Analysis of Antarctic Sea Level: Monthly Mean

Analysis of Antarctic Sea Level: Monthly Mean#

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Sea level measurement in the Antarctic region is of critical importance for understanding and monitoring global climate changes. Antarctica, with its extensive ice sheets, constitutes one of the planet’s primary freshwater reservoirs. The fluctuation of sea level in this region serves as a direct indicator of changes in the volume of glaciers and ice shelves. Ice loss in Antarctica contributes to sea level rise in Europe disproportionately, exceeding the global mean sea level rise (GMSLR). (source: https://web.archive.org/web/20240125202734id_/https://sp.copernicus.org/preprints/sp-2023-36/sp-2023-36.pdf)

Measuring sea level anomalies is crucial because they provide specific information about the phenomena directly influencing sea level. For example, positive anomalies may indicate water accumulation due to glacier melt. These measurements are essential for monitoring ocean dynamics, predicting extreme weather events, and understanding the impact of climate change on global coastlines and marine ecosystems. Accurate and continuous research in this region is thus vital to address the challenges of climate change and safeguard the global balance of marine ecosystems.

In this notebook, we explore monthly data in time range 1993-2020, focusing on monthly levels anomalies of Antarctic sea.

Note: The data and trends presented in this article are based on information available up until December 2020. Subsequent developments may not be included in the analysis.

The tool uses the following product:

EMODnet (https://prod-erddap.emodnet-physics.eu/erddap/griddap/EMODNET_CMCC_ASVL_DUAC_GLO_MONTHLYMAPS_v2022)

The two code cells below will generate a map of the Antarctic area with a layer representing sea anomalies. In this example the year 2020 has been chosen. The plot of the anomalies has been generated from a point at coordinates -60.375, 44.375.

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summer_data = Output()
winter_data = Output()

# DateTime slider control
def updating_values(value):
  selected_time = obs_dates[(value.new)-1]
  time_descr.value = datetime.strptime(selected_time, "%Y-%m-%dT%H:%M:%S.%fZ").strftime("%B %Y")
  wms.time = selected_time

play = Play(
    value=len(obs_dates),
    min=1,
    max=len(obs_dates),
    step=1,
    interval=2000,
    description='',
    disabled=False
)
slider = IntSlider(min=1, max=len(obs_dates), readout = False, continuous_update=False)
time_descr = Text(value= datetime.strptime(obs_dates[-1], "%Y-%m-%dT%H:%M:%S.%fZ").strftime("%B %Y") , description='', layout=Layout(width='180px'), disabled=True)

jslink((play, 'value'), (slider, 'value'))

slider.observe(updating_values, names='value')

graph_area = Output()
graph_area.layout.width = '80%'

# Map click functions:
# Show DataTable
def show_dataTable(table, df):

  # Remove previous table
  table.clear_output()

  with table:
    display(df)

# Show loading...
def show_loading_animation():
    loading_html = """
    <style>
    .loader {
        border: 8px solid #f3f3f3;
        border-top: 8px solid #3498db;
        border-radius: 50%;
        width: 50px;
        height: 50px;
        animation: spin 1s linear infinite;
    }

    @keyframes spin {
        0% { transform: rotate(0deg); }
        100% { transform: rotate(360deg); }
    }
    </style>
    <div class="loader"></div>
    """
    graph_area.clear_output()
    with graph_area:
        display(HTML(loading_html))

# Making a data graph
def generate_sealevel_anomalies_data_plot(latlon):
    show_loading_animation()
    lat = latlon[0]
    lon = latlon[1]
    url = ERDDAP_SLA_URL + TIME_RANGE + f'[({lat}):1:({lat})][({lon}):1:({lon})]'

    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()

        global summer_data
        global winter_data

        df_oct_to_mar = pd.DataFrame()
        df_apr_to_sep = pd.DataFrame()

        column_names = data['table']['columnNames']
        rows = data['table']['rows']
        df = pd.DataFrame(rows, columns=column_names)

        df['timestamp'] = pd.to_datetime(df['time'])
        df['month'] = df['timestamp'].dt.month
        df['year'] = df['timestamp'].dt.year

        # Separate data into seasons
        df_oct_to_mar = df[df['month'].isin([10, 11, 12, 1, 2, 3])].reset_index()
        df_apr_to_sep = df[df['month'].isin([4, 5, 6, 7, 8, 9])].reset_index()

        # Calculate mean sea level anomalies for each season
        mean_sla_oct_to_mar = df_oct_to_mar.groupby('year')['sla'].mean().reset_index()
        mean_sla_apr_to_sep = df_apr_to_sep.groupby('year')['sla'].mean().reset_index()

        # Display DataTables for both seasons
        show_dataTable(summer_data, mean_sla_oct_to_mar)
        show_dataTable(winter_data, mean_sla_apr_to_sep)

        # Ensure data exists before plotting
        if not mean_sla_oct_to_mar.empty and not mean_sla_apr_to_sep.empty:
            # set Font sizes
            plt.rcParams.update({
            'font.size': 15
            })
            # Plot
            plt.figure(figsize=(15, 10))

