{ "cells": [ { "cell_type": "markdown", "id": "ddeeea4b-753f-4dc0-8427-5ed695d5790b", "metadata": {}, "source": [ "# Some very simple GMIP3 data example use cases" ] }, { "cell_type": "markdown", "id": "f62b9987-26b9-4c59-b0d9-0286dc474fd1", "metadata": {}, "source": [ "- [1. I want to get the glacier model simulations from an individual glacier model for a specific period and compare it with my study/model](#1.-I-want-to-get-the-glacier-model-simulations-from-the-individual-glacier-models-and-compare-it-with-my-study-...)\n", "- [2. I want to analyse the relationship of steady-state mass changes of a glacier model to global warming for a specific region](#2.-I-want-to-analyse-the-relationship-of-steady-state-mass-changes-of-a-glacier-model-to-global-warming-for-a-specific-region) \n", "- [3. I want to extract LOWESS fitted steady-state glacier mass estimates](#3.-I-want-to-extract-LOWESS-fitted-steady-state-glacier-mass-estimates)\n", "- [4. Ask us to get more use case examples! -> contact](mailto:lilian.schuster@uibk.ac.at,harry.zekollari@vub.be)\n", " \n", "--> all code for the first GlacierMIP3 manuscript analysis and creation of the figures is available at: [https://github.com/GlacierMIP/GlacierMIP3](https://github.com/GlacierMIP/GlacierMIP3)" ] }, { "cell_type": "code", "execution_count": 1, "id": "6fcc4a11-541c-4a10-a782-79a07caf3e44", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "id": "20d86f5c-90ca-4c41-b0fe-6ed12a9c3bc8", "metadata": {}, "source": [ "\n", "## 1. I want to get the glacier model simulations from the individual glacier models and compare it with my study ...\n", " " ] }, { "cell_type": "code", "execution_count": 2, "id": "891a039a-9838-4d87-b789-cb4afbcb3148", "metadata": {}, "outputs": [], "source": [ "ds = xr.open_dataset('GMIP3_reg_glacier_model_data/glacierMIP3_Feb12_2024_models_all_rgi_regions_sum.nc')" ] }, { "cell_type": "code", "execution_count": 3, "id": "dc0c6dd3-a17d-4ba2-97ae-50933c80c9ba", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:          (model_author: 10, simulation_year: 5001, gcm: 5,\n",
       "                      rgi_reg: 19, period_scenario: 16)\n",
       "Coordinates:\n",
       "  * model_author     (model_author) <U14 'CISM2' 'GO' ... 'GloGEMflow3D'\n",
       "  * simulation_year  (simulation_year) int16 0 1 2 3 4 ... 4997 4998 4999 5000\n",
       "  * gcm              (gcm) <U13 'gfdl-esm4' 'ipsl-cm6a-lr' ... 'ukesm1-0-ll'\n",
       "  * rgi_reg          (rgi_reg) <U2 '01' '02' '03' '04' ... '16' '17' '18' '19'\n",
       "  * period_scenario  (period_scenario) <U16 '1851-1870_hist' ... '2081-2100_s...\n",
       "Data variables:\n",
       "    volume_m3        (model_author, gcm, rgi_reg, simulation_year, period_scenario) float32 ...\n",
       "    area_m2          (model_author, gcm, rgi_reg, simulation_year, period_scenario) float32 ...
