Estimating the maritime component of aerosol optical depth and its dependency on surface wind speed using MODIS and QuikSCAT data

Estimating the maritime component of aerosol optical depth and its dependency on surface wind speed using MODIS and QuikSCAT data Y. Lehahn, I. Koren, E. Boss, Y. Ben-Ami, and O. Altaratz Department of Geophysics and Planetary Sciences, Tel Aviv University, Tel Aviv, Israel Department of Environmental Sciences, Weizmann Institue, Rehovot, Israel School of Marine Sciences, University of Maine, Orono, Maine, USA Received: 12 December 2009 – Accepted: 21 January 2010 – Published: 29 January 2010 Correspondence to: Y. Lehahn (yoav.lehahn@weizmann.ac.il) Published by Copernicus Publications on behalf of the European Geosciences Union.


Introduction
Covering approximately 70% of the Earth's surface, the World Ocean is one of the major sources of natural aerosol.Marine aerosols play an important role in altering Earth's energy balance, either through direct scattering of solar radiation or indirectly through altering cloud microphysical properties, and therefore are important constituents of the Figures

Back Close Full Screen / Esc
Printer-friendly Version Interactive Discussion climate system and of the hydrological cycle.Changes in marine aerosol properties are likely to have important climatological implications (Kaufman et al., 2002;Murphy et al., 1998;Latham and Smith, 1990;Charlson et al., 1992).Marine aerosols comprise two components: (i) primary aerosols, which are generated at the sea surface through wind driven processes (especially the bursting of entrained air bubbles associated with whitecap formation) resulting in mechanical production of sea-spray particles and (ii) secondary aerosols, which are the outcome of gas-to-particle processes (O'Dowd and de Leeuw, 2007).Sea spray aerosol contribute to both fine and coarse aerosol modes, with dominant fractions of inorganic sea salt and organic matter in the supermicron and submicron modes, respectively (Cavalli et al., 2004).It is well established that the generation of sea spray aerosols (Nilsson et al., 2001), as well as the consequent particle concentration (Hoppel et al., 1990;Glantz et al., 2004;O'Dowd et al., 1997) and size distribution (Hoppel et al., 1990;Gong et al., 1997) are strongly dependent on the wind speed.
Thus, correct representation of marine aerosol characteristics and their dependency on surface wind speed are essential for climate forcing estimations and accurate modeling of the climate system.Currently, our understanding of these phenomena is limited by the availability of observational data.Measurements of aerosol parameters such as aerosol optical depth (τ), fraction of coarse aerosol (f c ) and the relative contribution to τ by fine (τ f ) and coarse (τ c ) particles from the Moderate Resolution Imaging Spectroradiometer (MODIS) (Remer et al., 2008), in conjunction with satellite measurements of the surface wind speed (W ), provide the means for continuous, long term global observations of this complex system.A major obstacle for making a comprehensive use of satellite data for quantifying phenomena associated with marine aerosols stems from the fact that the majority of the oceans cannot be considered pristine, as they show evidence of aerosol loading from terrestrial origin (Huang et al., 2009).Overall, aerosols over the ocean can be classified into three main groups: marine aerosol, anthropogenic aerosol (biomass burning and pollution) and dust.Accordingly, satellite measurements of aerosol optical depth are the sum of contributions from all aerosol groups: Where the subscripts a, d, and m denote anthropogenic, dust, and marine aerosol components, respectively.Currently, our ability to distinguish between the different types of aerosols is limited.Given that the fraction of fine particles in anthropogenic aerosol is much higher as compared to dust or maritime aerosol (Kaufman et al., 2002), a limited classification of aerosol type is possible by the spectral dependence that is linked to the aerosol size.Nevertheless, we do not have an efficient mechanism to distinguish between maritime aerosol and dust in the MODIS data (Kaufman et al., 2005b).In previous works, τ m was determined empirically as a constant or as a function of near-surface wind speed from the National Center for Environmental Prediction (NCEP) (Kaufman et al., 2001(Kaufman et al., , 2005a,b),b).
Here we suggest a novel approach to distinguish the maritime from other components of τ, based on a systematic comparison of satellite measurements of aerosol optical parameters and W .We show that satellite measured surface wind speed can serve as a reliable proxy for the spatial distribution of coarse marine aerosol.More specifically, this work is aimed at providing (i) a mechanism for identifying the maritime component of satellite derived τ and τ c (τ cm ), and (ii) a global perspective on the dependency of τ m and τ cm on W .
Previous studies of the link between τ and W were carried out in specific locations over the world ocean, resulting in a variety of linear (Smirnov et al., 2003;Villevalde et al., 1994), power-law (Mulcahy et al., 2008;Glantz et al., 2009) and exponential (Moorthy and Satheesh, 2000;Vinoj and Satheesh, 2003) relationships between the fields.Exponential increase in aerosol optical depth with increasing wind speeds, were also found by Satheesh et al. (2006)  the European Center for Medium-Range Weather Forecast (ECMWF).Their results from 3 pristine ocean regions show linear relationship between the fields.The methodology and satellite datasets used in this research are described in the following section.The proposed mechanism for separating the maritime component of τ and τ c and the results of the comparison between the fields are described in Sect. 3 followed by a summary and conclusions in Sect. 4.

