Install the library

You will need to install the library from GitHub. For this step, you will need to use devtools (if you haven’t done so already, install it from CRAN with install.packages("devtools"). Once you have devtools, simply use:

devtools::install_github("EvolEcolGroup/pastclim")

There is a vignette with detailed step by step examples on how to use the library. You can find it on the website for the package. Or, if needed, you can build it when installing pastclim :

devtools::install_github("EvolEcolGroup/pastclim", build_vignettes = TRUE)

And read it directly in R with:

vignette("pastclim_overview", package = "pastclim")

Depending on the operating system you use, you might need additional packages to build a vignette.

NOTE: pastclim relies on terra to process rasters. There is a known bug in terra that leads to the occasional message:

"Error in x$.self$finalize() : attempt to apply non-function"

being reported. This is an error related to garbage collection, which does not affect the script being correctly executed, so it can be ignored. More discussion of this issue can be found on stackoverflow

Download the data

You will need to download climatic reconstructions before being able to do any work with pastclim. Currently the library contains two datasets: Beyer2020 which covers the last 120k years; and, for project that go further back in time, Krapp2021 which goes back to 800kya. It is possible to add additional, custom datasets, but you will need some familiarity with handling netcdf files (see the vignette on ‘custom dataset’).

vignette("custom_datasets", package = "pastclim")

A list of all datasets available can be obtained by typing

vignette("available_datasets", package = "pastclim")

Please be aware that using any dataset made available to pastclim will require to cite both pastclim and the original publication presenting the dataset. The reference to cite for pastclim can be obtained by typing

citation("pastclim")
#> 
#> To cite pastclim in publications use:
#> 
#>   Leonardi M, Hallet EY, Beyer R, Krapp M, Manica A (2022). “pastclim:
#>   an R package to easily access and use palaeoclimatic
#>   reconstructions.” bioRxiv. <URL:
#>   https://www.biorxiv.org/content/10.1101/2022.05.18.492456v1>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {pastclim: an R package to easily access and use palaeoclimatic reconstructions},
#>     author = {Michela Leonardi and Emily Y. Hallet and Robert Beyer and Mario Krapp and Andrea Manica},
#>     journal = {bioRxiv},
#>     year = {2022},
#>     url = {https://www.biorxiv.org/content/10.1101/2022.05.18.492456v1},
#>   }

while the reference associated to any dataset of choice (in this case “Beyer2020”) is displayed together with the general information on it through the command:

help("Beyer2020")

#> Documentation for the Beyer2020 dataset
#> 
#> Description:
#> 
#>      This dataset covers the last 120k years, at intervals of 1/2 k
#>      years, and a resolution of 0.5 degrees in latitude and longitude.
#> 
#> Details:
#> 
#>      If you use this dataset, make sure to cite the original
#>      publication:
#> 
#>      Beyer, R.M., Krapp, M. & Manica, A. High-resolution terrestrial
#>      climate, bioclimate and vegetation for the last 120,000 years. Sci
#>      Data 7, 236 (2020). doi:doi.org/10.1038/s41597-020-0552-1
#>      <https://doi.org/doi.org/10.1038/s41597-020-0552-1>
#> 
#>      The version included in `pastclim` has the ice sheets masked, as
#>      well as internal seas (Black and Caspian Sea) removed. The latter
#>      are based on:
#> 
#>      <https://www.marineregions.org/gazetteer.php?p=details&id=4278>
#> 
#>      <https://www.marineregions.org/gazetteer.php?p=details&id=4282>
#> 
#>      As there is no reconstruction of their depth through time, modern
#>      outlines were used for all time steps.
#> 
#>      Also, for bio15, the coefficient of variation was computed after
#>      adding one to monthly estimates, and it was multiplied by 100
#>      following <https://pubs.usgs.gov/ds/691/ds691.pdf>
#> 
#>      Changelog
#> 
#>      v1.1.0 Added monthly variables. Files can be downloaded from:
#>      <https://zenodo.org/deposit/7062281>
#> 
#>      v1.0.0 Remove ice sheets and internal seas, and use correct
#>      formula for bio15. Files can be downloaded from:
#>      doi:doi.org/10.6084/m9.figshare.19723405.v1
#>      <https://doi.org/doi.org/10.6084/m9.figshare.19723405.v1>

