CambioClimaticoIII.md

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:class: danger

Las actividades humanas, en particular la quema de combustibles fósiles, están alterando el sistema climático del planeta desde mediados del siglo XX.

Además, otras actividades como la deforestación, la urbanización y los cambios en los patrones de vegetación también estan  alterando el clima.


Pero, ¿por qué esta actividades tiene consecuencia sobre el clima de la Tierra?

<!-- Además, los cambios provocados por el hombre en el uso de la tierra y la cobertura del suelo, como la deforestación, la urbanización y los cambios en los patrones de vegetación también alteran el clima, lo que produce cambios en la reflectividad de la superficie de la Tierra (albedo), en las emisiones causadas por los incendios forestales, en los efectos de las islas de calor urbano y en los cambios en el ciclo natural del agua.-->


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:tags: [hide-input]
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.express as px
import numpy as np
import pandas as pd


df = pd.read_csv(r'./datos/owid-co2-data.csv')
world = df[df['country'] == 'World' ]

continents = ['Europe','North America','South America','Asia','Oceania']
co2continents = df[df['country'].isin(continents)]

co2continents = co2continents.rename(columns={"country": "Región"})

labels = ['Europa','A. del Norte','A. del Sur','Asia','Oceania']

i=0
N= len(labels)
values_ghg = np.zeros(N)

for c in continents:
    df2 = co2continents[co2continents['Región']== c ]
    values_ghg[i] = df2[df2["year"]==2019]["total_ghg"]

    i+=1


labels_gas = ['methane','nitrous_oxide','co2', "total_ghg"]
labels_gas2 = ['CH_4','NO_2','CO_2', "otros"]
#labels_gas2 = ['Metano','Oxido de nitrogeno','Dioxido de carbono', "otros"]
N = len(labels_gas)
values_gas = np.zeros(N)
i=0
for c in labels_gas:
    values_gas[i] = world[world["year"]==2019][c]

    i+=1

ghg_tot=values_ghg.sum()
values_ghg=values_ghg/ghg_tot*100

ghg_total = (world[world["year"]==2019]["total_ghg"]).iloc[0]
values_gas = values_gas/ghg_total*100

values_gas[-1]=100-np.sum(values_gas[:3])

# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

irises_colors =['rgb(56, 75, 126)', 'rgb(18, 36, 37)', 'rgb(34, 53, 101)',
                'rgb(36, 55, 57)', 'rgb(6, 4, 4)']
night_colors = ['rgb(175, 51, 21)', 'rgb(35, 36, 21)',
                 'rgb(146, 123, 21)','rgb(206, 206, 40)',
                ]

fig.add_trace(go.Pie(labels=labels, values=values_ghg, name="Continente",marker_colors=irises_colors, textinfo='percent+label',showlegend=False ), 1, 1)

fig.add_trace(go.Pie(labels=labels_gas2, values=values_gas, name="Gas",marker_colors=night_colors, textinfo='percent+label',showlegend=False ),
             1, 2)


# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+percent+name")

fig.update_layout(
    title_text="Emision de gases de efecto invernadero (2019)",
    # Add annotations in the center of the donut pies.
    annotations=[dict(text='Total', x=0.18, y=0.5, font_size=20, showarrow=False),
                 dict(text='Gases', x=0.82, y=0.5, font_size=20, showarrow=False)])


fig.update_layout (
    autosize=False,
    width=780,
    height=400,
    margin=dict(
        l=50,
        r=50,
        b=50,
        t=50,
        pad=4),
    font=dict(
        family="Rockwell",
        size=16,
        #color="RebeccaPurple"
    ),
    plot_bgcolor='rgba(0,0,0,0)'
)

fig.show()
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National Geographic. Foto: Luke Sharrett
:tags: [hide-input]

import plotly.graph_objs as go
import pandas as pd
from plotly.subplots import make_subplots

