3.   Aerosols and Radiation


3.1.   Aerosol Monitoring


E. Andrews (Editor), D. Delene, D. Jackson, A. Jefferson, J. Ogren, P. Sheridan, and J. Wendell


3.1.1.   Scientific Background


            Aerosol particles affect the radiative balance of Earth both directly, by scattering and absorbing solar and terrestrial radiation, and indirectly, through their action as cloud condensation nuclei (CCN) with subsequent effects on the microphysical and optical properties of clouds.  Evaluation of the climate forcing by aerosols, defined here as the perturbation of the Earth's radiation budget induced by the presence of airborne particles, requires knowledge of the spatial distribution of the particles, their optical and cloud-nucleating properties, and suitable models of radiative transfer and cloud physics.  Obtaining a predictive relationship between the aerosol forcing and the physical and chemical sources of the particles additionally requires knowledge of regional and global-scale chemical processes, physical transformation, and transport models for calculating the spatial distributions of the major chemical species that control the optical and cloud-nucleating properties of the particles.  Developing and validating these various models requires a diverse suite of in situ and remote observations of the aerosol particles on a wide range of spatial and temporal scales.

            Aerosol measurements began at the CMDL baseline observatories in the mid-1970s as part of the Geophysical Monitoring for Climatic Change (GMCC) program.  The objective of these "baseline" measurements was to detect a response, or lack of response, of atmospheric aerosols to changing conditions on a global scale.  Since the inception of the program, scientific understanding of the behavior of atmospheric aerosols has improved considerably.  One lesson learned is that residence times of tropospheric aerosols are generally less than 1 week, and that human activities primarily influence aerosols on regional/continental scales rather than global scales.  In response to this increased understanding, and to more recent findings that anthropogenic aerosols create a significant perturbation in the Earth's radiative balance on regional scales [Charlson et al., 1992; National Research Council, 1996], CMDL expanded its aerosol research program to include regional aerosol monitoring stations.  The goals of this regional-scale monitoring program are: (1) to characterize means, variabilities, and trends of climate-forcing properties of different types of aerosols, and (2) to understand the factors that control these properties.

            No single approach to observing the atmospheric aerosol can provide the necessary data for monitoring all the relevant dimensions and spatial/temporal scales required to evaluate climate forcing by anthropogenic aerosols.  In situ observations from fixed surface sites, ships, balloons, and aircraft can provide very detailed characterizations of the atmospheric aerosol but on limited spatial scales.  Remote sensing methods from satellites, aircraft, or from the surface can determine a limited set of aerosol properties from local to global spatial scales, but they cannot provide the chemical information needed for linkage with global chemical models.  Fixed ground stations are suitable for continuous observations over extended time periods but lack vertical resolution.  Aircraft and balloons can provide the vertical dimension, but not continuously.  Only when systematically combined can these various types of observations produce a data set where point measurements can be extrapolated with models to large geographical scales where satellite measurements can be compared to the results of large-scale models, and where process studies have a context for drawing general conclusions from experiments conducted under specific conditions.

            Measurements of atmospheric aerosols are used in three fundamentally different ways for aerosol/climate research: algorithm development for models and remote-sensing retrievals, parameter characterization, and model validation.  Laboratory and field process studies guide the development of parameterization schemes and the choice of parameter values for chemical transport models that describe the relationship between emissions and concentration fields of aerosol species.  Systematic surveys and monitoring programs provide characteristic values of aerosol properties that are used in radiative transfer models for calculating the radiative effects of the aerosols, and for retrieving aerosol properties from satellites and other remote sensing platforms.  And finally, monitoring programs provide spatial and temporal distributions of aerosol properties that are compared to model results to validate the models.  Each of these three modes of interaction between applications and measurements requires different types of data and entails different measurement strategies.  Ogren [1995] applied the thermodynamic concept of “intensive” and “extensive” properties of a system to emphasize the relationship between measurement approach and applications of aerosol observations.

            Intensive properties do not depend on the amount of aerosol present and are used as parameters in chemical transport and radiative transfer models (e.g., atmospheric residence time, single-scattering albedo).  Extensive properties vary strongly in response to mixing and removal processes and are most commonly used for model validation (e.g., mass concentration, optical depth).  Intensive properties are more difficult and expensive to measure than extensive properties because they generally are defined as the ratio of two extensive properties.  As a result, different measurement strategies are needed for meeting the data needs of the various applications.  Measurements of a few carefully chosen extensive properties, of which aerosol optical depth and species mass concentrations are prime candidates, are needed in many locations to test the ability of the models to predict spatial and temporal variations on regional to global scales and to detect changes in aerosol concentrations resulting from changes in aerosol sources.  The higher cost of determining intensive properties suggests a strategy of using a limited number of highly instrumented sites to characterize means and variabilities of intensive properties for different regions or aerosol types, supplemented with surveys by aircraft and ships to characterize the spatial variability of these parameters.  CMDL's regional aerosol monitoring program is primarily focused on characterizing intensive properties.

            CMDL's measurements provide ground truth for satellite observations and global models, as well as key aerosol parameters for global-scale models (e.g., scattering efficiency of sulfate particles and hemispheric backscattering fraction).  An important aspect of this strategy is that the chemical measurements are linked to the physical measurements through simultaneous, size-selective sampling that allows the observed aerosol properties to be connected to the atmospheric cycles of specific chemical species [e.g., Quinn et al., 2001]. 


3.1.2.   Experimental Methods


            Extensive aerosol properties monitored by CMDL include condensation nucleus (CN) concentration, aerosol optical depth (d), and components of the aerosol extinction coefficient at one or more wavelengths (total scattering (ssp), backwards hemispheric scattering (sbsp), and absorption (sap)).  At the regional sites, size-resolved impactor and filter samples (submicrometer and supermicrometer size fractions) are obtained for gravimetric and chemical (ion chromatograph) analyses.  All size-selective sampling, as well as the measurements of the components of the aerosol extinction coefficient at the regional stations, is performed at a low, controlled relative humidity (<40%) to eliminate confounding effects due to changes in ambient relative humidity.  Data from the continuous sensors are screened to eliminate contamination from local pollution sources.  At the regional stations the screening algorithms use measured wind speed, direction, and total particle number concentration in real-time to prevent contamination of the chemical samples.  Algorithms for the baseline stations use measured wind speed and direction to exclude data that are likely to have been locally contaminated.

            Prior to 1995, data from the baseline stations were manually edited to remove spikes from local contamination.  Since 1995 an automatic editing algorithm has been applied to the baseline data in addition to manual editing of local contamination spikes.  For the baseline stations (Barrow, Alaska (BRW), Mauna Loa, Hawaii (MLO), American Samoa (SMO), and South Pole, Antarctica (SPO), as well as Sable Island (WSA)), data are automatically removed when the wind direction is from local sources of pollution (such as generators and buildings) as well as when the wind speed is less than a threshold value (0.5-1 m s-1). In addition, at MLO data for upslope conditions (1800-1000 UTC) are excluded since the airmasses do not represent “background” free tropospheric air for this case. A summary of the data-editing criteria for each station is presented in Table 3.1.

