CMCC-BioClimInd: new data for biogeographers

A global and free access dataset of 35 bioclimatic indicators at 0.5° by 0.5° resolution for historical and future conditions

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It was March 2019 when our team published a work on the impacts of global warming on Russian forests from a biogeographical perspective. To publish that study based essentially on the SDMs (Species Distrubution Models) approach we produced as input a long list of bioclimatic indicators covering the Russian territory. However, at the end of that work, we said: "great job guys, but what if we made available all these input data for everyone?" and also "what if we extend the coverage to the entire globe?". So, in those days, CMCC-BioClimInd was born.

In truth, our dataset born with another name: in the beginning, we named it "BioClim" and only many months later we changed into the definitive "CMCC-BioClimInd". The original "BioClim" according to our co-author Monia Santini looked like the name of a pharmaceutical!

A new dataset or a new medicine??

Beyond the name, our target was to widen the availability of bioclimatic information by an ensemble of bioclimatic indicators valuable both for historical and future robust climate change impacts’ assessments. We're thinking about wildlife ecology, natural resources’ conservation, botanic, forestry and many other fields involving biogeography.

But, what is meant by biogeography and why bioclimatic indicators are so important?  Biogeography studies the distribution of species and ecosystems in a geographic space and the relationships between these communities and, among others, the climatic conditions: bioclimatic indicators summarize these conditions.

SSo, in CMCC-BioClimInd we provide a global dataset of 35 indicators, calculated both for a 40-years historical interval (1960–1999) and for two future 40-years time intervals (mid term 2040–2079 and long term 2060–2099) from, respectively, post-processing of climate reanalysis and an ensemble of 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) bias corrected climate simulations.

Potential Evapotranspiration of the historical time interval 1960–1999 (left) and the ensemble anomaly of the future simulations expressed in percentage (center) and the variation among simulations (right)

But our work is ongoing and we wish, in the near future, to keep CMCC-BioClimInd up-to-date adding more climate simulations such as Representative Concentration Pathways (RCPs) 2.6 and 6.0 and/or further Earth System Model outputs. Data are available in NetCDF4 format in PANGAEA, but if needed we can provide any other GIS format (i.e. ESRI grid, GEOTIFF).

Our paper in Scientific Data is available here.

Sergio Noce

Researcher, Euro-Mediterranean Centre on Climate Change Foundation (CMCC)

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