" ], "text/plain": [ "\n", "Dimensions: (model_author: 10, simulation_year: 5001, gcm: 5,\n", " rgi_reg: 19, period_scenario: 16)\n", "Coordinates:\n", " * model_author (model_author) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'model_author' (model_author: 10)>\n",
       "array(['CISM2', 'GO', 'PyGEM-OGGM_v13', 'GloGEMflow', 'Kraaijenbrink', 'GLIMB',\n",
       "       'OGGM_v153', 'OGGM_v16', 'OGGM-VAS', 'GloGEMflow3D'], dtype='<U14')\n",
       "Coordinates:\n",
       "  * model_author  (model_author) <U14 'CISM2' 'GO' ... 'OGGM-VAS' 'GloGEMflow3D'
" ], "text/plain": [ "\n", "array(['CISM2', 'GO', 'PyGEM-OGGM_v13', 'GloGEMflow', 'Kraaijenbrink', 'GLIMB',\n", " 'OGGM_v153', 'OGGM_v16', 'OGGM-VAS', 'GloGEMflow3D'], dtype=' [see all changes](https://docs.oggm.org/en/stable/whats-new.html#v1-6-0-march-10-2023))\n", "- `OGGM-VAS` (i.e. an experimental OGGM with only volume-area scaling: [https://github.com/OGGM/oggm-vas](https://github.com/OGGM/oggm-vas))" ] }, { "cell_type": "code", "execution_count": 5, "id": "4be37924-87d1-4cb2-8975-27f01d5776b1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'gcm' (gcm: 5)>\n",
       "array(['gfdl-esm4', 'ipsl-cm6a-lr', 'mpi-esm1-2-hr', 'mri-esm2-0',\n",
       "       'ukesm1-0-ll'], dtype='<U13')\n",
       "Coordinates:\n",
       "  * gcm      (gcm) <U13 'gfdl-esm4' 'ipsl-cm6a-lr' ... 'ukesm1-0-ll'
" ], "text/plain": [ "\n", "array(['gfdl-esm4', 'ipsl-cm6a-lr', 'mpi-esm1-2-hr', 'mri-esm2-0',\n", " 'ukesm1-0-ll'], dtype='\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'period_scenario' (period_scenario: 16)>\n",
       "array(['1851-1870_hist', '1901-1920_hist', '1951-1970_hist', '1995-2014_hist',\n",
       "       '2021-2040_ssp126', '2021-2040_ssp370', '2021-2040_ssp585',\n",
       "       '2041-2060_ssp126', '2041-2060_ssp370', '2041-2060_ssp585',\n",
       "       '2061-2080_ssp126', '2061-2080_ssp370', '2061-2080_ssp585',\n",
       "       '2081-2100_ssp126', '2081-2100_ssp370', '2081-2100_ssp585'],\n",
       "      dtype='<U16')\n",
       "Coordinates:\n",
       "  * period_scenario  (period_scenario) <U16 '1851-1870_hist' ... '2081-2100_s...
" ], "text/plain": [ "\n", "array(['1851-1870_hist', '1901-1920_hist', '1951-1970_hist', '1995-2014_hist',\n", " '2021-2040_ssp126', '2021-2040_ssp370', '2021-2040_ssp585',\n", " '2041-2060_ssp126', '2041-2060_ssp370', '2041-2060_ssp585',\n", " '2061-2080_ssp126', '2061-2080_ssp370', '2061-2080_ssp585',\n", " '2081-2100_ssp126', '2081-2100_ssp370', '2081-2100_ssp585'],\n", " dtype='" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for gcm in ds_sel.gcm:\n", " plt.plot(ds_sel.simulation_year, ds_sel.sel(gcm=gcm).volume_m3, '-', label=gcm.values)\n", "plt.ylabel('Regional glacier volume (m3)')\n", "plt.xlabel('Simulation year')\n", "plt.legend(loc='upper right');" ] }, { "cell_type": "code", "execution_count": null, "id": "f4a8785d-3f86-4a23-ba6b-1d66259686a7", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "b68ee48a-45b7-4e3a-8e55-e136e2b0d4cf", "metadata": {}, "source": [ "# 2. I want to analyse the relationship of steady-state mass changes of a glacier model to global warming for a specific region" ] }, { "cell_type": "markdown", "id": "061f7246-edbc-4f30-a15c-142b4baff74d", "metadata": {}, "source": [ "we can also compare all experiments of the Kraaijenbrink model by looking into the warming of these experiments:" ] }, { "cell_type": "code", "execution_count": 9, "id": "51de0d65-17e9-4dd7-8ddc-4dc4fbd5c16d", "metadata": {}, "outputs": [], "source": [ "# let's first get the data that links experiments to warming\n", "pd_temp = pd.read_csv('climate_input_data/temp_ch_ipcc_ar6_isimip3b.csv', index_col=[0])" ] }, { "cell_type": "code", "execution_count": 10, "id": "7721dc1f-3ad2-4bab-aed6-60d195e92ed9", "metadata": {}, "outputs": [], "source": [ "# we need this to later assign the coordinates to the glacier volume change dataset\n", "pd_temp = pd_temp.set_index(['gcm', 'period_scenario'])" ] }, { "cell_type": "code", "execution_count": 11, "id": "bbbd03cf-4ba2-4b28-aeb8-0e160fd6d38d", "metadata": {}, "outputs": [], "source": [ "# create a \"experiments\" variable out of gcm and period_scenario\n", "ds_stack = ds.stack(experiments=['gcm','period_scenario'])\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "0d9b99ca-b5ca-45b6-a93b-3d733a843e80", "metadata": {}, "outputs": [], "source": [ "# let's assign the coordinates by making sure that we use the correct experiment order\n", "ds_stack = ds_stack.assign_coords(temp_ch_ipcc = ('experiments',\n", " pd_temp.loc[ds_stack.experiments,'temp_ch_ipcc']))" ] }, { "cell_type": "code", "execution_count": 13, "id": "d7ffd0b2-05d4-402a-be13-a2889b6fb890", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'experiments' (experiments: 80)>\n",
       "array([('gfdl-esm4', '1851-1870_hist'), ('gfdl-esm4', '1901-1920_hist'),\n",
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       "       ('gfdl-esm4', '2021-2040_ssp126'), ('gfdl-esm4', '2021-2040_ssp370'),\n",
       "       ('gfdl-esm4', '2021-2040_ssp585'), ('gfdl-esm4', '2041-2060_ssp126'),\n",
       "       ('gfdl-esm4', '2041-2060_ssp370'), ('gfdl-esm4', '2041-2060_ssp585'),\n",
       "       ('gfdl-esm4', '2061-2080_ssp126'), ('gfdl-esm4', '2061-2080_ssp370'),\n",
       "       ('gfdl-esm4', '2061-2080_ssp585'), ('gfdl-esm4', '2081-2100_ssp126'),\n",
       "       ('gfdl-esm4', '2081-2100_ssp370'), ('gfdl-esm4', '2081-2100_ssp585'),\n",
       "       ('ipsl-cm6a-lr', '1851-1870_hist'), ('ipsl-cm6a-lr', '1901-1920_hist'),\n",
       "       ('ipsl-cm6a-lr', '1951-1970_hist'), ('ipsl-cm6a-lr', '1995-2014_hist'),\n",
       "       ('ipsl-cm6a-lr', '2021-2040_ssp126'),\n",
       "       ('ipsl-cm6a-lr', '2021-2040_ssp370'),\n",
       "       ('ipsl-cm6a-lr', '2021-2040_ssp585'),\n",
       "       ('ipsl-cm6a-lr', '2041-2060_ssp126'),\n",
       "       ('ipsl-cm6a-lr', '2041-2060_ssp370'),\n",
       "       ('ipsl-cm6a-lr', '2041-2060_ssp585'),\n",
       "       ('ipsl-cm6a-lr', '2061-2080_ssp126'),\n",
       "       ('ipsl-cm6a-lr', '2061-2080_ssp370'),\n",
       "       ('ipsl-cm6a-lr', '2061-2080_ssp585'),\n",
       "       ('ipsl-cm6a-lr', '2081-2100_ssp126'),\n",
       "       ('ipsl-cm6a-lr', '2081-2100_ssp370'),\n",
       "       ('ipsl-cm6a-lr', '2081-2100_ssp585'),\n",
       "       ('mpi-esm1-2-hr', '1851-1870_hist'),\n",
       "       ('mpi-esm1-2-hr', '1901-1920_hist'),\n",
       "       ('mpi-esm1-2-hr', '1951-1970_hist'),\n",
       "       ('mpi-esm1-2-hr', '1995-2014_hist'),\n",
