Merge pull request #41 from EvolEcolGroup/paleopgem2

Paleopgem2

NEWS.md

@@ -1,6 +1,10 @@
 # development
+* Add Barreto et al 2023 (based on PALEO-PGEM, covering the last 5 M years),
+and all the WorldClim data (present, and future projections with multiple models
+and emission scenarios).
 * Improve `get_ice_mask()`, `get_land_mask()`, and `distance_from_sea()` to work
   on series rather than just on slices.
+* Speed up `region_*()` functions when subsetting the extent/cropping.
 
 # pastclim 1.2.4
 * Updates on how time is handled to stay in sync with changes in `terra`.

R/datasets_docs.R

@@ -81,7 +81,12 @@ NULL
 #' Documentation for the WorldClim datasets
 #'
 #' WorldClim version 2.1 is a database of high spatial resolution global weather and 
-#' climate data, covering both the present and future projections. 
+#' climate data, covering both the present and future projections. If you use this
+#' dataset, make sure to cite the original publication:
+#'
+#' Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution 
+#' climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315. 
+#' \doi{doi.org/10.1002/joc.5086}
 #' 
 #' **Present-day reconstructions** are based on the mean for the period 1970-2000, 
 #' and are available at multiple resolutions of
@@ -116,11 +121,6 @@ NULL
 #' are automatically downloaded for each combination of GCM model and SSP, and are selected
 #' as usual by defining the time in functions such as [region_slice()].
 #' 
-#' If you use this dataset, make sure to cite the original publication:
-#'
-#' Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution 
-#' climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315. 
-#' \doi{doi.org/10.1002/joc.5086}
 #'
 #' @name WorldClim_2.1
 NULL
@@ -140,3 +140,46 @@ NULL
 NULL
 #> NULL
 
+#' Documentation for the Barreto et al 2023 dataset
+#'
+#' Spatio-temporal series of monthly temperature and precipitation and 17
+#' derived bioclimatic variables covering the last 5 Ma (Pliocene–Pleistocene),
+#' at intervals of 1,000 years, and a spatial resolution of 1
+#' degrees (see Barreto et al., 2023 for details).
+#' 
+#' PALEO-PGEM-Series is downscaled to 1° × 1° spatial resolution from the 
+#' outputs of the PALEO-PGEM emulator (Holden et al., 2019), which emulates 
+#' reasonable and extensively validated global estimates of monthly temperature 
+#' and precipitation for the Plio-Pleistocene every 1 kyr at a spatial 
+#' resolution of ~5° × 5° (Holden et al., 2016, 2019).
+#' 
+#' PALEO-PGEM-Series includes the mean and the standard deviation (i.e.,
+#' standard error) of the emulated climate over 10 stochastic GCM emulations
+#' to accommodate aspects of model uncertainty. This allows users to estimate 
+#' the robustness of their results in the face of the stochastic aspects of 
+#' the emulations. For more details, see Section 2.4 in Barreto et al. (2023).
+#' 
+#' Note that this is a very large dataset, with 5001 time slices. It takes
+#' approximately 1 minute to set up each variable when creating a region_slice or
+#' region_series. However, once the object has been created, other operations tend
+#' to be much faster (especially if you subset the dataset to a small number
+#' of time steps of interest).
+#' 
+#' If you use this dataset, make sure to cite the original publications:
+#' 
+#'  Barreto, E., Holden, P. B., Edwards, N. R., & Rangel, T. F. (2023). 
+#'  PALEO-PGEM-Series: A spatial time series of the global climate over the
+#'   last 5 million years (Plio-Pleistocene). Global Ecology and
+#'    Biogeography, 00, 1– 12.
+#'    \doi{doi.org/10.1111/geb.13683}
+#'    
+#' Holden, P. B., Edwards, N. R., Rangel, T. F., Pereira, E. B., Tran, G. T.,
+#'  and Wilkinson, R. D. (2019): PALEO-PGEM v1.0: a statistical emulator of
+#'   Pliocene–Pleistocene climate, Geosci. Model Dev., 12, 5137–5155,
+#'    \doi{doi.org/10.5194/gmd-12-5137-2019}.
+#'
+#' @name Barreto2023
+NULL
+#> NULL
+
+

