The Impact of ENSO on Precipitation and Temperature#
Content creators: Olawale Ikuyajolu & Patrick Orenstein
Content reviewers: Marguerite Brown, Yuxin Zhou
Content editors: Zane Mitrevica, Natalie Steinemann, Jenna Pearson, Chi Zhang, Ohad Zivan
Production editors: Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan
Our 2023 Sponsors: NASA TOPS, Google DeepMind, and CMIP
In this project you will work with climate model output, reanalysis data, and Niño 3.4 indices from CMIP5/6, ERA5, NOAA, and HadISST to understand the historical and future impacts of El Niño Southern Oscillation (ENSO) events on rainfall and temperature. You will focus on variables like sea surface temperature, surface air temperature, and precipitation. You will also be able to investigate the relationships between these variables and how they affect community efforts to prepare for the impacts of El Niño phases.
Recall from W1D1 that ENSO is a climate phenomena that originates in the tropical Pacific ocean but has global impacts on atmospheric circulation, temperature and precipitation. The two phases of ENSO are El Niño (warmer than average SSTs in the central and eastern tropical Pacific Ocean) and La Niña (cooler than average SSTs in the central and eastern tropical Pacific Ocean). The Niño 3.4 region is an area in the central and eastern Pacific Ocean that is often used for determining the phase of ENSO.
You may also reference W1D5, W2D1, and W2D4 tutorials on CMIP6 and read more about the different CMIP6 scenarios here. Please see the Resources section at the bottom of this notebook for more information.
Project Template#
Note: The dashed boxes are socio-economic questions.
Data Exploration Notebook#
Project Setup#
# google colab installs
# !pip install condacolab &> /dev/null
# import condacolab
# condacolab.install()
# install all packages in one call (+ use mamba instead of conda)
# !mamba install xarray-datatree intake-esm gcsfs xmip aiohttp cartopy nc-time-axis cf_xarray xarrayutils "esmf<=8.3.1" xesmf &> /dev/null
# imports
import time
tic = time.time()
import intake
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
import xesmf as xe
from xmip.preprocessing import combined_preprocessing
from xarrayutils.plotting import shaded_line_plot
from datatree import DataTree
from xmip.postprocessing import _parse_metric
import cartopy.crs as ccrs
import pooch
import os
import tempfile
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[2], line 7
3 import time
5 tic = time.time()
----> 7 import intake
8 import numpy as np
9 import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'intake'
# functions
%matplotlib inline
col = intake.open_esm_datastore(
"https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
) # open an intake catalog containing the Pangeo CMIP cloud data
def load_cmip6(source_id, variable_id, member_id, table_id): # load selected model
cat = col.search(
source_id=source_ids,
variable_id=variable_id,
member_id=member_id,
table_id=table_id,
grid_label="gn",
experiment_id=[
"historical",
"ssp126",
"ssp245",
"ssp585",
], # downloading the scenarios out of the total 5+historical
require_all_on=["source_id"],
)
kwargs = dict(
preprocess=combined_preprocessing,
xarray_open_kwargs=dict(use_cftime=True),
storage_options={"token": "anon"},
)
cat.esmcat.aggregation_control.groupby_attrs = ["source_id", "experiment_id"]
dt = cat.to_datatree(**kwargs)
return dt
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[3], line 3
1 # functions
----> 3 get_ipython().run_line_magic('matplotlib', 'inline')
5 col = intake.open_esm_datastore(
6 "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
7 ) # open an intake catalog containing the Pangeo CMIP cloud data
10 def load_cmip6(source_id, variable_id, member_id, table_id): # load selected model
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2417, in InteractiveShell.run_line_magic(self, magic_name, line, _stack_depth)
2415 kwargs['local_ns'] = self.get_local_scope(stack_depth)
2416 with self.builtin_trap:
-> 2417 result = fn(*args, **kwargs)
2419 # The code below prevents the output from being displayed
2420 # when using magics with decodator @output_can_be_silenced
2421 # when the last Python token in the expression is a ';'.
2422 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/IPython/core/magics/pylab.py:99, in PylabMagics.matplotlib(self, line)
97 print("Available matplotlib backends: %s" % backends_list)
98 else:
---> 99 gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui)
100 self._show_matplotlib_backend(args.gui, backend)
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3588, in InteractiveShell.enable_matplotlib(self, gui)
3567 def enable_matplotlib(self, gui=None):
3568 """Enable interactive matplotlib and inline figure support.
3569
3570 This takes the following steps:
(...)
3586 display figures inline.
3587 """
-> 3588 from matplotlib_inline.backend_inline import configure_inline_support
3590 from IPython.core import pylabtools as pt
3591 gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select)
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/matplotlib_inline/__init__.py:1
----> 1 from . import backend_inline, config # noqa
2 __version__ = "0.1.6" # noqa
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/matplotlib_inline/backend_inline.py:6
1 """A matplotlib backend for publishing figures via display_data"""
3 # Copyright (c) IPython Development Team.
4 # Distributed under the terms of the BSD 3-Clause License.
