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Title: ARM: Derived: Aerosol intensive properties from AOS (FITRH), Delene and Ogren et al, 2001

Derived: Aerosol intensive properties from AOS (FITRH), Delene and Ogren et al, 2001
Authors:
;
Publication Date:
DOE Contract Number:
DE-AC05-00OR22725
Product Type:
Dataset
Research Org(s):
Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US)
Collaborations:
PNL, BNL,ANL,ORNL
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Subject:
54 Environmental Sciences; Aerosol absorption; Aerosol backscattered radiation; Aerosol optical properties; Aerosol scattering; Hygroscopic growth
OSTI Identifier:
1095300
  1. ARM focuses on obtaining continuous measurements—supplemented by field campaigns—and providing data products that promote the advancement of climate models. ARM data include routine data products, value-added products (VAPs), field campaign data, complementary external data products from collaborating programs, and data contributed by ARM principal investigators for use by the scientific community. Data quality reports, graphical displays of data availability/quality, and data plots are also available from the ARM Data Center. Serving users worldwide, the ARM Data Center collects and archives approximately 20 terabytes of data per month. Datastreams are generally available for download within 48 hours.
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  1. Derived: Aerosol intensive properties from AOS, Delene and Ogren et al, 2001
  2. Derived: Hourly Averages of Aerosol intensive properties from AOS, Delene and Ogren et al, 2001
  3. Derived: Aerosol intensive properties from AOS (FITRH), Delene and Ogren et al, 2001
  4. This field campaign will address multiple uncertainties in aerosol intensive properties, which are poorly represented in climate models, by means of aircraft measurements in biomass burning plumes. Key topics to be investigated are: 1. Aerosol mixing state and morphology 2. Mass absorption coefficients (MACs) 3.more » Chemical composition of non-refractory material associated with light-absorbing carbon (LAC) 4. Production rate of secondary organic aerosol (SOA) 5. Microphysical processes relevant to determining aerosol size distributions and single scattering albedo (SSA) 6. CCN activity. These topics will be investigated through measurements near active fires (0-5 hours downwind), where limited observations indicate rapid changes in aerosol properties, and in biomass burning plumes aged >5 hours. Aerosol properties and their time evolution will be determined as a function of fire type, defined according to fuel and the mix of flaming and smoldering combustion at the source. « less
  5. Understanding sources of uncertainty in aerosol direct radiative forcing (DRF), the difference in a given radiative flux component with and without aerosol, is essential to quantifying changes in Earth's radiation budget. We examine the uncertainty in DRF due to measurement uncertainty in the quantities onmore » which it depends: aerosol optical depth, single scattering albedo, asymmetry parameter, solar geometry, and surface albedo. Direct radiative forcing at the top of the atmosphere and at the surface as well as sensitivities, the changes in DRF in response to unit changes in individual aerosol or surface properties, are calculated at three locations representing distinct aerosol types and radiative environments. The uncertainty in DRF associated with a given property is computed as the product of the sensitivity and typical measurement uncertainty in the respective aerosol or surface property. Sensitivity and uncertainty values permit estimation of total uncertainty in calculated DRF and identification of properties that most limit accuracy in estimating forcing. Total uncertainties in modeled local diurnally averaged forcing range from 0.2 to 1.3 W m-2 (42 to 20%) depending on location (from tropical to polar sites), solar zenith angle, surface reflectance, aerosol type, and aerosol optical depth. The largest contributor to total uncertainty in DRF is usually single scattering albedo; however decreasing measurement uncertainties for any property would increase accuracy in DRF. Comparison of two radiative transfer models suggests the contribution of modeling error is small compared to the total uncertainty although comparable to uncertainty arising from some individual properties. « less