Collaborative Research: P2C2--Constraining Cloud and Convective Parameterizations Using Paleoclimate Data Assimilation

合作研究：P2C2——利用古气候数据同化约束云和对流参数化

• 批准号: 2203001
• 负责人: Gregory Elsaesser
• 金额: $ 25.6万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203001&HistoricalAwards=false
• 关键词: Collaborative; Research; P2C2; Constraining; Cloud

The general goal of the research is to develop a new framework for advancing model representations of cloud and convective processes by integrating paleoclimate data and climate model simulations. Specifically, the research strategy consists of three main objectives: (1) conduct fully coupled, isotope-enabled simulations for the Last Glacial Maximum (LGM), mid-Holocene (MH), and pre-industrial (PI) time periods using a wide ensemble of cloud and convective parameter sets; (2) perform paleoclimate data assimilation on the perturbed parameter ensemble (PPE) using sea surface temperature (SST) and water isotope proxies, respectively; and (3) use the posteriors from the LGM, MH, and PI assimilations to identify which cloud and convection parameters provide the best match to the proxy data, then conduct present-day and doubled carbon dioxide (CO2) experiments with these parameter sets to calculate a narrowed estimate of equilibrium climate sensitivity.Equilibrium climate sensitivity (ECS) is a key climate metric that quantifies the rise in global mean surface temperature in response to doubling of atmospheric CO2 relative to PI levels. Changes in hydroclimate, temperature extremes, and other aspects of the climate system in future projections are closely tied to a model’s ECS. For decades, estimates of ECS have remained wide despite improvements from using multiple lines of evidence. One persistent source of this spread is related to cloud and convective processes, which occur at scales too small to be explicitly resolved, and thus require parameterizations to be represented in climate models. This project seeks to address this issue.The potential Broader Impacts include improvement in climate models, school term and summer internship experience for a high school teacher and graduate student in collaboration with NASA, involvement of undergraduate students in research through the Garden State Louis Stokes Alliance for Minority Participation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究的总体目标是通过整合古气候数据和气候模式模拟，开发一个新的框架，以推进云和对流过程的模型表示。 具体而言，研究策略包括三个主要目标：（1）使用广泛的云和对流参数集集合对末次盛冰期（LGM）、全新世中期（MH）和工业化前（PI）时间段进行完全耦合的同位素模拟;（2）分别利用海表温度（SST）和水同位素代用指标对扰动参数集合（PPE）进行古气候资料同化;以及（3）使用LGM、MH和PI同化的后验来识别哪些云和对流参数提供与代理数据的最佳匹配，然后用这些参数集进行现在和加倍的二氧化碳（CO2）实验，以计算平衡气候敏感性的缩小估计。是一个关键的气候指标，量化了全球平均表面温度的上升，以响应大气CO2相对于PI水平的加倍。在未来的预测中，水文气候、极端温度和气候系统的其他方面的变化与模式的ECS密切相关。几十年来，尽管使用多条证据线有所改进，但ECS的估计仍然很宽。这种扩散的一个持久来源与云和对流过程有关，这些过程发生在尺度太小而无法明确解决的情况下，因此需要在气候模式中表示参数化。 该项目旨在解决这一问题。潜在的更广泛的影响包括改进气候模型，与美国宇航局合作，为高中教师和研究生提供学期和暑期实习经验，通过花园州路易斯斯托克斯少数民族参与联盟参与研究的本科生。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用评估支持基金会的学术价值和更广泛的影响审查标准。