            Integrating nephelometers are used to determine the light scattering coefficient of the aerosol.  These instruments operate by illuminating a fixed sample volume from the side and observing the amount of light that is scattered by particles and gas molecules in the direction of a photomultiplier tube.  The instrument integrates over scattering angles of 7-170°.  Depending on the station, measurements are performed at three or four wavelengths in the visible and near-infrared.  Newer instruments allow determination of the hemispheric backscattering coefficient by using a shutter to prevent illumination of the portion of the instrument that yields scattering angles less than 90°.  A particle filter is inserted periodically into the sample stream to measure the light scattered by gas molecules, which is subtracted from the total scattered signal to determine the contribution from the particles alone.  The instruments are calibrated by filling the sample volume with CO2 gas, which has a known scattering coefficient.

            The aerosol light absorption coefficient is determined with a continuous light absorption photometer.  This instrument continuously measures the amount of light transmitted through a quartz filter while particles are being deposited on the filter.  The rate of decrease of transmissivity, divided by the sample flow rate, is directly proportional to the light absorption coefficient of the particles.  Newer instruments  (Particle Soot Absorption Photometers (PSAP), Radiance Research, Seattle, Washington) are calibrated in terms of the difference of light extinction and scattering in a long-path extinction cell, for laboratory test aerosols.  Older instruments at the baseline stations (aethalometers, Magee Scientific, Berkeley, California) were calibrated by the manufacturer in terms of the equivalent amount of black carbon (BC) from which the light absorption coefficient is calculated, assuming a mass absorption efficiency of the calibration aerosols of 10 m2 g-1.

            Particle number concentration is determined with a CN counter that exposes the particles to a high supersaturation of butanol vapor.  This causes the particles to grow to a size where they can be optically detected and counted.  The instruments in use have lower particle-size detection limits of 10-20 nm diameter. 

            Summaries of the extensive measurements obtained at each site are given in Tables 3.2 and 3.3.  Table 3.4 lists the intensive aerosol properties that can be determined from the directly measured extensive properties.  These properties are used in chemical transport models to determine the radiative effects of the aerosol concentrations calculated by the models.  Inversely, these properties are used in algorithms for interpreting satellite remote-sensing data to determine aerosol amounts based on measurements of the radiative effects of the aerosol.


3.1.3.   Annual Cycles


            The annual cycles of aerosol optical properties for the four baseline and three regional stations are illustrated in Figures 3.1 and 3.2, respectively.  The data are presented in the form of box and whisker plots that summarize the distribution of values.  Each box ranges from the lower to upper quartiles with a central bar at the median value, while the whiskers extend to the 5th and 95th percentiles.  The statistics are based on hourly averages of each parameter for each month of the year; also shown are the annual statistics for the entire period of record.  The horizontal line represents the annual median, so measurements above and below the median can easily be discerned.  The annual cycles for the baseline stations are based on data through the end of 2001 except at SMO where scattering measurements were only made from 1977 – 1991.

            In general, changes in long-range transport patterns dominate the annual cycles of the baseline stations.  For BRW, the high values of CN, ssp, and sap are observed during the arctic haze period when anti-cyclonic activity transports pollution from the lower latitudes of Central Europe and Russia.  A more stable polar front characterizes the summertime meteorology.  High cloud coverage and precipitation scavenging of accumulation mode (0.1-1.0-mm diameter) aerosols account for the annual minima in ssp and sap from June to September.  In contrast, CN values have a secondary maximum in the summer which is thought to be the result of sulfate aerosol production from gas to particle conversion of DMS oxidation products from local oceanic emissions [Radke et al., 1990].  The aerosol single-scattering albedo displays little annual variability and is indicative of highly scattering sulfate and seasalt aerosol.  A September minimum is observed in å when ssp and accumulation mode aerosols are also low but when primary production of coarse mode seasalt aerosols from open water is high [Quinn et al., 2001; Delene and Ogren, 2001].  Quinn et al. [2001] have also shown, based on their chemical analysis of the sub-micrometer aerosol particles at BRW, that sea salt has a dominant role in controlling scattering in the winter; non-sea-salt sulfate is the dominant scatterer in the spring; while both components contribute to scattering in the summer.

            For MLO, the highest ssp and sap values occur in the springtime and result from the long-range transport of pollution and mineral dust from Asia.  However, little seasonality is seen in CN concentrations at MLO, indicating that the smallest particles (<0.1-mm diameter), which usually dominate CN concentration, are not enriched during these long-range transport events.  Both the aerosol ssp and Ångström exponent display seasonal cycles at SPO with a ssp maximum and an å minimum in austral winter associated with the transport of coarse mode seasalt from the Antarctic coast to the interior of the continent.  The summertime peaks in CN and å are associated with fine mode sulfate aerosol and correlate with a seasonal sulfate peak found in the ice core, presumably from coastal biogenic sources [Bergin et al., 1998].  The aerosol extensive properties at SMO display no distinct seasonal variation.  Albedo values above 1.0 evident at BRW and MLO are due to instrument noise at low aerosol concentration.  These high albedo values are not present in daily averaged data.  Furthermore, these high albedo values are not present if data are excluded where ssp is below 1 Mm-1.  Hence, the high albedo values result from an instrument detection limitation problem.

            Based on only 4-7 years of measurements, the annual cycles for the regional stations are less certain than those of the baseline stations.  The proximity of the regional sites to North American pollution sources is apparent in the results, with monthly median values of ssp that are up to two orders of magnitude higher than values from the baseline stations.  The Bondville site (BND), situated in a rural agricultural region, displays an autumn high in sap and a low in wo that coincide with anthropogenic and dust aerosols emitted during the harvest [Delene and Ogren, 2001].  As evident in the lower ssp and sap values, the Southern Great Plains site (SGP) is more remote than BND.  SGP has a similar but less pronounced annual cycle with late summer highs in ssp and sap, and a corresponding minimum in wo [Delene and Ogren, 2001; Sheridan et al., 2001].  Little seasonal variability is observed in aerosol properties at WSA.  Values of å tend to be higher in the summer and likely result from transport of fine mode sulfate aerosol from the continent and lower coarse-mode production of particles with lower summer wind speeds [Delene and Ogren, 2001]. 


3.1.4.   Long-term Trends


          Long-term trends in CN concentration, ssp, sap, wo, and å are plotted in Figures 3.3 and 3.4 for the baseline observatories.  The monthly means are plotted along with a linear trend line fitted to the data.  The aerosol properties at BRW exhibit an annual decrease in ssp of about 2% per year since 1980.  This reduction in aerosol scattering has been attributed to decreased anthropogenic emissions from Europe and Russia [Bodhaine, 1989] and is most apparent during March when the arctic haze effect is largest.  The corresponding decrease in the Ångström exponent over the same time period points to a shift in the aerosol size distribution to a larger fraction of coarse mode seasalt aerosol.  Stone [1997] noted a long-term increase in both surface temperatures and cloud coverage at BRW from 1965-1995 that derives from changing circulation patterns and may account for the reduction in ssp by enhanced scavenging of accumulation mode aerosols.

