Goodwin (2025) Indicators and predictions of climate change

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-11-29
• Authors: Paul Goodwin
• DOI: 10.1016/b978-0-443-32838-1.00005-9

Research Groups

• Paul Goodwin, University of Bath, Bath, Somerset, United Kingdom

Short Summary

This chapter reviews the desirable features of climate forecasting methods and discusses various approaches, including statistical, physical, expert judgment, and combined forecasts, to inform policymakers and the public about future climate changes.

Objective

• To review and discuss the desirable features and various methodologies for forecasting climate change, providing a framework for understanding and generating reliable predictions for decision-making.

Study Configuration

• Spatial Scale: Global (implied by discussion of Earth's climate and global temperatures)
• Temporal Scale: Future (long-term climate projections, implied by "predictions of climate change" and "future conditions")

Methodology and Data

• Models used: Not applicable (N/A) - This is a review chapter discussing categories of models (e.g., univariate statistical methods, models based on physical and chemical processes), not applying specific models.
• Data sources: Not applicable (N/A) - This is a review chapter discussing types of data implicitly used by forecasting methods (e.g., historical climate data for statistical models, physical parameters for process models), not using specific data sources.

Main Results

• Reliable climate forecasts should possess accuracy, provide information on uncertainty, be acceptable to users, and be computationally efficient.
• Key forecasting methods include univariate statistical approaches, models based on physical and chemical processes, judgmental forecasts from experts, and techniques for combining forecasts.
• Each forecasting method offers distinct characteristics and trade-offs concerning the desirable features, influencing their applicability for different decision-making contexts.

Contributions

• Provides a comprehensive overview of the essential characteristics and diverse methodologies for climate change forecasting.
• Offers a structured framework for evaluating the suitability and reliability of different forecasting approaches for informing policy and public understanding.
• Synthesizes the strengths and weaknesses of various forecasting techniques, from statistical to physical and expert-based methods.

Funding

• Not specified in the provided text.

Citation

@article{Goodwin2025Indicators,
  author = {Goodwin, Paul},
  title = {Indicators and predictions of climate change},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-32838-1.00005-9},
  url = {https://doi.org/10.1016/b978-0-443-32838-1.00005-9}
}