MOCCHA Analysis of Dynamic, Cloud, and Aerosol Processes

北极的气候正在迅速变化，其变暖速度至少是全球平均速度的两倍。虽然气候模式总体上与这种放大的变暖相一致，但它们在北极的模式之间显示出比其他地方大得多的差异，并且明显未能重现观测到的气候变化的一些关键特征——特别是夏季海冰范围的迅速减少。在北极气候模型中，不确定性的最大来源，实际上是总体气候，是云。云通过对太阳和红外辐射的影响，对地表能量收支起着主要的控制作用。云对地面辐射收支的影响通常表示为“云辐射效应”（CRE）：即晴空下和有云时的辐射之差。最新一期气候模式比对项目（CMIP5）的结果表明，所有模式的年平均CRE偏差为-10 W m-2，在相似量级的模式之间存在标准偏差。仅夏季月份的偏差为+20 W m-2。一年的偏压仅为1瓦/平方，就足以融化/冻结10厘米厚的海冰层；因此，这些模型偏差非常重要。我们的目标是通过提高对影响北极夏季云形成、发展和消散的关键过程的理解和参数化，减少模式云造成的偏差和不确定性。北极夏季边界层云是混合相的——既包含液态水又包含冰晶。这是一种热力学不稳定的非平衡状态，因为冰上的饱和水蒸气压力低于水上的饱和水蒸气压力，因此冰晶应该以牺牲水滴为代价生长。云层通过气溶胶（水滴和冰晶在其上形成）、热力学、微物理、辐射和湍流混合过程之间一系列复杂的相互作用保持在准平衡状态。所有这些单独的过程都有显著的不确定性，目前的模式参数化几乎完全是基于对中纬度云的测量而发展起来的，那里的条件非常不同。我们将结合最先进的云和边界层特性和过程的测量，并使用英国气象局统一模型进行详细的建模研究。测量利用高分辨率主动遥感系统，如云雷达和激光雷达，通过CLOUDNET算法处理，生成详细的云属性数据。这些补充了边界层结构和气溶胶特性的现场测量。对综合模型和观测数据的分析将用于确定模型参数化中的关键缺陷，以及对必须在模型中表示的物理的新理解。参数化更新和对观测结果进行测试的迭代过程将用于改进模型物理。然后，这些更新将在20年的气候模式运行中进行测试，使用来自大气模式比对项目（AMIP）的案例来评估它们对气候预测的影响。统一模型被选为理想的工具，因为它的无缝哲学，在业务预测和气候模型中使用相同的模型物理。这样就可以直接对观测案例进行测试，并且可以很容易地将新的模式更新转移到气候版本。最终的模型更新将提交给英国气象局，与正式的模型发布相结合。

The climate of the Arctic is changing rapidly, warming at least twice as fast as the global average. While climate models are generally consistent with this amplified warming, they show much greater variability between model in the Arctic than elsewhere, and have conspicuously failed to reproduce some key features of observed climate change - notably the rapid reduction in summer sea ice extent. The greatest source of uncertainty in models of Arctic climate, indeed for climate in general, is clouds. Clouds are the dominant control of the surface energy budget through their impact on both solar and infra red radiation. The effect of clouds on the surface radiation budget is commonly expressed as the 'cloud radiative effect' (CRE): the difference between the radiation under clear skies and with clouds present. Results from the latest Climate Model Intercomparison Project (CMIP5) show that the annual mean CRE of all the models is biased by -10 W m-2 with a standard deviation across the models of similar magnitude. The bias for the summer months only is +20 W m-2. A bias of just 1 W m-2 over a year is sufficient to melt/freeze a 10 cm thick layer of sea ice; these model biases are thus highly significant. We aim to reduce the bias and uncertainty resulting from model summertime clouds in the Arctic, by improving understanding and parameterization of key processes affecting their formation, development, and dissipation. Arctic summer boundary-layer clouds are mixed-phase - containing both liquid water and ice crystals. This is a thermodynamically unstable non-equilibrium state because saturation water vapour pressure over ice is lower than that over water, thus ice crystals should grow at the expense of water droplets. The clouds are maintained in a quasi-equilbrium through a complex set of interactions between aerosol (on which droplets and ice crystals form), thermodynamic, microphysical, radiative, and turbulent mixing processes. All of these individual processes have significant uncertainties associate with them, and current model parameterizations have been developed almost entirely based on measurements in mid-latitude clouds where conditions are very different. We will use a combination of state-of-the-art measurements of cloud and boundary layer properties and processes, with detailed modelling studies using the Met Office Unified Model. The measurements utilise high resolution active remote sensing systems such as cloud radar and lidar, processed through the CLOUDNET algorithm to generate detail cloud property data. These are supplemented with in situ measurements of boundary layer structure and aerosol properties. Analysis of combined model and observational data will be used to identify key failings in model parameterizations, and new understanding of the physics that must be represented in the models. An iterative process of parameterization updates and testing against observations will be used to improve the model physics. The updates will then be tested in 20-year climate model runs using cases from the Atmospheric Model Intercomparison Project (AMIP) to evaluate their impact on climate projections. The Unified Model is chosen as an ideal tool because of its seamless philosophy, using the same model physics in both operational forecast and climate models. This allows direct testing against observational cases, and easy transfer of new model updates to the climate version. The final model updates will then be put forward to the Met Office for integration into the official model releases.