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A new global dataset of bioclimatic indicators
Sergio Noce1, Luca Caporaso2,3, Monia Santini2
1Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Viterbo, Italy. sergio.noce@cmcc.it.
Scientific Data
|November 17, 2020
Summary
A new global dataset, CMCC-BioClimInd, offers 35 bioclimatic indicators for historical and future climate conditions. This spatialized data aids ecological and environmental research, including biodiversity conservation and forestry.
Area of Science:
- Environmental Science
- Climate Science
- Ecology
Background:
- Bioclimatic indicators are crucial for understanding ecological patterns and environmental changes.
- Spatialized bioclimatic data is essential for large-scale ecological and environmental applications.
- Existing datasets may not fully cover historical and future climate projections with sufficient detail.
Purpose of the Study:
- To present a new global gridded dataset of bioclimatic indicators (CMCC-BioClimInd).
- To provide bioclimatic data for both historical (1960-1999) and future climate conditions (2040-2079, 2060-2099).
- To support applications in forestry, biodiversity conservation, and landscape ecology.
Main Methods:
- Post-processing climate reanalysis for the historical period.
- Utilizing an ensemble of 11 bias-corrected CMIP5 simulations for future projections.
- Calculating 35 bioclimatic indices at 0.5° by 0.5° resolution.
Main Results:
- A comprehensive global gridded dataset (CMCC-BioClimInd) of 35 bioclimatic indices is generated.
- Data covers historical (1960-1999) and future climate scenarios (2040-2079, 2060-2099).
- The dataset is available in NetCDF4 format for public download.
Conclusions:
- The CMCC-BioClimInd dataset significantly enhances the availability of spatialized bioclimatic information.
- This resource is vital for diverse ecological and environmental research fields.
- Public accessibility of the data promotes wider scientific application and discovery.
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