Intro#
Overview#
Today you will utilize the climate modeling background from the prior day to explore future projections of Earth’s climate by analyzing an ensemble of Earth System Models (ESMs) from the most recent Coupled Model Intercomparison Project (CMIP6). In yesterday’s last two tutorials, you analyzed data from a CMIP6 ESM to develop fundamental model analysis techniques such as diagnosing geographic variations of future climate change, contrasting projections from different socioeconomic & emission scenarios, calculating global mean properties on arbitrary ESM data grids, and re-gridding CMIP6 datasets to allow comparison with observations and/or other CMIP6 models. In today’s tutorials, you will synthesize data from five different CMIP6 models to develop ensemble analysis techniques. You will analyze this CMIP6 ensemble to contrast the climate states of distinct Earth System Models to estimate the uncertainty associated with CMIP6 future projections, and to separate the natural- and human-driven components of this uncertainty. Finally, you will synthesize these CMIP6 projections with data from observations, proxy records, and CMIP6 paleo-simulations, to create a long-term past & future record of global mean sea surface temperature.
Day Learning Objectives#
Identify the physical differences between future climate scenarios (SSPs) used by the Intergovernmental Panel on Climate Change (IPCC).
Understand the basics of an Earth System (climate) model and how their information can be used.
Evaluate and quantify the agreement amongst these models, and recognize why multiple models exist.
Manipulate climatic data from observations and models to quantify the Earth system’s past physical changes and to project its future under different SSPs.
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 = "W2D1_Intro"