While stand alone satellite and model aerosol products see wide utilization,
there is a significant need in numerous atmospheric and climate applications
for a fused product on a regular grid. Aerosol data assimilation is an
operational reality at numerous centers, and like meteorological reanalyses,
aerosol reanalyses will see significant use in the near future. Here we
present a standardized 2003–2013 global
The importance of aerosol particles in the atmosphere and climate system is
recognized across the Earth sciences. Long implicated in climate change
investigations (e.g., IPCC, 2007, 2013), aerosol particles influence
countless other aspects of science and society. Obvious impacts include
biologic and visual air quality, including health outcomes (Laden et al.,
2000; Kappos, et al., 2004), defense operations and transportation
(Wilkinson et al., 2012). Further, aerosol particles interfere with many
aspects of Earth system surveillance, such retrievals of sea surface
temperature (e.g., May et al., 1992; Reynolds et al., 1989; Robock, 1989) and
ocean color (e.g., Gordon, 1997) and land use systems (Song et al., 2001).
Aerosols can also affect atmospheric retrievals or radiances used to
constrain temperature, water vapor and CO
To meet aerosol monitoring requirements, the climate and Earth systems science community has historically presented aerosol data as either a free-running model (with the advantage of regularly gridded and timed products, e.g., Tanaka et al., 2003, Miller et al., 2006, Morcrette et al., 2009, Colarco et al., 2010 and Pérez et al., 2011), or irregularly timed and located satellite data (e.g., Mishchenko et al., 1999; Torres et al., 2002; Hsu et al., 2004; Levy et al., 2010; Kahn et al., 2010). In both cases, the products are underdetermined. Models have poorly resolved emissions, evolution and sinks, and can be affected by errors in the underlying meteorological model, whereas satellite data have limited coverage and underdetermined retrievals based on assumptions that lead to a series of spatially and temporally correlated biases (e.g., Shi et al., 2011a). Ultimately, models and remote sensing products present different aspects of atmospheric characteristics. When model and satellite products are compared, contextual and sampling biases appear (e.g., Zhang and Reid, 2009). For daily and more rapid analysis, such as for many specific Earth system science process study questions or intersensor correction, neither approach can adequately represent the full state of the aerosol system.
To bridge modeling and remote sensing data sources, numerous operational numerical weather prediction centers have embarked on sophisticated aerosol data assimilation efforts of both passive and lidar satellite sensors (e.g., Collins et al., 2001; Weaver et al., 2007; Zhang et al., 2008, 2011; Benedetti et al., 2009; Sekiyama et al., 2010). Satellite products are screened, empirically corrected and assimilated into models to provide systematic best-available analyses of the aerosol environment. The next step in this process is to develop best-available reanalyses for community use. Just as meteorological reanalysis such as the National Center for Atmospheric Research/National Centers for Environmental Prediction (NCAR/NCEP) (e.g., Kalnay et al., 1996) and European Centre for Medium-Range Weather Forecasts (ECMWF) (e.g., Uppala et al., 2005; Dee et al., 2011) are commonly applied for meteorological applications, aerosol reanalyses are likely to be destined to be useful data sources for initial analysis or systematic global studies for aerosol sciences.
Like meteorological reanalyses, aerosol reanalyses are generated through a rerun of a model that assimilates historical observational data. Aerosol reanalyses aim to be a best-available, contiguous, gridded product with consistent temporal reporting. It combines advantages of data accuracy from satellite products and data consistency from modeling. The data should have good spatial and temporal coverage and be easy to use. But an aerosol reanalysis is not simply just a rerunning of the model with aerosol data assimilation. First, strict quality assurance and quality control processes need to be applied to the satellite data that goes into an assimilation system, such that the model input is as consistent as possible over the reanalysis period. Biased retrievals in the data assimilation system could result in erroneous features that can propagate in the short term. Lack of consistency in the model or data can lead to artifacts that could be mistaken for climatological trends or spurious aerosol events. Second, the performance of the underlying aerosol forward model should be optimized to its upper limit through a series of tunings to the aerosol sources and wet/dry removal processes. This helps to avoid large and frequent corrections via the data assimilation cycle, so that the natural model field is as close as possible to the satellite product and the final reanalysis product is smooth and fluent in space and time.
In this paper, we present the Naval Research Laboratory's development of an aerosol reanalysis product for applied science use through the assimilation of NASA Terra and A-train satellite sensors into the Navy Aerosol Analysis and Prediction System (NAAPS). The goal is to provide a best-available aerosol optical thickness (AOT) product for applications that require this parameter. As the system develops and verification data sets become available, the publicly released analysis will include many other aspects of the aerosol system, including three-dimensional concentrations and radiative effects such as fluxes and heating rates. Our goals for the initial development of the NAAPS reanalysis and this paper are threefold.
While the aerosol system is a highly complex internal mixture of
anthropogenic, biogenic, open-burning and wind-driven emissions, ultimately
it is AOT and its simple partition into fine- and coarse-mode contributions
that we can actually measure and verify globally. Reanalyses on atmospheric
gas composition and/or aerosols are also in development at ECMWF (Inness et
al., 2013) and NASA (Buchard et al., 2015). The aerosol models used for
generating these reanalyses are independent in their underlying meteorology,
as well as aerosol sources, sinks, microphysics and chemistry. The AOT
assimilation methodologies, the observed AOT data to be assimilated, and the
pre-assimilation treatments of input data are also different. Validation of
multivariate reanalyses of atmospheric composition is a very complex task,
and a comprehensive evaluation is needed. This study focuses exclusively on
the development and validation of a 550 nm modal (fine mode, coarse mode and
total) AOT reanalysis.
In this paper, we provide an up-to-date description of the primary NAAPS model, noting differences between the reanalysis and operational versions. Our emphasis is on the development of a modal NAAPS AOT analysis. We describe the methods used to tune modeled aerosol processes. The data assimilation system used to fuse the model and observations is described, as well as the satellite data products used in the reanalysis. This is followed by a basic description of the reanalyzed global fine- and coarse-mode 550 nm AOT fields and their verification. We conclude with a brief synopsis and discussion of our findings. We provide documentation of strengths and pitfalls of reanalysis products including advice on interpreting like products. For example, we discuss how the data assimilation system affects diurnal aerosol representation or how long-term trends are represented in the simulation that has static industrial emissions. We also discuss the difficulty in keeping meteorological input consistent at decadal levels. We conclude with a project synopsis and outlook for future experiments.
The foundation of this AOT reanalysis is the NAAPS and its associated aerosol data assimilation components. NAAPS is an offline aerosol transport model, which has seen wide use in the community for global aerosol life-cycle research, contextual information, field mission planning and operations.
The original NAAPS model was based on the Danish Eulerian hemispheric model
(Christensen, 1997), although since then there have been a number of upgrades
to model advection and microphysics. NAAPS has been run quasi-operationally
at the United States Naval Research Laboratory (NRL) since 1998, and became the world's first operational global aerosol
model in 2006 with implementation at the Fleet Numerical Meteorology and
Oceanography Center (FNMOC). The Navy Atmospheric Variational Data
Assimilation System (NAVDAS) for AOT (NAVDAS-AOT; Zhang
et al., 2008) was operationally implemented in 2010. The system assimilates
quality assured and quality controlled two-dimensional Moderate Resolution Imaging Spectroradiometer (MODIS) AOT at 550 nm. In
its current operational configuration, NAAPS makes 6-day forecasts, 4 times a
day at 1080
In converting NAAPS from a forecast model to a reanalysis system for the A-train 2003–2013 time period, we desire a system that is consistent spatially and temporally in time and fits within our computational constraints. This requires, at times, significant departures from the operational model, and some reduction in resolution. In this section, we describe the NAAPS model configured for reanalysis mode, its AOT assimilation package and the associated MODIS, Multi-angle Imaging SpectroRadiometer (MISR) and precipitation satellite data used to initialize and assimilate into the model. We also describe the tuning processes necessary to help ensure spatial and temporal consistency within the reanalysis period.
