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

该研究的总体科学目标是促进对云和潮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-氘梯度的模式参数，来推进气候模拟和未来气候预测的几个关键方面。研究策略的合理性基于这样一种观念，即热带至中纬度海面温度梯度的降低，如太平洋和大西洋盆地的代理记录所显示的那样，是过去暖期的共同特征，但气候模式仍然难以复制。这种模式与数据的差异（称为“低梯度问题”）与云、潮湿对流和海洋混合的参数化密切相关，这些参数化仍然高度不确定，限制了模式预测未来气候变化的可信度。缩小这种不确定性可以从过去的气候数据和有针对性的古气候模拟中受益。该项目潜在的更广泛的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，以及为高中生举办的研讨会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.