DeepGreen: A Deep Learning Based Tree-Ring Width Data Model for Paleoclimatic Data Assimilation

对过去千年的气候变异性和变化进行检测和归因分析，是一种手段，可以据此对未来几十年和几个世纪的气候进行预测，为气候政策提供信息，建设有复原力的社会。该项目旨在研究什么影响因素导致气候在十年尺度上变化，为什么以及如何变化？ 该研究将利用现有的树木年轮数据，这些数据高度复制，精确日期，并可在全球范围内获得。因此，它们构成了气候模式同化观测的主要来源。然而，使用树木年轮作为模型数据存在挑战，包括（1）响应的季节性;（2）生物信号与气候信号的区别;以及森林作为气候传感器的阈值响应。在这个项目中，研究人员建议使用深度机器学习来开发，验证和解释树木年轮的新数据模型（特别是树木年轮的宽度），方法是为机器学习组装足够大的数据集。这种新的方法框架，解释和同化的树木年轮记录在古气候模型有可能改善重建的共同时代的气候场，这反过来又可以加快检测和归因的气候变率和变化的时间尺度上的几年到几十年。该项目将通过为博士后研究科学家提供监督研究、教育推广和辅导活动，并通过支持重要的本科生研究经验，建设科学能力。该项目与国家科学基金会项目（“提供地球科学研究性学习的抢先体验”）合作，将支持地球科学多样化的努力。我们将组织一个开放的虚拟工作坊，培训和指导早期职业研究人员，旨在建立用于古气候学数据建模的深度学习框架。该项目将使用基于深度学习的方法（DeepGreen）开发，验证和解释新的树木年轮宽度（TRW）数据模型。研究人员将通过将具有相似响应特征的TRW系列聚类为聚合物来组装足够大的机器学习数据集。使用伪代理实验，将确定适合TRW建模的最小数据集大小要求和算法。然后将开发来自真实世界TRW网络的数据模型，并相对于现有的线性统计和非线性以及基于多变量过程的TRW模型评估其技能。通过从深度学习练习中推导和验证树轮宽度的数据模型，该研究旨在：（1）进一步了解现存TRW数据中包含的环境信息;（2）识别deepGreen和过程或模拟模型中的结构和观测不确定性;（3）补充针对现存TRW数据中的信息内容和观测不确定性的现有建模工作;（4）支持通过机器学习识别其他古气候观测数据模型的努力。在工作范围之外，deepGreen数据模型可以在现有的数据同化框架内使用，以开发和评估新的古气候重建。对结果的分析可能会加速对几年和几十年时间尺度上的气候变率和变化的检测和归因。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知识价值和更广泛的影响审查标准进行评估来支持。

Using detection and attribution analyses of past climate variability and change at multidecadal timescale over the last millennium is a means by which climate projections for coming decades and centuries can be contextualized to inform climate policy and build resilient societies. This project aims to investigate what influential factors cause the climate to vary on decadal timescales, why and how? The research will leverage existing Tree-ring data which are highly replicated, precisely dated, and available at global scale. As such, they constitute a major source of observations for assimilation in climate models. However, there are challenges for using tree rings as model data including (1) the seasonal nature of the response; (2) distinction of biological vs. climatic signals; and threshold responses in forests as climate sensors. In this project, the researchers propose to use deep machine learning to develop, validate and interpret new data models for tree rings (specifically, the width of tree rings) by assembling sufficiently large datasets for machine learning. This new methodological framework to interpret and assimilate tree-ring records in paleoclimate models has the potential for improving the reconstruction of climate fields over the common era which in turn could accelerate the detection and attribution of climate variability and change on timescales of years to decades. The project will build capacity for science by providing supervised research, education outreach and mentoring activities for a postdoctoral research scientist, and by supporting a significant undergraduate research experience. In partnership with NSF project (“Providing Early Access to Research & Learning in geoscienceS: PEARLS), this project will support efforts to diversify the geosciences. One open virtual workshop will be organized to train and mentor early career researchers with the aim to establish deep learning framework for data modeling in paleoclimatology.This project will use deep learning-based approach (DeepGreen) to develop, validate and interpret new data models for tree-ring width (TRW). The researchers will assemble sufficiently large datasets for machine learning by clustering TRW series with similar response characteristics into aggregates. Using pseudoproxy experiments, a minimum dataset size requirements and algorithms suitable for TRW modeling will be identified. Data models from the real-world TRW network will then be developed and their skill evaluated relative to that of existing linear statistical and nonlinear and multivariate process-based TRW models. By deriving and validating data models for tree-ring width from deep learning exercises, the research seeks to: (1) further understand the environmental information contained in extant TRW data; (2) identify structural and observational uncertainties in deepGreen and process or mimic models; (3) complement existing modeling efforts targeting the information content and observational uncertainty in extant TRW data; (4) support efforts to identify data models by machine learning for other paleoclimatic observations. Beyond the scope of the work, deepGreen data models may be used within existing data assimilation frameworks, to develop and evaluate new paleoclimate reconstructions. Analysis of the results may accelerate the detection and attribution of climate variability and change on timescales of years and decades.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.