{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "2cbyh88dRy-4" }, "source": [ "# Analysis of Antarctic Sea Level: Monthly Mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For an interactive version of this page please visit the Google Colab: \n", "[ Open in Google Colab ](https://colab.research.google.com/drive/1IxVK1dC2P-Cyf31GRbXlS7Y83PkM3yUT)
\n", "(To open link in new tab press Ctrl + click)" ] }, { "cell_type": "markdown", "metadata": { "id": "07zueoUGRwdS" }, "source": [ "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)\n", "\n", "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.\n", "\n", "In this notebook, we explore monthly data in time range 1993-2020, focusing on monthly levels anomalies of Antarctic sea.\n", "\n", "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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The tool uses the following product:\n", "\n", "- EMODnet (https://prod-erddap.emodnet-physics.eu/erddap/griddap/EMODNET_CMCC_ASVL_DUAC_GLO_MONTHLYMAPS_v2022)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eGvq9XDvOpfL", "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "%%capture\n", "!pip install ipyleaflet ipywidgets matplotlib\n", "%reload_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PT0Z2SEXm2Z1", "tags": [ "hide-input" ] }, "outputs": [], "source": [ "import requests\n", "import xml.etree.ElementTree as ET\n", "import calendar\n", "import time\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from datetime import datetime, timedelta, timezone\n", "from traitlets import Unicode\n", "from ipyleaflet import (\n", " Map,\n", " Marker,\n", " basemaps,\n", " projections,\n", " WMSLayer,\n", " Popup,\n", " Icon,\n", " AwesomeIcon\n", ")\n", "from ipywidgets import (\n", " Play,\n", " jslink,\n", " IntSlider,\n", " HBox,\n", " Text,\n", " Layout,\n", " Output,\n", " VBox,\n", " HTML\n", ")\n", "from IPython.display import display, clear_output" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "arl_GxyITppD", "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Configurations\n", "ERDDAP_SLA_URL = 'https://prod-erddap.emodnet-physics.eu/erddap/griddap/EMODNET_CMCC_ASVL_DUAC_GLO_MONTHLYMAPS_v2022.json?sla'\n", "\n", "WMS_URL = 'https://prod-erddap.emodnet-physics.eu/ncWMS/wms'\n", "WMS_LAYER = 'EMODNET_CMCC_ASVL_DUAC_GLO_MONTHLYMAPS_v2022/sla'\n", "GET_CAPABILITIES_URL = f\"{WMS_URL}?SERVICE=WMS&REQUEST=GetCapabilities&VERSION=1.3.0&DATASET={WMS_LAYER}\"\n", "\n", "TIME_RANGE = '[(1993-01-16T00:00:00Z):1:(2020-12-16T00:00:00Z)]'\n", "\n", "EPSG3031 = dict(\n", " name='EPSG:3031',\n", " custom=True,\n", " proj4def=\"\"\"+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1\n", " +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs\"\"\",\n", " bounds=[[-3500000,-3500000],[3500000,3500000]]\n", ")\n", "\n", "GEOSERVER_SOOS_URL = 'https://gs1webapps.emodnet-physics.eu/geoserver/wfs?request=getfeature&service=wfs&version=2.0.0&srsName=EPSG:3031&typeName=SOOS:dat_platformmapcache&outputFormat=application%2Fjson'\n", "EMODNET_LINK = 'https://map.emodnet-physics.eu/platformpage/?source=cp&platformcode='" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "V51ggUgcO2CO", "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Retrieving observation datetimes\n", "get_capabilities = requests.get(GET_CAPABILITIES_URL)\n", "\n", "if get_capabilities.status_code == 200:\n", " root = ET.fromstring(get_capabilities.content)\n", " for elem in root.findall(\".