skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: COLLABORATIVE RESEARCH:USING ARM OBSERVATIONS & ADVANCED STATISTICAL TECHNIQUES TO EVALUATE CAM3 CLOUDS FOR DEVELOPMENT OF STOCHASTIC CLOUD-RADIATION

Abstract

The long-range goal of several past and current projects in our DOE-supported research has been the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data, and the implementation and testing of these parameterizations in global models. The main objective of the present project being reported on here has been to develop and apply advanced statistical techniques, including Bayesian posterior estimates, to diagnose and evaluate features of both observed and simulated clouds. The research carried out under this project has been novel in two important ways. The first is that it is a key step in the development of practical stochastic cloud-radiation parameterizations, a new category of parameterizations that offers great promise for overcoming many shortcomings of conventional schemes. The second is that this work has brought powerful new tools to bear on the problem, because it has been a collaboration between a meteorologist with long experience in ARM research (Somerville) and a mathematician who is an expert on a class of advanced statistical techniques that are well-suited for diagnosing model cloud simulations using ARM observations (Shen).

Authors:
Publication Date:
Research Org.:
The Regents of the University of California / Scripps Institution of Oceanography
Sponsoring Org.:
USDOE
OSTI Identifier:
1091953
Report Number(s):
Final Report
DOE Contract Number:
SC0000658
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
15 GEOTHERMAL ENERGY; cloud, radiation, statistical, ARM, observations

Citation Formats

Somerville, Richard. COLLABORATIVE RESEARCH:USING ARM OBSERVATIONS & ADVANCED STATISTICAL TECHNIQUES TO EVALUATE CAM3 CLOUDS FOR DEVELOPMENT OF STOCHASTIC CLOUD-RADIATION. United States: N. p., 2013. Web. doi:10.2172/1091953.
Somerville, Richard. COLLABORATIVE RESEARCH:USING ARM OBSERVATIONS & ADVANCED STATISTICAL TECHNIQUES TO EVALUATE CAM3 CLOUDS FOR DEVELOPMENT OF STOCHASTIC CLOUD-RADIATION. United States. doi:10.2172/1091953.
Somerville, Richard. 2013. "COLLABORATIVE RESEARCH:USING ARM OBSERVATIONS & ADVANCED STATISTICAL TECHNIQUES TO EVALUATE CAM3 CLOUDS FOR DEVELOPMENT OF STOCHASTIC CLOUD-RADIATION". United States. doi:10.2172/1091953. https://www.osti.gov/servlets/purl/1091953.
@article{osti_1091953,
title = {COLLABORATIVE RESEARCH:USING ARM OBSERVATIONS & ADVANCED STATISTICAL TECHNIQUES TO EVALUATE CAM3 CLOUDS FOR DEVELOPMENT OF STOCHASTIC CLOUD-RADIATION},
author = {Somerville, Richard},
abstractNote = {The long-range goal of several past and current projects in our DOE-supported research has been the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data, and the implementation and testing of these parameterizations in global models. The main objective of the present project being reported on here has been to develop and apply advanced statistical techniques, including Bayesian posterior estimates, to diagnose and evaluate features of both observed and simulated clouds. The research carried out under this project has been novel in two important ways. The first is that it is a key step in the development of practical stochastic cloud-radiation parameterizations, a new category of parameterizations that offers great promise for overcoming many shortcomings of conventional schemes. The second is that this work has brought powerful new tools to bear on the problem, because it has been a collaboration between a meteorologist with long experience in ARM research (Somerville) and a mathematician who is an expert on a class of advanced statistical techniques that are well-suited for diagnosing model cloud simulations using ARM observations (Shen).},
doi = {10.2172/1091953},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2013,
month = 8
}

Technical Report:

