Ocean Acidification

Content creators: C. Gabriela Mayorga Adame, Lidia Krinova

Content reviewers: Jenna Pearson, Abigail Bodner, Ohad Zivan, Chi Zhang

Content editors: Zane Mitrevica, Natalie Steinemann, Ohad Zivan, Chi Zhang, Jenna Pearson

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

Our 2023 Sponsors: NASA TOPS, Google DeepMind

# @title Project Background

from ipywidgets import widgets
from IPython.display import YouTubeVideo
from IPython.display import IFrame
from IPython.display import display


class PlayVideo(IFrame):
    def __init__(self, id, source, page=1, width=400, height=300, **kwargs):
        self.id = id
        if source == "Bilibili":
            src = f"https://player.bilibili.com/player.html?bvid={id}&page={page}"
        elif source == "Osf":
            src = f"https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render"
        super(PlayVideo, self).__init__(src, width, height, **kwargs)


def display_videos(video_ids, W=400, H=300, fs=1):
    tab_contents = []
    for i, video_id in enumerate(video_ids):
        out = widgets.Output()
        with out:
            if video_ids[i][0] == "Youtube":
                video = YouTubeVideo(
                    id=video_ids[i][1], width=W, height=H, fs=fs, rel=0
                )
                print(f"Video available at https://youtube.com/watch?v={video.id}")
            else:
                video = PlayVideo(
                    id=video_ids[i][1],
                    source=video_ids[i][0],
                    width=W,
                    height=H,
                    fs=fs,
                    autoplay=False,
                )
                if video_ids[i][0] == "Bilibili":
                    print(
                        f"Video available at https://www.bilibili.com/video/{video.id}"
                    )
                elif video_ids[i][0] == "Osf":
                    print(f"Video available at https://osf.io/{video.id}")
            display(video)
        tab_contents.append(out)
    return tab_contents


video_ids = [('Youtube', 'NAgrB8HxMMk'), ('Bilibili', 'BV1fM4y1x7g8')]
tab_contents = display_videos(video_ids, W=730, H=410)
tabs = widgets.Tab()
tabs.children = tab_contents
for i in range(len(tab_contents)):
    tabs.set_title(i, video_ids[i][0])
display(tabs)

# @title Tutorial slides
# @markdown These are the slides for the videos in all tutorials today
from IPython.display import IFrame
link_id = "n7wdy"

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

Note: The dashed boxes are socio-economic questions.

Data Exploration Notebook

Project Setup

# google colab installs

# !mamaba install netCDF4

# 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

# 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:

# code to retrieve and load the data
# url_SurfacepH= 'https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0206289/Surface_pH_1770_2100/Surface_pH_1770_2000.nc' $ old CMIP5 dataset
filename_SurfacepH='pHT_median_historical.nc'
url_SurfacepH='https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0259391/nc/median/pHT_median_historical.nc'
ds_pH = xr.open_dataset(pooch_load(url_SurfacepH,filename_SurfacepH))
ds_pH

<xarray.Dataset>
Dimensions:    (time: 18, lat: 180, lon: 360)
Coordinates:
  * time       (time) float64 1.75e+03 1.85e+03 1.86e+03 ... 2e+03 2.01e+03
Dimensions without coordinates: lat, lon
Data variables:
    pHT        (time, lat, lon) float64 ...
    longitude  (lat, lon) float64 ...
    latitude   (lat, lon) float64 ...
Attributes:
    title:               Global surface ocean pH on total hydrogen ion scale ...
    comment:             This gridded data product contains pH on total hydro...
    reference:           Jiang, L-Q., J. Dunne, B. R. Carter, J. Tjiputra,\n ...
    Fair_use_statement:  This data product is made freely available\n   to th...
    created_by:          Li-Qing Jiang
    institution:         (a) Cooperative Institute for Satellite Earth System...
    contact:             <Liqing.Jiang@noaa.gov>
    creation_date:       August 14, 2022

xarray.Dataset

• Dimensions:
  • time: 18
  • lat: 180
  • lon: 360
• Coordinates: (1)
  • time
    (time)
    float64
    1.75e+03 1.85e+03 ... 2.01e+03

    long name :

