Hello all,
I am a wildlife ecologist planning to summarize projections for global heat and drought and how it may affect endangered wildlife species to.
My approach was a global study, using monthly data, preferably on a resolution of 1 degree or coarser.
I am not a climate scientist, so I don’t know how to judge the different Global Climate Models underlying CMIP6, or which ones would best suit my purposes. Therefore, the “Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas” seemed like a great option, because it appeared I could use the output from an ensemble model, rather than have to choose what GCMs I want to in-or exclude.
I downloaded the monthly mean of precipitation and monthly mean of maximum temperature for four SSP scenarios and the historical period, and started investigating the .NC files.
To my surprise, it seems like each .NC file in fact has data for each Global Climate Model used in the ensemble model. So instead of having monthly values for a certain scenario, for a certain variable, I have 31 values for each month.
I was wondering whether there is a way to just get the final values that, I presume, are used in the Atlas? Would it be appropriate to just average the values across all individual models?
If this is not possible, this may not be the right dataset for me and I may need to select data from global climate models manually, though I currently do not have the knowledge to do so.
On a sidenote, there’s a warning when I convert the .nc file to a rasterstack (which is the format I want to work with):
dat.stack <- stack(f)[1] “vobjtovarid4: **** WARNING **** I was asked to get a varid for dimension named member BUT this dimension HAS NO DIMVAR! Code will probably fail at this point”
Warning message:
In .rasterObjectFromCDF(x, type = objecttype, band = band, …) :
“level” set to 1 (there are 1032 levels)
The 1032 levels make sense, there’s 1032 months that I would need a value for. Nevertheless I’m unsure what the warning implies.
And then, unfortunately, the rasterstack has 31 layers, one for each global climate model.
dat.stack
class : RasterStack
dimensions : 180, 360, 64800, 31 (nrow, ncol, ncell, nlayers)
resolution : 1, 1 (x, y)
extent : -540, -180, -90, 90 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
names : X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, …