Collaborative Research: Reducing Model Uncertainty by Improving Understanding of Pacific Meridional Climate Structure during Past Warm Intervals

合作研究：通过提高对过去温暖时期太平洋经向气候结构的理解来降低模型不确定性

• 批准号: 2303568
• 负责人: Tripti Bhattacharya
• 金额: $ 30.09万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-15 至 2026-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303568&HistoricalAwards=false
• 关键词: Collaborative; Research; Reducing; Model; Uncertainty

The general science goal of the research is to advance understanding of feedback from cloud and moist convection to carbon dioxide radiative forcing, which is one of the most fundamental questions in modern climate science. This project specifically seeks to advance several key aspects of climate modeling and future climate projections by identifying model parameters that can reproduce both past and present-day sea surface temperature gradients and north-south precipitation delta-deuterium gradients. The rational for the research strategy is founded on the notion that the reduction in the tropical to mid-latitude sea surface temperature gradient, as shown by proxy records across Pacific and Atlantic Ocean basins, is a common feature of past warm intervals but remains difficult for climate models to replicate. This model-data discrepancy (termed “the low-gradient problem”) is closely tied to the parameterizations of cloud, moist convection, and ocean mixing, which remain highly uncertain and limit the confidence of predicted future climate change by models. Narrowing down this uncertainty can benefit from data from past climates and targeted paleoclimate simulations.The potential Broader Impacts of this project include supporting early career scientists, post-doctoral scholar, graduate student, and undergraduate support and mentoring, and workshops for high school students.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.

这项研究的一般科学目标是促进对云和湿对流对二氧化碳辐射强迫的反馈的理解，这是现代气候科学中最基本的问题之一。该项目特别寻求通过确定能够再现过去和现在的海面温度梯度和南北降水三角洲氘梯度的模型参数，推进气候建模和未来气候预测的几个关键方面。 研究战略的合理性是建立在这样一个概念上的，即太平洋和大西洋盆地的代用记录所显示的热带到中纬度海面温度梯度的减少是过去温暖时期的一个共同特征，但气候模型仍然难以复制。这种模式数据的差异（称为“低梯度问题”）与云、湿对流和海洋混合的参数化密切相关，这些参数化仍然具有高度的不确定性，并限制了模式预测未来气候变化的信心。缩小这种不确定性可以从过去气候和有针对性的古气候模拟数据中受益。该项目的潜在更广泛影响包括支持早期职业科学家，博士后学者，研究生和本科生的支持和指导，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。