//{http://www.opengis.net/wms}Dimension\"):\n", " if 'name' in elem.attrib and elem.attrib['name'] == 'time':\n", " obs_dates = elem.text.strip().split(',')\n", "else:\n", " print(\"ERROR:\", get_capabilities.status_code)" ] }, { "cell_type": "markdown", "metadata": { "id": "EawIwBtRSSz-" }, "source": [ "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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uI1mgrT0eU7P", "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Antarctic Map\n", "m = Map(\n", " center=(-90, 0),\n", " zoom=2,\n", " min_zoom=1,\n", " basemap=basemaps.Esri.AntarcticBasemap,\n", " crs=projections.EPSG3031.ESRIBasemap,\n", ")\n", "\n", "m.layout.width = '70%'\n", "\n", "class TimeWMSLayer(WMSLayer):\n", " time = Unicode('').tag(sync=True, o=True)\n", "\n", "wms = TimeWMSLayer(\n", " url=WMS_URL,\n", " layers=WMS_LAYER,\n", " attribution=\"EMODNET Data\",\n", " format='image/png',\n", " transparent=True,\n", " styles=\"default-scalar/x-Rainbow\",\n", " colorscalerange=(-1, 1),\n", " crs=EPSG3031,\n", " bounds = [(-90,-180), (0,180)]\n", ")\n", "\n", "m.add_layer(wms)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 638, "referenced_widgets": [ "3958ced97b8c43148ca71634e45e5529", "c8a89ea8b9ee4cbda03d6090d1abe4f6", "5824581465e345218a4c6d17db1521f0", "a3d833ac90504e12a7e80d63df3ee340", "86d1df8d6e3a495ead7c996db0cec853", "2254fc0f7a2248319b865db9d28d3200", "d1639a66b95941d1948f9b22e696c7d1", "a754a787cef6413e8d53aead410f3c08", "ad0663ca5c334be689fd401f27659ac1", "4136e37cb54d43e4ac237bb775a81152", "781ad5a589954cc297668510b2efb9a7", "e77003beac234d1b8c1627f875575f5f", "6f02e1c2bf0342d3af9a2541167e97a4", "e00969f80a8c4800bc61ee233759cff0", "508e22668882439abb56870c57977443", "32f353ff1fd54827b1103da4f80558d2", "64ad8ccbc87d42609a11d736f276ed12", "a82e3b6a38f04ed98e75d6a8a04ce8cd", "8acfbd15c8b94f7996c4e925d99fa932", "749c741466824120b473de9d210407a0", "fe9e1e23af96427b8db48499bc31b7b4", "4239f766514244a3a5f57a7ca6e8afd8", "652f8e6b35314e348e1fed857abc0a44", "f49e90f18f9a405ea273075e7abe0a5c", "cd489649521b4c55b2c357ea13df16a7", "1a2b84890a9f476f8f76da481e47483a", "486f9046e9b34dfa818e5d06374c58b3" ] }, "id": "tKbJfN25ktxs", "outputId": "73016ea9-88e8-4a66-f81e-904fef0f602b", "tags": [ "hide-input" ] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d1639a66b95941d1948f9b22e696c7d1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Play(value=336, interval=2000, max=336, min=1), IntSlider(value=1, continuous_up…" ] }, "metadata": { "application/vnd.jupyter.widget-view+json": { "colab": { "custom_widget_manager": { "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" } } } }, "output_type": "display_data" } ], "source": [ "\n", "summer_data = Output()\n", "winter_data = Output()\n", "\n", "# DateTime slider control\n", "def updating_values(value):\n", " selected_time = obs_dates[(value.new)-1]\n", " time_descr.value = datetime.strptime(selected_time, \"%Y-%m-%dT%H:%M:%S.