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Title: A Study to Investigate Cloud Feedback Processes and Evaluate GCM Cloud Variations Using Statistical Cloud Property Composites From ARM Data

Abstract

The representation of clouds in Global Climate Models (GCMs) remains a major source of uncertainty in climate change simulations. Cloud climatologies have been widely used to either evaluate climate model cloud fields or examine, in combination with other data sets, climate-scale relationships between cloud properties and dynamical or microphysical parameters. Major cloud climatologies have been based either on satellite observations of cloud properties or on surface observers views of cloud type and amount. Such data sets provide either the top-down view of column-integrated cloud properties (satellites) or the bottom-up view of the cloud field morphology (surface observers). Both satellite-based and surface cloud climatologies have been successfully used to examine cloud properties, to support process studies, and to evaluate climate and weather models. However, they also present certain limitations, since the satellite cloud types are defined using radiative cloud boundaries and surface observations are based on cloud boundaries visible to human observers. As a result, these data sets do not resolve the vertical distribution of cloud layers, an issue that is important in calculating both the radiative and the hydrologic effects of the cloud field. Ground-based cloud radar observations, on the other hand, resolve with good accuracy the vertical distribution ofmore » cloud layers and could be used to produce cloud type climatologies with vertical layering information. However, these observations provide point measurements only and it is not immediately clear to what extent they are representative of larger regimes. There are different methods that can be applied to minimize this problem and to produce cloud layering climatologies useful for both cloud process and model evaluation studies. If a radar system is run continuously over a number of years, it eventually samples a large number of dynamical and microphysical regimes. If additional data sets are used to put the cloud layering information into the context of large-scale dynamical regimes, such information can be used to study interactions among cloud vertical distributions and dynamical and microphysical processes and to evaluate the ability of models to simulate those interactions. The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program has established several Climate Research Facilities (ACRF) that provide continuous, long-term observations of clouds and radiation. ARM, with its overall goal of improving the treatment of radiation and clouds in climate models has provided unique observing systems for accelerating progress on the representation of cloud processes. In this project, six and a half years (January 1998 to June 2004) of cloud observations collected at the Southern Great Plains (SGP) Oklahoma ACRF were used to produce a cloud-type climatology. The climatology provides cloud amounts for seven different cloud types as well as information on the detailed structure of multi-layer cloud occurrences. Furthermore, the European Centre for Medium-Range Weather Forecasts (ECMWF) model output was used to define the dynamic regimes present during the observations of the cloud conditions by the vertically pointing radars at the SGP ACRF. The cloud-type climatology and the ECMWF SGP data set were then analyzed to examine and map dynamical conditions that favor the creation of single-layer versus multi-layer cloud structures as well as dynamical conditions that favor the occurrence of drizzle in continental stratus clouds. In addition, output from the ECMWF weather model forecasts was analyzed with the objective to compare model and radar derived cloud type statistics, in order to identify the major model deficiencies in cloud vertical distribution and map their seasonal variations. The project included two primary goals. The first was to create a cloud type climatology over the Southern Great Planes site that will show how cloud vertical distribution varies with dynamic and thermodynamic regime and how these variations would affect cloud climate feedbacks. The second was to compare this climatology to clouds derived by a numerical model in order to identify the major model deficiencies in cloud vertical distribution and map their seasonal variations.« less

Authors:
Publication Date:
Research Org.:
Columbia University, New York, N.Y.
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
962208
Report Number(s):
DOE/ER/63725-1
TRN: US201002%%1169
DOE Contract Number:  
FG02-04ER63725
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; ACCURACY; CLIMATE MODELS; CLIMATES; CLOUDS; DISTRIBUTION; EVALUATION; FEEDBACK; MORPHOLOGY; RADAR; RADIATIONS; SATELLITES; SEASONAL VARIATIONS; STATISTICS; THERMODYNAMICS; WEATHER

