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

DeepGreen：基于深度学习的树木年轮宽度数据模型，用于古气候数据同化

• 批准号: 2303530
• 负责人: Michael Evans
• 金额: $ 49.46万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303530&HistoricalAwards=false
• 关键词: DeepGreen; Deep; Learning; Based; Tree

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.

在过去的千年中，使用过去气候变化和变化的检测和属性分析是一种手段，这是一种在未来几十年和几个世纪以来的气候项目，可以将其依据，以告知气候政策并建立弹性的社会。该项目旨在调查什么影响因素导致气候在年代际时标不同，为什么以及如何？该研究将利用现有的树木环数据，这些数据被高度复制，精确过时并在全球范围内获得。因此，它们构成了攀岩模型中同化的主要观测来源。但是，使用树环作为模型数据存在挑战，包括（1）响应的季节性。 （2）生物学与气候信号的区别；作为气候传感器的森林中的阈值反应。在这个项目中，研究人员提出，使用深机学习来开发，验证和解释树环的新数据模型（特别是树环的宽度），通过组装适当的大型数据集来进行机器学习。在古气候模型中解释和吸收树木环记录的新方法学框架具有改善共同时代气候领域的重建，这反过来又可以加速气候变化的检测和归因于数十年来的时间表。该项目将通过为博士后研究科学家提供监督的研究，教育宣传和心理活动，并支持重要的本科研究经验，来增强科学能力。与NSF项目合作（“在地球科学：珍珠中提供早期进入研究和学习的访问），该项目将支持对地球科学多样化的努力。将组织一个开放的虚拟研讨会来培训和早​​期的职业研究人员，旨在建立深层学习模型的深度学习框架，以在古质学项目中使用深度学习的方法来开发基于深度学习的方法，以开发新的数据，并开发了新的数据，并开展了新的数据，并开发了（绘制新的数据）。研究人员将使用相似的响应特征来汇集机器学习，以使用相似的响应特征，并将算法的算法与现实和统计数据相对，并从事统计数据从深度学习练习中验证树木宽度的数据模型，研究试图：（1）进一步了解额外的TRW数据中包含的环境信息； （2）在深绿和过程或模拟模型中确定结构和观察不确定性； （3）完成针对额外TRW数据中信息内容和观察不确定性的现有建模工作； （4）支持通过机器学习来确定其他古气候观察的努力。除了工作范围之外，还可以在现有数据同化框架中使用深绿色数据模型，以开发和评估新的古气候重建。对结果的分析可能会加速气候变异性的检测和属性以及数十年和数十年的时间尺度的变化。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来诚实地通过评估来诚实地支持。