StableClim, continuous projections of climate stability from 21000 BP to 2100 CE at multiple spatial scales

Paleoclimatic data are used in eco-evolutionary models to improve knowledge of biogeographical processes that drive patterns of biodiversity through time, opening windows into past climate–biodiversity dynamics. Applying these models to harmonised simulations of past and future climatic change can strengthen forecasts of biodiversity change. StableClim provides continuous estimates of climate stability from 21,000 years ago to 2100 C.E. for ocean and terrestrial realms at spatial scales that include biogeographic regions and climate zones. Climate stability is quantified using annual trends and variabilities in air temperature and precipitation, and associated signal-to-noise ratios. Thresholds of natural variability in trends in regional- and global-mean temperature allow periods in Earth’s history when climatic conditions were warming and cooling rapidly (or slowly) to be identified and climate stability to be estimated locally (grid-cell) during these periods of accelerated change. Model simulations are validated against independent paleoclimate and observational data. Projections of climatic stability, accessed through StableClim, will improve understanding of the roles of climate in shaping past, present-day and future patterns of biodiversity.


warming_periods <-piThresh[["Warm periods"]]
A quick look at the warming_periods data.table shows how the thresholds for climate change are organised in the table. We can see that the first column desribes the region type (e.g. Wallace region), the second column describes the region of interest (e.g. Eurasian), while the remaining columns describe the thresholds of climate change in 1, 2.5, and 5% increments.  We can then extract the threshold we're interested in, for example 90%, using the following code This threshold shows us that we would expect extreme warming (90th percentile) 'natural climate variability' to be characterised by temperature changes of 0.38°C/century in the Eurasian zoogeographic region.

Applying Thresholds
Now that we know our threshold of rapid climate change we can subset the past and the historical/future data to these periods only. This is a two step process: 1. Subset the regional regressions, and extract a list of windows which are ≥ the threshold 2. Subset the rasters for the respective time periods to windows ≥ the threshold.
A quick look at the names of the pastReg and futReg lists shows that they are a little more complex than the piThresh list.
names (   We can now subset the pastReg and futReg data.tables based on our threshold of extreme climate change.
pastReg_RCC <-pastReg[pastReg$Slope >= thresh, ] futReg_RCC <-futReg[futReg$Slope >= thresh, ] The pastReg_RCC data.table now contains only 2392 rows, from a total of 20901 rows previously. Likewise, the futReg_RCC data.table contains 120 rows, from a total of 152 suggesting for the Eurasian zone, more often that not (~79 % of the time) rates of climate change since wide-scale industrialisation (~1850 C.E.) have been greater than expected under extremely high levels of natural climate variability.
Using the Start column from the pastReg_RCC data.table we can create a vector of layer names which can be matched to the names of the pastRast raster brick we imported earlier.

Subsetting raster data
We can the use this subset raster to calculate median rates of extreme climate change given the threshold.

Masking
Now that we have the rasters subset to the correct time periods and we have calculated the median rates of climate change, we can crop and mask the raster data to the Eurasian region and then plot it.
In the usage notes of the manuscript we also advocate using a pattern scaled trend. We can calculate this here too and then recalculate SNR using the pattern scaled trend.
For masking and cropping we first transform the eurasia polygons into a raster. As of writing this appendix, sf objects don't play nicely with rasters, so we can either rasterize the polygons or turn them to sp polygon objects. Here we first convert to SpatialPolygons, then rasterize, and then remove cells that have less than 10% coverage in Eurasia.

Rates of climate change in Eurasia
From the eurasia_trend raster layer for Eurasia we can see that the minimum and maximum rates of median climate change during periods of rapid warming were 0.14°C/century and 1.42°C/century. eurasia_trend * 100

Plotting
We can now plot maps of the Eurasian region showing trend, variability, and SNR for the past.
NB -Here we plot the non-pattern scaled trend in°C/Century.°C  We will leave it up to you to create the necessary rasters for the future period, using the steps above.