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2003-08-30 Global and Local Dust over N. America Observations and Analysis Tools
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2003-08-30 Global and Local Dust over N. America Observations and Analysis Tools

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    2003-08-30 Global and Local Dust over N. America Observations and Analysis Tools 2003-08-30 Global and Local Dust over N. America Observations and Analysis Tools Presentation Transcript

    • Global and Local Dust over N. America Observations and Analysis Tools Rudolf Husar CAPITA , Washington University , St. Louis Presented at: Second Workshop on Mineral Dust Paris, France, September 10-12, 2003 Sahara Local Sahara Local
    • Global Aerosol: Dominance of Dust, Smoke & Some Sulfate Note: Each satellite senses different aerosol parameter, indicates different pattern
      • UV Absorption
      • Elevated Layer
      Herman et. al., 1997 TOMS POLDER MISR Polarization Small Particles Backscattering Scattering Particles Jun, Jul, Aug Duze et. al., 1997
    • Aerosols: Many Dimensions
      • Compared to gases (X, Y, Z, T) , the aerosol system has four extra dimensions (D, C, F, M).
        • Spatial dimensions X, Y Satellites, dense networks
        • Height Z Lidar, soundings
        • Time T Continuous monitoring
        • Particle size D Size-segregated sampling
        • Particle Composition C Speciated analysis
        • Particle Shape/Form F Microscopy
        • Ext/Internal Mixture M Microscopy
      • Bad News: The mere characterization requires many tools.
      • Some tools sample a small subset of the xDim aerosol data space
      • These need extrapolation, e.g. single particle analysis
      • Other tools get integral measures of several dimensions
      • These require de-convolution of the integral, e.g. satellite sensors
      Satellite-Integral
    • Aerosols: Opportunity and Challenge
      • Good news: The aerosol system is self-describing .
        • Once the aerosol is characterized (size-composition, shape) and
        • Spatio-temporal pattern are established,
        • => The aerosol system describes much of its history through the properties and pattern, e.g source type (dust, smoke, haze), formation mechanisms, atmospheric interactions. and transformations.
        • The ‘aerosol’ dimensions (D, C, F, M) are most useful for establishing the sources and effects, including some of the processes .
        • The Source of can be considered an additional, ‘derived’ aerosol dimension.
      • Analysts challenge: Deciphering the handwriting contained in the data
        • Chemical fingerprinting/source apportionment
        • Meteorological transport analysis
        • Multidimensional data extrapolation, de-convolution and fusion
    • Dust Particle Size and Shape
      • Near-Source dust mass mean diameter (MMD) is over 5-10  m, virtually all in the coarse mode
      • Long range transported dust (3-10 days old) has MMD of 2-5  m, 30-50% of the mass in the PM2.5 range
    • Atmospheric Residence Time of Dust
      • Coarse dust particles with 10 and and 100  m size, settle out within 1 day and 15 minutes, respectively.
      • Fine dust particles are removed by clouds and rain
      PM2.5 Residence Time Increase with Height
      • Within the atmospheric boundary layer (the lowest 1-2 km), the residence time is 3-5 days.
      • Dust lifted to 3-10 km is transported for weeks over thousands of miles.
      Residence Time in the Atmosphere (Jaenicke, 1978) 1  m ~ 15 days 100  m ~ 15 min 10  m ~ 1 day
    • Local, Sahara and Gobi Dust over N. America
      • The dust over N. America originates from local sources as well as from the Sahara and Gobi Deserts
      • Each dust source region has distinct chemical signature in the crustal elements.
    • Attribution of Fine Dust (<2.5  m) Local and Sahara
      • In Florida, virtually all the Fine Particle Dust appears to originate from Sahara throughout the year
      • At other sites over the Southeast, Sahara dominates in July
      • The Spring and Fall dust is evidently of local origin
      The two dust peeks at Big Bend have different Al/Si ratios During the year, Al/Si = 0.4 In July, Al/Si reaches 0.55, closer to the Al/Si of the Sahara dust (0.65-0.7) The spring peak is identified as as ‘Local Dust’, while the July peak is dominated by Sahara dust.
    • Seasonal Fine Aerosol Composition, E. US Upper Buffalo Smoky Mtn Everglades, FL Big Bend, TX
    • Seasonal and Secular Trends of Sahara Dust over the US
      • Seasonally, dust peaks sharply in July when the Sahara plume swings into the Caribbean.
      Regional Sahara Dust events occur several times each summer
    • Sahara and Local Dust Apportionment: Annual and July
      • The maximum annual Sahara dust contribution is about 1  g.m 3
      • In Florida, the local and Sahara dust contributions are about equal but at Big Bend, the Sahara contribution is < 25%.
      The Sahara and Local dust was apportioned based on their respective source profiles.
