Expected Climate Changes in Erlangen at +4 °C Global Warming
Author
Antonios Isidoros Avrithis
Aim of this tutorial
This tutorial examines how annual mean temperature and annual precipitation are expected to change in Erlangen, Germany, when global warming reaches +4 °C above the pre-industrial baseline (1850–1900) under the CMIP6 SSP5-8.5 scenario.
The tutorial will:
locate Germany and Erlangen on a world map;
read annual temperature- and precipitation-change rasters from the IPCC Interactive Atlas;
crop and mask the rasters to Germany;
use the Robinson projection on all maps;
plot temperature and precipitation change with different colour palettes; and
extract the raster values for Erlangen.
1. Packages
library(viridis) # colour palettes
Warning: package 'viridis' was built under R version 4.5.3
Loading required package: viridisLite
Warning: package 'viridisLite' was built under R version 4.5.2
library(terra) # raster data
Warning: package 'terra' was built under R version 4.5.3
terra 1.9.11
library(sf) # vector data
Warning: package 'sf' was built under R version 4.5.3
Linking to GEOS 3.14.1, GDAL 3.12.1, PROJ 9.7.1; sf_use_s2() is TRUE
library(rgplates) # map edge
Warning: package 'rgplates' was built under R version 4.5.3
2. Read the vector data
The data for the map plotting were already provided and downloaded to the working directory.Spatial analyses often combine vector and raster data. Here, Natural Earth provides the outlines of the world’s land areas, and the World Administrative Boundaries dataset provides country polygons. Germany will later be selected from the global dataset so that later analyses can focus only on the study area.
# Natural Earthne <- sf::st_read("ne_10m_land.shx",quiet=TRUE)# countriescountries <- sf::st_read("world-administrative-boundaries.shx",quiet=TRUE)# Germany - using a logical expressiongermany <- countries[which(countries$name=="Germany"), ]germany
Simple feature collection with 1 feature and 8 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 5.865 ymin: 47.27472 xmax: 15.03382 ymax: 55.05653
Geodetic CRS: WGS 84
status color_code region iso3 continent name iso_3166_1_
42 Member State DEU Western Europe DEU Europe Germany DE
french_shor geometry
42 Allemagne MULTIPOLYGON (((14.22555 53...
3. Create the Erlangen location
Using the coordinates of Erlangen to plot it aferwards in the map
A first, wide view plot of the whole global map with erlangen and Germany marked
plot( edgeRob,col="#9bbff4",border=NA,reset=FALSE,main="Location of Germany and Erlangen")plot( neRob$geometry,col="white",border="gray",add=TRUE)plot( germanyRob$geometry,col="orange",border="black",add=TRUE)plot( erlangenRob$geometry,pch=3,col="red",cex=2,add=TRUE)
Location of Germany and Erlangen in Robinson projection.
6. Read the IPCC Atlas raster data
The selected climate setting should be CMIP6, SSP5-8.5, +4 °C global warming, the 1850–1900 baseline, and Annual season. Under these prefenreces, the data for mean temperature and average precipitation where obtained by the IPCC Atlas webpage. For this work we use the .shx format of the files.
temperature <- terra::rast("MAT_map.nc")precipitation <- terra::rast("PREC_map.nc")temperature
temperature <- temperature[[1]]precipitation <- precipitation[[1]]
After rendering, check the output of names(temperature) and names(precipitation) above. If either file contains several layers, replace [[1]] with the correct layer name.
8. Crop and mask the rasters to Germany
Now, the rasters of temperature and precipitation that project on the area of Germany are cropped from the global reconstruction and masked on the map of the country
With extract function, the values for Erlangen climatological change are obtained by identifying the raster cell containing the coordinates of Erlangen.
Different colour palettes improve map readability, and we ensure to use a color-blind friendly pallete. The ‘inferno’ palette is used for temperature because its gradual transition from dark to bright colours is well correlated conceptually with the warming increase trend.
plot( temperatureRob,col=inferno(100),axes=FALSE,main="Annual temperature change at +4 degrees C")plot( germanyRob$geometry,col=NA,border="black",add=TRUE)plot( erlangenRob$geometry,pch=3,col="red",cex=2,add=TRUE)
Expected annual temperature change in Germany at +4 °C global warming.
12. Annual precipitation change
For the annual precipitation change a different pallete with the same criteria should be used.
plot( precipitationRob,col=viridis(100),axes=FALSE,main="Annual precipitation change at +4 degrees C")plot( germanyRob$geometry,col=NA,border="black",add=TRUE)plot( erlangenRob$geometry,pch=3,col="red",cex=2,add=TRUE)
Expected annual precipitation change in Germany at +4 °C global warming.
13. Results for Erlangen
The expected annual temperature change for the raster cell containing Erlangen is 5.04 °C relative to the 1850–1900 baseline.
The expected annual precipitation change for the raster cell containing Erlangen is 2.03% relative to the 1850–1900 baseline.
cat("Temperature change in Erlangen:", temperatureValue,"degrees C\n")
Temperature change in Erlangen: 5.038018 degrees C
cat("Precipitation change in Erlangen:", precipitationValue,"percent\n")
Precipitation change in Erlangen: 2.028173 percent
These values represent the climate-model raster cell containing Erlangen.
14. Interpretation
The +4 °C warming level refers to global mean warming. The local annual temperature change over Germany and Erlangen does not have to be exactly ranges from 4.2 to +5.2 °C but Erlangen shows about +5 °C MAT change. This is reasonable taking into account that in other global warming phases (PETM) higher latitudes showed greater warming than lower latitudes.
On the other hand, precipitation shows only value shifts, indicating an increase in annual precipitation relative to 1850–1900. However, it should be noted that there is no clue about intensified or weakened seasonality of precipitation or temperature in this investigation.
15. Conclusion
This tutorial demonstrated how raster and vector data can be combined to investigate expected climate changes in Erlangen. The analysis imported the spatial datasets, selected Germany, created the Erlangen point, cropped and masked the climate rasters, extracted the local values, projected all maps to Robinson, and plotted annual temperature and precipitation change.
Data sources
The climate data were obtained from the IPCC Working Group I Interactive Atlas and represent CMIP6 SSP5-8.5 conditions at +4 °C global warming relative to 1850–1900.
Natural Earth supplied the world land polygons. World Administrative Boundaries supplied the country polygons.