    The decade of 1750, and all decades from 1850 to 2010, i.e., 1750, 1850, 1860, 1870, 1880, 1890, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010

    CF_name :

    time

    unit :

    year

    array([1750., 1850., 1860., 1870., 1880., 1890., 1900., 1910., 1920., 1930.,
           1940., 1950., 1960., 1970., 1980., 1990., 2000., 2010.])

• Data variables: (3)
  • pHT
    (time, lat, lon)
    float64
    ...

    long name :

    pH on total hydrogen ion scale

    CF_name :

    sea_water_ph_reported_on_total_scale

    units :

    N/A

    [1166400 values with dtype=float64]

  • longitude
    (lat, lon)
    float64
    ...

    long name :

    Longitude on the 1 x 1 degree global grid (20.5 to 379.5 degrees).

    CF_name :

    grid_longitude

    units :

    decimal degrees

    [64800 values with dtype=float64]

  • latitude
    (lat, lon)
    float64
    ...

    long name :

    Latitude on the 1 x 1 degree grid (-89.5 to 89.5 degrees) at this depth level.

    CF_name :

    grid_latitude

    units :

    decimal degrees

    [64800 values with dtype=float64]

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(Index([1750.0, 1850.0, 1860.0, 1870.0, 1880.0, 1890.0, 1900.0, 1910.0, 1920.0,
           1930.0, 1940.0, 1950.0, 1960.0, 1970.0, 1980.0, 1990.0, 2000.0, 2010.0],
          dtype='float64', name='time'))

• Attributes: (8)

  title :

  Global surface ocean pH on total hydrogen ion scale (unit: N/A) on a 1 x 1 grid in 1750, and all decades from 1850 to 2010.

  comment :

  This gridded data product contains pH on total hydrogen ion scale [median of 14 Earth System Models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6)] on a global [20.5:1:379.5] degree longitude x [-89.5:1:89.75] degree latitude grid in the global surface ocean in 1750 and all decades from 1850 to 2010, i.e., 1750, 1850, 1860, 1870, 1880, 1890, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010. At each grid point, temperature, salinity, DIC, and TA were estimated based on their temporal changes out of fourteen Earth System Models of the sixth phase of the Coupled Model Intercomparison Project (CMIP6): (1) ACCESS-ESM1-5, Ziehn et al., 2020, https://doi.org/10.1071/es19035, (2) CanESM5, Swart et al., 2019, https://doi.org/10.5194/gmd-12-4823-2019, (3) CESM2, Danabasoglu et al., 2020, https://doi.org/10.1029/2019ms001916, (4) CMCC-ESM2, Lovato et al., 2022, https://doi.org/10.1029/2021ms002814, (5) CNRM-ESM-2, Séférian et al., 2019, https://doi.org/10.1029/2019ms001791, (6) EC-Earth3-CC, Döscher et al., 2021, https://doi.org/10.5194/gmd-2020-446, (7) GFDL-CM4, Held et al., 2019, https://doi.org/10.1029/2019MS001829, (8) GFDL-ESM4, Dunne et al., 2020, https://doi.org/10.1029/2019MS002015, (9) IPSL-CM6A-LR, Boucher et al., 2020, https://doi.org/10.1029/2019ms002010, (10) MIROC-ES2L, Hajima et al., 2020, https://doi.org/10.5194/gmd-13-2197-2020, (11) MPI-ESM1-2-LR, Mauritsen et al., 2019, https://doi.org/10.1029/2018ms001400, (12) MRI-ESM2-0, Yukimoto et al., 2019, https://doi.org/10.2151/jmsj.2019-051, (13) NorESM2-LM, Seland et al., 2020, https://doi.org/10.5194/gmd-13-6165-2020, (14) UKESM1-0-LL), Sellar et al., 2019, https://doi.org/10.1029/2019ms001739, with bias corrections based on three latest observational data products: (1) the Surface Ocean CO2 Atlas (SOCAT, version 2021, Bakker et al., 2016, https://doi.org/10.5194/essd-8-383-2016), (2) the Global Ocean Data Analysis Product Version 2 (GLODAPv2, version 2021, Lauvset et al., 2021, https://doi.org/10.5194/essd-13-5565-2021), and (3) the Coastal Ocean Data Analysis Product in North America (CODAP-NA, version 2021, Jiang et al., 2021, https://doi.org/10.5194/essd-13-2777-2021). Once temperature, salinity, DIC, and TA at that decade were calculated, they were then used to calculate the rest of the OA indicators. The CO2 system calculation was conducted using CO2System.jl (Humphreys et al., 2022) with the dissociation constants and parameter as recommended by Jiang et al. (2022), i.e., dissociation constants for carbonic acid of Lueker et al. (2000), potassium bisulfate (KHSO4–) of Dickson (1990), hydrofluoric acid (HF) of Perez and Fraga (1987), and the total borate concentration equations of Lee et al. (2010). The values reported in this NetCDF are the median values among all 14 ESMs at each of the grid point