            # Plotting Bars
            bar_width = 0.35
            x_oct_to_mar = np.arange(len(mean_sla_oct_to_mar))
            x_apr_to_sep = np.arange(len(mean_sla_apr_to_sep))
            plt.bar(x_oct_to_mar, mean_sla_oct_to_mar['sla'], width=bar_width, color='orange', label='Sea Level Anomalies (Austral summer)')
            plt.bar(x_apr_to_sep + bar_width, mean_sla_apr_to_sep['sla'], width=bar_width, color='skyblue', label='Sea Level Anomalies (Austral winter)')

            # Trend Lines
            z_oct_to_mar = np.polyfit(x_oct_to_mar, mean_sla_oct_to_mar['sla'], 3)
            p_oct_to_mar = np.poly1d(z_oct_to_mar)
            plt.plot(x_oct_to_mar, p_oct_to_mar(x_oct_to_mar), color='red', linestyle='--', label='Trendline (Austral summer)')

            z_apr_to_sep = np.polyfit(x_apr_to_sep, mean_sla_apr_to_sep['sla'], 3)
            p_apr_to_sep = np.poly1d(z_apr_to_sep)
            plt.plot(x_apr_to_sep, p_apr_to_sep(x_apr_to_sep), color='blue', linestyle='--', label='Trendline (Austral winter)')

            plt.xlabel('Year')
            plt.ylabel('Anomalies (m)')
            plt.title(f"Sea Level Anomalies in time (Season mean)\n ({df['latitude'][0]} {df['longitude'][0]})")
            plt.legend()
            plt.xticks(x_oct_to_mar + bar_width / 2, mean_sla_oct_to_mar['year'], rotation=45)
            plt.grid(True)
            plt.tight_layout()

            # Clear loading animation and show the plot
            graph_area.clear_output()
            plt.show()

        else:
            # No data available
            graph_area.clear_output()
            print('No Data Available...')

    except Exception as e:
        graph_area.clear_output()
        plt.close()
        print('No Data Available...')

random_point = (-60.375, 44.375)
marker = Marker(location=random_point)
# Adding the marker on the map
m.add_layer(marker)

# Generate a new graph
with graph_area:
    generate_sealevel_anomalies_data_plot(random_point)

def onChangeLatlon(**kwargs):
  global graph_area
  global m
  latlon = kwargs.get('coordinates')
  if kwargs.get('type') == 'click':

    # removing old markers
    for layer in m.layers:
        if isinstance(layer, Marker):
            m.remove_layer(layer)

    marker = Marker(location=latlon)
    m.add_layer(marker)

    # Remove previous graph
    graph_area.clear_output()

    # Genera a new graph
    with graph_area:
      generate_sealevel_anomalies_data_plot(latlon)

m.on_interaction(onChangeLatlon)

VBox([HBox([time_descr]), VBox([m, graph_area])])

Below Sea Level Anomalies [meters] are presented in two tables: the one on the left pertains to summer data, while the one on the right addresses winter data.

The last code cell below will generate a map of the antarctic area with positions and data from tide gauge platforms:

Show code cell source Hide code cell source

def format_date(date_str):
    # this function convers the datetime to the formatted date as YYYY-MM-DDThh:mm:ssZ
    date_time = datetime.strptime(date_str, '%Y-%m-%dT%H:%M:%SZ')
    # Adding time zone +1 h
    date_time = date_time.replace(tzinfo=timezone.utc).astimezone(timezone(timedelta(hours=1)))
    formatted_date = date_time.strftime('%d/%m/%Y %H:%M GMT%z')
    return formatted_date

geoserver_filter = "&cql_filter=(platformType IN ('TG')) AND (lastDateObservationDT>='1993-01-16T00:00:00.000Z')"
pl_url = GEOSERVER_SOOS_URL + geoserver_filter

try:
  response = requests.get(pl_url)
  response.raise_for_status()
  data = response.json()

  m2 = Map(
      basemap=basemaps.Esri.AntarcticBasemap,
      crs=projections.EPSG3031.ESRIBasemap,
      center=(-90, 0),
      zoom=2,
      min_zoom=2,
      layout=Layout(height='600px'),
      close_on_escape_key=False
  )

  data_marker = data['features']
  for data in data_marker:
    position = (data['properties']['latitude'], data['properties']['longitude'])

    marker = Marker(location=position)
    EMODNET_URL = EMODNET_LINK + data['properties']['platformcode'] + '&integrator=' + data['properties']['source']

    html = HTML()
    infos = f'''<b>Platform Code:</b> {data['properties']['platformcode']}<br/>
    <b>Platform Name:</b> {data['properties']['platformName']}<br/>
    <b>Lat:</b> {data['properties']['latitude']} <b>Lon:</b> {data['properties']['longitude']}<br/>
    <b>Source:</b> {data['properties']['source']}<br/>
    <b>DataOwner:</b> {data['properties']['dataOwner']}<br/>
    <b>Last Observation:</b> {format_date(data['properties']['lastDateObservation'])}<br/>
    <a href='{EMODNET_URL}' target='_blank'>View data</a>'''
    html.value = infos

    popup = Popup(
        location=position,
        child=html,
        close_button=True,
        auto_close=False,
    )
    marker.popup = popup

    m2.add_layer(marker)
except Exception as e:
  print('No Data Available...')

m2

Additional resources#

The Python libraries that have been used in this notebook are:

Analysis of Antarctic Sea Level: Monthly Mean

Contents

Analysis of Antarctic Sea Level: Monthly Mean#

Additional resources#