            In contrast to the reduction in ssp at BRW, CN concentrations, which are most sensitive to particles with diameters <0.1 mm, have increased since 1976.  There is an offset in CN concentration starting in 1998 that corresponds to a change to a new CN sampling inlet.  Similarly the step increases in CN concentration in late 1991 at MLO and 1989 at SPO are due to replacement of the CN counter with a butanol-based instrument with a lower size detection limit.  The reason for the decrease in CN and increase in å at SMO is not readily apparent, but it could stem from changes in long-term circulation patterns.

            Previous reports describing the aerosol data sets include: BRW: Bodhaine [1989, 1995]; Quakenbush and Bodhaine [1986]; Bodhaine and Dutton [1993]; Barrie [1996]; Delene and Ogren [2001]; MLO: Bodhaine [1995]; Delene and Ogren [2001]; SGP: Delene and Ogren [2001]; Sheridan et al. [2001]; Bergin et al. [2000]; SMO: Bodhaine and DeLuisi [1985]; SPO: Bodhaine et al. [1986, 1987, 1992]; Bergin et al. [1998]; WSA: McInnes et al. [1998]; Delene and Ogren [2001].


3.1.5.   Special Studies


Comparison of Aerosol Light Scattering and Absorption Measurements – Mauna Loa, Hawaii

            NOAA’s Climate Monitoring and Diagnostics Lab (CMDL) has measured both aerosol light scattering (since 1974) and aerosol light absorption (since 1990) at Mauna Loa (MLO).  In  Spring 2000 new light scattering and absorption instruments were installed at Mauna Loa (within two meters of the old instruments).  Prior to the upgrade, light scattering was measured with a three wavelength nephelometer (MS Electron, Seattle, Washington), and light absorption was measured using an aethalometer (McGee Scientific, Berkeley, California).  This original sampling system did not include size or relative humidity control of the aerosol sample.  The new instruments are designated MLN (for Mauna Loa New) to distinguish them from the co-located MLO instruments.  The new system obtains measurements at two size cuts by using a valve to switch between a 10-mm and 1-mm impactor.  Relative humidity is maintained at no more than 40% by heating the air sample as necessary.  Table 3.5 lists the instruments and their period of operation at Mauna Loa.

            It is important to assess how measurements from these instruments compare in order to maintain data consistency for the entire measurement period.  The MLO and MLN systems were operated simultaneously for approximately 1 year (Spring 2000 – Spring 2001).  One year of simultaneous light scattering and three months of light absorption (the aethalometer broke in August 2000) measurements from the co-located instruments are compared. 

The comparison procedure was:


  • Select data for the overlap period, May 2000 through April 2001
  • Estimate the absorption coefficient for the aethalometer using:     sap = 10 m2/g * [BC]
  • Apply quality control edit corrections to data sets (e.g., remove spikes and contaminated data)
  • For the MLN data set, use only 10-mm size cut data 
  • Correct the MLN PSAP data for: (a) scattering by particles within the filter and (b) spot size.  Also, remove low transmittance data (e.g., transmittance < 0.5 as per Bond et al. [1999])
  • Calculate hourly averages of scattering and absorption coefficients
  • Determine the least squares linear fit and correlation coefficient for the data sets.  Perform the regression for both a calculated and forced-to-zero y-intercept.


            Figure 3.5 shows that, on an hourly basis, there is excellent correlation between the nephelometer measurements (R2=0.94), and the instruments agree fairly well – the slope is 1.1.  This agreement is also seen when the data are separated for upslope (polluted) and downslope (cleaner) conditions.  Because the two instruments appear to be in good agreement, the old nephelometer was removed during annual maintenance in May 2001.

            The comparison between the PSAP and the aethalometer (Figure 3.6) was over a much shorter time period: May 2000 - early August 2000.  In early August the aethalometer feed sprocket was replaced, and after the replacement, measurements from the two instruments became completely uncorrelated (R2=0.02).  Prior to the feed sprocket replacement, the absorption measurements show that the PSAP measured absorption coefficients ~3x higher than the aethalometer although the instruments were fairly well correlated.  (R2~0.6).   The difference between the two instruments suggests that the assumed BC absorption efficiency of 10 m2 g-1 may not be appropriate for Mauna Loa.  Additional comparisons between the PSAP and aethalometer are planned to resolve this discrepancy.


Determination of the Light Absorption Efficiency of Graphitic Carbon in Indian Ocean Aerosols

            Carbonaceous particles in the atmosphere are generally thought to contain up to two major classes of carbon; these are organic carbon (OC) and black carbon (BC, also called elemental carbon, EC).  Visible light absorption by atmospheric aerosols is typically dominated by particles containing BC.  The mass measurement of BC in atmospheric aerosol samples has most often been performed by thermal evolved gas analysis [e.g., Cachier et al., 1989] or thermal optical reflectance (TOR) methods [e.g., Chow et al., 1993].  The light absorption coefficient (sap) of aerosol samples has been determined using optical (usually filter-based) instruments, such as the aethalometer [e.g., Bodhaine, 1995], the Particle Soot Absorption Photometer (PSAP) [e.g., Bond et al., 1999], or photoacoustic techniques [e.g., Arnott et al., 1999].  All of these measurement techniques depend directly on the amount of aerosol sampled for the analysis, and all have associated measurement uncertainties, artifacts, interferences, and other problems that must be taken into account.

            A measure of the efficiency with which atmospheric aerosols absorb visible radiation is desirable for model inputs and other applications.  The aerosol light absorption efficiency, a, is defined as:


                                                                           a = sap / mC                                                          (1)


where sap is measured in Mm-1, and mC is the mass concentration of absorbing carbon in mg m-3.  The parameter a has also been called the specific attenuation cross-section, the specific absorption coefficient, and the BC mass absorption coefficient in previous studies.  Over the past two decades, reported values of a have ranged from ~1 to 25 m2 g-1.  Most of the variability in a has been attributed to differences in aerosol composition, shape, size, mixing state (i.e., internal vs. external), and amount of scattering material present.  The challenge for researchers is to determine what portion of the variability in a is caused by instrument uncertainty or differences in measurement methods and what portion is due to real differences in the studied aerosols.

            In this study we report on a new method of determining the a of graphitic carbon (GC), a specific and dominant component of BC.  We have used the PSAP instrument to obtain the sap of aerosol samples collected during the Indian Ocean Experiment (INDOEX) in early 1999.  The PSAP is a filter-based instrument in which aerosol particles are continuously deposited onto a filter.  A transmittance measurement is made through the particle deposit and simultaneously compared with an identical measurement through a second, particle-free filter.  Through careful calibration of the raw transmittance signals against derived absorption from an extinction cell – nephelometer system [Bond et al., 1999], a measurement of suspended-state aerosol absorption is obtained.

