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Updated: Feb 10, 2026
06:10
Using Generative Art to Convey Past and Future Climate Transitions
Published on: March 31, 2023
1.5K
Selection of climate change scenario data for impact modelling.
M Sloth Madsen1, C Fox Maule, N MacKellar
1Danish Climate Centre, Danish Meteorological Institute, Lyngbyvej 100, DK-2100 Copenhagen, Denmark. msm@dmi.dk
Summary
Selecting appropriate climate change projection data is crucial for food safety impact models. This study recommends using multiple climate models and a novel statistical method to generate localized climate data for accurate crop and mycotoxin impact assessments.
Area of Science:
- Climate science
- Agricultural science
- Food safety
Background:
- Climate change impact models require detailed climate data for accurate food safety assessments.
- Existing climate projection data may not be suitable for local-scale impact analysis.
Purpose of the Study:
- To select appropriate climate change projection data for crop phenology and mycotoxin impact models.
- To address limitations in weather generator approaches for local-scale climate impact analysis.
Main Methods:
- Utilized the ENSEMBLES database for climate model output.
- Employed a weather generator approach for local-scale climate projections.
- Developed an ad-hoc statistical method to synthesize missing climate variables, demonstrated with relative humidity.
Main Results:
- Projected climate change signals (temperature, precipitation, relative humidity) vary significantly based on the chosen climate model.
- The developed statistical method successfully synthesized realistic values for missing variables.
Conclusions:
- Using climate change projections from multiple climate models is essential to account for model uncertainty.
- The novel statistical method enhances the suitability of climate projections for local-scale impact studies, particularly for food safety and crop modeling.
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