{ "cells": [ { "cell_type": "markdown", "id": "d4185627-8c90-40df-be43-405dd22a6cf0", "metadata": {}, "source": [ "# Workflow to analyse the raw oggm output per glacier projection files " ] }, { "cell_type": "code", "execution_count": 1, "id": "89e71823-f643-4620-8b81-2aa2864435af", "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import pandas as pd\n", "import progressbar\n", "import numpy as np\n", "from oggm.utils import mkdir\n", "import matplotlib.pyplot as plt\n", "import json\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "id": "71b8a117-a7e4-43fc-91dc-8cce26277771", "metadata": {}, "outputs": [], "source": [ "# raw output files -> change that to your local path\n", "dirpath = '/home/www/oggm/oggm-standard-projections/oggm_v16/2023.3'" ] }, { "cell_type": "code", "execution_count": 3, "id": "0e635d01-50fb-43fa-9108-e28744994798", "metadata": {}, "outputs": [], "source": [ "### get the GCMs and SSPs until 2100 (those \n", "# can download it like that, or do it manually ... \n", "import oggm\n", "fp_metadata = oggm.utils.file_downloader('/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.1/common_running_2100/metadata.csv')\n", "pd_meta_2100 = pd.read_csv(fp_metadata, index_col=0)\n", "pd_meta_2100 = pd_meta_2100.loc[pd_meta_2100.end_year == 2100]\n", "pd_meta_2100['gcm_scenario'] = pd_meta_2100.gcm_original + '_' + pd_meta_2100.scenario # we use here gcm_original, as some GCMs have upper and lower cases \n", "pd_meta_2100 = pd_meta_2100.loc[(pd_meta_2100.cmip == 'CMIP6')]\n", "gcms_scenarios = pd_meta_2100['gcm_scenario'].unique()\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "61b7b431-bcc7-48ce-becf-8c6857316f98", "metadata": {}, "outputs": [], "source": [ "RGI_Shylok = pd.read_csv('RGI_Shylok.csv')\n", "RGI_Upper_Indus = pd.read_csv(\"RGI_Upper_Indus.csv\")\n", "\n", "list_RGI_Shylok = list(RGI_Shylok[\"RGIId\"])\n", "list_RGI_Upper_Indus = list(RGI_Upper_Indus[\"RGIId\"])\n", "\n", "#list_RGI_Upper_Indus" ] }, { "cell_type": "code", "execution_count": 5, "id": "0c178b78-dd40-442f-a4ce-dbf1e102ef5e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GFDL-ESM4_ssp370\n", "GFDL-ESM4_ssp119\n", "EC-Earth3-Veg_ssp245\n", "CESM2-WACCM_ssp534-over\n", "EC-Earth3_ssp126\n", "NorESM2-MM_ssp126\n", "FGOALS-f3-L_ssp245\n", "ACCESS-CM2_ssp585\n", "CESM2_ssp126\n", "CanESM5_ssp534-over\n", "IPSL-CM6A-LR_ssp585\n", "BCC-CSM2-MR_ssp245\n", "MRI-ESM2-0_ssp534-over\n", "BCC-CSM2-MR_ssp370\n", "CAMS-CSM1-0_ssp585\n", "FGOALS-f3-L_ssp370\n", "CESM2-WACCM_ssp585\n", "MPI-ESM1-2-HR_ssp585\n", "EC-Earth3-Veg_ssp370\n", "EC-Earth3-Veg_ssp119\n", "INM-CM5-0_ssp126\n", "INM-CM4-8_ssp126\n", "CanESM5_ssp126\n", "GFDL-ESM4_ssp245\n", "MRI-ESM2-0_ssp370\n", "MRI-ESM2-0_ssp119\n", "IPSL-CM6A-LR_ssp126\n", "ACCESS-CM2_ssp126\n", "CESM2_ssp585\n", "NorESM2-MM_ssp585\n", "EC-Earth3_ssp585\n", "MPI-ESM1-2-HR_ssp126\n", "CESM2-WACCM_ssp126\n", "CAMS-CSM1-0_ssp126\n", "CMCC-CM2-SR5_ssp245\n", "CanESM5_ssp585\n", "INM-CM5-0_ssp585\n", "INM-CM4-8_ssp585\n", "MRI-ESM2-0_ssp245\n", "EC-Earth3-Veg_ssp585\n", "MPI-ESM1-2-HR_ssp370\n", "CESM2-WACCM_ssp370\n", "CAMS-CSM1-0_ssp370\n", "FGOALS-f3-L_ssp585\n", "ACCESS-ESM1-5_ssp585\n", "BCC-CSM2-MR_ssp585\n", "CAMS-CSM1-0_ssp119\n", "IPSL-CM6A-LR_ssp534-over\n", "CAMS-CSM1-0_ssp245\n", "CESM2-WACCM_ssp245\n", "MPI-ESM1-2-HR_ssp245\n", "MRI-ESM2-0_ssp126\n", "MRI-ESM2-0_ssp460\n", "GFDL-ESM4_ssp585\n", "EC-Earth3-Veg_ssp126\n", "INM-CM5-0_ssp370\n", "INM-CM4-8_ssp370\n", "BCC-CSM2-MR_ssp126\n", "ACCESS-ESM1-5_ssp126\n", "CESM2_ssp245\n", "FGOALS-f3-L_ssp126\n", "TaiESM1_ssp585\n", "NorESM2-MM_ssp245\n", "EC-Earth3_ssp245\n", "MRI-ESM2-0_ssp434\n", "EC-Earth3_ssp370\n", "NorESM2-MM_ssp370\n", "CESM2_ssp370\n", "CMCC-CM2-SR5_ssp585\n", "GFDL-ESM4_ssp126\n", "INM-CM4-8_ssp245\n", "INM-CM5-0_ssp245\n", "MRI-ESM2-0_ssp585\n" ] } ], "source": [ "bc = '_bc_2000_2019' # bias correction from 2000 to 2019 (at the moment, this is the only option that has been simulated globally)\n", "cmip = 'CMIP6' \n", "endyr = 2100 # take the simulations that go until 2300\n", "hist = 'gcm_from_2000' # take the simulations where we use the GCMs from 2000 onwards\n", "\n", "dfs = []\n", "for rgi_reg in [14]: # all RGI regions would be np.arange(1,20,1)\n", " df = []\n", " for gcm_scen in gcms_scenarios: \n", " #try:\n", " # need to download the data first, if you are not on the oggm cluster and then adapt the path here:\n", " dpath = f'{dirpath}/{cmip}/{endyr}/RGI{rgi_reg:02}'\n", " with xr.open_mfdataset(f'{dpath}/run_hydro_{hist}_{gcm_scen}{bc}*.nc') as ds:\n", " ds = ds.sel(rgi_id = list_RGI_Upper_Indus) #list_RGI_Shylok\n", " ds = ds[['melt_off_glacier_monthly', 'melt_on_glacier_monthly', 'liq_prcp_off_glacier_monthly', 'liq_prcp_on_glacier_monthly',\n", " 'volume']].load()\n", " #ds['runoff'] = ds['melt_off_glacier']+ds['melt_on_glacier']+ ds['liq_prcp_off_glacier'] +ds['liq_prcp_on_glacier']\n", " ds['runoff_monthly'] = ds['melt_off_glacier_monthly']+ds['melt_on_glacier_monthly']+ ds['liq_prcp_off_glacier_monthly'] +ds['liq_prcp_on_glacier_monthly']\n", " \n", " # could also instead select the common running glaciers if they have been computed already\n", " # ds = ds.sel(rgi_id=rgi_reg_glaciers_working)\n", " # and then directly do the sum or anything\n", " n_running_rgis = len(ds.rgi_id)\n", " #ds = ds.sum(dim='rgi_id')\n", " ds.coords['rgi_reg'] = f'{rgi_reg:02}'\n", " ds.coords['gcm_scenario'] = gcm_scen\n", " ds.coords['gcm'] = gcm_scen.split('_')[0]\n", " ds.coords['scenario'] = gcm_scen.split('_')[1]\n", " df.append(ds)\n", "\n", " \n", " print(gcm_scen) \n", " #dff = xr.concat(df, dim='gcm_scenario', fill_value=np.NaN)\n", " #df = df.expand_dims('rgi_reg')\n", " #dfs.append(dff)\n", "\n", "#dff_sel_1 = xr.concat(dfs, dim='rgi_reg')\n", "#dff_sel_1.coords['bias_correction'] = bc[1:]\n", "#dff_sel_1.coords['hist'] = hist" ] }, { "cell_type": "code", "execution_count": 6, "id": "8ca35330-514b-467a-81b6-6648e238e4c5", "metadata": {}, "outputs": [], "source": [ "dff = xr.concat(df, dim='gcm_scenario', fill_value=np.NaN)" ] }, { "cell_type": "code", "execution_count": 7, "id": "05fbac25-ada5-4370-ad50-d7046e0e0b83", "metadata": {}, "outputs": [], "source": [ "dff.to_netcdf('runoff_monthly_upper_indus.nc')\n", "#dff.to_netcdf('runoff_monthly_shylok.nc')" ] }, { "cell_type": "code", "execution_count": null, "id": "251a907e-2bf1-48d9-8d37-7ddabd2d010a", "metadata": {}, "outputs": [], "source": [ "#dff.to_netcdf(\"Upper_Indus_monthly\")" ] }, { "cell_type": "code", "execution_count": null, "id": "680af684-878c-49cf-a3d6-f92f6ff5f7a7", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "cc55cfc4-cf30-4700-a4ae-6aa70505a444", "metadata": {}, "outputs": [], "source": [ "# IPCC colors from: https://pyam-iamc.readthedocs.io/en/stable/tutorials/ipcc_colors.html\n", "colors_scenario = {'ssp126': '#003466',\n", " 'ssp370':'#df0000',\n", " 'ssp585':'#980002'}\n", "\n", "import seaborn as sns\n", "# create a pandas dataframe which facilitates plotting with seaborn\n", "pd_runoff = dff.runoff.to_dataframe().reset_index().dropna()\n", "for scen in ['ssp126', 'ssp370', 'ssp585']:\n", " pd_sel = pd_runoff.loc[pd_runoff.scenario==scen]\n", " n_gcm = len(pd_sel.gcm.unique())\n", " sns.lineplot(data=pd_sel,\n", " x='time', y= 'runoff',\n", " errorbar=('pi', 100), estimator='median',\n", " color= colors_scenario[scen],\n", " label=f'{scen}, n={n_gcm} GCMs'\n", " )\n", "plt.ylabel('runoff (kg year-1)')\n", "plt.legend(title='median and total range')" ] }, { "cell_type": "code", "execution_count": null, "id": "d0f1483a-05af-4291-84d8-d1438718b2a5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "fb4722df-0532-4ec4-8702-40598c377ca2", "metadata": {}, "source": [ "### Load a single file" ] }, { "cell_type": "code", "execution_count": 3, "id": "a13c426b-f2d4-4ac3-ab71-46ebd91ff424", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_660/3982805575.py:4: DeprecationWarning: dropping variables using `drop` is deprecated; use drop_vars.\n", " ds = ds.drop(['hydro_year', 'hydro_month', 'calendar_year', 'calendar_month','calendar_month_2d'])\n" ] }, { "data": { "text/html": [ "
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<xarray.Dataset> Size: 46MB\n",
       "Dimensions:                       (time: 101, rgi_id: 1000, month_2d: 12)\n",
       "Coordinates:\n",
       "  * time                          (time) float64 808B 2e+03 ... 2.1e+03\n",
       "  * rgi_id                        (rgi_id) <U14 56kB 'RGI60-14.00001' ... 'RG...\n",
       "  * month_2d                      (month_2d) int64 96B 1 2 3 4 5 ... 9 10 11 12\n",
       "Data variables: (12/29)\n",
       "    volume                        (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    volume_bsl                    (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    volume_bwl                    (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    area                          (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    length                        (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    calving                       (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    ...                            ...\n",
       "    snowfall_on_glacier_monthly   (time, month_2d, rgi_id) float32 5MB dask.array<chunksize=(101, 12, 1000), meta=np.ndarray>\n",
       "    snow_bucket_monthly           (time, month_2d, rgi_id) float32 5MB dask.array<chunksize=(101, 12, 1000), meta=np.ndarray>\n",
       "    residual_mb_monthly           (time, month_2d, rgi_id) float32 5MB dask.array<chunksize=(101, 12, 1000), meta=np.ndarray>\n",
       "    water_level                   (rgi_id) float32 4kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
       "    glen_a                        (rgi_id) float32 4kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
       "    fs                            (rgi_id) float32 4kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    description:    OGGM model output\n",
       "    oggm_version:   1.6.1.dev26+gf8a1745\n",
       "    calendar:       365-day no leap\n",
       "    creation_date:  2023-07-27 12:45:22
