Ocean-atmosphere influences on tropical Pacific cloud feedbacks

海洋大气对热带太平洋云反馈的影响

• 批准号: 2886174
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886174
• 关键词: Ocean; atmosphere; influences; tropical; Pacific

Cloud feedback, particularly resulting from tropical high cloud, is the most uncertain contribution to equilibrium climate sensitivity (i.e. global temperature rise from doubling of CO2)1,2. Clouds in the eastern tropical Pacific region exhibit some of the largest spread in cloud feedback across climate models. A focus on drivers of cloud radiative effects in the region is needed in order to better understand controls on cloud feedback.The El Nino Southern Oscillation typifies the large-scale coupling of ocean and atmosphere in the Pacific; a coupling that must be considered in relation to drivers of cloud feedback. However, radiative effects stem directly from the distribution and microphysical properties of clouds. The project proposed here will use Earth observations, existing in-situ campaign data and reanalysis data to characterise the small and large-scale ocean-atmosphere influences on the Pacific cloud radiative effect, and then relate this to global model variability in the region.An ongoing NERC programme, CloudSense, aims to reduce uncertainty in climate sensitivity. Two of the programme's projects, DCMEX and CIRCULATES, are between them studying the effects of microphysics, aerosol and large-scale circulation on cloud feedback. But neither has focused on the ocean-atmosphere interaction in the equatorial Pacific. These projects can guide, as well as learn from, the PhD research proposed here.Objectives / Methods / DatasetsCharacterise variability in clouds and their radiative effect in satellite (GOES/CERES) observations of tropical Pacific cloud, building on previous analysis3.Explore the potential importance to Pacific cloud variability of observed ocean-atmosphere interaction variables (e.g SST, ocean heat content variability, near surface air temperature and humidity, wind speed, strength of Walker/Hadley circulations, cloud base temperature, or aerosol concentration). The work will begin by decomposing "input" variables that influence cloud and "output" cloud variables into principal components and associating inputs and outputs using multiple linear regression. If it is necessary to address the scientific questions, we will progress to more sophisticated techniques, such as an autoencoder, that are better able to represent variance by using nonlinear relationships but retain a small number of features for ease of interpretability.This empirical approach will further develop the "cloud-controlling factors" approach4, which so far has not included a focus on ocean interactions and important cloud microphysical processes. Key datasets include MODIS/CALIPSO satellite data, HadISST, UK Met Office EN4, and TOGA-COARE/CEPEX Pacific field campaigns5,6,7.Apply identified important variables (including their interactions) to global model understanding. Assess the representation of variables in global climate models and whether the variables can explain the large model spread in Pacific cloud feedback, or whether different variables are important in the models.

云反馈，尤其是由热带高云引起的，是对气候敏感性平衡的最不确定的贡献（即CO2的加倍，全球温度升高）1,2。东部热带太平洋地区的云层在气候模型中表现出云反馈中最大的传播。为了更好地理解云反馈的控制，需要关注该地区云辐射效应的驱动因素。ElNino Southern振荡代表了太平洋海洋和大气的大规模耦合。必须考虑与云反馈驱动程序有关的耦合。但是，辐射效应直接源于云的分布和微物理特性。此处提出的项目将使用地球观测，现有的现场运动数据和重新分析数据来表征对太平洋云辐射效应的大小和大规模的海洋 - 大气影响，然后将其与该地区的全球模型变异性相关联。一个正在进行的NERC计划，CloudSense，旨在降低气候敏感性的不可能。该计划的两个项目，即DCMEX和循环系统，它们研究了微物理学，气溶胶和大规模循环对云反馈的影响。但是，这两个都不集中在赤道太平洋中的海洋 - 大气相互作用上。这些项目可以指导并从这里提出的博士学位研究。对象 /方法 /数据表达云中的变异性及其在卫星（GOOS / CERES）观察到的热带太平洋云的观察结果上，建立在先前的分析上。速度，沃克/哈德利循环的强度，云基温度或气溶胶浓度）。这项工作将首先分解影响云和“输出”云变量为主组件的“输入”变量，并使用多个线性回归将输入和输出关联。如果有必要解决科学问题，我们将发展到更复杂的技术（例如自动编码器），可以通过使用非线性关系来更好地代表方差，但保留了少数功能以易于解释性。这种经验方法将进一步发展为“云控制因素”的方法。到那时，它不再是对海洋互动的焦点和重要的云量构造的方法。关键数据集包括MODIS/CALIPSO卫星数据，Hadisst，UK MET Office EN4和Toga-Coare/Cepex Pacific Field活动5,6,7。应用确定了重要的变量（包括其交互），以了解全球模型的理解。评估变量在全球气候模型中的表示形式，以及变量是否可以解释太平洋云反馈中的大型模型，或者在模型中是否重要的​​变量很重要。