2  Overview

The climr App is a graphical interface for climr, an R package that provides downscaled climate data for North America. This app allows users to download custom climate data for their areas of interest. It also provides visualizations of the climates of North America and how they are changing. The climr App is created by the Future Forest Ecosystem Centre of the BC Ministry of Forests. If you have ideas for how we can improve it, let us know via email or GitHub.

2.1 Structure

The climr App is organized into three tabs.

  1. Get Data: Users can select points or areas of interest using one of 2 methods:

    1. Interactively select map points or draw polygons
    2. Upload a batch file (csv), digital elevation model, or spatial boundary

    Users can then select the desired parameters and downscale the data. The downscaled data can be previewed and is available for download as csv and GeoTIFF.

  2. Visualization: Users can select from 3 types of locations:

    1. Ecoregions of North America
    2. Forest Landscape Plan (FLP) Area
    3. Single map point

    Users can then visualize data for the selected area using available plots (Time Series, Bivariate, and Walter-Lieth plots).

    Users can also add climate maps as an overlay on North America.

  3. Documentation: Help files and data sources

    1. Overview: You’re reading it!
    2. Instructions: Instructions on how to use the tool
    3. Methods: Detailed background information about what’s happening “under the hood”
    4. Known issues: Documentation on current known issues with the tool
    5. Definitions: Glossary of climate variables?
    6. Providing feedback: Ways users can report issues or provide suggestions for future improvements
    7. FAQs: Frequently asked questions
    8. Resources:

2.2 Open source code

The code for this app is open source:

  • climr R package: https://github.com/bcgov/climr
  • climr App: https://github.com/bcgov/climr-app

2.3 Acknowledgements

The collaborators on the climr app are: Tirion Grice (front end developer), Kiri Daust (lead developer), Colin Mahony (science lead), and Bruno Tremblay (back end developer). We also thank Ceres Barros and Aseem Sharma for their contributions to the climr package.

climr builds on the downscaling concepts pioneered by Dr. Tongli Wang (University of British Columbia) and Dr. Andreas Hamann (University of Alberta) in the ClimateNA tool (Wang et al. 2016, 2024).

We gratefully acknowledge the following groups for making their climate data available:

  • PRISM Climate Group (PRISM monthly climatological normals for USA) (Daly et al. 2008).
  • NASA Oak Ridge National Laboratory (Daymet climatological normals for North America) (Thornton et al. 2021).
  • Pacific Climate Impacts Consortium (PRISM monthly climatological normals for BC) (PCIC 2014).
  • Climatic Research Unit, University of East Anglia (CRU gridded time series) (Harris et al. 2020).
  • GloH2O (MSWX and MSWEP gridded time series) (Beck et al. 2019, 2022).
  • Deutscher Wetterdienst Global Precipitation Climatology Centre (GPCC gridded precipitation time series) (Becker et al. 2013)

Finally, we acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for the Coupled Model Intercomparison Project (CMIP6), and we thank the global climate climate modelling groups for producing and making available their model output.

2.4 References:

Beck, Hylke E., Albert I. J. M. van Dijk, Pablo R. Larraondo, Tim R. McVicar, Ming Pan, Emanuel Dutra, and Diego G. Miralles. 2022. “MSWX: Global 3-Hourly 0.1° Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles.” Bulletin of the American Meteorological Society 103 (3): E710–32. https://doi.org/10.1175/BAMS-D-21-0145.1.

Beck, Hylke E., Eric F. Wood, Ming Pan, Colby K. Fisher, Diego G. Miralles, Albert I. J. M. van Dijk, Tim R. McVicar, and Robert F. Adler. 2019. “MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment.” Bulletin of the American Meteorological Society 100 (3): 473–500. https://doi.org/10.1175/BAMS-D-17-0138.1.

Becker, Andreas, Peter Finger, Anja Meyer-Christoffer, Bruno Rudolf, Kathrin Schamm, Udo Schneider, and Markus Ziese. 2013. “A Description of the Global Land-Surface Precipitation Data Products of the Global Precipitation Climatology Centre with Sample Applications Including Centennial (Trend) Analysis from 1901–Present.” Earth System Science Data 5 (1): 71–99. https://doi.org/10.5194/essd-5-71-2013.

Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. A. Pasteris. 2008. “Physiographically Sensitive Mapping of Climatological Temperature and Precipitation Across the Conterminous United States.” International Journal of Climatology 28 (15): 2031–64. https://doi.org/10.1002/joc.1688.

Harris, Ian, Timothy J. Osborn, Phil Jones, and David Lister. 2020. “Version 4 of the CRU TS Monthly High-Resolution Gridded Multivariate Climate Dataset.” Scientific Data 7 (1): 109. https://doi.org/10.1038/s41597-020-0453-3.

Pacific Climate Impacts Consortium. 2014. “High-Resolution 1981-2010 Gridded Precipitation and Temperature Climatologies for British Columbia.” Unpublished. https://www.pacificclimate.org/data/prism-climatology-and-monthly-timeseries.

Thornton, P. E., R. Shrestha, M. Thornton, S.-C. Kao, Y. Wei, and B. E. Wilson. 2021. “Gridded Daily Weather Data for North America with Comprehensive Uncertainty Quantification.” Scientific Data 8 (190): 1–17. https://doi.org/10.1038/s41597-021-00973-0.

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.