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{"title"=>"Locally downscaled and spatially customizable climate data for historical and future periods for North America", "type"=>"journal", "authors"=>[{"first_name"=>"Tongli", "last_name"=>"Wang", "scopus_author_id"=>"56026541800"}, {"first_name"=>"Andreas", "last_name"=>"Hamann", "scopus_author_id"=>"7102254283"}, {"first_name"=>"Dave", "last_name"=>"Spittlehouse", "scopus_author_id"=>"6603054220"}, {"first_name"=>"Carlos", "last_name"=>"Carroll", "scopus_author_id"=>"7101866082"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84974849166", "pui"=>"610786592", "doi"=>"10.1371/journal.pone.0156720", "isbn"=>"1932-6203", "sgr"=>"84974849166", "pmid"=>"27275583"}, "id"=>"5a6c5bff-920e-3978-8203-cc16475ecbf9", "abstract"=>"Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.", "link"=>"http://www.mendeley.com/research/locally-downscaled-spatially-customizable-climate-data-historical-future-periods-north-america", "reader_count"=>111, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>3, "Researcher"=>28, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>3, "Student > Master"=>25, "Other"=>4, "Student > Bachelor"=>3, "Lecturer"=>5, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>3, "Researcher"=>28, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>3, "Student > Master"=>25, "Other"=>4, "Student > Bachelor"=>3, "Lecturer"=>5, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Engineering"=>4, "Environmental Science"=>47, "Biochemistry, Genetics and Molecular Biology"=>4, "Mathematics"=>1, "Agricultural and Biological Sciences"=>36, "Social Sciences"=>2, "Computer Science"=>1, "Earth and Planetary Sciences"=>10}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Social Sciences"=>{"Social Sciences"=>2}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>10}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>36}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>47}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>2, "United Kingdom"=>2, "Spain"=>1}, "group_count"=>3}

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F1000Prime