            For the analysis of GC mass, we collaborated with colleagues at the Institute for Tropospheric Research (IFT) in Leipzig, Germany.  The Raman instrument used in this study to quantify GC mass was a Bruker IFS 55 spectrometer equipped with a FRA-106 Raman module in a backscatter configuration.  Calibration of the Raman spectrometer for GC mass on the PSAP filters was performed as documented in Mertes et al. [2001] with a commercially available carbon black standard (Monarch 71, Cabot Corporation, Boston, Massachusetts).  The atmospheric concentration of GC can then be calculated from the GC filter mass by using the volume of air sampled through the PSAP filters.

            Our investigation has two advantages over previous studies.  First, the GC responsible for light absorption and not the thermographic EC is used.  Thus, a quantifiable, dominant component of light absorbing carbon is obtained.  Second, the correlation between sap and mC is analyzed from identical aerosol samples (i.e., the same particles), so any variability between samples does not come from examining different populations of particles in the two analyses.

            The INDOEX Intensive Field Phase (IFP) was conducted during February and March 1999.  The site for our measurements was the Kaashidhoo Climate Observatory (KCO), on the island of Kaashidhoo in the Republic of Maldives.  During this period of the winter monsoon, dry winds from the northeast sweep over the area.  These winds bring very polluted air from India and Southeast Asia over the Maldives.  The INDOEX aerosols have been found to contain significant fractions of BC [Anderson et al., 2001]; probable sources of these aerosols include biomass and biofuel burning, vehicle exhaust, industrial releases, and other anthropogenic emissions.

            Figure 3.7 shows a time series of sap plotted alongside GC concentration.  Overall, there was good agreement between the two parameters over the course of the IFP.  The initial portion of the experiment (through DOY 70) was a period of higher aerosol concentrations at KCO punctuated by two rainfall events that removed aerosols from the atmosphere.  From DOY 71 through nearly the end of the IFP, light absorption coefficients were relatively lower, but still several times larger than at typical rural, midcontinental sites in the US [Delene and Ogren, 2001].  In Figure 3.7, each symbol represents the analysis of one PSAP filter.  When high concentrations of aerosols were present, filters were changed up to five times per day to prevent the PSAP filter transmittance measurement from dropping below 0.5.  During cleaner periods (e.g., the rainfall events), filters were changed as infrequently as once per day.

            Figure 3.8 shows sap plotted against GC.  In this analysis, we have grouped the PSAP filters according to their filter loading and have removed one obvious outlier from the “Medium Loading” category.  The PSAP manufacturer’s manual states that absorption measurements should be reliable as long as the filter transmittance (Tr, or I/I0) remains above 0.5.  The calibrations recommended by Bond et al. [1999], however, were performed using PSAPs with Tr values maintained above 0.7, so this grouping of lightly loaded filters is the only one in which the Bond et al. [1999] PSAP calibration corrections can be applied reliably.  The slope of the linear least squares regression line through the lightly loaded filters equates to an a of 9.7 m2 g-1, with a very small offset and an R2 value of 0.86.  The filters with higher GC loadings show regression slopes between roughly 9.0 and 10.4, but with significant y-intercepts.  These zero offsets may be due to uncertainties in the nonlinear filter loading corrections used in the PSAP instrument.  While GC currently is thought to be the major aerosol absorber of visible radiation, other forms of carbonaceous material (e.g., amorphous C, organic C) need to be studied in a similar manner to quantify their contributions to visible light absorption by aerosols.


In Situ Aerosol Profile Measurements over the Southern Great Plains CART Site

            The objective of this project is to obtain a statistically significant data set of in-situ measurements of the vertical distribution of aerosol properties (e.g., light scattering and absorption). The measurements will be used to answer the following scientific questions:


  • How do aerosol properties vary throughout the year?
  • Under what conditions can surface-based measurements of these properties be used to calculate the direct aerosol radiative forcing from a measured aerosol optical depth? (and what conditions inhibit such calculations?)
  • How do local and regional perturbations (e.g., fires) influence the vertical profile characteristics?
  • How do data from these flights compare with other measurements of atmospheric characteristics (e.g., aerosol optical depth (AOD))?


The data are obtained by flying an instrumented light aircraft (Cessna C-172N) over the Southern Great Plains (SGP) site in Lamont, Oklahoma.  The aerosol instrument package on the aircraft is similar to the one operating at the surface SGP site.  The aircraft flew 195 profile flights between March 2000 and September 2001.  For each profile flight, the Cessna flew nine level legs over (or near) the SGP site.  The legs were flown at altitudes of 467, 610, 915, 1220, 1525, 1830, 2440, 3050, and 3660 m above sea level, and flights were made several times each week.  Aerosol data were collected at 1 Hz and averaged over the duration of each level flight segment (~10-min averages for the 4 highest levels, ~5-min averages for the lowest 5 levels).  Aerosol optical properties obtained for each level leg include ssp, sbsp, and sap coefficients.  From these extensive properties the following intensive aerosol optical properties were derived: wo, b, and å.  All measurements on the aircraft were made at low humidity (RH < 40 %).

            There is good agreement (R2 = 0.82) between lowest level leg and surface extinction
(sext = sap + ssp) values, indicating submicrometer aerosol (predominantly scattering aerosol) in the 150 m above the surface is well-mixed.  Much of the variability in the parameters measured at the surface site appears to be captured by the weekly profiling flights. The comparison is not as good (R2 = 0.49) for single-scattering albedo, which is due to measured differences in absorption between the surface and the lowest flight level. These observed differences appear to be real because side-by-side tests of the two particle soot absorption photometers show good agreement (within 8%).  Comparison of other derived properties at the lowest flight level with surface properties are excellent for backscatter fraction and green-blue Ångström exponent, but less so for the green-red Ångström exponent. 

            Figure 3.9 shows the medians and ranges (as indicated by percentiles) of a representative extensive aerosol optical property (extinction) and a representative intensive aerosol optical property (single-scattering albedo) at STP, low RH, and Dp < 1 mm, obtained at the surface and during vertical profiling flights.  The line in the center of the box represents the median, while the edges of the box give the 25th and 75th percentiles, and the whiskers extend to the 5th and 95th percentiles.  The values for extensive properties (extinction, absorption, and scattering coefficients) vary by up to a factor of 3, while the medians for intensive aerosol properties (single scattering albedo, backscatter fraction, Ångström exponent) are much less variable (less than 10% variation).  Figure 3.9 suggests that the median values of the extensive properties tend to decrease with altitude from the surface upward.  Such behavior is expected as distance from the ground-based sources of aerosol particles increases.  The median values of the intensive properties do not display a strong dependence with altitude.


            More indicative of the overall variability of the aerosol are the ranges of the parameters as indicated by the percentiles in Figure 3.9.  Extensive properties can differ by up to two orders of magnitude between flights and even between individual levels of the same flight.  The intensive properties, while still displaying a range of values, vary at most by an order of magnitude (i.e., green-red Ångström exponent, level 3660 m) but more commonly by less than a factor of two.  The parameter ranges display different tendencies with height.  Extensive properties become less variable at higher altitudes, due to consistently low concentrations of aerosol particles.  Conversely, intensive properties become more variable with altitude for a similar reason: low concentrations of aerosol particles result in more noise when calculating the values of these parameters.