----> 6 import matplotlib
7 from matplotlib import colors
8 from matplotlib.backends import backend_agg
ModuleNotFoundError: No module named 'matplotlib'
# helper functions
def pooch_load(filelocation=None,filename=None,processor=None):
shared_location='/home/jovyan/shared/Data/Projects/ENSO' # this is different for each day
user_temp_cache=tempfile.gettempdir()
if os.path.exists(os.path.join(shared_location,filename)):
file = os.path.join(shared_location,filename)
else:
file = pooch.retrieve(filelocation,known_hash=None,fname=os.path.join(user_temp_cache,filename),processor=processor)
return file
Dataset 1: Load CMIP6 Model of Your Choice#
Following W2D1 (Week 2 Day 1) tutorial notebooks:
We use the CESM2 model (source_id) and ensemble member r4i1p1f1 (member_id) in this template, but you are free to select any model and ensemble member. Make sure the member_id selected is available for your model. You can learn more about the member_id and other CMIP6 facets through the links at the end of the CMIP Resource Bank
load_cmip6
function load both historical and ssp585 (future: climate change)
To learn more about CMIP, including additional ways to access CMIP data, please see our CMIP Resource Bank and the CMIP website.
# pick your model
source_ids = "CESM2"
dm_tas = load_cmip6(
source_ids, "tas", "r4i1p1f1", "Amon"
) # tas is atmoerhpere temprature
dm_pr = load_cmip6(source_ids, "pr", "r4i1p1f1", "Amon") # pr is precipitation rate
dm_sst = load_cmip6(
source_ids, "tos", "r4i1p1f1", "Omon"
) # tos is surface ocean temprature
print(
dm_tas.keys()
) # an example for one of the datatrees, you can duplicate this for the other DT
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[5], line 5
1 # pick your model
3 source_ids = "CESM2"
----> 5 dm_tas = load_cmip6(
6 source_ids, "tas", "r4i1p1f1", "Amon"
7 ) # tas is atmoerhpere temprature
8 dm_pr = load_cmip6(source_ids, "pr", "r4i1p1f1", "Amon") # pr is precipitation rate
9 dm_sst = load_cmip6(
10 source_ids, "tos", "r4i1p1f1", "Omon"
11 ) # tos is surface ocean temprature
NameError: name 'load_cmip6' is not defined
# load cell areas for computing ocean surface temparuters means
dt_ocean_area = load_cmip6(source_ids, "areacello", "r4i1p1f1", "Ofx")
dt_atmos_area = load_cmip6(source_ids, "areacella", "r4i1p1f1", "fx")
dt_ocean_with_area = DataTree()
dt_atmos_with_area = DataTree()
for model, subtree in dm_sst.items():
metric_ocean = dt_ocean_area[model]["historical"].ds["areacello"]
dt_ocean_with_area[model] = subtree.map_over_subtree(_parse_metric, metric_ocean)
for model, subtree in dm_pr.items():
metric_atmos = dt_atmos_area[model]["historical"].ds["areacella"]
dt_atmos_with_area[model] = subtree.map_over_subtree(_parse_metric, metric_atmos)
print(dt_ocean_with_area.keys())
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[6], line 3
1 # load cell areas for computing ocean surface temparuters means
----> 3 dt_ocean_area = load_cmip6(source_ids, "areacello", "r4i1p1f1", "Ofx")
4 dt_atmos_area = load_cmip6(source_ids, "areacella", "r4i1p1f1", "fx")
6 dt_ocean_with_area = DataTree()
NameError: name 'load_cmip6' is not defined
Dataset 2: Load Observations#
We use the NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5 product, a widely used and trusted gridded compilation of historical data going back to 1854. Since the data is provided via an OPeNDAP server, we can load it directly without downloading anything.
For precipitation, we are using CPC Merged Analysis of Precipitation (CMAP). We can download this dataset from the NOAA PSL, Boulder, Colorado, USA website at https://psl.noaa.gov
For air temperature, we are using anomalies from NASA GISS Surface Temperature Analysis which we can also download from NOAA PSL, Boulder, Colorado, USA website at https://psl.noaa.gov
# Ocean surface temprature
filename_SST='sst.mnmean.nc'
url_SST = 'https://downloads.psl.noaa.gov/Datasets/noaa.ersst.v5/sst.mnmean.nc'
do_sst = xr.open_dataset(pooch_load(url_SST,filename_SST), drop_variables=['time_bnds'])
# Precipitation rate (notice the units in the plot below)
filename_prec_rate='precip.mon.mean.nc'
url_prec_rate='https://downloads.psl.noaa.gov/Datasets/cmap/enh/precip.mon.mean.nc'
do_pr = xr.open_dataset(pooch_load(url_prec_rate,filename_prec_rate))
# Air Temperature Anomalies
filename_tas='air.2x2.1200.mon.anom.comb.nc'
url_tas='https://downloads.psl.noaa.gov/Datasets/gistemp/combined/1200km/air.2x2.1200.mon.anom.comb.nc'
do_tas = xr.open_dataset(pooch_load(url_tas,filename_tas))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[7], line 5
2 filename_SST='sst.mnmean.nc'
3 url_SST = 'https://downloads.psl.noaa.gov/Datasets/noaa.ersst.v5/sst.mnmean.nc'
----> 5 do_sst = xr.open_dataset(pooch_load(url_SST,filename_SST), drop_variables=['time_bnds'])
7 # Precipitation rate (notice the units in the plot below)
8 filename_prec_rate='precip.mon.mean.nc'
NameError: name 'xr' is not defined
We can now visualize the content of the dataset.
