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 and decreases in precipitation, which could affect the life cycle, quality, and type of winegrape grown.
Having data on the changing climate can help winegrowers better prepare for what may come. We leverage predictions from General Circulation Models (GCMs), which are created by climate scientists worldwide. These models are designed to predict how environments are likely to change using fundamental models of our physical and biological systems to project future temperatures, precipitation, and other variables (such as fog and clouds) for different greenhouse gas emission scenarios.
Currently, each scenario is categorized by what are called ‘Representative Concentration Pathways,’ that try to summarize how emissions change through 2100. We used RCP 8.5 (a higher emissions scenario, which is slightly worse than our current trajectory today) to generate the data shown in the maps. One of the biggest sources of uncertainty in our work is the emissions scenario, thus we hope the projections shown here for the end of the century will not be the realized future. However, models across almost all scenarios converge on similar estimates for the next several decades.
No one climate model can perfectly predict the future, and all provide projections with some uncertainty. This is in part because these models build on physical and biological processes that will reach slightly different ending points based on their starting point, and because of certain processes, we do not understand well enough to perfectly predict. To address this, all GCMs have model ‘members,’ where the same exact model is run with slightly different starting conditions. Here, we present results averaged over these 5 members of the Canadian GCM, CanESM2.
Want to learn more details about the process behind our maps? Click below on Map & Data Details, Data Summary, and References.
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
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.
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.
6. sp – Pebesma, E.J., R.S. Bivand, 2005. Classes and methods for spatial data in R. R News 5 (2)
Roger S. Bivand, Edzer Pebesma, Virgilio Gomez-Rubio, 2013. Applied spatial data analysis with R, Second edition. Springer, NY.
7. rgdal – Roger Bivand, Tim Keitt and Barry Rowlingson (2020). rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.5-10.
8. RColorBrewer – Erich Neuwirth (2014). RColorBrewer: ColorBrewer Palettes. R package version 1.1-2.