%fZ\").strftime(\"%B %Y\")\n", " wms.time = selected_time\n", "\n", "play = Play(\n", " value=len(obs_dates),\n", " min=1,\n", " max=len(obs_dates),\n", " step=1,\n", " interval=2000,\n", " description='',\n", " disabled=False\n", ")\n", "slider = IntSlider(min=1, max=len(obs_dates), readout = False, continuous_update=False)\n", "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)\n", "\n", "jslink((play, 'value'), (slider, 'value'))\n", "\n", "slider.observe(updating_values, names='value')\n", "\n", "graph_area = Output()\n", "graph_area.layout.width = '80%'\n", "\n", "# Map click functions:\n", "# Show DataTable\n", "def show_dataTable(table, df):\n", "\n", " # Remove previous table\n", " table.clear_output()\n", "\n", " with table:\n", " display(df)\n", "\n", "# Show loading...\n", "def show_loading_animation():\n", " loading_html = \"\"\"\n", " \n", "
\n", " \"\"\"\n", " graph_area.clear_output()\n", " with graph_area:\n", " display(HTML(loading_html))\n", "\n", "# Making a data graph\n", "def generate_sealevel_anomalies_data_plot(latlon):\n", " show_loading_animation()\n", " lat = latlon[0]\n", " lon = latlon[1]\n", " url = ERDDAP_SLA_URL + TIME_RANGE + f'[({lat}):1:({lat})][({lon}):1:({lon})]'\n", "\n", " try:\n", " response = requests.get(url)\n", " response.raise_for_status()\n", " data = response.json()\n", "\n", " global summer_data\n", " global winter_data\n", "\n", " df_oct_to_mar = pd.DataFrame()\n", " df_apr_to_sep = pd.DataFrame()\n", "\n", " column_names = data['table']['columnNames']\n", " rows = data['table']['rows']\n", " df = pd.DataFrame(rows, columns=column_names)\n", "\n", " df['timestamp'] = pd.to_datetime(df['time'])\n", " df['month'] = df['timestamp'].dt.month\n", " df['year'] = df['timestamp'].dt.year\n", "\n", " # Separate data into seasons\n", " df_oct_to_mar = df[df['month'].isin([10, 11, 12, 1, 2, 3])].reset_index()\n", " df_apr_to_sep = df[df['month'].isin([4, 5, 6, 7, 8, 9])].reset_index()\n", "\n", " # Calculate mean sea level anomalies for each season\n", " mean_sla_oct_to_mar = df_oct_to_mar.groupby('year')['sla'].mean().reset_index()\n", " mean_sla_apr_to_sep = df_apr_to_sep.groupby('year')['sla'].mean().reset_index()\n", "\n", " # Display DataTables for both seasons\n", " show_dataTable(summer_data, mean_sla_oct_to_mar)\n", " show_dataTable(winter_data, mean_sla_apr_to_sep)\n", "\n", " # Ensure data exists before plotting\n", " if not mean_sla_oct_to_mar.empty and not mean_sla_apr_to_sep.empty:\n", " # set Font sizes\n", " plt.rcParams.update({\n", " 'font.size': 15\n", " })\n", " # Plot\n", " plt.figure(figsize=(15, 10))\n", "\n", " # Plotting Bars\n", " bar_width = 0.35\n", " x_oct_to_mar = np.arange(len(mean_sla_oct_to_mar))\n", " x_apr_to_sep = np.arange(len(mean_sla_apr_to_sep))\n", " plt.bar(x_oct_to_mar, mean_sla_oct_to_mar['sla'], width=bar_width, color='orange', label='Sea Level Anomalies (Austral summer)')\n", " 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)')\n", "\n", " # Trend Lines\n", " z_oct_to_mar = np.polyfit(x_oct_to_mar, mean_sla_oct_to_mar['sla'], 3)\n", " p_oct_to_mar = np.poly1d(z_oct_to_mar)\n", " plt.plot(x_oct_to_mar, p_oct_to_mar(x_oct_to_mar), color='red', linestyle='--', label='Trendline (Austral summer)')\n", "\n", " z_apr_to_sep = np.polyfit(x_apr_to_sep, mean_sla_apr_to_sep['sla'], 3)\n", " p_apr_to_sep = np.poly1d(z_apr_to_sep)\n", " plt.plot(x_apr_to_sep, p_apr_to_sep(x_apr_to_sep), color='blue', linestyle='--', label='Trendline (Austral winter)')\n", "\n", " plt.xlabel('Year')\n", " plt.ylabel('Anomalies (m)')\n", " plt.title(f\"Sea Level Anomalies in time (Season mean)\\n ({df['latitude'][0]} {df['longitude'][0]})\")\n", " plt.legend()\n", " plt.xticks(x_oct_to_mar + bar_width / 2, mean_sla_oct_to_mar['year'], rotation=45)\n", " plt.grid(True)\n", " plt.tight_layout()\n", "\n", " # Clear loading animation and show the plot\n", " graph_area.clear_output()\n", " plt.show()\n", "\n", " else:\n", " # No data available\n", " graph_area.clear_output()\n", " print('No Data Available...')