# code to print the shape, array names, etc of the dataset
# select just a single model and experiment
hist_precip = dm_pr["CESM2"]["historical"].ds.pr
fig, ax_july2000 = plt.subplots(
ncols=1, nrows=1, figsize=[12, 6], subplot_kw={"projection": ccrs.Robinson()}
)
hist_precip.sel(time="2000-07").squeeze().plot(
ax=ax_july2000,
x="lon",
y="lat",
transform=ccrs.PlateCarree(),
cmap="magma",
robust=True,
)
ax_july2000.coastlines()
ax_july2000.set_title("July 2000")
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[8], line 4
1 # code to print the shape, array names, etc of the dataset
2
3 # select just a single model and experiment
----> 4 hist_precip = dm_pr["CESM2"]["historical"].ds.pr
6 fig, ax_july2000 = plt.subplots(
7 ncols=1, nrows=1, figsize=[12, 6], subplot_kw={"projection": ccrs.Robinson()}
8 )
10 hist_precip.sel(time="2000-07").squeeze().plot(
11 ax=ax_july2000,
12 x="lon",
(...)
16 robust=True,
17 )
NameError: name 'dm_pr' is not defined
hist_sst = dm_sst["CESM2"]["historical"].ds.tos
fig, ax = plt.subplots(
ncols=1,
nrows=1,
figsize=[12, 6],
subplot_kw={"projection": ccrs.Robinson(central_longitude=180)},
)
ax.coastlines()
ax.gridlines()
hist_sst.sel(time="2000-07").squeeze().plot(
ax=ax,
x="lon",
y="lat",
transform=ccrs.PlateCarree(),
vmin=-2,
vmax=30,
cmap="magma",
robust=True,
)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[9], line 1
----> 1 hist_sst = dm_sst["CESM2"]["historical"].ds.tos
3 fig, ax = plt.subplots(
4 ncols=1,
5 nrows=1,
6 figsize=[12, 6],
7 subplot_kw={"projection": ccrs.Robinson(central_longitude=180)},
8 )
10 ax.coastlines()
NameError: name 'dm_sst' is not defined
Dataset 3: Oceanic Nino Index#
There are several indices used to identify ENSO in the tropical Pacific Ocean. These indices are based on SST anomalies averaged across a given region and are used to define El Niño and La Niña events. Two indices that you will explore in this project are the Nino 3.4 Index and the Oceanic Niño Index (ONI). Both of these indices are averaged over the same region in the tropical Pacific (5N-5S, 170W-120W), but use different running means and criteria for identifying El Niño and La Niña events (i.e. for ONI, SST anomalies must exceed +/- 0.5C for at least five consecutive months to be defined as an ENSO event, whereas for Nino 3.4, SST anomalies must exceed +/- 0.4C for at least six consecutive months). You can find additional information about these indices here. For now, we will download the ONI data that we used in W1D3.
# get El Nino data from W1D3 tutorial 7
filename_nino='t6_oceanic-nino-index.nc'
url_nino = "https://osf.io/8rwxb/download/"
oni = xr.open_dataset(pooch_load(url_nino,filename_nino))
print(oni.keys())
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[10], line 4
2 filename_nino='t6_oceanic-nino-index.nc'
3 url_nino = "https://osf.io/8rwxb/download/"
----> 4 oni = xr.open_dataset(pooch_load(url_nino,filename_nino))
5 print(oni.keys())
NameError: name 'xr' is not defined
Further Reading#
“El Niño & La Niña (El Niño-Southern Oscillation)”, NOAA/Climate.gov (https://www.climate.gov/enso)
“El Niño and La Niña”, OCHA (https://www.unocha.org/themes/el-niño/el-niño-and-la-niña)
“How will climate change change El Nino and La Nina?”, NOAA, 2020 (https://research.noaa.gov/2020/11/09/new-research-volume-explores-future-of-enso-under-influence-of-climate-change/)
L’Heureux, M., Karamperidou, C., DiNezio, P., Karnauskas, K., Anyamba, A. 2023. “El Niño and La Niña: Local and global effects”. Climate Central (https://www.climatecentral.org/climate-matters/local-and-global-effects-of-el-nino-and-la-nina-2023)
Kelly, M. 2023. “In Years After El Niño, Global Economy Loses Trillions” (https://home.dartmouth.edu/news/2023/05/years-after-el-nino-global-economy-loses-trillions)
Resources#
This tutorial uses data from the simulations conducted as part of the CMIP6 multi-model ensemble.
For examples on how to access and analyze data, please visit the Pangeo Cloud CMIP6 Gallery
For more information on what CMIP is and how to access the data, please see this page.