diff --git a/.Rbuildignore b/.Rbuildignore
index 89a8a3a8..155dccb1 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -10,3 +10,4 @@ ci_dependencies
 ^cran-comments.md
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
+^codecov.yml
\ No newline at end of file
diff --git a/.circleci/config.yml b/.circleci/config.yml
index f6ec96ab..e120021f 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -179,7 +179,7 @@ jobs:
       - run: rm deps_checksum  # Delete the temp file with the dir checksum
       
       - *devtools-check # (includes vignettes)
-      - *code-cov   
+#      - *code-cov   
 
 
 ###################################
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
new file mode 100644
index 00000000..1e4bbc70
--- /dev/null
+++ b/.github/workflows/test-coverage.yaml
@@ -0,0 +1,35 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master, rcheck]
+  pull_request:
+    branches: [main, master]
+
+name: test-coverage
+
+jobs:
+  test-coverage:
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-pandoc@v2
+#        with:
+#          pandoc-version: '2.17.1'
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::covr
+          needs: coverage
+
+      - name: Test coverage
+        run: covr::codecov(type="all")
+        shell: Rscript {0}
diff --git a/DESCRIPTION b/DESCRIPTION
index 2062b6c4..d5253577 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: pastclim
 Type: Package
 Title: Manipulate Time Series of Palaeoclimate Reconstructions 
-Version: 1.2.1
+Version: 1.2.2
 Authors@R: c(
        person("Michela", "Leonardi", role = "aut"),
        person(c("Emily","Y."), "Hallet", role = "ctb"),
diff --git a/NAMESPACE b/NAMESPACE
index 35ffae82..c3f7feeb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export(check_dataset_path)
+export(clean_data_path)
 export(climate_for_locations)
 export(climate_for_time_slice)
 export(df_from_region_series)
@@ -27,5 +28,6 @@ export(set_data_path_for_CRAN)
 export(slice_region_series)
 export(time_bp)
 export(time_series_for_locations)
+export(update_dataset_list)
 export(var_labels)
 import(terra)
diff --git a/NEWS.md b/NEWS.md
index d76bf47a..a3399a21 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,8 @@
+# pastclim 1.2.2
+* Update of Krapp2021 files to make them compatible with how terra now handles
+  time. Users will have to redownload datasets. Old files can be removed with
+  'clean_data_path()'
+
 # pastclim 1.2.1
 * Small updates for CRAN submission.
 
diff --git a/R/clean_data_path.R b/R/clean_data_path.R
new file mode 100644
index 00000000..4cdc67e9
--- /dev/null
+++ b/R/clean_data_path.R
@@ -0,0 +1,35 @@
+#' Clean the data path 
+#'
+#' This function deletes old reconstructions that have been superseded in the
+#' data_path. It assumes that the only files in data_path are part of pastclim
+#' (i.e. there are no custom datasets stored in that directory).
+#'
+#' @param ask boolean on whether the user should be asked before deleting
+#' @returns TRUE if files are deleted successfully
+#' @export
+
+clean_data_path <- function(ask=TRUE) {
+  if (is.null(get_data_path(silent=TRUE))){
+    message("The data path has not been set yet; use set_data_path() first!")
+    return(FALSE)
+  }
+  files_now <- list.files(get_data_path())
+  possible_files <- unique(getOption("pastclim.dataset_list")$file_name)
+  files_to_remove <- files_now[!files_now %in% possible_files]
+  if (length(files_to_remove)>0){
+    if (ask){
+      this_answer <- utils::menu(choices = c("yes","no"),
+                title = paste("The following files are obsolete:\n",
+                                   paste(files_to_remove,collapse = ", "),
+                                   "\n Do you want to delete them?"))
+    } else { # default to delete if we are not asking
+      this_answer <- 1
+    }
+    if (this_answer==1){
+      file.remove(file.path(get_data_path(),files_to_remove))
+    }
+  } else {
+    message("Everything is up-to-date; no files need removing.")
+  }
+  return(TRUE)
+}
diff --git a/R/download_dataset.R b/R/download_dataset.R
index f01b8216..bcb95890 100644
--- a/R/download_dataset.R
+++ b/R/download_dataset.R
@@ -16,7 +16,7 @@
 download_dataset <- function(dataset, bio_variables = NULL) {
 
   # check the dataset exists
-  available_datasets <- unique(files_by_dataset$dataset)
+  available_datasets <- unique(getOption("pastclim.dataset_list")$dataset)
   if (!dataset %in% available_datasets) {
     stop(
       "'dataset' must be one of ",
@@ -26,7 +26,7 @@ download_dataset <- function(dataset, bio_variables = NULL) {
 
   # check that the variable is available for this dataset
   available_variables <-
-    files_by_dataset$variable[files_by_dataset$dataset == dataset]
+    getOption("pastclim.dataset_list")$variable[getOption("pastclim.dataset_list")$dataset == dataset]
   # if variable is null, donwload all possible variables
   if (is.null(bio_variables)) {
     bio_variables <- available_variables
@@ -50,15 +50,21 @@ download_dataset <- function(dataset, bio_variables = NULL) {
   }
 
 
-  # download the dataset
-  for (this_var in bio_variables) {
-    file_details <- get_file_for_dataset(this_var, dataset)
-    # only download the file if it is needed
-    if (!file.exists(file.path(get_data_path(), file_details$file_name))) {
-      curl::curl_download(file_details$download_path,
-        destfile = file.path(get_data_path(), file_details$file_name),
-        quiet = FALSE
-      )
+  # special case for the example dataset
+  # as we have a copy on the package
+  if (dataset == "Example"){
+    copy_example_data()
+  } else {
+    # download the file for each variable
+    for (this_var in bio_variables) {
+      file_details <- get_file_for_dataset(this_var, dataset)
+      # only download the file if it is needed
+      if (!file.exists(file.path(get_data_path(), file_details$file_name))) {
+        curl::curl_download(file_details$download_path,
+                            destfile = file.path(get_data_path(), file_details$file_name),
+                            quiet = FALSE
+        )
+      }
     }
   }
   return(TRUE)
diff --git a/R/get_available_datasets.R b/R/get_available_datasets.R
index 7daf7660..2bd65b0e 100644
--- a/R/get_available_datasets.R
+++ b/R/get_available_datasets.R
@@ -7,5 +7,5 @@
 #' @export
 
 get_available_datasets <- function() {
-  return(unique(as.character(files_by_dataset$dataset)))
+  return(unique(as.character(getOption("pastclim.dataset_list")$dataset)))
 }
diff --git a/R/get_downloaded_datasets.R b/R/get_downloaded_datasets.R
index aa788bda..dcd1c7e2 100644
--- a/R/get_downloaded_datasets.R
+++ b/R/get_downloaded_datasets.R
@@ -12,7 +12,7 @@ get_downloaded_datasets <- function(data_path = NULL) {
     data_path <- get_data_path()
   }
   all_nc_files <- list.files(data_path)
-  files_subset <- files_by_dataset[files_by_dataset$file_name %in%
+  files_subset <- getOption("pastclim.dataset_list")[getOption("pastclim.dataset_list")$file_name %in%
     all_nc_files, ]
   downloaded_vars <- list()
   for (dataset in unique(files_subset$dataset)) {
diff --git a/R/get_file_for_dataset.R b/R/get_file_for_dataset.R
index fc749b9a..dff4b1fc 100644
--- a/R/get_file_for_dataset.R
+++ b/R/get_file_for_dataset.R
@@ -9,6 +9,6 @@
 
 get_file_for_dataset <- function(variable, dataset) {
   check_available_variable(variable, dataset)
-  return(files_by_dataset[files_by_dataset$variable %in% variable &
-    files_by_dataset$dataset == dataset, ])
+  return(getOption("pastclim.dataset_list")[getOption("pastclim.dataset_list")$variable %in% variable &
+    getOption("pastclim.dataset_list")$dataset == dataset, ])
 }
diff --git a/R/get_time_steps.R b/R/get_time_steps.R
index 54e620fe..f130b975 100644
--- a/R/get_time_steps.R
+++ b/R/get_time_steps.R
@@ -1,6 +1,6 @@
 #' Get time steps for a given dataset
 #'
-#' Get the time steps available in a given dataset.
+#' Get the time steps (in time_bp) available in a given dataset.
 #'
 #' @param dataset string defining dataset to be downloaded (a list of possible
 #' values can be obtained with \code{get_available_datasets}). If set to
@@ -21,8 +21,11 @@ get_time_steps <- function(dataset, path_to_nc = NULL) {
     path_to_nc <- file.path(get_data_path(), this_file)
   }
 
-  climate_nc <- ncdf4::nc_open(path_to_nc)
-  time_steps <- (climate_nc$dim$time$vals)
-  ncdf4::nc_close(climate_nc)
-  return(time_steps)
+  climate_nc <- terra::rast(path_to_nc, subds=1)
+  return(time_bp(climate_nc))
+  
+  # climate_nc <- ncdf4::nc_open(path_to_nc)
+  # time_steps <- (climate_nc$dim$time$vals)
+  # ncdf4::nc_close(climate_nc)
+  # return(time_steps)
 }
diff --git a/R/get_vars_for_dataset.R b/R/get_vars_for_dataset.R
index c2a84a8e..e10fe81f 100644
--- a/R/get_vars_for_dataset.R
+++ b/R/get_vars_for_dataset.R
@@ -21,9 +21,9 @@ get_vars_for_dataset <- function(dataset, path_to_nc = NULL, details=FALSE) {
     }
     check_available_dataset(dataset)
     if (!details){
-      return(files_by_dataset$variable[files_by_dataset$dataset == dataset])
+      return(getOption("pastclim.dataset_list")$variable[getOption("pastclim.dataset_list")$dataset == dataset])
     } else {
-      return(files_by_dataset[files_by_dataset$dataset == dataset,
+      return(getOption("pastclim.dataset_list")[getOption("pastclim.dataset_list")$dataset == dataset,
                               c("variable","long_name", "units")])
     }
   } else {
@@ -80,6 +80,6 @@ check_available_variable <- function(variable, dataset) {
 #'
 
