In EvolEcolGroup/pastclim: Manipulate Time Series of Palaeoclimate Reconstructions

knitr::opts_chunk$set(echo = TRUE)

First update pastclim to the latest commit in branch chelsa. Close RStudio and restart it before trying this out.

A quick check for the new datasets

We now have a lot of new dataset. Let's try a couple of variables to check that they all work

library(pastclim)
list_available_datasets()

CHELSA present

Start with a virtual dataset:

bio_vars <- c("bio12","temperature_01")
dataset = "CHELSA_2.1_0.5m_vsi"
download_dataset(dataset=dataset, bio_variables = bio_vars)

Now use it:

test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$bio12
test_rast$temperature_01

NOTE: bio12 is precipitation (it should go into the several thousands), whilst temperature_01 is the Jan temp (so range, from -50 to 40 or something along those lines depending on which dataset you are looking at). Ideally, the units should be informative.

Now the real dataset (downloading the files)

bio_vars <- c("bio12","temperature_01")
dataset = "CHELSA_2.1_0.5m"
download_dataset(dataset=dataset, bio_variables = bio_vars)
test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$bio12
test_rast$temperature_01

CHELSA future

Again, a virtual dataset first

bio_vars <- c("bio12","temperature_01")
dataset = "CHELSA_2.1_MPI-ESM1-2-HR_ssp370_0.5m_vsi"
download_dataset(dataset=dataset, bio_variables = bio_vars)
test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$bio12
test_rast$temperature_01

And now the downloading data:

bio_vars <- c("bio12","temperature_01")
dataset = "CHELSA_2.1_GFDL-ESM4_ssp126_0.5m"
download_dataset(dataset=dataset, bio_variables = bio_vars)
test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$bio12
test_rast$temperature_01

Paleoclim

bio_vars <- c("bio12","bio12")
dataset = "paleoclim_1.0_10m"
download_dataset(dataset=dataset, bio_variables = bio_vars)
test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$bio12
test_rast$bio12

WorldClim

bio_vars <- c("bio01","bio12")
dataset = "WorldClim_2.1_10m"
download_dataset(dataset=dataset, bio_variables = bio_vars)
test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$bio01
test_rast$bio12

bio_vars <- c("temperature_min_03")
dataset = "WorldClim_2.1_10m"
download_dataset(dataset=dataset, bio_variables = bio_vars)
test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$temperature_min_03

bio_vars <- c("altitude")
dataset = "WorldClim_2.1_10m"
download_dataset(dataset=dataset, bio_variables = bio_vars)
test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$altitude

Future projections

bio_vars <- c("bio01","bio12")
dataset = "WorldClim_2.1_MPI-ESM1-2-HR_ssp370_10m"
download_dataset(dataset=dataset, bio_variables = bio_vars)
test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$bio01
test_rast$bio12

Chelsa Trace21k

bio_vars <- c("bio06")
dataset = "CHELSA_trace21k_1.0_0.5m_vsi"
download_dataset(dataset=dataset, bio_variables = bio_vars)
test_rast<-region_series(bio_variables = bio_vars, dataset = dataset)
test_rast$bio06

Don't plot this series, it has over 200 remote time steps, with each time step hundreds of megabytes in size. But you could try etracting climate for a couple of points:

locations <- data.frame(
  name = c("Iho Eleru", "La Riera", "Chalki", "Oronsay", "Atlantis"),
  longitude = c(5, -4, 27, -6, -24), latitude = c(7, 44, 36, 56, 31),
  time_bp = c(-11200, -18738, -10227, -10200, -11600)
)
location_slice(
  x = locations, bio_variables = c("bio06"),
  dataset = "CHELSA_trace21k_1.0_0.5m_vsi", nn_interpol = FALSE
)