Climate#

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Part I: Natural and Anthropogenic Variability#

Objectives

Describe the pattern of ocean ¹⁸O variations over the past 2 million years, using data from benthic foraminifera.
Use this data to infer the pattern of ice-volume changes and identify glacial and inter-glacial periods.
Compare the benthic foraminifera results to CO2 record of ancient atmosphere trapped in bubbles in the Antarctica ice cores.

Mini-tutorial#

Oxygen isotopes and Climate

The concentration of ¹⁸O in water and sediment is represented as \(\delta\)¹⁸O values with units of per mil (parts per thousand, or ‰). Average ocean water is assigned a value of 0‰, and is the standard against which variations in ¹⁸O/¹⁶O are measured and reported. The process of fractionation, the preferential removal of one isotope over another during a phase change, results from the mass difference of water molecules containing ¹⁸O (a.k.a., “heavy water”) and of water molecules containing ¹⁶O (“light water”) in the Earth climate system.

Question 1.1 Will ocean water be isotopically light or heavy during a glacial period, compared to during an interglacial period? Explain why. Hint: Think about ease of evaporation.

Answer:

\(\delta\)¹⁸O data and climate change from benthic forams#

A series of programs funded by the U.S. National Science Foundation and 22 international partners has used the scientific drill ship JOIDES Resolution (Joint Oceanographic Institute for Deep Earth Sampling) to drill into the ocean floor, both into unconsolidated sediment and into the ocean crust below the sediment:

1968-1984: Deep Sea Drilling Project (DSDP)
1985-2003: Ocean Drilling Project (ODP)
2004-2012: Integrated Ocean Drilling Program (IODP)
2013 and ongoing: International Ocean Discovery Program (IODP).

Question 1.2 Describe the IODP expedition the Resolution is currently involved in and tell where the ship is right now! See current expedition information at https://iodp.tamu.edu/scienceops/sitesumm.html

Answer:

Benthic forams

Here, you will analyze \(\delta\)¹⁸O data measured in foraminifera. “Forams” are shelled microorganisms found in aquatic environments. There are both planktonic (floating in the water column) and benthic (bottom dwelling) varieties. Foram shells are made up of calcium carbonate (CaCO₃) and when the organisms die, their shells get buried and preserved in sediment. The chemical make-up of the shells reflects the water chemistry at the time of shell formation. For this problem set, we focus on the benthic forams which better reflect bulk ocean chemistry as opposed to planktonic forams which better reflect surface water temperature.

Figure: Live Ammonia tepida benthic foraminiferan collected from San Francisco Bay. Phase-contrast photomicrograph by Scott Fay, UC Berkeley, 2005.

Figure: Scanning electron microscope photographs of 16-28 My foram shells recovered from an Indian Ocean sediment core. Scale bars are 0.1 mm. Source: Ridha et al., 2019

Shown below is a plot of \(\delta\)¹⁸O (on the y-axis) as a function of age (on the x-axis) for the past several million years. Modern times are to the right, and age increases to the left.

Global benthic stack for the last 5.5 Ma accessed from ncei.noaa.gov based on the following study Lisiecki, L.E. and M.E. Raymo. 2005. A Pliocene-Pleistocene stack of 57 globally distributed benthic D18O records. Paleoceanography, 20, PA1003. doi: 10.1029/2004PA001071

import pandas as pd
benthic2005d18O = pd.read_excel('Files/lisiecki2005.xls', sheet_name='benthic_d18O')

benthic2005d18O

	Time_(ka)	Benthic_d18O_(per_mil)	Standard_error_(per_mil)
0	0	3.23	0.03
1	1	3.23	0.04
2	2	3.18	0.03
3	3	3.29	0.03
4	4	3.30	0.03
...	...	...	...
1810	5300	2.91	0.06
1811	5305	2.79	0.04
1812	5310	2.79	0.09
1813	5315	2.84	0.07
1814	5320	2.91	0.09