The current operational version of NAAPS is driven by NAVGEM (Hogan et al.,
2014), a global T425L60 spectral model that has only been available since
September 2013. The NAAPS reanalysis described in this paper is driven by the
recently decommissioned Navy Operational Global Atmospheric Prediction System
(NOGAPS) analysis fields for 2003–2013. A full NAVGEM reanalysis is under
construction that will allow for higher horizontal and vertical resolution to
better constrain future runs of the reanalysis. The NOGAPS model is a global
model that is spectral horizontally and energy-conserving finite difference
(sigma coordinate) in the vertical (Hogan and Rosmond, 1991; Hogan and Brody,
1993); 4 times a day, the weather forecast models provide 6-day forecasts
of the dynamical and surface analysis fields to NAAPS at 3 h intervals. The
reanalysis uses only the 00:00, 06:00, 12:00 and 18:00 Z analyses with the
associated 3 h forecast fields to make up the 3 h time series of dynamical
forcing. NOGAPS variables used by NAAPS are the topography, sea ice, surface
stress, surface heat flux, surface moisture flux, surface temperature,
surface wetness, snow cover, stratiform precipitation, convective
precipitation, lifting condensation level, cumulus fractional coverage,
cumulus cloud height, surface pressure as well as three components of the wind,
temperature and relative humidity. For data assimilation, NOGAPS uses the
NAVDAS, which is still
used operationally for assimilation of a large variety of conventional and
satellite-based observations (Daley and Barker, 2001). While NOGAPS has had
some resolution changes over the 2003–2013 study period (ranging from T159
to T319), spectrally truncated NOGAPS meteorology data are incorporated into
the NAAPS reanalysis for each 6 h time step at the prescribed
As the primary sink of aerosol particles, the precipitation component of NOGAPS is worth special attention. Often in large-scale models the parameterized precipitation schemes for tropical regimes generate widespread light precipitation, while the long-term total precipitation amount is comparable to observations (Dai, 2006; Sun et al., 2007). Similarly, global models also have difficulty placing significant convective cells, particularly moderately sized squall lines or coastal thunderstorms. Diurnal precipitation cycles are also poorly represented by numerical models. These characteristics of model precipitation are shown to affect removal of aerosol particles and can have significant impact on regional AOT simulations (Wilcox and Ramanathan, 2004; Xian et al., 2009). For the reanalysis, tropical precipitation from NOAA Climate Prediction Center (CPC) MORPHing technique (CMORPH; Joyce et al., 2004) is used whenever available to improve aerosol wet deposition in the manner described in Xian et al. (2009), in which cloud structure from the model is retained but precipitation flux is changed accordingly. CMORPH combines infrared (IR) and passive microwave data (PMW) retrieved from instruments onboard multiple geostationary and lower-orbiter satellites. CMORPH was chosen for this role as it appears to have the best representation of temporal and spatial patterns of tropical precipitation among satellite precipitation products (Janowiak et al., 2005; Sapiano and Arkin, 2009).
As noted above, NAAPS is a global aerosol model originated in the mid-1990s
from a hemispheric sulfate chemistry model developed by Christensen (1997).
Dust, sea salt and smoke have been added to the original model, and are
documented in Westphal et al. (2009), Witek et al. (2007) and Reid et
al. (2009), respectively. Given that what is commonly referred to as regional
pollution or haze is a result of complex anthropogenic and biogenic emissions
and chemistry, here we replaced the simplified Christensen (1997) SO
The equations solved in the model have the form
Equation (1) is solved on a spherical grid with
1
Optical properties for dry aerosol particles at 550 nm in NAAPS.
where
Aerosol microphysics are treated relatively simply in NAAPS. This is in
response to the computational needs of an efficient operational forecast
model, its operational requirements (e.g., forecast severe visibility
reducing events) and the fact that in comparison with the uncertainties in
source functions as well as transport meteorology, microphysics is relatively
well constrained. Dry mass concentrations are forecasted with Eq. (1) and AOT
for each aerosol species is computed assuming an effective particle size with
respect to mass. Aerosol particles in NAAPS are treated as external mixture
of the aforementioned species and do not interact with each other. With these
assumptions, extinction and AOT can be calculated using bulk values of
optical properties that have been derived from theory and observations. The
calculations for scattering (
The bulk mass extinction, scattering and absorption efficiencies, along with
single-scattering albedo and asymmetry factor for the four aerosol species at
wavelength
The effect of humidity on particle light scattering for each aerosol species
is represented by the Hänel (1976) formulation of the hygroscopic growth
factor
Dry deposition to the surface is accounted for through a decrease of the
aerosol concentration in the lowermost model layer, assuming a dry
deposition flux
For particle deposition over water, the dry deposition velocity
For particle deposition over land, the method of Walcek et al. (1986) is used
and the explicit expression for
Gravitational settling is also applied to the aerosol particles in the
model. Dry deposition is only applied in the lowermost model layer, whereas
gravitational sedimentation takes place within the whole vertical domain
except the lowermost model layer, as it is taken into account in
The wet deposition of particles is assumed to be similar to that for sulfate
aerosol, based on a simple scavenging ratio formulation (e.g., Iversen, 1989).
The scavenging coefficient is calculated in the same way as in Witek et
al. (2007), as a function of the precipitation mass flux with different
below-cloud and in-cloud scavenging ratios, written as
Dust emissions occur whenever the friction velocity exceeds a threshold
value, snow depth is less than a critical value, and the surface moisture is
less than a critical value (Westphal et al., 1988). The dust emission flux
follows the equation
Regional source tuning is also applied in the NAAPS reanalysis, which is described in Sect. 2.4. Dust is emitted into the bottom two layers of the model (below 100 m) when friction velocity exceeds the threshold and surface wetness is below a critical value (0.4). Then, dust is transported by model dynamics both horizontally and vertically in the boundary layer and the free troposphere. Dust removal includes sedimentation, dry deposition and wet removal, which is constrained with CMORPH precipitation within the tropics. Dust is assumed to be totally hydrophobic and hence the hygroscopic growth factor is set to 1.
The sea salt component for operational NAAPS and the NAAPS reanalysis was
developed by Witek et al. (2007). Sea salt emissions are driven dynamically
by sea surface wind. The sea salt dry mass flux
The most significant change to NAAPS microphysics for the reanalysis is the
development of a method to account for complex anthropogenic and biogenic
species while not significantly increasing the computational cost of the
model. Originally, the only anthropogenic emissions and predictive variables
within NAAPS were SO
For realistic simulation of AOT, primary and secondary organic aerosols must
both be included in the NAAPS model in some form. To be consistent with the
NAAPS reanalysis' philosophy of simple and tractable physics, the
sulfur-related species has been replaced with a bulk ABF mass category to account for the entire class of
anthropogenic and biogenic emissions and their secondary particle products.
This species class includes all accumulation mode particles, including
biogenic marine, outside of open biomass burning, as described in
Sect. 2.2.7. The first component of this mixture is the original sulfur
chemistry. Sulfate aerosols are produced by chemical processes in the
atmosphere from gaseous precursors, mainly sulfur dioxide (SO
Inclusion of POA in the NAAPS reanalysis is straightforward, including the major VOC species that act as precursors for the SOA. We apply the 2005–2010 monthly mean MACCity data base for anthropogenic (industrial and transport) emissions of POA and SOA precursors (Granier et al., 2011), the Bond et al. (2004) biofuels data with a monthly scaling factor based on Jeong (2011), and the Precursors of Ozone and their Effects in the Troposphere (POET) climatological monthly emissions inventory for biogenic VOCs (Olivier et al., 2003). For the actual SOA formation process, the Volatility Basis Set (VBS) approach has been adopted (Donahue et al., 2006; Ahmadov et al., 2012). This greatly reduces both the number of necessary precursor species and the number of SOA products from the vast numbers needed to explicit represent SOA formation and evolution by formulating the conversion process in terms of a limited number of precursor species and volatility classes (four in our case) for the reaction products. The reaction yields for the various VBS classes, upon which the approach ultimately depends, are derived from numerous chamber studies as cited, for example, in Ahmadov et al. (2012) and Donahue et al. (2006). Phase partitioning is done as per Pankow (1994).
To further simplify the inclusion of organic aerosols in the NAAPS model, both the POA and SOA are calculated in a “preprocessor” at model initialization. For the SOA, this includes calculation of the yield of SOA product mass from the emissions inventory VOC's, based on the VBS model, and the treatment of this mass as a primary aerosol emission, similar to the POA. Utilizing the similarity in microphysical and optical properties of organic aerosol (OA) and sulfate, the model carries POA and SOA together with sulfate as aforementioned “anthropogenic and biogenic fine”. This approach has some obvious shortcomings, but it carries minimal computational cost and has much improved the simulation of AOT, especially the model bias and correlation with Aerosol Robotic Network (AERONET) over India, China and eastern United States.