 get_varname <- function(variable, dataset) {
-  return(files_by_dataset$ncvar[files_by_dataset$variable == variable &
-    files_by_dataset$dataset == dataset])
+  return(getOption("pastclim.dataset_list")$ncvar[getOption("pastclim.dataset_list")$variable == variable &
+    getOption("pastclim.dataset_list")$dataset == dataset])
 }
diff --git a/R/load_dataset_list.R b/R/load_dataset_list.R
new file mode 100644
index 00000000..3c17c04f
--- /dev/null
+++ b/R/load_dataset_list.R
@@ -0,0 +1,38 @@
+#' Load the dataset list
+#'
+#' This function returns a dataframe with the details for each variable
+#' available in every dataset. It defaults to the copy stored within the
+#' package, but it checks in case there is an udpated version stored as
+#' 'data_list.csv' in 
+#' `tools::R_user_dir("pastclim","config")`. If the latter is present, the last
+#' column, named 'dataset_list_v', provides the version of this table, and the
+#' most advanced table is used.
+#'
+#' @param on_cran boolean to make this function run on ci tests using tempdir
+#' @returns the dataset list
+#' @keywords internal
+
+load_dataset_list <- function(on_cran=FALSE) {
+  if (!on_cran){
+    config_dir <- tools::R_user_dir("pastclim", "config")
+  } else {
+    config_dir <- tempdir()
+  }
+  if (file.exists(file.path(
+    config_dir,
+    "dataset_list_included.csv"
+  ))) {
+    table_in_config <- utils::read.csv(file.path(
+      config_dir,
+      "dataset_list_included.csv"
+    ))
+    table_in_config$dataset <- as.factor(table_in_config$dataset)
+    # we should check that the new table includes all the columns in the original file
+    if (utils::compareVersion(table_in_config$dataset_list_v[1], 
+                          dataset_list_included$dataset_list_v[1])==1){
+      # need to update
+      return(table_in_config)
+    }
+  }
+  return(dataset_list_included)
+}
diff --git a/R/location_series.R b/R/location_series.R
index cb43879e..82c62064 100644
--- a/R/location_series.R
+++ b/R/location_series.R
@@ -116,7 +116,7 @@ location_series <-
 #' @export
 
 time_series_for_locations <- function(...) {
-  warning("DEPRECATED: use 'location_slice' instead")
+  warning("DEPRECATED: use 'location_series' instead")
   # if (!is.null(path_to_nc)) {
   #   stop(
   #     "the use of pastclimData is now deprecated",
diff --git a/R/set_data_path.R b/R/set_data_path.R
index 0a5309f9..5c4b0049 100644
--- a/R/set_data_path.R
+++ b/R/set_data_path.R
@@ -92,12 +92,13 @@ set_data_path <- function(path_to_nc = NULL, ask = TRUE, write_config = TRUE,
 #' @keywords internal
 
 copy_example_data <- function() {
-  if (!file.exists(file.path(get_data_path(), "example_climate_v2.nc"))) {
+  example_filename <- unique(getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset == "Example"])
+  if (!file.exists(file.path(get_data_path(), example_filename))) {
     file.copy(
-      from = system.file("/extdata/example_climate_v2.nc",
+      from = system.file(file.path("/extdata",example_filename),
                          package = "pastclim"
       ),
-      to = file.path(get_data_path(), "example_climate_v2.nc")
+      to = file.path(get_data_path(), example_filename)
     )
   }
   return(TRUE)
diff --git a/R/sysdata.rda b/R/sysdata.rda
index 88523f60..6c07e06b 100644
Binary files a/R/sysdata.rda and b/R/sysdata.rda differ
diff --git a/R/time_bp.R b/R/time_bp.R
index 79e0f755..fa93ccee 100644
--- a/R/time_bp.R
+++ b/R/time_bp.R
@@ -13,7 +13,8 @@ time_bp <- function(x){
     stop("x is not a SpatRaster")
   }
   if (x@ptr$timestep!="years"){
-    stop("the time units of SpatRaster are not 'years'",
+    # this should be escalated to an error once terra can properly set times in years (it's in dev)
+    warning("the time units of SpatRaster are not 'years'",
          " it might be a problem with the time units not being properly set in the original nc file")
   }
   time_yr<-terra::time(x)
diff --git a/R/update_dataset_list.R b/R/update_dataset_list.R
new file mode 100644
index 00000000..b5010d5b
--- /dev/null
+++ b/R/update_dataset_list.R
@@ -0,0 +1,32 @@
+#' Update the dataset list
+#'
+#' If a newer dataset list (which includes all the information about the files
+#' storing the data for pastclim), download it and start using it as
+#' 'dataset_list_included.csv' in 
+#' `tools::R_user_dir("pastclim","config")`. If the latter is present, the last
+#' column, named 'dataset_list_v', provides the version of this table, and the
+#' most advanced table is used.
+#'
+#' @param on_cran boolean to make this function run on ci tests using tempdir
+#' @returns TRUE if the dataset was updated
+#' @export
+
+update_dataset_list <- function(on_cran=FALSE) {
+  curl::curl_download("https://raw.githubusercontent.com/EvolEcolGroup/pastclim/dataset_list/dataset_list_included.csv",
+                      destfile = file.path(tempdir(), "dataset_list_included.csv"),
+                      quiet = FALSE)
+  new_table_github <- utils::read.csv(file.path(tempdir(), "dataset_list_included.csv"))
+  # if the github version is more recent, copy it into config
+  if (utils::compareVersion(new_table_github$dataset_list_v[1], 
+                            getOption("pastclim.dataset_list")$dataset_list_v[1])==1){
+    file.copy(utils::read.csv(file.path(tempdir(), "dataset_list_included.csv")),
+              to= file.path(tools::R_user_dir("pastclim", "config"),"dataset_list_included.csv"))
+    load_dataset_list()
+    message("The dataset list was updated.")
+    return(TRUE)
+  } else {
+    message("The dataset list currently installed is already the latest version.")
+    return(FALSE)
+  }
+  
+}
diff --git a/R/var_labels.R b/R/var_labels.R
index b4692816..001da5d6 100644
--- a/R/var_labels.R
+++ b/R/var_labels.R
@@ -47,7 +47,7 @@ var_labels <- function(x, dataset, with_units=TRUE,
   }
   
   # get variable details for this dataset
-  sub_table <- files_by_dataset[files_by_dataset$dataset==dataset,]
+  sub_table <- getOption("pastclim.dataset_list")[getOption("pastclim.dataset_list")$dataset==dataset,]
 
   indeces <- match(variables, sub_table$variable)
   if (any(is.na(indeces))){
@@ -69,4 +69,4 @@ var_labels <- function(x, dataset, with_units=TRUE,
     pretty_names <- c(pretty_names,this_name)
   }
   return(parse(text = pretty_names))
-}
\ No newline at end of file
+}
diff --git a/R/zzz.R b/R/zzz.R
index 972ff65f..ee3bd9c3 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -2,13 +2,13 @@
   # store the data path as an option for easy retrieval
   op <- options()
   op.pastclim <- list(
-    pastclim.data_path = get_data_path(silent=TRUE)
+    pastclim.data_path = get_data_path(silent=TRUE),
+    pastclim.dataset_list = load_dataset_list()
   )
   toset <- !(names(op.pastclim) %in% names(op))
   if (any(toset)) options(op.pastclim[toset])
 
   # check that gdal was compiled with netcdf support
-  d <- gdal(drivers=TRUE)
   if (!"netCDF" %in% terra::gdal(drivers=TRUE)$name){
     stop("The installed version of terra lacks support for reading netcdf files.\n",
          "pastclim needs netcdf support: you will need to reinstall terra,\n",
diff --git a/README.md b/README.md
index 8f621069..13b24262 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,16 @@
 # pastclim <img src="./man/figures/logo.png" align="right" alt="" width="150" />
 
 <!-- badges: start -->
-[![CircleCI](https://circleci.com/gh/EvolEcolGroup/pastclim/tree/master.svg?style=shield&circle-token=928bdbe8f065e17b22642f66a8b9c13f29f2e3fb)](https://app.circleci.com/pipelines/github/EvolEcolGroup/pastclim?branch=master)
-[![R-CMD-check dev](https://github.com/EvolEcolGroup/pastclim/actions/workflows/R-CMD-check.yaml/badge.svg?branch=dev)](https://github.com/EvolEcolGroup/pastclim/actions/workflows/R-CMD-check.yaml)
+[![R-CMD-check master](https://img.shields.io/github/checks-status/EvolEcolGroup/pastclim/master?label=master&logo=GitHub)](https://github.com/EvolEcolGroup/pastclim/actions/workflows/R-CMD-check.yaml)
+[![R-CMD-check dev](https://img.shields.io/github/checks-status/EvolEcolGroup/pastclim/dev?label=dev&logo=GitHub)](https://github.com/EvolEcolGroup/pastclim/actions/workflows/R-CMD-check.yaml)
 [![codecov](https://codecov.io/gh/EvolEcolGroup/pastclim/branch/master/graph/badge.svg?token=NflUsWlnQR)](https://app.codecov.io/gh/EvolEcolGroup/pastclim)
 <!-- badges: end -->
 
+<!-- old badges, kept for future reference
+[![CircleCI](https://circleci.com/gh/EvolEcolGroup/pastclim/tree/master.svg?style=shield&circle-token=928bdbe8f065e17b22642f66a8b9c13f29f2e3fb)](https://app.circleci.com/pipelines/github/EvolEcolGroup/pastclim?branch=master)
+[![R-CMD-check dev](https://github.com/EvolEcolGroup/pastclim/actions/workflows/R-CMD-check.yaml/badge.svg?branch=dev)](https://github.com/EvolEcolGroup/pastclim/actions/workflows/R-CMD-check.yaml)
+
+-->
 
