Collaborative Research: EaSM3 Integration of Decision-Making with Predictive Capacity for Decadal Climate Impacts

人们普遍认识到，需要进行区域和十年尺度的气候影响预测。工业、政府和社会越来越需要与决策相关的预测信息，以便能够对气候变异性和变化所产生的未来危害进行适当的规划和适应，从而降低未来成本并最大限度地提高潜在效益。气候科学家正在努力了解气候系统的十年可预测性，并开发预测5-30年时间尺度气候的模型。然而，同时了解气候影响的预测能力和实现年代际预测科学的全部社会价值的努力却很少受到关注。这个合作项目将利用现有的和发展中的动力学模型，结合观测数据，使用先进的统计方法来了解我们对年代际时间尺度气候影响的预测能力，并整合预测方法，以便更直接地满足社会对预测信息的需求。总体目标是通过在决策相关方面实现年代际预测，以提高气候变率和变化的规划和适应能力，改变来自多学科的科学家和从业人员概念化年代际气候预测的方式。第一部分将在多个高影响天气和气候现象以及多个利益相关者群体中发展对当前预测信息需求和使用的理解。核心研究将在第二部分中进行，并将通过开发新的结合了不确定性并对不确定性具有鲁棒性的组合动力学建模方法来确定我们对所需信息的预测能力。信息需求和信息提供之间的迭代过程将促进熟练的预测信息与决策的有效结合。一个关键的项目贡献是开发一个原型的广义跨学科研究框架，将预测能力与决策相结合。

The need for climate impact predictions on regional and decadal scales is widely recognized. Industry, government and society increasingly require predictive information in decision-relevant terms to enable appropriate planning and adaptation to future hazards arising from climate variability and change, both to mitigate future costs and maximize potential benefits. Climate scientists are engaged in significant effort to understand the decadal predictability of the climate system and to develop models to predict the climate on 5-30 year timescales. However, parallel efforts to understand predictive capacity of climate impacts and realize the full societal value of decadal prediction science has received little attention.This collaborative project will utilize existing and developing dynamical models combined with observed data using advanced statistical methods to understand our predictive capacity for climate impacts on decadal timescales, and integrate predictive methods so that they more directly meet the societal needs for predictive information. The overarching goal is to transform how scientists from multiple disciplines and practitioners conceptualize decadal climate prediction, by enabling decadal predictions in decision relevant terms to improve planning and adaptive capacity for climate variability and change.The approach follows in two concurrent parts. Understanding of current predictive information needs and usage will be developed in Part I across multiple high-impact weather and climate phenomena and across multiple stakeholder groups. The core research will take place in Part II and will identify our predictive capacity for the needed information through development of new combined statistical-dynamical modeling approaches that incorporate and are robust to uncertainty. An iterative process between information needs and information provision will promote effective integration of skillful predictive information with decision-making. A key project contribution is the development of a prototype generalized interdisciplinary research framework to integrate predictive capacity with decision-making.