Biomass burning has a wide coverage globally, from the tropics to the high latitudes, and it significantly impacts the total light absorption budget (Bond et al., 2013). Unlike other aerosol sources that are meteorologically driven (e.g., dust and sea salt) or prescribed in a seasonal or monthly inventory (e.g., pollution), smoke emissions have significant variability that hinders easy parameterization. Configuring the NAAPS model with biomass-burning aerosols as a separate species permits explicit hypothesis testing about the sources, sinks and optical properties of these aerosols. Operational NAAPS has adopted the satellite-active fire hotspot-based approach through the Fire Locating and Modeling of Burning Emissions (FLAMBE1.0; Reid et al., 2009; Hyer et al., 2013). The model converts the smoke emission to total mass injected by multiplying by the fire size. This value is then divided by the area of the grid cell and the fire duration to create a flux as an hourly input to the model. FLAMBE can use satellite fire products from either geostationary sensors, which offer faster refresh rates and observation of the full diurnal cycle, or polar orbiters, which have greater sensitivity. Polar orbiting satellites have significant biases not only in their daily sampling pattern, but also additional artifacts from day to day shifts in the orbital pattern (e.g., Heald et al., 2003; Hyer et al., 2013). Over the reanalysis period, multiple changes in the geostationary constellation posed a challenge for consistency of the smoke source function. Therefore, a polar-only version of FLAMBE was created for the reanalysis.
Given that the NAAPS reanalysis coincides with the NASA Earth Observing System (EOS) system, MODIS-based fire products and emissions are applied. MODIS orbits have a 16-day repeat cycle, with daily coverage of the globe excepting small gaps between orbits at the Equator. Areas that are not covered one day are centered on the orbit the next. The Fire Inventory from NCAR (FINN; Wiedinmyer et al., 2011), which is also based on MODIS active fire detections, uses a 3-day moving average to account for gaps and orbital variations. After testing multiple coverage corrections, we found that for the reanalysis a simple two-day maximum (previous day and present day) fire signal largely mitigated orbital effects and thick clouds in a tractable way. This correction is consistent with the self-sustained nature of regional fire emissions, and further improves upon the scores presented in Reid et al. (2009).
Smoke injection height combined with boundary layer mixing has a strong influence on how smoke is dispersed. Most plumes are observed as constrained within the planetary boundary layers, especially within the tropics and subtropics (Tosca et al., 2011; Campbell et al., 2013). Large boreal fires can pump smoke to higher altitudes, though these fires constitute only a very small portion of the total fires and global budget of AOT (Fromm and Servranckx, 2003; Kahn et al., 2008). In NAAPS, smoke is injected into the bottom four layers of the model, which is approximately the bottom 400 m of the model. Tuning of injection height to match observed aerosol vertical profiles is feasible in regional studies (e.g., Wang et al., 2013). However, we use the uniform injection height in NAAPS, considering that boundary layer processes generally quickly mix aerosols well within the boundary layer or below the models significant inversion height to produce a result similar to the observations of Kahn et al. (2008).
The core of the NAAPS AOT reanalysis is AOT assimilation using the NAVDAS-AOT (Zhang et al., 2008). NAVDAS-AOT is a system that, by default, assimilates quality-controlled two-dimensional MODIS AOT at 550 nm into NAAPS. It additionally has the ability to perform three-dimensional (3DVAR) assimilation using the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) product of Campbell et al. (2010) in Zhang et al. (2011). The main impact of 3DVAR assimilation is redistribution of aerosol mass vertically, while conserving the total column mass and AOT. CALIOP data are available for only part (2006–2013) of the reanalysis period; therefore, in this first study we perform 2DVAR AOT assimilation only.
The NAAPS prognostic variable is the three-dimensional aerosol mass concentration. A 2DVAR
approach is adopted for AOT assimilation simply because AOT retrievals from
MODIS and MISR are a column-integrated aerosol optical property. The 2DVAR
AOT assimilation is realized through three steps:
Convert NAAPS mass concentration AOT: Two-dimensional variational assimilation of the AOT field: Convert the analysis AOT vectors to NAAPS mass concentration:
Both observational and model errors could contain systematic bias, either of which could be removed or minimized through pre-processing. For example, our quality assurance (QA) and quality control (QC) methodology (Sect. 2.3.3) attempts to remove systematic bias as much as possible from the AOT observations. Likewise the tuning process described in Sect. 2.4 attempts to remove systematic bias from the model background. Thus, both model background and observations are assumed to be unbiased in NAVDAS-AOT.
In NAVDAS-AOT, observational errors are assumed to be uncorrelated. Thus,
only observational error variances are needed. The error variances for the
gridded satellite AOT data are computed by the summation of
instrumental
error variances and sample error variances (Zhang et al., 2008). The
instrumental error variance is estimated through the comparison of
satellite and ground-based sun-photometer data as shown in Zhang and
Reid (2006) and Shi et al. (2011a) for MODIS Dark Target, and Shi et
al. (2014) for MISR aerosol products. The sample error variance measures the
variance in the gridded mean (or the representative error variance). For a
1
The background error covariance is computed for any given two horizontal
model grid locations
The basis of input data for the reanalysis is operational MODIS Collection 5
AOT (Levy et al., 2007, 2010; Remer et al., 2005, 2008) and MISR AOT products (Martonchik et al., 2009; Kahn
et al., 2009, 2010). MODIS Deep Blue for Collection 5 is not used here due to
bias issues, but it is expected that improvements in Collection 6 will be
made and the data could be assimilated (Shi et al., 2013). Extensive QA and QC procedures applied to the MODIS C5
AOT are conducted as described in Zhang et al. (2006) and Shi et al. (2011a)
for over water and Hyer et al. (2011) for over land. These QA/QC procedures
are especially important for this application, because the analysis must be
heavily weighted to the observations to allow assimilation for correction of
errors such as missing dust and smoke sources. Under these circumstances, the
impact of noisy data is large and proper filtering and correction of data is
critical. QA/QC procedures implemented for MODIS and MISR AOT include
(a) strict checks for removal of possible cloud contamination,
(b) corrections for the lower boundary condition, such as wind speed to
correct for white caps and specular reflection over water and surface albedo
over land and (c) aerosol micro-physical corrections based on derived fine-mode fraction over water and regionally over land. This strict quality
assuring and quality control procedure is necessary to remove outliers and
minimize erroneous aerosol features in MODIS that would adversely impact the
model and propagate through the system. Currently, the total global data loss
through screening of MODIS is about 40 %, with a reduction of absolute
errors of 10–30 % over water (Zhang et al., 2006; Shi et al., 2011a).
over land, the QA/QC procedures reduce data volume by
A benefit of a reanalysis is that observations that are not timely enough to
be incorporated into an operational run can be utilized. Thus, while MODIS
products are used in all versions of NAAPS, for the reanalysis we can make
use of MISR. Though narrower in swath than MODIS, and thus providing less
relative coverage, MISR has two key benefits. First, MISR is on Terra and its
imaging swath is in the MODIS sun-glint region. Hence, MODIS plus MISR
completes the MODIS swath with full coverage. Second, the MISR over land
algorithm has an advantage over retrievals conducted with other sensors in
its handling of the lower boundary condition, provided that AOT
An example of the general pattern of data coverage from MODIS
(Aqua
Data assimilation using NAVDAS-AOT is used to produce a new analysis after every 6 h of NAAPS integration time. The MODIS and MISR level 2 aerosol products are typically acquired in a 6 h range centered on the nominal valid time of the analysis (i.e., 00:00, 06:00, 12:00 and 18:00 UTC) from NASA data servers. Then QA/QC processes convert MODIS and MISR level 2 data into filtered, corrected and aggregated AOT observations with associated uncertainty estimates for assimilation in NAVDAS-AOT. After QA/QC processes, the general pattern of data coverage from MODIS and MISR for each assimilation cycle is shown in Fig. 1. The observed geographic pattern is attributed to the fact that MODIS and MISR AOT retrievals are limited to daytime and a limited range of sun-sensor geometries. The longitudinal range for which MODIS and MISR data available in a given assimilation cycle are limited because Terra and Aqua are in sun-synchronous orbits with equatorial overpass times of 10:30 and 13:30 local solar time, respectively.
For the MODIS sensors, overlapping coverage between Terra and Aqua over the
6 h data acquisition period does occur and a mean of Terra and Aqua weighted
to the number of level 2 retrievals from each sensor. The contribution of
each individual sensor to the total volume of the MODIS DA-quality data is
about 50 % on average, although this number is highly variable on the
6-hourly basis, with the variability depending on the observability of the
sensors (cloudy vs. non-cloudy, land vs. ocean, etc.). Because
of its narrower swath compared to MODIS, the data volume of the MISR
DA-quality data is only about 22 % on average of that of MODIS.
