Data from: Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally
Data files
Sep 06, 2024 version files 294.13 MB
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example_script.r
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global_raster_template.RData
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mask_of_terrestrial_cells.RData
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README.md
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SCP_shifts.RData
Abstract
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.
README: Data from: Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally
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/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