Workshop: Creating a Virtual Climate Station with ClimateNA

Researchers occasionally need high-quality historical climate data for locations not found near an official WMO weather station. One solution to this problem is to generate computer simulated climate data from actual measurements using downscaling techniques.

ClimateNA is a climate software database that uses numerical methods to downscale and spatially interpolate a variety of climate variables to the local scale using inputs of latitude, longitude, and elevation for North America. This scale-free software is available as a Microsoft Windows computer program, R programming language package, or as a web-based application. ClimateNA can also generate a continental scale overlay raster file that can be used in a Geographic Information System.  

In this workshop, Dr. Michael Pidwirny, Associate Professor in the Department of Earth, Environmental and Geographic Sciences will demonstrate how ClimateNA can be used to generate data to be used in research.  Dr. Pidwirny with use the example of how human-caused climate change has and will affect winter snowfall, mean temperature, and rainfall at Whistler Ski Resort (elevation of 1835 meters).

WHO SHOULD ATTEND THIS WORKSHOP?

Anyone interested in how to include climate data without a nearby weather station available at their field sites.  No technical experience is required to be able to participate in this workshop.  Although we will be exploring ClimateNA which is focused on North America, tools exist for other regions of the world; the skills developed in this workshop can be applied to other regions. 

MORE INFORMATION ABOUT CLIMATENA: 

ClimateNA has been developed through research at UBC’s Faculty of Forestry.  For more information please see the open access article Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America by T Wang, A Hamann, D Spittlehouse, and C Carroll C.

The latest version of this software produces monthly climate data for the historical period 1901-2022. This data is numerically generated by using a PRISM baseline from the 1971-2000 normal period at 800-meter grid resolution and a 1901-2022 dataset from the Climate Research Unit (CRU) at the University of East Anglia. ClimateNA can also generate future predictions up to the year 2100 from a selection of IPCC AR6 climate models for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 emission scenarios.

References

Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. doi:10.1371/journal.pone.0156720

If future climate data are used, please also refer:
Mahony, CR, Wang, T; Hamann, A and Cannon, AJ, 2022. A CMIP6 ensemble for downscaled monthly climate normals over North America. International Journal of Climatology 42 (11), 5871-5891 DOI: https://doi.org/10.1002/joc.7566