# 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.index = pd.to_datetime(df[['year', 'month', 'day']])

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

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)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Input In [1], in <cell line: 10>()
8 url = 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_weekly_mlo.csv'
9 df = pd.read_csv(url, skiprows=51, na_values=-999.99)
---> 10 df.index = pd.to_datetime(df[['year', 'month', 'day']])
12 ppm_weekly = df['average']
13 ppm_daily = ppm_weekly.resample('d').interpolate()

File ~/miniconda3/envs/kevbase-dev/lib/python3.8/site-packages/pandas/core/frame.py:3511, in DataFrame.__getitem__(self, key)
3509     if is_iterator(key):
3510         key = list(key)
-> 3511     indexer = self.columns._get_indexer_strict(key, "columns")[1]
3513 # take() does not accept boolean indexers
3514 if getattr(indexer, "dtype", None) == bool:

File ~/miniconda3/envs/kevbase-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py:5782, in Index._get_indexer_strict(self, key, axis_name)
5779 else:
5780     keyarr, indexer, new_indexer = self._reindex_non_unique(keyarr)
-> 5782 self._raise_if_missing(keyarr, indexer, axis_name)
5784 keyarr = self.take(indexer)
5785 if isinstance(key, Index):
5786     # GH 42790 - Preserve name from an Index

File ~/miniconda3/envs/kevbase-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py:5842, in Index._raise_if_missing(self, key, indexer, axis_name)
5840     if use_interval_msg:
5841         key = list(key)
-> 5842     raise KeyError(f"None of [{key}] are in the [{axis_name}]")
5845 raise KeyError(f"{not_found} not in index")

KeyError: "None of [Index(['year', 'month', 'day'], dtype='object')] are in the [columns]"

Hide code cell source
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()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [2], in <cell line: 4>()
1 fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 6))
3 # plot this first because it has a shorter index
----> 4 yoy_changes.plot(ax=axes[1])
5 yoy_changes.rolling(52, center=True).median().plot(ax=axes[1])
6 axes[1].set_xlabel(None)

NameError: name 'yoy_changes' is not defined


## EIA Monthly: Electric Power Sector¶

Hide code cell source
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 = 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()

Hide code cell source
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()

Hide code cell source
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()

Hide code cell source
# 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()

Hide code cell source
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()

Hide code cell source
fractions.assign(clean_fraction=clean_fraction).tail(24)

source Nuclear Hydroelectric Wind Solar Coal Natural Gas Petroleum clean_fraction
date
2021-07-01 17.749486 5.672725 5.596148 3.123736 26.059623 41.411726 0.386556 32.142095
2021-08-01 17.582022 5.121672 6.847001 3.009276 25.658286 41.306510 0.475233 32.559971
2021-09-01 19.471349 5.108749 8.744410 3.363259 23.656471 39.193898 0.461864 36.687767
2021-10-01 19.225829 5.609387 10.597540 3.032225 20.451449 40.595974 0.487596 38.464981
2021-11-01 21.074837 6.472696 11.997935 2.601607 19.124287 38.191189 0.537449 42.147075
2021-12-01 22.124844 7.342375 12.457838 1.894092 18.635210 37.079001 0.466641 43.819148
2022-01-01 19.648130 7.266734 10.593376 2.258741 24.220136 35.052329 0.960554 39.766981
2022-02-01 19.882530 7.330795 12.200840 2.979657 22.598647 34.503456 0.504076 42.393822
2022-03-01 20.599277 8.234918 14.019994 3.854628 19.652440 33.638743 NaN 46.708817
2022-04-01 19.213153 6.768627 15.960220 4.651368 18.971811 34.024399 0.410424 46.593367
2022-05-01 19.454481 7.049620 12.781013 4.635027 18.948576 36.684719 0.446564 43.920141
2022-06-01 18.046523 7.355952 9.188972 4.380111 19.992738 40.621075 0.414628 38.971559
2022-07-01 16.943656 5.930102 7.205831 3.849752 21.104321 44.618513 0.347826 33.929341
2022-08-01 17.434188 5.469584 6.159135 3.629690 21.380218 45.543385 0.383801 32.692597
2022-09-01 19.037261 4.997759 8.069551 4.005375 19.227136 44.200185 0.462733 36.109945
2022-10-01 19.764535 4.883314 10.998682 4.079339 17.986522 41.785433 0.502176 39.725870
2022-11-01 20.248213 6.095464 13.640923 2.750624 18.196231 38.581512 0.487033 42.735224
2022-12-01 19.936510 6.280340 11.326968 2.015887 20.977570 38.183218 1.279508 39.559705
2023-01-01 21.435912 6.909181 11.811051 2.444816 18.054992 38.964551 0.379497 42.600961
2023-02-01 20.631132 6.529218 14.245167 3.163071 15.583960 39.386561 0.460890 44.568589
2023-03-01 20.023073 6.543128 14.127610 3.886425 15.770735 39.273379 0.375649 44.580236
2023-04-01 19.751619 6.233706 14.969008 5.202725 13.810082 39.632281 0.400578 46.157059
2023-05-01 19.688290 8.928018 10.254452 5.412216 13.857508 41.507467 0.352050 44.282975
2023-06-01 19.045293 5.733255 8.047143 5.180946 16.820428 44.835486 0.337448 38.006638
Hide code cell source
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)');


import datetime