Climate Report

Additional time series data about climate change come in all the time. Here are some plots that I like to track.

I think that what is essential for this problem is a global consciousness,
a view that transcends our exclusive identifications with the generational
and political groupings into which by accident we have been born.

The solution to these problems requires a perspective that embraces the
planet and the future, because we are all in this greenhouse together. 

Carl Sagan, https://youtu.be/Wp-WiNXH6hI?t=985

Keeling Curve (Mauna Loa CO2)

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import rdtools

plt.rcParams['figure.dpi'] = 100

url = 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_weekly_mlo.csv'
df = pd.read_csv(url, skiprows=51, na_values=-999.99)
df.index = pd.to_datetime(df[['year', 'month', 'day']])

ppm_weekly = df['average']
ppm_daily = ppm_weekly.resample('d').interpolate()

_, _, calc_info = rdtools.degradation_year_on_year(ppm_daily)
yoy_changes = calc_info['YoY_values']

moving_avg = ppm_daily.rolling(365, center=True).mean()
ppm_deseasonalized = ppm_daily - moving_avg
grouper = ppm_deseasonalized.groupby(ppm_deseasonalized.index.dayofyear)
median_seasonality = grouper.median()
upper_seasonality = grouper.quantile(0.95)
lower_seasonality = grouper.quantile(0.05)

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fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 6))

# plot this first because it has a shorter index
yoy_changes.plot(ax=axes[1])
yoy_changes.rolling(52, center=True).median().plot(ax=axes[1])
axes[1].set_xlabel(None)
axes[1].set_ylabel('Year-on-year change [%/yr]')
axes[1].grid()

moving_avg.plot(ax=axes[0])
ppm_daily.plot(ax=axes[0])
axes[0].set_ylabel('Dry CO$_2$ Fraction [ppm]')
axes[0].grid()

fig.tight_layout()

EIA Monthly: Electric Power Sector

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

url = 'https://www.eia.gov/totalenergy/data/browser/csv.php?tbl=T07.02B'
df = pd.read_csv(url)
df = df.loc[~df['Description'].str.contains('Generation Total')]
df = df.loc[~df['YYYYMM'].astype(str).str.endswith('13')]
df['source'] = df['Description'].str.split(",").str[0].str.replace('Electricity Net Generation From ', '')
df['date'] = pd.to_datetime(df['YYYYMM'], format='%Y%m')
df2 = df.pivot(index='date', columns='source', values='Value')
df2 = df2.rename(columns={'Conventional Hydroelectric Power': 'Hydroelectric',
                          'Nuclear Electric Power': 'Nuclear'})
df2 = df2.replace('Not Available', np.nan).replace('Not Meaningful', np.nan).astype(float)
df2 = df2.loc[:, df2.max() > 5000]  # only keep the main players

fractions = 100 * df2.divide(df2.sum(axis=1), axis=0).clip(lower=0)

fig, ax = plt.subplots(figsize=(10, 6))
df2.rolling(12, center=True).mean().plot(ax=ax, colormap='tab10')
ax.legend()
ax.set_ylabel('Million kWh')
fig.tight_layout()
df2.plot(ax=ax, alpha=0.2, colormap='tab10', legend=False)
plt.show()

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fig, axes = plt.subplots(3, 1, sharex=True, figsize=(6, 7))
fig.suptitle('Utility-Scale Solar Monthly Generation')

df2['Solar'].dropna().loc['2009':].plot(ax=axes[0])
axes[0].set_ylabel('Million kWh')

fractions['Solar'].loc['2009':].plot(ax=axes[1])
moving_average = fractions['Solar'].rolling(12).mean()
moving_average.loc['2009':].plot(ax=axes[1])
axes[1].set_ylabel('Fraction of total [%]')

yoy_change = moving_average - moving_average.shift(12)
yoy_change.loc['2009':].plot(ax=axes[2])
axes[2].set_ylabel('Year-on-year change [% of total]')

for ax in axes:
    ax.axvline('2017-02-01', ls='--', c='k')
    ax.grid(which='both')

fig.tight_layout()

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fig, axes = plt.subplots(3, 1, sharex=True, figsize=(6, 7))
fig.suptitle('Utility-Scale Wind Monthly Generation')

df2['Wind'].dropna().loc['2000':].plot(ax=axes[0])
axes[0].set_ylabel('Million kWh')

fractions['Wind'].loc['2000':].plot(ax=axes[1])
moving_average = fractions['Wind'].rolling(12).mean()
moving_average.loc['2000':].plot(ax=axes[1])
axes[1].set_ylabel('Fraction of total [%]')

yoy_change = moving_average - moving_average.shift(12)
yoy_change.loc['2000':].plot(ax=axes[2])
axes[2].set_ylabel('Year-on-year change [% of total]')

for ax in axes:
    ax.grid(which='both')

fig.tight_layout()

