The global trend of increasing temperature is predicted to affect different locations differently.
These maps display important changes for the Okanagan Valley–home of the highest density of wineries in British Columbia and the majority of vineyard acreage. Models predict higher maximum temperatures during summer, as well as decreases in precipitation, which could affect the life cycle, quality, and type of winegrape grown.
Having comprehensive data on the changing climate can help wine growers be better prepared for what may come. We leverage predictions from General Circulation Models (GCMs), created by climate scientists worldwide. These models are designed to predict how environments are likely to change using atmospheric physics to project future temperatures, precipitation, and more variables for different greenhouse gas emission scenarios. Currently, each scenario is categorized by Representative Concentration Pathways, summarizing how emissions change through 2100. The RCP with the greatest emissions (RCP 8.5) was used to generate the data shown in the maps. A consensus on climate variables was reached by using an ensemble of 15 GCMs.
Find out more about the Okanagan Valley wine regions here.
Data Source: ClimateBC (Wang et al.)
General Circulation Model: CanESM2
No Warming Scenario: 1970 – 1989
Moderate Warming Scenario: 2040 – 2059
High Warming Scenario: 2070 – 2089
Anomaly Calculation: (moderate / high warming scenario) – (no warming)
Monthly data was output by ClimateBC for the periods of years associated with the warming scenarios. Each warming scenario was averaged over five members (r11i1p1, r21i1p1, r31i1p1, r41i1p1, r51i1p1), standard deviation and coefficient of variation were calculated from the differences between each member. Mean minimum daily temperature, mean maximum daily temperature, monthly accumulated precipitation, and growing degree days > 5 were selected from each year and averaged over the warming scenario period.
Data aggregation and visualization were scripted in R. Packages listed in “References.”
1. 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
2. R Core Team (2020). R: A language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
1. tmap – Tennekes M (2018). “tmap: Thematic Maps in R.” _Journal of Statistical Software_, *84*(6), 1-39. doi: 10.18637/jss.v084.i06 (URL: https://doi.org/10.18637/jss.v084.i06).
2. tidyverse – Wickham et al., (2019). Welcome to the tidyverse.
Journal of Open Source Software, 4(43), 1686,
3. raster – Robert J. Hijmans (2020). raster: Geographic Data Analysis and Modeling. R package version 3.1-5. https://CRAN.R-project.org/package=raster
5. htmlwidgets – Ramnath Vaidyanathan, Yihui Xie, JJ Allaire, Joe Cheng and Kenton Russell (2019). htmlwidgets: HTML Widgets for R. R package version 1.5.1. https://CRAN.R-project.org/package=htmlwidgets
6. sp – Pebesma, E.J., R.S. Bivand, 2005. Classes and methods for spatial data in R. R News 5 (2), https://cran.r-project.org/doc/Rnews/.
Roger S. Bivand, Edzer Pebesma, Virgilio Gomez-Rubio, 2013. Applied spatial data analysis with R, Second edition. Springer, NY. https://asdar-book.org/
7. rgdal – Roger Bivand, Tim Keitt and Barry Rowlingson (2020). rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.5-10. https://CRAN.R-project.org/package=rgdal
8. RColorBrewer – Erich Neuwirth (2014). RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer