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Title: SCM Forcing Data Derived from NWP Analyses

Forcing data, suitable for use with single column models (SCMs) and cloud resolving models (CRMs), have been derived from NWP analyses for the ARM (Atmospheric Radiation Measurement) Tropical Western Pacific (TWP) sites of Manus Island and Nauru.
Publication Date:
DOE Contract Number:
Product Type:
Research Org(s):
Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
54 Environmental Sciences; Advective tendency; Atmospheric moisture; Atmospheric pressure; Atmospheric temperature; Horizontal wind; Soil surface temperature; Vertical velocity
OSTI Identifier:
  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. 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
  2. The Carbon Dioxide Research Group, Scripps Institution of Oceanography, University of California, San Diego, has provided this data set, which includes long-term measurements of near-surface atmospheric CO2 concentrations at 10 locations spanning latitudes 82 degrees N to 90 degrees S. Most of the data aremore » based on replicated (collected at the same time and place) flask samples taken at intervals of approximately one week to one month and subsequently subjected to infrared analysis. Periods of record begin in various years, ranging from 1957 (for the South Pole station) to 1985 (for Alert, Canada), and all flask data records except for Christmas Island and Baring Head, New Zealand extend through year 2001. Christmas Island data end with August, 2001 and Baring Head data end with October 2001. Weekly averages of continuous data from Mauna Loa Observatory, Hawaii, are available back to March 1958. Similar weekly averages are also available for La Jolla, California, from November 1972 to October 1975, and for the South Pole from June 1960 to October 1963. At the South Pole, however, this weekly averaged data is usually based on only one day of continuous sampling, and only about 2 averages per month are given. Flask data from all stations include replicate measurements and flagged questionable data; thus, they differ from the usual presentations of CO2 data (e.g., Keeling and Whorf, 2004) which are monthly averaged values fitted to curves as discussed by Keeling et al. (1989). Questionable data are flagged with asterisks; the user is accordingly advised to use caution in including them in analysis or in interpreting them without reference to the flag codes that provide the rationale for data rejections.The data are available in 13 ASCII files: 10 files give the flask measurements corresponding to each of the 10 locations; 2 additional files, one for La Jolla and another for the South Pole, each give about three years of averages, derived from continuous samples, to represent the corresponding weekly averages; another file gives weekly averages of the continuous record since 1958 at Mauna Loa, Hawaii.These long-term records of atmospheric CO2 concentration complement the continuous records made by SIO, and also complement the long term flask records of the Climate Monitoring and Diagnostics Laboratory of the National Oceanic and Atmospheric Administration. All these data are useful for characterizing seasonal and geographical variations in atmospheric CO2 over several years, and for assessing results of global carbon models. Flask data provide information about instantaneous departures from the hourly or multi-hourly averages derived from the continuous data, and at the same time serve as a quality check on those averages. Additionally, flask samples can be archived for future analyses as more refined measuring techniques become available. Temporal and geographical variations in the flask data are similar to those in the continuous data. Annual averages and amplitudes of the annual cycle of atmospheric CO2 concentration both decrease from high northern latitudes to high southern latitudes. Peak annual CO2 concentrations occur in spring, around May in mid latitudes of the Northern Hemisphere and September or October in mid latitudes in the Southern Hemisphere. « less
  3. Cloud ice water concentration is one of the most important, yet poorly observed, cloud properties. Developing physical parameterizations used in general circulation models through single-column modeling is one of the key foci of the ARM program. In addition to the vertical profiles of temperature, watermore » vapor and condensed water at the model grids, large-scale horizontal advective tendencies of these variables are also required as forcing terms in the single-column models. Observed horizontal advection of condensed water has not been available because the radar/lidar/radiometer observations at the ARM site are single-point measurement, therefore, do not provide horizontal distribution of condensed water. The intention of this product is to provide large-scale distribution of cloud ice water by merging available surface and satellite measurements. The satellite cloud ice water algorithm uses ARM ground-based measurements as baseline, produces datasets for 3-D cloud ice water distributions in a 10 deg x 10 deg area near ARM site. The approach of the study is to expand a (surface) point measurement to an (satellite) areal measurement. That is, this study takes the advantage of the high quality cloud measurements at the point of ARM site. We use the cloud characteristics derived from the point measurement to guide/constrain satellite retrieval, then use the satellite algorithm to derive the cloud ice water distributions within an area, i.e., 10 deg x 10 deg centered at ARM site. « less
  4. Single-Column Model (SCM) Forcing Data are derived from the ARM facility observational data using the constrained variational analysis approach (Zhang and Lin 1997 and Zhang et al., 2001). The resulting products include both the large-scale forcing terms and the evaluation fields, which can be usedmore » for driving the SCMs and Cloud Resolving Models (CRMs) and validating model simulations. « less
  5. Convective processes play a critical role in the Earth's energy balance through the redistribution of heat and moisture in the atmosphere and their link to the hydrological cycle. Accurate representation of convective processes in numerical models is vital towards improving current and future simulations ofmore » Earths climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales important to convective processes and therefore must turn to parameterization schemes to represent these processes. In turn, parameterization schemes in cloud-resolving models need to be evaluated for their generality and application to a variety of atmospheric conditions. Data from field campaigns with appropriate forcing descriptors have been traditionally used by modelers for evaluating and improving parameterization schemes. « less