For the datasets available in pastclim, there are functions that help you download the data and choose the variables. When you start pastclim for the first time, you will need to set the path where reconstructions are stored using set_data_path. By default, the package data path will be used:

library(pastclim)
set_data_path()

#> Loading required package: terra
#> terra 1.6.49

#> The data_path will be set to /home/andrea/.local/share/R/pastclim.
#> A copy of the Example dataset will be copied there.
#> This path will be saved by pastclim for future use.
#> Proceed? 
#> 
#> 1: Yes
#> 2: No

Press 1 if you are happy with the offered choices, and pastclim will remember your data path in future sessions. Note that your data path will look different than in this example, as it depends on your user name and operating system.

If you prefer using a custom path (e.g. in “~/my_reconstructions”), it can be set with:

set_data_path(path_to_nc = "~/my_reconstructions")

The package includes a small dataset, Example, that we will use in this vignette but is not suitable for running analyses; the real datasets are large (from 100s of Mb to a few Gb), and you will need to specify what you want to download (see below).

Let us start by inspecting the Example dataset. We can get a list of variables available for this dataset with:

get_vars_for_dataset(dataset = "Example")
#> [1] "bio01" "bio10" "bio12" "biome"

and the available time steps can be obtained with:

get_time_steps(dataset = "Example")
#> [1] -20000 -15000 -10000  -5000      0

For Beyer2020 and Krapp2021, you can get a list of available variables for each dataset with:

get_vars_for_dataset(dataset = "Beyer2020")
#>  [1] "bio01"                "bio04"                "bio05"               
#>  [4] "bio06"                "bio07"                "bio08"               
#>  [7] "bio09"                "bio10"                "bio11"               
#> [10] "bio12"                "bio13"                "bio14"               
#> [13] "bio15"                "bio16"                "bio17"               
#> [16] "bio18"                "bio19"                "npp"                 
#> [19] "lai"                  "biome"                "altitude"            
#> [22] "rugosity"             "temperature_01"       "temperature_02"      
#> [25] "temperature_03"       "temperature_04"       "temperature_05"      
#> [28] "temperature_06"       "temperature_07"       "temperature_08"      
#> [31] "temperature_09"       "temperature_10"       "temperature_11"      
#> [34] "temperature_12"       "precipitation_01"     "precipitation_02"    
#> [37] "precipitation_03"     "precipitation_04"     "precipitation_05"    
#> [40] "precipitation_06"     "precipitation_07"     "precipitation_08"    
#> [43] "precipitation_09"     "precipitation_10"     "precipitation_11"    
#> [46] "precipitation_12"     "cloudiness_01"        "cloudiness_02"       
#> [49] "cloudiness_03"        "cloudiness_04"        "cloudiness_05"       
#> [52] "cloudiness_06"        "cloudiness_07"        "cloudiness_08"       
#> [55] "cloudiness_09"        "cloudiness_10"        "cloudiness_11"       
#> [58] "cloudiness_12"        "relative_humidity_01" "relative_humidity_02"
#> [61] "relative_humidity_03" "relative_humidity_04" "relative_humidity_05"
#> [64] "relative_humidity_06" "relative_humidity_07" "relative_humidity_08"
#> [67] "relative_humidity_09" "relative_humidity_10" "relative_humidity_11"
#> [70] "relative_humidity_12" "wind_speed_01"        "wind_speed_02"       
#> [73] "wind_speed_03"        "wind_speed_04"        "wind_speed_05"       
#> [76] "wind_speed_06"        "wind_speed_07"        "wind_speed_08"       
#> [79] "wind_speed_09"        "wind_speed_10"        "wind_speed_11"       
#> [82] "wind_speed_12"        "mo_npp_01"            "mo_npp_02"           
#> [85] "mo_npp_03"            "mo_npp_04"            "mo_npp_05"           
#> [88] "mo_npp_06"            "mo_npp_07"            "mo_npp_08"           
#> [91] "mo_npp_09"            "mo_npp_10"            "mo_npp_11"           
#> [94] "mo_npp_12"

