Using Scripps Institution of Oceanography’s Global Observatories to Study Atmospheric CO2 pt. 2

This is a follow up post to my last post (which can be found here) where we did a deep dive into the data gathered from the Scripps Insitution of Oceanography’s Mauna Loa Observatory. In this post I will be looking at Scripps’s global observatories and comparing their findings to gather insight about how latitude affects our atmospheric CO₂ concentrations.

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First thing I did was to import the data and do some preprocessing which involved renaming the columns to something a bit more readable:

import pandas as pd

# I skip 53-58 rows because that is where the metadata is so it is not helpful to read those lines 
# it actively messes up the data actually. I originally tried to do it iteratively but then thought
# it would just be easier to do in place

# I also opted to use the flask data since a lot of the observatories did not have in-situ readings.
# This way we would have a bit more standard/controlled readings.
df_ALT = pd.read_csv('data/monthly_flask_co2_alt.csv', skiprows=53)
df_CHR = pd.read_csv('data/monthly_flask_co2_chr.csv', skiprows=56)
df_KER = pd.read_csv('data/monthly_flask_co2_ker.csv', skiprows=56)
df_KUM = pd.read_csv('data/monthly_flask_co2_kum.csv', skiprows=56)
df_NZD = pd.read_csv('data/monthly_flask_co2_nzd.csv', skiprows=56)
df_SAM = pd.read_csv('data/monthly_flask_co2_sam.csv', skiprows=56)
df_MLO = pd.read_csv('data/monthly_flask_co2_mlo.csv', skiprows=56)
df_LJO = pd.read_csv('data/monthly_merge_co2_ljo.csv', skiprows=58)
df_PTB = pd.read_csv('data/monthly_merge_co2_ptb.csv', skiprows=58)
df_SPO = pd.read_csv('data/monthly_merge_co2_spo.csv', skiprows=58)


all_dataframes = [df_ALT, df_CHR, df_KER, df_KUM, df_NZD, df_SAM, df_MLO, df_LJO, df_PTB, df_SPO]
dataframe_names = ['ALT', 'CHR', 'KER', 'KUM', 'NZD', 'SAM', 'MLO', 'LJO', 'PTB', 'SPO']

column_names = ["Year", "Month", "Date1", "Date2", "CO2", "Szn_Adj", "Fit", "Szn_Fit", "CO2_Filled", "Szn_Fill"]

for i, df in enumerate(all_dataframes):
    df.columns = column_names
    df['Station'] = dataframe_names[i]
    
df_LJO.head()

I then plotted the data (fig. 1) in separate subplots to get an overall look of how the data differs from station to station.

One thing that was immediately apparent was the variation in the sinusoidal nature from one observatory to the next. For example, the Atmospheric CO₂ at PTB (Barrow, Alaska) which has great variability versus SPO (South Pole) which has almost no variability. I decided to see if the latitude affected the average variation in CO₂. My initial thought is that it would be relatively unrelated, but, let’s find out and see! The method that I used was to go through a year long cycle (Jan – Dec) and calculated the difference in the maximum and minimum CO₂ concentrations and averaged that year over year from each observatory (fig. 3).

Judging from the graph alone, there it seems like there is a definite trend where once you cross the equator the farther north one goes the more seasonal variation in atmospheric CO₂ we see (this is evident in fig. 4). But why do we find these results? First we should consider some experimental factors, like where are these observatories placed? The three observatories with the biggest seasonal variation were LJO, PTB, and ALT. All three of which are connected to North America, a much larger landmass than any of the other stations (other than the South Pole) and therefore could be affected by more vegetative growth and decay. For example, the CHR station (Christmas Island – which I bring up both for it’s small size but also because I noticed it is actually on Kiribati according to their latitude and longitude records) is on an island in the middle of the central Pacific Ocean with an area of 811.2 km², which for reference is just a tiny bit bigger than the size of New York City. Other experimental factors include the altitude. The Mauna Loa Observatory and the Cape Kumukahi Observatory share the same latitude and are only 80km apart but have nearly 3.4km difference in altitude which likely leads to the Kumukahi observatory having more variation in CO₂.