 
 This `R` library is designed to provide an easy way to extract and manipulate palaeoclimate
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 00000000..edbacf5a
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,14 @@
+coverage:
+  status:
+    project:
+      default:
+        # basic
+        target: 10%
+        threshold: 20%
+        base: auto
+    patch:
+      default:
+        # basic
+        target: 10%
+        threshold: 20%
+        base: auto
diff --git a/inst/extdata/example_climate_v2.nc b/inst/extdata/example_climate_v1.2.2.nc
similarity index 100%
rename from inst/extdata/example_climate_v2.nc
rename to inst/extdata/example_climate_v1.2.2.nc
diff --git a/inst/rawdata_scripts/create_dataset_list_included.R b/inst/rawdata_scripts/create_dataset_list_included.R
new file mode 100644
index 00000000..b7c97150
--- /dev/null
+++ b/inst/rawdata_scripts/create_dataset_list_included.R
@@ -0,0 +1,6 @@
+# Run this script from the package root to update the internal dataset
+# of filenames for each variable, based on the dataset_list_included.csv
+dataset_list_included <-
+  read.csv("./inst/rawdata_scripts/data_files/dataset_list_included.csv")
+dataset_list_included$dataset <- as.factor(dataset_list_included$dataset)
+usethis::use_data(dataset_list_included, internal = TRUE, overwrite = TRUE)
diff --git a/inst/rawdata_scripts/create_files_by_dataset.R b/inst/rawdata_scripts/create_files_by_dataset.R
deleted file mode 100644
index d8a294d4..00000000
--- a/inst/rawdata_scripts/create_files_by_dataset.R
+++ /dev/null
@@ -1,6 +0,0 @@
-# Run this script from the package root to update the internal dataset
-# of filenames for each variable, based on the variable_table.csv
-files_by_dataset <-
-  read.csv("./inst/rawdata_scripts/data_files/variable_table.csv")
-files_by_dataset$dataset <- as.factor(files_by_dataset$dataset)
-usethis::use_data(files_by_dataset, internal = TRUE, overwrite = TRUE)
diff --git a/inst/rawdata_scripts/data_files/dataset_list_included.csv b/inst/rawdata_scripts/data_files/dataset_list_included.csv
new file mode 100644
index 00000000..8fb0c014
--- /dev/null
+++ b/inst/rawdata_scripts/data_files/dataset_list_included.csv
@@ -0,0 +1,168 @@
+variable,ncvar,dataset,monthly,file_name,download_path,file_name_orig,download_path_orig,version,long_name,abbreviated_name,time_frame,units,units_exp,dataset_list_v
+bio01,BIO1,Example,FALSE,example_climate_v1.2.2.nc,,,,1.2.2,annual mean temperature,ann. mean T,year,degrees Celsius,*degree*C*,1.2.2
+bio10,BIO10,Example,FALSE,example_climate_v1.2.2.nc,,,,1.2.2,mean temperature of warmest quarter,mean T of warmest qtr,year,degrees Celsius,*degree*C*,
+bio12,BIO12,Example,FALSE,example_climate_v1.2.2.nc,,,,1.2.2,annual precipitation,ann. P,year,mm per year,*mm~yr^-1*,
+biome,biome,Example,FALSE,example_climate_v1.2.2.nc,,,,1.2.2,biome (from BIOME4),biome,year,,,
+bio01,bio01,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,annual mean temperature,ann. mean T,year,degrees Celsius,*degree*C*,
+bio04,bio04,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,temperature seasonality,T season.,year,degrees Celsius,*degree*C*,
+bio05,bio05,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,maximum temperature of warmest month,max. T of warmest mo.,year,degrees Celsius,*degree*C*,
+bio06,bio06,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,minimum temperature of coldest month,min. T of coldest mo.,year,degrees Celsius,*degree*C*,
+bio07,bio07,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,temperature annual range (bio05-bio06),T ann. Range,year,degrees Celsius,*degree*C*,
+bio08,bio08,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature of wettest quarter,mean T of wettest qtr,year,degrees Celsius,*degree*C*,
+bio09,bio09,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature of driest quarter,mean T of driest qtr,year,degrees Celsius,*degree*C*,
+bio10,bio10,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature of warmest quarter,mean T of warmest qtr,year,degrees Celsius,*degree*C*,
+bio11,bio11,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature of coldest quarter,mean T of coldest qtr,year,degrees Celsius,*degree*C*,
+bio12,bio12,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,annual precipitation,ann. P,year,mm per year,*mm~yr^-1*,
+bio13,bio13,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation of wettest month,P of wettest mo.,year,mm per month,*mm~mo^-1*,
+bio14,bio14,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation of driest month,P of driest mo.,year,mm per month,*mm~mo^-1*,
+bio15,bio15,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation seasonality (coeff var),P season.,year,,,
+bio16,bio16,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation of wettest quarter,P of wettest qtr,year,mm per quarter,*mm~qtr^-1*,
+bio17,bio17,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation of driest quarter,P of driest qtr,year,mm per quarter,*mm~qtr^-1*,
+bio18,bio18,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation of warmest quarter,P of warmest qtr,year,mm per quarter,*mm~qtr^-1*,
+bio19,bio19,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation of coldest quarter,P of coldest qtr,year,mm per quarter,*mm~qtr^-1*,
+npp,npp,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity,NPP,year,gC per m^2 per year,*gC~m^-2~yr^-1*,
+lai,lai,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,leaf area index,LAI,year,gC per m^2,*gC~m^-2*,
+biome,biome,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,biome (from BIOME4),biome,year,,,
+altitude,altitude,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,,,1.2.2,altitude over the sea level,altitude,year,meters,*m*,
+rugosity,rugosity,Beyer2020,FALSE,Beyer2020_annual_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_annual_vars_v1.2.2.nc?download=1,,,1.2.2,rugosity (st. dev. altitude at 1 min),rugosity,year,,,
+temperature_01,temperature_01,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Jan,mean T Jan,january,degrees Celsius,*degree*C*,
+temperature_02,temperature_02,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Feb,mean T Feb,february,degrees Celsius,*degree*C*,
+temperature_03,temperature_03,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Mar,mean T Mar,march,degrees Celsius,*degree*C*,
+temperature_04,temperature_04,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Apr,mean T Apr,april,degrees Celsius,*degree*C*,
+temperature_05,temperature_05,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature May,mean T May,may,degrees Celsius,*degree*C*,
+temperature_06,temperature_06,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Jun,mean T Jun,june,degrees Celsius,*degree*C*,
+temperature_07,temperature_07,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Jul,mean T Jul,july,degrees Celsius,*degree*C*,
+temperature_08,temperature_08,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Aug,mean T Aug,august,degrees Celsius,*degree*C*,
+temperature_09,temperature_09,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Sep,mean T Sep,september,degrees Celsius,*degree*C*,
+temperature_10,temperature_10,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Oct,mean T Oct,october,degrees Celsius,*degree*C*,
+temperature_11,temperature_11,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Nov,mean T Nov,november,degrees Celsius,*degree*C*,
+temperature_12,temperature_12,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,mean temperature Dec,mean T Dec,december,degrees Celsius,*degree*C*,
+precipitation_01,precipitation_01,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Jan,P Jan,january,mm per month,*mm~mo^-1*,
+precipitation_02,precipitation_02,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Feb,P Feb,february,mm per month,*mm~mo^-1*,
+precipitation_03,precipitation_03,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Mar,P Mar,march,mm per month,*mm~mo^-1*,
+precipitation_04,precipitation_04,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Apr,P Apr,april,mm per month,*mm~mo^-1*,
+precipitation_05,precipitation_05,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation May,P May,may,mm per month,*mm~mo^-1*,
+precipitation_06,precipitation_06,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Jun,P Jun,june,mm per month,*mm~mo^-1*,
+precipitation_07,precipitation_07,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Jul,P Jul,july,mm per month,*mm~mo^-1*,
+precipitation_08,precipitation_08,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Aug,P Aug,august,mm per month,*mm~mo^-1*,
+precipitation_09,precipitation_09,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Sep,P Sep,september,mm per month,*mm~mo^-1*,
+precipitation_10,precipitation_10,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Oct,P Oct,october,mm per month,*mm~mo^-1*,
+precipitation_11,precipitation_11,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Nov,P Nov,november,mm per month,*mm~mo^-1*,
+precipitation_12,precipitation_12,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,precipitation Dec,P Dec,december,mm per month,*mm~mo^-1*,
+cloudiness_01,cloudiness_01,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Jan,cloudiness Jan,january,%,*'%'*,
+cloudiness_02,cloudiness_02,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Feb,cloudiness Feb,february,%,*'%'*,
+cloudiness_03,cloudiness_03,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Mar,cloudiness Mar,march,%,*'%'*,
+cloudiness_04,cloudiness_04,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Apr,cloudiness Apr,april,%,*'%'*,
+cloudiness_05,cloudiness_05,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness May,cloudiness May,may,%,*'%'*,
+cloudiness_06,cloudiness_06,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Jun,cloudiness Jun,june,%,*'%'*,
+cloudiness_07,cloudiness_07,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Jul,cloudiness Jul,july,%,*'%'*,
+cloudiness_08,cloudiness_08,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Aug,cloudiness Aug,august,%,*'%'*,
+cloudiness_09,cloudiness_09,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Sep,cloudiness Sep,september,%,*'%'*,
+cloudiness_10,cloudiness_10,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Oct,cloudiness Oct,october,%,*'%'*,
+cloudiness_11,cloudiness_11,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Nov,cloudiness Nov,november,%,*'%'*,
+cloudiness_12,cloudiness_12,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,cloudiness Dec,cloudiness Dec,december,%,*'%'*,
+relative_humidity_01,relative_humidity_01,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,relative humidity Jan,RH Jan,january,%,*'%'*,
+relative_humidity_02,relative_humidity_02,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,relative humidity Feb,RH Feb,february,%,*'%'*,
+relative_humidity_03,relative_humidity_03,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,relative humidity Mar,RH Mar,march,%,*'%'*,
+relative_humidity_04,relative_humidity_04,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,relative humidity Apr,RH Apr,april,%,*'%'*,
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+wind_speed_07,wind_speed_07,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,wind speed Jul,WS Jul,july,m per second,*m~s^-1*,
+wind_speed_08,wind_speed_08,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,wind speed Aug,WS Aug,august,m per second,*m~s^-1*,
+wind_speed_09,wind_speed_09,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,wind speed Sep,WS Sep,september,m per second,*m~s^-1*,
+wind_speed_10,wind_speed_10,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,wind speed Oct,WS Oct,october,m per second,*m~s^-1*,
+wind_speed_11,wind_speed_11,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,wind speed Nov,WS Nov,november,m per second,*m~s^-1*,
+wind_speed_12,wind_speed_12,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,wind speed Dec,WS Dec,december,m per second,*m~s^-1*,
+mo_npp_01,mo_npp_01,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Jan,NPP Jan,january,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_02,mo_npp_02,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Feb,NPP Feb,february,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_03,mo_npp_03,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Mar,NPP Mar,march,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_04,mo_npp_04,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Apr,NPP Apr,april,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_05,mo_npp_05,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity May,NPP May,may,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_06,mo_npp_06,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Jun,NPP Jun,june,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_07,mo_npp_07,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Jul,NPP Jul,july,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_08,mo_npp_08,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Aug,NPP Aug,august,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_09,mo_npp_09,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Sep,NPP Sep,september,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_10,mo_npp_10,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Oct,NPP Oct,october,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_11,mo_npp_11,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Nov,NPP Nov,november,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_12,mo_npp_12,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.2.2.nc,https://zenodo.org/record/7388091/files/Beyer2020_monthly_vars_v1.2.2.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.2.2,net primary productivity Dec,NPP Dec,december,gC per m^2 per month,*gC~m^-2~mo^-1*,