  reference :

  Jiang, L-Q., J. Dunne, B. R. Carter, J. Tjiputra, J. Terhaar, J. D. Sharp, A. Olsen, R. A. Feely, R. Wanninkhof, P. Hogan, S. Alin, T. Ilyina, N. Lange, S. K. Lauvset, T. Lovato, Julien Palmieri, Y. Santana-Falcón, J. Schwinger, R. Séférian, G Strand, N. Swart, T. Tanhua, H. Tsujino, M. Watanabe, A. Yamamoto, T. Ziehn. (2022). Global surface ocean acidification indicators: past, present, and future. Global Biogeochemical Cycles, under review.

  Fair_use_statement :

  This data product is made freely available to the public and the scientific community. If it is used in scientific publications, please cite the paper on GBC, so that readers can access all of the detailed information about this data product. If these data are essential to the work, or if an important result or conclusion depends on these data, co-authorship may be appropriate. This should be discussed at an early stage in the work.

  created_by :

  Li-Qing Jiang

  institution :

  (a) Cooperative Institute for Satellite Earth System Studies, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland 20740, United States, and (b) National Centers for Environmental Information, National Oceanic and Atmospheric Administration, Silver Spring, Maryland 20910, United States

  contact :

  <Liqing.Jiang@noaa.gov>

  creation_date :

  August 14, 2022

For those feeling adventurouts, 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.

Copernicus

Copernicus is the Earth observation component of the European Union’s Space programme, looking at our planet and its environment to benefit all European citizens. It offers information services that draw from satellite Earth Observation and in-situ (non-space) data.

The European Commission manages the Programme. It is implemented in partnership with the Member States, the European Space Agency (ESA), the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), the European Centre for Medium-Range Weather Forecasts (ECMWF), EU Agencies and Mercator Océan.

Vast amounts of global data from satellites and ground-based, airborne, and seaborne measurement systems provide information to help service providers, public authorities, and other international organisations improve European citizens’ quality of life and beyond. The information services provided are free and openly accessible to users.

Source: https://www.copernicus.eu/en/about-copernicus

ECMWF Atmospheric Composition Reanalysis: Carbon Dioxide (CO₂)

From this dataset we will use CO₂ column-mean molar fraction from the Single-level chemical vertical integrals variables & Sea Surface Temperature from the Single-level meteorological variables (in case you need to download them direclty from the catalog).

This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO₂) and methane (CH₄). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO₂ fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual. The CH₄ chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets.

Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.

