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SoilFutures-BR: Bias-Corrected Soil Temperature Projections for Brazil
Dimaghi Schwamback1,2,3, Bruna S Aguiar4, Jullian S Sone4,5
1Department of Hydraulics and Sanitation, São Carlos School of Engineering, University of São Paulo, CxP. 359, São Carlos, 13566-590, São Paulo, Brazil. dimaghis@professores.utfpr.edu.br.
Scientific Data
|June 25, 2026
Summary
A new dataset, STEM-BR, offers bias-corrected soil temperature projections for Brazil under climate change. This crucial data aids in understanding future warming trends and developing adaptation strategies for agriculture and water resources.
Area of Science:
- Earth and Environmental Sciences
- Climate Science
- Data Science
Background:
- Soil temperature is a critical Earth system variable highly sensitive to climate change, especially in Brazil.
- Existing climate projections often contain biases, necessitating correction for reliable impact assessments.
- No bias-corrected soil temperature dataset based on CMIP6 projections is available for Brazil.
Purpose of the Study:
- To create a comprehensive, bias-corrected soil temperature dataset for Brazil using CMIP6 projections.
- To provide historical and future soil temperature data under various climate change scenarios (SSP2-4.5, SSP3-7.0, SSP5-8.5).
- To support climate impact studies and adaptation strategies in Brazil.
Main Methods:
- Generated a gridded dataset (STEM-BR) from 15 CMIP6 General Circulation Models (GCMs).
- Applied Quantile Delta Mapping (QDM) for bias correction against the ERA5 dataset at three soil depths (0.07, 0.28, 1.00m).
- Systematic regridding, statistical refinement, and monthly time series analysis were performed.
Main Results:
- The bias-corrected dataset shows significant improvements in representing observed soil temperature climatology.
- Residual errors were reduced to within ±10%, preserving essential spatial and climatic gradients.
- Future projections indicate consistent warming across Brazil, with significant increases by 2100 under different scenarios, particularly in the Northeast, North, and Central-West regions.
Conclusions:
- The STEM-BR dataset fills a critical data gap for soil temperature in Brazil under climate change.
- Bias correction enhances the reliability of climate projections for impact assessments.
- This dataset is a valuable resource for climate adaptation strategies in agriculture, food security, and water management.
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