Computational Tools in Climate Science
NFDI4Earth
About This Course
The course "Computational Tools in Climate Science" spans most areas of climate science. Its content is taken and adapted from the syllabus of the international two week long summer school at Climatematch Academy. The syllabus covers fundamentals of climate science, different types of climate data and the future of the climate system as predicted through earth system and socioeconomic modeling. The course also discusses responses to climate change, including identification of extreme climate events, like heatwaves or floods, and the use of basic AI algorithms for tracking the impacts of climate change.
Outline
- Climate System Overview
- Ocean-Atmosphere Reanalysis
- Remote Sensing
- Paleoclimate
- Introduction to Climate Modelling
- An Ensemble of Futures
- The Socioeconomics of Climate Change
- Extremes and Variability
- AI and Climate Change
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
- Analyze global climate data using python and a suite of computational methods.
- Identify various types of global and local climate data, as well as their public access points.
- Understand the atmospheric, oceanic, cryospheric, biospheric, and terrestrial components of the climate system by exploring and comparing climate data from various sources (e.g., in situ measurements, remote observations, reanalysis, models) across multiple timescales (e.g., days, years, millennia) spanning the past, present, and future.
- Assess the impacts of climate change by examining projections of future variability and current climate response.
- Apply analysis techniques to explore the relationships between physical and socioeconomic variables.
Resources
This course is adapted from the following resources, which also benefit from various CC BY licensed resources that are linked in the respective tutorials:
Administration
Farzaneh Sadeghi, Remon Sadikni