Source & further information: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4-monthly?tab=overview

We can load and visualize the sea surface temperature and CO₂ concentration (from NOAA Global Monitoring Laboratory):

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

	year	month	decimal	average	average_unc	trend	trend_unc
0	1979	1	1979.042	336.56	0.10	335.92	0.09
1	1979	2	1979.125	337.29	0.09	336.25	0.09
2	1979	3	1979.208	337.88	0.10	336.51	0.09
3	1979	4	1979.292	338.32	0.11	336.72	0.09
4	1979	5	1979.375	338.26	0.04	336.71	0.10
...	...	...	...	...	...	...	...
527	2022	12	2022.958	418.72	0.10	418.12	0.06
528	2023	1	2023.042	419.34	0.10	418.25	0.06
529	2023	2	2023.125	419.70	0.10	418.27	0.06
530	2023	3	2023.208	420.06	0.10	418.40	0.06
531	2023	4	2023.292	420.54	0.10	418.72	0.06

532 rows × 7 columns

# from W1D3 tutorial 6 we have Sea Surface Temprature from 1981 to the present:
# download the monthly sea surface temperature data from NOAA Physical System
# Laboratory. The data is processed using the OISST SST Climate Data Records
# from the NOAA CDR program.
# the data downloading may take 2-3 minutes to complete.

filename_sst='sst.mon.mean.nc'
url_sst = "https://osf.io/6pgc2/download/"

ds_SST = xr.open_dataset(pooch_load(url_sst,filename_sst))
ds_SST

Downloading data from 'https://osf.io/6pgc2/download/' to file '/tmp/sst.mon.mean.nc'.

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[8], line 10
      7 filename_sst='sst.mon.mean.nc'
      8 url_sst = "https://osf.io/6pgc2/download/"
---> 10 ds_SST = xr.open_dataset(pooch_load(url_sst,filename_sst))
     11 ds_SST

Cell In[5], line 10, in pooch_load(filelocation, filename, processor)
      8     file = os.path.join(shared_location,filename)
      9 else:
---> 10     file = pooch.retrieve(filelocation,known_hash=None,fname=os.path.join(user_temp_cache,filename),processor=processor)
     12 return file

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/pooch/core.py:239, in retrieve(url, known_hash, fname, path, processor, downloader, progressbar)
    236 if downloader is None:
    237     downloader = choose_downloader(url, progressbar=progressbar)
--> 239 stream_download(url, full_path, known_hash, downloader, pooch=None)
    241 if known_hash is None:
    242     get_logger().info(
    243         "SHA256 hash of downloaded file: %s\n"
    244         "Use this value as the 'known_hash' argument of 'pooch.retrieve'"
   (...)
    247         file_hash(str(full_path)),
    248     )

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/pooch/core.py:803, in stream_download(url, fname, known_hash, downloader, pooch, retry_if_failed)
    799 try:
    800     # Stream the file to a temporary so that we can safely check its
    801     # hash before overwriting the original.
    802     with temporary_file(path=str(fname.parent)) as tmp:
--> 803         downloader(url, tmp, pooch)
    804         hash_matches(tmp, known_hash, strict=True, source=str(fname.name))
    805         shutil.move(tmp, str(fname))

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/pooch/downloaders.py:226, in HTTPDownloader.__call__(self, url, output_file, pooch, check_only)
    224     progress = self.progressbar
    225     progress.total = total
--> 226 for chunk in content:
    227     if chunk:
    228         output_file.write(chunk)

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/requests/models.py:816, in Response.iter_content.<locals>.generate()
    814 if hasattr(self.raw, "stream"):
    815     try:
--> 816         yield from self.raw.stream(chunk_size, decode_content=True)
    817     except ProtocolError as e:
    818         raise ChunkedEncodingError(e)

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/urllib3/response.py:628, in HTTPResponse.stream(self, amt, decode_content)
    626 else:
    627     while not is_fp_closed(self._fp):
--> 628         data = self.read(amt=amt, decode_content=decode_content)
    630         if data:
    631             yield data

