Computational Tools in Climate Science: Part 8 - Extremes and Variability
NFDI4Earth
About This Course
Welcome to the "Extremes and Variability" course. This is the eighth part of the "Computational Tools for Climate Science" course series! Today we will discuss extreme climate events in the context of climate change and introduce you to statistical methods to study their variability. Around the world, people experience increased intensities and frequencies of extreme weather due to climate change, leading to significant costs to societies. To develop efficient adaptation strategies we need to understand how exactly extreme events change, yet systematic observed data of extremes is sparse. Extreme Value Theory (EVT) addresses some of these challenges and can be used to estimate extreme event probabilities. In these tutorials we show you how you can use EVT to estimate return periods of storms, droughts, floods and more.
Level
Intro, Beginner, Intermediate
Requirements
Prerequisites include some introductory programming skills in Python, as well as core math and science concepts. We expect participants to be familiar with fundamental Python and data storage concepts (variables, lists, dictionaries, data formats) as well as some key Python libraries like NumPy, matplotlib, cartopy, datetime, pandas, and Xarray.
Subject Area
Geosciences
Learning Objectives
- Understand the relevance of the Generalized Extreme Value distribution to extreme events, apply this distribution to observation and model data, and assess the fit.
- Explain how the moments and parameters of the Generalized Extreme Value distribution vary with time.
- Compute extreme event probabilities (return periods/levels).
- Characterize extreme events (e.g. precipitation, sea level height, and heat) by these probabilities and prescribed thresholds.
What You Will Learn
- Introduction to Extremes in a Changing Climate
- Distributions
- What is an Extreme Event? Empirical Return Levels
- Extreme Value Analysis - the GEV Distribution
- Return Levels Using Normal and GEV Distributions
- Non-stationarity in Historical Records
- Scenario-dependence of Future Changes in Extremes
- Scenario-dependence of Future Changes in Extremes
- Thresholds
Resources
Computational Climate Science syllabus by Climatematch
Computational Computational Tools in Climate Science by Climatematch
Computational Tools for Climate Science Course by neuromatch