Approximately half of the MISR DA-quality data overlaps with MODIS. When
overlapping of MISR and MODIS 1
Properties of the 6-hourly
The seasonal geographic distribution of the total number of 6-hourly
1
The time series of 6-hourly data count of the global
The start date of the reanalysis is 1 January 2003, based on the availability
of the observational data used in the reanalysis. Terra MODIS and MISR AOT
data are first available in March 2000, and Aqua MODIS AOT is first available
in July 2002. An additional consideration is CMORPH precipitation data, which
is used to replace model precipitation within the tropics, is not available
until December 2002. Since the required spin-up time for the aerosol model is
1 month, the reanalysis starts at 1 January 2003. Figure 3 shows the time
evolution of 6-hourly data counts of the global MODIS, MISR and the fused
1
Selection of regions for this study. Antarctica is excluded. All AERONET sites that have valid L2 data for the study period (2003–2013) are in black dots. The selected sites for detailed validation (Sect. 3.2.3) are highlighted with red diamonds.
While AOT data assimilation from sensors such as MODIS and MISR improves NAAPS performance (Zhang et al., 2014), the natural NAAPS model performance is equally important for generating a final reanalysis product that aims to match observations. Previous studies have shown that aerosol source functions, inherent within the natural runs, are one of the largest uncertainties with respect to aerosol modeling of AOT (e.g., Kinne et al., 2003). As a result, a series of source-tuning exercises have been carried out on the natural model, using AERONET and satellite AOT observations for constraint. The tuning exercises consisted of running the model multiple times while iteratively adjusting model source and sink parameters. Smoke emissions and dust erodibility, for regions as shown in Fig. 4 with some additional divisions as shown in Table S1 in the Supplement, were tuned by iterative comparison between NAAPS model output without data assimilation and AERONET data, as described in Sect. 2.4.1. Emissions for some regions not covered by AERONET, as well as aerosol sink parameters, were constrained using the AOT assimilation correction field as described in Sect. 2.4.2. A list of the corrections applied is given in Table S1. The range of variation in optical properties of dry aerosols reported in the literature (e.g., Hess et al., 1998; Kinne et al., 2003) is small compared to other uncertainties; therefore, we adopted the optical properties described in Sect. 2.2.2 without additional tuning.
The AERONET (
Only cloud-screened, quality-assured level 2 AERONET data are used in this
study (Smirnov et al., 2000), and the sites are marked with black dots in
Fig. 4. Within the reanalysis time period, nearly 600 regular sites provided
valid observational data. AERONET Distributed Regional Aerosol Gridded
Observation Networks (DRAGON) observations are concentrated over a small area
and a short period of time, and they are excluded from this study to avoid
the effect of uneven sampling on the results from the statistical analysis.
Spatially, the
Empirical regional tuning of smoke and dust emissions is based on the fine- and coarse-mode AOT comparisons with AERONET. The globe is divided into 16 regions, as shown in Fig. 4, each having their own distinct aerosol characteristics. For example, South America, southern Africa, peninsular Southeast Asia and insular Southeast Asia have a prevailing smoke aerosol species during burning seasons, while northern Africa and Southwest Asia are dust dominated. East Asia and Indian Peninsular have mixed dust and pollution. Regional emission tuning factors were generated by using the regional bias and slope of the linear regression between pairwise NAAPS and AERONET AOT. This is done for 2009–2011 when AERONET data is more abundant than earlier years. Seasonally, data are grouped into the boreal winter–spring (December to next-May) and boreal summer–fall (June to November) time periods. These bi-seasonal temporal stratifications account for the major monsoonal and climatic shifts in the atmosphere while preserving major aerosol seasons such as, for the boreal summer/fall, the August–October biomass-burning seasons in southern Africa, South America and maritime continent, the June–August African dust season, and the United States and European summer haze seasons.
Regional emission factors, in the form of linear scaling factors applied to the original source functions for smoke and dust, are derived for each aerosol active season for the 3 years. For a single tuning factor, it differs slightly from year to year and season to season to a certain range. An average over the six seasons is taken to generalize this tuning factor for the reanalysis. The model is then run using the corrected emissions and the results are validated regionally against AERONET to determine whether the tuning improved bias, correlation, and root mean square error (RMSE). Additionally, the fine-/coarse-mode AOT time series of NAAPS and AERONET are reviewed for each site in the region to ensure the tuning is sensible. This process is repeated iteratively to refine the tuning. In Table S1, the values of the regional multipliers for smoke emission based on the 2-day maximum MODIS-only FLAMBE data base are listed. Also provided are the regional multipliers for soil erodibility, which are used to modify the dust source (Ginoux et al., 2001). The tuning factor for soil erodibility changes twice over the 11 years to accommodate the land surface parameterization changes in the meteorological analysis.
The total number of operational AERONET sites has grown to over 300 in recent years. However, the network's global coverage is uneven with the majority of sites located over land where they are easily accessible. The available AERONET data are often not representative of major aerosol impact regions, and it does not optimally sample for the biases that remote sensing products may have (Shi et al., 2011b). In particular, open oceans have few AERONET sites.
In regions with sparse AERONET data coverage, aerosol sources and parameters, such as sedimentation and dry deposition for ocean regions, are tuned using satellite AOT assimilation correction/increment fields. The monthly means of the daily AOT corrections (i.e., the difference between the assimilation posterior and the model prior) are a good indicator of the model performance globally. The correction maps can be used to quickly identify geographic regions where the model succeeds or does poorly. A region in which the data assimilation consistently suppresses aerosol mass could indicate a region with excessive aerosol emissions, or deficient removal, with the assumption that aerosol transport has much smaller uncertainty.
2003–2013 averaged bi-seasonal (June–November, i.e., JJASON, and
December–May, i.e., DJFMAM) total (upper), fine (middle) and coarse (bottom)
AOTs at 550 nm from NAAPS with and without AOT data assimilation.
Annotations at the bottom left in the figures show the area mean AOTs over
ocean and over land averaged for 40
Since satellite products have uncertainties, especially over land, we rely on
source corrections inferred from AERONET except where there are no
representative sites close to the known source area (e.g., southern African
biomass-burning region). Over the ocean where AERONET has only a few sites
globally, satellite data assimilation plays an irreplaceable role, not only
because of the good spatial and temporal coverage of satellite AOT data, but
also because of its much smaller uncertainty compared to the over land AOT
product (Hyer et al., 2011). Dust dry deposition velocity over water is tuned
based on the AOT correction over the tropical Atlantic where African
continental dust outflow is located, and is set to 0.001 m s
In this section, we focus on evaluating the reanalysis AOT at 550 nm apportioned into fine- and coarse-mode contributions. The sum of the fine- and coarse-mode AOTs constitutes the total AOT. These are what we consider the key reanalysis output variables. Dust and sea salt are considered coarse-mode aerosols and the ABF and smoke aerosols are considered fine-mode aerosols, given the simple microphysics of the NAAPS model. Seasonally, the boreal winter–spring (December to next-May, i.e., DJFMAM) and boreal summer–fall (June to November, i.e., JJASON) time periods are investigated. When performing bi-seasonal long-term averaging, we use only data in the June 2003–May 2013 time period, so that each individual month has an even weighting.
The bi-seasonally averaged total, fine-mode and coarse-mode AOTs at 550 nm for the 2003–2013 time period are presented in Fig. 5. Results are shown for the reanalysis and a parallel model run using tuned source and sink parameters but without AOT data assimilation. The fused MODIS–MISR DA-quality AOT for the same time period are shown in Fig. 2 (right column) for comparison. The total AOTs for both the NAAPS runs with and without AOT data assimilation look very similar to the fused DA-quality MODIS–MISR AOT. Prominent fine-mode features include pollution over East Asia and India, as well as biomass burning in southern Africa, South America and the maritime continent in JJASON. Distinguishable coarse-mode features include Saharan dust, Arabian and central Asian dust and the circumpolar sea salt belt over the Southern Ocean. For DJFMAM, the total AOTs for both the NAAPS runs with and without AOT data assimilation also look very similar to the fused DA-quality MODIS–MISR AOT. As for the fine-mode AOT, in addition to the year-round pollution over East Asia and India, biomass burning in central Africa and peninsular Southeast Asia shows up for the DJFMAM season. As for the coarse-mode AOT, dust over Sahara, Sahel, Arabian Peninsula and East Asia are clear and the circumpolar sea salt belt over the southern ocean is persistent. The seasonal global average total AOTs for over ocean and over land from the reanalysis are also similar to those of the fused DA-quality MODIS–MISR AOT. The NAAPS run without AOT assimilation has slightly higher global average total AOTs for over ocean and over land, mainly attributed to higher fine-mode AOT averages.
