Intro#
Overview#
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.
Day 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.
Install and import feedback gadget#
Show code cell source
# @title Install and import feedback gadget
!pip3 install vibecheck datatops --quiet
from vibecheck import DatatopsContentReviewContainer
def content_review(notebook_section: str):
return DatatopsContentReviewContainer(
"", # No text prompt
notebook_section,
{
"url": "https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab",
"name": "comptools_4clim",
"user_key": "l5jpxuee",
},
).render()
feedback_prefix = "W2D3_Intro"
[notice] A new release of pip is available: 24.2 -> 24.3.1
[notice] To update, run: pip install --upgrade pip