1815 rows × 3 columns

import plotly.graph_objects as go
fig = go.Figure(data=go.Scatter(x=benthic2005d18O['Time_(ka)']*1000, y=benthic2005d18O['Benthic_d18O_(per_mil)'], mode='lines+markers'))
# Create axis objects
fig.update_layout(
    xaxis=dict(
        domain=[0., 1.],
        title="Years before present",
        titlefont=dict(
            color="#000000"
        ),
        tickfont=dict(
            color="#000000"
        )
    ),
    yaxis=dict(
        title="Benthic d18O (per mil ‰)",
        titlefont=dict(
            color="#1f77b4"
        ),
        tickfont=dict(
            color="#1f77b4"
        ),
        anchor="x",
        overlaying="y",
        side="left",
        position=0.15
    )
)

fig['layout']['xaxis']['autorange'] = "reversed"
fig.show()

Question 1.3 Exploring the long-term trends.

(A) What is the range through which \(\delta\)¹⁸O values fluctuate between 5.5 million and 1 million years ago?

(B) Between 500,000 yr and the present?

(B) What could this indicate about the difference in global ice volume during these two intervals?

Answer:

Question 1.4 Zoom into the past 500,000 years to focus in on that time interval. Within the \(\delta\)¹⁸O record, you can identify intervals of isotope change or “excursions.” Intervals of greatest ice volume are termed glaciations, whereas intervals of least ice volume are termed interglacials. Attach an annotate figure of your 500,000 year graph that indicates:

the five times that indicate the greatest ice volume (peak glaciation)
the times that indicate the interglacial periods.

Answer:

Question 1.5 Describe the cyclicity of the \(\delta\)¹⁸O signal on the 500,000 graph. For example, what is the duration of each cycle (from peak of glaciation to the next)? What is the average duration?

Answer:

Question 1.6 Consider the rate of change in \(\delta\)¹⁸O values. Which is faster, the building up of global ice volume (more snow falling in the winter than melts in the summer) or the waning of global ice volume (less snow falling in the winter than melts in the summer).

Answer:

Question 1.7 How long ago was the peak of the most recent glaciation? Do you think we are overdue for the onset of the next ice age? Explain why or why not?

Answer:

CO₂ data and climate change from Ice Cores#

Ice Cores drilled in the Antarctic give us a peek into the climate of the past several thousands of years. Antarctic CO₂ data core records both the precipitation (snow transformed into glacial ice) and ancient atmosphere (trapped air bubbles) in Antarctica. Samples from the entire length of the core have been analyzed for dust, methane, CO2, and deuterium (“heavy hydrogen”). We focus on the CO₂ content of trapped bubbles of ancient atmosphere from a composite study of all ice cores that goes back 800,000 years, accessed from ncei.noaa.gov based on the following study: Bereiter, B., S. Eggleston, J. Schmitt, C. Nehrbass-Ahles, T. F. Stocker, H. Fischer, S. Kipfstuhl, J. Chappellaz. 2015. Revision of the EPICA Dome C CO2 record from 800 to 600 kyr before present. Geophysical Research Letters, 42(2), 542-549. doi: 10.1002/2014GL061957

Figure: A slice of ice core, showing trapped air bubbles. Photograph: British Antarctic Survey/PA

import pandas as pd
antarctica2015co2 = pd.read_excel('Files/antarctica2015co2.xls', sheet_name='CO2 Composite', header=14)

antarctica2015co2

	Gasage (yr BP)	CO2 (ppmv)	sigma mean CO2 (ppmv)
0	-51.030000	368.022488	0.060442
1	-48.000000	361.780737	0.370000
2	-46.279272	359.647793	0.098000
3	-44.405642	357.106740	0.159923
4	-43.080000	353.946685	0.043007
...	...	...	...
1896	803925.284376	202.921723	2.064488
1897	804009.870607	207.498645	0.915083
1898	804522.674630	204.861938	1.642851
1899	805132.442334	202.226839	0.689587
1900	805668.868405	207.285440	2.202808

