---
title: "present and future"
# output: rmarkdown::pdf_document
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{present and future}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  markdown: 
    wrap: 72
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
# boolean to turn off chunks that download large datasets
eval_chunk <- FALSE
```


Whilst `pastclim` is mostly designed to handle palaeoclimate time series, it can
also be used to manipulate present reconstructions and future projections.
Currently, it covers the WorldClim and the CHELSA datasets, but it could easily be adapted to 
use other sources.

# Present reconstructions

## WorldClim
Present-day reconstructions for WorldClim v2.1 are based on the mean for the period 1970-2000, 
and are available at multiple resolutions of
10 arc-minutes, 5 arc-minutes, 2.5 arc-minute and 0.5 arc-minutes. The resolution
of interest can be obtained by changing the ending of the dataset name 
*WorldClim_2.1_RESm*, e.g. *WorldClim_2.1_10m* or *WorldClim_2.1_5m*. In `pastclim`, the datasets are given
a date of 1985 CE (the mid-point of the period of interest). There are 19 “bioclimatic” variables, as well as monthly 
estimates for minimum, mean, and maximum temperature, and precipitation.

So, the annual variables for the 10m arc-minutes dataset are:
```{r}
library(pastclim)
get_vars_for_dataset("WorldClim_2.1_10m")
```

And the monthly variables
```{r}
get_vars_for_dataset("WorldClim_2.1_10m", monthly = TRUE, annual = FALSE)
```

We can manipulate data in the usual way. We start by downloading the dataset:
```{r eval=eval_chunk}
download_dataset(
  dataset = "WorldClim_2.1_10m",
  bio_variables = c("bio01", "bio02", "altitude")
)
```

We can then use `region_slice` to extract the data as a `SpatRaster`:
```{r eval=eval_chunk}
climate_present <- region_slice(
  time_ce = 1985,
  bio_variables = c("bio01", "bio02", "altitude"),
  dataset = "WorldClim_2.1_10m"
)
```

## CHELSA
Present-day reconstructions for CHELSA v2.1 are based on the mean for the period 1981-2000, 
and are available at the high resolution of 0.5 arc-minutes. *CHELSA_2.1_0.5m*. In `pastclim`, the datasets are given
a date of 1990 CE (the mid-point of the period of interest). There are 19 “bioclimatic” variables, as well as monthly 
estimates for minimum, mean, and maximum temperature, and precipitation.

So, the annual variables for the CHELSA dataset are:
```{r}
library(pastclim)
get_vars_for_dataset("CHELSA_2.1_0.5m")
```

And the monthly variables
```{r}
get_vars_for_dataset("CHELSA_2.1_0.5m", monthly = TRUE, annual = FALSE)
```

We can manipulate data in the usual way. We start by downloading the dataset:
```{r eval=eval_chunk}
download_dataset(
  dataset = "CHELSA_2.1_0.5m",
  bio_variables = c("bio01", "bio02")
)
```

We can then use `region_slice` to extract the data as a `SpatRaster`:
```{r eval=eval_chunk}
climate_present <- region_slice(
  time_ce = 1990,
  bio_variables = c("bio01", "bio02"),
  dataset = "CHELSA_2.1_0.5m"
)
```

The datasets for each variable are very large due to the high resolution. Besides
downloading the data, it is also possible to use a virtual raster, leaving the 
files on the server, and only downloading the pixels that are needed. We can achieve
that by using the dataset *CHELSA_2.1_0.5_vsi*. 

We still need to download the 
dataset first, but rather than downloading all the files, this sets up the virtual raster
(and so it is very fast!):