       "       ('mpi-esm1-2-hr', '2021-2040_ssp126'),\n",
       "       ('mpi-esm1-2-hr', '2021-2040_ssp370'),\n",
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       "       ('mpi-esm1-2-hr', '2041-2060_ssp126'),\n",
       "       ('mpi-esm1-2-hr', '2041-2060_ssp370'),\n",
       "       ('mpi-esm1-2-hr', '2041-2060_ssp585'),\n",
       "       ('mpi-esm1-2-hr', '2061-2080_ssp126'),\n",
       "       ('mpi-esm1-2-hr', '2061-2080_ssp370'),\n",
       "       ('mpi-esm1-2-hr', '2061-2080_ssp585'),\n",
       "       ('mpi-esm1-2-hr', '2081-2100_ssp126'),\n",
       "       ('mpi-esm1-2-hr', '2081-2100_ssp370'),\n",
       "       ('mpi-esm1-2-hr', '2081-2100_ssp585'), ('mri-esm2-0', '1851-1870_hist'),\n",
       "       ('mri-esm2-0', '1901-1920_hist'), ('mri-esm2-0', '1951-1970_hist'),\n",
       "       ('mri-esm2-0', '1995-2014_hist'), ('mri-esm2-0', '2021-2040_ssp126'),\n",
       "       ('mri-esm2-0', '2021-2040_ssp370'), ('mri-esm2-0', '2021-2040_ssp585'),\n",
       "       ('mri-esm2-0', '2041-2060_ssp126'), ('mri-esm2-0', '2041-2060_ssp370'),\n",
       "       ('mri-esm2-0', '2041-2060_ssp585'), ('mri-esm2-0', '2061-2080_ssp126'),\n",
       "       ('mri-esm2-0', '2061-2080_ssp370'), ('mri-esm2-0', '2061-2080_ssp585'),\n",
       "       ('mri-esm2-0', '2081-2100_ssp126'), ('mri-esm2-0', '2081-2100_ssp370'),\n",
       "       ('mri-esm2-0', '2081-2100_ssp585'), ('ukesm1-0-ll', '1851-1870_hist'),\n",
       "       ('ukesm1-0-ll', '1901-1920_hist'), ('ukesm1-0-ll', '1951-1970_hist'),\n",
       "       ('ukesm1-0-ll', '1995-2014_hist'), ('ukesm1-0-ll', '2021-2040_ssp126'),\n",
       "       ('ukesm1-0-ll', '2021-2040_ssp370'),\n",
       "       ('ukesm1-0-ll', '2021-2040_ssp585'),\n",
       "       ('ukesm1-0-ll', '2041-2060_ssp126'),\n",
       "       ('ukesm1-0-ll', '2041-2060_ssp370'),\n",
       "       ('ukesm1-0-ll', '2041-2060_ssp585'),\n",
       "       ('ukesm1-0-ll', '2061-2080_ssp126'),\n",
       "       ('ukesm1-0-ll', '2061-2080_ssp370'),\n",
       "       ('ukesm1-0-ll', '2061-2080_ssp585'),\n",
       "       ('ukesm1-0-ll', '2081-2100_ssp126'),\n",
       "       ('ukesm1-0-ll', '2081-2100_ssp370'),\n",
       "       ('ukesm1-0-ll', '2081-2100_ssp585')], dtype=object)\n",
       "Coordinates:\n",
       "  * experiments      (experiments) object MultiIndex\n",
       "  * gcm              (experiments) <U13 'gfdl-esm4' ... 'ukesm1-0-ll'\n",
       "  * period_scenario  (experiments) <U16 '1851-1870_hist' ... '2081-2100_ssp585'\n",
       "    temp_ch_ipcc     (experiments) float64 0.2314 0.4783 0.3923 ... 5.84 6.884
" ], "text/plain": [ "\n", "array([('gfdl-esm4', '1851-1870_hist'), ('gfdl-esm4', '1901-1920_hist'),\n", " ('gfdl-esm4', '1951-1970_hist'), ('gfdl-esm4', '1995-2014_hist'),\n", " ('gfdl-esm4', '2021-2040_ssp126'), ('gfdl-esm4', '2021-2040_ssp370'),\n", " ('gfdl-esm4', '2021-2040_ssp585'), ('gfdl-esm4', '2041-2060_ssp126'),\n", " ('gfdl-esm4', '2041-2060_ssp370'), ('gfdl-esm4', '2041-2060_ssp585'),\n", " ('gfdl-esm4', '2061-2080_ssp126'), ('gfdl-esm4', '2061-2080_ssp370'),\n", " ('gfdl-esm4', '2061-2080_ssp585'), ('gfdl-esm4', '2081-2100_ssp126'),\n", " ('gfdl-esm4', '2081-2100_ssp370'), ('gfdl-esm4', '2081-2100_ssp585'),\n", " ('ipsl-cm6a-lr', '1851-1870_hist'), ('