1901 rows × 3 columns

import plotly.graph_objects as go
fig = go.Figure(data=go.Scatter(x=antarctica2015co2['Gasage (yr BP)'], y=antarctica2015co2['CO2 (ppmv)'], mode='lines+markers'))
# Create axis objects
fig.update_layout(
    xaxis=dict(
        domain=[0., 1.],
        title="Years before present",
        titlefont=dict(
            color="#000000"
        ),
        tickfont=dict(
            color="#000000"
        )
    ),
    yaxis=dict(
        title="CO2 (ppmv)",
        titlefont=dict(
            color="#1f77b4"
        ),
        tickfont=dict(
            color="#1f77b4"
        ),
        anchor="x",
        overlaying="y",
        side="left",
        position=0.15
    )
)

fig['layout']['xaxis']['autorange'] = "reversed"
fig.show()

Question 1.8 How would you expect the CO₂ content of the atmosphere during a glacial period to compare to the CO₂ content of the atmosphere during an interglacial period? Explain your reasoning in a few sentences.

Answer:

Comparison of climate records from Antarctica versus benthic forams globally#

Question 1.9 Compare the CO₂ data from the Antarctic ice cores, with the \(\delta\)¹⁸O data from benthic forams. Do they tell a consistent picture of global climate over the last 800,000 years? Explain in a brief, well-written paragraph or two, referring appropriately to the data sets and graphs.

fig = go.Figure()
fig.add_trace(go.Scatter(x=benthic2005d18O['Time_(ka)']*1000, y=benthic2005d18O['Benthic_d18O_(per_mil)'],
                    mode='lines+markers',
                    name='d18O'))
fig.add_trace(go.Scatter(x=antarctica2015co2['Gasage (yr BP)'], y=antarctica2015co2['CO2 (ppmv)'],
                    mode='lines+markers',
                    name='CO2',yaxis="y2"))

# Create axis objects
fig.update_layout(
    xaxis=dict(
        domain=[0., 1.],
        title="Years before present",
        titlefont=dict(
            color="#000000"
        ),
        tickfont=dict(
            color="#000000"
        )
    ),
    yaxis=dict(
        title="Benthic d18O (per mil ‰)",
        titlefont=dict(
            color="#1f77b4"
        ),
        tickfont=dict(
            color="#1f77b4"
        )
    ),
    yaxis2=dict(
        title="CO2 (ppmv)",
        titlefont=dict(
            color="#ff7f0e"
        ),
        tickfont=dict(
            color="#ff7f0e"
        ),
        anchor="x",
        overlaying="y",
        side="right",
        position=0.15
    )
)


fig['layout']['xaxis']['autorange'] = "reversed"
fig.show()

Answer:

Question 1.10 Modern Measurements: What is the CO₂ content of Earth’s atmosphere today. Provide the latest values from Mauna Loa in Hawaii available from https://gml.noaa.gov/ccgg/trends/data.html

Answer:

Question 1.11 Revisit your response regarding potential ice age based on the curve above in Question 4.7. Do you think we are due for the onset of the next ice age? Explain why or why not.

Answer:

Fundamentals of Solid Earth Science

Climate

Contents

Climate#

Part I: Natural and Anthropogenic Variability#

Mini-tutorial#

\(\delta\)¹⁸O data and climate change from benthic forams#

CO₂ data and climate change from Ice Cores#

Comparison of climate records from Antarctica versus benthic forams globally#

Fundamentals of Solid Earth Science

Climate

Contents

Climate#

Part I: Natural and Anthropogenic Variability#

Mini-tutorial#

\(\delta\)18O data and climate change from benthic forams#

CO2 data and climate change from Ice Cores#

Comparison of climate records from Antarctica versus benthic forams globally#

\(\delta\)¹⁸O data and climate change from benthic forams#

CO₂ data and climate change from Ice Cores#