and

get_vars_for_dataset(dataset = "Krapp2021")
#>  [1] "bio01"            "bio04"            "bio05"            "bio06"           
#>  [5] "bio07"            "bio08"            "bio09"            "bio10"           
#>  [9] "bio11"            "bio12"            "bio13"            "bio14"           
#> [13] "bio15"            "bio16"            "bio17"            "bio18"           
#> [17] "bio19"            "npp"              "biome"            "lai"             
#> [21] "altitude"         "rugosity"         "temperature_01"   "temperature_02"  
#> [25] "temperature_03"   "temperature_04"   "temperature_05"   "temperature_06"  
#> [29] "temperature_07"   "temperature_08"   "temperature_09"   "temperature_10"  
#> [33] "temperature_11"   "temperature_12"   "precipitation_01" "precipitation_02"
#> [37] "precipitation_03" "precipitation_04" "precipitation_05" "precipitation_06"
#> [41] "precipitation_07" "precipitation_08" "precipitation_09" "precipitation_10"
#> [45] "precipitation_11" "precipitation_12" "mo_npp_01"        "mo_npp_02"       
#> [49] "mo_npp_03"        "mo_npp_04"        "mo_npp_05"        "mo_npp_06"       
#> [53] "mo_npp_07"        "mo_npp_08"        "mo_npp_09"        "mo_npp_10"       
#> [57] "mo_npp_11"        "mo_npp_12"        "cloudiness_01"    "cloudiness_02"   
#> [61] "cloudiness_03"    "cloudiness_04"    "cloudiness_05"    "cloudiness_06"   
#> [65] "cloudiness_07"    "cloudiness_08"    "cloudiness_09"    "cloudiness_10"   
#> [69] "cloudiness_11"    "cloudiness_12"

For monthly variables, months are coded as “_xx” at the end of the variable names; e.g. “temperature_02” is the mean monthly temperature for February. A more thorough description of each variable (including the units) can be obtained with:

get_vars_for_dataset(dataset="Example", details = TRUE)
#>   variable                           long_name           units
#> 1    bio01             annual mean temperature degrees Celsius
#> 2    bio10 mean temperature of warmest quarter degrees Celsius
#> 3    bio12                annual precipitation     mm per year
#> 4    biome                 biome (from BIOME4)

You will not be able to get the available time steps until you download the data. pastclim offers an interface to download the necessary files into your data path.

To inspect which datasets and variables have already been downloaded in the data path, we can use:

get_downloaded_datasets()
#> $Example
#> [1] "bio01" "bio10" "bio12" "biome"

Let’s now download bio01 and bio05 for the Beyer2020 dataset (this operation might take several minutes, as the datasets are large; R will pause until the download is complete):

download_dataset(dataset = "Beyer2020", bio_variables = c("bio01", "bio05"))

Note that multiple variables can be packed together into a single file, so get_downloaded_datasets() might list more variables than the ones that we chose to download (it depends on the dataset).

When upgrading pastclim, new version of various datasets might become available. This will make the previously downloaded datasets obsolete, and you might suddenly be told by pastclim that some variables have to be re-downloaded. This can lead to the accumulation of old datasets in your data path. The function clean_data_path() can be used to delete old files that are no longer needed.

Get climate for locations

Often we want to get the climate for specific locations. We can do so by using the function location_slice. With this function, we will get slices of climate for the times relevant to the locations of interest.

