DOI: 10.5061/dryad.6m905qg8j
title: Data from: Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally

description: Modeling how climate change may affect the potential distribution of
 species and communities typically utilizes bioclimatic variables.
 Distribution predictions rely on the values of the bioclimatic variable
 (e.g., precipitation of the wettest quarter). However, the ecological
 meaning of most of these variables depends strongly on the within-year
 position of a specific climate period (SCP), e.g., the wettest quarter of
 the year, which is often overlooked. Our aim was to determine how the
 within-year position of the SCPs would shift (SCP shift) in reaction to
 climate change in a global context. We calculated the deviations of the
 future within-year position of the SCPs relative to the reference period.
 We used four future time periods, four scenarios, and four CMIP6 global
 climate models (GCMs) to provide an ensemble of expectations regarding SCP
 shifts and locate the spatial hotspots of the shifts. Also, the size and
 frequency of the SCP shifts were subjected to linear models to evaluate
 the importance of the impact modeler's decision on time period,
 scenario, and GCM. We found ample examples of SCP shifts exceeding 2
 months, with 6-month shifts being predicted as well. Many areas in the
 tropics are expected to experience both temperature and
 precipitation-related shifts, but precipitation-related shifts are
 abundantly predicted for the temperate and arctic zones as well. The
 combined shifts at the Equator reinforce the likelihood of the emergence
 of no-analogue climates there. The shifts become more pronounced as time
 and scenario progress, while GCMs could not be ranked in a clear order in
 this respect. For most SCPs, the modeler's decision on the GCM was
 the least important, while the choice of time period was typically more
 important than the choice of scenario. Future predictive distribution
 models should account for SCP shifts and incorporate the phenomenon in the
 modeling efforts.
description: # Data from: Precipitation and temperature timings underlying bioclimatic
 variables rearrange under climate change globally
 [https://doi.org/10.5061/dryad.6m905qg8j](https://doi.org/10.5061/dryad.6m905qg8j) ## Description of the data and file structure Format: RData The dataset contains the processed data (SCP shifts) and some auxiliary files and script. ### 1 Global raster template RData file called `global_raster_template.RData` that contain a `RasterLayer` object called `global_raster_template`. It is a 0-layer global raster at 2.5' (~5 km) resolution using WGS-84 coordinate reference system. The raster can be used as a template for converting the numeric vectors of the SCP shifts to rasters (please refer to the example script). ### 2 Mask of the terrestrial cells RData file called `mask_of_terrestrial_cells.RData` that contain a logical vector called `mask_of_terrestrial_cells`. It is `TRUE` for terrestrial cells and `FALSE` for marine cells of the global raster template. SCP shifts are stored only for the terrestrial cells (for computational reasons), therefore the mask of the terrestrial cells should be used if the numeric vectors of the SCP shifts are converted to rasters (please refer to the example script). ### 3 SCP shifts RData file called `SCP_shifts.RData` that contain a list of `data.frame`s called `SCP_shifts`. The list has 4×4×4 elements named as `[period]_[scenario]_[GCM]` (e.g., `2090_370_bcc`). `[period]` can be one of the following options: * 2030: 2021-2040 * 2050: 2041-2060 * 2070: 2061-2080 * 2090: 2081-2100 `[scenario]` can be one of the following options: * 126: SSP1-2.6 * 245: SSP2-4.5 * 370: SSP3-7.0 * 585: SSP5-8.5 `[GCM]` can be one of the following options: * miroc6: MIROC-ES2L * bcc: BCC-CSM2-MR * ipsl: IPSL-CM6A-LR * canesm: CanESM5 Each list element is a `data.frame` of 8 variables called `wettestM`, `driestM`, `warmestM`, `coldestM`, `wettestQ`, `driestQ`, `warmestQ`, `coldestQ`, where `M` stands for month and `Q` stands for quarter. Each column contains integer numbers ranging from 0 to 6 describing the magnitude of the SCP shift (measured as the number of months). SCP shifts are stored only for the terrestrial cells (for computational reasons), therefore the number of rows of the `data.frame` is the same as the number of terrestrial cells. Please refer to the example script for converting the data stored in the `SCP_shifts` list to raster. ## Example script R script file called `example_script.r` demonstrating how the abovementioned three RData files can be used to extract the shift of the wettest quarter according to the 2081-2100 period, the SSP3-7.0 scenario, and the BCC-CSM2-MR global climate model, and to convert to `RasterLayer`, display, and save as geoTiff raster file. The script can be run in R statistical software. ## Access information The raw climate data from which the processed data (i.e., SCP shifts) were calculated can be downloaded from the WorldClim database (Fick and Hijmans 2017): * [https://www.worldclim.org/data/worldclim21.html](https://www.worldclim.org/data/worldclim21.html) * [https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html](https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html) Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. [https://doi.org/10.1002/joc.5086](https://doi.org/10.1002/joc.5086)