#df = pd.read_csv('./datos/owid-co2-data.csv')
df = pd.read_csv('./datos/owid-co2-data.csv')

continents = ['Europe','North America','South America','Asia','Oceania']
co2continents = df[df['country'].isin(continents)]

co2continents = co2continents.rename(columns={"country": "Región"})
#fig = make_subplots(rows=1, cols=2)
fig= go.FigureWidget(make_subplots(rows=1, cols=2,horizontal_spacing = 0.13))

labels = ['Europa','América del Norte','América del Sur','Asia','Oceania']
colors= ['rgb(31, 119, 180)', 'rgb(255, 127, 14)',
                       'rgb(44, 160, 44)', 'rgb(214, 39, 40)',
                       'rgb(148, 103, 189)', 'rgb(140, 86, 75)',
                       'rgb(227, 119, 194)', 'rgb(127, 127, 127)',
                       'rgb(188, 189, 34)', 'rgb(23, 190, 207)']
ymax = co2continents["year"].max()+10
ymin = co2continents["year"].min()-10

i=0
for c in continents:
    df = co2continents[co2continents['Región']== c ]
    fig.add_scatter( x = df['year'], y = df['co2'] , name=labels[i],legendgroup=labels[i],text=labels[i], row=1, col=1,
                  line=dict(color=colors[i]) )
    i+=1

i=0
for c in continents:
    df = co2continents[co2continents['Región']== c ]
    fig.add_scatter( x = df['year'], y = df["share_global_co2"] ,  name=labels[i], legendgroup=labels[i],text=labels[i], row=1, col=2,
                 showlegend=False, line=dict(color=colors[i]) )
    i+=1


note = 'datos tomados de https://ourworldindata.org/co2-and-greenhouse-gas-emissions'
fig.add_annotation(
    showarrow=False,
    text=note,
    font=dict(size=10),
    xref='x domain',
    x=2.3,
    yref='y domain',
    y=1.08
    )

fig.update_layout (
    xaxis_title="Año",
    yaxis_title="co2 [Toneladas]",
    xaxis2_title="Año",
    yaxis2_title="Porcentaje global [%]",
    autosize=False,
    width=900,
    height=400,
    margin=dict(
        l=50,
        r=50,
        b=50,
        t=50,
        pad=4),
    legend=dict(
        title="Región: ", orientation="h", y=1.05, yanchor="bottom", x=0.5, xanchor="center"
    ),
    font=dict(
        family="Rockwell",
        size=16,
        #color="RebeccaPurple"
    ),
    plot_bgcolor='rgba(0,0,0,0)'
)

fig.update_xaxes(
    ticks='outside',
    showline=True,
    linecolor='black',
    gridcolor='lightgrey',
    range=[ymin,ymax],
    mirror=True
)
fig.update_yaxes(
    ticks='outside',
    showline=True,
    linecolor='black',
    gridcolor='lightgrey',
    mirror=True
)


fig.show()

:tags: [hide-input]
import plotly.graph_objs as go
import numpy as np
import pandas as pd
from plotly.subplots import make_subplots

df = pd.read_csv('./datos/owid-co2-data.csv')
world = df[df['country'] == 'World' ]

fig= go.FigureWidget(make_subplots(rows=1, cols=1))

columns = ['coal_co2_per_capita','gas_co2_per_capita','oil_co2_per_capita','cement_co2_per_capita', 'flaring_co2_per_capita','other_co2_per_capita']
labels = ["Carbón" , "Gas", "Petróleo","Cemento","Quema", "otros"]
colors= ['rgb(31, 119, 180)', 'rgb(255, 127, 14)',
                       'rgb(44, 160, 44)', 'rgb(214, 39, 40)',
                       'rgb(148, 103, 189)', 'rgb(140, 86, 75)',
                       'rgb(227, 119, 194)', 'rgb(127, 127, 127)',
                       'rgb(188, 189, 34)', 'rgb(23, 190, 207)']
ymax = co2continents["year"].max()+10
ymin = co2continents["year"].min()-10

