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

Journal Article · Mon Jan 01 00:00:00 EST 2018 · Bulletin of the American Meteorological Society

DOI:https://doi.org/10.1175/BAMS-D-16-0258.1· OSTI ID:1422299

Zhang, Yuying ^[1]; Xie, Shaocheng ^[1]; Klein, Stephen A. ^[1]; Marchand, Roger ^[2]; Kollias, Pavlos ^[3]; Clothiaux, Eugene E. ^[4]; Lin, Wuyin ^[5]; Johnson, Karen ^[5]; Swales, Dustin ^[6]; Bodas-Salcedo, Alejandro ^[7]; Tang, Shuaiqi ^[1]; Haynes, John M. ^[8]; Collis, Scott ^[9]; Jensen, Michael ^[5]; Bharadwaj, Nitin ^[10]; Hardin, Joseph ^[10]; Isom, Bradley ^[10]

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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

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.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1422299
• Report Number(s): PNNL-SA-124525; 830403000
• Journal Information: Bulletin of the American Meteorological Society, Vol. 99, Issue 1; ISSN 0003-0007
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English