Wiuff (2025) Probability of Climate Records and Their Likely Magnitudes Given Climate Data with an Idealized Linear Trend

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Climate
• Year: 2025
• Date: 2025-11-17
• Authors: Rasmus Wiuff
• DOI: 10.1175/jcli-d-24-0717.1

Research Groups

Not available in the provided abstract.

Short Summary

This study develops a simple stochastic model, incorporating a linear trend and a normal stochastic component, to analyze historical climate data and estimate the probabilities and magnitudes of climate extremes. It finds that these probabilities and magnitudes depend on the time scale (ratio of standard deviation to trend slope) and the number of observational years, stabilizing when the observational period exceeds approximately twice the time scale, and suggests climate change is accelerating based on a global temperature case study.

Objective

• To develop a simple stochastic model for analyzing historical climate data and estimating the probabilities and magnitudes of future climate extremes, considering a linear trend and a normally distributed stochastic component.

Study Configuration

• Spatial Scale: Global (for the illustrative case study); general for the theoretical model.
• Temporal Scale: Multi-year, with annual resolution (e.g., yearly average temperatures); analysis considers the "number of observational years."

Methodology and Data

• Models used: Simple stochastic model incorporating a linear trend and a normally distributed stochastic component; Monte Carlo simulations; analytical solutions for specific cases.
• Data sources: Historical climate observational data, specifically global yearly average temperatures for the case study.

Main Results

• The probabilities and magnitudes of climate extremes are determined by the time scale (defined as the ratio between the standard deviation and the slope of the trend) and the number of observational years.
• When the number of observational years exceeds approximately twice the time scale, the stochastic contributions to probabilities and magnitudes stabilize and become exclusively dependent on the time scale.
• Graphical representations and approximated formulas are provided to facilitate rapid determination of dependent parameters.
• A case study using global yearly average temperatures illustrates the method's applicability and suggests that climate change appears to be occurring at an accelerated pace.

Contributions

• Presents a simple, generalizable stochastic model for analyzing historical climate data and estimating the probability and magnitude of future climate records.
• Identifies the critical dependencies of extreme event probabilities and magnitudes on the "time scale" (standard deviation to trend slope ratio) and the length of the observational period.
• Provides practical tools, including graphs and approximate formulas, for rapid estimation of these parameters.
• Illustrates the model's applicability with a real-world example of global annual mean temperatures, offering insights into the perceived acceleration of climate change.

Funding

Not available in the provided abstract.

Citation

@article{Wiuff2025Probability,
  author = {Wiuff, Rasmus},
  title = {Probability of Climate Records and Their Likely Magnitudes Given Climate Data with an Idealized Linear Trend},
  journal = {Journal of Climate},
  year = {2025},
  doi = {10.1175/jcli-d-24-0717.1},
  url = {https://doi.org/10.1175/jcli-d-24-0717.1}
}