            The surface measurements are representative of the frequency distributions aloft, particularly for intensive properties such as albedo (Figure 3.9).  However, the correlations between column average and surface values (not shown) are lower (e.g., extinction R2 = 0.65; albedo R2 = 0.30) than correlations between lowest flight level and surface values.  Thus, while surface aerosol measurements are statistically representative of the air aloft, they may not be representative of day-to-day variations in the column.

            Measurements from the profile flights can also be compared with measurements by remote sensing instruments located at SGP (i.e., the Cimel sun/sky radiometer and the multi-filter rotating shadowband radiometer (MFRSR)).  After incorporating corrections for supermicrometer, upper tropospheric, and stratospheric aerosol particles, comparison of aerosol optical depth (AOD) (Figure 3.10) calculated from aircraft measurements with AOD obtained from the remote sensing instruments shows fair correlation (R2 ~ 0.5 Cimel, R2 ~ 0.8 MFRSR), although the aircraft AODs are lower than those derived from the radiation instruments, with an offset in the range of -0.03 or -0.04.

            Long-term surface measurements can represent statistical distribution of aerosol properties aloft.  However, day-to-day variability between the surface and aloft may not always be captured and causes a poor relationship between surface and column average quantities.  Comparison of the in-situ and remote sensing instruments shows fair correlation for AOD although the aircraft AOD is consistently lower than that of the remote sensing instruments.


Measurements of Aerosol Optical Properties From a Surface Site in Korea (ACE-Asia)

            An intensive field campaign known as the Aerosol Characterization Experiment (ACE-Asia) took place in April 2001 in eastern Asia. The goal of ACE-Asia was to gather data for regional climate models as well as to understand better the interactions between aerosols and radiation, gas phase species, transport, and clouds.

            This large field study was a multi-platform, international effort with scientific measurements being recorded from land-based sites, ships, aircraft, and satellites.  Scientists from Korea, United States, Great Britain, China, Japan, and Australia worked together to characterize aerosol radiative, chemical, and size properties.  These aerosol properties, as well as their covariance, strongly influence the Earth’s radiative balance.  The primary surface site was located at the Upper Air Meteorological Observation Facility near the small town of Kosan on Cheju Island, South Korea.  The site is strategically located between several major aerosol source regions.  Depending on wind conditions, the scientists observed either anthropogenic aerosol from Japan, Korea, and China, large dust outbreaks from northern China, relatively clean marine air, or even local crop burning.

            As part of this effort, CMDL conducted in situ measurements of the aerosol optical properties as well as full column measurements of the solar radiation at Kosan, South Korea. The ground-based measurements included aerosol scattering coefficient as a function of particle size, wavelength, and relative humidity and the aerosol absorption coefficient as a function of size.  These observations provide a direct measure of the surface aerosol extinction of visible radiation. For full column measurements of the atmosphere, radiometers from CMDL measured the total, direct, and diffuse (scattered light by aerosol) solar radiation. These observations can be used to derive the AOD or amount of solar radiation attenuated by aerosols and the aerosol forcing efficiency.

            The aerosol scattering coefficient was highly variable during the campaign, ranging between 20 and 250 Mm-1.  Spring in Korea is known as the dust season when southeasterly winds bring dust to the region from the Gobi Desert.  Several such events (note particularly DOY 101-104 and DOY 110) are apparent from the data (Figure 3.11).  On these days over 60% of the aerosol scattering was in the total size mode as indicated by the low values of the ratio of the submicrometer to total aerosol scattering coefficients (Figure 3.11c).  The aerosol single- scattering albedo during the dust events declined slightly to ~0.80 for total aerosol and as low as 0.63 for submicrometer aerosol.  Most of the aerosol absorption during the campaign was in the submicrometer particles. The aerosol hygroscopic growth factor (f(RH)= ssp(RH=85)/ssp(RH=40)), a measure of the increase in scattering due to aerosol water uptake, was relatively high during the dust events, ranging from 1.5 to 2.5. The low single-scattering albedo and high hygroscopic growth factor indicate the aerosol at the site was composed of not only dust but also likely had absorbing elemental and hygroscopic species such as sulfate, oxidized organics, and sea salt. 

            Mean aerosol optical properties from Kosan as well as from two other anthropogenically-perturbed sites are given in Table 3.6. Although all three sites receive anthropogenic aerosol, there are significant differences in the aerosol optical properties between the sites, demonstrating the importance of long term, regional measurements at a variety of locations.  Kosan, South Korea, and Kaashidhoo, Republic of Maldives are anthropogenically perturbed marine sites, and Bondville, Illinois, is an anthropogenically perturbed continental site. The aerosol scattering coefficient from Kosan is higher than either the Kaashidhoo or the Bondville site, indicating a high aerosol loading in eastern Asia during the spring.  Kosan and Kaashidhoo have higher light absorption coefficients than Bondville, consistent with a larger contribution of combustion aerosol (e.g., from biomass burning or limited pollution control on vehicles and industries).  In keeping with the higher absorption coefficients at Kosan and Kaashidhoo, those sites have lower single-scattering albedo values than Bondville, with Kaashidhoo being significantly lower than Kosan.  The f(RH) value at Kosan was significantly higher than at Kaashidhoo or Bondville.  The f(RH) at Kosan did not correlate well with sea salt aerosol, but was likely high due to extensive mixing with polluted air masses containing sulfates and nitrates from both China and Korea as well as cloud processing.  At Kaashidhoo the aerosol was more reflective of the source emissions, which were high in black carbon and, periodically, dust and had undergone little cloud processing.  The strong inversion layer present during the INDOEX sampling at Kaashidhoo prevented significant processing of the aerosol in the boundary layer, whereas the frontal systems present during ACE-Asia promoted strong vertical mixing between the boundary layer and lower free troposphere and hence faster aerosol cloud processing.  The similarity of the f(RH) values at KCO and BND is likely coincidental and not from a similar aerosol composition.  Finally, at all three sites, the absorbing aerosol was concentrated in the submicron aerosol, as suggested by the high values of the submicrometer fraction of absorbing aerosol (Table 3.6).  However, Kosan and Kaashidhoo had lower values of the submicrometer fraction of scattering aerosol than Bondville, likely due to the presence of sea salt and/or dust at those two sites.


The sunphotometers give a measure of the direct solar irradiance in seven narrow band wavelength channels.  Preliminary data from these instruments (screened for clouds) give a measure of the AOD. On days with the highest pollution, the AOD for 500-nm radiation was as high as 0.7, and on days with clean marine air the value dropped to a low of 0.1.  During the major dust event on DOYs 101 to 104 the AOD was 0.45 to 0.7, indicating relatively low sunlight levels from a heavy loading of aerosol.  Figure 3.12 shows both the AOD at 500 nm and the aerosol Ångström exponent, a measure of the aerosol size, for a 3-month period from March 31 to July 9, 2001.