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+bio16,bio16,Krapp2021,FALSE,Krapp2021_bio16_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_bio16_v1.2.2.nc?download=1,bio16_800ka.nc,https://osf.io/kn8ma/?action=download,1.2.2,precipitation of wettest quarter,P of wettest qtr,year,mm per quarter,*mm~qtr^-1*,
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+bio18,bio18,Krapp2021,FALSE,Krapp2021_bio18_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_bio18_v1.2.2.nc?download=1,bio18_800ka.nc,https://osf.io/a2uhm/?action=download,1.2.2,precipitation of warmest quarter,P of warmest qtr,year,mm per quarter,*mm~qtr^-1*,
+bio19,bio19,Krapp2021,FALSE,Krapp2021_bio19_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_bio19_v1.2.2.nc?download=1,bio19_800ka.nc,https://osf.io/3mbd9/?action=download,1.2.2,precipitation of coldest quarter,P of coldest qtr,year,mm per quarter,*mm~qtr^-1*,
+npp,npp,Krapp2021,FALSE,Krapp2021_npp_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_v1.2.2.nc?download=1,biome4output_800ka.nc,https://osf.io/cf5zp/?action=download,1.2.2,net primary productivity,NPP,year,gC per m^2 per year,*gC~m^-2~yr^-1*,
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+altitude,altitude,Krapp2021,FALSE,Krapp2021_topography_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_topography_v1.2.2.nc?download=1,,,1.2.2,altitude over the sea level,altitude over the sea level,year,m,*m*,
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+temperature_01,temp_01,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Jan,mean T Jan,january,degrees Celsius,*degree*C*,
+temperature_02,temp_02,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Feb,mean T Feb,february,degrees Celsius,*degree*C*,
+temperature_03,temp_03,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Mar,mean T Mar,march,degrees Celsius,*degree*C*,
+temperature_04,temp_04,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Apr,mean T Apr,april,degrees Celsius,*degree*C*,
+temperature_05,temp_05,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature May,mean T May,may,degrees Celsius,*degree*C*,
+temperature_06,temp_06,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Jun,mean T Jun,june,degrees Celsius,*degree*C*,
+temperature_07,temp_07,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Jul,mean T Jul,july,degrees Celsius,*degree*C*,
+temperature_08,temp_08,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Aug,mean T Aug,august,degrees Celsius,*degree*C*,
+temperature_09,temp_09,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Sep,mean T Sep,september,degrees Celsius,*degree*C*,
+temperature_10,temp_10,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Oct,mean T Oct,october,degrees Celsius,*degree*C*,
+temperature_11,temp_11,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Nov,mean T Nov,november,degrees Celsius,*degree*C*,
+temperature_12,temp_12,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_temp_monthly_v1.2.2.nc?download=1,,,1.2.2,mean temperature Dec,mean T Dec,december,degrees Celsius,*degree*C*,
+precipitation_01,prec_01,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Jan,P Jan,january,mm per month,*mm~mo^-1*,
+precipitation_02,prec_02,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Feb,P Feb,february,mm per month,*mm~mo^-1*,
+precipitation_03,prec_03,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Mar,P Mar,march,mm per month,*mm~mo^-1*,
+precipitation_04,prec_04,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Apr,P Apr,april,mm per month,*mm~mo^-1*,
+precipitation_05,prec_05,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation May,P May,may,mm per month,*mm~mo^-1*,
+precipitation_06,prec_06,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Jun,P Jun,june,mm per month,*mm~mo^-1*,
+precipitation_07,prec_07,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Jul,P Jul,july,mm per month,*mm~mo^-1*,
+precipitation_08,prec_08,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Aug,P Aug,august,mm per month,*mm~mo^-1*,
+precipitation_09,prec_09,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Sep,P Sep,september,mm per month,*mm~mo^-1*,
+precipitation_10,prec_10,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Oct,P Oct,october,mm per month,*mm~mo^-1*,
+precipitation_11,prec_11,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Nov,P Nov,november,mm per month,*mm~mo^-1*,
+precipitation_12,prec_12,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_prec_monthly_v1.2.2.nc?download=1,,,1.2.2,precipitation Dec,P Dec,december,mm per month,*mm~mo^-1*,
+mo_npp_01,mo_npp_01,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Jan,NPP Jan,january,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_02,mo_npp_02,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Feb,NPP Feb,february,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_03,mo_npp_03,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Mar,NPP Mar,march,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_04,mo_npp_04,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Apr,NPP Apr,april,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_05,mo_npp_05,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity May,NPP May,may,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_06,mo_npp_06,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Jun,NPP Jun,june,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_07,mo_npp_07,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Jul,NPP Jul,july,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_08,mo_npp_08,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Aug,NPP Aug,august,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_09,mo_npp_09,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Sep,NPP Sep,september,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_10,mo_npp_10,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Oct,NPP Oct,october,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_11,mo_npp_11,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Nov,NPP Nov,november,gC per m^2 per month,*gC~m^-2~mo^-1*,
+mo_npp_12,mo_npp_12,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_npp_monthly_v1.2.2.nc?download=1,,,1.2.2,net primary productivity Dec,NPP Dec,december,gC per m^2 per month,*gC~m^-2~mo^-1*,
+cloudiness_01,tcc_01,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Jan,cloudiness Jan,january,%,*'%'*,
+cloudiness_02,tcc_02,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Feb,cloudiness Feb,february,%,*'%'*,
+cloudiness_03,tcc_03,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Mar,cloudiness Mar,march,%,*'%'*,
+cloudiness_04,tcc_04,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Apr,cloudiness Apr,april,%,*'%'*,
+cloudiness_05,tcc_05,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness May,cloudiness May,may,%,*'%'*,
+cloudiness_06,tcc_06,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Jun,cloudiness Jun,june,%,*'%'*,
+cloudiness_07,tcc_07,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Jul,cloudiness Jul,july,%,*'%'*,
+cloudiness_08,tcc_08,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Aug,cloudiness Aug,august,%,*'%'*,
+cloudiness_09,tcc_09,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Sep,cloudiness Sep,september,%,*'%'*,
+cloudiness_10,tcc_10,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Oct,cloudiness Oct,october,%,*'%'*,
+cloudiness_11,tcc_11,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Nov,cloudiness Nov,november,%,*'%'*,
+cloudiness_12,tcc_12,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.2.2.nc,https://zenodo.org/record/7388149/files/Krapp2021_tcc_monthly_v1.2.2.nc?download=1,,,1.2.2,cloudiness Dec,cloudiness Dec,december,%,*'%'*,
diff --git a/inst/rawdata_scripts/data_files/variable_table.csv b/inst/rawdata_scripts/data_files/variable_table.csv
deleted file mode 100644
index 01540a44..00000000
--- a/inst/rawdata_scripts/data_files/variable_table.csv
+++ /dev/null
@@ -1,168 +0,0 @@
-variable,ncvar,dataset,monthly,file_name,download_path,file_name_orig,download_path_orig,version,long_name,abbreviated_name,time_frame,units,units_exp
-bio01,BIO1,Example,FALSE,example_climate_v2.nc,,,,1.1.0,annual mean temperature,ann. mean T,year,degrees Celsius,*degree*C*
-bio10,BIO10,Example,FALSE,example_climate_v2.nc,,,,1.1.0,mean temperature of warmest quarter,mean T of warmest qtr,year,degrees Celsius,*degree*C*
-bio12,BIO12,Example,FALSE,example_climate_v2.nc,,,,1.1.0,annual precipitation,ann. P,year,mm per year,*mm~yr^-1*
-biome,biome,Example,FALSE,example_climate_v2.nc,,,,1.1.0,biome (from BIOME4),biome,year,,
-bio01,bio01,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,annual mean temperature,ann. mean T,year,degrees Celsius,*degree*C*
-bio04,bio04,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,temperature seasonality,T season.,year,degrees Celsius,*degree*C*
-bio05,bio05,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,maximum temperature of warmest month,max. T of warmest mo.,year,degrees Celsius,*degree*C*
-bio06,bio06,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,minimum temperature of coldest month,min. T of coldest mo.,year,degrees Celsius,*degree*C*
-bio07,bio07,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,temperature annual range (bio05-bio06),T ann. Range,year,degrees Celsius,*degree*C*
-bio08,bio08,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,mean temperature of wettest quarter,mean T of wettest qtr,year,degrees Celsius,*degree*C*
-bio09,bio09,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,mean temperature of driest quarter,mean T of driest qtr,year,degrees Celsius,*degree*C*
-bio10,bio10,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,mean temperature of warmest quarter,mean T of warmest qtr,year,degrees Celsius,*degree*C*
-bio11,bio11,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,mean temperature of coldest quarter,mean T of coldest qtr,year,degrees Celsius,*degree*C*
-bio12,bio12,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,annual precipitation,ann. P,year,mm per year,*mm~yr^-1*
-bio13,bio13,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,precipitation of wettest month,P of wettest mo.,year,mm per month,*mm~mo^-1*
-bio14,bio14,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,precipitation of driest month,P of driest mo.,year,mm per month,*mm~mo^-1*
-bio15,bio15,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,precipitation seasonality (coeff var),P season.,year,,
-bio16,bio16,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,precipitation of wettest quarter,P of wettest qtr,year,mm per quarter,*mm~qtr^-1*
-bio17,bio17,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,precipitation of driest quarter,P of driest qtr,year,mm per quarter,*mm~qtr^-1*
-bio18,bio18,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,precipitation of warmest quarter,P of warmest qtr,year,mm per quarter,*mm~qtr^-1*
-bio19,bio19,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,precipitation of coldest quarter,P of coldest qtr,year,mm per quarter,*mm~qtr^-1*
-npp,npp,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity,NPP,year,gC per m^2 per year,*gC~m^-2~yr^-1*
-lai,lai,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,leaf area index,LAI,year,gC per m^2,*gC~m^-2*
-biome,biome,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,biome (from BIOME4),biome,year,,
-altitude,altitude,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,,,1.1.0,altitude over the sea level,altitude,year,meters,*m*
-rugosity,rugosity,Beyer2020,FALSE,Beyer2020_annual_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_annual_vars_v1.1.0.nc?download=1,,,1.1.0,rugosity (st. dev. altitude at 1 min),rugosity,year,,
-temperature_01,temperature_01,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,mean temperature Jan,mean T Jan,january,degrees Celsius,*degree*C*
-temperature_02,temperature_02,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,mean temperature Feb,mean T Feb,february,degrees Celsius,*degree*C*
-temperature_03,temperature_03,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,mean temperature Mar,mean T Mar,march,degrees Celsius,*degree*C*
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-relative_humidity_04,relative_humidity_04,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,relative humidity Apr,RH Apr,april,%,*'%'*
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-relative_humidity_06,relative_humidity_06,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,relative humidity Jun,RH Jun,june,%,*'%'*
-relative_humidity_07,relative_humidity_07,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,relative humidity Jul,RH Jul,july,%,*'%'*
-relative_humidity_08,relative_humidity_08,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,relative humidity Aug,RH Aug,august,%,*'%'*
-relative_humidity_09,relative_humidity_09,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,relative humidity Sep,RH Sep,september,%,*'%'*
-relative_humidity_10,relative_humidity_10,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,relative humidity Oct,RH Oct,october,%,*'%'*
-relative_humidity_11,relative_humidity_11,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,relative humidity Nov,RH Nov,november,%,*'%'*
-relative_humidity_12,relative_humidity_12,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,relative humidity Dec,RH Dec,december,%,*'%'*
