Chapter 1: Greenhouse Gase - The prime suspect

Chapter 1: Greenhouse Gase - The prime suspect#

Long-Term Development of CO2 and CH4 Concentrations#

Generation of plots based on the relevant Climate Indicators’ report.#

The ongoing climate change and the subsequent incease of earth’s temperature is a hot topic in recent years; a topic that has lately expanded from the purely scientific communities and now touches almost all aspects of our lives. There is much said about the role that the GreenHouse Gases (GHG) play in this climatic crisis. Let’s follow this tutorial and find out if and how the concentrations of two key GHG (carbon dioxide - CO2, and methane - CH4) changed in the last decades.

In this tutorial we will:

Search, download, and view data freely available in Climate Data Store.
Calculate global and regional monthly timeseries.
Mask the data and use spatiotemporal subsets for plotting.
View time series and analyse trends.
Create gridded plots and analyse spatial variations.

NOTE: Before interacting with the following notebook, please ensure you've reviewed the How to Execute the Notebooks section.

Run the tutorial via free cloud platforms:

Section 1. Install & import the necessary packages.#

The first step for being able to analyse and plot the data is to download and import the necessary libraries for this tutorial. We categorized the libraries based on that they are used for: general libraries, libraries for data analysis, and plotting libraries.

# General libraries
from string import ascii_lowercase as ABC # String operations
import datetime # date
import calendar # date calculations
import zipfile # for unzipping data
import os # operating system interfaces library
import cdsapi # CDS API
import urllib3 # Disable warnings for data download via API
urllib3.disable_warnings()

# Libraries for working with multidimensional arrays
import numpy as np # for n-d arrays
import pandas as pd # for 2-d arrays (including metadata for the rows & columns)
import xarray as xr # for n-d arrays (including metadata for all the dimensions)

# Libraries for plotting and visualising data
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import seaborn as sns
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.mpl.geoaxes

The below is for having a consistent plotting across all tutorials. It will NOT work in Google Colab or other cloud services, unless you include the file (available in the Github repository) in the cloud and in the same directory as this notebook, and use the correct path, e.g. plt.style.use('copernicus.mplstyle').

plt.style.use('../copernicus.mplstyle') # use the predefined matplotlib style for consistent plotting across all tutorials

Section 4. Useful links#

For the ones interested in finding out more about GHG, please find below some links for relevant articles and visualizations:

Greenhouse gas concentrations Climate Indicators Report
Global Monitorin Laboratory from NOAA, with a lot of information, relevant plots, and raw data that can be freely donwloaded.
Lashof, D., Ahuja, D. Relative contributions of greenhouse gas emissions to global warming. Nature 344, 529–531 (1990). https://doi.org/10.1038/344529a0
Buchwitz, M., Reuter, M., Schneising, O., Noël, S., Gier, B., Bovensmann, H., Burrows, J. P., Boesch, H., Anand, J., Parker, R. J., Somkuti, P., Detmers, R. G., Hasekamp, O. P., Aben, I., Butz, A., Kuze, A., Suto, H., Yoshida, Y., Crisp, D., and O’Dell, C.: Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003–2016, Atmos. Chem. Phys., 18, 17355–17370 (2018), https://doi.org/10.5194/acp-18-17355-2018
Buchwitz, M., Dils, B., Boesch, H., Brunner, D., Butz, A., Crevoisier, C., Detmers, R., Frankenberg, C., Hasekamp, O., Hewson, W., Laeng, A., Noël, S., Notholt, J., Parker, R., Reuter, M., Schneising, O., Somkuti, P., Sundström, A.-M., and De Wachter, E.: ESA Climate Change Initiative (CCI) Product Validation and Intercomparison Report (PVIR) for the Essential Climate Variable (ECV) Greenhouse Gases (GHG) for data set Climate Research Data Package No. 4 (CRDP#4), Technical Report, version 5, available at: http://www.esa-ghg-cci.org/?q=webfm_send/352, 253 pp. (2017)

Appendix: Monthly climatology of the variables#

As we stated previously, we only used global mean and standard deviation for comparing both variables, and we did not use monthly climatology. Let’s see how the monthly climatology looks like, and why using it is not the best approach.

# what is normally done assuming a 'stationary' behaviour of the variable
climatology_mean = concentration.groupby('time.month').mean('time') # climatological mean for each month
climatology_std = concentration.groupby('time.month').std('time') # climatological standard deviation for each month
anomalies = (concentration.groupby('time.month') - climatology_mean) # anomalies after removing mean
anomalies = anomalies.groupby('time.month')/climatology_std # final anomalies after dividing with standard deviation
anomalies = area_weighted_spatial_average(anomalies.sel(lat=slice(-60, 60))) # get global average

fig, ax = plt.subplots(1, 1, figsize=(9, 4))
anomalies.plot.line(x='time', ax=ax, label=[aux_var[i]['shortname'] for i in spatial_aver_norm.ghg.values])
ax.legend()
ax.axhline(0, color='k', ls='--', lw=0.5)
ax.set_xlim(anomalies.time.values[0], anomalies.time.values[-1])
sns.move_legend(ax, loc='lower right')
sns.despine(ax=ax, trim=True, offset=10)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
ax.set_title('Monthly anomalies of GHG')
ax.set_ylabel('Standardized anomalies')

date_annotate = pd.to_datetime('201312', format='%Y%m')
ax.annotate(
            'Notice the big step that occurs \nfor CH$_4$ at the end of each year.\nWhy is this happening?',
            xy=(date_annotate, anomalies.sel(ghg='xch4', time=date_annotate).values),
            xytext=(0.05, 0.9),
            textcoords=ax.transAxes,
            ha='left', va='top', color='.3',
            arrowprops=dict(arrowstyle='->', color='.4', connectionstyle='arc3,rad=-0.2')
           )

plt.show()