Systematic Variation of Aerosol Optical Properties

            Aerosol optical properties measured over several years at BND, SGP, WSA, and BRW have been analyzed to determine the importance of the variability in aerosol optical properties to direct aerosol radiative forcing calculations and to investigate whether  systematic relationships exist between various aerosol optical properties (sap, wo, b, å, and aerosol radiative forcing (DF/d)) and the amount of aerosol present (measured by ssp).  Systematic relationships among aerosol properties can be used to check for consistency among measured and modeled climatologies.  Also, systematic relationships can be used in model parameterization to reduce uncertainties resulting from insufficient knowledge of aerosol properties.  The performance of different models can be evaluated with measurements, and if models predict the parameters that are observed, the measurements can be used to validate the models.

            Knowledge concerning systematic relationships among aerosol properties can be useful in reducing uncertainties in remotely sensed data because they can be used to make better assumptions about unknown aerosol properties.  Remer and Kaufman [1998] illustrated the importance of using a dynamic model where aerosol properties vary with aerosol load for the inversion of remote sensing data.  The reason for using a dynamic aerosol model is to represent systematic changes in one aerosol property as another property changes.  Due to their importance and usefulness, systematic relationships among aerosol optical properties were investigated at the four surface sites.  Figure 3.13 shows systematic relationships among low relative humidity (<40%) aerosol optical properties (sap, wo, b, and DF/d) and the aerosol load (measured as ssp at 550-nm wavelength).  The mean aerosol optical properties were calculated over ssp intervals of 10 Mm-1 with the corresponding maximum standard error for each station given in the graph.  The standard errors are considerably smaller than the changes over 10-Mm-1 bins, which implies a high level of significance to the relationships.  The sap does not increase as rapidly as the ssp, resulting in a systematic increase in wo as ssp increases at all four stations.  All four stations also show a systematic decrease in b as ssp increases.  In terms of DF/d, the relationship between b and ssp acts to offset the relationship between wo and ssp.  The b-ssp relationship results in a decrease in the magnitude of the DF/d as ssp increases, while the wo-ssp relationship results in an increase in the magnitude of the DF/d as ssp increases.  For SGP and WSA, the b-ssp relationship is more important and results in a decrease in the magnitude of DF/d as ssp increases.  For BND, the wo-ssp relationship is more important for ssp less than 40 Mm-1 and results in an increase in the magnitude of DF/d as ssp increases; however above 40 Mm-1 b-ssp relationship is more important and results in a decrease in the magnitude of DF/d as ssp increases.  Similarly at BRW, the relative importance of one relationship compared to the other determines how DF/d changes as ssp increases.

            Figure 3.14 illustrates two systematic relationships, one between å and ssp and the other between Rsp and å.  The strong relationship between Rsp and å indicates that the å is sensitive to changes in the relative amount of submicrometer scattering aerosol.  At BND and SGP there is a drop in å as ssp drops below 30 Mm-1, while at WSA and BRW the å increases as the ssp drops below 30 Mm-1.  Above 30 Mm-1 all four stations show a fairly constant å with increasing ssp.  This systematic relationship suggests that during low aerosol concentration events, the continental sites (BND and SGP) have more relatively larger particles present, while the marine sites (WSA and BRW) have more relatively smaller particles present.  The å-ssp relationship at BND and SGP (Figure 3.14a) is consistent with the relationship between the Ångström exponent and aerosol optical thickness (derived from Sun/sky scanning spectral radiometer measurements) for the mid-Atlantic region of the eastern United States [Remer and Kaufman, 1998].  It is interesting to note that while the å-ssp relationship is different between SGP and WSA, the DF/d-ssp relationship is very similar.  This suggests that the DF/d-ssp relationship is not influenced by changes in the submicrometer to total aerosol fraction but rather by changes in the submicrometer aerosol size distribution (b-ssp relationship) and the aerosol composition (å-ssp relationship).

            Systematic relationships exist between various aerosol properties (sap, wo, b, DF/d, and å) and ssp and also between å and Rsp.  These systematic relationships are qualitatively similar among the four stations; however, the quantitative relationships are different at each station, which is indicative of the occurrence of different aerosol types and size distributions at each station.  Systematic relationships and the regional, yearly, weekly, and daily variations in optical properties can be used to check for consistency between climatologies based either on observations or models.  The existence of systematic changes in aerosol optical properties with changes in aerosol concentration indicate that care should be taken when using average values in algorithms to retrieve aerosol properties, such as optical depth, from satellite data.  An algorithm that uses a static representation for aerosol optical properties will have a systematic bias in derived values. 


3.1.6   References


Anderson, J.R., P. Crozier, and S. Howell, Electron microscopy of sulfate particles north and south of the ITCZ during INDOEX, J. Geophys. Res., in revision, 2001. 

Arnott, W.P., H. Moosmüller, C.F. Rogers, T. Jin, and R. Bruch, Photoacoustic spectrometer for measuring light absorption by aerosol: Instrument description, Atmos. Environ., 33, 2845-2852, 1999. 

Barrie, L. A., Occurrence and trends of pollution in the Arctic troposphere, in Chemical Exchange Between the Atmosphere and Snow, Edited by E. W. Wolff and R. C. Bales, Springer-Verlag, Berlin, 1996. 

Bergin, M.H., R.S. Halthorne, S.E. Schwartz, J.A. Ogren, and S. Nemesure, Comparison of aerosol column properties based on nephelometer and radiometer measurements at the SGP ARM site, J. Geophys. Res., 105, 6807-6818, 2000. 

Bergin, M.H., E. Meyerson, J.E. Dibb, and P. Mayewski, Comparison of continuous aerosol measurements and ice core chemistry over a 10 year period at the South Pole, Geophys. Res. Lett., 25, 1189-1192, 1998.

Bodhaine, B. A., Barrow surface aerosol: 1976-1987, Atmos. Environ., 23(11), 2357-2369, 1989.

Bodhaine, B. A., Aerosol absorption measurements at Barrow, Mauna Loa and South Pole, J. Geophys. Res., 100, 8967-8975, 1995.

Bodhaine, B. A., and E. G. Dutton, A long-term decrease in Arctic Haze at Barrow, Alaska, Geophys. Res. Lett., 20, 947-950, 1993.

Bodhaine, B. A. and J.J. DeLuisi,  An aerosol climatology of Samoa, J. Atmos. Chem., 3, 107-122, 1985.

Bodhaine, B. A., J. J. DeLuisi, J. M. Harris, P. Houmere, and S. Bauman, Aerosol measurements at the South Pole, Tellus, 38B, 223-235, 1986.

Bodhaine, B. A., J.J. De Luisi, J. M. Harris, P. Houmere, and S. Bauman, PIXE analysis of South Pole aerosol, in Nuclear Instruments and Methods in Physics Research, B22, 241-247, Elsevier, Holland, 1987.

