#W2D1 Tutorial 2: Time series, global averages, and scenario comparison

Indented block

Week 2, Day 1, Future Climate: The Physical Basis

Content creators: Brodie Pearson (Day Lead), Julius Busecke (Tutorial co-lead), Tom Nicholas (Tutorial co-lead)

Content reviewers: Jenna Pearson, Ohad Zivan

Content editors: TBD

Production editors: TBD

Our 2023 Sponsors: TBD

#Tutorial Objectives

Today’s tutorials demonstrate how to work with data from Earth System Models (ESMs) simulations conducted for the recent Climate Model Intercomparison Project (CMIP6)

By the end of today’s tutorials you will be able to:

Manipulate raw data from multiple CMIP6 models
Evaluate the spread of future projections from several CMIP6 models
Synthesize climate data from observations and models

#Setup

# #Imports

# !pip install condacolab &> /dev/null
# import condacolab
# condacolab.install()

# # Install all packages in one call (+ use mamba instead of conda)
# # hopefully this improves speed
# !mamba install xarray-datatree intake-esm gcsfs xmip aiohttp nc-time-axis cf_xarray xarrayutils &> /dev/null

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

Figure settings#

# @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)}

Plotting functions#

# @title Plotting functions

# You may have functions that plot results that aren't
# particularly interesting. You can add these here to hide them.


def plotting_z(z):
    """This function multiplies every element in an array by a provided value

    Args:
      z (ndarray): neural activity over time, shape (T, ) where T is number of timesteps

    """

    fig, ax = plt.subplots()

    ax.plot(z)
    ax.set(xlabel="Time (s)", ylabel="Z", title="Neural activity over time")

Helper functions#

# @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

Tutorial 2: Time series, global averages, and scenario comparison#

Video 1: Video 1 Name#

# @title Video 1: Video 1 Name
# Tech team will add code to format and display the video

##Section 2.1: Load CMIP6 SST data from several models using xarray

Let’s expand on Tutorial 1 by loading five different CMIP6 models on last week’s Climate Modelling day.

col = intake.open_esm_datastore(
    "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
)  # open an intake catalog containing the Pangeo CMIP cloud data

# pick our five example models
# There are many more to test out! Try executing `col.df['source_id'].unique()` to get a list of all available models
source_ids = ["IPSL-CM6A-LR", "GFDL-ESM4", "ACCESS-CM2", "MPI-ESM1-2-LR", "TaiESM1"]

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[7], line 1
----> 1 col = intake.open_esm_datastore(
   "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
)  # open an intake catalog containing the Pangeo CMIP cloud data
# pick our five example models
# There are many more to test out! Try executing `col.df['source_id'].unique()` to get a list of all available models
source_ids = ["IPSL-CM6A-LR", "GFDL-ESM4", "ACCESS-CM2", "MPI-ESM1-2-LR", "TaiESM1"]

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/intake_esm/core.py:107, in esm_datastore.__init__(self, obj, progressbar, sep, registry, read_csv_kwargs, columns_with_iterables, storage_options, **intake_kwargs)
   self.esmcat = ESMCatalogModel.from_dict(obj)
else:
--> 107     self.esmcat = ESMCatalogModel.load(
       obj, storage_options=self.storage_options, read_csv_kwargs=read_csv_kwargs
   )
self.derivedcat = registry or default_registry
self._entries = {}

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/intake_esm/cat.py:264, in ESMCatalogModel.load(cls, json_file, storage_options, read_csv_kwargs)
       csv_path = f'{os.path.dirname(_mapper.root)}/{cat.catalog_file}'
   cat.catalog_file = csv_path
--> 264     df = pd.read_csv(
       cat.catalog_file,
       storage_options=storage_options,
       **read_csv_kwargs,
   )
else:
   df = pd.DataFrame(cat.catalog_dict)

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/pandas/io/parsers/readers.py:912, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
kwds_defaults = _refine_defaults_read(
   dialect,
   delimiter,
   (...)
   dtype_backend=dtype_backend,
)
kwds.update(kwds_defaults)
--> 912 return _read(filepath_or_buffer, kwds)

