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Aligning Climate Models With Stakeholder Needs: Advances in Communicating Future Rainfall Uncertainties for South Florida Decision Makers
Earth and Space Science ( IF 3.1 ) Pub Date : 2020-07-07 , DOI: 10.1029/2019ea000725 Johnna M. Infanti 1, 2, 3, 4 , Ben P. Kirtman 2 , Nicholas G. Aumen 5 , John Stamm 5 , Colin Polsky 3
Earth and Space Science ( IF 3.1 ) Pub Date : 2020-07-07 , DOI: 10.1029/2019ea000725 Johnna M. Infanti 1, 2, 3, 4 , Ben P. Kirtman 2 , Nicholas G. Aumen 5 , John Stamm 5 , Colin Polsky 3
Affiliation
Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. Geological Survey workshop, “Increasing Confidence in Precipitation Projections for Everglades Restoration,” highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate‐model simulations based on user‐defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high‐resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.
中文翻译:
使气候模型与利益相关者的需求保持一致:为南佛罗里达决策者传达未来降雨不确定性的进展
未来降水量的变化对南佛罗里达州的气候数据用户至关重要。最近在美国地质调查局举办的研讨会上,“增加对大沼泽地恢复的降水预测的信心”,强调了标准气候模型输出与一些佛罗里达重要自然资源管理者的气候信息需求之间的差距。这些自然资源管理者(以下简称为“气候数据用户”)需要的定制输出要比气候建模社区通常提供的输出更多。这项研究通过概述和测试一种适应性方法,使用科学界通用的统计方法,根据用户定义的降水驱动因素从整体气候模型模拟中选择输出,从而满足这些用户需求。开发此方法是为了提供一个“决策矩阵”,该矩阵可指导气候数据用户根据每个用户的季节(冬季,夏季和每年)和状况(干燥,潮湿,中性,并且没有感兴趣的阈值事件)。决策矩阵旨在更好地传达最能代表降水驱动因素的模型子集。这些信息可以增加用户对气候模型作为自然资源规划资源的信心,并可用于指导未来的动态降尺度工作。此方法基于通过远程和本地远程连接控制降水的动态过程。我们还建议,未来在南佛罗里达州进行的气候研究应包括高分辨率的气候模型运行(即,
更新日期:2020-07-07
中文翻译:
使气候模型与利益相关者的需求保持一致:为南佛罗里达决策者传达未来降雨不确定性的进展
未来降水量的变化对南佛罗里达州的气候数据用户至关重要。最近在美国地质调查局举办的研讨会上,“增加对大沼泽地恢复的降水预测的信心”,强调了标准气候模型输出与一些佛罗里达重要自然资源管理者的气候信息需求之间的差距。这些自然资源管理者(以下简称为“气候数据用户”)需要的定制输出要比气候建模社区通常提供的输出更多。这项研究通过概述和测试一种适应性方法,使用科学界通用的统计方法,根据用户定义的降水驱动因素从整体气候模型模拟中选择输出,从而满足这些用户需求。开发此方法是为了提供一个“决策矩阵”,该矩阵可指导气候数据用户根据每个用户的季节(冬季,夏季和每年)和状况(干燥,潮湿,中性,并且没有感兴趣的阈值事件)。决策矩阵旨在更好地传达最能代表降水驱动因素的模型子集。这些信息可以增加用户对气候模型作为自然资源规划资源的信心,并可用于指导未来的动态降尺度工作。此方法基于通过远程和本地远程连接控制降水的动态过程。我们还建议,未来在南佛罗里达州进行的气候研究应包括高分辨率的气候模型运行(即,