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-mo_npp_03,mo_npp_03,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity Mar,NPP Mar,march,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_04,mo_npp_04,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity Apr,NPP Apr,april,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_05,mo_npp_05,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity May,NPP May,may,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_06,mo_npp_06,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity Jun,NPP Jun,june,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_07,mo_npp_07,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity Jul,NPP Jul,july,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_08,mo_npp_08,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity Aug,NPP Aug,august,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_09,mo_npp_09,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity Sep,NPP Sep,september,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_10,mo_npp_10,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity Oct,NPP Oct,october,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_11,mo_npp_11,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity Nov,NPP Nov,november,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_12,mo_npp_12,Beyer2020,TRUE,Beyer2020_monthly_vars_v1.1.0.nc,https://zenodo.org/record/7062281/files/Beyer2020_monthly_vars_v1.1.0.nc?download=1,LateQuaternary_Environment.nc,https://ndownloader.figshare.com/files/22659026,1.1.0,net primary productivity Dec,NPP Dec,december,gC per m^2 per month,*gC~m^-2~mo^-1*
-bio01,bio01,Krapp2021,FALSE,Krapp2021_bio01_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio01_v1.0.0.nc?download=1,bio01_800ka.nc,https://osf.io/a39gh/?action=download,1.1.0,annual mean temperature,ann. mean T,year,degrees Celsius,*degree*C*
-bio04,bio04,Krapp2021,FALSE,Krapp2021_bio04_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio04_v1.0.0.nc?download=1,bio04_800ka.nc,https://osf.io/p82ue/?action=download,1.1.0,temperature seasonality,T season.,year,degrees Celsius,*degree*C*
-bio05,bio05,Krapp2021,FALSE,Krapp2021_bio05_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio05_v1.0.0.nc?download=1,bio05_800ka.nc,https://osf.io/emhp9/?action=download,1.1.0,maximum temperature of warmest month,maximum T of warmest mo.,year,degrees Celsius,*degree*C*
-bio06,bio06,Krapp2021,FALSE,Krapp2021_bio06_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio06_v1.0.0.nc?download=1,bio06_800ka.nc,https://osf.io/cm923/?action=download,1.1.0,minimum temperature of coldest month,minimum T of coldest mo.,year,degrees Celsius,*degree*C*
-bio07,bio07,Krapp2021,FALSE,Krapp2021_bio07_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio07_v1.0.0.nc?download=1,bio07_800ka.nc,https://osf.io/prq8n/?action=download,1.1.0,temperature annual range (bio05-bio06),T ann. range (bio05-bio06),year,degrees Celsius,*degree*C*
-bio08,bio08,Krapp2021,FALSE,Krapp2021_bio08_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio08_v1.0.0.nc?download=1,bio08_800ka.nc,https://osf.io/7jsa8/?action=download,1.1.0,mean temperature of wettest quarter,mean T of wettest qtr,year,degrees Celsius,*degree*C*
-bio09,bio09,Krapp2021,FALSE,Krapp2021_bio09_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio09_v1.0.0.nc?download=1,bio09_800ka.nc,https://osf.io/zaxku/?action=download,1.1.0,mean temperature of driest quarter,mean T of driest qtr,year,degrees Celsius,*degree*C*
-bio10,bio10,Krapp2021,FALSE,Krapp2021_bio10_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio10_v1.0.0.nc?download=1,bio10_800ka.nc,https://osf.io/b3kx8/?action=download,1.1.0,mean temperature of warmest quarter,mean T of warmest qtr,year,degrees Celsius,*degree*C*
-bio11,bio11,Krapp2021,FALSE,Krapp2021_bio11_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio11_v1.0.0.nc?download=1,bio11_800ka.nc,https://osf.io/vaune/?action=download,1.1.0,mean temperature of coldest quarter,mean T of coldest qtr,year,degrees Celsius,*degree*C*
-bio12,bio12,Krapp2021,FALSE,Krapp2021_bio12_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio12_v1.0.0.nc?download=1,bio12_800ka.nc,https://osf.io/kg9v6/?action=download,1.1.0,annual precipitation,ann. P,year,mm per year,*mm~yr^-1*
-bio13,bio13,Krapp2021,FALSE,Krapp2021_bio13_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio13_v1.0.0.nc?download=1,bio13_800ka.nc,https://osf.io/2u5c6/?action=download,1.1.0,precipitation of wettest month,P of wettest mo.,year,mm per month,*mm~mo^-1*
-bio14,bio14,Krapp2021,FALSE,Krapp2021_bio14_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio14_v1.0.0.nc?download=1,bio14_800ka.nc,https://osf.io/z2ewu/?action=download,1.1.0,precipitation of driest month,P of driest mo.,year,mm per month,*mm~mo^-1*
-bio15,bio15,Krapp2021,FALSE,Krapp2021_bio15_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio15_v1.0.0.nc?download=1,bio15_800ka.nc,https://osf.io/z52xr/?action=download,1.1.0,precipitation seasonality (coefficient of variation),P season. (coefficient of variation),year,,
-bio16,bio16,Krapp2021,FALSE,Krapp2021_bio16_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio16_v1.0.0.nc?download=1,bio16_800ka.nc,https://osf.io/kn8ma/?action=download,1.1.0,precipitation of wettest quarter,P of wettest qtr,year,mm per quarter,*mm~qtr^-1*
-bio17,bio17,Krapp2021,FALSE,Krapp2021_bio17_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio17_v1.0.0.nc?download=1,bio17_800ka.nc,https://osf.io/2z8ej/?action=download,1.1.0,precipitation of driest quarter,P of driest qtr,year,mm per quarter,*mm~qtr^-1*
-bio18,bio18,Krapp2021,FALSE,Krapp2021_bio18_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio18_v1.0.0.nc?download=1,bio18_800ka.nc,https://osf.io/a2uhm/?action=download,1.1.0,precipitation of warmest quarter,P of warmest qtr,year,mm per quarter,*mm~qtr^-1*
-bio19,bio19,Krapp2021,FALSE,Krapp2021_bio19_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_bio19_v1.0.0.nc?download=1,bio19_800ka.nc,https://osf.io/3mbd9/?action=download,1.1.0,precipitation of coldest quarter,P of coldest qtr,year,mm per quarter,*mm~qtr^-1*
-npp,npp,Krapp2021,FALSE,Krapp2021_npp_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_biome_v1.0.0.nc?download=1,biome4output_800ka.nc,https://osf.io/cf5zp/?action=download,1.1.0,net primary productivity,NPP,year,gC per m^2 per year,*gC~m^-2~yr^-1*
-biome,biome,Krapp2021,FALSE,Krapp2021_biome_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_biome_v1.0.0.nc?download=1,biome4output_800ka.nc,https://osf.io/cf5zp/?action=download,1.1.0,biome (from BIOME4),biome (from BIOME4),year,,
-altitude,altitude,Krapp2021,FALSE,Krapp2021_topography_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_topography_v1.0.0.nc?download=1,,,1.1.0,altitude over the sea level,altitude over the sea level,year,m,*m*
-rugosity,rugosity,Krapp2021,FALSE,Krapp2021_topography_v1.0.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_topography_v1.0.0.nc?download=1,,,1.1.0,rugosity,rugosity,year,,
-temperature_01,temp_01,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Jan,mean T Jan,january,degrees Celsius,*degree*C*
-temperature_02,temp_02,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Feb,mean T Feb,february,degrees Celsius,*degree*C*
-temperature_03,temp_03,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Mar,mean T Mar,march,degrees Celsius,*degree*C*
-temperature_04,temp_04,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Apr,mean T Apr,april,degrees Celsius,*degree*C*
-temperature_05,temp_05,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature May,mean T May,may,degrees Celsius,*degree*C*
-temperature_06,temp_06,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Jun,mean T Jun,june,degrees Celsius,*degree*C*
-temperature_07,temp_07,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Jul,mean T Jul,july,degrees Celsius,*degree*C*
-temperature_08,temp_08,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Aug,mean T Aug,august,degrees Celsius,*degree*C*
-temperature_09,temp_09,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Sep,mean T Sep,september,degrees Celsius,*degree*C*
-temperature_10,temp_10,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Oct,mean T Oct,october,degrees Celsius,*degree*C*
-temperature_11,temp_11,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Nov,mean T Nov,november,degrees Celsius,*degree*C*
-temperature_12,temp_12,Krapp2021,TRUE,Krapp2021_temp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_temp_monthly_v1.1.0.nc?download=1,,,1.1.0,mean temperature Dec,mean T Dec,december,degrees Celsius,*degree*C*
-precipitation_01,prec_01,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Jan,P Jan,january,mm per month,*mm~mo^-1*
-precipitation_02,prec_02,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Feb,P Feb,february,mm per month,*mm~mo^-1*
-precipitation_03,prec_03,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Mar,P Mar,march,mm per month,*mm~mo^-1*
-precipitation_04,prec_04,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Apr,P Apr,april,mm per month,*mm~mo^-1*
-precipitation_05,prec_05,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation May,P May,may,mm per month,*mm~mo^-1*
-precipitation_06,prec_06,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Jun,P Jun,june,mm per month,*mm~mo^-1*
-precipitation_07,prec_07,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Jul,P Jul,july,mm per month,*mm~mo^-1*
-precipitation_08,prec_08,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Aug,P Aug,august,mm per month,*mm~mo^-1*
-precipitation_09,prec_09,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Sep,P Sep,september,mm per month,*mm~mo^-1*
-precipitation_10,prec_10,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Oct,P Oct,october,mm per month,*mm~mo^-1*
-precipitation_11,prec_11,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Nov,P Nov,november,mm per month,*mm~mo^-1*
-precipitation_12,prec_12,Krapp2021,TRUE,Krapp2021_prec_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_prec_monthly_v1.1.0.nc?download=1,,,1.1.0,precipitation Dec,P Dec,december,mm per month,*mm~mo^-1*
-mo_npp_01,mo_npp_01,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Jan,NPP Jan,january,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_02,mo_npp_02,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Feb,NPP Feb,february,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_03,mo_npp_03,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Mar,NPP Mar,march,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_04,mo_npp_04,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Apr,NPP Apr,april,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_05,mo_npp_05,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity May,NPP May,may,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_06,mo_npp_06,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Jun,NPP Jun,june,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_07,mo_npp_07,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Jul,NPP Jul,july,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_08,mo_npp_08,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Aug,NPP Aug,august,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_09,mo_npp_09,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Sep,NPP Sep,september,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_10,mo_npp_10,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Oct,NPP Oct,october,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_11,mo_npp_11,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Nov,NPP Nov,november,gC per m^2 per month,*gC~m^-2~mo^-1*
-mo_npp_12,mo_npp_12,Krapp2021,TRUE,Krapp2021_npp_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_npp_v1.0.0.nc?download=1,,,1.1.0,net primary productivity Dec,NPP Dec,december,gC per m^2 per month,*gC~m^-2~mo^-1*
-cloudiness_01,tcc_01,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Jan,cloudiness Jan,january,%,*'%'*
-cloudiness_02,tcc_02,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Feb,cloudiness Feb,february,%,*'%'*
-cloudiness_03,tcc_03,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Mar,cloudiness Mar,march,%,*'%'*
-cloudiness_04,tcc_04,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Apr,cloudiness Apr,april,%,*'%'*
-cloudiness_05,tcc_05,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness May,cloudiness May,may,%,*'%'*
-cloudiness_06,tcc_06,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Jun,cloudiness Jun,june,%,*'%'*
-cloudiness_07,tcc_07,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Jul,cloudiness Jul,july,%,*'%'*
-cloudiness_08,tcc_08,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Aug,cloudiness Aug,august,%,*'%'*
-cloudiness_09,tcc_09,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Sep,cloudiness Sep,september,%,*'%'*
-cloudiness_10,tcc_10,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Oct,cloudiness Oct,october,%,*'%'*
-cloudiness_11,tcc_11,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Nov,cloudiness Nov,november,%,*'%'*
-cloudiness_12,tcc_12,Krapp2021,TRUE,Krapp2021_tcc_monthly_v1.1.0.nc,https://zenodo.org/record/7065055/files/Krapp2021_tcc_monthly_v1.1.0.nc?download=1,,,1.1.0,cloudiness Dec,cloudiness Dec,december,%,*'%'*
diff --git a/inst/rawdata_scripts/downsample_CHELSA_TraCE21k_tiff.R b/inst/rawdata_scripts/downsample_CHELSA_TraCE21k_tiff.R
new file mode 100644
index 00000000..d22f8ce3
--- /dev/null
+++ b/inst/rawdata_scripts/downsample_CHELSA_TraCE21k_tiff.R
@@ -0,0 +1,6 @@
+# ran in bash
+# this takes 3 files downloaded at high resolution and downsamples so that they can be
+# used as example files
+gdalwarp -tr 1 1 -r average CHELSA_TraCE21k_bio01_-1_V1.0.tif CHELSA_TraCE21k_bio01_-1_V1.0.small.tif
+gdalwarp -tr 1 1 -r average CHELSA_TraCE21k_bio01_-2_V1.0.tif CHELSA_TraCE21k_bio01_-2_V1.0.small.tif
+gdalwarp -tr 1 1 -r average CHELSA_TraCE21k_bio01_-3_V1.0.tif CHELSA_TraCE21k_bio01_-3_V1.0.small.tif
diff --git a/inst/rawdata_scripts/readme.md b/inst/rawdata_scripts/readme.md
index d02f4bad..d8695532 100644
--- a/inst/rawdata_scripts/readme.md
+++ b/inst/rawdata_scripts/readme.md
@@ -22,7 +22,7 @@ variables into two separate files.
 
