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

人们普遍认识到，需要在区域和十年尺度上进行气候影响预测。行业、政府和社会越来越需要与决策相关的预测性信息，以便能够适当规划和适应气候多变和变化引起的未来危险，从而降低未来成本并最大限度地发挥潜在效益。气候科学家正在进行重大努力，以了解气候系统的十年可预测性，并开发预测5-30年时间尺度上的气候的模型。然而，了解气候影响的预测能力和实现十年预测科学的全部社会价值的并行努力却很少受到关注。这一合作项目将利用现有的和正在开发的动力模型，结合使用先进统计方法的观测数据，了解我们在十年时间尺度上对气候影响的预测能力，并整合预测方法，以便它们更直接地满足社会对预测信息的需求。总体目标是改变来自多个学科的科学家和实践者对十年气候预测的概念化方式，通过实现与决策相关的十年气候预测，以改善对气候多变性和变化的规划和适应能力。将在第一部分中通过多种高影响的天气和气候现象以及多个利益攸关方群体，了解当前的预测性信息需求和使用情况。核心研究将在第二部分进行，并将通过开发结合了不确定性并对不确定性很强的新的综合统计-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性信息与决策的有效结合。一个关键的项目贡献是开发了一个原型通用跨学科研究框架，将预测能力与决策结合起来。

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.