Tutorial 5: Internal Climate Variability#
Week 2, Day 1, Future Climate: The Physical Basis
Content creators: Brodie Pearson, Julius Busecke, Tom Nicholas
Content reviewers: Younkap Nina Duplex, Zahra Khodakaramimaghsoud, Sloane Garelick, Peter Ohue, Jenna Pearson, Derick Temfack, Peizhen Yang, Cheng Zhang, Chi Zhang, Ohad Zivan
Content editors: Jenna Pearson, Ohad Zivan, Chi Zhang
Production editors: Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan
Our 2023 Sponsors: NASA TOPS
Tutorial Objectives#
In this tutorial, we will learn about the concept of internal climate variability, how it influences the predictability of climate phenomena and how it contributes to uncertainty in CMIP6 models. We will work with a single-model ensemble, which utilizes the MPI-ESM1-2-LR model from CMIP6, to isolate and quantify internal climate variability.
By the end of this tutorial, you would be able to:
Understand the importance of internal climate variability and its role in climate prediction and model uncertainty.
Create and evaluate a single-model ensemble using IPCC uncertainty bands, providing a visual representation of model uncertainty.
Contrast the uncertainty due to internal variability against the uncertainty within a multi-model ensemble (which includes internal variability and the impacts of human/coding choices).
Setup#
# !pip install condacolab &> /dev/null
# import condacolab
# condacolab.install()
# # Install all packages in one call (+ use mamba instead of conda), this must in one line or code will fail
# !mamba install xarray-datatree intake-esm gcsfs xmip aiohttp nc-time-axis cf_xarray xarrayutils &> /dev/null
# imports
import time
tic = time.time()
import intake
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
from xmip.preprocessing import combined_preprocessing
from xarrayutils.plotting import shaded_line_plot
from datatree import DataTree
from xmip.postprocessing import _parse_metric
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[2], line 6
2 import time
4 tic = time.time()
----> 6 import intake
7 import numpy as np
8 import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'intake'
Figure settings#
Figure settings#
Show code cell source
# @title Figure settings
import ipywidgets as widgets # interactive display
%config InlineBackend.figure_format = 'retina'
plt.style.use(
"https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle"
)
# model_colors = {k:f"C{ki}" for ki, k in enumerate(source_ids)}
%matplotlib inline
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[3], line 2
1 # @title Figure settings
----> 2 import ipywidgets as widgets # interactive display
4 get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'retina'")
5 plt.style.use(
6 "https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle"
7 )
ModuleNotFoundError: No module named 'ipywidgets'
Helper functions#
Helper functions#
Show code cell source
# @title Helper functions
# If any helper functions you want to hide for clarity (that has been seen before
# or is simple/uniformative), add here
# If helper code depends on libraries that aren't used elsewhere,
# import those libaries here, rather than in the main import cell
def global_mean(ds: xr.Dataset) -> xr.Dataset:
"""Global average, weighted by the cell area"""
return ds.weighted(ds.areacello.fillna(0)).mean(["x", "y"], keep_attrs=True)
# Calculate anomaly to reference period
def datatree_anomaly(dt):
dt_out = DataTree()
for model, subtree in dt.items():
# for the coding exercise, ellipses will go after sel on the following line
ref = dt[model]["historical"].ds.sel(time=slice("1950", "1980")).mean()
dt_out[model] = subtree - ref
return dt_out
def plot_historical_ssp126_combined(dt):
for model in dt.keys():
datasets = []
for experiment in ["historical", "ssp126"]:
datasets.append(dt[model][experiment].ds.tos)
da_combined = xr.concat(datasets, dim="time")
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[4], line 9
1 # @title Helper functions
2
3 # If any helper functions you want to hide for clarity (that has been seen before
4 # or is simple/uniformative), add here
5 # If helper code depends on libraries that aren't used elsewhere,
6 # import those libaries here, rather than in the main import cell
----> 9 def global_mean(ds: xr.Dataset) -> xr.Dataset:
10 """Global average, weighted by the cell area"""
11 return ds.weighted(ds.areacello.fillna(0)).mean(["x", "y"], keep_attrs=True)
NameError: name 'xr' is not defined
Video 5: Internal Climate Variability#
Video 5: Internal Climate Variability#
Show code cell source
# @title Video 5: Internal Climate Variability
# Tech team will add code to format and display the video
Section 1: Internal Climate Variability & Single-model Ensembles#
One of the CMIP6 models we are using in today’s tutorials, MPI-ESM1-2-LR is part of single-model ensemble, where its modelling center carried out multiple simulations of the model for many of the CMIP6 experiments.
Let’s take advantage of this single-model ensemble to quantify the internal variability of this model’s simulated climate, and contrast this against the multi-model uncertainty we diagnosed in the previous tutorial.
Coding Exercise 1.1#
Complete the following code to:
Load 5 different realizations of the MPI-ESM1-2-LR experiments (r1i1p1f1 through r5i1p1f1). This numbering convention means they were each initialized using a different time-snapshot of the base/spin-up simulation.
