Future climate data

using SimpleSDMLayers
using Plots
using Statistics

Justification for this use case: we will often want to forecast the range of species under a variety of climate change scenarios. For this reason, SimpleSDMLayers offers access to CMIP5 and CMIP6 scenarios or models, to be used in these situations.

For some data providers and datasets, SimpleSDMLayers offers access to future climate data. Future climates are usually specified by a model, and a scenario. For example, WorldClim 2.1 offers the full suite of BioClim variable under four SSPs and a number of CMIP6 models.

We can use this to look at, for example, the temperature difference between the current and future climate. To illustrate this, we will do a simple example where we contrast the "historical" climate (i.e. what is assumed to be the current data) to the projected data under SSP585 in the 2041-2060 period.

We will start by getting the contemporary data:

boundaries = (left=-169.0, right=-50.0, bottom=24.0, top=71.0)
baseline = SimpleSDMPredictor(WorldClim, BioClim, 1; boundaries...)
contour(baseline; c=:cork, frame=:box, fill=true, clim=(-30, 30), levels=6)

To get a future dataset, we need to specify the model - models are combinations of a CMIP version, and either a RCP or SSP:

instances(CMIP6)

(BCCCSM2MR, CNRMCMS61, CNRMESM21, CanESM5, GFDLESM4, IPSLCM6ALR, MIROCES2L, MIROC6, MRIESM2)

And we need to check the names of the SSP we want to use:

instances(SharedSocioeconomicPathway)

(SSP126, SSP245, SSP370, SSP585)

We can now get our future temperature layer (and plot it), for a valid model/scenario combination, and plot it. We will pick an extreme scenario at a long (yet frighteningly short) time in the future. In order to facilitate the comparison with the previous plot, we will re-use the same color limites, where blue is negatuve temperatures.

future = SimpleSDMPredictor(WorldClim, BioClim, CanESM5, SSP585, 1; year="2061-2080", boundaries...)
contour(future; c=:cork, frame=:box, fill=true, clim=(-30, 30), levels=6)

Note that the call to get the future data is almost the same as the historical one - the exception is the addition of the model and scenario, and the specification of the years. With this layer, we can now measure the difference in mean annual temperature, because layers can be substracted. Note that we are switching the scale: the difference between the two layers here is always positive.

plot(future - baseline, frame=:box, c=:lajolla)

We might actually be interested in averaging multiple models. Because we know the variety of models worldclim has (instances(CMIP6)), we can do this fairly easily. One of the model has no predictions for SSP585 (which we would learn in the form of an error message), so we will filter it out directly.

ensemble = [
    SimpleSDMPredictor(
        WorldClim, BioClim, model, SSP585, 1;
        year="2061-2080", boundaries...
    ) for model in instances(CMIP6) if model != GFDLESM4
];

We can create a layer of differences from each scenario to the baseline:

differences = [component - baseline for component in ensemble];

This can be plotted as a grid of differences, using the same colorscale as in the previous plot:

plot(plot.(differences, c=:lajolla, grid=:none, axes=false, frame=:none, leg=false)...)

Finally, we can plot the average of the expected conditions under the scenario we considered:

contour(mosaic(mean, ensemble); c=:cork, frame=:box, fill=true, clim=(-30, 30), levels=6)