\n", "\n", " except Exception as e:\n", " graph_area.clear_output()\n", " plt.close()\n", " print('No Data Available...')\n", "\n", "random_point = (-60.375, 44.375)\n", "marker = Marker(location=random_point)\n", "# Adding the marker on the map\n", "m.add_layer(marker)\n", "\n", "# Generate a new graph\n", "with graph_area:\n", " generate_sealevel_anomalies_data_plot(random_point)\n", "\n", "def onChangeLatlon(**kwargs):\n", " global graph_area\n", " global m\n", " latlon = kwargs.get('coordinates')\n", " if kwargs.get('type') == 'click':\n", "\n", " # removing old markers\n", " for layer in m.layers:\n", " if isinstance(layer, Marker):\n", " m.remove_layer(layer)\n", "\n", " marker = Marker(location=latlon)\n", " m.add_layer(marker)\n", "\n", " # Remove previous graph\n", " graph_area.clear_output()\n", "\n", " # Genera a new graph\n", " with graph_area:\n", " generate_sealevel_anomalies_data_plot(latlon)\n", "\n", "m.on_interaction(onChangeLatlon)\n", "\n", "VBox([HBox([time_descr]), VBox([m, graph_area])])" ] }, { "cell_type": "markdown", "metadata": { "id": "GnVXviQdUY_I" }, "source": [ "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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 927, "referenced_widgets": [ "02eb989caf1149669317c3fdd278d1d7", "67bf8f28609d450aab7e799e3a53fceb", "87f5301fb2094c0f921ff4762aeef28f", "90658d1355dc4f6a83dec6d185631ce7", "180ccb18077a40cabe97eebb02643bf9", "edb402d6a1e14e43a13e5a91082ca992" ] }, "id": "Oa23lvkL0Huf", "outputId": "6f90647e-3a2d-4b48-df8a-02c1b2d9632e", "tags": [ "hide-input" ] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "02eb989caf1149669317c3fdd278d1d7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Output(), Output()))" ] }, "metadata": { "application/vnd.jupyter.widget-view+json": { "colab": { "custom_widget_manager": { "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" } } } }, "output_type": "display_data" } ], "source": [ "display(HBox([summer_data, winter_data]))" ] }, { "cell_type": "markdown", "metadata": { "id": "xjbug4XSaVOI" }, "source": [ "The last code cell below will generate a map of the antarctic area with positions and data from tide gauge platforms:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 621, "referenced_widgets": [ "a2d0e0c040c947c0a3c01e418aa02462", "c0440ca018bb4b91bea596fd16912c5f", "1f7e22c0ddd54331b424b24c6bf78c34", "6db2025289de4f129b996ef6266eba4d", "72997c33e7c5494cbb7ce9359801f83d", "ab453026d01e4195ab63cd535fbbcb15", "9fa31b933e28450085aaee2647654816", "3db88a4869514bc6807ec655f3ad1036", "a5ffa77a5bb64234bf1e9a9628c6231d", "617e473ca73745f0aaa8125edbca51d9", "293806126e2740d2a66a66b3df9a4c58", "4f3d7dcb160d4a9a9bfde81a97c1ee29", "722e64a0db794919903820a39a8a210c", "f6c74a852c7040b48e2ab8f7c0b42b9a", "26dc899260df4ae88e8510f74d8e142a", "01a7fb42375148c38c085fea2eee5bae", "3cc3816d63214103b1a0c04e05fa508a", "ebc3528a03fb49e8a00c38880c3fd5a6", "4fd5108f2a574db3918606b9aac2dbda", "50c640c5410448f4b43d9c1a833d04cd", "916163e64b4442039652b6fdbaafa848", "25b463737c794d2bbf5211164eb5c3f0", "b2f5d29d2b4f4be2886528dbfbeb4d3a", "de1c81b298dd4dfc83a0ca30a71e2deb", "6e356a10d2ea4cd5b36edbf5be7d2f94", "9d02cac2544f4ce79e34ebc5b0b9553e", "fe5ee291327f4a93b690c9ed210a6929", "5e2bde6df55b4342b8a3f556d977fce2", "7014780469e04cc190ef88b0d011a26c", 