Intro#
Overview#
Welcome to the first day of the Computational Tools for Climate Science course! During this first day, the videos will provide an overview of Earth’s climate system. You’ll learn about various aspects of Earth’s climate including different parts of the climate system, forcings and mechanisms that drive changes in the climate system, and the importance of understanding past, present, and future climate variability. You’ll begin by learning about the effect of incoming solar radiation and Earth’s energy budget on the climate. You’ll then explore various processes within the atmospheric, oceanic, and terrestrial components of Earth’s climate system. Finally, you’ll investigate long-term, natural forcings and feedbacks that influence Earth’s climate and will begin to think about these processes in the context of past, present, and future climate variability.
Additionally, the notebooks in today’s tutorials will elaborate on the climate concepts introduced in the videos, but will primarily focus on introducing the Xarray Python package, which is commonly used to analyze large climate datasets and makes working with multi-dimensional arrays simple and efficient. Xarray introduces labels in the form of dimensions, coordinates, and attributes on top of the raw data array, and includes a large number of functions for advanced analytics and visualization with these data structures. Throughout the tutorials today, you’ll learn the basics of Xarray and apply some of these tools to climate datasets to further explore the climate system concepts introduced in the videos.
Day Learning Objectives#
Use the basic functions in Xarray to process, analyze, interpret, and present climate data.
Understand the main climate sub-systems, drivers of Earth’s climate, and the types of climate science data that exist.
Identify the main concepts covered in the course.
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 = "W1D1_Intro"