ipsl-cm6a-lr', '1901-1920_hist'),\n", " ('ipsl-cm6a-lr', '1951-1970_hist'), ('ipsl-cm6a-lr', '1995-2014_hist'),\n", " ('ipsl-cm6a-lr', '2021-2040_ssp126'),\n", " ('ipsl-cm6a-lr', '2021-2040_ssp370'),\n", " ('ipsl-cm6a-lr', '2021-2040_ssp585'),\n", " ('ipsl-cm6a-lr', '2041-2060_ssp126'),\n", " ('ipsl-cm6a-lr', '2041-2060_ssp370'),\n", " ('ipsl-cm6a-lr', '2041-2060_ssp585'),\n", " ('ipsl-cm6a-lr', '2061-2080_ssp126'),\n", " ('ipsl-cm6a-lr', '2061-2080_ssp370'),\n", " ('ipsl-cm6a-lr', '2061-2080_ssp585'),\n", " ('ipsl-cm6a-lr', '2081-2100_ssp126'),\n", " ('ipsl-cm6a-lr', '2081-2100_ssp370'),\n", " ('ipsl-cm6a-lr', '2081-2100_ssp585'),\n", " ('mpi-esm1-2-hr', '1851-1870_hist'),\n", " ('mpi-esm1-2-hr', '1901-1920_hist'),\n", " ('mpi-esm1-2-hr', '1951-1970_hist'),\n", " ('mpi-esm1-2-hr', '1995-2014_hist'),\n", " ('mpi-esm1-2-hr', '2021-2040_ssp126'),\n", " ('mpi-esm1-2-hr', '2021-2040_ssp370'),\n", " ('mpi-esm1-2-hr', '2021-2040_ssp585'),\n", " ('mpi-esm1-2-hr', '2041-2060_ssp126'),\n", " ('mpi-esm1-2-hr', '2041-2060_ssp370'),\n", " ('mpi-esm1-2-hr', '2041-2060_ssp585'),\n", " ('mpi-esm1-2-hr', '2061-2080_ssp126'),\n", " ('mpi-esm1-2-hr', '2061-2080_ssp370'),\n", " ('mpi-esm1-2-hr', '2061-2080_ssp585'),\n", " ('mpi-esm1-2-hr', '2081-2100_ssp126'),\n", " ('mpi-esm1-2-hr', '2081-2100_ssp370'),\n", " ('mpi-esm1-2-hr', '2081-2100_ssp585'), ('mri-esm2-0', '1851-1870_hist'),\n", " ('mri-esm2-0', '1901-1920_hist'), ('mri-esm2-0', '1951-1970_hist'),\n", " ('mri-esm2-0', '1995-2014_hist'), ('mri-esm2-0', '2021-2040_ssp126'),\n", " ('mri-esm2-0', '2021-2040_ssp370'), ('mri-esm2-0', '2021-2040_ssp585'),\n", " ('mri-esm2-0', '2041-2060_ssp126'), ('mri-esm2-0', '2041-2060_ssp370'),\n", " ('mri-esm2-0', '2041-2060_ssp585'), ('mri-esm2-0', '2061-2080_ssp126'),\n", " ('mri-esm2-0', '2061-2080_ssp370'), ('mri-esm2-0', '2061-2080_ssp585'),\n", " ('mri-esm2-0', '2081-2100_ssp126'), ('mri-esm2-0', '2081-2100_ssp370'),\n", " ('mri-esm2-0', '2081-2100_ssp585'), ('ukesm1-0-ll', '1851-1870_hist'),\n", " ('ukesm1-0-ll', '1901-1920_hist'), ('ukesm1-0-ll', '1951-1970_hist'),\n", " ('ukesm1-0-ll', '1995-2014_hist'), ('ukesm1-0-ll', '2021-2040_ssp126'),\n", " ('ukesm1-0-ll', '2021-2040_ssp370'),\n", " ('ukesm1-0-ll', '2021-2040_ssp585'),\n", " ('ukesm1-0-ll', '2041-2060_ssp126'),\n", " ('ukesm1-0-ll', '2041-2060_ssp370'),\n", " ('ukesm1-0-ll', '2041-2060_ssp585'),\n", " ('ukesm1-0-ll', '2061-2080_ssp126'),\n", " ('ukesm1-0-ll', '2061-2080_ssp370'),\n", " ('ukesm1-0-ll', '2061-2080_ssp585'),\n", " ('ukesm1-0-ll', '2081-2100_ssp126'),\n", " ('ukesm1-0-ll', '2081-2100_ssp370'),\n", " ('ukesm1-0-ll', '2081-2100_ssp585')], dtype=object)\n", "Coordinates:\n", " * experiments (experiments) object MultiIndex\n", " * gcm (experiments) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:          (simulation_year: 5001, experiments: 80)\n",
       "Coordinates:\n",
       "    model_author     <U14 'Kraaijenbrink'\n",
       "  * simulation_year  (simulation_year) int16 0 1 2 3 4 ... 4997 4998 4999 5000\n",
       "    rgi_reg          <U2 '13'\n",
       "  * experiments      (experiments) object MultiIndex\n",
       "  * gcm              (experiments) <U13 'gfdl-esm4' ... 'ukesm1-0-ll'\n",
       "  * period_scenario  (experiments) <U16 '1851-1870_hist' ... '2081-2100_ssp585'\n",
