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

被称为“云反馈”的云对全球变暖的表面温度介导的反应，仍然是气候模型气候预测的最大范围。由PI领导的先前工作已经证明，使用单一气候模型，混合相云中过冷液体与总云水含量的比例的低偏差导致低估了气候敏感性-一种量化全球平均地表空气温度最终变化的度量标准，以响应大气二氧化碳浓度的加倍。最近，参加耦合模式相互比较项目（CMIP 6）第六阶段的最新一代气候模式预测气候敏感性的增加，这与PI工作所确定的相同混合相云偏差的总体改善有关。确定更高的气候敏感性是否现实，对于制定气候变化政策和减缓战略至关重要。 拟议的五年计划将解决最新一代气候模型的更高气候敏感性值是否现实的问题，通过解决PI以前在两个不同但平行的研究主题中的工作中未解决的问题。第一个研究主题致力于利用卫星遥感观测诊断和约束气候模式中的热带云反馈。这将通过（i）应用PI的方法来评估CMIP 6模式中云光学厚度的模拟与两个不同卫星仪器的观测结果，（ii）执行单一模式扰动参数集合模拟，以确定社区气候模式中最重要的可约束物理过程，以及（iii）基于降水的卫星观测，确定降水作为对流层外云反馈中的可约束过程速率的影响。 第二个研究主题致力于正确解释气候模式中最突出的长期混合相云偏差。这将通过（i）使用两种不同的方法来表征混合相云中云热力学相的空间分布，（ii）计算冰成核粒子与云热力学相的空间分布的协变性，以及（iii）利用现场观测，在气候模式中首次对冰沉积增长进行明确的参数化，从而解释云的空间分布基于观测的混合相云的热力学相。拟议的五年计划的首要目标是限制热带云反馈，以评估CMIP 6模式中较高的气候敏感性是否现实。这些影响对加拿大尤其重要，尤其是我们脆弱的北极地区，过去几十年来，那里的气候变化迅速。

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.