Let us consider five possible locations of interest: Iho Eleru (a Late Stone Age inland site in Nigeria), La Riera (a Late Palaeolithic coastal site on Spain), Chalki (a Mesolithic site on a Greek island), Oronsay (a Mesolithic site in the Scottish Hebrides), and Atlantis (the fabled submersed city mentioned by Plato). For each site we have a date (realistic, but made up) that we are interested in associating with climatic reconstructions.

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)
)
locations
#>        name longitude latitude time_bp
#> 1 Iho Eleru         5        7  -11200
#> 2  La Riera        -4       44  -18738
#> 3    Chalki        27       36  -10227
#> 4   Oronsay        -6       56  -10200
#> 5  Atlantis       -24       31  -11600

And extract their climatic conditions for bio01 and bio12:

location_slice(
  x = locations, bio_variables = c("bio01", "bio12"),
  dataset = "Example", nn_interpol = FALSE
)
#>        name longitude latitude time_bp time_bp_slice     bio01    bio12
#> 1 Iho Eleru         5        7  -11200        -10000 25.346703 2204.595
#> 2  La Riera        -4       44  -18738        -20000  5.741851 1149.570
#> 3    Chalki        27       36  -10227        -10000        NA       NA
#> 4   Oronsay        -6       56  -10200        -10000  6.937467 1362.824
#> 5  Atlantis       -24       31  -11600        -10000        NA       NA

pastclim finds the closest time steps (slices) available for a given date, and outputs the slice used in column time_bp_slice (the Example dataset that we use in this vignette has a temporal resolution of only 5k years).

Note that the last Chalki and Atlantis, for the appropriate time steps, are not available. This occurs when a location, in the reconstructions, was either under water or ice, and so pastclim can not return any estimate. In some instances, this is due to the discretisation of space in the raster. We can interpolate climate among the nearest neighbours, thus using climate reconstructions for neighbouring pixels if the location is just off one or more land pixels:

location_slice(
  x = locations, bio_variables = c("bio01", "bio12"),
  dataset = "Example", nn_interpol = TRUE)
#>        name longitude latitude time_bp time_bp_slice     bio01     bio12
#> 1 Iho Eleru         5        7  -11200        -10000 25.346703 2204.5950
#> 2  La Riera        -4       44  -18738        -20000  5.741851 1149.5703
#> 3    Chalki        27       36  -10227        -10000 17.432425  723.1012
#> 4   Oronsay        -6       56  -10200        -10000  6.937467 1362.8245
#> 5  Atlantis       -24       31  -11600        -10000        NA        NA

For Chalki, we can see that the problem is indeed that, since it is a small island, it is not well represented in the reconstructions (bear in mind that the Example dataset is very coarse in resolution), and so we can reconstruct some appropriate climate by interpolating. Atlantis, on the other hand, is the middle of the ocean, and so there is no information on what the climate might have been before became submerged (assuming it ever existed…). Note that nn_interpol = TRUE is the default for this function.

Sometimes, we want to get a time series of climatic reconstructions, thus allowing us to see how climate changed over time:

locations_ts <- location_series(
  x = locations,
  bio_variables = c("bio01", "bio12"),
  dataset = "Example")

The resulting dataframe can be subsetted to get the time series for each location (the small Example dataset only contains 5 time slices):

subset(locations_ts, name == "Iho Eleru")
#>          name longitude latitude time_bp    bio01    bio12
#> 1   Iho Eleru         5        7  -20000 22.55133 1577.238
#> 1.1 Iho Eleru         5        7  -15000 23.27008 1850.715
#> 1.2 Iho Eleru         5        7  -10000 25.34670 2204.595
#> 1.3 Iho Eleru         5        7   -5000 25.65009 2109.735
#> 1.4 Iho Eleru         5        7       0 26.77033 1840.845