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"custom_widget_manager": { "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" } } } }, "output_type": "display_data" } ], "source": [ "def format_date(date_str):\n", " # this function convers the datetime to the formatted date as YYYY-MM-DDThh:mm:ssZ\n", " date_time = datetime.strptime(date_str, '%Y-%m-%dT%H:%M:%SZ')\n", " # Adding time zone +1 h\n", " date_time = date_time.replace(tzinfo=timezone.utc).astimezone(timezone(timedelta(hours=1)))\n", " formatted_date = date_time.strftime('%d/%m/%Y %H:%M GMT%z')\n", " return formatted_date\n", "\n", "geoserver_filter = \"&cql_filter=(platformType IN ('TG')) AND (lastDateObservationDT>='1993-01-16T00:00:00.000Z')\"\n", "pl_url = GEOSERVER_SOOS_URL + geoserver_filter\n", "\n", "try:\n", " response = requests.get(pl_url)\n", " response.raise_for_status()\n", " data = response.json()\n", "\n", " m2 = Map(\n", " basemap=basemaps.Esri.AntarcticBasemap,\n", " crs=projections.EPSG3031.ESRIBasemap,\n", " center=(-90, 0),\n", " zoom=2,\n", " min_zoom=2,\n", " layout=Layout(height='600px'),\n", " close_on_escape_key=False\n", " )\n", "\n", " data_marker = data['features']\n", " for data in data_marker:\n", " position = (data['properties']['latitude'], data['properties']['longitude'])\n", "\n", " marker = Marker(location=position)\n", " EMODNET_URL = EMODNET_LINK + data['properties']['platformcode'] + '&integrator=' + data['properties']['source']\n", "\n", " html = HTML()\n", " infos = f'''Platform Code: {data['properties']['platformcode']}
\n", " Platform Name: {data['properties']['platformName']}
\n", " Lat: {data['properties']['latitude']} Lon: {data['properties']['longitude']}
\n", " Source: {data['properties']['source']}
\n", " DataOwner: {data['properties']['dataOwner']}
\n", " Last Observation: {format_date(data['properties']['lastDateObservation'])}
\n", " View data'''\n", " html.value = infos\n", "\n", " popup = Popup(\n", " location=position,\n", " child=html,\n", " close_button=True,\n", " auto_close=False,\n", " )\n", " marker.popup = popup\n", "\n", " m2.add_layer(marker)\n", "except Exception as e:\n", " print('No Data Available...')\n", "\n", "m2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Additional resources**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Python libraries that have been used in this notebook are:\n", "- [ipyleaflet](https://ipyleaflet.readthedocs.io/en/latest/)\n", "- [ipywidgets](https://ipywidgets.readthedocs.io/en/stable/)\n", "- [requests](https://requests.readthedocs.io/en/latest/)\n", "- [datetime](https://docs.python.org/3/library/datetime.html)\n", "- [numpy](https://numpy.org/)\n", "- [pandas](https://pandas.pydata.org/)\n", "- [traitlets](https://traitlets.readthedocs.io/en/stable/)\n", "- [matplotlib](https://matplotlib.org/)\n", "- 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