Citation Formats

George Tselioudis. A Study to Investigate Cloud Feedback Processes and Evaluate GCM Cloud Variations Using Statistical Cloud Property Composites From ARM Data. United States: N. p., 2009. Web. doi:10.2172/962208.
George Tselioudis. A Study to Investigate Cloud Feedback Processes and Evaluate GCM Cloud Variations Using Statistical Cloud Property Composites From ARM Data. United States. doi:10.2172/962208.
George Tselioudis. Tue . "A Study to Investigate Cloud Feedback Processes and Evaluate GCM Cloud Variations Using Statistical Cloud Property Composites From ARM Data". United States. doi:10.2172/962208. https://www.osti.gov/servlets/purl/962208.
@article{osti_962208,
title = {A Study to Investigate Cloud Feedback Processes and Evaluate GCM Cloud Variations Using Statistical Cloud Property Composites From ARM Data},
author = {George Tselioudis},
abstractNote = {The representation of clouds in Global Climate Models (GCMs) remains a major source of uncertainty in climate change simulations. Cloud climatologies have been widely used to either evaluate climate model cloud fields or examine, in combination with other data sets, climate-scale relationships between cloud properties and dynamical or microphysical parameters. Major cloud climatologies have been based either on satellite observations of cloud properties or on surface observers views of cloud type and amount. Such data sets provide either the top-down view of column-integrated cloud properties (satellites) or the bottom-up view of the cloud field morphology (surface observers). Both satellite-based and surface cloud climatologies have been successfully used to examine cloud properties, to support process studies, and to evaluate climate and weather models. However, they also present certain limitations, since the satellite cloud types are defined using radiative cloud boundaries and surface observations are based on cloud boundaries visible to human observers. As a result, these data sets do not resolve the vertical distribution of cloud layers, an issue that is important in calculating both the radiative and the hydrologic effects of the cloud field. Ground-based cloud radar observations, on the other hand, resolve with good accuracy the vertical distribution of cloud layers and could be used to produce cloud type climatologies with vertical layering information. However, these observations provide point measurements only and it is not immediately clear to what extent they are representative of larger regimes. There are different methods that can be applied to minimize this problem and to produce cloud layering climatologies useful for both cloud process and model evaluation studies. If a radar system is run continuously over a number of years, it eventually samples a large number of dynamical and microphysical regimes. If additional data sets are used to put the cloud layering information into the context of large-scale dynamical regimes, such information can be used to study interactions among cloud vertical distributions and dynamical and microphysical processes and to evaluate the ability of models to simulate those interactions. The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program has established several Climate Research Facilities (ACRF) that provide continuous, long-term observations of clouds and radiation. ARM, with its overall goal of improving the treatment of radiation and clouds in climate models has provided unique observing systems for accelerating progress on the representation of cloud processes. In this project, six and a half years (January 1998 to June 2004) of cloud observations collected at the Southern Great Plains (SGP) Oklahoma ACRF were used to produce a cloud-type climatology. The climatology provides cloud amounts for seven different cloud types as well as information on the detailed structure of multi-layer cloud occurrences. Furthermore, the European Centre for Medium-Range Weather Forecasts (ECMWF) model output was used to define the dynamic regimes present during the observations of the cloud conditions by the vertically pointing radars at the SGP ACRF. The cloud-type climatology and the ECMWF SGP data set were then analyzed to examine and map dynamical conditions that favor the creation of single-layer versus multi-layer cloud structures as well as dynamical conditions that favor the occurrence of drizzle in continental stratus clouds. In addition, output from the ECMWF weather model forecasts was analyzed with the objective to compare model and radar derived cloud type statistics, in order to identify the major model deficiencies in cloud vertical distribution and map their seasonal variations. The project included two primary goals. The first was to create a cloud type climatology over the Southern Great Planes site that will show how cloud vertical distribution varies with dynamic and thermodynamic regime and how these variations would affect cloud climate feedbacks. The second was to compare this climatology to clouds derived by a numerical model in order to identify the major model deficiencies in cloud vertical distribution and map their seasonal variations.},
doi = {10.2172/962208},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2009},
month = {8}
}