Also note that for some locations, climate can be available only for certain time steps, depending on sea level and ice sheet extent. This is the case for Oronsay:

subset(locations_ts, name == "Oronsay")
#>        name longitude latitude time_bp    bio01    bio12
#> 4   Oronsay        -6       56  -20000       NA       NA
#> 4.1 Oronsay        -6       56  -15000       NA       NA
#> 4.2 Oronsay        -6       56  -10000 6.937467 1362.824
#> 4.3 Oronsay        -6       56   -5000 8.167976 1462.253
#> 4.4 Oronsay        -6       56       0 8.185000 1434.490

We can quickly plot bio1 through time for the locations:

library(ggplot2)
ggplot(data=locations_ts, aes(x=time_bp, y=bio01, group=name)) +
  geom_line(aes(col=name))+
  geom_point(aes(col=name))
#> Warning: Removed 12 rows containing missing values (`geom_line()`).
#> Warning: Removed 12 rows containing missing values (`geom_point()`).

As expected, we don’t have data for Atlantis (as it is always underwater), but we also fail to retrieve data for Chalki. This is because location_series does not interpolate from nearest neighbours by default (so, it differs from location_slice in behaviour). The rationale for this behaviour is that we are intersted in whether some locations might end underwater, and so we do not want to grab climate estimates if they have been submerged. However, in some cases (as for Chalki) it might be necessary to allow for interpolation.

Pretty labels for environmental variables can be generated with var_labels:

library(ggplot2)
ggplot(data=locations_ts, aes(x=time_bp, y=bio01, group=name)) +
  geom_line(aes(col=name))+
  geom_point(aes(col=name))+
  labs(y = var_labels("bio01", dataset="Example", abbreviated=TRUE),
       x = "time BP (yr)")
#> Warning: Removed 12 rows containing missing values (`geom_line()`).
#> Warning: Removed 12 rows containing missing values (`geom_point()`).

Note that these climatic reconstructions were extracted from the Example dataset, which is very coarse, so they should not be used to base any real inference about their environmental conditions. But note that higher resolution is not always better. It is important to consider the appropriate spatial scale that is relevant to the question at hand. Sometimes, it might be necessary to downscale the simulations (see section at the end of this vignette), or in other cases we might want to get estimates to cover an area around the specific location (e.g. if we are comparing to proxies that capture the climatology of a broad area, such as certain sediment cores that capture pollen from the broader region). location_slice and location_series can provide mean estimates for areas around the location coordinates by setting the buffer parameter (see the help pages of those functions for details).

Get climate for a region

Instead of focussing on specific locations, we might want to look at a whole region. For a given time step, we can extract a slice of climate with

climate_20k <- region_slice(
  time_bp = -20000,
  bio_variables = c("bio01", "bio10", "bio12"),
  dataset = "Example"
)

This returns a raster (technically a SpatRaster object as defined in the terra library, meaning that we can perform all standard terra raster operations on this object). To interact with SpatRaster objects, it is best to load the terra library (otherwise you might get errors as the correct method is not found, e.g. when plotting); we can then get a summary of this object:

library(terra)
climate_20k
#> class       : SpatRaster 
#> dimensions  : 150, 360, 3  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 
#> sources     : example_climate_v1.2.3.nc:BIO1  
#>               example_climate_v1.2.3.nc:BIO10  
#>               example_climate_v1.2.3.nc:BIO12  
#> varnames    : bio01 (annual mean temperature) 
#>               bio10 (mean temperature of warmest quarter) 
#>               bio12 (annual precipitation) 
#> names       :           bio01,           bio10,       bio12 
#> unit        : degrees Celsius, degrees Celsius, mm per year 
#> time (years): -18050

and plot these three variables (the layers of the raster):

terra::plot(climate_20k, main = names(climate_20k))

We can add more informative labels with var_labels:

terra::plot(climate_20k, main = var_labels(climate_20k, dataset = "Example"))

