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Uncertainty in weather and climate prediction.
1Met Office, Exeter, UK. julia.slingo@metoffice.gov.uk
Summary
Probabilistic forecasting, pioneered by Lorenz, now dominates weather and climate prediction. This paper explores its evolution, uncertainty representation, and challenges in a changing climate for better decision-making.
Area of Science:
- Meteorology and Climate Science
- Chaos Theory and Dynamical Systems
- Predictive Modeling and Uncertainty Quantification
Background:
- The foundational work of Edward Lorenz on chaos theory revolutionized weather and climate forecasting.
- Probabilistic prediction methods have become central to modern forecasting science.
- Understanding and representing forecast uncertainty is critical across various lead times.
Purpose of the Study:
- To provide a perspective on Lorenz's contributions to probabilistic forecasting.
- To examine the evolution of uncertainty representation in weather and climate models.
- To address challenges in climate change projections for improved decision support.
Main Methods:
- Review of Lorenz's seminal work on chaos theory.
- Analysis of probabilistic prediction systems and their methods for representing model uncertainty.
- Exploration of challenges posed by climate change to forecasting accuracy.
Main Results:
- Probabilistic approaches, stemming from chaos theory, are now standard in weather and climate prediction.
- Significant advancements have been made in representing forecast uncertainty across different time scales.
- Climate change introduces new complexities and uncertainties into prediction models.
Conclusions:
- Lorenz's work profoundly shaped modern probabilistic forecasting.
- Addressing model uncertainty is key to reliable climate change projections.
- Enhanced uncertainty assessments are vital for effective adaptation and mitigation strategies.
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