Bodhaine, B.A., J.M. Harris, and J.A. Ogren, Aerosol optical properties at Mauna Loa Obseratory: Long-range transport from Kuwait?, Geophys. Res. Lett., 19, 581-584, 1992.

Bond, T.C., T.L. Anderson and D. Campbell, Calibration and intercomparison of filter-based measurements of visible light absorption by aerosols, Aerosol Sci. Technol., 30, 582-600, 1999.

Cachier, H., M.P. Brémond, and P. Buat-Ménard, Determination of atmospheric soot carbon with a simple thermal method, Tellus, 41B, 379-390, 1989.

Charlson, R.J., S.E. Schwartz, J.M. Hales, R.D. Cess, J.A. Coakley, Jr., J.E. Hansen, and D.J. Hofmann, Climate forcing by anthropogenic aerosols, Science, 255, 423-430, 1992.

Chow, J.C., J.G. Watson, L.C. Pritchett, W.R. Pierson, C.A. Frazier, and R.G. Purcell, The DRI thermal/optical reflectance carbon analysis system: Description, evaluation and applications in US air quality studies, Atmos. Environ, Part A, 27, 1185-1201, 1993.

Delene, D.J. and J.A. Ogren, Variability of aerosol optical properties at four North American surface monitoring sites, J. Atmos. Sci., in press, 2002.

McInnes, L.M., M.H. Bergin, J.A. Ogren, and S.E. Schwartz, Differences in hygroscopic growth between marine and anthropogenic aerosols, Geophys. Res. Lett., 25, 513-516, 1998.

Mertes, S., B. Dippel, and A. Schwarzenböck, Comparison of the Particle Soot Absorption Photometer (PSAP) absorption measurement to graphitic carbon quantification on the PSAP internal filters, Aerosol Sci. Technol., in press, 2002.

NRC (National Research Council), Aerosol Radiative Forcing and Climatic Change, National Academy Press, Washington, D.C., 161 pp., 1996.

Ogren, J.A., A systematic approach to in situ observations of aerosol properties, in Aerosol Forcing of Climate, edited by R.J. Charlson and J. Heintzenberg, Wiley & Sons, Ltd., Chicester, UK, 215-226, 1995.

Quakenbush, T. K., and B. A. Bodhaine, Surface aerosols at the Barrow GMCC observatory: Data from 1976 through 1985, NOAA Data Rep. ERL ARL-10, 230 pp., NOAA Air Resources Laboratory, Silver Spring, MD, 1986.

Quinn, P.K., T.L. Miller, T.S. Bates, J.A. Ogren, E. Andrews, and G.E. Shaw, A three-year record of simultaneously measured aerosol chemical and optical properties at Barrow, Alaska, in press J. Geophys. Res., 2001.

Radke, L. F., C.A. Brock, R.J. Ferek, and D.J. Coffman, Summertime Arctic hazes, paper A52B-03 presented at the American Geophysical Union Fall Annual Meeting, San Francisco, December 3-7, 1990.

Remer, L.A. and Y.J. Kaufman, Dynamic aerosol model: Urban/industrial aerosol, J. Geophys. Res., 103, 13,859-1,3871, 1998.

Sheridan, P.J., D.J. Delene, and J.A. Ogren, Four years of continuous surface aerosol measurements from the DOE/ARM Southern Great Plains CART site, J .Geophys Res., 106, 20,735-20,747, 2001.

Stone, R.S., Variations in western Arctic temperatures in response to cloud radiative and synoptic-scale influences, J. Geophys. Res., 102, 21,769-21,776, 1997.





Table 3.1 Data Editing Summary for NOAA Baseline and Regional Stations


Table 3.2. CMDL Baseline Aerosol Monitoring Stations (Status as of December 2001)


Table 3.3. CMDL Regional Aerosol Monitoring Sites (Status as of December 2001)


Table 3.4. Intensive Aerosol Properties Derived From CMDL Network


Table 3.5 Instrument inventory at Mauna Loa Observatory


Table 3.6 Means and standard deviations of aerosol optical properties of anthropogenically-influenced aerosols at 550 nm





Fig. 3.1.  Annual cycles for baseline stations at BRW, MLO, SMO, and SPO showing statistics for condensation nuclei (CN) concentration, total scattering coefficient (ssp), Ångström exponent (å), absorption coefficient (sap) and single scattering albedo (wo). Statistics representing the entire period are given in the last column (ANN), with the horizontal line representing the median value.


Fig. 3.2.  Annual cycles for regional stations at BND, WSA, and SGP showing statistics for annual absorption coefficient (sap), total scattering coefficient (ssp), Ångström exponent (å),) and single-scattering albedo (wo). Statistics representing the entire period are given in the last column (ANN), with the horizontal line representing the median value.


Fig. 3.3.  Long-term trends for baseline stations of monthly averaged condensation nuclei concentration and total scattering coefficient at 550 nm.  A simple linear fit is given for the scattering coefficient but is omitted for the condensation nuclei since instrument changes make a trend line inappropriate.


Fig. 3.4.  Long-term trends for baseline stations of monthly averaged Ångström exponent (550/700 nm), absorption coefficient, and single scattering albedo.  A simple linear fit to the data is shown.


Fig. 3.5.  Comparison of scattering measured by new nephelometer (bspG MLN) with scattering measured by old nephelometer (bspG MLO) for 550 nm wavelength.  The solid black line shows the fit when the y-intercept is forced through the origin; the dashed line is the 1:1 line.


Fig. 3.6.  Comparison of absorption measured by PSAP (bap MLN) with absorption measured by aethalometer (bap MLO). The solid black line shows the fit when the y-intercept is forced through the origin; the dashed line is the 1:1 line.



Fig. 3.7.  Time series of absorption and graphitic carbon measured at KCO during February-March 1999.


Fig. 3.8.  Comparison of GC and absorption coefficient.  The solid dark line is the fit to the heavy loading data; the gray line is the fit to the medium loading data; and the dashed black line is the fit to the light loading data.  One outlier has been removed from the ‘Medium Loading’ data set. 


Fig. 3.9.  Statistical plots of vertical profiles of extinction (left) and albedo (right) over the SGP site.  Black box-whiskers are from aircraft flights. Gray box-whiskers are surface measurements.


Fig. 3.10.  Comparison of AOD measured by aircraft with AOD derived from remote sensing instruments at SGP.


Fig. 3.11.  Time series of aerosol measurements from Kosan during ACE-Asia in 2001: a) total absorption and scattering coefficients, b) submicrometer and total single-scattering albedo, c) submicrometer fraction of aerosol absorption and scattering, and d) total aerosol hygroscopic growth factor.  All values are reported for a wavelength of 550 nm.


Fig. 3.12.  Aerosol optical depth measured at 500 nm and aerosol Ångström exponent from the 412/862-nm wavelength pair.


