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

北极低对流层代表了地面、大气边界层和云过程之间的敏感平衡，形成了云-ABL-表面(CAS)系统。对此系统中某个组件的更改将在其他组件中产生物理响应，最终导致净系统对更改的响应。为了准确地模拟CAS对一个组分变化的净贡献和响应，例如对预测的从主要多年海冰到第一年海冰的变化，必须很好地理解CAS组分之间的过程相互作用，并在耦合的气候模式中进行定量表示。不幸的是，对各种关键过程及其相互作用的了解还处于起步阶段，导致当前全球或区域气候模型的代表性很差。因此，模拟的云量、冰水路径和液态水路径等参数存在很大的不确定性，导致不同模式之间的地面云辐射强迫变化40Wm-2，模拟的地表湍流热通量与观测值相关性较差。甚至基本的表面参数，如反照率，也往往没有实物表示，这给CAS系统提供了一个重大误差来源。该提案侧重于利用现有观测和过程模型了解北极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。分析中将使用大量现有的现场和遥感观测数据。流程建模将使用天气和研究预报模型(WRF)的多分辨率配置进行。将使用面向流程的诊断--揭示与流程和/或参数直接相关的物理关系的诊断--进行观察性分析和模型评估。通过与国家大气研究中心(NCAR)的非正式合作，还将对最先进的全球气候模式--社区气候系统模式(CCSM)忠实地表示这些过程相互作用的能力进行初步评估，初步了解在观测和过程模型中发现的关键过程和关系是否也在CCSM中表现出来。

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.