Data and methods
Analysis of the link between aerosol parameters and surface wind speed is based on comparison between daily satellite measurements over a seven years period, from 2002 to 2008.

Aerosol properties
Aerosol parameters were derived from MODIS aboard NASAs satellite Terra that follows a sun-synchronous orbit, crossing the equator at approximately 10:30 LT.The dataset used here is from the Terra Collection005, consisting of daily global level 3 images (LAADS, http://ladsweb.nascom.nasa.gov/).The spatial resolution is 1 • .MODIS aerosol optical parameters and aerosol size parameters are standard MODIS products and were found to be in good agreement with the same quantities derived by AERONET instruments on the ground (Remer et al., 2005(Remer et al., , 2008)).

Surface wind speed
Surface wind speeds were obtained from the SeaWinds scatterometer on board NASA's Quick Scatterometer (QuikSCAT) satellite (Freilich et al., 1994).The satellite's orbit, which is sun-synchronous, is optimized to measure winds over 90% of the

Regions of interest
The comparison between wind speed and aerosol parameters was performed over 5 5×5 • regions of interest (ROI) at different locations over the World Ocean (regions ae in Fig. 1a).In order to minimize contribution from terrestrial sources, the ROIs were defined in areas that are (i) located away from known sources and transport routes of terrestrial aerosol and (ii) characterized by low mean τ and hence less affected by long-range aerosol transport.Another issue that was taken into consideration when choosing the ROIs is the 4.5 h time difference between the wind and aerosol measurements (06:00 LT for QuikSCAT measurements and 10:30 LT for MODIS-Terra measurements).Hence, the ROIs were defined in regions away from the coastline, where the wind regime is not effected by sea breeze (Gille et al., 2003).In these regions, within the 4.5 h time frame the correlation between W and τ c is well kept.When increasing the time difference between the measurements to longer periods the correlation between the fields decreases significantly due to synoptic changes in the wind field (Fig. 2).
Based on these criteria, adequate areas were identified in the North and South Pacific (regions a,b centered respectively at 177.5 • W/27.5 • N and 147.5 • W/27.5 • S), North and South Atlantic (regions c,d centered respectively at 37.5 • W/37.5 • N and 17.5 • W/27.5 • S) and the Indian Ocean (region e, centered at 77.5 • E/27.5 • S).
ACPD 10,2010 Linking satellite derived wind speed and marine AOD

Statistical analysis
For each ROI, time series of wind speed and aerosol parameters were extracted separately over 25 1×1 • cells (each cell covered by one pixel of MODIS data and 16 pixels of QuikSCAT data).In order to avoid extreme (and hence poorly represented) values, the highest 3% of the W and τ c data were excluded.The data were sorted according to W and divided into 30 bins of equal number of observations.Regression equations and correlation coefficients were calculated from the full cloud of data points before binning.The statistical analysis was performed on the daily data, without spatial and temporal averaging.

