Tutorial 2: A Lot of Weather Makes Climate - Exploring the ERA5 Reanalysis

Tutorial 2: A Lot of Weather Makes Climate - Exploring the ERA5 Reanalysis#

Week 1, Day 2, Ocean-Atmosphere Reanalysis

Content creators: Momme Hell

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Content editors: Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan

Production editors: Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan

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Tutorial Objectives#

Estimated timing of tutorial: 25 mins

In the previous tutorial, we learned about the El Niño Southern Oscillation (ENSO), which is a specific atmosphere-ocean dynamical phenomenon. You will now examine the atmosphere and the ocean systems more generally.

In this tutorial, you will learn to work with reanalysis data. These data combine observations and models of the Earth system and are a critical tool for weather and climate science. You will first access a specific reanalysis dataset: ECMWF’s ERA5. You will then select variables and slices of interest from the preprocessed file, investigating how important climate variables change on medium-length timescales (hours to months) within a certain region.

By the end of this tutorial, you will be able to:

Access reanalysis data of climatically important variables.
Plot interactive maps to explore changes on various time scales.
Compute and compare time series of different variables from reanalysis data.

Setup#

# installations ( uncomment and run this cell ONLY when using google colab or kaggle )

# !pip install cartopy
# !pip install geoviews
# !pip install cdsapi

import cdsapi
import matplotlib.pyplot as plt
import xarray as xr
import numpy as np
import geoviews as gv
import geoviews.feature as gf
import holoviews

import os
import pooch
import tempfile

from cartopy import crs as ccrs

import warnings
#  Suppress warnings issued by Cartopy when downloading data files
warnings.filterwarnings('ignore')

Helper functions#

Figure Settings#

Video 1: ECMWF Reanalysis#

Section 2: Plotting Spatial Maps of Reanalysis Data#

First, let’s plot the region’s surface temperature for the first time step of the reanalysis dataset. To do this let’s extract the 2m air temperature data from the dataset that contains all the variables.

We will be plotting this a little bit differently than you have previously plotted a map (and differently from how you will plot in most tutorials) so we can look at a few times steps interactively later. To do this we are using the packages geoviews and holoviews.

holoviews.extension("bokeh")

dataset_plot = gv.Dataset(ds_surface_temp_2m.isel(time=0))  # select the first time step

# create the image
images = dataset_plot.to(
    gv.Image, ["longitude", "latitude"], ["t2m"], "hour"
)

# aesthetics, add coastlines etc.
images.opts(
    cmap="coolwarm",
    colorbar=True,
    width=600,
    height=400,
    projection=ccrs.PlateCarree(),
    clabel="2m Air Temperature (K)",
) * gf.coastline

In the above figure, coastlines are shown as black lines. Most of the selected region is land, with some ocean (lower right) and a lake (top middle).

Next, we will examine variability at two different frequencies using interactive plots:

Hourly variability
Daily variability

Note that in the previous tutorial, you computed the monthly variability, or climatology, but here you only have one month of data loaded (March 2018). If you are curious about longer timescales you will visit this in the next tutorial!

# average temperatures over the whole month after grouping by the hour
ds_surface_temp_2m_hour = ds_surface_temp_2m.groupby("time.hour").mean()

# interactive plot of hourly frequency of surface temperature
# this cell may take a little longer as it contains several maps in a single plotting function
dataset_plot = gv.Dataset(
    ds_surface_temp_2m_hour.isel(hour=slice(0, 12))
)  # only the first 12 time steps (midnight to noon), as it is a time-consuming task
images = dataset_plot.to(
    gv.Image, ["longitude", "latitude"], ["t2m"], "hour"
)
images.opts(
    cmap="coolwarm",
    colorbar=True,
    width=600,
    height=400,
    projection=ccrs.PlateCarree(),
    clabel="2m Air Temperature (K)",
) * gf.coastline

# average temperatures over the whole day after grouping by the day
ds_surface_temp_2m_day = ds_surface_temp_2m.groupby("time.day").mean()

# interactive plot of hourly frequency of surface temperature
# this cell may take a little longer as it contains several maps in a single plotting function
dataset_plot = gv.Dataset(
    ds_surface_temp_2m_hour.isel(hour=slice(0, 12))
)  # only the first 12 time steps (midnight to noon), as it is a time-consuming task
images = dataset_plot.to(
    gv.Image, ["longitude", "latitude"], ["t2m"], "hour"
)
images.opts(
    cmap="coolwarm",
    colorbar=True,
    width=600,
    height=400,
    projection=ccrs.PlateCarree(),
    clabel="2m Air Temperature (K)",
) * gf.coastline

# average temperatures over the whole day after grouping by the day
ds_surface_temp_2m_day = ds_surface_temp_2m.groupby("time.day").mean()

# interactive plot of daily frequency of surface temperature
# this cell may take a little longer as it contains several maps in a single plotting function holoviews.extension('bokeh')
dataset_plot = gv.Dataset(
    ds_surface_temp_2m_day.isel(day=slice(0, 10))
)  # only the first 10 time steps, as it is a time-consuming task
images = dataset_plot.to(
    gv.Image, ["longitude", "latitude"], ["t2m"], "day"
)
images.opts(
    cmap="coolwarm",
    colorbar=True,
    width=600,
    height=400,
    projection=ccrs.PlateCarree(),
    clabel="2m Air Temperature (K)",
) * gf.coastline

Question 2#

What differences do you notice between the hourly and daily interactive plots, and are there any interesting spatial patterns of these temperature changes?

Click for solution

Summary#

In this tutorial, you learned how to access and process ERA5 reanalysis data. You are now able to select specific slices within the reanalysis dataset and perform operations such as taking spatial and temporal averages to plot them interactively.

You also looked at different climate variables to distinguish and identify the variability present at different timescales.

Resources#

Data for this tutorial can be accessed here. We summarized the download procedure in a separate notebook named get_ERA5_reanalysis_data.ipynb.

Tutorial 2: A Lot of Weather Makes Climate - Exploring the ERA5 Reanalysis

Contents

Tutorial 2: A Lot of Weather Makes Climate - Exploring the ERA5 Reanalysis#

Tutorial Objectives#

Setup#

Helper functions#

Figure Settings#

Figure Settings#

Video 1: ECMWF Reanalysis#

Section 1: What is Reanalysis Data?#

Section 1.1: Accessing ERA5 Data#

Section 1.2: Selecting Regions of Interest#

Section 2: Plotting Spatial Maps of Reanalysis Data#

Question 2#

Section 3: Plotting Time Series of Reanalysis Data#

Section 3.1: Surface Air Temperature Time Series#

Questions 3.1#

Section 3.2: Comparing Time Series of Multiple Variables#

Questions 3.2#

Summary#

Resources#