R/download_dataset.R

@@ -22,9 +22,10 @@ download_dataset <- function(dataset, bio_variables = NULL, annual = TRUE,
   # check the dataset exists
   available_datasets <- unique(getOption("pastclim.dataset_list")$dataset)
   if (!dataset %in% available_datasets) {
+    cat("'dataset' must be one of ")
+    get_available_datasets()
     stop(
-      "'dataset' must be one of ",
-      paste(available_datasets, collapse = ", ")
+      "Invalid 'dataset', for a comprehensive list of all possible combinations, use `list_available_datasets()`"
     )
   }
 
@@ -55,13 +56,19 @@ download_dataset <- function(dataset, bio_variables = NULL, annual = TRUE,
     )
   }
 
-  if (dataset %in% c("Krapp2021", "Beyer2020", "Example")){
-    # add biome to list of variables (we need it to generate the landmask)
-    if (!"biome" %in% bio_variables) {
-      bio_variables <- c(bio_variables, "biome")
-    }    
-  }
+  # if (dataset %in% c("Krapp2021", "Beyer2020", "Example")){
+  #   # add biome to list of variables (we need it to generate the landmask)
+  #   if (!"biome" %in% bio_variables) {
+  #     bio_variables <- c(bio_variables, "biome")
+  #   }    
+  # }
 
+  # add biome to list of variables (we need it to generate the landmask)
+  if (all((!"biome" %in% bio_variables),("biome"%in%available_variables))) {
+    bio_variables <- c(bio_variables, "biome")
+  }
+
+
   # special case for the example dataset
   # as we have a copy on the package
   if (dataset == "Example"){

R/region_series.R

@@ -47,7 +47,9 @@ region_series <-
     time_bp <- check_time_vars(time_bp = time_bp, time_ce = time_ce)
 
     check_dataset_path(dataset = dataset, path_to_nc = path_to_nc)
-
+    if (dataset=="Barreto2023"){
+      message("This is a large dataset, it might take a while...")
+    }
 
     if (!is.null(ext)){
       if(!any(inherits(ext,"SpatExtent"),

README.md

@@ -12,7 +12,8 @@ dev](https://img.shields.io/github/checks-status/EvolEcolGroup/pastclim/dev?labe
 
 This `R` library is designed to provide an easy way to extract and
 manipulate palaeoclimate reconstructions for ecological and
-anthropological analyses.
+anthropological analyses. It is also able to handle timeseries of future
+reconstructions.
 
 The functionalities of `pastclim` are described in [Leonardi et al.
 (2023)](https://doi.org/10.1111/ecog.06481). Please cite it if you use
@@ -54,10 +55,12 @@ updated for the `dev` branch of `pastclim` (on the top left of the dev
 website, the version number is in red and in the format x.x.x.9xxx,
 indicating it is a development version).
 
-Pastclim currently includes data from Beyer et al 2020, a reconstruction
-of climate based on the HadCM3 model for the last 120k years, and Krapp
-et al 2021, which covers the last 800k years. The reconstructions are
-bias-corrected and downscaled to 0.5 degree. More details on these
+Pastclim currently includes data from Beyer et al 2020 (a reconstruction
+of climate based on the HadCM3 model for the last 120k years), Krapp
+et al 2021 (which covers the last 800k years with a statistical emulator of HadCM3),
+Barreto et al 2023 (covering the last 5M years using hte PALEO-PGEM emulator),
+and the WorldClim data (present, and future projections with a number of models and 
+emission scenarios). More details on each of these
 datasets can be found
 [here](https://evolecolgroup.github.io/pastclim/articles/a1_available_datasets.html).
 There are also instructions on how to build and use [custom

data-raw/data_files/.~lock.dataset_list_included.csv#

@@ -0,0 +1 @@
+,am315,zoopc805.biosci.cam.ac.uk,18.09.2023 15:12,file:///home/am315/.config/libreoffice/4;