       "    temp_ch_ipcc     (experiments) float64 0.2314 0.4783 0.3923 ... 5.84 6.884\n",
       "Data variables:\n",
       "    volume_m3        (simulation_year, experiments) float32 3.271e+12 ... nan\n",
       "    area_m2          (simulation_year, experiments) float32 4.93e+10 ... nan
" ], "text/plain": [ "\n", "Dimensions: (simulation_year: 5001, experiments: 80)\n", "Coordinates:\n", " model_author " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(ds_stack_sel_steady_state.temp_ch_ipcc, ds_stack_sel_steady_state.volume_m3, 'o')\n", "plt.ylabel('Regional glacier volume (m3)')\n", "plt.xlabel('Global mean temperature above pre-industrial (°C)')" ] }, { "cell_type": "markdown", "id": "8fea9d2c-f22d-4b8c-8157-2fe0727c318f", "metadata": {}, "source": [ "- IMPORTANT: we used now the raw data without any postprocessing\n" ] }, { "cell_type": "markdown", "id": "f637a8cd-12d0-4103-ab5a-0503b58858f6", "metadata": {}, "source": [ "The actual visualisations and analysis in the manuscript is done with this postprocessed variant of the dataset: `GMIP3_reg_glacier_model_data/all_shifted_glacierMIP3_Feb12_2024_models_all_rgi_regions_sum_scaled_extended_repeat_last_101yrs_via_5yravg.nc` where the data got scaled to always match at the start Farinotti et al. 2021, then extended to always go until the year 5000 and then shifted to start at a timepoint that is near to the 2020 regional glacier mass states (see methods)\n", "\n", "Let's look instead into the shifted data:" ] }, { "cell_type": "code", "execution_count": 19, "id": "4c890b69-245a-4f3c-b124-18d9937a5be4", "metadata": {}, "outputs": [], "source": [ "ds_shifted = xr.open_dataset('GMIP3_reg_glacier_model_data/all_shifted_glacierMIP3_Feb12_2024_models_all_rgi_regions_sum_scaled_extended_repeat_last_101yrs_via_5yravg.nc')" ] }, { "cell_type": "code", "execution_count": 20, "id": "6d69129d-cf71-4b7e-9c47-61a6481acc1e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:                           (model_author: 8, year_after_2020: 5051,\n",
       "                                       period_scenario: 16, gcm: 5, rgi_reg: 19)\n",
       "Coordinates:\n",
       "  * model_author                      (model_author) <U14 'CISM2' ... 'PyGEM-...\n",
       "  * year_after_2020                   (year_after_2020) float32 -50.0 ... 5e+03\n",
       "  * period_scenario                   (period_scenario) <U16 '1851-1870_hist'...\n",
       "  * gcm                               (gcm) <U13 'gfdl-esm4' ... 'ukesm1-0-ll'\n",
       "  * rgi_reg                           (rgi_reg) <U2 '01' '02' '03' ... '18' '19'\n",
       "Data variables:\n",
       "    simulation_year                   (model_author, rgi_reg, year_after_2020, period_scenario, gcm) float64 ...\n",
       "    temp_ch_ipcc                      (model_author, rgi_reg, year_after_2020, period_scenario, gcm) float64 ...\n",
       "    yrs_w_most_similar_state_to_2020  (model_author, rgi_reg, year_after_2020, period_scenario, gcm) float32 ...\n",
       "    volume_rel_2020_%                 (model_author, rgi_reg, year_after_2020, period_scenario, gcm) float64 ...\n",
       "    volume_m3                         (model_author, rgi_reg, year_after_2020, period_scenario, gcm) float64 ...