```{r eval=eval_chunk}
download_dataset(
  dataset = "CHELSA_2.1_0.5m_vsi",
  bio_variables = c("bio12", "temperature_01")
)
```

Once downloaded, we can use it as any other dataset:
```{r eval=eval_chunk}
climate_present <- region_slice(
  time_ce = 1990,
  bio_variables = c("bio12", "temperature_01"),
  dataset = "CHELSA_2.1_0.5m_vsi"
)
```

This is ideal if you just need to extract climate for a number of locations, and
do not need to get the full map.

# Future projections

## WorldClim

Future projections are based on the models in CMIP6, downscaled and de-biased
using WorldClim 2.1 for the present as a baseline. Monthly values of minimum
temperature, maximum temperature, and precipitation, as well as 19 bioclimatic
variables were processed for
23 global climate models (GCMs), and for four 
Shared Socio-economic Pathways (SSPs): 126, 245, 370 and 585. Model and
SSP can be chosen by changing the ending of the dataset name
*WorldClim_2.1_GCM_SSP_RESm*.

A complete list of available combinations can be obtained with:
```{r}
list_available_datasets()[grepl("WorldClim_2.1", list_available_datasets())]
```

So, if we are interested in the the HadGEM3-GC31-LL model, with ssp set
to 245 and at 10 arc-minutes, we can get the available variables:

```{r}
get_vars_for_dataset(dataset = "WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m")
```

We can now download "bio01" and "bio02" for that dataset with:
```{r eval=eval_chunk}
download_dataset(
  dataset = "WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m",
  bio_variables = c("bio01", "bio02")
)
```

The datasets are averages over 20 year 
periods (2021-2040, 2041-2060, 2061-2080, 2081-2100).
In `pastclim`, the midpoints of the periods (2030, 2050, 2070, 2090) are used as the time stamps. All 4 periods
are automatically downloaded for each combination of GCM model and SSP, and can
be selected
as usual by defining the time in `region_slice`.

```{r eval=eval_chunk}
future_slice <- region_slice(
  time_ce = 2030,
  dataset = "WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m",
  bio_variables = c("bio01", "bio02")
)
```

Alternatively, it is possible to get the full time series of 4 slices with:
```{r eval=eval_chunk}
future_series <- region_series(
  dataset = "WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m",
  bio_variables = c("bio01", "bio02")
)
```

It is possible to simply use `get_time_ce_steps(dataset = "WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m")` to get the available time points for
that dataset.

Help for the WorldClim datasets (modern and future) can be accessed with `help("WorldClim_2.1")`

## CHELSA

Future projections are based on the models in CMIP6, downscaled and de-biased
using CHELSA 2.1 for the present as a baseline. Monthly values of mean temperature, 
and precipitation, as well as 19 bioclimatic
variables were processed for
5 global climate models (GCMs), and for three 
Shared Socio-economic Pathways (SSPs): 126, 370 and 585. Model and
SSP can be chosen by changing the ending of the dataset name
*CHELSA_2.1_GCM_SSP_0.5m*.

A complete list of available combinations can be obtained with:
```{r}
list_available_datasets()[grepl("CHELSA_2.1", list_available_datasets())]
```

Note that there is a virtual option for each dataset.
So, if we are interested in the the GFDL-ESM4 model, with ssp set
to 126 , we can get the available variables:

```{r}
get_vars_for_dataset(
  dataset = "CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m",
  monthly = TRUE
)
```

We can now download "bio01" and "bio02" for that dataset, using the virtual version, with:
```{r eval=eval_chunk}
download_dataset(
  dataset = "CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi",
  bio_variables = c("bio01", "bio02")
)
```

The datasets are averages over 30 year 
periods (2011-2040, 2041-2070, 2071-2100).
In `pastclim`, the midpoints of the periods (2025, 2055, 2075)  are used as the time stamps. All 3 periods
are automatically downloaded for a given combination of GCM model and SSP, and can
be selected
as usual by defining the time in `region_slice`.

```{r eval=eval_chunk}
future_slice <- region_slice(
  time_ce = 2025,
  dataset = "CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi",
  bio_variables = c("bio01", "bio02")
)
```

Alternatively, it is possible to get the full time series of 4 slices with:
```{r eval=eval_chunk}
future_series <- region_series(
  dataset = "CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi",
  bio_variables = c("bio01", "bio02")
)
```

It is possible to simply use `get_time_ce_steps(dataset = "CHELSA_2.1_UKESM1-0-LL_ssp585_0.5m_vsi")` to get the available time points for
that dataset.

Help for the WorldClim datasets (modern and future) can be accessed with `help("CHELSA_2.1")`

