Development and Testing of a Life Cycle Model and a Parameterization of Thin Mid-level Stratiform Clouds

Krueger, Steven K.

关键词: CLIMATE MODELS;    CLIMATES;    CLOUDS;    COMPUTERS;    FORECASTING;    LIFE CYCLE;    RADIATIONS;    REMOTE SENSING;    SIMULATION;    TESTING;    WEATHER ARM;    Atmospheric Radiation Measurement program;    cloud-resolving model;    single-column model;    global climate model;    NCEP;    National Centers for Environmental Prediction;    climate research facility;    southern Great Plains;    cloud modeling;    climate modeling;    numerical weather prediction;    cloud parameterization;    cloud radar;    MMCR;    millimeter cloud radar;    GCSS;    GEWEX Cloud System Study;   

DOI  :  10.2172/924412
RP-ID  :  DOE/ER/61769
PID  :  OSTI ID: 924412
Others  :  TRN: US200814%%723

学科分类：环境科学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

We used a cloud-resolving model (a detailed computer model of cloud systems) to evaluate and improve the representation of clouds in global atmospheric models used for numerical weather prediction and climate modeling. We also used observations of the atmospheric state, including clouds, made at DOE's Atmospheric Radiation Measurement (ARM) Program's Climate Research Facility located in the Southern Great Plains (Kansas and Oklahoma) during Intensive Observation Periods to evaluate our detailed computer model as well as a single-column version of a global atmospheric model used for numerical weather prediction (the Global Forecast System of the NOAA National Centers for Environmental Prediction). This so-called Single-Column Modeling approach has proved to be a very effective method for testing the representation of clouds in global atmospheric models. The method relies on detailed observations of the atmospheric state, including clouds, in an atmospheric column comparable in size to a grid column used in a global atmospheric model. The required observations are made by a combination of in situ and remote sensing instruments. One of the greatest problems facing mankind at the present is climate change. Part of the problem is our limited ability to predict the regional patterns of climate change. In order to increase this ability, uncertainties in climate models must be reduced. One of the greatest of these uncertainties is the representation of clouds and cloud processes. This project, and ARM taken as a whole, has helped to improve the representation of clouds in global atmospheric models.