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Title: Clouds and more: ARM climate modeling best estimate data: A new data product for climate studies

Journal Article · · Bulletin of the American Meteorological Society
 [1];  [1];  [1];  [1];  [2];  [3];  [4];  [5];  [6];  [2];  [2];  [5];  [4];  [4];  [6];  [4];  [6];  [4];  [7]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
  3. The Pennsylvania State Univ., University Park, PA (United States)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  5. NOAA Geophysical Fluid Dynamics Lab., Princeton, NJ (United States)
  6. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  7. Univ. of Wisconsin, Madison, WI (United States)
+ Show Author Affiliations

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program (www.arm.gov) was created in 1989 to address scientific uncertainties related to global climate change, with a focus on the crucial role of clouds and their influence on the transfer of radiation atmosphere. Here, a central activity is the acquisition of detailed observations of clouds and radiation, as well as related atmospheric variables for climate model evaluation and improvement.

Research Organization:
Lawrence Livermore National Lab., Livermore, CA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1281659
Report Number(s):
LLNL-JRNL-412676
Journal Information:
Bulletin of the American Meteorological Society, Vol. 91, Issue 1; ISSN 0003-0007
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 115 works
Citation information provided by
Web of Science

Cited By (16)

Mixed-phase clouds cause climate model biases in Arctic wintertime temperature inversions journal October 2013
Cloud fraction at the ARM SGP site: Instrument and sampling considerations from 14 years of ARSCL journal March 2013
Impact of Cumulus Microphysics and Entrainment Specification on Tropical Cloud and Radiation in GFDL AM2 journal May 2019
Comparison of Cloudy-Sky Downward Longwave Radiation Algorithms Using Synthetic Data, Ground-Based Data, and Satellite Data journal May 2018
Causes of model dry and warm bias over central U.S. and impact on climate projections journal October 2017
Observationally derived rise in methane surface forcing mediated by water vapour trends journal April 2018
How close are time series to power tail Lévy diffusions?
  • Gairing, Jan M.; Högele, Michael A.; Kosenkova, Tania
  • Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 27, Issue 7 https://doi.org/10.1063/1.4986496
journal July 2017
Land surface skin temperature climatology: benefitting from the strengths of satellite observations journal October 2010
Statistical Characteristics of Long-Term High-Resolution Precipitable Water Vapor Data at Darwin journal October 2018
Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations journal January 2012
Impacts of increasing the aerosol complexity in the Met Office global numerical weather prediction model journal January 2014
Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment journal January 2016
An intercomparison of radar-based liquid cloud microphysics retrievals and implications for model evaluation studies journal January 2012
ReOBS: a new approach to synthesize long-term multi-variable dataset and application to the SIRTA supersite journal January 2018
An intercomparison of radar-based liquid cloud microphysics retrievals and implication for model evaluation studies journal January 2011
ReOBS: a new approach to synthetize long-term multi-variable dataset and application to the SIRTA supersite posted_content January 2018

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Related Subjects

54 ENVIRONMENTAL SCIENCES