The similarity between the NAAPS runs with and without AOT data assimilation implies that the AOT correction by the data assimilation process is small and the whole model tuning process is effective. The resemblance between the reanalysis (NAAPS with AOT data assimilation) AOT and the fused MODIS–MISR AOT indicates that the data assimilation system works well in adjusting model fields to the closest observations. In this study, the model tuning process is considered equally as significant as the AOT data assimilation in influencing the final reanalysis. As the DA-quality satellite AOT data can reflect relatively small global coverage (Figs. 1, 2), areas not covered by the DA-quality satellite AOT would be highly impacted by the natural model (NAAPS without data assimilation). More details on the impact of tuning versus the DA on the model performance are provided in Appendix A.
For this type of comparison (Fig. 5), which is done with all available model and satellite data, we should also expect some difference between the satellite retrievals and the reanalysis, resulting from contextual biases in satellite products such as clear-sky biases (Zhang and Reid, 2009). Satellite retrievals for AOT mainly occur over clear sky, while the model depicts both clear and cloudy situations. Aerosol conditions can be very different between clear and cloudy sky, which is often associated with weather systems. For example, during the South American and southern African burning season (corresponding to JJASON), the southeast outflow regions from the southeast coast of the continents into the southern oceans are found to have lower seasonal average AOT for clear sky compared to cloudy/all sky, as smoke plumes are often transported along with the cloud system (Zhang and Reid, 2009). This clear-sky bias is also discernable comparing MODIS AOT and the reanalysis AOT (Figs. 2 and 5).
For validation purposes, we use the quality-assured AERONET level-2 product. The reanalysis AOTs are compared with AERONET 6-hourly total, fine- and coarse-mode AOTs at 550 nm.
Over the reanalysis period (2003–2013), the number of AERONET observations that can be paired with model data gradually increases with time (Fig. 6 top). The daily volume of global 6-hourly AERONET data has more than doubled in 2012 compared with 2003. The data count in 2013 decreases slightly due to the long processing time required for validating AERONET level 2 data (instruments need to be removed from the field and recalibrated; Smirnov et al., 2000). As there are more AERONET sites in the Northern Hemisphere than in the Southern Hemisphere and AERONET measurement only occurs during daytime, there are more AERONET observations during boreal summers than winters. Polar and high-latitude sites have few or no observations in winter, which raises a temporal sampling issue in validation for these regions. AERONET sampling also covaries with the seasonal AOT assimilation cycle, as high-latitude regions are less influenced by AOT assimilation during the wintertime.
(Top) time series of the daily total number of global regular AERONET L2 observations (excluding observations at DRAGON sites) binned into 6-hourly intervals (to match the model output resolution) for the AOT reanalysis period. (Bottom) time series of the RMSE of the reanalysis total AOT (black), fine-mode AOT (blue) and coarse-mode AOT (red), all at 550 nm, validated with AERONET. The daily average 6 h RMSEs are in small dots and the corresponding 90-day-running averages are in solid lines.
Despite the uneven seasonal sampling, the 90-day-running average of the RMSE of reanalysis AOTs is quite stable throughout the reanalysis time period (Fig. 6 bottom), at around 0.1 for both fine- and coarse-mode AOTs and 0.14 for the total AOTs. Daily average RMSE can occasionally exceed 0.4.
Figure 7 provides the comparison of the pairwise 6-hourly reanalysis AOT and
AERONET AOT for all of the available global sites during the reanalysis time
period. The normalized data density is shown in color. AOT data from AERONET
and the reanalysis are binned at a resolution of 0.01 and density of each bin
is colored relative to the maximum density in the sample. Also shown are the
basic statistics of the comparison: the total number of stations and the
6-hourly observations, bias, RMSE, square of the Pearson correlation
coefficient (
Pairwise comparison of the global 6-hourly reanalysis AOT and
AERONET AOT with respect to total (left), fine (middle) and coarse (right)
modes at 550 nm for JJASON (upper) and DJFMAM (bottom) for the entire
reanalysis time (2003–2013). The normalized data density is shown in color.
The solid magenta line represents a Theil–Sen linear regression and the
corresponding equation is shown, where
For both JJASON and DJFMAM, the global reanalysis fine-mode AOT has a small
positive bias of slightly less than 0.01, while the coarse-mode AOT has a
negative bias close to
As monthly data is often used in climate studies, we also evaluate the
reanalysis monthly averaged AOTs (Fig. 8). Monthly averages are obtained only
when the total number of 6-hourly AERONET data exceeds 10. For validation
purposes, the monthly average reanalysis AOT is calculated based on the
available 6-hourly data that can be paired with AERONET data. With the high-frequency signals (e.g., daily variability) smoothed out, the monthly average
exhibits a better match with AERONET data over all. For both seasons and all
modal AOTs, the monthly averages in the scatter plots are more aligned with
the 1 : 1 lines, RMSE is roughly 50 % lower (0.07 for total AOT, 0.05
for fine- and coarse- mode AOTs) and
Same as Fig. 7, except for the monthly average of pairwise 6-hourly mode AOTs at 550 nm. Monthly average is obtained only when the total number of 6-hourly AERONET data exceed 10 to ensure temporal representativeness. The monthly average reanalysis AOT here is calculated based on the available 6-hourly data that can be paired with AERONET data.
Figure 9 shows the cumulative distribution function (CDF) of AOT errors
compared with AERONET for total, fine and coarse AOTs, respectively, using
6-hourly data. As a reassurance, the CDF of AOT errors compared with MODIS
and MISR DA-quality data is also shown. Because the seasonal differences for
the global validation statistics are small, the two seasons are combined for
the CDF analysis. As expected, the reanalysis total AOT is in good agreement
with MODIS and MISR DA quality AOTs, though slightly less agreement with MISR
than MODIS is found as the relative number of MISR data involved in AOT
assimilation is much less. More than 95 % of the reanalysis total AOT has
an AOT error falling in the AOT error range of [
Cumulative distribution function for the reanalysis 6-hourly AOT errors compared to AERONET L2, MODIS and MISR data assimilation-quality data with respect to the available total, fine and coarse modes at 550 nm for the entire reanalysis time period (2003–2013).
Comparison of regional fine-mode AOT at 550 nm of the reanalysis
(red) at 95, 90, 75, 50, 25, 10 and 5 % percentiles to the pairwise
AERONET L2 data (black) for the regions defined in Fig. 4 for the 10-year
time period (June 2003–May 2013). Also shown are the regional mean of the
reanalysis and AERONET fine-mode AOTs in “
Figures 10, 11 and 12 show box-whisker plots of the pairwise comparisons of
regional reanalysis 6-hourly modal AOT vs. AERONET: percentiles marked in the
plots are 95, 90, 75, 50, 25, 10 and 5 %, for the regions defined in
Fig. 4 for 2003–2013. Also shown are regional mean AOTs designated by a
diamond for AERONET and “
Same as Fig. 10, except for coarse-mode AOT at 550 nm.
Same as Fig. 10, except for total AOT at 550 nm. Also, AOT value greater than 1.0 is cropped in this figure.
