In [9]:
co2.plot(subplots=True, sharey=True, figsize=(12,8))
Out[9]:
array([<AxesSubplot:xlabel='Year'>, <AxesSubplot:xlabel='Year'>,
<AxesSubplot:xlabel='Year'>], dtype=object)
%matplotlib inline
import pandas as pd
from pandas_datapackage_reader import read_datapackage
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use("ggplot")
Historical CO2 emissions are taken from the Global Carbon Budget. After downloading the Excel file we can read it in:
gcb = pd.read_excel(
'Global_Carbon_Budget_2020v1.0.xlsx',
sheet_name="Historical Budget",
skiprows=15,
index_col="Year",
usecols="A:H"
)
gcb = gcb.rename(columns={
"fossil emissions excluding carbonation": "Fossil-Fuel-Industry",
"land-use change emissions": "Land-Use-Change"
})
gcb.head()
| Fossil-Fuel-Industry | Land-Use-Change | atmospheric growth | ocean sink | land sink | cement carbonation sink | budget imbalance | |
|---|---|---|---|---|---|---|---|
| Year | |||||||
| 1750 | 0.002552 | NaN | -0.077592 | NaN | 0.173773 | NaN | NaN |
| 1751 | 0.002552 | NaN | -0.073988 | NaN | -0.452640 | NaN | NaN |
| 1752 | 0.002553 | NaN | -0.070596 | NaN | -0.280658 | NaN | NaN |
| 1753 | 0.002553 | NaN | -0.067628 | NaN | -0.023358 | NaN | NaN |
| 1754 | 0.002554 | NaN | -0.064024 | NaN | -0.136870 | NaN | NaN |
gcb.tail()
| Fossil-Fuel-Industry | Land-Use-Change | atmospheric growth | ocean sink | land sink | cement carbonation sink | budget imbalance | |
|---|---|---|---|---|---|---|---|
| Year | |||||||
| 2019 | 9.945622 | 1.802637 | 5.42682 | 2.625991 | 3.13698 | 0.215298 | 0.343171 |
| NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020* | ~9.3 | ~1.6 | ~5.3 | NaN | NaN | NaN | NaN |
| NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| *2020 values are based on proxies and on projections for the most recent months, not based on actual emission data. See Friedlingstein et al. 2020 for methodology | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
gcb = gcb.dropna(how="all")
gcb = gcb.rename(index={"2020*": 2020})
gcb.loc[2020] = gcb.loc[2020].str.replace("~", "")
gcb = gcb.astype(float)
gcb
| Fossil-Fuel-Industry | Land-Use-Change | atmospheric growth | ocean sink | land sink | cement carbonation sink | budget imbalance | |
|---|---|---|---|---|---|---|---|
| Year | |||||||
| 1750 | 0.002552 | NaN | -0.077592 | NaN | 0.173773 | NaN | NaN |
| 1751 | 0.002552 | NaN | -0.073988 | NaN | -0.452640 | NaN | NaN |
| 1752 | 0.002553 | NaN | -0.070596 | NaN | -0.280658 | NaN | NaN |
| 1753 | 0.002553 | NaN | -0.067628 | NaN | -0.023358 | NaN | NaN |
| 1754 | 0.002554 | NaN | -0.064024 | NaN | -0.136870 | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 2016 | 9.613000 | 1.555205 | 6.074640 | 2.683507 | 3.183434 | 0.199106 | -0.972483 |
| 2017 | 9.742000 | 1.521109 | 4.577220 | 2.519991 | 3.829413 | 0.203694 | 0.132791 |
| 2018 | 9.940000 | 1.553637 | 5.086980 | 2.561091 | 3.776513 | 0.210856 | -0.141803 |
| 2019 | 9.945622 | 1.802637 | 5.426820 | 2.625991 | 3.136980 | 0.215298 | 0.343171 |
| 2020 | 9.300000 | 1.600000 | 5.300000 | NaN | NaN | NaN | NaN |
271 rows × 7 columns
Converting to GtCO2 from GtC
co2 = (gcb[["Fossil-Fuel-Industry", "Land-Use-Change"]].loc[1850:] * 3.66 )
co2.tail()
| Fossil-Fuel-Industry | Land-Use-Change | |
|---|---|---|
| Year | ||
| 2016 | 35.183580 | 5.692049 |
| 2017 | 35.655720 | 5.567260 |
| 2018 | 36.380400 | 5.686311 |
| 2019 | 36.400977 | 6.597651 |
| 2020 | 34.038000 | 5.856000 |
co2["Total"] = co2[["Fossil-Fuel-Industry", "Land-Use-Change"]].sum(axis=1)
co2.tail()
| Fossil-Fuel-Industry | Land-Use-Change | Total | |
|---|---|---|---|
| Year | |||
| 2016 | 35.183580 | 5.692049 | 40.875629 |
| 2017 | 35.655720 | 5.567260 | 41.222980 |
| 2018 | 36.380400 | 5.686311 | 42.066711 |
| 2019 | 36.400977 | 6.597651 | 42.998629 |
| 2020 | 34.038000 | 5.856000 | 39.894000 |
co2.plot(subplots=True, sharey=True, figsize=(12,8))
array([<AxesSubplot:xlabel='Year'>, <AxesSubplot:xlabel='Year'>,
<AxesSubplot:xlabel='Year'>], dtype=object)
Export for visualisation
export = pd.DataFrame({"value": co2["Total"]})
export.index.name = "year"
export.reset_index().to_csv("../public/emissions.csv", index=False, float_format='%g')
export.tail()
| value | |
|---|---|
| year | |
| 2016 | 40.875629 |
| 2017 | 41.222980 |
| 2018 | 42.066711 |
| 2019 | 42.998629 |
| 2020 | 39.894000 |
co2.loc[:2019].Total.sum()
2408.005837404804
CO2 concentrations are taken from the CMIP6 concentration dataset, version from 1 July 2016, combined with data from ESRL/NOAA.
