climr

An R package of downscaled climate data for North America.
https://github.com/bcgov/climr

Category: Climate Change
Sub Category: Climate Downscaling

Keywords

climate-data flnr r rstats-package

Keywords from Contributors

shinyapps arcpy python-script ecological-modelling landscape-ecosystem-modelling spades

Last synced: about 13 hours ago
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Repository metadata

An R package for downscaling monthly climate data for North America

Host: GitHub
URL: https://github.com/bcgov/climr
Owner: bcgov
Created: 2021-11-02T00:23:27.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2025-12-08T20:24:53.000Z (18 days ago)
Last Synced: 2025-12-10T07:29:52.157Z (16 days ago)
Topics: climate-data, flnr, r, rstats-package
Language: R
Homepage: https://bcgov.github.io/climr/
Size: 521 MB
Stars: 18
Watchers: 5
Forks: 6
Open Issues: 51
Releases: 6
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- Contributing: CONTRIBUTING.md
- Code of conduct: CODE_OF_CONDUCT.md

`climr`

`climr`: An R package of downscaled climate data for North America

climr is an R package that builds on the downscaling concepts operationalized in the ClimateNA tool (Wang et al. 2016).
It 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.
climr is designed to be fast and to minimize local data storage requirements.
To do so, it uses a remote PostGIS database, and optionally caches data locally.

We are actively developing climr and releasing minor versions every month or two.
If you would like to receive email updates when new versions of climr are released,
subscribe to the climr GitHub repo using the following steps:

Navigate to https://github.com/bcgov/climr.
Click the "Watch" button at the top right of the repository page.
Choose "Custom".
Select "Releases".

Features

climr provides the following data:

Three historical observational time series: (1) the 1901-2022 combined Climatic Research Unit TS dataset
(for temperature) and Global Precipitation Climatology Centre dataset (for precipitation); (2) the 1901-2023 ClimateNA time series
(Wang et al., 2024); and (3) the 1981-2024 Multi-Source Weather (MSWX; for temperature) and
Multi-Source Weighted-Ensemble Precipitation (MSWEP; for precipitation), extended back to 1901 using the CRU/GPCC dataset.
Multiple historical (1851-2014) and future (2015-2100) climate model simulations
for each of 13 CMIP6 global climate models, in monthly time series and 20-year normals.
User selection of single or multiple climate variables, with derived variables
following the ClimateNA methodology of Wang et al. (2016).

Data Sources

The default reference climate maps for North America are a
custom 2.5km-resolution mosaic of BC PRISM,
US PRISM,
deep learning prediction (Yukon, Northwest Territories, and Alberta), and
Daymet (rest of North America).
The climr mosaic is described in vignette("climr_methods_mosaic").
The alternative 4km-resolution ClimateNA mosaics of PRISM (BC, US, W. Canada) and WorldClim (rest of North America) are accessed from AdaptWest.

The default historical observational time series are obtained from Climatic Research Unit, Global Precipitation Climatology Centre, and ClimateNA (Wang et al. 2016), .

CMIP6 global climate model simulations were downloaded from the Earth System Grid Federation. The majority of these downloads were conducted by Tongli Wang, Associate Professor at the UBC Department of Forest and Conservation Sciences.
The 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").

Installation

climr is only available on GitHub. To install please use:

remotes::install_github("bcgov/climr")

If you want to install the development version:

remotes::install_github("bcgov/climr@devl")

If you get an error on installation, try the following:

options(download.file.method = "libcurl")

and then rerun install_github(). On some computers, the default R download method doesn't play nicely with github.

Usage

See vignette("climr_workflow_beg") to get started with simple climr workflows;

Methods

For an overview of downscaling methods used in climr see vignette("methods_downscaling")

Known issues

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.
We are still working on the documentation, examples, and vignettes. Please let us know if something isn't clear, preferably as a GitHub issue.

License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless 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.
See the License for the specific language governing permissions and limitations under the License.

climr logo uses icon designed by Freepik, Flaticon.com, available here.