It is possible to also load a time series of rasters with the function region_series. In this case, the function returns a SpatRasterDataset, with each variable as a sub-dataset:

climate_region <- region_series(
  time_bp = list(min = -15000, max = 0), 
  bio_variables = c("bio01", "bio10", "bio12"),
  dataset = "Example")
climate_region
#> class       : SpatRasterDataset 
#> subdatasets : 3 
#> dimensions  : 150, 360 (nrow, ncol)
#> nlyr        : 4, 4, 4 
#> resolution  : 1, 1  (x, y)
#> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 
#> source(s)   : example_climate_v1.2.3.nc 
#> names       : bio01, bio10, bio12

Each of these sub-dataset is a SpatRaster, with time steps as layers:

climate_region$bio01
#> class       : SpatRaster 
#> dimensions  : 150, 360, 4  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 
#> source      : example_climate_v1.2.3.nc:BIO1 
#> varname     : bio01 (annual mean temperature) 
#> names       :    bio01_-15000,    bio01_-10000,     bio01_-5000,         bio01_0 
#> unit        : degrees Celsius, degrees Celsius, degrees Celsius, degrees Celsius 
#> time (years): -13050 to 1950

Note that terra stores dates in years a AD, not BP.

We can then plot the time series of a given variable (we relabel the plots to use years bp):

terra::plot(climate_region$bio01, main=time_bp(climate_region$bio01))

To plot all climate variables for a given time step, we can slice the time series:

slice_10k <- slice_region_series(climate_region, time_bp = -10000)
terra::plot(slice_10k, main = names(slice_10k))

Instead of giving a minimum and maximum time step, you can also provide specific time steps to region_series. Note that pastclim has a function to get a vector of the time steps for a given MIS in a dataset. For example, for MIS 1, we get:

mis1_steps <- get_mis_time_steps(mis = 1, dataset = "Example")
mis1_steps
#> [1] -10000  -5000      0

Which we can then use:

climate_mis1 <- region_series(
  time_bp = mis1_steps, 
  bio_variables = c("bio01", "bio10", "bio12"),
  dataset = "Example"
)
climate_mis1
#> class       : SpatRasterDataset 
#> subdatasets : 3 
#> dimensions  : 150, 360 (nrow, ncol)
#> nlyr        : 3, 3, 3 
#> resolution  : 1, 1  (x, y)
#> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 
#> source(s)   : example_climate_v1.2.3.nc 
#> names       : bio01, bio10, bio12

Cropping

Often we want to focus a given region. There are a number of preset rectangular extents in pastclim:

names(region_extent)
#> [1] "Africa"    "America"   "Asia"      "Europe"    "Eurasia"   "N_America"
#> [7] "Oceania"   "S_America"

We can get the corners of the European extent:

region_extent$Europe
#> [1] -15  70  33  75

And then we can extract climate only for Europe by setting ext in region_slice:

europe_climate_20k <- region_slice(
  time_bp = -20000,
  bio_variables = c("bio01", "bio10", "bio12"),
  dataset = "Example",
  ext = region_extent$Europe
)
terra::plot(europe_climate_20k,
            main = names(europe_climate_20k))

As we can see in the plot, cutting Europe using a rectangular shape keeps a portion of Northern Africa in the map. pastclim includes a number of pre-generated masks for the main continental masses, stored in the dataset region_outline in an sf::sfc object. We can get a list with:

names(region_outline)
#> [1] "Africa"    "Eurasia"   "N_America" "Oceania"   "S_America" "Europe"

We can then use the function crop within region_slice to only keep the area within the desired outline.

europe_climate_20k <- region_slice(
  time_bp = -20000,
  c("bio01", "bio10", "bio12"),
  dataset = "Example",
  crop = region_outline$Europe
)
terra::plot(europe_climate_20k,
            main = names(europe_climate_20k))

We can combine multiple regions together. For example, we can crop to Africa and Eurasia by unioning the two individual outlines:

library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
afr_eurasia <- sf::st_union(region_outline$Africa, region_outline$Eurasia)
climate_20k_afr_eurasia <- region_slice(
  time_bp = -20000,
  bio_variables  = c("bio01", "bio10", "bio12"),
  dataset = "Example",
  crop = afr_eurasia)
terra::plot(climate_20k_afr_eurasia,
            main = names(climate_20k_afr_eurasia))

Note that outlines that cross the antimeridian are split into multiple polygons (so that they can be used without projecting the rasters). For Eurasia, we have the eastern end of Siberia on the left hand side of the plot. continent_outlines_union provides the same outlines as single polygons (in case you want to use a projection).

