1. Visualization
Regarding the visualization of results to increase readability and comparability, we would like to suggest the method for uncertainty plots for multi-model analysis used in Kaye et al. (2012), which was presented by Ron Kahana at the ISI-MIP Analysis Workshop. We have attached the paper for your consideration and reference. Of course, this is only a suggestion and it is not obligatory to adhere to it.
The colour coding for the RCPs should be as follows (taken from IPCC WGI, also attached to this email):
2. Set of regions
We have decided to switch from using the Giorgi regions to the GEO-3 subregions for the spatial aggregation of ISI-MIP results. The GEO-regions have the advantage that they are defined along national borders (rather than simple rectangles), and therefore data at the country level can easily be aggregated to these regions.
You can find definitions which countries belong to which regions and subregions via the following link given below:
http://geocompendium.grid.unep.ch/geo_regions/index_geo_regions.htm
For your convenience you can find masks we created for these regions in the attachment, both for the larger regions and the subregions.
3. IRIS - data analysis and visualisation tool developed by the Met Office
The Met Office is developing IRIS - a data analysis and visualisation tool for doing analysis / visualisation in python. Iris is released as free and open source software under the Lesser Gun Public License and you can get it here:
https://github.com/scitools/iris or http://scitools.github.com/iris/
The JULES team at the Met Office is currently trying this tool out, and would like to invite more people using it so that codes can be shared. (Please note that this information was already shared on the ISI-MIP blog - http://isimip.wordpress.com/ - which continues to be a very useful medium for exchange.)
