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It was written at the `Pacific Climate Impacts\nConsortium`_ (PCIC) with support from `Environment and Climate Change\nCanada`_.\n\nThe package provides routines for statistical downscaling of coarse\nscale global climate model (GCM) output to a fine spatial resolution.\n\nPCIC's suite of routines include several different (yet related)\ndownscaling techniques. The entire process is named Bias\nCorrection/Constructed Analogues with Quantile mapping reordering\n(BCCAQ) and is composed of the following steps.\n\n* Constructed Analogues (CA)\n* Climate Imprint (CI)\n* Quantile Delta Mapping (QDM)\n* Rerank\n\nSee refer to the package documentation for details on each step and\nreferences to the corresponding scientific literature.\n\n  .. _R statistical programming language: http://www.r-project.org/\n  .. _Pacific Climate Impacts Consortium: https://pacificclimate.org/\n  .. _Environment and Climate Change Canada: http://ec.gc.ca/\n\nClimate Downscaling: What and Why?\n==================================\n\nChanges in global climate have widespread impacts on the environment,\neconomic activity, and human health, especially in high latitudes\nwhere warming is proceeding more rapidly and where ecosystems and\ntraditional lifestyles are particularly sensitive to the impacts of\nwarming.\n\nPlanning for adapting to climate change requires scientifically sound\ninformation about the future climate. Global climate models (GCMs)\nsimulate future climate under different emission scenarios. However,\nGCMs simulate average conditions over large grid cells--typically on\nthe order of 10,000 square kilometers or more per cell--which is often\ntoo coarse a resolution for regional and local applications. The use\nof original GCM data is not always the best option to provide\nadaptation-relevant information at the local scale.\n\nBias in model simulated local climate is of concern for many\napplications. For example, compared with observations, the median\ntemperature simulated by GCMs from the Coupled Model Intercomparison\nProject Phase 5 (CMIP5) shows biases relative to Climate Research Unit\nhigh-resolution gridded dataset (`CRU TS3.10`_) ranging from -3° C to\n1.5° C for seasonal and annual mean temperatures in 26 global land\nareas (`Flato et al. 2013`_).  Precipitation simulated by CMIP5 models\nis also biased relative to observations (`Flato et al. 2013`_). These\nbiases hinder the direct application of model simulated future climate\nfor impacts modelling and adaptation planning since climate impacts\nare often related to certain physical or biophysical thresholds. As a\nresult, adaptation planning often uses model simulated future climate\ninformation that has incorporated some sort of downscaling and bias\ncorrection. Additionally, climate information is more useable and is\nless prone to misinterpretation when presented in a manner specific to\nthe local community and/or impacts most relevant to a particular\nsector. High-resolution future projections of impact-relevant climate\nindices can be particularly useful in this regard.\n\nClimDown has been used to produce such bias corrected, downscaled GCMs\nfor current and future climates, and could be used to do so for\nanywhere else in the world.\n\n.. _Flato et al. 2013: http://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter09_FINAL.pdf\n.. _CRU TS3.10: http://dx.doi.org/10.1002/joc.3711\n\nInstallation\n============\n\nYou can install the latest `ClimDown release from CRAN`_ using the R\ninterpreter: ``\u003e install.packages('climdex.pcic')``\n\n.. _ClimDown release from CRAN: http://cran.r-project.org/web/packages/ClimDown/index.html\n\nIf you are interested in a development version or a specific release\nof ClimDown, you can use the `devtools` package as an installation\nalternative.::\n\n    \u003e install.packages('devtools')\n    \u003e devtools::install_github(\"pacificclimate/ClimDown\", ref=\"release\")\n    # Or\n    \u003e devtools::install_github(\"pacificclimate/ClimDown\", ref=\"1.0.1\")\n\nSystem dependencies\n-------------------\n\nClimDown reads all of its input and produces its output in `NetCDF\nformat`_ and manages numeric units using the `UDUNITS2 library`_. The\nNetCDF and udunits2 libraries are system dependency of the\npackage. Ensure that the following packages are installed under\nDebian/Ubuntu Linux systems: libnetcdf-dev, netcdf-bin, and\nlibudunits2-dev. For other systems, follow the installation `NetCDF\ninstall instructions`_ and `UDUNITS2 install instructions`_ provided\nby `Unidata`_.