Collaborative Research: Understanding and Modeling Key Arctic Cloud-ABL-Surface Processes and Interactions

合作研究：理解和建模关键的北极云-ABL-地表过程和相互作用

• 批准号: 1023366
• 负责人: Ola P Persson
• 金额: $ 66.47万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1023366&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Modeling; Key

The Arctic lower troposphere represents a sensitive balance between surface, atmospheric boundary layer, and cloud processes, forming a cloud-ABL-surface (CAS) system. Changes to a component in this system will produce physical responses in the other components, culminating in the net system response to the change. In order to accurately simulate the net CAS contributions and responses to a component change, such as to the predicted change from primarily multi-year sea-ice to first-year sea-ice, the process interactions between the CAS components must be well understood and quantitatively represented in coupled climate models. Unfortunately, the understanding of various key processes and their interactions is very nascent, leading to poor representations in current global or regional climate models. Hence, significant uncertainties exist in modeled parameters such as cloud fraction, ice water path, and liquid water path, resulting in surface cloud radiative forcing that varies by 40 W m-2 among models and modeled surface turbulent heat fluxes that have poor correlations with observations. Even basic surface parameters, such as albedo, are often not physically represented and provide a source of significant errors to the CAS system. This proposal focuses on understanding the complex processes and interactions in the Arctic CAS system using existing observations and process models and on improving key parameterizations in these process models. A broad range of existing in-situ and remotely sensed observational data will be used in the analyses. The process modeling will be done with multiple-resolution configurations of the Weather and Research Forecasting Model (WRF). Observational analyses and model evaluations will be conducted using process-oriented diagnostics -- diagnostics that reveal physical relationships directly relevant to a process and/or parameterization. Through informal collaboration with the National Center for Atmospheric Research (NCAR), preliminary evaluations of the ability of a state-of-the-art global climate model (GCM), the Community Climate System Model (CCSM), to faithfully represent these process interactions will also be performed, providing initial insight as to whether key processes and relationships found in observations and process models are also represented in CCSM.

北极对流层低层代表了地表、大气边界层和云过程之间的敏感平衡，形成了云-ABL-地表（CAS）系统。该系统中某个组件的更改将在其他组件中产生物理响应，最终导致网络系统对更改做出响应。 为了准确模拟 CAS 净贡献和对组件变化的响应，例如从主要多年海冰到第一年海冰的预测变化，必须充分理解 CAS 组件之间的过程相互作用，并在耦合气候模型中定量表示。 不幸的是，对各种关键过程及其相互作用的理解还处于初级阶段，导致当前全球或区域气候模型的代表性较差。 因此，云分数、冰水路径、液态水路径等模型参数存在显着的不确定性，导致模型间地表云辐射强迫相差40 W·m-2，模型地表湍流热通量与观测值相关性较差。 。 即使是基本的表面参数，例如反照率，通常也无法以物理方式表示，并为 CAS 系统提供重大误差来源。该提案的重点是利用现有的观测和过程模型来了解北极 CAS 系统中的复杂过程和相互作用，并改进这些过程模型中的关键参数化。分析中将使用广泛的现有现场和遥感观测数据。 过程建模将通过天气和研究预测模型 (WRF) 的多分辨率配置来完成。 观察分析和模型评估将使用面向过程的诊断进行——揭示与过程和/或参数化直接相关的物理关系的诊断。 通过与国家大气研究中心 (NCAR) 的非正式合作，对最先进的全球气候模型 (GCM)、社区气候系统模型 (CCSM) 的能力进行初步评估，以忠实地表示这些过程相互作用还将执行，提供关于观察和过程模型中发现的关键过程和关系是否也在 CCSM 中得到体现的初步见解。