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Change","sub_category":"Climate Downscaling","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# `climr` \u003cimg src=\"logo.svg\" align=\"right\" alt=\"\" width=\"120\"/\u003e\n\n\u003c!-- badges: start --\u003e\n\n[![Lifecycle:Maturing](https://img.shields.io/badge/Lifecycle-Maturing-007EC6)](\u003cRedirect-URL\u003e)\n[![Codecov test coverage - main](https://codecov.io/gh/bcgov/climr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/bcgov/climr/?branch=main)\n\n\u003c!-- badges: end --\u003e\n\n## `climr`: An R package of downscaled climate data for North America\n\n`climr` is an R package that builds on the downscaling concepts operationalized in the \u003ca href='https://climatena.ca/' target='_blank'\u003eClimateNA\u003c/a\u003e tool (Wang et al. 2016).\nIt provides downscaling of observational and simulated climate data using change-factor (a.k.a. climate imprint) downscaling, a simple method that adds low-spatial-resolution climate anomalies to a high-spatial-resolution reference climatological map, with additional elevation adjustment for \"scale-free\" downscaling.\n`climr` is designed to be fast and to minimize local data storage requirements.\nTo do so, it uses a remote PostGIS database, and optionally caches data locally.\n\n### Subscribe\n\nWe are actively developing `climr` and releasing minor versions every month or two. \nIf you would like to receive email updates when new versions of climr are released, \nsubscribe to the `climr` GitHub repo using the following steps: \n\n1. Navigate to [https://github.com/bcgov/climr](https://github.com/bcgov/climr). \n2. Click the \"Watch\" button at the top right of the repository page.\n3. Choose \"Custom\".\n4. Select \"Releases\".\n\n### Features\n\n`climr` provides the following data:\n\n-   Three historical observational time series: (1) the 1901-2022 combined Climatic Research Unit TS dataset \n(for temperature) and Global Precipitation Climatology Centre dataset (for precipitation); (2) the 1901-2023 ClimateNA time series \n(Wang et al., 2024); and (3) the 1981-2024 Multi-Source Weather (MSWX; for temperature) and \nMulti-Source Weighted-Ensemble Precipitation (MSWEP; for precipitation), extended back to 1901 using the CRU/GPCC dataset. \n\n-   Multiple historical (1851-2014) and future (2015-2100) climate model simulations \nfor each of 13 CMIP6 global climate models, in monthly time series and 20-year normals.\n\n-   User selection of single or multiple climate variables, with derived variables \nfollowing the ClimateNA methodology of Wang et al. (2016).\n\n### Data Sources\n\nThe default reference climate maps for North America are a \ncustom 2.5km-resolution mosaic of [BC PRISM](https://www.pacificclimate.org/data/prism-climatology-and-monthly-timeseries), \n[US PRISM](https://prism.oregonstate.edu/normals/), \ndeep learning prediction (Yukon, Northwest Territories, and Alberta), and \n[Daymet](https://daymet.ornl.gov/overview) (rest of North America). \nThe climr mosaic is described in [`vignette(\"climr_methods_mosaic\")`](https://bcgov.github.io/climr/articles/methods_mosaic.html). \nThe alternative 4km-resolution ClimateNA mosaics of PRISM (BC, US, W. Canada) and WorldClim (rest of North America) are accessed from [AdaptWest](https://adaptwest.databasin.org/pages/adaptwest-climatena/).\n\nThe default historical observational time series are obtained from [Climatic Research Unit](https://crudata.uea.ac.uk/cru/data/hrg/), [Global Precipitation Climatology Centre](https://psl.noaa.gov/data/gridded/data.gpcc.html), and [ClimateNA](https://climatena.ca/) (Wang et al. 2016), . \n\nCMIP6 global climate model simulations were downloaded from the [Earth System Grid Federation](https://aims2.llnl.gov/search/cmip6). The majority of these downloads were conducted by Tongli Wang, Associate Professor at the UBC Department of Forest and Conservation Sciences.\nThe 13 global climate models selected for `climr`, and best practices for ensemble analysis, are described in Mahony et al. (2022) and summarized in [`vignette(\"climr_methods_ensembleSelection\")`](https://bcgov.github.io/climr/articles/methods_ensembleSelection.html). \n\n## Installation\n\n`climr` is only available on GitHub. To install please use:\n\n``` r\nremotes::install_github(\"bcgov/climr\")\n```\n\nIf you want to install the development version:\n\n``` r\nremotes::install_github(\"bcgov/climr@devl\")\n```\nIf you get an error on installation, try the following:\n```r\noptions(download.file.method = \"libcurl\")\n```\nand then rerun `install_github()`. On some computers, the default R download method doesn't play nicely with github. \n\n## Usage\n\nSee `vignette(\"climr_workflow_beg\")` to get started with simple `climr` workflows;\n\n## Methods\n\nFor an overview of downscaling methods used in `climr` see `vignette(\"methods_downscaling\")`\n\n## Known issues\n\n-   Downloads of time series take a long time. We recommend users dedicate some time prior to analysis to cache their time series of interest for their areas of interest in a batch. Once the time series are cached, they don't need to be downloaded again. \n-   We are still working on the documentation, examples, and vignettes. Please let us know if something isn't clear, preferably as a [GitHub](https://github.com/bcgov/climr) issue. \n\n### License\n\nCopyright 2024 Province of British Columbia\n\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n\u003chttp://www.apache.org/licenses/LICENSE-2.0\u003e\n\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and limitations under the License.\n\n`climr` logo uses icon designed by Freepik, Flaticon.com, available [here](https://www.flaticon.com/free-icon/pin_6093139).\n\n### Acknowledgements\n\nWe acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. \n\n### References\n\nMahony, C.R., T. Wang, A. Hamann, and A.J. Cannon. 2022. [A global climate model ensemble for downscaled monthly climate normals over North America](https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.7566). International Journal of Climatology. 42:5871-5891. [doi.org/10.1002/joc.7566](https://doi.org/10.1002/joc.7566)\n\nWang, Tongli, Andreas Hamann, Dave Spittlehouse, and Carlos Carroll. 2016. “Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.” Edited by Inés Álvarez. PLOS ONE 11 (6): [e0156720](https://doi.org/10.1371/journal.pone.0156720).\n\nWang, Tongli, Andreas Hamann, and Zihaohan Sang. 2024. “Monthly High-Resolution Historical Climate Data for North America Since 1901.” International Journal of Climatology. early view: [https://doi.org/10.1002/joc.8726](https://doi.org/10.1002/joc.8726).\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1002/joc.7566","https://doi.org/10.1371/journal.pone.0156720","https://doi.org/10.1002/joc.8726"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["shinyapps","arcpy","python-script","ecological-modelling","landscape-ecosystem-modelling","spades"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/9143","html_url":"https://ost.ecosyste.ms/projects/9143"}