You can also use your own custom outline (i.e. a polygon, coded as a terra::vect object) as a mask to limit the area covered by the raster. Note that you need to reuse the first vertex as the last vertex, to close the polygon:

custom_vec <- terra::vect("POLYGON ((0 70, 25 70, 50 80, 170 80, 170 10,
                              119 2.4, 119 0.8, 116 -7.6, 114 -12, 100 -40,
                              -25 -40, -25 64, 0 70))")
climate_20k_custom <- region_slice(
  time_bp = -20000,
  bio_variables = c("bio01", "bio10", "bio12"),
  dataset = "Example",
  crop = custom_vec)
terra::plot(climate_20k_custom, 
            main = names(climate_20k_custom))

region_series takes the same ext and crop options as region_slice to limit the extent of the climatic reconstructions.

Working with biomes and ice sheets

The Beyer2020 and Krapp2021 datasets include a categorical variable detailing the extension of biomes.

get_biome_classes("Example")
#>    id                           category
#> 1   0                       Water bodies
#> 2   1          Tropical evergreen forest
#> 3   2     Tropical semi-deciduous forest
#> 4   3 Tropical deciduous forest/woodland
#> 5   4         Temperate deciduous forest
#> 6   5           Temperate conifer forest
#> 7   6                  Warm mixed forest
#> 8   7                  Cool mixed forest
#> 9   8                Cool conifer forest
#> 10  9                  Cold mixed forest
#> 11 10      Evegreen taiga/montane forest
#> 12 11     Deciduous taiga/montane forest
#> 13 12                   Tropical savanna
#> 14 13      Tropical xerophytic shrubland
#> 15 14     Temperate xerophytic shrubland
#> 16 15     Temperate sclerophyll woodland
#> 17 16      Temperate broadleaved savanna
#> 18 17              Open conifer woodland
#> 19 18                    Boreal parkland
#> 20 19                 Tropical grassland
#> 21 20                Temperate grassland
#> 22 21                             Desert
#> 23 22                      Steppe tundra
#> 24 23                       Shrub tundra
#> 25 24                 Dwarf shrub tundra
#> 26 25             Prostrate shrub tundra
#> 27 26    Cushion forb lichen moss tundra
#> 28 27                             Barren
#> 29 28                           Land ice

If we need to plot the extent of a specific biome, for example the desert, we first extract the variable and then subset it to just the class we are interested in using its ID (21, in this case):

climate_20k <- region_slice(
  time_bp = -20000,
  bio_variables = c("bio01", "bio10", "biome"),
  dataset = "Example"
)
climate_20k$desert <- climate_20k$biome
climate_20k$desert[climate_20k$desert != 21] <- FALSE
climate_20k$desert[climate_20k$desert == 21] <- TRUE
terra::plot(climate_20k, main = names(climate_20k))

The climate reconstructions do not show areas under permanent ice. Ice sheets are stored as class 28 in the “biome” variable:

To plot it, we use functions designed to get ice and land masks.

climate_20k <- region_slice(
  time_bp = -20000,
  bio_variables = c("bio01", "bio10"),
  dataset = "Example"
)
climate_20k$ice_mask <- get_ice_mask(-20000, dataset = "Example")
climate_20k$land_mask <- get_land_mask(-20000, dataset = "Example")
terra::plot(climate_20k, main = names(climate_20k))

pastclim overview

Install the library

Download the data

Get climate for locations

Get climate for a region

Cropping

Working with biomes and ice sheets

Set the samples within the background

Random sampling of background

Downscaling