Open In Colab   Open in Kaggle

Tutorial 6: Implementing the Analysis#

Good Research Practices

Content creators: Yuxin Zhou, Marguerite Brown, Zane Mitrevica, Natalie Steinemann

Content reviewers: Sherry Mi, Maria Gonzalez, Nahid Hasan, Beatriz Cosenza Muralles, Katrina Dobson, Sloane Garelick, Cheng Zhang

Content editors: Jenna Pearson, Chi Zhang, Ohad Zivan

Production editors: Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan

Our 2023 Sponsors: NASA TOPS and Google DeepMind

Tutorials Objectives#

In Tutorials 5-8, you will learn about the research process. This includes how to

  1. Draft analyses of data to test a hypothesis

  2. Implement analysis of data

  3. Interpret results in the context of existing knowledge

  4. Communicate your results and conclusions

By the end of these tutorials you will be able to:

  • Understand the principles of good research practices

  • Learn to view a scientific data set or question through the lens of equity: Who is represented by this data and who is not? Who has access to this information? Who is in a position to use it?

Activity: Implement the Analysis#

In this tutorial, you will be implementing a linear regression model as outlined in Step 5 on real-world CO2 and temperature records.

The CO2 and temperature records we will be analyzing are both examples of paleoclimate data (for more information, refer back to Step 3). The CO2 record (Bereiter et al., 2015) was generated by measuring the CO2 concentration in ancient air bubbles trapped inside ice from multiple ice cores retrieved from Antarctica. The temperature record (Shakun et al., 2015) is based on chemical analysis done on the shells of planktic foraminifera. The foraminifera shells were identified and picked from deep-sea sediments, and the temperature record combined multiple sea-surface temperature records from a range of sites globally.

Why are we focusing on these two records specifically? The CO2 record from Antarctic ice core is the gold standard of air CO2 variability on glacial-interglacial time scales, and it has a temporal resolution unmatched by any other reconstruction methods. The temperature record comes from sediment cores all over the global ocean, and therefore is likely representative of the global sea surface temperature (SST) variability. All SST records were shifted to a mean of zero and combined as unweighted global averages. Polar air temperature records are also available from ice core studies, but such records may represent an exaggerated view of the global temperature because of polar amplification.

If you would like to learn more, the data sources are listed at the bottom of the page.

# imports

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
from scipy import interpolate
from scipy import stats
import os
import pooch
import tempfile

Helper functions#

Hide code cell source
# @title Helper functions


def pooch_load(filelocation=None, filename=None, processor=None):
    shared_location = "/home/jovyan/shared/Data/tutorials/W2D1_FutureClimate-IPCCIPhysicalBasis"  # this is different for each day
    user_temp_cache = tempfile.gettempdir()

    if os.path.exists(os.path.join(shared_location, filename)):
        file = os.path.join(shared_location, filename)
    else:
        file = pooch.retrieve(
            filelocation,
            known_hash=None,
            fname=os.path.join(user_temp_cache, filename),
            processor=processor,
        )

    return file
# time series
# read SST data "Shakun2015_SST.txt"
filename_Shakun2015_SST = "Shakun2015_SST.txt"
url_Shakun2015_SST = "https://osf.io/kmy5w/download"
SST = pd.read_table(pooch_load(url_Shakun2015_SST, filename_Shakun2015_SST))
SST.set_index("Age", inplace=True)
SST
Downloading data from 'https://osf.io/kmy5w/download' to file '/tmp/Shakun2015_SST.txt'.
SHA256 hash of downloaded file: e3af19895ffddb73a3d8b10b1cdb9e7f6412ab1e930e8f3154c146999b645ff9
Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.
SST stack _18Op stack _18Ob stack _18Osw stack _18Osw uncertainty (2_) Deep ocean temperature Detrended sea-level equivalent Sea level uncertainty (2_) Unnamed: 9 Unnamed: 10
Age
3 1.2 -0.81 -0.88 -0.58 0.11 0.0 0 14 NaN NaN
6 1.3 -0.76 -0.87 -0.50 0.11 0.1 -10 14 NaN NaN
9 1.4 -0.57 -0.62 -0.28 0.11 0.5 -38 14 NaN NaN
12 1.0 -0.16 -0.23 0.06 0.09 0.1 -80 12 NaN NaN
15 0.1 0.31 0.27 0.33 0.09 -1.4 -115 12 NaN NaN
... ... ... ... ... ... ... ... ... ... ...
786 0.3 -0.10 -0.34 -0.14 0.22 0.1 -50 28 NaN NaN
789 -0.1 0.03 -0.21 0.09 0.29 0.0 -79 36 NaN NaN
792 -0.3 0.40 0.26 0.30 0.23 -0.8 -106 30 NaN NaN
795 -1.6 0.37 0.44 0.07 0.31 -3.2 -78 39 NaN NaN
798 -0.8 0.23 0.37 0.17 0.20 -2.7 -90 26 NaN NaN

