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.

云的反馈，特别是热带高云的反馈，是对平衡气候敏感性（即二氧化碳加倍导致的全球温度上升）最不确定的贡献。热带太平洋东部地区的云在气候模式中表现出云反馈的最大传播。为了更好地了解对云反馈的控制，需要重点关注该区域云辐射效应的驱动因素，厄尔尼诺南方涛动是太平洋海洋和大气大规模耦合的典型;必须考虑与云反馈驱动因素有关的耦合。然而，辐射效应直接源于云的分布和微物理特性。这里提议的项目将利用地球观测、现有的现场活动数据和再分析数据来确定小型和大型海洋-大气对太平洋云辐射效应的影响，然后将其与该区域的全球模式变异性联系起来，一个正在进行的NERC方案CloudSense旨在减少气候敏感性方面的不确定性。该方案的两个项目DCMEX和CIRCULATES正在研究微物理学、气溶胶和大规模环流对云反馈的影响。但是，这两项研究都没有关注赤道太平洋的海洋-大气相互作用。这些项目可以指导，以及从这里提出的博士研究中学习。目的/方法/数据集描述云的变化及其在卫星上的辐射效应（GOES/CERES）对热带太平洋云的观测，建立在以前的分析基础上3.探讨观测到的海洋-大气相互作用变量对太平洋云变率的潜在重要性（例如SST、海洋热含量变率、近地面空气温度和湿度、风速、步行者/Hadley环流的强度、云底温度或气溶胶浓度）。这项工作将开始，首先将影响云的“输入”变量和“输出”云变量分解为主成分，并使用多元线性回归将输入和输出相关联。如果有必要解决科学问题，我们将发展更复杂的技术，例如自动编码器，它能够通过使用非线性关系更好地表示方差，但保留少量特征以便于解释。这种经验方法将进一步发展“云控制因素”方法4，到目前为止还没有包括对海洋相互作用和重要的云微物理过程的关注。关键数据集包括MODIS/CALIPSO卫星数据，HadISST，英国气象局EN 4，和TOGA-COARE/CEPEX太平洋场信号5，6，7。将识别的重要变量（包括它们的相互作用）应用于全球模式理解。评估变量在全球气候模式中的代表性，以及这些变量是否可以解释太平洋云反馈中的大规模模式传播，或者不同的变量在模式中是否重要。