Climate Analytics Lab

Improving climate projections with machine learning

Welcome!

Week 1 is upon us and we are going to spend it getting everyone situated, goiong over the primary paper, and beginning to read up on the domain of climate model simulation and emulation.

Topics

You will be touching on these topics over the first week

  • What is climate change, and how do we model it?
  • What is CMIP and how does it relate to the IPCC?
  • What is the ClimateBench dataset?
  • Reproducibility/replicability in data science

Tasks

  • Gain access to Casper, see instructions on home page
  • Complete all assigned readings
  • Submit question answers to the online form (linked below)
  • Join the class Slack Channel

Readings

  • Skim the latest UN Intergovernmental Panel on Climate Change Synthesis Report to get a summary of the latest climate change science, especially the figures.
  • Fully read the ClimateBench primary paper by Watson-Parris et al.
  • Pick two citations within the introduction to the ClimateBench paper and skim-read them (just read the abstract, conclusion and look at the figures. Maybe look at the methods if relevent.).

Questions

Answer the following questions using this form.

  1. What are the primary goals of the research paper we aim to replicate? Additionally, what do you anticipate to be the major hurdles in this process? Lastly, define what outcomes would you consider as a successful completion of Phase I in this capstone project.
  2. Provide a brief explanation of each of the ClimateBench inputs, what they represent, and the relative magnitude of their climate impact.
  3. Write a brief summary of each citation you read and how it relates to the ClimateBench paper. Note: You will be doing this for each paper we read in the course, so it is good to get some practice in now. The aim is for each team to gain a broad understanding of the field, so ensure that each team member chooses different citations.