Understanding and constraining extratropical clouds in Earth's present and changing climate

在气候模型的气候预测中，被称为“云反馈”的云对全球变暖的地表温度调节的反应仍然是最大的传播。PI领导的之前的工作使用一个单一的气候模式证明，混合相云中过冷液体与总云水含量比例的低偏差导致气候敏感性被低估-一种量化全球平均地表气温最终变化的指标，以响应大气二氧化碳浓度的加倍。最近，参与耦合模式对比项目(CMIP6)第六阶段的最新一代气候模式预测，气候敏感性的增加与PI的工作确定的相同混合相云偏差的普遍改善有关。确定更高的气候敏感度是否现实，对于制定气候变化政策和缓解战略至关重要。拟议的五年计划将解决最新一代气候模型的较高气候敏感值是否现实的问题，方法是解决PI之前在两个不同但平行的研究主题下的工作所产生的悬而未决的问题。第一个研究主题是利用卫星遥感观测对气候模型中的温带云反馈进行诊断和限制。这将通过以下方式实现：(I)应用PI的方法，根据来自两个不同卫星仪器的观测，评估CMIP6模式中云光学厚度的模拟，(Ii)执行单模式摄动参数集合模拟，以确定社区气候模式中最重要的约束物理过程，以及(Iii)根据卫星对降水的观测，确定降水作为温带云反馈中的约束过程速率的影响。第二个研究主题努力恰当地解释气候模型中最突出的长期存在的混合相云偏向。这将通过以下方式实现：(1)使用两种不同的方法描述混合相云中云热力相的空间分布，(2)计算冰成核粒子与云热力相空间分布的协变性，以及(3)利用现场观测建立气候模式中冰沉积增长的第一个显式参数，其中解释了混合相云中云热力相的空间分布。拟议的五年计划的总体目标是限制温带云反馈，以评估CMIP6模式中较高的气候敏感度是否现实。这一影响与加拿大特别相关，尤其是我们脆弱的北极，在过去几十年里，北极经历了快速的气候变化。

The surface temperature-mediated response of clouds to global warming, known as "cloud feedback", continues to contribute the largest spread in climate projections by climate models. Previous work led by the PI has demonstrated using a single climate model that low biases in the proportion of supercooled liquid to total cloud water content in mixed-phase clouds causes underestimates in climate sensitivity --- a metric that quantifies the ultimate change in global mean surface air temperature in response to a doubling of atmospheric carbon dioxide concentrations. Recently, the newest generation of climate models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) predicts an increase in climate sensitivity that has been linked to general improvements to the same mixed-phase cloud biases identified by the PI's work. Establishing whether the higher climate sensitivity is realistic is of utmost importance for the development of climate change policy and mitigation strategies. The proposed five-year program will address the question of whether the higher climate sensitivity values of the latest generation of climate models are realistic by addressing the unresolved issues stemming from the PI's previous work in two distinct but parallel research themes. The first research theme strives to diagnose and constrain the extratropical cloud feedback in climate models using satellite remote sensing observations. This will be achieved by (i) applying the PI's methodology to evaluate the simulation of cloud optical depth in the CMIP6 models against observations from two different satellite instruments, (ii) performing single-model perturbed parameter ensemble simulations to identify the most important constrainable physical processes in a community climate model, and (iii) determining the impact of precipitation as a constrainable process rate in the extratropical cloud feedback based on satellite observations of precipitation. The second research theme strives to properly account for the most prominent long-standing mixed-phase cloud bias in climate models. This will be achieved by (i) characterizing the spatial distribution of cloud thermodynamic phase in mixed-phase clouds using two different approaches, (ii) calculating the covariability of ice nucleating particles with the spatial distribution of cloud thermodynamic phase, and (iii) using in situ observations to develop the first explicit parameterization of ice depositional growth in climate models that accounts for the spatial distribution of cloud thermodynamic phase in mixed-phase clouds based on observations. The overarching goal of the proposed five-year program is to constrain the extratropical cloud feedback in order to assess whether the higher climate sensitivity in the CMIP6 models is realistic. The implications are particularly pertinent to Canada, and especially our vulnerable Arctic, which has seen rapid climate change in the past few decades.