i=0
for c in columns:
    fig.add_scatter( x = world['year'], y = world[c] , name=labels[i],legendgroup=labels[i],text=labels[i],
                     row=1, col=1,
                  line=dict(color=colors[i]) )
    i+=1


note = 'datos tomados de https://ourworldindata.org/co2-and-greenhouse-gas-emissions'
fig.add_annotation(
    showarrow=False,
    text=note,
    font=dict(size=10),
    xref='x domain',
    x=1.6,
    yref='y domain',
    y=-0.255
    )

fig.update_layout (
    xaxis_title="Año",
    yaxis_title="co2 [Toneladas por persona]",
    autosize=True,
    #width=600,
    #height=400,
    margin=dict(
        l=50,
        r=50,
        b=50,
        t=50,
        pad=4),
    legend=dict(title="Industria",y=.45, yanchor="bottom", x=0.15, xanchor="center"),
    font=dict(
        family="Rockwell",
        size=14,
        #color="RebeccaPurple"
    ),
    plot_bgcolor='rgba(0,0,0,0)'
)

fig.update_xaxes(
    ticks='outside',
    showline=True,
    mirror=True,
    linecolor='black',
    gridcolor='lightgrey',
    range=[1800,ymax]
)
fig.update_yaxes(
    ticks='outside',
    mirror=True,
    showline=True,
    linecolor='black',
    gridcolor='lightgrey'
)


fig.show()

:class: note

Mira este [mapa](http://www.globalcarbonatlas.org/en/CO2-emissions) global sobre las emisiones y consumo de CO$_2$

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El sol. Créditos: NASA
:tags: [hide-input]

import numpy as np
import pandas as pd
import plotly.graph_objs as go

df = pd.read_csv('./datos/historical_tsi.csv')
df = df.rename(columns={"time (yyyy)": "Time", "Irradiance (W/m^2)": "II"})

df2 = pd.read_table('./datos/graph.txt',
                   skiprows=[0,1,2,4], sep='\s+')
df2 = df2.rename(columns={"No_Smoothing": "temperature anomaly", "Lowess(5)": "lowess"})


data = [
    go.Scatter(x = df['Time'], y=df['II'], name='Irradiancia'),
    go.Scatter(x = df2['Year'], y= df2['temperature anomaly'], name='Anomalía', yaxis='y2')
]

y1 = go.layout.YAxis(title='Irradiancia solar [W / m^2]', color='SteelBlue',
    linecolor='black',
    mirror=True )

y2 = go.layout.YAxis(title= 'Anomalía Térmica', color='Red',
    linecolor='black',
    mirror=True)
y2.update(overlaying='y', side='right')

layout = go.Layout(yaxis1 = y1, yaxis2 = y2)

fig = go.Figure(data=data, layout=layout)

fig.add_vline(x=1957, line_dash="dot", row="all", col="all",
              annotation_text="Máximo <br> era moderna",
              annotation_position="bottom right",
             annotation=dict(font_size=15, font_family="Times New Roman"),)

fig.update_layout (
    xaxis_title="Año",
    yaxis_title="Irradiancia solar [W / m^2]",
    autosize=True,
    #width=790,
    #height=500,
    margin=dict(
        l=50,
        r=50,
        b=50,
        t=50,
        pad=4),
    font=dict(
        family="Rockwell",
        size=16,
        #color="RebeccaPurple"
    ),
    legend=dict(
        title=None, orientation="h", y=1.05, yanchor="bottom", x=0.5, xanchor="center"
    ),
    plot_bgcolor='rgba(0,0,0,0)'
)

fig.update_xaxes(
    ticks='outside',
    showline=True,
    linecolor='black',
    mirror=True,
    range=[1600,2020],
    rangeslider=dict(
            visible = True,

            )
)


fig.add_vrect(x0=1645, x1=1715,
              annotation_text="Pequeña edad<br> de hielo", annotation_position="top left",
              annotation=dict(font_size=15, font_family="Times New Roman"),
              fillcolor="green", opacity=0.25, line_width=0)


display(fig)
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Nat Geo. Foto: Madden, Robert