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/pandas/io/parsers/readers.py:577, in _read(filepath_or_buffer, kwds)
_validate_names(kwds.get("names", None))
# Create the parser.
--> 577 parser = TextFileReader(filepath_or_buffer, **kwds)
if chunksize or iterator:
   return parser

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1407, in TextFileReader.__init__(self, f, engine, **kwds)
   self.options["has_index_names"] = kwds["has_index_names"]
self.handles: IOHandles | None = None
-> 1407 self._engine = self._make_engine(f, self.engine)

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1661, in TextFileReader._make_engine(self, f, engine)
   if "b" not in mode:
       mode += "b"
-> 1661 self.handles = get_handle(
   f,
   mode,
   encoding=self.options.get("encoding", None),
   compression=self.options.get("compression", None),
   memory_map=self.options.get("memory_map", False),
   is_text=is_text,
   errors=self.options.get("encoding_errors", "strict"),
   storage_options=self.options.get("storage_options", None),
)
assert self.handles is not None
f = self.handles.handle

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/pandas/io/common.py:716, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
   codecs.lookup_error(errors)
# open URLs
--> 716 ioargs = _get_filepath_or_buffer(
   path_or_buf,
   encoding=encoding,
   compression=compression,
   mode=mode,
   storage_options=storage_options,
)
handle = ioargs.filepath_or_buffer
handles: list[BaseBuffer]

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/pandas/io/common.py:373, in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
       if content_encoding == "gzip":
           # Override compression based on Content-Encoding header
           compression = {"method": "gzip"}
--> 373         reader = BytesIO(req.read())
   return IOArgs(
       filepath_or_buffer=reader,
       encoding=encoding,
   (...)
       mode=fsspec_mode,
   )
if is_fsspec_url(filepath_or_buffer):

File ~/miniconda3/envs/climatematch/lib/python3.10/http/client.py:482, in HTTPResponse.read(self, amt)
else:
   try:
--> 482         s = self._safe_read(self.length)
   except IncompleteRead:
       self._close_conn()

File ~/miniconda3/envs/climatematch/lib/python3.10/http/client.py:631, in HTTPResponse._safe_read(self, amt)
def _safe_read(self, amt):
   """Read the number of bytes requested.

   This function should be used when <amt> bytes "should" be present for
   reading. If the bytes are truly not available (due to EOF), then the
   IncompleteRead exception can be used to detect the problem.
   """
--> 631     data = self.fp.read(amt)
   if len(data) < amt:
       raise IncompleteRead(data, amt-len(data))

File ~/miniconda3/envs/climatematch/lib/python3.10/socket.py:705, in SocketIO.readinto(self, b)
while True:
   try:
--> 705         return self._sock.recv_into(b)
   except timeout:
       self._timeout_occurred = True

File ~/miniconda3/envs/climatematch/lib/python3.10/ssl.py:1274, in SSLSocket.recv_into(self, buffer, nbytes, flags)
   if flags != 0:
       raise ValueError(
         "non-zero flags not allowed in calls to recv_into() on %s" %
         self.__class__)
-> 1274     return self.read(nbytes, buffer)
else:
   return super().recv_into(buffer, nbytes, flags)

File ~/miniconda3/envs/climatematch/lib/python3.10/ssl.py:1130, in SSLSocket.read(self, len, buffer)
try:
   if buffer is not None:
-> 1130         return self._sslobj.read(len, buffer)
   else:
       return self._sslobj.read(len)

KeyboardInterrupt: 

If the following cell crashes, run the cell a second time#

# from the full `col` object, create a subset using facet search
cat = col.search(
    source_id=source_ids,
    variable_id="tos",
    member_id="r1i1p1f1",
    table_id="Omon",
    grid_label="gn",
    experiment_id=["historical", "ssp126", "ssp585"],
    require_all_on=[
        "source_id"
    ],  # make sure that we only get models which have all of the above experiments
)

# 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.esmcat.aggregation_control.groupby_attrs = ["source_id", "experiment_id"]
dt = cat.to_datatree(**kwargs)

###Coding Exercise 2.1: Load additional CMIP6 data sets

In the following tutorials we will be looking at the global mean sea surface temperature. To calculate this global mean, will need to know the horizontal area of every ocean grid cell in all the models we are using.