Approach
In order to separate the maritime component of τ, we suggest a novel method that takes into account the additional (satellite derived) information on the surface wind speed.
The approach we take is based on the assumption that, as a first approximation, the component of τ c that correlates with W over the oceans is τ cm (Satheesh et al., 2006).
This assumption is supported by Kaufman et al. (2005b) analysis of vertical profiles of wind driven aerosol concentration over the Atlantic Ocean, that showed that the lowest 500 m of the atmosphere are mostly occupied by sea salt aerosol, whereas dust away from the source is transported at higher altitudes.The lack of correlation between W and the dust component of τ c (τ cd ) is emphasized in Fig. 3, where we plot W and τ c over a 5×5 • region (region DD in Fig. 1a) in an area known to be dominated by desert dust offshore Africa (Kaufman et al., 2002).Regression analysis shows no significant correlation between W and τ c in this area.Anthropogenic aerosols are predominately fine-mode (Kaufman et al., 2002) and are considered to have very little effect on τ c (Satheesh et al., 2006).Introduction

Conclusions References
Tables Figures

Back Close
Full Screen / Esc Printer-friendly Version Interactive Discussion

Comparison with ground measurements
Correlating data from QuikSCAT and MODIS for studying wind-aerosol interactions over the ocean may be limited by the effect of reflection by whitecaps, which are also strongly linked to the wind speed and might bias aerosol retrieval (Yu et al., 2009).In order to evaluate the feasibility of the methodology, a comparison was made with data from the land-based AERONET station in Midway Island, where wind speed effects on marine aerosols optical properties were thoroughly investigated by Smirnov et al. (2003).AERONET data (level 1.5, ONEILL-15 -provisional status) for the years 2001-2008 were downloaded from the AERONET web-page (http://aeronet.gsfc.nasa.gov/).A daily sample was calculated as the mean of all measurements taken between 6 a.m. and 10 a.m.MODIS and QuikSCAT data were averaged daily over a 2×2 • area around the station.The relationship between W and aerosol optical parameters (τ, τ c and τ f ) from MODIS (Fig. 4, left panels) and from the AERONET station (Fig. 4, right panels) are similar.For moderate winds (W smaller than approximately 5 m/s) both MODIS and AERONET τ (Fig. 4a and b) and τ c (Fig. 4c and d) are almost constant, with typical values of approximately 0.1 and 0.05, respectively.For stronger winds the two parameters increase linearly with W . τ f from both MODIS and AERONET show little or no change over the whole range of wind speeds, with MODIS τ f being slightly higher than that from AERONET.

Analysis and results
On an annual time scale W and f c show similar distribution patterns over large parts of the World Ocean (Fig. 1).Note Ocean east of Africa and in the Arabian Sea, where prominent features of high τ c associated with dust (Kaufman et al., 2002) are found in regions of weak or intermediate W .
As can be seen in Fig. 5, the relationship between W and τ c in the 5 ROIs can be divided into two phases associated with the wind intensity: (i) a "tranquil" phase where τ c is nearly constant and (ii) a "windy" phase where τ c is linearly correlated with W . Since, as suggested above, any correlation between W and τ c actually reflects correlation with τ cm , we can now identify a threshold wind speed (W thc ) for triggering contribution to τ c from wind induced marine aerosols.W thc is objectively defined as the highest value of W corresponding to τ c equal or smaller than τ c of the first bin (i.e. the strongest wind associated with the "tranquil" phase).In the 5 ROIs W thc (red circle in Fig. 5) is remarkably consistent, with an average value of 4.1±0.1 m/s.When excluding data corresponding to winds below W thc , the correlation coefficient R between τ c and W range from 0.37 to 0.45 and are statistically significant at the 99% confidence level.The slope (α c ) linking τ c to W is remarkably consistent, with an average value of 0.0082±0.0004s/m.
Following that, we identify a background aerosol (τ c0 , marked by the horizontal line in Fig. 5), which is a measure to the contribution to τ c from all aerosols not produced in situ through wind driven processes, by averaging τ c associated with winds below W thc .τ c0 varies between 0.03 to 0.05 with an average value of 0.04±0.01.Then, τ cm is simply calculated as: Further investigation of the link between W and marine aerosol optical properties is done by plotting τ, τ c and τ f against W for all the data associated with the 5 ROIs (Fig. 6).The relationship between τ and W (Fig. 6a) is remarkably similar to the relationship between τ c and W (Fig. 6b), with a threshold wind speed (W th ) of 3.8±0.2m/s and a slope α of 0.011 ± 0.002 s/m.The background component of τ (τ 0 ) is twice higher than τ c0 (0.08).Introduction