Fig. 3.13. (a) Mean aerosol light absorption coefficient (sap), (b) single-scattering albedo (wo), (c) hemispheric backscatter fraction (b), and (d) forcing efficiency (DF/d) versus the aerosol light scattering coefficient (ssp) for BND, SGP, WSA, and BRW.  Plots are based on all valid hourly averaged aerosol measurements (>50% 1–min data within the hour) for systems with TSI 3563 nephelometers. The mean values were calculated over 10-Mm-1 ssp bins.  The maximum standard error (sample standard deviation /square root of the number of points) at each station is given in the text boxes.


Fig. 3.14.  (a) Mean angstrom exponent (å) versus aerosol light scattering coefficient (ssp) and (b) sub micrometer scattering fraction (Rsp) versus Ångström exponent for BND, SGP, WSA, and BRW. Plots are based on all valid hourly averaged aerosol measurements (>50% 1–min data within the hour) for systems with TSI 3563 nephelometers.  The å values were calculated over 10-Mm-1 ssp bins and the mean Rsp was calculated from 0.25-Ångström exponent bins.  The maximum standard error (sample standard deviation /square root of the number of points) at each station is given in the text boxes.



TABLE 3.1.   Data-Editing Summary for NOAA Baseline and
Regional Stations



Clean Sector

South Pole


0° < WD < 110°, 330°<WD < 360°



0° < WD < 165°, 285°<WD < 360°

Mauna Loa


90° < WD < 270°



0° < WD < 130°

Sable Island


0° < WD < 35°, 85° < WD < 360°

Southern Great Plains






   a: Manual removal of local contamination spikes;

   b: Automatic removal of data not in clean sector;

   c: Automatic removal of data for low wind speeds;

   d: Removal of data for upslope wind conditions;

   WD: Wind direction.




TABLE 3.2.   CMDL Baseline Aerosol Monitoring Stations (Status as of December 2001)


Baseline Arctic

Baseline Free Troposphere

Baseline Marine

Baseline Antarctic


Point Barrow

Mauna Loa

American Samoa

South Pole
















Elevation (m)





Responsible Institute






Operational, 1976 
   Major upgrade, 1997

Operational, 1974

   Major upgrade, 2000

Operational, 1977

Operational, 1974

Sample RH

RH <40%

RH <40%



Sample Size Fractions

D<1 µm

D<10 µm

D<1 µm

D<10 µm



Optical measurements

ssp(3l), sbsp(3l), sap(1l)

ssp(3l), sbsp(3l), sap(1l), d(6l)




CN concentration

CN concentration

CN concentration

CN concentration

Chemical measurements

Major ions, mass








TABLE 3.3.   CMDL Regional Aerosol Monitoring Sites (Status as of December 2001)


Perturbed Marine

Perturbed Continental

Perturbed Continental


Sable Island, Nova Scotia, Canada

Bondville, Illinois

Lamont, Oklahoma













Elevation (m)




Responsible Institute




Collaborating Institute(s)


University of Illinois, Illinois State Water Survey



Operational, August 1992

   Inactive, April 2000

Operational, July 1994

Operational, July 1996

   Chemistry added, February 2000

Sample RH

RH <40%

RH <40%

RH <40%

Sample size fractions

D<1 µm, D<10 µm

D<1 µm, D<10 µm

D<1 µm, D<10 µm

Optical  measurements

ssp(3l), sbsp(3l) sap(1l)

ssp(3l), sbsp(3l), sap(1l)

ssp(3l),sbsp(3l), sap(1l),  d(7l)


CN concentration

CN concentration

CN, n(D) concentration

Chemical measurements

Major ions, mass

Major ions, mass

Major ions, mass






TABLE 3.4.   Intensive Aerosol Properties Derived From CMDL Network




The Ångström exponent, defined by the power-law sspµl-å, describes the wavelength-dependence of scattered light.  In the figures below, å is calculated from measurements at 550 and 700 nm wavelengths.  Situations where the scattering is dominated by submicrometer particles typically have values around 2, while values close to 0 occur when the scattering is dominated by particles larger than a few microns in diameter. 



The aerosol single-scattering albedo, defined as ssp/(sap + ssp), describes the relative contributions of scattering and absorption to the total light extinction.  Purely scattering aerosols (e.g., sulfuric acid) have values of 1, while very strong absorbers (e.g., elemental carbon) have values around 0.3. 


g, b

Radiative transfer models commonly require one of two integral properties of the angular distribution of scattered light (phase function):  the asymmetry factor g or the hemispheric backscatter fraction b.  The asymmetry factor is the cosine-weighted average of the phase function, ranging from a value of -1 for entirely backscattered light to +1 for entirely forward-scattered light.  The hemispheric backscatter fraction b is defined as sbsp/ssp. 



The hygroscopic growth factor, defined as ssp(RH=85)/ssp(RH=40), describes the humidity dependence of scattering on relative humidity (RH).



The mass scattering efficiency for species i, defined as the slope of the linear regression line relating ssp and the mass concentration of the chemical species, is used in chemical transport models to evaluate the radiative effects of each chemical species predicted by the
model.  This parameter has typical units of m
2 g-1. 



TABLE 3.5.   Instrument Inventory at Mauna Loa Observatory


Period of Operation



MRI nephelometer

1974 - Spring 2000


4 wavelengths, no size cut

MRI nephelometer (operating at 1 wavelength due to instrument problems)

Summer 1982 - Spring 1984


1 wavelength (550 nm), no size cut

MS Electron nephelometer

Spring 1994 - Spring 2001


3 wavelengths, no size cut

Magee Scientific aethalometer

1990 - present


Broadband, no size cut

Specific absorption of 10 m2/g used to convert [BC] to sap

TSI nephelometer

(model number 3563)

Spring 2000 - present


3 wavelengths, total and back scatter, 1-and 10-mm size cuts

Radiance Research particle soot absorption photometer (PSAP)

Spring 2000 - present


565-nm wavelength, 1-and 10- mm size cuts



TABLE 3.6.   Means and Standard Deviations of Aerosol Optical Properties of Anthropogenically Influenced Aerosols at 550 nm.  KOS data for April, 2001; KCO data for mid-February-March, 1999; BND data for 2000










92 (53)

73 (28)

54 (43)


12 (8)

16 (9)

4 (3)


0.87 (0.05)

0.82 (0.03)

0.92 (0.06)


2.2 (0.5)

1.7 (0.1)

1.7 (0.4)b


0.61 (0.16 )

0.67 (0.08)

0.86 (0.09)


0.83 (0.09)

0.84 (0.06)

0.92 (0.38)

     aValues are for total (Dp <10 mm) aerosol.

     bRood, personal  communication[2002]

     cFssp and Fsap are the submicrometer fractions of aerosol scattering and absorption, respectively.







Figure 3.1




Figure 3.2

Figure 3.3


Figure 3.4





Fig. 3.5





Fig. 3.6





Fig. 3.7. 







Fig. 3.8. 


Fig. 3.9.   





Fig. 3.10






Fig. 3.11a. 




Fig. 3.11b. 



Fig. 3.11c





Fig. 3.11d





Fig. 3.12






Fig. 3.13a


Fig. 3.13b






Fig. 3.13c




Fig. 3.13d



Fig. 3.14a


Fig. 3.14b