 ## scripts to create internal variables for pastclim
 
-1. `create_files_by_dataset.R` is a script to update the internal dataframe which stores the link between variable names and file names and locations. It is based on `variable_table.csv`.
+1. `create_getOption("pastclim.dataset_list").R` is a script to update the internal dataframe which stores the link between variable names and file names and locations. It is based on `variable_table.csv`.
 
 2. `creat_mis_boundaries.R` create internal dataset of mis boundaries.
 
diff --git a/inst/rawdata_scripts/update_meta_data_of_vars_in_nc.R b/inst/rawdata_scripts/update_meta_data_of_vars_in_nc.R
new file mode 100644
index 00000000..b75755cf
--- /dev/null
+++ b/inst/rawdata_scripts/update_meta_data_of_vars_in_nc.R
@@ -0,0 +1,42 @@
+#read.csv(system.file("rawdata_scripts/data_files/variable_table_complete_meta.csv",
+#            library="pastclim"))
+library(ClimateOperators)
+rm(list=ls())
+dataset <- "Krapp2021"
+version_number <- "1.2.2"
+out_dir <- "../../project_temp/past_climate/new_meta"
+full_meta <- read.csv("./inst/rawdata_scripts/data_files/variable_table_complete_meta.csv")
+data_path <- get_data_path()
+sub_meta <- full_meta[full_meta$dataset == dataset,]
+target_files <- unique(sub_meta$file_name)
+
+for (i in target_files){
+  name_components<-unlist(strsplit(i,"_",fixed=TRUE))
+  # replace the last component with new version 1.2.2
+  name_components<-name_components[-length(name_components)]
+  new_name <- paste(paste(name_components, collapse="_"),paste0("v", version_number,".nc"),sep="_")
+  new_target_path <- file.path(out_dir, new_name)
+  old_target_path <- file.path(get_data_path(),i)
+  file.copy(old_target_path,new_target_path)
+  nc_in <- ncdf4::nc_open(new_target_path, write = TRUE)
+  # update time units
+  ncdf4::ncatt_put(nc_in, varid="time", attname = "units", attval = "years since 1950-01-01 00:00:00.0")
+  ncdf4::ncatt_put(nc_in,varid="time", attname = "long_name",attval = "years BP")
+  # update meta units
+  this_var_names <- names(nc_in$var)
+  for (x in this_var_names){
+    ncdf4::ncatt_put(nc_in, varid=x, attname = "long_name",
+                     attval = sub_meta$long_name[sub_meta$ncvar==x])
+    ncdf4::ncatt_put(nc_in, varid=x, attname = "units",
+                     attval = sub_meta$units[sub_meta$ncvar==x])
+  }
+  ncdf4::ncatt_put(nc_in, varid = 0, attname = "pastclim_version",
+                   attval=version_number)
+  
+  ncdf4::nc_close(nc_in)
+  # now remove the attribute unit if present (it should be units)
+  for (x in this_var_names){
+    ncatted(paste0('-a unit,',x,',d,, -h ',new_target_path))
+  }
+  
+}
diff --git a/inst/rawdata_scripts/verify_files_by_dataset.R b/inst/rawdata_scripts/verify_files_by_dataset.R
new file mode 100644
index 00000000..cbf753b1
--- /dev/null
+++ b/inst/rawdata_scripts/verify_files_by_dataset.R
@@ -0,0 +1,13 @@
+# verify that all the variables in the tables are actually found in the files
+# this requires all data to have been downloaded
+full_meta <- read.csv("./inst/rawdata_scripts/data_files/variable_table.csv")
+in_dir <- get_data_path()
+in_dir <- "~/project_temp/past_climate/new_meta"
+for (i in 1:nrow(full_meta)){
+  nc_in <- ncdf4::nc_open(file.path(in_dir, full_meta$file_name[i]))
+  if (!full_meta$ncvar[i] %in% names(nc_in$var)){
+    ncdf4::nc_close(nc_in)
+    stop("problem with ",full_meta$ncvar[i]," in ", full_meta$file_name[i])
+  }
+  ncdf4::nc_close(nc_in)
+}
diff --git a/inst/temp/_delta_downscale_3.R b/inst/temp/_delta_downscale_3.R
index d0bdce40..27d501d3 100644
--- a/inst/temp/_delta_downscale_3.R
+++ b/inst/temp/_delta_downscale_3.R
@@ -14,7 +14,7 @@
 #'
 #' @param x a \code{terra::SpatRaster} for the variable of interest, with all
 #' time steps of interest
-#' @param ref_time the time of the slice that is used to compute the delta
+#' @param ref_time the time (BP) of the slice that is used to compute the delta
 #' @param obs the observations
 #'
 #' @export
@@ -33,7 +33,7 @@ delta_compute <- function(x, ref_time, obs) {
   x_modern_high<-disagg(x_modern, fact = terra::res( x_modern)/terra::res(obs),
          method="bilinear")
   # compute anomalies against the modern
-  delta <- x_modern_high - obs
+  delta <- obs - x_modern_high
   # mask for maximum land extent
   max_land <- max(x,na.rm=TRUE)
   max_land <- disagg(max_land, fact = terra::res( x)/terra::res(obs),
@@ -143,7 +143,9 @@ make_land_mask <- function(topo_rast, time_bp, sea_level = NULL) {
       add(land_mask)<-sea_patches
     }
   }
-  terra::time(land_mask) <- (time_bp+1950)
+  # TODO work around problem in terra (fixed in dev)
+  #terra::time(land_mask, tstep="years") <- (time_bp+1950)
+  terra::time(land_mask, tstep="") <- (time_bp+1950)
   return(land_mask)
   