Conclusions References
Tables Figures

Back Close
Full Screen / Esc Printer-friendly Version

Interactive Discussion
Hence, equation in the form of Eq. ( 2), can be used for estimating the wind induced marine aerosol contribution to τ: The similarity between τ and τ c relationships with W can be explained by the low correlation between W and τ f (Fig. 6c).Since τ is simply the sum of τ c and τ f , and since τ f shows little dependency on the wind speed, increase in τ due to wind driven emission of marine aerosols is attributed almost entirely to contribution from coarse mode particles.τ f has an important contribution to the part of τ that is independent of the wind speed (i.e. the background aerosol), with a value 0.04 being about half of the total τ 0 .
Seasonal and geographical variations in the parameters describing the relationships of τ and τ c with W are examined in Fig. 7.The threshold wind speed for triggering contribution of maritime aerosols to τ and τ c (Fig. 7a and b, respectively), show very little variability, maintaining a value of approximately 4 m/s throughout the year in all 5 ROIs.The slopes α and α c linking W with τ c and τ c , respectively, have mean values of 0.011 ± 0.001 s/m and 0.0083±0.0001s/m, (Fig. 7c and d, respectively).
The parameters showing the largest spatio-temporal variability are τ 0 , with values ranging from 0.05 to 0.17 and 0.18 (Fig. 7e) and τ c0 , with values ranging from 0.02 to 0.07 (Fig. 7f).The large geographical and seasonal variations in τ 0 and τ c0 are attributed to changes in the emission and transport of aerosol from terrestrial sources (Kaufman et al., 2002).Since the background aerosol optical depth is a measure to all aerosols not produced in situ through wind driven processes, regions characterized by low values of τ 0 (e.g.ROI e during the months June-August) and τ c0 (e.g.ROI d during the months December-February) can be considered as relatively pristine, with little contribution of aerosols from terrestrial sources.
Comparison with previous works (dashed lines in Fig. 6a), suggests that the power law relationship obtained by Mulcahy et al. (2008) can be used as a first approximation to the link between satellite derived surface wind speed and marine aerosol optical depth found here.

Conclusions References
Tables Figures

Back Close
Full

Summary and conclusions
Using a novel methodology based on a systematic comparison between satellite measurements of surface wind speed from QuikSCAT and aerosol parameters from MODIS, we conclude that: 1.At a given location over the open sea, instantaneous measurements within a few hours time frame are adequate for quantifying the link between wind and coarse marine aerosols.
2. Changes in aerosol optical depth due to wind induced maritime aerosols are dominated by contribution from coarse mode particles.
3. Similarly to the "threshold wind" for emission of desert dust (Chomette et al., 1999), there is a remarkably consistent threshold wind speed of 4.1±0.1 m/s for triggering emission of coarse maritime aerosol allover our study area.This threshold value is strongly supported by the fact that 4 m/s is typically estimated as the wind speed at which whitecap formation onset occurs (O'Dowd and de Leeuw, 2007).
4. When surface wind speed exceeds the threshold value, coarse marine aerosol optical depth is linearly correlated to the surface wind speed, with a consistent slope of 0.0082±0.0004s/m. 5. Assuming that over the open sea, away from the continents, optical depth by other types of coarse particles (such as dust) is not well correlated with surface wind, having the wind information, the total aerosol optical depth and the fine fraction enables estimation of the marine aerosol contribution to the total coarse optical depth and therefore, to a good approximation, the contribution of coarse mode dust.Moreover, by deducting the wind induced marine aerosol, the average background aerosol loading can be estimated per location and season.The satellite based analysis demonstrated here covers a large variety of oceanic and atmospheric regimes throughout a relatively long (7 years) time span, hence including a wide range of wind speeds and aerosol properties.The results from this analysis significantly improve our ability to distinguish between different aerosol types over the World Ocean and provide a framework for continuous, long term observations on the production of marine aerosols, and on the way they affect, and are affected by, climate variability.Introduction