In general, the reanalysis follows the regional variation found in AERONET
for fine-mode, coarse-mode and total AOTs. For the fine-mode AOT, the
reanalysis matches well with AERONET with respect to the regional means,
medians, and variance. However, the results vary by region (Fig. 10). The
regional means and medians are the same or slightly larger than those of
AERONET for all regions, except East Asia and insular Southeast Asia, where
the means are smaller than AERONET. The high AOT regions are the developing
East Asia, Indian subcontinents, peninsular and insular Southeast Asia. These
regions also have the highest RMSE values varying between 0.15 and 0.2, while
RMSE values of other regions are all below 0.1. The low bias in mean fine-mode AOT in East Asia and insular Southeast Asia is mostly due to the model's
inability to capture the magnitude of large fine aerosol events (e.g., extreme
pollution and biomass-burning events). The
The coarse-mode AOT, overall, agrees less well with AERONET than the fine-mode AOT with respect to the regional means, medians, variances and
correlations (Fig. 11). Many regions have generally very low coarse AOT; RMSE
for these regions will be low, but
The
List of AERONET sites for further validation and statistics of the
reanalysis total AOT at 550 nm compared with AERONET at these sites for
December 2011–November 2012 breaking into two seasons DJFMAM (winter) and
JJASON (summer). The selected sites and time periods match Sessions et
al. (2015), where the International Cooperative for Aerosol Prediction (ICAP)
Multi-Model Ensemble (ICAP-MME) AOT is described and evaluated. The mean of
total AOT of AERONET L2 data, the paired reanalysis data bias, root mean
square error (RMSE), square of the Pearson correlation coefficient (
The total AOT, which is the sum of the coarse-mode AOT and fine-mode AOT, has
a validation feature that combines the validation properties of the two AOT
modes (Fig. 12). The regional variation of total AOT follows that of AERONET
well. The variance of the reanalysis for each region is smaller overall than
that of AERONET, suggesting the difficulty in capturing extreme events with
the model and assimilation system and a tendency to underestimate the
magnitude of extreme events and overestimate in very clean conditions. A
smaller AOT variance is known to be a typical model behavior among aerosol
models (Kinne et al., 2006; Sessions et al., 2015) and is a persistent
challenge to the aerosol modeling community. The reanalysis does not perform
as well with respect to mean bias and RMSE over East Asia, the Indian
subcontinent and insular and peninsular Southeast Asia, where complicated
aerosol environments often exist. For example, dust is often mixed with
various kinds of pollutants over East Asia and the Indian subcontinent, which
hinders satellite AOT retrievals and impacts model performance through AOT
data assimilation. Over insular Southeast Asia, constant high cloud cover
poses significant observability issues (Reid et al., 2013), reducing the
availability of successful satellite retrievals of AOT, in addition to
artificial high AOTs caused by cirrus contamination in AERONET data. This
region also has a complicated fire regime that is systematically undersampled
by the observations used to drive the smoke emissions in the model (Miettinen
et al., 2013). The large discrepancies between the reanalysis and AERONET for
coarse AOTs over insular and peninsular Southeast Asia affect the reanalysis
means and medians for total AOTs, but to a lesser degree, since fine-mode
aerosols are the dominant aerosol type for the these regions. Most regions
have
Same as Table 2, except for fine-mode AOT at 550 nm.
Site-by-site validation of the NAAPS reanalysis was conducted relative to the International Cooperative for Aerosol Prediction (ICAP) Multi-Model Ensemble (ICAP-MME; Sessions et al., 2015) as a baseline. Overall, ICAP-MME was shown to outperform any individual models with regard to RMSE in 550 nm AOT forecast (Sessions et al., 2015). By ranking, the ICAP-MME was typically first or second against all models at individual sites using 1-year worth of data. Since most of the ICAP models include AOT assimilation as well, the NAAPS reanalysis was compared to the ICAP-MME. The 21 AERONET sites used in the ICAP-MME study were agreed upon by the world's major center developers, as the most representative of each region. The same two seasonal periods (DJFMAM and JJASON of 2012) are used. In Fig. 4, these sites are marked with red squares. The ICAP-MME is run daily at 00:00 UTC for 6-hourly forecasts out to 120 h. The best-available ICAP MME data (closest to analysis) for this comparison is the consensus mean of 6 h forecast at 00:00 UTC; thus, the NAAPS reanalysis is at an advantage in this comparison due to the lagged AOT assimilation cycle in the ICAP-MME.
Table 2 shows the name of each site, its location and the prevailing aerosol type, along with all statistics relating to the total AOT at 550 nm for the two seasons. The same statistics for fine- and coarse-mode AOTs are listed in Tables 3 and 4, respectively. The values of bias and RMSE are in bold, bold with underline and italic, depending on whether the reanalysis performance is the same, better or worse than the ICAP MME mean 6 h forecast, respectively. Over a majority of the sites, the total AOT of the reanalysis is the same or better than the ICAP-MME with respect to bias and RMSE. The exceptions are the Beijing and Solar Village AERONET sites. Singapore is uncertain, as the low biases in fine-mode AOT contributes less than half of the total low bias, implying the dominant bias is the coarse-mode AOT bias, which is affected by thin cloud contamination in AERONET data. Cases, where the reanalysis is the same or better than the ICAP-MME in bias and RMSE occur less for the coarse-mode AOT than for the total AOT. On the one hand, the total AOT is assimilated in the reanalysis while the coarse-mode AOT is not. So, the total AOT is better constrained with satellite observations. On the other hand, the ICAP-MME consensus mean for dust/coarse-mode AOT includes an additional independent aerosol model relative to the total AOT consensus (five vs. four models), which makes the dust AOT ensemble exhibit better performance among all the models compared with the total AOT ensemble performance (Sessions et al., 2015).
Same as Table 2, except for coarse-mode AOT at 550 nm and for sites in which the coarse mode is dominated by dust.
The AOT seasonal difference is very clear for sites with outstanding seasonal
aerosol features. For example, higher total and fine-AOT values attributed to
biomass burning are observed in JJASON over Alta Floresta, Rio Branco and
Singapore and in DJFMAM over Chiang Mai. Seasonal differences are also found
over Ilorin with higher AOT in DJFMAM relative to JJASON, due to both dust
and biomass-burning activities. It is generally true that absolute bias and
RMSE increase with increasing values of AOT, so a seasonal variation in bias
and RMSE is also discernable for the sites with large seasonal AOT
variations.
Overall, the sign of the bias and the order of magnitude of the bias and RMSE
values for the selected sites are consistent with the regional evaluations in
Figs. 10–12 (and the tables in the Supplement). For high AOT sites (e.g.,
Banizoumbou, Beijing, Chiang Mai, Gandhi College, Ilorin and Kanpur), the
reanalysis generally has a low bias, as a result of the model and/or the data
assimilation system being incapable of capturing the amplitude of high AOT
events. An exception is Solar Village, though its dominant aerosol species,
which is dust/coarse-mode aerosol, is also biased low in AOT during DJFMAM.
Low bias in high AOT events is quite common among aerosols models (Kinne et
al., 2006; Sessions et al., 2015). The discrepancy can arise solely as a
function of spatial and temporal resolution: the average AOT for a grid cell
in an aerosol plume will be systematically lower than the peak observed point
AOT in that plume. However, shortcomings of aerosol sources or insufficient
representation of near-source aerosol processes can also cause bias.
Sometimes the discrepancy can be reduced by AOT assimilation, but the
probability of a successful retrieval declines for higher AOT events, and
this phenomenon is amplified by the application of AOT QA/QC procedures. The
largest departure for both seasons in total AOT occurs over Beijing, where
the coarse-mode bias contributes a little more to the total bias in DJFMAM
and the fine-mode bias contributes a little more in JJASON. Among all sites,
the maximum RMSE occurs over Beijing in both seasons for the total and the
fine-mode AOT and in DJFMAM for coarse-mode AOT. JJASON RMSE is smaller for
the reanalysis than for the ICAP-MME, implying that global models uniformly
do not do well here.
For moderate to low AOT sites, including Cart Site, Chapais, Goddard Space Flight Center (GSFC), Minsk,
Moldova, Monterey and Palma de Mallorca, the reanalysis performs well, with
the biases falling between
Several sites are affected by similar aerosol sources at different distances,
allowing us to examine transport phenomena using these sites. Banizoumbou,
which is located deep in the Sahara, has the largest bias (negative) and
RMSE, and the lowest
The performance of the reanalysis has a tendency to increase with the distance from the source region, especially over water. The main reasons for this are (1) aerosol models normally have larger uncertainties in aerosol sources than aerosol transports (Kinne et al., 2003), (2) there is limited satellite AOT data over the bright desert regions for the model to assimilate (Fig. 2), while there are a lot more opportunities for the model AOT to be corrected by assimilation along dust transport paths and (3) the atmosphere acts to smooth out near-source variability that is often at finer scales than the effective resolution of the model. These effects can also be seen when comparing the reanalysis performance over Beijing and Baengyueong, an island site in South Korea downwind of Beijing, for both fine- and coarse-mode AOTs.
There is debate over the use of AOT reanalyses to document and understand climatic trends, similar to the debate associated with meteorological reanalysis. However, the decadal trends derived from the reanalysis are largely in line with other studies using stand alone satellite products (Zhang and Reid, 2010; Hsu et al., 2012) for a similar time period. This helps to evaluate the reanalysis from another perspective. Figure 13 shows the trend of the deseasonalized total AOT over the whole reanalysis period (2003–2013), using the same calculation method as in Zhang and Reid (2010), where the significance of the trend analysis is estimated following the method of Weatherhead et al. (1998). Many areas show trends consistent with the satellite-only results of Zhang and Reid (2010) and Hsu et al. (2012): Indian Bay of Bengal, Arabian Peninsula and Arabian Sea, Bohai Sea in East Asia and the downwind region of southern African biomass-burning area, which have a positive trend, and the east coast of North America, Europe, central South America biomass-burning area and southern Indian Ocean, which have a negative trend. The reanalysis also exhibits a weak negative trend off the coast of dusty western Africa that is similar to other studies, though not statistically significant. The non-trend (zero trend) region with statistical significance in the south subtropical Pacific Ocean is also consistent with other studies.
