Collaborative Research: Evaluation of Snow Simulations in the Second Phase of the Atmsopheric Model Intercomparison Project (AMIP-II)
Collaborative Research: Evaluation of Snow Simulations in the Second Phase of the Atmsopheric Model Intercomparison Project (AMIP-II)
批准号:
9820920
负责人:
David Robinson
金额:
$9.91万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
1999
资助国家:
美国
项目状态:
已结题
起止时间:
1999-05-15 至 2002-04-30
中文摘要
大气模式相互比较项目(AMIP-II)第二阶段中雪模拟的评估该项目的总体目标是评估在大气模式相互比较项目(AMIP-II)第二阶段赞助下提交的大气环流模式(GCM)中雪覆盖面积和雪质量的模拟。 AMIP是一项国际性的努力,旨在确定大气气候模式中的系统误差。 确定了三个主要目标,以评估AMIP-II雪模拟。 这些是:(1)雪覆盖区(SCA)气候学和运动学评估;(2)雪团气候学评估;(3)与雪相关的大气环流模式评估。 雪是地表能量通量的重要调节因子,也是北方季节变化最大的地表参数之一。 这些调查将帮助模型制作者从大气动力学和表面参数化的角度评估他们对雪的处理。 通过识别区域,模型和模型特性与偏雪模拟,这项研究将有助于建模者在他们的诊断地面通量,一个重要的物理过程相关的气候预测。
英文摘要
AbstractATM-9820920Robinson, David A.Rutgers UniversityTitle: Evaluation of Snow Simulation in the Second Phase of the Atmospheric Model Intercomparison Project (AMIP-II)The overall goal of this project is to evaluate simulations of snow covered area and snow mass in General Circulation Models (GCM) submitted under the auspices of the second phase of the Atmospheric Model Intercomparison Project (AMIP-II). AMIP is an international effort to determine the systematic errors in atmospheric climate models. Three main objectives are identified to evaluate AMIP-II snow simulations. These are: (1) evaluation of snow covered area (SCA) climatology and kinematics; (2) evaluation of snow mass climatologies; and (3) evaluation of atmospheric circulation patterns associated with snow. Snow is an important modulator of surface energy fluxes, and one of the largest seasonally varying surface parameters over the Northern Hemisphere. These investigations will help modelers evaluate their treatment of snow, from the perspectives of both atmospheric dynamics as well as surface parameterizations. By identifying regions, models, and model characteristics with biased snow simulations, this research will aid modelers in their diagnoses of surface fluxes, an important physical process relevant to climate prediction.
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