" ], "text/plain": [ "\n", "Dimensions: (model_author: 8, year_after_2020: 5051,\n", " period_scenario: 16, gcm: 5, rgi_reg: 19)\n", "Coordinates:\n", " * model_author (model_author) " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# we repeat the same as above:\n", "ds_stack_shifted = ds_shifted.stack(experiments=['gcm','period_scenario'])\n", "ds_stack_shifted = ds_stack_shifted.assign_coords(temp_ch_ipcc = ('experiments',\n", " pd_temp.loc[ds_stack.experiments,'temp_ch_ipcc']))\n", "ds_stack_sel_shifted = ds_stack_shifted.sel(model_author=model_author).sel(rgi_reg=rgi_reg)\n", "# but here we use now the `year_after_2020` coordinate\n", "ds_stack_sel_steady_state_shifted = ds_stack_sel_shifted.sel(year_after_2020=slice(1900,2000)).mean(dim='year_after_2020')\n", "\n", "\n", "plt.plot(ds_stack_sel_steady_state_shifted.temp_ch_ipcc, ds_stack_sel_steady_state_shifted['volume_rel_2020_%'], 'o')\n", "plt.ylabel('Regional glacier volume (% rel. to 2020)')\n", "plt.xlabel('Global mean temperature above pre-industrial (°C)');" ] }, { "cell_type": "code", "execution_count": null, "id": "021a5461-74fa-432c-8cc2-cef84065c347", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "b9bfe18e-7ee5-4864-bf06-a803cd134f0e", "metadata": {}, "source": [ "## 3. I want to extract LOWESS fitted steady-state glacier mass estimates" ] }, { "cell_type": "code", "execution_count": 62, "id": "6ae8024d-a9e4-48f9-bf4d-242e64d7f10f", "metadata": {}, "outputs": [], "source": [ "# lets load the most used lowess fit variant: fit uses relative remaining mass at \n", "# steady-state (101-year rolling average of the last simulation years)\n", "# together with global mean warming above preindustrial\n", "pd_lowess = pd.read_csv('lowess_fit_rel_2020_101yr_avg_steady_state_Feb12_2024.csv', index_col=[0])" ] }, { "cell_type": "code", "execution_count": 64, "id": "cbf54b66-9f77-498b-a504-d06a68339a54", "metadata": {}, "outputs": [], "source": [ "pd_lowess_reg =pd_lowess.loc[pd_lowess.region==rgi_reg]" ] }, { "cell_type": "markdown", "id": "99c65e04-aa37-4852-b8b6-e67da7163156", "metadata": {}, "source": [ "- we can for example extract values not available from the Extended Data Table 1:" ] }, { "cell_type": "code", "execution_count": 71, "id": "13f546ac-1dc0-4ff3-a998-df7f5c1677a6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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temp_ch0.050.170.250.50.750.830.95fracregionyearitN
17262.218.44835522.15841324.51338735.1593459.67783363.69942369.692570.211350002500
\n", "
" ], "text/plain": [ " temp_ch 0.05 0.17 0.25 0.5 0.75 \\\n", "1726 2.2 18.448355 22.158413 24.513387 35.15934 59.677833 \n", "\n", " 0.83 0.95 frac region year it N \n", "1726 63.699423 69.69257 0.21 13 5000 2 500 " ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# regional lowess fit estimates at 2.2°C for that region\n", "pd_lowess_reg.loc[pd_lowess_reg.temp_ch==2.2]" ] }, { "cell_type": "markdown", "id": "537d5d99-1bd6-4889-826a-b0c9297d7e7e", "metadata": {}, "source": [ "- or show the entire fit by also showing another distribution range of the fitted model spread:" ] }, { "cell_type": "code", "execution_count": 81, "id": "521b4f04-6ed4-47a3-bf5e-be5f9e923347", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lineplot(data=pd_lowess_reg, x='temp_ch', y='0.5', label='median', color='C0')\n", "plt.fill_between(pd_lowess_reg.temp_ch, pd_lowess_reg['0.17'], pd_lowess_reg['0.83'], alpha=0.4,\n", " label = '17th to 83rd percentiles (likely range)', color='C0')\n", "plt.fill_between(pd_lowess_reg.temp_ch, pd_lowess_reg['0.05'], pd_lowess_reg['0.95'], alpha=0.2,\n", " label = '5th to 95th percentile range', color='C0')\n", "\n", "# note that regional relative glacier volume and mass are the same\n", "plt.ylabel('Regional glacier mass at steady-state (% rel. to 2020)')\n", "plt.xlabel('Global mean temperature above pre-industrial (°C)')\n", "plt.legend(title='LOWESS fit');" ] }, { "cell_type": "code", "execution_count": null, "id": "9ffef7f3-f56d-4d79-9f4d-a456c5cc731a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:oggm_gmip3_working]", "language": "python", "name": "conda-env-oggm_gmip3_working-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 }