 }
@@ -182,6 +184,10 @@ idw_interp <- function(x, y, ...){
   x_gap[x_gap==0]<-NA
   x_df <- terra::as.data.frame(x,xy=TRUE,na.rm=TRUE)
   x_gap_df <- terra::as.data.frame(x_gap, xy=TRUE, na.rm=TRUE)
+  # if there is not gap between the values and the mask, just return the values
+  if (nrow(x_gap_df)==0){
+    return(x)
+  }
   names(x_df)[3] <-"this_var"
   names(x_gap_df)[3] <-"this_var"
   # interpolate those gaps with idw (time consuming...)
diff --git a/inst/temp/a4_delta_downscale.Rmd b/inst/temp/a4_delta_downscale.Rmd
index 7b53b300..c25c8200 100644
--- a/inst/temp/a4_delta_downscale.Rmd
+++ b/inst/temp/a4_delta_downscale.Rmd
@@ -49,7 +49,7 @@ model_rast
 ```
 And we can now plot it:
 ```{r, fig.width=6, fig.height=5}
-plot(model_rast)
+plot(model_rast, main=time_bp(model_rast))
 ```
 
 We can see how that the reconstructions are rather coarse (the Example dataset
@@ -97,7 +97,7 @@ for `make_land_mask` for details):
 ```{r, fig.width=6, fig.height=5}
 high_res_mask <- make_land_mask(topo_rast = topo_rast, 
                                 time_bp = time_bp(model_rast))
-plot(high_res_mask)
+plot(high_res_mask, main=time_bp(high_res_mask))
 ```
 
 We can now compute a delta raster and use it to downscale the model reconstructions:
@@ -110,11 +110,11 @@ model_downscaled
 
 Let's inspect the resulting data:
 ```{r, fig.width=6, fig.height=5}
-plot(model_downscaled)
+plot(model_downscaled, main=time_bp(model_downscaled))
 ```
 
 And, as a reminder, the original reconstructions (note that the colour scales are not
-the same! The interpolated data have a wider temperature range):
+the same! `terra` chooses a scale for each time step based on the time specific range):
 ```{r, fig.width=6, fig.height=5}
-plot(model_rast)
+plot(model_rast, main=time_bp(model_rast))
 ```
diff --git a/inst/temp/notes_on_saving_nc_files.R b/inst/temp/notes_on_saving_nc_files.R
deleted file mode 100644
index 67c848a3..00000000
--- a/inst/temp/notes_on_saving_nc_files.R
+++ /dev/null
@@ -1,39 +0,0 @@
-# testing times
-
-s <- rast(system.file("ex/logo.tif", package="terra"))   
-
-# Date"
-#d <- as.Date("2001-05-04") + 0:2
-d <- c(0,-1000,-2000)
-d <- c(0, 1000, 2000)
-time_bp(s) <- d
-
-time_bp(s)
-
-setwd(tempdir())
-rr <- writeCDF(s, "test.nc", overwrite=TRUE)
-foo<-rast("test.nc",  drivers="NETCDF")
-
-foo<-rast("test.nc",  drivers="blah")
-
-nc_in <- ncdf4::nc_open("test.nc", write=TRUE)
-ncdf4::ncatt_put(nc_in, varid="time", attname="axis", attval = "T")
-ncdf4::ncatt_put(nc_in, varid="easting", attname="axis", attval = "X")
-ncdf4::ncatt_put(nc_in, varid="northing", attname="axis", attval = "Y")
-ncdf4::ncatt_put(nc_in, varid="time", attname="units", attval = "years since present")
-ncdf4::nc_close(nc_in)
-
-## time not recognised unless it is both axis=T and a unit that is NOT unknown
-## is that a terra problem, or a gdal?
-foo<-rast("test.nc",  drivers="NETCDF")
-
-# but years in terra does not allow for negative values
-
-rr <- writeCDF(f, "test2.nc", overwrite=TRUE, varname="alt", 
-               longname="elevation in m above sea level", unit="m")
-
-
-f <- system.file("ex/elev.tif", package="terra")
-f <-rast(f)
-writeCDF(f)
-g <- rast("test2.nc")
diff --git a/man/clean_data_path.Rd b/man/clean_data_path.Rd
new file mode 100644
index 00000000..b526dd01
--- /dev/null
+++ b/man/clean_data_path.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/clean_data_path.R
+\name{clean_data_path}
+\alias{clean_data_path}
+\title{Clean the data path}
+\usage{
+clean_data_path(ask = TRUE)
+}
+\arguments{
+\item{ask}{boolean on whether the user should be asked before deleting}
+}
+\value{
+TRUE if files are deleted successfully
+}
+\description{
+This function deletes old reconstructions that have been superseded in the
+data_path. It assumes that the only files in data_path are part of pastclim
+(i.e. there are no custom datasets stored in that directory).
+}
diff --git a/man/get_time_steps.Rd b/man/get_time_steps.Rd
index 01cd6256..5620b304 100644
--- a/man/get_time_steps.Rd
+++ b/man/get_time_steps.Rd
@@ -16,5 +16,5 @@ reconstructions. All the variables of interest need to be included in
 this file.}
 }
 \description{
-Get the time steps available in a given dataset.
+Get the time steps (in time_bp) available in a given dataset.
 }
diff --git a/man/load_dataset_list.Rd b/man/load_dataset_list.Rd
new file mode 100644
index 00000000..3c9a69cd
--- /dev/null
+++ b/man/load_dataset_list.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/load_dataset_list.R
+\name{load_dataset_list}
+\alias{load_dataset_list}
+\title{Load the dataset list}
+\usage{
+load_dataset_list(on_cran = FALSE)
+}
+\arguments{
+\item{on_cran}{boolean to make this function run on ci tests using tempdir}
+}
+\value{
+the dataset list
+}
+\description{
+This function returns a dataframe with the details for each variable
+available in every dataset. It defaults to the copy stored within the
+package, but it checks in case there is an udpated version stored as
+'data_list.csv' in 
+`tools::R_user_dir("pastclim","config")`. If the latter is present, the last
+column, named 'dataset_list_v', provides the version of this table, and the
+most advanced table is used.
+}
+\keyword{internal}
diff --git a/man/update_dataset_list.Rd b/man/update_dataset_list.Rd
new file mode 100644
index 00000000..6d0c8354
--- /dev/null
+++ b/man/update_dataset_list.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/update_dataset_list.R
+\name{update_dataset_list}
+\alias{update_dataset_list}
+\title{Update the dataset list}
+\usage{
+update_dataset_list(on_cran = FALSE)
+}
+\arguments{
+\item{on_cran}{boolean to make this function run on ci tests using tempdir}
+}
+\value{
+TRUE if the dataset was updated
+}
+\description{
+If a newer dataset list (which includes all the information about the files
+storing the data for pastclim), download it and start using it as
+'dataset_list_included.csv' in 
+`tools::R_user_dir("pastclim","config")`. If the latter is present, the last
+column, named 'dataset_list_v', provides the version of this table, and the
+most advanced table is used.
+}
diff --git a/tests/testthat/test_check_dataset_path.R b/tests/testthat/test_check_dataset_path.R
index 3e212861..e308a10e 100644
--- a/tests/testthat/test_check_dataset_path.R
+++ b/tests/testthat/test_check_dataset_path.R
@@ -18,11 +18,12 @@ test_that("check_dataset_path errors", {
     check_dataset_path("custom", NULL),
     "you need to set path_to_nc if dataset='custom'"
   )
+  example_filename <- getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset=="Example"][1]
   expect_true(check_dataset_path(
     "custom",
     file.path(
       get_data_path(),
-      "example_climate_v2.nc"
+      example_filename
     )
   ))
   expect_error(
diff --git a/tests/testthat/test_clean_data_path.R b/tests/testthat/test_clean_data_path.R
new file mode 100644
index 00000000..78b81ff8
--- /dev/null
+++ b/tests/testthat/test_clean_data_path.R
@@ -0,0 +1,31 @@
+# set up data path for this test
+data_path <- file.path(tempdir(),"pastclim_data")
+unlink(data_path, recursive = TRUE) # it should not exist, but remove it just in case
+# set data path
+set_data_path(path_to_nc = data_path,
+              ask = FALSE,
+              write_config = FALSE,
+              copy_example = TRUE)
+################################################################################
+
+test_that("clean data path", {
+  files_in_data_path <- list.files(get_data_path())
+  expect_message(clean_data_path(ask = FALSE),
+                 "Everything is")
+  # the files should not be touched
+  expect_equal(files_in_data_path,list.files(get_data_path()))
+  # now create a spurious file
+  file_to_add <- "example_climate_v0.0.1.nc"
+  write.csv("blash",file.path(get_data_path(),file_to_add))
+  # now the extra file is there
+  expect_true(file_to_add %in% list.files(get_data_path()))
+  expect_true(clean_data_path(ask = FALSE))
+  # now it's gone
+  expect_false(file_to_add %in% list.files(get_data_path()))
+  
+})
+
+################################################################################
+# clean up for the next test
+unlink(data_path, recursive = TRUE)
+
diff --git a/tests/testthat/test_download_dataset.R b/tests/testthat/test_download_dataset.R
index e13d31cf..6646ffb2 100644
--- a/tests/testthat/test_download_dataset.R
+++ b/tests/testthat/test_download_dataset.R
@@ -20,7 +20,8 @@ test_that("download_dataset", {
     "^foo not available "
   )
   # check that only the example climate is in the data directory
-  expect_true("example_climate_v2.nc" %in% list.files(get_data_path()))
+  example_filename <- getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset=="Example"][1]
+  expect_true(example_filename %in% list.files(get_data_path()))
   # expect no error as the dataset exists
   expect_error(download_dataset("Example"), NA)
 })
diff --git a/tests/testthat/test_get_mis_time_steps.R b/tests/testthat/test_get_mis_time_steps.R
index c4500f2f..3f219dd3 100644
--- a/tests/testthat/test_get_mis_time_steps.R
+++ b/tests/testthat/test_get_mis_time_steps.R
@@ -16,7 +16,8 @@ test_that("get_mis_time_steps for standard dataset", {
 })
 
 test_that("get_mis_time_steps for local file", {
-  path_to_example_nc <- system.file("/extdata/", "example_climate_v2.nc",
+  example_filename <- getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset=="Example"][1]
+  path_to_example_nc <- system.file("/extdata/", example_filename,
     package = "pastclim"
   )
   expect_equal(get_mis_time_steps(
@@ -40,4 +41,4 @@ test_that("get_mis_time_steps requires correct variables", {
 
 ################################################################################
 # clean up for the next test
-unlink(data_path, recursive = TRUE)
\ No newline at end of file
+unlink(data_path, recursive = TRUE)
diff --git a/tests/testthat/test_get_set_data_path.R b/tests/testthat/test_get_set_data_path.R
index df4b934f..c8ae92be 100644
--- a/tests/testthat/test_get_set_data_path.R
+++ b/tests/testthat/test_get_set_data_path.R
@@ -1,14 +1,20 @@