Trends of the deseasonalized reanalysis total AOT at 550 nm over
2003–2013 (unit: 100
An arguable trend appears in the maritime continent, where Zhang and Reid (2010) report a non-significant positive trend, while Hsu et al. (2012) and our reanalysis here report a non-significant or significant negative trend based on slightly different study periods (Study periods are 2000–2010, 1998–2010, and 2003–2013 in Zhang and Reid, 2010, Hsu et al., 2012, and this paper, respectively). Because 1997–1998 was a strong El Niño period and 2010–2012 are La Niña years, corresponding to strong and weak fire activities in the maritime continent, respectively, trends for these different periods can be expected to differ systematically. Studies show that the climate and the associated fire/smoke activity in the maritime continent are controlled by El Niño–Southern Oscillation (ENSO) on the interannual timescale (e.g., Reid et al., 2012; van der Werf et al., 2004). The maritime continent is anomalously dry during El Niño years and experiences more fire activity and thus smoke aerosols compared to La Niña years, and there is a good correlation between ENSO and AOT there (e.g., Hsu et al., 2012; Xian et al., 2013). The different AOT trends over the maritime continents obtained with the use of slightly different time periods suggest the importance of checking the possible controlling climate variability on aerosol trend analysis depending on the timescales of interest. Similarly, the negative AOT trend in northern Africa and off the coast of western Africa is likely impacted by the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO) and ENSO activities as Saharan dust is also shown to be correlated with these climate variabilities (Evan et al., 2006; Hsu et al., 2012; Wang et al., 2012).
This reanalysis uses non-trending source functions for sulfate, DMS, organic aerosol emissions and dust erodibility. It is worth noting that even with static source functions and no volcanic source, the data assimilation has successfully picked up the positive trend downwind of the Hawaiian Islands due to the enhanced degassing activity of the Kilauea volcano since 2008 (e.g., Beirle et al., 2014). In a parallel model run, where AOT data assimilation is turned off, trends disappear over the east coast of North America and Europe or change sign over the Bay of Bengal while retaining their signs in most other regions (not shown). This indicates that AOT trends over the eastern United States, Europe and Bay of Bengal are related to anthropogenic emission changes. Opposite to the trend shown in the DA run, western African and the downwind subtropical Atlantic region show a strong positive trend in the natural run. There could be many possible reasons, such as an artifact of stronger surface wind in the meteorological model over the study period, or changes in vegetation which are not captured in the meteorological model or the dust source function.
Monthly mean 550 nm reanalysis and AERONET L2 mode AOTs at six
AERONET sites, Alta Floresta in the Amazon, Beijing in East Asia, Capo Verde
off the west coast of northern Africa, GSFC in East CONUS, Solar Village in
Arabian Peninsula, and Venice in Italy. The solid blue line is a linear
regression of the reanalysis total AOT. The red solid line is a linear
regression of the AERONET total AOT, only available when there is continuous
data through the time. Monthly mean AERONET AOT is obtained only when the
total number of 6-hourly AERONET data exceeds 10 to ensure temporal
representativeness. Annotations for each time series show bias, RMSE and
The positive trend over the southern African biomass-burning area and its downwind subtropical Atlantic region and the negative trend over central South American biomass-burning region are by and large a result of increasing fire emissions over southern Africa and decreasing fire emissions over South America exhibited in FLAMBE (not shown). The smoke emission trends in the above regions are consistent with the trends found with other satellite fire detection products for the same time period (Giglio et al., 2013). Trends over other regions are most likely relevant to climate variability or changes in climate, especially changes in meteorological variables that covary with aerosol processes. For example, the aforementioned negative trend over the maritime continent is very likely closely related to ENSO cycles. In another example, the decreasing dust trend in the northern Africa dust outflow region of the tropical Atlantic is shown to be caused mainly by a reduction in surface winds over dust source regions rather than changes in land surface properties in modeling studies (Chin et al., 2014; Ridley et al., 2014).
The Arabian Peninsula experiences increasing AOT, which may result from the observed decreasing precipitation for the similar time period (Almazroui et al., 2012). The negative AOT trend over the southern Indian Ocean is consistent with the trend analysis using MISR AOT data (Murphy, 2013). However, this trend in our analysis results solely from trends in the source and sink function, because AOT is not assimilated in this region in our system. The decreasing trend in the southern Indian Ocean AOT in the model is mainly caused by a decreasing trend in the surface winds in the meteorological model, NOGAPS (not shown). Observational studies, however, have found that wind speed over the southern oceans has increased in the past 2 decades (Young et al., 2011; Hande et al., 2012). The question of why the surface wind in NOGAPS decreases and AOT decreases in the southern oceans during the 2003–2013 time period requires additional investigation but is beyond the scope of this study.
Figure 14 shows the monthly mean NAAPS reanalysis and AERONET L2 modal AOT at
six AERONET sites chosen for their relatively long-term record under
different aerosol regimes: Alta Floresta in the Amazon, dominated by biomass-burning smoke during the burning season; Beijing in East Asia, dominated by
anthropogenic fine-mode aerosols year round with mixed dust and pollution in
the spring time; Capo Verde off the west coast of northern Africa, dominated by
Sahara/Sahel dust; GSFC in east CONUS, dominated by anthropogenic fine-mode
aerosols; Solar Village in the Arabian Peninsula, dominated by dust; and
Venice in Italy, dominated by pollution-related fine-mode aerosols and
influenced by Saharan dust in spring time. Also shown are linear regression
lines based on the total AOTs, indicative of AOT trends. Annotations in each
time series show bias, RMSE and
Overall, the reanalysis follows the seasonal and interannual variability in AERONET data for the total AOT quite well, and to a lesser extent for the coarse- and fine-mode AOTs. The pairwise comparison shows better correlation with AERONET than that calculated with all data, and, generally smaller absolute bias and RMSE. The decreasing trends over Alta Floresta, GSFC and Venice, the increasing trend over Beijing (slight) and Solar Village, and the insignificant trend over Capo Verde are consistent with the regional trends shown in Fig. 13, and qualitatively agree with AERONET. Over GSFC, the reanalysis has captured the evident decrease in total and fine-mode AOT since 2008. The June–July–August average AOT drops about 0.14 (from 0.37 to 0.23) for the total AOT and 0.12 (from 0.29 to 0.17) for the fine-mode AOT comparing the years before and after 2008. It drops about 0.09 (from 0.31 to 0.22) for the total AOT and 0.08 (from 0.27 to 0.19) for the fine-mode AOT in the reanalysis, with a low bias in total AOT and a minimal bias in fine-mode AOT for the season.
This paper describes a nearly 11-year global 550 nm modal AOT reanalysis
product developed at the Naval Research Laboratory, with a spatial resolution
of 1
Compared with 6 h average AERONET data, global mean RMSE values for both
fine-
and coarse-mode AOTs are around 0.1, and the RMSE for the total AOT is
Since total AOT is being assimilated, the total AOT has a smaller uncertainty relative to the coarse- and fine-mode AOT. Currently, there is no way to validate speciated AOTs if two or more aerosol species are present in the same size mode. We would expect the relative uncertainty of the speciated AOTs to be larger than the modal AOTs. The data quality of satellite-retrieved AOT is generally better over water than over land because of the relatively simple surface optical properties of water (e.g., Levy et al., 2005; Remer et al., 2005). Under the same AOT data assimilation frequency (or same amount of data to be assimilated), the reanalysis performs relatively better over oceanic and coastal regions/sites than land regions/sites.
The reanalysis captures the regional and seasonal AOT variations skillfully. The range of the regional reanalysis AOT values are generally smaller than those of AERONET (i.e., high bias for small AOTs and low bias for high AOTs), which is commonly seen among aerosol models, especially with coarse spatial and temporal resolution (e.g., Kinne et al., 2006; Sessions et al., 2015). Challenging regions for the reanalysis are East Asia, Indian subcontinent and Sahel, where there are often mixed fine- and coarse-mode aerosols. The reanalysis generally performs better in the long-range transport regions than the source regions. For example, the reanalysis AOT of the Caribbean islands sites, which are the receptor sites of African dust, matches AERONET observations better than the land sites within the African continent. A field campaign analysis of remotely transported smoke aerosols from Borneo and Sumatra islands found good agreement between the reanalysis AOT and the smoke concentrations therein and in situ measurements taken in the open ocean west of the Philippines (Reid et al., 2015).
