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Title: The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

Abstract

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have had difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to improve and to facilitate the comparison of modeled clouds with observations. Many simulators have (and continue to be developed) for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Klein et al. 2013; Zhang et al. 2010). This article introduces a ground-basedmore » cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to CloudSat radar observations, ARM radar measurements occur with higher temporal resolution and finer vertical resolution. This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to surface contamination (Mace et al. 2007; Marchand et al. 2008). Therefore, the ARM ground-based cloud observations can provide important observations of clouds that complement measurements from space.« less

Authors:
 [1];  [1];  [1];  [2];  [3];  [4];  [5];  [5];  [6];  [7];  [1];  [8];  [9];  [5];  [10];  [10];  [10]
  1. Lawrence Livermore National Laboratory, Livermore, California
  2. University of Washington, Seattle, Washington
  3. Stony Brook University, Stony Brook, New York
  4. The Pennsylvania State University, University Park, Pennsylvania
  5. Brookhaven National Laboratory, Upton, New York
  6. CIRES and NOAA/Earth System Research Laboratory, Boulder, Colorado
  7. Met Office Hadley Centre, Exeter, United Kingdom
  8. Cooperative Institute for Research in the Atmosphere/Colorado State University, Fort Collins, Colorado
  9. Argonne National Laboratory, Argonne, Illinois
  10. Pacific Northwest National Laboratory, Richland, Washington
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1422299
Report Number(s):
PNNL-SA-124525
Journal ID: ISSN 0003-0007; 830403000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Bulletin of the American Meteorological Society; Journal Volume: 99; Journal Issue: 1
Country of Publication:
United States
Language:
English

Citation Formats

Zhang, Yuying, Xie, Shaocheng, Klein, Stephen A., Marchand, Roger, Kollias, Pavlos, Clothiaux, Eugene E., Lin, Wuyin, Johnson, Karen, Swales, Dustin, Bodas-Salcedo, Alejandro, Tang, Shuaiqi, Haynes, John M., Collis, Scott, Jensen, Michael, Bharadwaj, Nitin, Hardin, Joseph, and Isom, Bradley. The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models. United States: N. p., 2018. Web. doi:10.1175/BAMS-D-16-0258.1.
Zhang, Yuying, Xie, Shaocheng, Klein, Stephen A., Marchand, Roger, Kollias, Pavlos, Clothiaux, Eugene E., Lin, Wuyin, Johnson, Karen, Swales, Dustin, Bodas-Salcedo, Alejandro, Tang, Shuaiqi, Haynes, John M., Collis, Scott, Jensen, Michael, Bharadwaj, Nitin, Hardin, Joseph, & Isom, Bradley. The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models. United States. doi:10.1175/BAMS-D-16-0258.1.
Zhang, Yuying, Xie, Shaocheng, Klein, Stephen A., Marchand, Roger, Kollias, Pavlos, Clothiaux, Eugene E., Lin, Wuyin, Johnson, Karen, Swales, Dustin, Bodas-Salcedo, Alejandro, Tang, Shuaiqi, Haynes, John M., Collis, Scott, Jensen, Michael, Bharadwaj, Nitin, Hardin, Joseph, and Isom, Bradley. Mon . "The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models". United States. doi:10.1175/BAMS-D-16-0258.1.
@article{osti_1422299,
title = {The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models},
author = {Zhang, Yuying and Xie, Shaocheng and Klein, Stephen A. and Marchand, Roger and Kollias, Pavlos and Clothiaux, Eugene E. and Lin, Wuyin and Johnson, Karen and Swales, Dustin and Bodas-Salcedo, Alejandro and Tang, Shuaiqi and Haynes, John M. and Collis, Scott and Jensen, Michael and Bharadwaj, Nitin and Hardin, Joseph and Isom, Bradley},
abstractNote = {Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have had difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to improve and to facilitate the comparison of modeled clouds with observations. Many simulators have (and continue to be developed) for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Klein et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to CloudSat radar observations, ARM radar measurements occur with higher temporal resolution and finer vertical resolution. This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to surface contamination (Mace et al. 2007; Marchand et al. 2008). Therefore, the ARM ground-based cloud observations can provide important observations of clouds that complement measurements from space.},
doi = {10.1175/BAMS-D-16-0258.1},
journal = {Bulletin of the American Meteorological Society},
number = 1,
volume = 99,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2018},
month = {Mon Jan 01 00:00:00 EST 2018}
}