../_images/abb07e174d295d8dfcfe0989fef8cd3a95c2d41021bab0dc91891dac4a348e00.png

Plot the monthly climatology (global average ) to understand if the issue is related to that step.

clim_spatial = climatology_mean.sel(lat=slice(-60, 60)).weighted(np.cos(np.deg2rad(climatology_mean.lat)).clip(0, 1)).mean(['lat', 'lon'])

fig, ax = plt.subplots(2, 1, figsize=(10, 6))

for i, j in enumerate(clim_spatial.ghg.values):

    ax[i].plot(clim_spatial.month.values, clim_spatial.sel(ghg=j).values*aux_var[j]['unit_factor']) 
    
    ax[i].set_xlim(1, 13)

    ax[i].set_title(f'({ABC[i]}) '+ aux_var[j]['name'] + ' (' + aux_var[j]['shortname'] + ')')
    ax[i].set_ylabel('Colunn-averaged '+ aux_var[j]['shortname']  + ' [' + aux_var[j]['units'] + ']')

    sns.despine(ax=ax[i], trim=True) # trimming the y and x axis to be visible only from the fist till the last tick

fig.subplots_adjust(hspace=.3)

../_images/9053b1d1a44ef9ae2544f145b9003b58bb05c87effe412cc1494c670055e88bf.png

Maybe its more useful to present the results in polar coordinates, since from a climatological point of view the months follow a circular behaviour with December being similar to January.

fig, ax = plt.subplots(1, 2, figsize=(10, 10), subplot_kw={'projection': 'polar'})

for i, j in enumerate(clim_spatial.ghg.values):

    yy_values = clim_spatial.sel(ghg=j).values*aux_var[j]['unit_factor'] # get the values and multiply with the unit factor
    ax[i].plot( (clim_spatial.month.values)/12*2*np.pi, yy_values, linewidth=5 ) # plot the line
    
    # color the graph based on y value for easily differentiating between the magnitudes for the months
    segments = np.linspace(np.array(yy_values).min()*1, np.array(yy_values).max()*1., 5)
    for seg_min, seg_max, i_alpha in zip(segments[:-1], segments[1:], np.arange(0.25, 1.01, .25)):
        ax[i].fill_between(x=np.linspace(0, 2*np.pi, 360), y1=seg_max, y2=seg_min, color='.4', alpha=i_alpha)

    ax[i].set_ylim(np.array(yy_values).min()*1, np.array(yy_values).max()*1.)
    
    ax[i].set_xticks((clim_spatial.month.values)/12*2*np.pi)
    ax[i].set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
    ax[i].set_yticks(segments[::2], labels=np.round(segments, 1)[::2])
    ax[i].set_axisbelow(False) # otherwise the green line hiddes the yticks (red values) 
    ax[i].tick_params(axis='y', colors='red', size=14, zorder=10)
    ax[i].set_title(f'({ABC[i]}) '+ aux_var[j]['name'] + ' (' + aux_var[j]['shortname'] + ')')
    ax[i].yaxis.set_label_coords(-0.2, 1)
    ax[i].set_ylabel('Colunn-averaged '+ aux_var[j]['shortname']  + ' [' + aux_var[j]['units'] + ']', color='red')

fig.subplots_adjust(wspace=1)

../_images/0265416375c26620528d6cf9ba2241291ef542cf5e47ea888f0b2fd480c37aed.png

Notice the difference in the values between January mean state and December mean state for both GHG, especially so for CH4. This indicates that on average just in the course of 1 single year there is already a substantial increase in the concentrations, that do alter the expected seasonal cycle, as we normally would see the Jan and Dec values being very very close.

Because of this substantial reduction in the mean state of January compared to December we see the high jump for every January, as the climatological mean is small, thus the anomalies get large.

Therefore, in order to get the anomalies in a robust way, we should not only remove the seasonal cycle, but also detrend the data, as this dataset is highly non-stationary!

Chapter 1: Greenhouse Gase - The prime suspect

Contents

Chapter 1: Greenhouse Gase - The prime suspect#

Long-Term Development of CO2 and CH4 Concentrations#

Generation of plots based on the relevant Climate Indicators’ report.#

Section 1. Install & import the necessary packages.#

Section 2. Download data from CDS.#

Section 3. Calculate the GHG concentrations and growth rates#

Focus on sub-polar regions#

Temporal smoothing#

Spatial average#

Annnual growth rates#

Uncertainty estimates of growth rates#

Plot the evolution of GHG concentration and growth rates.#

Comparing CO2 with CH4#

Regional spatial averages#

Section 4. Useful links#

Appendix: Monthly climatology of the variables#