cloudy pixels, which increase spatial variability. Absolute reflectance at 1380 nm and the ratio of reflectances at 1380 and 1240 nm as well as several infrared tests are used for additional screening of thin cirrus. Over ocean, the brightest and darkest 25% of the remaining pixels are arbitrarily removed and the average reflectance in each channel is calculated from the remaining pixels. Over land, the brightest 50% and darkest 30% of pixels are discarded before the averaging step (Remer et al. 2012). While this usually produces a reasonable result, in the case that unscreened clouds remain before the discarding of the brightest and darkest retrievals, this can produce AOD estimates that are biased high (Kahn et al. 2007). It is known that MODIS AOD is biased very positively (high) in the Southern Hemisphere mid–high latitudes over the Southern Ocean because of extensive broken stratocumulus and cirrus cloud contamination (Toth et al. 2013). We have thus limited these datasets to extend from 60°N to 50°S.
Level-2, version 3, AOD and fine-mode fraction from select sites, as will be discussed in section 2b, retrieved from AERONET (Holben et al. 1998, 2001) sunphotometers using the Spectral Deconvolution Algorithm (SDA), version 4.1 (O’Neill 2003; https://aeronet.gsfc.nasa.gov) are used to estimate
MODIS C061 L3 Deep Blue products [AOD, single scattering albedo (SSA), and Ångstrom exponent (AE); https://ladsweb.modaps.eosdis.nasa.gov] were used in the estimation of
The 630-nm AVHRR AOT climate data record (CDR) (Zhao and NOAA CDR Program 2017; accessed at https://www.ncei.noaa.gov/data/) was used in the estimation of
Daily mean surface wind speed from the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) (Gelaro et al. 2017) 1-h time-averaged surface flux assimilation product (accessed at https://disc.gsfc.nasa.gov/) was used in the estimation of marine AOD. This was regridded from its native resolution of 0.625° longitude × 0.5° latitude to a resolution of 1° × 1° using bilinear interpolation. MERRA-2 surface wind patterns have been shown to be similar to other datasets, but greater in magnitude in most regions than MERRA and ERA-Interim and weaker in magnitude than NCEP-R2 (Bosilovich et al. 2015). Similar to most reanalysis products, MERRA-2 winds are weaker than 93% of observations considered in Bosilovich et al. (2015).
b. MODIS dust optical depth over ocean
Our method for estimating
Summary of variables and data types used in Eqs. (1)–(3). The fd, fm, and fa represent characteristic AERONET fine-mode fractions for dust, marine, and anthropogenic or biomass-burning-dominated AERONET stations, respectively; τ and f represent the total AOD and fine-mode fraction; and τm represents the marine aerosol contribution to the total AOD.
AERONET stations used for calculation of fa, fd, and fm, with the years of data used from each station in this calculation.
Marine aerosol contribution
Kaufman (2005) parameterized marine aerosol based on NCEP surface (1000 hPa) wind speed at 2.5° × 2.5° resolution. This parameterization was based on the relationship between surface wind speed from a National Climatic Data Center meteorological station and an AERONET station on Midway Island from 14 months of data, excluding the dust season (February–May) (Smirnov et al. 2003).
Sea salt aerosol has been parameterized previously using linear (Lehahn et al. 2010; Smirnov et al. 2003; Mulcahy et al. 2008; Glantz et al. 2009; Huang et al. 2010), power-law (Mulcahy et al. 2008; Glantz et al. 2009), and exponential (Moorthy and Satheesh 2000) relationships to 10-m or surface wind speeds from in situ and remote sensing measurements. It is well known that, to first order, sea salt emission is primarily dependent on wind speed (Jaeglé et al. 2011). However, other factors including sea surface temperature (SST), organic material, vertical mixing, advection, wet and dry deposition, and relative humidity could impact sea salt emission to a lesser extent. The second most commonly used predictor, after wind speed, is SST (Jaeglé et al. 2011; Gong 2003). However, laboratory and in situ investigations of the SST dependence of marine aerosol have had contradictory results, indicating that we do not yet fully understand how temperature impacts SSA (Grythe et al. 2014; Mårtensson et al. 2003; Zábori et al. 2012). Therefore, we have chosen to retain a parameterization that is only based upon surface winds.