\n\n.. _NetCDF format: https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_introduction.html\n.. _UDUNITS2 library: https://www.unidata.ucar.edu/software/udunits/udunits-current/doc/udunits/udunits2.html\n.. _NetCDF install instructions: https://www.unidata.ucar.edu/software/netcdf/docs/getting_and_building_netcdf.html\n.. _UDUNITS2 install instructions: https://www.unidata.ucar.edu/software/udunits/udunits-current/doc/udunits/udunits2.html#Installation\n.. _Unidata: https://www.unidata.ucar.edu/\n\nNecessary Resources, Performance, and Platform\n==============================================\n\nThe BCCAQ algorithm implemented by ClimDown is a complex, multi-stage\noperation, and as such performance will vary widely depending on the\nsize of the input, the degree of parallelism selected by the user, and\nthe performance characteristics of user's system (CPU speed, available\nRAM, I/O speed).\n\nWe have `previously written`_ about some of the `complexities involved\nin downscaling performance`_, but it remains an area of active study.\n\nConsider our experience as a matter of anecdote. We typically run\nClimDown for downscaling 150 year, daily GCM simulations to a\nCanada-wide ANUSPLIN grid (approximately 10km resolution, 1068 by 510\ncells). On our Linux systems, such runs can take up to 7 days to\ncomplete. However each of the different downscaling steps has\ndifferent opportunities for parallelism and different performance\ncharacteristics. Typical of our runs is something like this:\n\n* CI: 1 core, 10 GB RAM, Run time ~ 7 hours\n* CA: 8 cores, 10 GB RAM, Run time ~ 2 hours\n* QDM: 1 core, 36 GB RAM, Run time ~ 1.5 days\n* rerank: 4 cores, 8 GB RAM, Run time ~ 4 days\n\nIn general, this downscaling technique is very expensive for large\nspatiotemporal domains. The more you can limit your domain, the faster\nyour runtime will be. For small domains, it may be possible to run\nClimDown on a typical workstation, but in general we do all of our\nproduction runs on rack-mounted supercomputers.\n\nThough Windows binaries for ClimDown are available `from CRAN`_, no\neffort has been made to optimize this package for Windows and your\nmileage may vary.\n\n.. _previously written: http://james.hiebert.name/blog/work/2016/04/26/BCCA.html\n.. _complexities involved in downscaling performance: https://github.com/pacificclimate/ClimDown/blob/doc/doc/report.md#rewriting-numerous-algorithms\n.. _from CRAN: https://cran.r-project.org/web/packages/ClimDown/index.html\n","funding_links":[],"readme_doi_urls":["http://dx.doi.org/10.1002/joc.3711"],"works":{"http://dx.doi.org/10.1002/joc.3711":{"id":"https://openalex.org/W1867005924","doi":"https://doi.org/10.1002/joc.3711","title":"Updated high‐resolution grids of monthly climatic observations – the \u003cscp\u003eCRU TS3\u003c/scp\u003e.10 Dataset","display_name":"Updated high‐resolution grids of monthly climatic observations – the \u003cscp\u003eCRU TS3\u003c/scp\u003e.10 Dataset","publication_year":2013,"publication_date":"2013-05-21","ids":{"openalex":"https://openalex.org/W1867005924","doi":"https://doi.org/10.1002/joc.3711","mag":"1867005924"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1002/joc.3711","pdf_url":null,"source":{"id":"https://openalex.org/S32061424","display_name":"International Journal of Climatology","issn_l":"0899-8418","issn":["0899-8418","1097-0088"],"is_oa":false,"is_in_doaj":false,"host_organization":"https://openalex.org/P4310320503","host_organization_name":"Wiley-Blackwell","host_organization_lineage":["https://openalex.org/P4310320503","https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley-Blackwell","Wiley"],"type":"journal"},"license":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-article","open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ueaeprints.uea.ac.uk/id/eprint/47192/1/badc_work_supplementary_information_post_review2_final.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011410890","display_name":"Ian Harris","orcid":null},"institutions":[{"id":"https://openalex.org/I1118541","display_name":"University of East Anglia","ror":"https://ror.org/026k5mg93","country_code":"GB","type":"education","lineage":["https://openalex.org/I1118541"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"I. 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