Time series plots of climate change — plot

Time series plots of 20th and 21st century climate change for user-selected locations and climate variables. The purposes of the plot are to:

view differences in interannual variability and climate change trends among global climate models (GCMs),
view the differences between multiple simulations of each model,
compare simulated and observed climate change from 1901 to present, and
compare time series of two different variables.

All global climate model anomalies are bias-corrected to the 1961-1990 reference period normals.

Usage

plot_timeSeries(
  X,
  var1 = "Tmin_sm",
  var2 = NULL,
  showObserved = TRUE,
  obs_ts_dataset = "climatena",
  gcms = list_gcms()[c(1, 4, 5, 6, 7, 10, 11, 12)],
  ssps = list_ssps()[1:3],
  showrange = TRUE,
  yfit = TRUE,
  cex = 1,
  mar = c(3, 3, 0.1, 4),
  showmean = TRUE,
  compile = TRUE,
  simplify = TRUE,
  refline = FALSE,
  refline.obs = TRUE,
  pal = "scenario",
  label.endyear = FALSE,
  endlabel = "change",
  yearmarkers = TRUE,
  yearlines = FALSE,
  legend_pos = "topleft"
)

Arguments

X: A data.table object produced using the function plot_timeSeries_input(). This table can include more models, scenarios, and variables than are used in individual calls to plot_timeSeries().
var1: character. A climate var. options are list_vars().
var2: character. A second climate var to plot in combination with var1. options are list_vars().
showObserved: logical. Plot a time series of observed climate.
obs_ts_dataset: character. The dataset to use for observational time series data. Options are "climatena" for the ClimateNA gridded time series or "cru.gpcc" for the combined Climatic Research Unit TS dataset (for temperature) and Global Precipitation Climatology Centre dataset (for precipitation). Defaults to NULL.
gcms: character. Vector of global climate model names. Options are list_gcms(). Defaults to NULL.
ssps: character. Vector of SSP-RCP scenarios (representative concentration pathways paired with shared socioeconomic pathways). Options are list_ssps(). Defaults to all scenarios available.
showrange: logical. Plot a shaded region indicating the minimum and maximum of the selected ensemble of GCM simulations for each selected scenario.
yfit: logical. Set the range of the y axis to the range of the visible data. If FALSE the y axis is the range of all values of var1 (and var2 if applicable) in the input table defined by X.
cex: Numeric. The magnification factor for text size. Default is 1.
mar: A numerical vector of length 4, giving the margin sizes in number of lines of text: c(bottom, left, top, right). The default is c(3,3,0.1,4).
showmean: logical. Plot the ensemble mean time series. Multi-model ensemble means are calculated from the mean of simulations for each model.
compile: logical. Compile multiple global climate models into a multi-model ensemble. If FALSE the single-model ensembles are plotted individually.
simplify: logical. Simplify the ensemble range and mean using a smoothing spline.
refline: logical. Plot the 1961-1990 reference period mean for the selected var and extend this line to the year 2100 as a visual reference.
refline.obs: logical. Plot the 1961-1990 reference period mean for the observational data. This should be the same as the reference line for the GCM time series.
pal: character. color palette. Options are "scenario", for use when comparing scenarios, and "gcms", for use when comparing GCMs.
label.endyear: logical. Add a label of the final year of the observational time series.
endlabel: character. Add a label to the end of each simulated time series. Options are "change", to indicate the change in year 2100 relative to the 1961-1990 baseline, or "gcms" to indicate the global climate model.
yearmarkers: logical. Add white points to the observational time series as a visual aid.
yearlines: logical. Add vertical lines on every fifth year as a visual reference
legend_pos: character. Position of the legend. Viable options are c("bottomright", "bottomleft", "topleft", and "topright").

Value

NULL. Draws a plot in the active graphics device.

Details

The input table X provides climate data for a single location or the average of multiple locations. The purpose of conducting the generation of the input table in a separate function is to allow users to make multiple calls to plot_timeSeries() without needing to generate the inputs each time.

Some combinations of var1 and var2 are not compatible or meaningful. Examples of meaningful combinations are winter vs summer values of the same climate var or minimum vs. maximum temperatures.

Downloads of GCM time series take a long time. The plot_timeSeries_input() function can take >1hr to run for the first time it is called for a location. We are looking into ways to speed this up, but until then we recommend users dedicate some time to run this function in background. Once the time series are cached, they don't need to be downloaded again.

Examples

if(FALSE){
# data frame of arbitrary points
my_points <- data.frame(lon = c(-127.7300,-127.7500), lat = c(55.34114, 55.25), elev = c(711, 500), id = 1:2)

# generate the input data
my_data <- plot_timeSeries_input(my_points)

# use the input to create a plot
plot_timeSeries(my_data, var1 = "Tmin_sm")

# compare observational time series
plot_timeSeries(my_data, var1 = "Tmin_sm", obs_ts_dataset = c("cru.gpcc", "climatena"))

# compare mean daily minimum and maximum temperatures
plot_timeSeries(my_data, var1 = "Tmin_sm", var2 = "Tmax_sm")

# compare summer and winter temperatures (without simplifying the ensemble range)
plot_timeSeries(my_data, var1 = "Tmax_sm", var2 = "Tmax_wt", simplify = FALSE)

# compare global climate models
plot_timeSeries(my_data, gcms = list_gcms()[c(7,13)], pal = "gcms", ssps = list_ssps()[2], showmean = FALSE, compile = FALSE, simplify = FALSE, endlabel = "gcms", mar=c(3,3,0.1,6), showObserved = FALSE)

# export plot to a temporary directory, including a title
figDir <- tempdir()
png(
  filename = file.path(figDir, "plot_test.png"), type = "cairo", units = "in",
  width = 6, height = 5, pointsize = 10, res = 300
)
plot_timeSeries(my_data, var1 = "Tmin_sm", mar=c(3,3,2,4))
title("Historical and projected summer night-time warming in the Bulkley Valley, BC")
dev.off()
}