Open-Source and Open-Workflow Climate Futures Toolbox for Adaptation Planning
Summary
Global climate models are a key source of climate information and produce large amounts of spatially explicit data for various physical parameters. However, these projections have substantial uncertainties associated with them, and the datasets themselves can be difficult to work with. The project team created the first version (cst 0.1.0) of the Climate Futures Toolbox, an open source workflow in R that allows users to access downscaled climate projections data, clip data by spatial boundaries (shapefile), save the output, and generate summary tables and plots. A detailed R vignette guides users to easily generate derived variables in order to answer specific questions about their region of interest (e.g. how will the daytime high [...]
Summary
Global climate models are a key source of climate information and produce large amounts of spatially explicit data for various physical parameters. However, these projections have substantial uncertainties associated with them, and the datasets themselves can be difficult to work with. The project team created the first version (cst 0.1.0) of the Climate Futures Toolbox, an open source workflow in R that allows users to access downscaled climate projections data, clip data by spatial boundaries (shapefile), save the output, and generate summary tables and plots. A detailed R vignette guides users to easily generate derived variables in order to answer specific questions about their region of interest (e.g. how will the daytime high temperature in the month of June change by midcentury?). Managers and scientists will be able to use this resource to evaluate the potential impacts of climate change and identify appropriate and robust adaptation strategies.
CFT Concept: The image attached below shows an overview of the Climate Futures Toolbox package’s two main functions. The cstdata() function streams downscaled MACAv2 climate data for spatial regions of interest, which can be summarized into data.frame objects with daily climate data using the cst_df() function. This enables subsequent tidy climate data analysis, including generation of derived climate variables and data visualizations.
Principal Investigator : Aparna Bamzai Co-Investigator : Brian W Miller, Brian Johnson, Max Joseph, Imtiaz Rangwala Cooperator/Partner : John Gross, Gregor Schuurmann, David Lawrence