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.

../_images/isotopes_18O.png

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.

../_images/foram.png

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

../_images/sem_foram.png

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()