noaa = pd.read_csv("co2_mm_gl.txt", comment="#", parse_dates=[[0, 1]], index_col=[0], delim_whitespace=True, header=None,
names=["year", "month", "decimal", "average", "trend"])
noaa.head()
| decimal | average | trend | |
|---|---|---|---|
| year_month | |||
| 1980-01-01 | 1980.042 | 338.55 | 337.93 |
| 1980-02-01 | 1980.125 | 339.27 | 338.22 |
| 1980-03-01 | 1980.208 | 339.60 | 338.25 |
| 1980-04-01 | 1980.292 | 340.00 | 338.37 |
| 1980-05-01 | 1980.375 | 340.43 | 338.90 |
cmip6 = pd.read_csv(
"mole_fraction_of_carbon_dioxide_in_air_input4MIPs_GHGConcentrations_CMIP_UoM-CMIP-1-1-0_gr3-GMNHSH_000001-201412.csv",
)
cmip6.index = (cmip6.year.astype(str).apply(lambda x: x.zfill(4)) +
"-" +
cmip6.month.astype(str).apply(lambda x: x.zfill(2)) +
"-01"
)
cmip6 = cmip6.iloc[21000:]
cmip6.index = pd.to_datetime(cmip6.index)
cmip6 = cmip6.drop(['datenum', 'datetime', 'day'], axis=1)
cmip6.head()
| year | month | data_mean_global | data_mean_nh | data_mean_sh | |
|---|---|---|---|---|---|
| 1750-01-01 | 1750 | 1 | 277.813529 | 278.492749 | 277.134309 |
| 1750-02-01 | 1750 | 2 | 278.196558 | 279.148898 | 277.244218 |
| 1750-03-01 | 1750 | 3 | 278.538475 | 279.791418 | 277.285531 |
| 1750-04-01 | 1750 | 4 | 278.779124 | 280.326118 | 277.232131 |
| 1750-05-01 | 1750 | 5 | 278.723560 | 280.230610 | 277.216510 |
fig, ax = plt.subplots(1,1, figsize=(16,10))
cmip6.loc["1850":]["data_mean_global"].plot(ax=ax)
noaa["average"].plot(ax=ax)
<AxesSubplot:xlabel='year_month'>
cmip6.loc["1850":].rename(columns={"data_mean_nh": "value"}).to_csv("../public/concentrations_nh.csv", index=False)
cmip6.loc["1850":].rename(columns={"data_mean_sh": "value"}).to_csv("../public/concentrations_sh.csv", index=False)
combined = pd.concat([cmip6["data_mean_global"].loc["1850":"1979-12"], noaa["average"]])
combined = pd.DataFrame({"value": combined})
combined["year"] = combined.index.year
combined["month"] = combined.index.month
combined.index.name = "date"
combined["day"] = 15
combined = combined[["year", "month", "value"]]
combined.to_csv("../public/concentrations.csv", index=False)
combined.tail()
| year | month | value | |
|---|---|---|---|
| date | |||
| 2021-03-01 | 2021 | 3 | 415.61 |
| 2021-04-01 | 2021 | 4 | 415.93 |
| 2021-05-01 | 2021 | 5 | 416.12 |
| 2021-06-01 | 2021 | 6 | 415.34 |
| 2021-07-01 | 2021 | 7 | 413.73 |
Global temperature data is taken from the HadCRUT4 near surface temperature dataset.
http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html
hadcrut = pd.read_csv(
"HadCRUT.4.6.0.0.monthly_ns_avg.txt",
delim_whitespace=True,
usecols=[0, 1],
header=None
)
hadcrut['year'] = hadcrut.iloc[:, 0].apply(lambda x: x.split("/")[0]).astype(int)
hadcrut['month'] = hadcrut.iloc[:, 0].apply(lambda x: x.split("/")[1]).astype(int)
hadcrut = hadcrut.rename(columns={1: "value"})
hadcrut = hadcrut.iloc[:, 1:]
hadcrut = hadcrut.set_index(['year', 'month'])
hadcrut -= hadcrut.loc[1850:1900].mean()
hadcrut.plot(figsize=(16,10))
hadcrut = hadcrut.reset_index()
plt.xlabel("Time")
plt.ylabel(u"Temperature anomalies (°C) (1850-1990 mean)")
plt.legend("")
<matplotlib.legend.Legend at 0x7fe4389e9610>
hadcrut.tail()
| year | month | value | |
|---|---|---|---|
| 2056 | 2021 | 5 | 0.940441 |
| 2057 | 2021 | 6 | 0.985441 |
| 2058 | 2021 | 7 | 1.038441 |
| 2059 | 2021 | 8 | 1.024441 |
| 2060 | 2021 | 9 | 0.994441 |
hadcrut.to_csv("../public/temperatures.csv", index=False)