c. over land
The
In all three dust-dominated regions, 97%–100% of pixels meet the criterion for SSA at 412 nm < 0.95; in the region dominated by other aerosol types, fewer than 26% of pixels meet this criterion. Similarly, in each dust-dominated region, more than 99% of pixels meet the criterion of SSA at 412 nm ≥ SSA at 660 nm, and only 40% meet this criterion for the nondust region. The criterion used in Ginoux et al. (2012) of AOD greater than 0.1 was not used in the creation of
Ginoux et al. (2012) used a conservative requirement of AE less than 0 to identify dust. However, the updated algorithm used to create the C006 Deep Blue AE product now limits the valid range of values to 0 ≤ AE ≤ 1.8 (Sayer et al. 2013; Hsu et al. 2013). Therefore, we chose a new threshold that is based on the distribution of AE in our three desert regions. When this threshold was replaced with a criterion of AE less than 1, 90% of pixels in the Sahara Desert region, 91% of pixels in the Arabian Desert region, and 73% of pixels in the Taklamakan Desert region met the new threshold and only 5% met it in the nondust region. The new threshold of 1 we have used here is within the values of AE reported for dust regimes in Dubovik et al. (2002). In summary, daily estimates of
MODIS C061 AE 470–670 nm, which is inversely proportional to particle size, of less than 1,
MODIS C061 SSA at 412 nm, the ratio of aerosol scattering to extinction coefficients at that wavelength, of less than 0.95, and
MODIS C061 SSA at 412 nm is less than or equal to SSA at 660 nm.
If a pixel did not meet these criteria, its τd was set equal to zero. An analysis of the accuracy of this method, through comparison with AERONET, is presented in the online supplemental materials.
d. (1981–2018)
The
Following Evan and Mukhopadhyay (2010), a time series of zonal mean stratospheric aerosol optical thickness τs as estimated in Sato et al. (1993) was removed from AVHRR AOT before calculation of
Our resultant global mean time series of
3. Results
The primary results of this work are two nearly global, daily, observational datasets of τd at a 1° horizontal resolution that can be used for studies of the global dust cycle. The long-term mean global
a. Comparison of and
Globally, monthly mean estimates of
We also calculated the linear least squares best fit between the daily
The discrepancy in retrievals over large dust plumes can be easily visualized in the case of 27 June 2014, over the tropical Atlantic (Fig. 6). In this case,
b. Comparison with existing datasets
It is informative to compare our τd with previously published estimates of atmospheric dust. Here, we compare
We also compared our new climatology of
We also compared our dataset with a monthly time series of dust optical depth (DOD) over Syria as published by Pu and Ginoux (2016) (Fig. 9). Similar to Ginoux et al. (2012), Pu and Ginoux (2016) used MODIS Deep Blue C006 AOD, SSA, and AE to construct DOD, and thus the datasets are not independent. However, Pu and Ginoux (2016) updated their method by interpolating optical properties to a 0.1° × 0.1° grid and using a threshold of SSA at 470 nm along with an empirical relationship between AE and f to derive coarse DOD. The time series are related with a correlation coefficient of 0.87 (p value < 0.01) and show the same trend of increasing dust over Syria (Fig. 9). We are not aware of another independent estimate of
c. Seasonal dust AOD
Having shown that this satellite-based climatology of dust appears to reflect changes in the atmospheric concentration of dust, we next summarize some of the broader characteristics of the global dust cycle, starting with seasonality. Maxima in seasonal mean
Discrepancies between
A region of high summertime mean
In addition, because of extensive low cloud cover this region in the western North Pacific has few retrievals during the summer months. The limited number of retrievals is evident in the number of pixels used in the estimate of MODIS Dark Target AOD during the summer in that region. While greater than 36 pixels are incorporated on average in the equatorial Pacific estimations of MODIS Dark Target AOD, only approximately 26 on average are used in this estimation in parts of the western North Pacific. Long-term seasonal mean
4. Discussion
Having presented two datasets of τd derived from satellite observations, we next present regional trends in τd (section 4a) and a comparison of
a. Trends in
In Fig. 11a we show regional trends in monthly mean
Interestingly, the greatest increase in
τd derived from MODIS Aqua (2003–18) displays the same trends of decreasing dust over northeast China and southwest Asia and increasing trends over the Arabian Peninsula (approximately 20°N, 70°E) and central Sahara (approximately 20°N, 10°E) (Fig. 11b). There are some regional changes that are more pronounced in this record, including a statistically significant increase in τd over Southern California (approximately 34°N, 119°W). There is also a large increase in dust over Oman (0.20; approximately 20°N, 57°E) that is not present in the
Trends in overwater τd, from 1981 to 2018, were also calculated using monthly mean
We note that Zhao et al. (2013) found a slight positive trend in the CDR AVHRR AOT record that is the result of residual cloud contamination. As such, trends in the tropical North Atlantic and Arabian Peninsula could be similarly biased.
