TRIPODS+X:RES: Collaborative Research: Data Science Frontiers in Climate Science

了解决定区域气候变异性和变化的因素是一项挑战，对全球许多地区的经济、安全和环境可持续性具有重要影响。我们对地球气候系统的大尺度动态以及相关的区域尺度气候变异性的理解和建模，极大地影响了我们预测和减轻极端气候和灾害的能力。地球观测和气候模型输出正在经历前所未有的数据量增长，这为推进气候科学创造了新的机会，但也带来了新的数据科学挑战，必须使用数学、统计学和计算机科学的工具来应对。该项目侧重于现代数据气候科学核心的两个核心挑战：(1)通过发现和量化(非)可预测性的来源，包括已知和新出现的气候模式及其相互作用和非平稳性，提高亚季节预报的预测能力；(2)了解和量化气候系统复杂的时空动态，为气候模型评估和区域预报提供指导。该项目汇集了一个跨学科团队，他们结合了水文气候科学和统计机器学习方面的专业知识，为气候诊断和预测创建了新的平台。加强对气候系统的了解以及稳健和准确的季节预报对整个社会具有广泛的影响。例如，更好的季节性预报将使水资源管理者能够为水资源分配做出可持续的决策。这个三脚架+气候项目将开发新的机器学习和网络估计方法，用于在一系列空间和时间尺度上分析气候系统，了解气候变化和变化的气候模式，并探索它们对区域水文气候学的预测能力。该项目的两个主要目标如下。目标1：开发新的分类和回归工具，以考虑高度相关的特征或协变量、高维环境中的非线性相互作用项以及气候观测中的非平稳性。这些工具将用于在存在动态演变和非平稳性的情况下，利用多维气候模式和特征向量改进区域降水的季节到次季节预报。目标2：开发网络识别方法，利用机器学习和统计学方面的最新进展，并能够解释气候数据的非平稳性和有限的时间框架。网络表示将用于分析学习到的依赖关系的结构和动态，对它们进行物理上的背景分析和解释，并量化气候模式的变化模式及其区域预测能力。重点将放在西太平洋的动态上，在那里最近发现了半球间的双向联系，有望在美国西南部和世界其他地区更早和更准确地预测季节性到亚季节性。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding the factors that determine regional climate variability and change is a challenge with important implications for the economy, security, and environmental sustainability of many regions around the globe. Our understanding and modeling of the large-scale dynamics of the Earth climate system and associated regional-scale climate variability significantly affects our ability to predict and mitigate climatic extremes and hazards. Earth observations and climate model outputs are witnessing an unprecedented increase in data volume, creating new opportunities to advance climate science but also leading to new data science challenges that must be addressed using tools from mathematics, statistics, and computer science. This project focuses on two central challenges at the heart of modern data-enabled climate science: (1) Increasing the predictive capacity of subseasonal forecasts by discovering and quantifying the sources of (un)predictability, including known and emergent climate modes and their interactions and non-stationarities; and (2) Understanding and quantifying the intricate space-time dynamics of the climate system to provide guidance for climate model assessment and regional forecasting. This project brings together an interdisciplinary team that combines expertise in both hydroclimate science and statistical machine learning to create new platforms for climate diagnostics and prognostics. The broader impacts of an enhanced knowledge of the climate system and robust and accurate seasonal forecasts have wide-ranging implications for society as a whole. For example, better seasonal forecasts will allow water resource managers to make sustainable decisions for water allocation.This TRIPODS+CLIMATE project will develop novel machine learning and network estimation methodologies for analyzing the climate system over a range of space and time scales, to understand climate modes of variability and change and to explore their predictive ability for regional hydroclimatology. The two main objectives of this project are the following. Objective 1: Develop novel classification and regression tools that account for highly-correlated features or covariates, nonlinear interaction terms in high-dimensional settings, and nonstationarity in climate observations. These tools will be used to improve seasonal-to-subseasonal forecasts of regional precipitation using multidimensional climate modes and feature vectors in the presence of evolving dynamics and nonstationarities. Objective 2: Develop network identification methods that leverage recent advances in machine learning and statistics and that can account for the nonstationarity and limited timeframe of climate data. The network representation will be used to analyze the structure and dynamics of the learned dependencies to contextualize and interpret them physically, and to quantify changing patterns in climate modes and their regional predictive capacity. Emphasis will be placed on the western Pacific dynamics where an interhemispheric bi-directional connection has recently been discovered, promising earlier and more accurate seasonal-to-subseasonal forecasts in the southwestern US and other parts of the world.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.