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Katharine Hayhoe

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A limited amount of valid scientific information about global climate change and its detrimental impacts has reached the public and exerted a positive impact on the public policy process or future planning for adaptation and mitigation. This project was designed to address this limitation by bringing together expertise in the social and communication sciences from targeted academic institutions affiliated with the Department of the Interior’s Climate Science Centers (CSCs) through a workshop. The project team brought together expertise in the social and communication sciences from targeted academic institutions, particularly experts and scholars who are affiliated with the nation’s CSCs, by means of an invited...
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In this project, we used an advanced statistical downscaling method that combines high-resolution observations with outputs from 16 different global climate models based on 4 future emission scenarios to generate the most comprehensive dataset of daily temperature and precipitation projections available for climate change impacts in the U.S. The gridded dataset covers the continental United States, southern Canada and northern Mexico at one-eighth degree resolution and Alaska at one-half degree resolution. The high-resolution projections produced by this work have been rigorously quality-controlled for both errors and biases in the global climate and statistical downscaling models. We also calculated projected future...
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In this project, we used an advanced statistical downscaling method that combines high-resolution observations with outputs from 16 different global climate models based on 4 future emission scenarios to generate the most comprehensive dataset of daily temperature and precipitation projections available for climate change impacts in the U.S. The gridded dataset covers the continental United States, southern Canada and northern Mexico at one-eighth degree resolution and Alaska at one-half degree resolution. The high-resolution projections produced by this work have been rigorously quality-controlled for both errors and biases in the global climate and statistical downscaling models. We also calculated projected future...
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This project brought together a team of researchers from the U.S. Geological Survey (USGS) and universities to develop a comprehensive web-based dataset of high-resolution (or ‘downscaled’) climate change projections, to enable scientists and decision-makers to better assess climate related ecosystem impacts. Currently, scientists and resource managers often find it difficult to use downscaled climate projections because of the multiple methodologies used to produce them and the time-consuming process required to obtain model output. In response, the research team implemented a three-part plan to provide high resolution climate data for the impact modeling community. First, a database was developed of up-to-date...
Abstract (from http://link.springer.com/article/10.1007/s10584-016-1598-0): Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies...
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