Abstract
Climate models provide information that resource managers, policy makers, and researchers can use when planning for the future. While this information is valuable in the broad sense, the low spatial-resolution often lacks local details that resource managers and decision makers need to plan for their communities. Therefore, statistically downscaled climate projections provide a high-resolution output and offer local information that is more beneficial than coarse-resolution global climate model output. In the Red River Basin, located in the south-central U.S., this detailed information is used to develop long-term water plans. This area is prone to drought conditions and heavy precipitation events, and studies have consistently estimated that this will continue in the future. This paper introduces a dataset of statistically downscaled climate projections of daily minimum and maximum temperature and daily precipitation that is a useful tool for studies regarding climatological and hydrological aspects in the region. The dataset was created using two quantile mapping techniques to downscale the CCSM4, MPI-ESM-LR, and MIROC5 model outputs to a 0.1-degree spatial resolution. Furthermore, we describe the added value of coproduction of knowledge between climate scientists and end users, or in this case impacts modelers and decision makers, for creating climate projections that can be used for climate risk assessments. A case study of the data’s development and application is provided, detecting the mean daily changes in temperature and precipitation through the end of the century in the Red River Basin for two representative concentration pathways. After applying the users’ inputs to develop the datasets, results for this example estimate an increase in mean daily precipitation in the eastern portion of the basin and as much as a 15% decline in the west by the end of the century. Furthermore, mean daily temperature is expected to rise across the entire basin in all scenarios by up to 6–7°C.
Highlights
As the global climate changes, numerical projections from climate models provide information that aids resource managers, policy makers, and researchers in planning for the future [1, 2]
Is paper discusses the creation of downscaled datasets that include historical and future projections of daily minimum and maximum temperature and daily precipitation. e intent was to develop an ensemble of projections that stakeholders in the Red River Basin could use for climate risk assessment
We will discuss some of these nuances for the case study of building an ensemble of projections for the Red River Basin in the south-central U.S While we detail the methods for developing the dataset, we highlight some of the issues that arise as the climate science community works with those in other disciplines, including modelers and decision makers
Summary
Development of Downscaled Climate Projections: A Case Study of the Red River Basin, South-Central U.S. Received 1 March 2019; Revised 27 May 2019; Accepted 5 September 2019; Published 22 October 2019. Climate models provide information that resource managers, policy makers, and researchers can use when planning for the future. While this information is valuable in the broad sense, the low spatial-resolution often lacks local details that resource managers and decision makers need to plan for their communities. A case study of the data’s development and application is provided, detecting the mean daily changes in temperature and precipitation through the end of the century in the Red River Basin for two representative concentration pathways. Mean daily temperature is expected to rise across the entire basin in all scenarios by up to 6–7°C
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