However, after looking for more data on the matter I stumbled upon this NASA post that visualizes the seasonal changes in CO₂. As a matter of fact the Northern Hemisphere does experience a higher fluctuation in atmospheric CO₂. This is partially a result of the Northern Hemisphere accounting for roughly 68% of the Earth’s Surface¹, and consequently roughly two thirds of the earth’s vegetation. But we need to also consider the human factor. Roughly 87% of the world’s population lives in the Northern Hemisphere ² and thus contributes much more to the global CO₂ emissions (fig. 5). This is also likely what contributes to the greater seasonal variation. The winter/spring peaks of CO₂ concentration are higher in the Northern Hemisphere and then in the summer/autumn the vegetation can absorb more CO₂. Furthermore, as we continue to increase the concentration of atmospheric CO₂ the vegetation and carbon cycle are shifting as well. As a result, we have and will continue to see greater and greater swings from season to season as time goes on (fig. 6). This has already been show to have altered some of the vegetation in northern parts of the world³, and it will likely only further intensify these seasonal variations.

Today we looked at how atmospheric CO₂ concentrations change across the globe and looked into the reasons why. We discovered that the Northern Hemisphere experiences more seasonal variation of atmospheric CO₂, and generally has higher concentrations in general. We looked deeper into this question and realized that a major reason for why this happens is due to the increased land mass in the Northern Hemisphere, but also the increased human activity that is found north of the equator. Unfortunately we also saw that as a result of our constant CO₂ (and general greenhouse gas) emissions we have seen more volatility in the atmospheric CO₂ and has been reshaping how vegetation grows in northern parts of the world.

So what does this all mean? And what are the next questions we should be asking? As we look towards solutions for our climate crisis we need consider whom we are holding accountable and what it means to reduce our CO₂ emissions. As we move forward we need acknowledge that those who will be most affected by our changing climate are poorer nations⁴ who are more reliant on non-renewable energy sources. What we know now is that we are continuously increasing our atmospheric CO₂ concentration year after year and that is not going to change until we reach net neutrality. Some questions I have are: how can we be considerate to the needs of developing nations while also working towards net neutrality? What other factors and greenhouse gases are contributing to seasonal variations, and how much of a role do each of these GHGs play?

If you have any questions I would love to hear them!

Resources:

Data:
https://scrippsco2.ucsd.edu/data/atmospheric_co2/sampling_stations.html

Research Citations:
C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and oceans from 1978 to 2000. I. Global aspects, SIO Reference Series, No. 01-06, Scripps Institution of Oceanography, San Diego, 88 pages, 2001.

Featured Image:
https://svs.gsfc.nasa.gov/vis/a000000/a005100/a005115/TotalCO2_Comp_1920x1920p30_00080.png

Link to github with Jupyter Notebook Files and Data:
https://github.com/RaphaelMako/scripps-climate-examination

Additional Resources:
https://svs.gsfc.nasa.gov/4565
https://sos.noaa.gov/catalog/datasets/carbon-dioxide-concentration-geos-5-model/
https://svs.gsfc.nasa.gov/5115/
https://www.vox.com/energy-and-environment/23458617/cop27-fossil-fuels-energy-developing-countries-coal-oil-gas-africa-finance

1. https://www.worldatlas.com/articles/which-hemisphere-has-the-largest-area-covered-by-land.html ↩︎
2. https://www.businessinsider.com/90-of-people-live-in-the-northern-hemisphere-2012-5 ↩︎
3. Matthias Forkel et al., Enhanced seasonal CO₂ exchange caused by amplified plant productivity in northern ecosystems.Science351,696-699(2016).DOI:10.1126/science.aac4971 ↩︎
4. https://www.un.org/ohrlls/news/frontline-climate-crisis-worlds-most-vulnerable-nations-suffer-disproportionately ↩︎