      • In July the Sahara dust contributions are 4-8  g.m 3
      • Throughout the Southeast, the Sahara dust exceeds the local source contributions by w wide margin (factor of 2-4)
      Annual July
    • Supporting Evidence: Transport Analysis Satellite data (e.g. SeaWiFS) show Sahara Dust reaching Gulf of Mexico and entering the continent. The air masses arrive to Big Bend, TX form the east (July) and from the west (April)
    • Sahara PM10 Events over Eastern US
      • The highest July, Eastern US, 90 th percentile PM10 occurs over the Gulf Coast ( > 80 ug/m3)
      • Sahara dust is the dominant contributor to peak July PM10 levels.
      Much previous work by Prospero, Cahill, Malm, Scanning the AIRS PM10 and IMPROVE chemical databases several regional-scale PM10 episodes over the Gulf Coast (> 80 ug/m3) that can be attributed to Sahara. June 30, 1993 July 5, 1992 June 21 1997
    • Sahara Dust Passage over the EUS (Poirot, 2003) Dirty dust composition based on Positive Matrix Factorization, PMF
      • At Brigantine, NJ, dust composition is enriched by SO4 (30% dirty dust mass) and NO3 (8%)
      ‘ Dirty’ dust and salt composition
    • Direction of Dust Origin at 5 IMPROVE Sites Ad hoc Data Processing Value Chain High ‘dust’ concentration at 5 sites indicate the same airmass pathway from the tropical Atlantic Weather Serv. Upper Air Data NOAA ARL ATAD ATAD Traject Gebhart (2002) NPS-CIRA IMPROVEData PMF Tool Pareto (2001) PMF “Sources” Coutant (2002) CATT Tool Husar (2003) Aggregation Poirot (2003)
    • Global Scale Dust Transport: The April 1998 Asian Dust Event Location of the April 19 dust cloud over the Pacific Ocean based on daily SeaWiFS, GMS5/GOES9/GOES10 and TOMS satellite data. The April 1998 Asian dust event caused 2-3 times higher dust concentrations then any other event during 1988-1998
    • Decomposition of Spectral Reflectance: Dust, Haze, Sea Water
      • The shape of dust, haze and sea reflectance are measured
      • The total spectral reflectance is measured
      • The dust, haze and sea reflectances are scaled to fit the total refl.
    • Topography: Sahara Dust Near the Surface
      • SeaWiFS shows a dense dust layer emanating from W. Africa
      3D View 3D view shows that shallow islands are submerged in dust, while high islands extrude from the ~1 km deep dust layer
    • In West-Central Africa, winter haze is surface- based; summer haze is elevated
      • In Jan-Feb the horizontal (Bext) extinction and vertical optical thickness (AOT) are correlated.
      • This implies that the haze is surface-based and has a scale-height of of about 1 km.
      • In Jun-Jul, the Bext is below detection limit, while the AOT is the same as in Jan-Feb.
      • Evidently, the summer dust layer is elevated while the surface layer is dust-free.
      Based on AERONET Sun Photometer Network, NOAA SOD Visibility data
    • Challenge: Putting together the BIG PICTURE
      • ‘ Increasing amount of satellite-derived information about air, land and sea are helping scientists to study how they are interconnected to form a finely balanced system.’
      • National Geographic, Oct 2000
      • Will we be able to put together the Big Picture?
    • SUMMARY
      • The atmospheric dust system occupies at least 8 key dimensions
      • g (x, y, x, t, size, comp, shape, mixture)
      • The current observational revolution (satellites, surface networks) allows monitoring many aspects of the global daily aerosol pattern and transport.
      • Each sensor/system measures different aspects of aerosols, usually resolving some and integrating over other dimensions.
      • Data from multiple sensors/systems (satellites AND surface) along with models are required to characterize the 8D system and to derive actionable knowledge.
      • Current data and analysis tools allow the estimation of transcontinental transport of dust to N. America.
      • The yearly average fine (<2.5 um) Sahara dust concentration over the SE US is 0.2 – 1 ug/m3, with July peak concentration of 2-6 ug/m3.
      • During specific transcontinental dust transport episodes from Africa and Asia, the globally transported surface dust concentrations approach 50-100 ug.m3 over 1000 km - scale regions of North America.
      • These events constitute significant perturbations to the aerosol pattern of North America.
    • SUMMARY: New Opportunities
      • We are in the midst of a sensory revolution regarding the detection of global aerosol sources, transport and some of the effects. Satellite and surface network provide daily pattern of aerosol.
      • Still, the available aerosol data provides only a sparse characterization of the aerosol system.
      • The Internet facilitates communication and the sharing, (reuse) of data and tools. There is a growing collaborative-sharing spirit in the scientific community; The winds of change are here – but we need to harness them for faster learning
      • Establishing trans-continental source-receptor relationship for dust is attainable with available observational and modeling tools but will require:
        • Open flow of data/knowledge and sharing of tools
        • Creation of scientific ‘value-adding chains’
        • Decomposition and reintegration of the 8D aerosol system