Plot the historical and SSP1-2.6 experiment data for each realization, using a distinct color for each realization, but keeping that color the same across both the historical period and future period for a given realization.
col = intake.open_esm_datastore(
"https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
) # open an intake catalog containing the Pangeo CMIP cloud data
cat_ensemble = col.search(
source_id="MPI-ESM1-2-LR",
variable_id="tos",
table_id="Omon",
member_id=["r1i1p1f1", "r2i1p1f1", "r3i1p1f1", "r4i1p1f1", "r5i1p1f1"],
grid_label="gn",
experiment_id=["historical", "ssp126", "ssp585"],
require_all_on=["source_id", "member_id"],
)
# convert the sub-catalog into a datatree object, by opening each dataset into an xarray.Dataset (without loading the data)
kwargs = dict(
preprocess=combined_preprocessing, # apply xMIP fixes to each dataset
xarray_open_kwargs=dict(
use_cftime=True
), # ensure all datasets use the same time index
storage_options={
"token": "anon"
}, # anonymous/public authentication to google cloud storage
)
# hopefully we can implement https://github.com/intake/intake-esm/issues/562 before the
# actual tutorial, so this would be a lot cleaner
cat_ensemble.esmcat.aggregation_control.groupby_attrs = ["source_id", "experiment_id"]
dt_ensemble = cat_ensemble.to_datatree(**kwargs)
cat_area = col.search(
source_id=["MPI-ESM1-2-LR"],
variable_id="areacello", # for the coding exercise, ellipses will go after the equals on this line
member_id="r1i1p1f1",
table_id="Ofx", # for the coding exercise, ellipses will go after the equals on this line
grid_label="gn",
experiment_id=[
"historical"
], # for the coding exercise, ellipses will go after the equals on this line
require_all_on=["source_id"],
)
# hopefully we can implement https://github.com/intake/intake-esm/issues/562 before the
# actual tutorial, so this would be a lot cleaner
cat_area.esmcat.aggregation_control.groupby_attrs = ["source_id", "experiment_id"]
dt_area = cat_area.to_datatree(**kwargs)
# add the area (we can reuse the area from before, since for a given model the horizontal are does not vary between members)
dt_ensemble_with_area = DataTree()
for model, subtree in dt_ensemble.items():
metric = dt_area["MPI-ESM1-2-LR"]["historical"].ds["areacello"].squeeze()
dt_ensemble_with_area[model] = subtree.map_over_subtree(_parse_metric, metric)
# global average
# average every dataset in the tree globally
dt_ensemble_gm = dt_ensemble_with_area.map_over_subtree(global_mean)
# calculate anomaly
dt_ensemble_gm_anomaly = datatree_anomaly(dt_ensemble_gm)
def plot_historical_ssp126_ensemble_combined(dt):
for model in dt.keys():
datasets = []
for experiment in ["historical", "ssp126"]:
datasets.append(dt[model][experiment].ds.coarsen(time=12).mean().tos)
# concatenate the historical and ssp126 timeseries for each ensemble member
da_combined = ...
# plot annual averages
da_combined.plot(hue="member_id")
plt.figure()
_ = ...
plt.title("Global Mean SST Anomaly in SSP1-2.6 from a 5-member single-model ensemble")
plt.ylabel("Global Mean SST Anomaly [$^\circ$C]")
plt.xlabel("Year")
plt.legend()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[6], line 1
----> 1 col = intake.open_esm_datastore(
2 "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
3 ) # open an intake catalog containing the Pangeo CMIP cloud data
5 cat_ensemble = col.search(
6 source_id="MPI-ESM1-2-LR",
7 variable_id="tos",
(...)
12 require_all_on=["source_id", "member_id"],
13 )
15 # convert the sub-catalog into a datatree object, by opening each dataset into an xarray.Dataset (without loading the data)
NameError: name 'intake' is not defined
Example output:
Coding Exercise 1.2#
Complete the following code to:
Repeat the final figure of the last tutorial, except now display means and uncertainty bands of the single-model ensemble that you just loaded, rather than the multi-model ensemble analyzed in the previous tutorial.
for experiment, color in zip(["historical", "ssp126", "ssp585"], ["C0", "C1", "C2"]):
da = (
dt_ensemble_gm_anomaly["MPI-ESM1-2-LR"][experiment]
.ds.tos.coarsen(time=12)
.mean()
.load()
)
# shading representing spread between members
x = da.time.data
# diagnose the lower range of the likely bounds
da_lower = ...
# diagnose the upper range of the likely bounds
da_upper = ...
_ = ...
# calculate the mean across ensemble members and plot
_ = ...
plt.title("Global Mean SST Anomaly in SSP1-2.6 from a 5-member single-model ensemble")
plt.ylabel("Global Mean SST Anomaly [$^\circ$C]")
plt.xlabel("Year")
plt.legend()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[7], line 3
1 for experiment, color in zip(["historical", "ssp126", "ssp585"], ["C0", "C1", "C2"]):
2 da = (
----> 3 dt_ensemble_gm_anomaly["MPI-ESM1-2-LR"][experiment]
4 .ds.tos.coarsen(time=12)
5 .mean()
6 .load()
7 )
9 # shading representing spread between members
10 x = da.time.data
NameError: name 'dt_ensemble_gm_anomaly' is not defined
Example output:
Question 1.2: Climate Connection#
How does this figure compare to the multi-model ensemble figure from the previous tutorial (included below)? Can you interpret differences using the science we have discussed today?
Summary#
In this tutorial, we explored the internal climate variability and its implications for climate modeling and prediction. We discussed the utility of single-model ensembles for isolating the effects of internal variability by contrasting simulations with identical physics, numerics, and discretization. We quantified the internal variability of MPI-ESM1-2-LR model’s simulated climate and compared it to the uncertainty introduced by multi-model ensembles. Through this tutorial, we better understand the boundaries of climate prediction and the different sources of uncertainty in CMIP6 models.
Resources#
Data for this tutorial can be accessed here.