 # start with a clean slate in case other tests set up the path
 options(pastclim.data_path = NULL) # reset the option
 
-test_that("set and get data path", {
+test_that("get data path when config not present", {
+  skip_if((file.exists(file.path(
+    tools::R_user_dir("pastclim", "config"),
+    "pastclim_data.txt"
+  ))),"config file already exists on this system")
   # try to get path when none is set
   expect_message(null_data_path <- get_data_path())
   expect_null(null_data_path)
   # now do the same in silent mode
   expect_no_message(null_data_path <- get_data_path(silent = TRUE))
   expect_null(null_data_path)
+})
 
+test_that("set and get data path", {
   # now set the path in a subdirectory of tempdir
   data_path <- file.path(tempdir(),"pastclim_data")
   unlink(data_path, recursive = TRUE) # it should not exist, but remove it just in case
diff --git a/tests/testthat/test_get_time_steps.R b/tests/testthat/test_get_time_steps.R
index c3ea551e..d9c18d14 100644
--- a/tests/testthat/test_get_time_steps.R
+++ b/tests/testthat/test_get_time_steps.R
@@ -27,8 +27,9 @@ test_that("get_time_steps for standard dataset", {
 })
 
 test_that("get_time_steps for local file", {
-  path_to_example_nc <- system.file("/extdata/", "example_climate_v2.nc",
-    package = "pastclim"
+  example_filename <- getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset=="Example"][1]
+  path_to_example_nc <- system.file("/extdata/", example_filename,
+                                    package = "pastclim"
   )
   expect_equal(get_time_steps(
     dataset = "custom",
diff --git a/tests/testthat/test_get_vars_for_dataset.R b/tests/testthat/test_get_vars_for_dataset.R
index ea0f38a1..11638447 100644
--- a/tests/testthat/test_get_vars_for_dataset.R
+++ b/tests/testthat/test_get_vars_for_dataset.R
@@ -33,8 +33,9 @@ test_that("get_vars_for_dataset returns appropriate object", {
 
 
 test_that("get_vars_for_dataset for local file", {
-  path_to_example_nc <- system.file("/extdata/", "example_climate_v2.nc",
-    package = "pastclim"
+  example_filename <- getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset=="Example"][1]
+  path_to_example_nc <- system.file("/extdata/", example_filename,
+                                    package = "pastclim"
   )
   vars <- get_vars_for_dataset(dataset = "custom", path_to_nc = path_to_example_nc)
   expect_true(inherits(vars,"character"))
@@ -45,4 +46,4 @@ test_that("get_vars_for_dataset for local file", {
 
 ################################################################################
 # clean up for the next test
-unlink(data_path, recursive = TRUE)
\ No newline at end of file
+unlink(data_path, recursive = TRUE)
diff --git a/tests/testthat/test_load_dataset_list.R b/tests/testthat/test_load_dataset_list.R
new file mode 100644
index 00000000..b8d88bef
--- /dev/null
+++ b/tests/testthat/test_load_dataset_list.R
@@ -0,0 +1,31 @@
+# set up data path for this test
+data_path <- file.path(tempdir(),"pastclim_data")
+unlink(data_path, recursive = TRUE) # it should not exist, but remove it just in case
+# set data path
+set_data_path(path_to_nc = data_path,
+                          ask = FALSE,
+                          write_config = FALSE,
+                          copy_example = TRUE)
+################################################################################
+
+test_that("load_dataset_list", {
+  # check that the version returned by load_dataset is the default on
+  expect_identical(load_dataset_list(on_cran=TRUE)$dataset_list_v[1],
+                   utils::read.csv(system.file("rawdata_scripts/data_files/dataset_list_included.csv",
+                                                package="pastclim"))$dataset_list_v[1])
+  # now create a file in the tempdir
+  new_table <- utils::read.csv(system.file("rawdata_scripts/data_files/dataset_list_included.csv",
+                                           package="pastclim"))
+  # now update to the version
+  new_table$dataset_list_v[1]<-"99.0.0"
+  
+  write.csv(new_table,
+            file.path(tempdir(),"dataset_list_included.csv"),row.names = FALSE)
+  # now check the version
+  expect_true(load_dataset_list(on_cran=TRUE)$dataset_list_v[1]=="99.0.0")
+})
+
+################################################################################
+# clean up for the next test
+unlink(data_path, recursive = TRUE)
+
diff --git a/tests/testthat/test_location_series.R b/tests/testthat/test_location_series.R
index d6fd188c..17d1dd62 100644
--- a/tests/testthat/test_location_series.R
+++ b/tests/testthat/test_location_series.R
@@ -92,8 +92,9 @@ test_that("time_series_for_location", {
   
   
   # now use a custom dataset
-  path_to_example_nc <- system.file("/extdata/example_climate_v2.nc",
-    package = "pastclim"
+  example_filename <- getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset=="Example"][1]
+  path_to_example_nc <- system.file("/extdata/", example_filename,
+                                    package = "pastclim"
   )
   locations_ts <- location_series(
     x = locations[, c("longitude", "latitude")],
@@ -123,4 +124,4 @@ test_that("time_series_for_location", {
 
 ################################################################################
 # clean up for the next test
-unlink(data_path, recursive = TRUE)
\ No newline at end of file
+unlink(data_path, recursive = TRUE)
diff --git a/tests/testthat/test_location_slice.R b/tests/testthat/test_location_slice.R
index 27bbd7ef..a5a62527 100644
--- a/tests/testthat/test_location_slice.R
+++ b/tests/testthat/test_location_slice.R
@@ -91,8 +91,9 @@ test_that("location_slice", {
   )
 
   # now test a custom dataset
-  path_to_example_nc <- system.file("/extdata/example_climate_v2.nc",
-    package = "pastclim"
+  example_filename <- getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset=="Example"][1]
+  path_to_example_nc <- system.file("/extdata/", example_filename,
+                                    package = "pastclim"
   )
   this_climate <- location_slice(
     x = locations[, c("longitude", "latitude")],
@@ -143,4 +144,4 @@ test_that("location_slice", {
 
 ################################################################################
 # clean up for the next test
-unlink(data_path, recursive = TRUE)
\ No newline at end of file
+unlink(data_path, recursive = TRUE)
diff --git a/tests/testthat/test_region_series.R b/tests/testthat/test_region_series.R
index 19755eb2..192b3dda 100644
--- a/tests/testthat/test_region_series.R
+++ b/tests/testthat/test_region_series.R
@@ -19,8 +19,10 @@ test_that("region series", {
   expect_true(all(terra::nlyr(climate_region) == c(2, 2)))
 
   # do the same for a custom dataset
-  path_to_example_nc <- system.file("/extdata/example_climate_v2.nc",
-                                    package = "pastclim")
+  example_filename <- getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset=="Example"][1]
+  path_to_example_nc <- system.file("/extdata/", example_filename,
+                                    package = "pastclim"
+  )
   climate_region <- region_series(c(-20000, -10000), c("BIO1", "BIO10"),
     "custom",
     path_to_nc = path_to_example_nc
diff --git a/tests/testthat/test_region_slice.R b/tests/testthat/test_region_slice.R
index 48b2a297..0c187069 100644
--- a/tests/testthat/test_region_slice.R
+++ b/tests/testthat/test_region_slice.R
@@ -19,8 +19,10 @@ test_that("region slice", {
   expect_true(terra::nlyr(climate_slice) == c(2))
 
   # do the same for a custom dataset
-  path_to_example_nc <- system.file("/extdata/example_climate_v2.nc",
-                                    package = "pastclim")
+  example_filename <- getOption("pastclim.dataset_list")$file_name[getOption("pastclim.dataset_list")$dataset=="Example"][1]
+  path_to_example_nc <- system.file("/extdata/", example_filename,
+                                    package = "pastclim"
+  )
   climate_slice <- region_slice(c(-10000), c("BIO1", "BIO10"),
     "custom",
     path_to_nc = path_to_example_nc
diff --git a/vignettes/a0_pastclim_overview.Rmd b/vignettes/a0_pastclim_overview.Rmd
index 9c265d94..27703c46 100644
--- a/vignettes/a0_pastclim_overview.Rmd
+++ b/vignettes/a0_pastclim_overview.Rmd
@@ -174,6 +174,12 @@ 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
diff --git a/vignettes/a2_custom_datasets.Rmd b/vignettes/a2_custom_datasets.Rmd
index 1a0afcdd..12da2295 100644
--- a/vignettes/a2_custom_datasets.Rmd
+++ b/vignettes/a2_custom_datasets.Rmd
@@ -45,7 +45,7 @@ names(bio01)<-paste("bio01",terra::time(bio01),sep="_")
 Now we save the data as a *nc* file (we will use the temporary directory)
 ```{r}
 nc_name <- file.path(tempdir(),"CHELSA_TraCE21k_bio01.nc")
-terra::writeCDF(bio01, filename = nc_name, varname = "bio01")
+terra::writeCDF(bio01, filename = nc_name, varname = "bio01", overwrite=TRUE)
 ```
 
 We also need to make sure that the *time* dimension has units and is labelled
@@ -117,7 +117,7 @@ store information about available datasets. This table is found in
 "./inst/rawdata_scripts/data_files/variable_table.csv".
 
 ```{r}
-head(read.csv(system.file("rawdata_scripts/data_files/variable_table.csv",
+head(read.csv(system.file("rawdata_scripts/data_files/dataset_list_included.csv",
                      package="pastclim")), n=2)
 ```
 
@@ -153,9 +153,9 @@ the *nc* dataset.
 
 `units_exp`: units formatted to be included in `expression` when creating plot labels
 
-3) Once you have added lines detailing the variables in your dataset, run the script "./inst/rawdata_scripts/create_files_by_dataset.R" to store
+3) Once you have added lines detailing the variables in your dataset, run the script "./inst/rawdata_scripts/create_getOption("pastclim.dataset_list").R" to store
 that information into the appropriate dataset in `pastclim`.
 
 4) Provide information on the new dataset in the file "./R/dataset_docs", using `roxygen2` syntax. Make sure that you provide an appropriate reference for the original data, as it is important that users can refer back to the original source.
 
-4) Make a Pull Request on GitHub.
\ No newline at end of file
+4) Make a Pull Request on GitHub.