The trends calculated from the reanalysis are similar to other studies using stand alone satellite products (Zhang and Reid, 2010; Hsu et al., 2012) in both aerosol transport regions and source regions. Over regionally representative sites, the reanalysis trend in modal AOT also agrees qualitatively well with the trend in AERONET data. This provides a reassurance of the quality of the reanalysis product. It is also worth noting that without trending source functions for sulfate and organic aerosols precursors, the data assimilation system has successfully reproduced regional AOT trends that are related to emission changes in the past decade. For example, a positive trend over India is attributed to emission growth. Signals of other low-frequency climate variability are also discernable in the reanalysis AOT. For example, using an earlier version of the NAAPS AOT analysis, the modulation effect of the Madden–Julian Oscillation on smoke AOT over the maritime continent is found (Reid et al., 2012).
Overall, the data assimilation system is very effective in correcting the
modeled AOT and bringing it as close as possible to the satellite
observations, and spreading the information to the neighboring grid cells
through a correlation length scale. In the time steps following assimilation,
the information is further propagated downstream. The data assimilation
system plays an indispensable role in picking up AOT trends in the regions
affected by emission changes that are not represented in the model. However,
the data assimilation system, associated with the assimilable data, also
has limitations. Satellite AOT retrievals characterize the optical properties
of a column, and it does not carry any information about aerosol vertical
profiles or speciation. So the total AOT is constrained through AOT data
assimilation. The relative vertical profile in three-dimensional extinction and speciation
of the aerosols are uniformly varied to match the posterior AOT. The
geographical coverage of the MODIS
Even though the data assimilation system has the capability of capturing the trend observed in stand alone satellite or AERONET AOT analyses, the inconsistency in the meteorological analysis of Navy Operational Global Atmospheric Prediction System (NOGAPS) in the past decade poses a big challenge in the development of a long-term global AOT reanalysis product. NOGAPS experienced several upgrades in the reanalysis period, including improved land surface parameterization, which impacts dust production trends.
A meteorological reanalysis is intended to provide a more consistent atmospheric state for aerosol simulations. But meteorological reanalyses have a data consistency issue as well, because observations being assimilated change significantly with time (e.g., Dee et al., 2011). For example, with the ever-increasing satellite observations of the past 2 decades, more and more satellite data are being assimilated for one or more meteorological variables. With the demise or periodic malfunction of some satellite instruments, some data became unavailable. This impacts the final meteorological reanalysis, and consequently the AOT reanalysis. The NOAA Climate Prediction Center MORPHing (CMORPH) precipitation data, which is used to replace NOGAPS precipitation in the tropics, is only available after December 2002. Its usage can impact regional AOT significantly in a natural model run (Xian et al., 2009). For areas not covered by the CMORPH product, any model precipitation performance change in time can be a potential issue for AOT trend analysis.
It is ideal for quick and consistent identification of large aerosol
events globally or regionally. It can serve as a reference and provide the
general background aerosol information without temporal or spatial
discontinuity for field campaign analysis. The reanalysis AOT can be used to provide global and regional AOT
climatologies for climate and applied science applications. The reanalysis AOT can be used in different scale analysis, from
daily to interannual. The diurnal AOT analysis should be performed with
caution considering the possible artifact feature introduced by the AOT
assimilation cycle.
Our future direction for the NAAPS aerosol reanalysis will be focused on three-dimensional extinction and mass concentration of single aerosol species, with special emphasis on the vertical dimension. The ability of NAAPS assimilating the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar backscatter coefficient data (Campbell et al., 2010; Zhang et al., 2011, 2014) will aid in this effort.
The NAAPS model code is a property of the US Naval Research Laboratory and is
not available to the public. However, the NAAPS reanalysis data are available
at
To show the relative importance of the tuning process on sources and sinks
versus the AOT data assimilation to reanalysis performance, four model runs
with difference configurations were conducted. AOT results from these four
runs were intercompared and validated with AERONET L2 data. The four model
configurations are NAAPS without tuning (that is to say the original native
version of NAAPS from which the reanalysis was originally based), NAAPS with
tuning, NAAPS without tuning but with AOT data assimilation, and the final
reanalysis version, which is with both tuning and AOT assimilation. The four
model runs all cover December 2010–November 2011 1-year time period. Interannual
tuning was not conducted to preserve a measure of consistency within the
model itself. The AOT data assimilation process, the input data and its
pre-DA treatment are kept the same for the DA runs. The “tuning” processes
on the sources and sinks include the addition of organic aerosols, updated
SO
Table A1 shows the 550 nm total, fine- and coarse-mode AOT bias, RMSE,
AOT data assimilation based on the tuned NAAPS further improves the
validation statistics. For example, the RMSE is reduced about 20 % for the
coarse, fine and total AOTs comparing the reanalysis to the
NAAPS_tuned. When comparing the DA runs (reanalysis
vs. DA_untuned), there are also discernable improvements
on bias, RMSE and
The Figs. A1 and A2 show the global coarse, fine and total AOT distributions from the four model runs for the two seasons of 2011, i.e., JJASON and DJFMAM respectively. For both seasons, it is obvious that the natural NAAPS run without tunings has the most different AOT distributions and global averages among the four runs. The three other runs look more similar to each other, which is consistent with the validation statistics shown in Table A1. For JJASON the natural NAAPS run without tunings has the lowest global mean AOTs among the four runs, yet the highest AOTs near dust and smoke source regions in South America and southern Africa. This indicates possible excessive emissions in these regions and excessive removal over water, which are tuned through applying smaller emission factors for smoke and dust and lower dry deposition velocity for dust over water in the tuning process. For both seasons, the tuned NAAPS run without DA has slightly high bias in the fine AOT (see also Table A1) and the bias is slightly larger in DJFMAM than in JJASON, most probably resulted from excessive addition of organic aerosols during boreal winter.
Compared to the reanalysis, the DA run without source and sink tuning,
exhibits similar global total AOT distribution. However, some differences
between the two are noticeable for the fine and coarse AOTs. For example,
over the Indian subcontinent the AOT partitioning between the fine and
coarse AOTs differs significantly. The contribution of the fine-mode
aerosols to the total AOT dominates the contribution of the coarse-mode
aerosols in the reanalysis. Whereas the total AOT is predominantly
attributed to the coarse-mode aerosols in the DA run without tunings. Over
the southern flank of the Himalayas, where fine-mode aerosols from
industrial and biofuel emissions often prevail over coarse-mode (refer to
Kanpur site in Tables 2–4), the fine-mode fraction is increased from
Statistics of the coarse, fine and total AOTs at 550 nm from four model runs compared with AERONET L2 data. The four model runs are from four different model configurations, including NAAPS without sources and sinks tuning, NAAPS with tuning, NAAPS without tuning but with AOT data assimilation, and the reanalysis version, which is with both the tuning and the AOT assimilation. The comparison is based on 1-year time period (December 2010 to November 2011). The global AERONET mean is 0.085, 0.102 and 0.187 for coarse, fine and total AOT, respectively, obtained with averaging 97 654 valid 6-hourly L2 data from 285 stations.
6-month-average (June–November 2011) total (upper), fine
(middle) and coarse (bottom) AOTs at 550 nm from four NAAPS runs with
different configuration: NAAPS without tuning, NAAPS with tuning processes
on sources and sinks, NAAPS without tuning but with AOT data assimilation
and the reanalysis version, which is with both tuning and AOT assimilation.
Annotations at the bottom left in the figures show the area mean AOTs over
ocean and over land averaged for 40
Same as the Fig. A1, except for December 2010–May 2011 6-month-average.
The development of the NAAPS reanalysis was an outcome of the needs of multiple projects, and largely supported by the Office of Naval Research code 322 and the NASA Interdisciplinary Science Program. Additional support was provided by the NRL Base Program and the Office of Naval Research 35. The development team is grateful to the effort of the operational NASA-MODIS and MISR aerosol teams for the development and implementation of their level two products. We are likewise grateful to the NASA land team for the development of their fire products. The NASA Aerosol Robotic Network (AERONET) data are key to verifying models such as the NAAPS reanalysis and the use of this federated network's data is gratefully acknowledged. Edited by: O. Boucher