b. Comparison with a modern reanalysis dust product
To understand whether this observation-based dataset is consistent with a modern reanalysis dust product, we compared
5. Conclusions
These global, daily, observation-based datasets of dust aerosol optical depth are rooted in observations of optical characteristics of the aerosol column from Terra Moderate resolution Imaging Spectroradiometer and from the Advanced Very High Resolution Radiometer, but incorporate observations of characteristic fine-mode fraction in dusty, clean marine, and anthropogenically dominated regions from the Aerosol Robotic Network as well as surface wind speed from the second Modern-Era Retrospective Analysis for Research and Applications. As previously mentioned, the long-term mean global
We had hoped to create a reliable dataset of
In 2011, the Suomi National Polar-Orbiting Partnership (SNPP) satellite was launched carrying the Visible Infrared Imaging Radiometer Suite (VIIRS), which is intended to continue the observational records that have been provided by sensors such as MODIS and AVHRR (Sayer et al. 2017). VIIRS has been shown to be capable of identifying aerosol type and partitioning between fine- and coarse-mode aerosol. As such, observational studies that require a shorter record (beginning in 2012) may be able to rely on these products. In addition, Chen et al. (2018) demonstrated that an inversion technique, utilizing the GOES-Chem model, can be applied to certain satellite aerosol optical property retrievals in order to retrieve desert dust. This was applied to one year of satellite-derived aerosol information generated by the General Retrieval of Atmosphere and Surface Properties (GRASP) algorithm from Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (POLDER/PARASOL) retrievals. This technique can provide daily dust at 2° × 2° resolution with uncertainty below 25.8%, but it is computationally costly and has not been applied to longer records of remotely sensed aerosol optical properties.
The current work provides daily τd generated from a simple algorithm and observed aerosol data. Previous estimates of τd have enhanced our understanding of the global dust cycle and have been a valuable tool for evaluating the performance of global models (Pu and Ginoux 2018a; Evan and Mukhopadhyay 2010; Kaufman 2005; Ginoux et al. 2012). This longer, higher-temporal-resolution record will facilitate additional advances in our understanding of the dust cycle.
Acknowledgments
This work was funded by the California Department of Water Resources Contract 4600010378, Task Order OSCOP215, and the Army Corps of Engineers USACE (CESU) W912HZ–15–0019. We thank the AERONET principal investigators and their staff for establishing and maintaining the 39 sites used in this investigation. We are grateful for the datasets and data-archiving centers that supported this work and appreciate those who made our study possible, including the MERRA-2 team at the GMAO and staff at GSFC, and the MODIS teams at NASA, as well as Tom Zhao and the NOAA Climate Data Record team. We thank Drs. Joseph Prospero, Jennifer Hand, and Daniel Tong for their contributions of long-term datasets for comparisons made in this work, and Drs. Martin Ralph, Leah Campbell, and Nora Mascioli for helpful comments on this paper. We also thank three anonymous reviewers for helpful comments on this paper. The datasets described in this paper will be made publicly available at Pangea Open Access (https://doi.pangaea.de/10.1594/PANGAEA.909140).
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