Ocean Acidification

Ocean Acidification#

Content creators: C. Gabriela Mayorga Adame, Lidia Krinova

Content reviewers: Will Gregory, Paul Heubel, Laura Paccini, Jenna Pearson, Ohad Zivan

Content editors: Paul Heubel

Production editors: Paul Heubel, Konstantine Tsafatinos

Our 2024 Sponsors: CMIP, NFDI4Earth

Project Background#

Human activities release CO2 into the atmosphere, which leads to atmospheric warming and climate change. A portion of this CO2 released by human activities is absorbed into the oceans, which has a direct, chemical effect on seawater, known as ocean acidification. When CO2 combines with water in the ocean it forms carbonic acid, which makes the ocean more acidic and can have negative impacts on certain marine ecosystems (e.g., reduce the ability of calcifying organisms to form their shells and skeletons). The degree of ocean acidification is often expressed in terms of the pH of seawater, which is the measure of acidity or alkalinity such that a pH below 7 is considered acidic, and a pH greater than 7 is considered alkaline, or basic. Additional background information on ocean acidification can be found here. In this project, you will explore spatial and temporal patterns of and relationships between pH, CO2, and temperature to assess changes in ocean acidification and the impact on marine ecosystems.

In this project, you will analyse ocean model and observational data from global databases to extract variables like pH, CO₂, and temperature, and to investigate ocean acidification process in your region of interest. This project will also be an opportunity to investigate the relationships between these variables as well as their impact on the marine ecosystems.

Project Template#

../../_images/2024_OceanAcidification.svg

Note: The dashed boxes are socio-economic questions.

Data Exploration Notebook#

Project Setup#

# google colab installs

# !mamba install

# imports

import random
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
import pooch
import pandas as pd
import os
import tempfile
from requests.exceptions import ReadTimeout

# helper functions

def pooch_load(filelocation=None,filename=None,processor=None):
    shared_location='/home/jovyan/shared/Data/Projects/Ocean_Acidification' # 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

NOAA Ocean pH and Acidity#

Global surface ocean acidification indicators from 1750 to 2100 (NCEI Accession 0259391)#

This data package contains a hybrid surface ocean acidification (OA) data product that is produced based on three recent observational data products:

Surface Ocean CO2 Atlas (SOCAT, version 2022)
Global Ocean Data Analysis Product version 2 (GLODAPv2, version 2022)
Coastal Ocean Data Analysis Product in North America (CODAP-NA, version 2021), and 14 Earth System Models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6).

The trajectories of ten OA indicators are included in this data product:

Fugacity of carbon dioxide
pH on Total Scale
Total hydrogen ion content
Free hydrogen ion content
Carbonate ion content
Aragonite saturation state
Calcite saturation state
Revelle Factor
Total dissolved inorganic carbon content
Total alkalinity content

These OA trajectories are provided under preindustrial conditions, historical conditions, and future Shared Socioeconomic Pathways: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 from 1750 to 2100 on a global surface ocean grid. These OA trajectories are improved relative to previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs over the 21st century.

Citation: Jiang, L.-Q., Dunne, J., Carter, B. R., Tjiputra, J. F., Terhaar, J., Sharp, J. D., et al. (2023). Global surface ocean acidification indicators from 1750 to 2100. Journal of Advances in Modeling Earth Systems, 15, e2022MS003563. https://doi.org/10.1029/2022MS003563

Dataset: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html

We can load and visualize the surface pH as follows:

For those feeling adventurous, there are also files of future projected changes under various scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, recall W2D1 tutorials):

pHT_median_ssp119.nc
pHT_median_ssp126.nc
pHT_median_ssp245.nc
pHT_median_ssp370.nc
pHT_median_ssp585.nc

To load them, replace the filename in the path/filename line above. These data were calculated from CMIP6 models. To learn more about CMIP please see our CMIP Resource Bank and the CMIP website.

For the advanced questions in red you can use sea surface temperature and CO₂ concentration from NOAA Global Monitoring Laboratory) which were previously introduced in the tutorials. We can load and visualize this variables as follows:

filename_CO2= 'co2_mm_gl.csv'
url_CO2= 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_gl.csv'
ds_CO2 = pd.read_csv(pooch_load(url_CO2,filename_CO2),header=55)

ds_CO2

Downloading data from 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_gl.csv' to file '/tmp/co2_mm_gl.csv'.

SHA256 hash of downloaded file: d1ffeb2883bd50c2af00fa9109538087b87f61d05b78b0c2e4b0f4a8c252bff4
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.

	1980	5	1980.375	340.44	0.10	338.89	0.07
0	1980	6	1980.458	339.99	0.09	339.22	0.07
1	1980	7	1980.542	338.44	0.16	339.28	0.07
2	1980	8	1980.625	337.21	0.19	339.50	0.06
3	1980	9	1980.708	337.05	0.12	339.44	0.06
4	1980	10	1980.792	337.83	0.07	339.17	0.07
...	...	...	...	...	...	...	...
526	2024	4	2024.292	423.72	0.10	421.89	0.06
527	2024	5	2024.375	423.81	0.10	422.17	0.06
528	2024	6	2024.458	423.11	0.10	422.54	0.06
529	2024	7	2024.542	421.45	0.10	422.75	0.06
530	2024	8	2024.625	419.92	0.10	422.77	0.06

531 rows × 7 columns

Hints for socio-economic question (dashed boxes):

Use the attached image (Figure 4 in Kroaker et al., 2013 research paper) and this mapping tool. Search for species of interest on the mapping tool to see their spatial global distribution and changes over time. Be critical about what you see.

effects of ocean acidification

Summary of effects of acidification among key taxonomic groups. Effects are represented as either mean percent (+) increase or percent (−) decrease in a given response.

Other Resources#

This tutorial uses data from the simulations conducted as part of the CMIP6 multi-model ensemble.

For examples on how to access and analyze data, please visit the Pangeo Cloud CMIP6 Gallery

For more information on what CMIP is and how to access the data, please see this page.