Save / Share:
  • The long-range goal of several past and current projects in our DOE-supported research has been the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data, and the implementation and testing of these parameterizations in global models. The main objective of the present project being reported on here has been to develop and apply advanced statistical techniques, including Bayesian posterior estimates, to diagnose and evaluate features of both observed and simulated clouds. The research carried out under this project has been novel in two important ways. The first is that it is a key stepmore » in the development of practical stochastic cloud-radiation parameterizations, a new category of parameterizations that offers great promise for overcoming many shortcomings of conventional schemes. The second is that this work has brought powerful new tools to bear on the problem, because it has been an interdisciplinary collaboration between a meteorologist with long experience in ARM research (Somerville) and a mathematician who is an expert on a class of advanced statistical techniques that are well-suited for diagnosing model cloud simulations using ARM observations (Shen). The motivation and long-term goal underlying this work is the utilization of stochastic radiative transfer theory (Lane-Veron and Somerville, 2004; Lane et al., 2002) to develop a new class of parametric representations of cloud-radiation interactions and closely related processes for atmospheric models. The theoretical advantage of the stochastic approach is that it can accurately calculate the radiative heating rates through a broken cloud layer without requiring an exact description of the cloud geometry.« less
  • Uncertainties in representing the atmospheric water cycle are major obstacles to an accurate prediction of future climate. This project focused on addressing some of these uncertainties by implementing new physics for convection and radiation into the NCAR climate model. To better understand and eventually better represent these processes, we modified CAM3.5 to use the convection and cloud schemes developed by the Massachusetts Institute of Technology (MIT) and the RRTMG rapid radiation code for global models developed by Atmospheric and Environmental Research, Inc. (AER). The impact of the new physics on the CAM3.5 simulation of convection on diurnal and intra-seasonal scales,more » intra-seasonal oscillations and the distribution of water vapor has been investigated. The effect of the MIT and AER physics also has been tested in the Weather Research and Forecasting (WRF) regional forecast model. It has been found that the application of the AER radiation and MIT convection produces significant improvements in the modeled diurnal cycle of convection, especially over land, in the NCAR climate model. However, both the standard CAM3.5 (hereinafter STD) and the modified CAM3.5 with the new physics (hereinafter MOD) are still unable to capture the proper spectrum and propagating characteristics of the intra-seasonal oscillations (ISOs). The new physics methods modify, but do not substantially improve, the distribution of upper tropospheric water vapor relative to satellite measurements.« less
  • Mixed-phase clouds are composed of a mixture of cloud droplets and ice crystals. The cloud microphysics in mixed-phase clouds can significantly impact cloud optical depth, cloud radiative forcing, and cloud coverage. However, the treatment of mixed-phase clouds in most current climate models is crude and the partitioning of condensed water into liquid droplets and ice crystals is prescribed as temperature dependent functions. In our previous 2007 ARM metric reports a new mixed-phase cloud microphysics parameterization (for ice nucleation and water vapor deposition) was documented and implemented in the NCAR Community Atmospheric Model Version 3 (CAM3). The new scheme was testedmore » against the Atmospheric Radiation Measurement (ARM) Mixed-phase Arctic Cloud Experiment (M-PACE) observations using the single column modeling and short-range weather forecast approaches. In this report this new parameterization is further tested with CAM3 in its climate simulations. It is shown that the predicted ice water content from CAM3 with the new parameterization is in better agreement with the ARM measurements at the Southern Great Plain (SGP) site for the mixed-phase clouds.« less
  • This report briefly summarizes the progress made by ARM postdoctoral fellow, Yanluan Lin, at GFDL during the period from October 2008 to present. Several ARM datasets have been used for GFDL model evaluation, understanding, and improvement. This includes a new ice fall speed parameterization with riming impact and its test in GFDL AM3, evaluation of model cloud and radiation diurnal and seasonal variation using ARM CMBE data, model ice water content evaluation using ARM cirrus data, and coordination of the TWPICE global model intercomparison. The work illustrates the potential and importance of ARM data for GCM evaluation, understanding, and ultimately,more » improvement of GCM cloud and radiation parameterizations. Future work includes evaluation and improvement of the new dynamicsPDF cloud scheme and aerosol activation in the GFDL model.« less
  • GFDL CM3 convective vertical velocities, parameterized because their scales are below those resolved in current climate models, have been compared with observations from DOE ARM. Single-column forcing has been used for this comparison for two time periods from TWP-ICE and three from MC3E. These are the first independent evaluations of the parameterized vertical velocities against observations. The results show that basic characteristics of the observations are captured by CM3 when forced with single-column observations and constrained to observed large-scale states. In many, but not all, cases, the parameterized vertical velocities exceed those observed, especially at higher levels in the clouds.more » Similar problems have been noted by others in cloud-resolving models. The results have important implications for cloud-aerosol interactions, which depend on vertical velocities, and likely pose constraints on entrainment by convection, which may be related to climate sensitivity.« less