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/urllib3/response.py:567, in HTTPResponse.read(self, amt, decode_content, cache_content)
    564 fp_closed = getattr(self._fp, "closed", False)
    566 with self._error_catcher():
--> 567     data = self._fp_read(amt) if not fp_closed else b""
    568     if amt is None:
    569         flush_decoder = True

File ~/miniconda3/envs/climatematch/lib/python3.10/site-packages/urllib3/response.py:533, in HTTPResponse._fp_read(self, amt)
    530     return buffer.getvalue()
    531 else:
    532     # StringIO doesn't like amt=None
--> 533     return self._fp.read(amt) if amt is not None else self._fp.read()

File ~/miniconda3/envs/climatematch/lib/python3.10/http/client.py:466, in HTTPResponse.read(self, amt)
    463 if self.length is not None and amt > self.length:
    464     # clip the read to the "end of response"
    465     amt = self.length
--> 466 s = self.fp.read(amt)
    467 if not s and amt:
    468     # Ideally, we would raise IncompleteRead if the content-length
    469     # wasn't satisfied, but it might break compatibility.
    470     self._close_conn()

File ~/miniconda3/envs/climatematch/lib/python3.10/socket.py:705, in SocketIO.readinto(self, b)
    703 while True:
    704     try:
--> 705         return self._sock.recv_into(b)
    706     except timeout:
    707         self._timeout_occurred = True

File ~/miniconda3/envs/climatematch/lib/python3.10/ssl.py:1274, in SSLSocket.recv_into(self, buffer, nbytes, flags)
   1270     if flags != 0:
   1271         raise ValueError(
   1272           "non-zero flags not allowed in calls to recv_into() on %s" %
   1273           self.__class__)
-> 1274     return self.read(nbytes, buffer)
   1275 else:
   1276     return super().recv_into(buffer, nbytes, flags)

File ~/miniconda3/envs/climatematch/lib/python3.10/ssl.py:1130, in SSLSocket.read(self, len, buffer)
   1128 try:
   1129     if buffer is not None:
-> 1130         return self._sslobj.read(len, buffer)
   1131     else:
   1132         return self._sslobj.read(len)

KeyboardInterrupt: 

cams-global-ghg-reanalysis-egg4-monthly

https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-ghg-reanalysis-egg4-monthly

From this dataset we will use CO₂ column-mean molar fraction from the Single-level chemical vertical integrals variables & Sea Surface Temperature from the Single-level meteorological variables (in case you need to download them direclty from the catalog).

This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO₂) and methane (CH₄). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO₂ fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual. The CH₄ chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets.

Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.

Source & further information: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4-monthly?tab=overview

filename_CO2_CAMS= 'SSTyCO2_CAMS_Copernicus_data.nc'
url_CO2_CAMS= ''
ds_CO2_CAMS =xr.open_dataset(pooch_load(url_CO2_CAMS,filename_CO2_CAMS))

ds_CO2_CAMS

Hint for question 4:

Use the attached image (figure 5 in this website) and this mapping tool. Search for each species on the mapping tool to see the spatial global distribution.

Further Reading

• Understanding Ocean Acidification”, NOAA (https://www.fisheries.noaa.gov/insight/understanding-ocean-acidification)

• “Ocean acidification and its effects”, CoastAdapt (https://coastadapt.com.au/ocean-acidification-and-its-effects)

• “Scientists Pinpoint How Ocean Acidification Weakens Coral Skeletons”, WHOI (https://www.whoi.edu/press-room/news-release/scientists-identify-how-ocean-acidification-weakens-coral-skeletons/)

• “Ocean acidification and reefs”, Smithonian Tropical Research Institute (https://stri.si.edu/story/ocean-acidification-and-reefs)

• Henry, Joseph, Joshua Patterson, and Lisa Krimsky. 2020. “Ocean Acidification: Calcifying Marine Organisms: FA220, 3/2020”. EDIS 2020 (2):4. https://doi.org/10.32473/edis-fa220-2020.

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.