Conclusions References
Tables Figures

Back Close
Full      1 for locations).Binning and statistical operations are similar to those in Fig. 5.The red data points in the upper and middle panels mark respectively W th and W thc .The horizontal black lines mark τ 0 , τ c0 and τ f 0 (panels a-c, respectively).The magenta, red and green dashed lines in the upper panel correspond respectively to the relationship between τ and W found by Smirnov et al. (2003), Mulcahy et al. (2008) and Huang et al. (2009).Introduction

Conclusions References
Tables Figures

Back Close
Full who made use of τ from MODIS and W from NCEP over the Arabian Sea.More recently,Huang et al. (2009) studied the effect of wind speed on τ at 550 nm over remote pristine ocean regions using satellite estimates of τ from Advanced Along-Track Scanning Radiometer (AATSR) and wind speeds from that the correlation are better over the open oceans away form the shores.Moreover, it is shown that areas of strong annual winds are characterized by large fraction of coarse mode aerosol and high values of τ c (Fig. 1d).Regions of moderate winds are usually characterized by a relatively small fraction of coarse mode aerosol and low values of τ c .A different picture is found in the Atlantic

Fig. 1 .
Fig. 1. 2004 annual mean distribution of (a) W ; (b) f c ; (c) τ; (d) τ c .The black boxes and the letters in panel (a) indicate regions used in the analysis presented in the following figures.

Fig. 2 .
Fig. 2. Correlation coefficients between MODIS τ c and QuikSCAT W as a function of the time difference between measurements, at the 5 regions of interest (regions a-e in Fig.1).Only data associated with wind speeds higher than 4 m/s were used.

Fig. 3 .Fig. 4 .
Fig. 3. MODIS τ c plotted against QuikSCAT W in an area dominated by desert dust (region DD in Fig.1a).The data were sorted according to W and divided into 30 bins of equal number of observations.Points and error bars represent respectively the mean and standard deviation of τ c within each bin.

Fig. 5 .
Fig. 5. MODIS τ c plotted against QuikSCAT W at the 5 ROIs (regions a-e in Fig.1).The data were sorted according to W and divided into 30 bins of equal number of observations.Circles and error bars represent respectively the mean and standard deviation of τ c within each bin.The red data point and the horizontal line mark respectively the threshold wind speed for triggering coarse marine aerosol emission, W thc , and the value of the background aerosol, τ c0 (see text).The regression equations and correlation coefficients were calculated from the full cloud of data points before binning.The statistical analysis was performed on the daily data, without spatial and temporal averaging.The regression equations and correlation coefficients were calculated for data corresponding to W >W thc and are shown for τ c after subtracting the background component.

Fig. 6 .
Fig. 6.MODIS (a) τ; (b) τ c ; (c) τ f plotted against QuikSCAT W from the 5 regions of interest (see Fig.1for locations).Binning and statistical operations are similar to those in Fig.5.The red data points in the upper and middle panels mark respectively W th and W thc .The horizontal black lines mark τ 0 , τ c0 and τ f 0 (panels a-c, respectively).The magenta, red and green dashed lines in the upper panel correspond respectively to the relationship between τ and W found bySmirnov et al. (2003),Mulcahy et al. (2008) andHuang et al. (2009).
Earth at least once per day.The level 3 data that were used in this research are provided on an approximately 0.25 • ×0.25 • global grid.Only data from ascending passes (06:00 LT equator crossing) were included.QuikSCAT data were obtained through the on line PO.DAAC Ocean ESIP Tool (POET) at the Physical Oceanography Distributed Active Archive Center (PO.DAAC), NASA Jet Propulsion Laboratory, Pasadena, CA (http://podaac.jpl.nasa.gov/poet).