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

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.