266 rows × 10 columns

# read CO2 dataantarctica2015co2composite_cleaned.txt
filename_antarctica2015co2composite_cleaned = "antarctica2015co2composite_cleaned.txt"
url_antarctica2015co2composite_cleaned = "https://osf.io/45fev/download"
CO2 = pd.read_table(
    pooch_load(
        url_antarctica2015co2composite_cleaned,
        filename_antarctica2015co2composite_cleaned,
    )
)
CO2.set_index("age_gas_calBP", inplace=True)
CO2
Downloading data from 'https://osf.io/45fev/download' to file '/tmp/antarctica2015co2composite_cleaned.txt'.
SHA256 hash of downloaded file: e86ec8dba4ca9a1e8404117bdd370fd830352d07164fe8093fff12330e371aa8
Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.
co2_ppm co2_1s_ppm
age_gas_calBP
-51.03 368.02 0.06
-48.00 361.78 0.37
-46.28 359.65 0.10
-44.41 357.11 0.16
-43.08 353.95 0.04
... ... ...
803925.28 202.92 2.06
804009.87 207.50 0.92
804522.67 204.86 1.64
805132.44 202.23 0.69
805668.87 207.29 2.20

1901 rows × 2 columns

# plot
# set up two subplots in a grid of 2 rows and 1 column
# also make sure the two plots share the same x(time) axis
fig, axes = plt.subplots(2, 1, sharex=True)
# move the two subplots closer to each other
fig.subplots_adjust(hspace=-0.5)
axes[0].plot(SST.index, SST["SST stack"], color="C4")
axes[1].plot(CO2.index / 1000, CO2["co2_ppm"], color="C1")

# beautification
# since sharex=True in plt.subplots(), this sets the x axis limit for both panels
axes[1].set_xlim((0, 805))
# axis labels
axes[1].set_xlabel("Age (ka BP)")
axes[0].set_ylabel(r"Sea Surface Temperature" "\n" "Anomaly (°C)", color="C4")
axes[1].set_ylabel(r"CO${}_\mathrm{2}$ (ppm)", color="C1")

# despine makes the plots look cleaner
sns.despine(ax=axes[0], top=True, right=False, bottom=True, left=True)
sns.despine(ax=axes[1], top=True, right=True, bottom=False, left=False)
# clean up top panel x axis ticks
axes[0].xaxis.set_ticks_position("none")
# move top panel xlabel to the right side
axes[0].yaxis.set_label_position("right")
# the following code ensures the subplots don't overlap
for ax in axes:
    ax.set_zorder(10)
    ax.set_facecolor("none")
# color the axis
axes[0].spines["right"].set_color("C4")
axes[1].spines["left"].set_color("C1")
axes[0].tick_params(axis="y", colors="C4")
axes[1].tick_params(axis="y", colors="C1")
../../../_images/54937380c927641d95704771a17b538497004b65b8b254ca55e1ce4d6103c5e7.png

Now that we’ve taken a look at the two time series, let’s make a scatter plot between them and fit a linear regression model through the data.

# in this code block, we will make a scatter plot of CO2 and temperature
# and fit a linear regression model through the data


def age_model_interp(CO2_age, CO2, SST_age):
    """
    This helper function linearly interpolates CO2 data, which
    have a very high temporal resolution, to temperature data,
    which have a relatively low resolution
    """
    f = interpolate.interp1d(CO2_age, CO2)
    all_ages = f(SST_age)
    return all_ages


# interpolate CO2 data to SST age
CO2_interpolated = age_model_interp(CO2.index / 1000, CO2["co2_ppm"], SST.index)

# plot
# set up two subplots in a grid of 2 rows and 1 column
# also make sure the two plots share the same x(time) axis
fig, ax = plt.subplots(1, 1, sharex=True)

ax.scatter(CO2_interpolated, SST["SST stack"], color="gray")

# regression
X = CO2_interpolated
y = SST["SST stack"]
res = stats.linregress(X, y)  # ordinary least sqaure

x_fit = np.arange(180, 280)
# intercept
y_fit = x_fit * res.slope + res.intercept
ax.plot(x_fit, y_fit, color="k")

# beautification
# axis labels
ax.set_xlabel(r"CO${}_\mathrm{2}$ (ppm)")
ax.set_ylabel(r"Sea Surface Temperature" "\n" "Anomaly (°C)")
print(
    "pearson (r^2) value: "
    + "{:.2f}".format(res.rvalue**2)
    + " \nwith a p-value of: "
    + "{:.2e}".format(res.pvalue)
)
pearson (r^2) value: 0.55 
with a p-value of: 3.37e-47
../../../_images/d301dfb1c72c73499ad808a81f77397b4c838ff8196ae02b404e798dc632e8c7.png

Figure Making Through the Equity Lense#

Click here for some information Are the colors in your figure distinguishable for people with color-vision deficiencies?

More readings on this topic:

Contrast checker: https://www.color-blindness.com/coblis-color-blindness-simulator/

Coloring for color blindness: https://davidmathlogic.com/colorblind

Python-specific color palettes that are friendly to those with color-vision deficiency: https://seaborn.pydata.org/tutorial/color_palettes.html

Resources#

Data from the following sources are used in this tutorial:

CO2: Bereiter, B., Eggleston, S., Schmitt, J., Nehrbass-Ahles, C., Stocker, T.F., Fischer, H., Kipfstuhl, S., Chappellaz, J., 2015. Revision of the EPICA Dome C CO2 record from 800 to 600 kyr before present. Geophysical Research Letters 42, 542–549. https://doi.org/10.1002/2014GL061957

Temperature: Shakun, J.D., Lea, D.W., Lisiecki, L.E., Raymo, M.E., 2015. An 800-kyr record of global surface ocean δ18O and implications for ice volume-temperature coupling. Earth and Planetary Science Letters 426, 58–68. https://doi.org/10.1016/j.epsl.2015.05.042