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)

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=47, 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)

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

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()

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()

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()

# 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()

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()

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

source	Nuclear	Hydroelectric	Wind	Solar	Coal	Natural Gas	Petroleum	clean_fraction
date
2020-09-01	20.722888	5.867653	7.283648	2.419150	21.415741	41.939091	0.351829	36.293339
2020-10-01	19.947467	6.298296	9.649658	2.363718	19.939519	41.418723	0.382618	38.259140
2020-11-01	21.671042	7.302242	11.583107	2.008710	21.316108	35.654483	0.464306	42.565102
2020-12-01	21.400579	6.557329	9.764229	1.549242	23.921286	36.317728	0.489607	39.271379
2021-01-01	21.568254	7.726720	9.123949	1.708809	24.358307	35.057843	0.456117	40.127733
2021-02-01	20.232108	6.918272	8.599784	2.047226	28.088203	33.377148	0.737259	37.797390
2021-03-01	21.524656	7.253476	13.464893	3.109543	20.804169	33.388248	0.455015	45.352568
2021-04-01	20.613114	6.896434	13.027281	3.881814	19.333900	35.859000	0.388457	44.418643
2021-05-01	20.953200	7.500017	11.065173	4.034643	20.960580	35.080056	0.406332	43.553032
2021-06-01	18.522126	6.721281	7.438509	3.297800	24.347450	39.326462	0.346372	35.979716
2021-07-01	17.781657	5.687010	5.549288	3.056849	26.115728	41.439960	0.369507	32.074804
2021-08-01	17.557164	5.270914	6.747998	2.965353	25.629573	41.366647	0.462351	32.541429
2021-09-01	19.442799	5.389966	8.625739	3.325565	23.629964	39.140676	0.445292	36.784069
2021-10-01	18.888144	5.939761	10.723468	3.043927	20.607307	40.797393	NaN	38.595299
2021-11-01	21.006165	6.816418	12.023371	2.615439	18.956266	38.065861	0.516480	42.461393
2021-12-01	21.970067	7.933967	12.594438	1.959319	18.445046	36.661973	0.435191	44.457791
2022-01-01	19.540750	7.449320	10.566333	2.201032	24.084032	35.139284	1.019249	39.757435
2022-02-01	19.826930	7.554435	12.221207	2.929947	22.529049	34.451338	0.487094	42.532520
2022-03-01	20.422092	8.416541	13.968529	3.819068	19.483063	33.452772	0.437936	46.626230
2022-04-01	19.139718	6.957363	15.987214	4.635510	18.903234	33.972141	0.404819	46.719805
2022-05-01	19.372439	7.288297	12.795123	4.601087	18.882402	36.619500	0.441151	44.056947
2022-06-01	18.069341	7.394303	9.264253	4.351615	20.005025	40.502984	0.412478	39.079513
2022-07-01	16.913401	6.303477	7.203069	3.819279	21.076070	44.358010	0.326694	34.239225
2022-08-01	17.376960	5.824824	6.143558	3.578472	21.313493	45.381963	0.380730	32.923814

This page was last regenerated on:

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

'2022-12-04'

previous

Blog