Acknowledgements

We 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.

References

Mahony, C.R., T. Wang, A. Hamann, and A.J. Cannon. 2022. A global climate model ensemble for downscaled monthly climate normals over North America. International Journal of Climatology. 42:5871-5891. doi.org/10.1002/joc.7566

Wang, 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.

Wang, 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.

Owner metadata

Name: bcgov
Login: bcgov
Email: Developer.Experience@gov.bc.ca
Kind: organization
Description: This is the home for code that is open
Website: https://github.com/bcgov/BC-Policy-Framework-For-GitHub
Location: Canada
Twitter:
Company:
Icon url: https://avatars.githubusercontent.com/u/916280?v=4
Repositories: 2150
Last ynced at: 2024-12-16T17:13:08.511Z
Profile URL: https://github.com/bcgov

GitHub Events

Total

Create event: 23
Release event: 2
Issues event: 151
Watch event: 2
Member event: 2
Issue comment event: 126
Push event: 153
Pull request review event: 1
Pull request event: 18
Fork event: 2

Last Year

Create event: 22
Release event: 2
Issues event: 98
Watch event: 1
Issue comment event: 117
Push event: 143
Pull request review event: 1
Pull request event: 16
Fork event: 1

Committers metadata

Last synced: 17 days ago

Total Commits: 1,333
Total Committers: 15
Avg Commits per committer: 88.867
Development Distribution Score (DDS): 0.583

Commits in past year: 249
Committers in past year: 6
Avg Commits per committer in past year: 41.5
Development Distribution Score (DDS) in past year: 0.55

Name	Email	Commits
CeresBarros	c**s@g**a	556
Colin Mahony	c**y@g**a	341
Kiri	k**t@g**m	176
Bruno Tremblay	m**z@n**m	90
Ceres Barros	c**s@n**a	63
Grice	t**e@g**a	32
Daust	K**t@g**a	26
Francois Bornais	f**t@l**m	22
Nicolas Gauthier	n**r@l**m	7
Alex Chubaty	a**y@f**a	7
ewhmacken	y**u@e**m	4
Nicolas Gauthier	n**s@b**a	4
repo-mountie[bot]	4****]	3
Eliot McIntire	e**i@m**a	1
Bruno Tremblay	b**y@l**m	1

Committer domains:

Issue and Pull Request metadata

Last synced: 16 days ago

Total issues: 228
Total pull requests: 84
Average time to close issues: about 2 months
Average time to close pull requests: 21 days
Total issue authors: 15
Total pull request authors: 7
Average comments per issue: 1.77
Average comments per pull request: 1.14
Merged pull request: 59
Bot issues: 0
Bot pull requests: 0

Past year issues: 47
Past year pull requests: 12
Past year average time to close issues: 26 days
Past year average time to close pull requests: 8 days
Past year issue authors: 9
Past year pull request authors: 4
Past year average comments per issue: 2.02
Past year average comments per pull request: 0.0
Past year merged pull request: 6
Past year bot issues: 0
Past year bot pull requests: 0

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Top Issue Authors

cmahony (118)
CeresBarros (51)
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ElizabethKleynhans (2)
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question (8)
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Dependencies

DESCRIPTION cran

R >= 4.0 depends
data.table * imports
gh * imports
methods * imports
parallel * imports
terra * imports
rmarkdown * suggests
testthat >= 3.0.0 suggests

.github/workflows/R-CMD-check.yaml actions

actions/checkout v3 composite
actions/upload-artifact main composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/pkgdown.yaml actions

.github/workflows/test-coverage.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

Score: 6.942156705699469

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`climr`

`climr`: An R package of downscaled climate data for North America

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README.md

climr

climr: An R package of downscaled climate data for North America

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Features

Data Sources

Installation

Usage

Methods

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Acknowledgements

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`climr`

`climr`: An R package of downscaled climate data for North America