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# reorder for plotting purposes
col_order = ['Nuclear', 'Hydroelectric', 'Wind', 'Solar',
             'Coal', 'Natural Gas', 'Petroleum']
fractions = fractions[col_order]
fractions.iloc[-12*10:].plot.area(lw=0, figsize=(10, 4))
plt.ylabel('Share of total generation [%]')

handles, labels = plt.gca().get_legend_handles_labels()
plt.legend(reversed(handles), reversed(labels),
           loc='center right', bbox_to_anchor=(1.2, 0.5))
plt.tight_layout()
plt.grid()

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clean = ['Solar', 'Wind', 'Hydroelectric', 'Nuclear']
clean_fraction = 100 * df2[clean].sum(axis=1) / df2.sum(axis=1)
clean_fraction.plot()
clean_fraction_rolling = 100 * (
    df2[clean].sum(axis=1).rolling(12, center=True).sum() /
    df2.sum(axis=1).rolling(12, center=True).sum()
)
clean_fraction_rolling.plot()
plt.ylabel('Clean Fraction [%]')
plt.tight_layout()
plt.grid()

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fractions.assign(clean_fraction=clean_fraction).tail(24)

source	Nuclear	Hydroelectric	Wind	Solar	Coal	Natural Gas	Petroleum	clean_fraction
date
2020-12-01	21.400579	6.557329	9.764229	1.549242	23.921286	36.317728	0.489607	39.271379
2021-01-01	21.652945	7.380077	9.067195	1.667184	24.379383	35.385158	0.468057	39.767401
2021-02-01	20.372133	6.489166	8.637934	2.036516	28.162106	33.607846	0.694300	37.535749
2021-03-01	21.575713	7.144087	13.266523	3.126851	20.809850	33.617503	0.459473	45.113174
2021-04-01	20.547507	6.938258	13.003650	3.893373	19.268645	35.932516	0.416051	44.382788
2021-05-01	20.845940	7.629358	11.102723	4.069981	20.853299	35.073657	0.425043	43.648002
2021-06-01	18.512837	6.548268	7.467721	3.395785	24.317522	39.386703	0.371163	35.924611
2021-07-01	17.748344	5.672360	5.595788	3.123535	26.057947	41.415359	0.386667	32.140028
2021-08-01	17.581077	5.121396	6.846633	3.009114	25.656907	41.309265	0.475608	32.558220
2021-09-01	19.473236	5.109244	8.745258	3.363585	23.658764	39.187692	0.462221	36.691323
2021-10-01	18.836057	5.636242	10.648276	3.046742	20.549359	40.793280	0.490045	38.167316
2021-11-01	21.078726	6.473890	12.000150	2.602087	19.089695	38.217652	0.537800	42.154853
2021-12-01	22.120823	7.341041	12.455574	1.893748	18.631823	37.090194	0.466797	43.811186
2022-01-01	19.627165	7.279458	10.583065	2.252881	24.190437	35.107484	0.959510	39.742569
2022-02-01	19.877272	7.350111	12.200232	2.972134	22.589292	34.507037	0.503922	42.399749
2022-03-01	20.580642	8.261454	14.007771	3.841555	19.631201	33.677377	NaN	46.691421
2022-04-01	19.211334	6.762649	15.948068	4.638046	18.967643	34.061900	0.410360	46.560097
2022-05-01	19.456704	7.056149	12.775585	4.624140	18.947615	36.693213	0.446595	43.912577
2022-06-01	18.048510	7.354000	9.188879	4.352820	19.997726	40.643408	0.414657	38.944209
2022-07-01	16.956261	5.912921	7.203022	3.837776	21.114304	44.627601	0.348115	33.909980
2022-08-01	17.442244	5.447476	6.159429	3.615273	21.386342	45.565218	0.384018	32.664422
2022-09-01	19.028847	4.999918	8.063690	3.974746	19.215507	44.254758	0.462534	36.067202
2022-10-01	19.768851	4.884765	11.001220	4.058524	17.982895	41.801571	0.502174	39.713361
2022-11-01	20.232302	6.093371	13.677540	2.740820	18.181965	38.587374	0.486628	42.744033

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rolling = df2.rolling(12).sum()
rolling['Renewables'] = rolling[['Solar', 'Wind', 'Hydroelectric']].sum(axis=1)
rolling.loc['2015':, ['Coal', 'Nuclear', 'Renewables']].plot()
plt.ylabel('Million kWh (12-month moving)');

This page was last regenerated on:

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import datetime
datetime.date.today().strftime('%Y-%m-%d')

'2023-03-05'

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