Write code to load this ocean-grid area data using the previously shown method for SST data, noting that:

We now need a variable called areacello (area of cells in the ocean)
This variable is stored in table_id Ofx (it is from the ocean model and is fixed/constant in time)
A model’s grid does not change between experiments so you only need to get grid data from the historical experiment for each model

#################################################
## TODO for students: details of what they should do ##
# Fill out function and remove
raise NotImplementedError("Student exercise: load the ocean-grid area data from the 5 CMIP6 models using the information above")
#################################################

cat_area = col.search(
    source_id=source_ids,
    # Add the appropriate variable_id
    variable_id=...,
    member_id='r1i1p1f1',
    # Add the appropriate table_id
    table_id=...,
    grid_label='gn',
    # Add the appropriate experiment_id
    experiment_id = [...],
    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)

dt_with_area = DataTree()

for model,subtree in dt.items():
    metric = dt_area[model]['historical'].ds['areacello']
    dt_with_area[model] = subtree.map_over_subtree(_parse_metric,metric)

# to_remove solution
cat_area = col.search(
    source_id=source_ids,
    # Add the appropriate variable_id
    variable_id="areacello",
    member_id="r1i1p1f1",
    # Add the appropriate table_id
    table_id="Ofx",
    grid_label="gn",
    # Add the appropriate experiment_id
    experiment_id=["historical"],
    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)

dt_with_area = DataTree()

for model, subtree in dt.items():
    metric = dt_area[model]["historical"].ds["areacello"]
    dt_with_area[model] = subtree.map_over_subtree(_parse_metric, metric)

###Coding Exercise 2.2: Calculate and plot projected global mean sea surface temperature

The data files above contain spatial maps of the sea surface temperature for every month of each experiment’s time period. For the rest of today’s tutorials, we’re going to focus on the global mean sea surface temperature, rather than maps, as a way to visualize the ocean’s changing temperature at a global scale*.

Complete the following code so that it calculates and plots a timeseries of global mean sea surface temperature from the TaiESM1 model for both the historical experiment and the two future projection experiments, SSP1-2.6 (low emissions) and SSP5-8.5 (high emissions).

As you complete this exercise this, consider the following questions:

In the first function, what xarray operation is the following line doing, and why is it neccessary?

return ds.weighted(ds.areacello.fillna(0)).mean(['x', 'y'], keep_attrs=True)

How would your time series plot might change if you instead used took a simple mean of all the sea surface temperatures across all grid cells? (Perhaps your previous maps could provide some help here)

*Note: we could alternatively look at ocean heat content, which depends on temperature at all depths, but it is a more intensive computation that would take too long to calculate in these tutorials.

#################################################
## TODO for students: details of what they should do ##
# Fill out function and remove
raise NotImplementedError("Student exercise: Calculate the global mean of each model, experiment, and time")
#################################################
%matplotlib inline

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)

# average every dataset in the tree globally
dt_gm = dt_with_area.map_over_subtree(...)

for experiment in ['historical', 'ssp126', 'ssp585']:
    da = dt_gm['TaiESM1'][experiment].ds.tos
    da.plot(label=experiment)
plt.title('Global Mean SST from TaiESM1')
plt.ylabel('Global Mean SST [$^\circ$C]')
plt.xlabel('Year')
plt.legend()

plt.show()

# to_remove solution
%matplotlib inline


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)


# average every dataset in the tree globally
dt_gm = dt_with_area.map_over_subtree(global_mean)


with plt.xkcd():
    for experiment in ["historical", "ssp126", "ssp585"]:
        da = dt_gm["TaiESM1"][experiment].ds.tos
        da.plot(label=experiment)
    plt.title("Global Mean SST from TaiESM1")
    plt.ylabel("Global Mean SST [$^\circ$C]")
    plt.xlabel("Year")
    plt.legend()

plt.show()

Post-figure questions#

Is anything about this plot interesting or surprising to you?
Do you find this plot easy to read? (BONUS TASK: If you do not find it easy to read, try improving this plot to address this)

Climatematch Academy: Computational Tools for Climate Science

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