Extremes are by definition rare. Statistical methods are critical for characterizing extreme weather in time and space, estimating changes in extreme weather, and quantifying our uncertainty about the frequency and trends in extremes.
The Statistics team is developing, implementing and advising on statistical methods for characterizing extremes in observations and model output. We are particularly focused on detection and attribution of changes in extreme events, quantifying the evidence that the probability of extreme events are changing over time and that changes are caused by anthropogenic influences. As part of this work we are addressing the question of uncertainty quantification – attempting to quantify the uncertainty in our results contributed by factors such as initial condition uncertainty/sampling uncertainty, forcing uncertainty, model parameter uncertainty, model structural uncertainty, and grid resolution uncertainty.
Some current efforts include
- Developing a statistical framework for, and advising the CASCADE detection and attribution team on, detection and attribution of changes in extreme events,
- Advising the CASCADE climate modeling team on methods for characterizing the ability of different model formulations to reproduce observed extremes,
- Analyzing the relationship between atmospheric rivers and precipitation on the west coast of the United States using statistical extreme value methods, and
- Developing software for fitting statistical extreme value distributions to climate data using Python and R.