" ], "text/plain": [ " Size: 46MB\n", "Dimensions: (time: 101, rgi_id: 1000, month_2d: 12)\n", "Coordinates:\n", " * time (time) float64 808B 2e+03 ... 2.1e+03\n", " * rgi_id (rgi_id) \n", " volume_bsl (time, rgi_id) float32 404kB dask.array\n", " volume_bwl (time, rgi_id) float32 404kB dask.array\n", " area (time, rgi_id) float32 404kB dask.array\n", " length (time, rgi_id) float32 404kB dask.array\n", " calving (time, rgi_id) float32 404kB dask.array\n", " ... ...\n", " snowfall_on_glacier_monthly (time, month_2d, rgi_id) float32 5MB dask.array\n", " snow_bucket_monthly (time, month_2d, rgi_id) float32 5MB dask.array\n", " residual_mb_monthly (time, month_2d, rgi_id) float32 5MB dask.array\n", " water_level (rgi_id) float32 4kB dask.array\n", " glen_a (rgi_id) float32 4kB dask.array\n", " fs (rgi_id) float32 4kB dask.array\n", "Attributes:\n", " description: OGGM model output\n", " oggm_version: 1.6.1.dev26+gf8a1745\n", " calendar: 365-day no leap\n", " creation_date: 2023-07-27 12:45:22" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = xr.open_mfdataset(dirpath+'/CMIP6/2100/RGI14/run_hydro_gcm_from_2000_CanESM5_ssp126_bc_2000_2019_Batch_0_1000.nc')\n", "# We can ignore the `hydro_year`, `hydro_month`, `calendar_year`, `calendar_month` and `calendar_month_2d` coordinates. \n", "ds = ds.reset_coords()\n", "ds = ds.drop(['hydro_year', 'hydro_month', 'calendar_year', 'calendar_month','calendar_month_2d'])\n", "ds" ] }, { "cell_type": "code", "execution_count": 4, "id": "01077d19-868b-4c01-ae6a-1cb3f8799d4b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 46MB\n",
       "Dimensions:                       (time: 101, rgi_id: 1000, month_2d: 12)\n",
       "Coordinates:\n",
       "  * time                          (time) float64 808B 2e+03 ... 2.1e+03\n",
       "  * rgi_id                        (rgi_id) <U14 56kB 'RGI60-14.00001' ... 'RG...\n",
       "  * month_2d                      (month_2d) int64 96B 1 2 3 4 5 ... 9 10 11 12\n",
       "Data variables: (12/29)\n",
       "    volume                        (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    volume_bsl                    (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    volume_bwl                    (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    area                          (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    length                        (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    calving                       (time, rgi_id) float32 404kB dask.array<chunksize=(101, 1000), meta=np.ndarray>\n",
       "    ...                            ...\n",
       "    snowfall_on_glacier_monthly   (time, month_2d, rgi_id) float32 5MB dask.array<chunksize=(101, 12, 1000), meta=np.ndarray>\n",
       "    snow_bucket_monthly           (time, month_2d, rgi_id) float32 5MB dask.array<chunksize=(101, 12, 1000), meta=np.ndarray>\n",
       "    residual_mb_monthly           (time, month_2d, rgi_id) float32 5MB dask.array<chunksize=(101, 12, 1000), meta=np.ndarray>\n",
       "    water_level                   (rgi_id) float32 4kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
       "    glen_a                        (rgi_id) float32 4kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
       "    fs                            (rgi_id) float32 4kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    description:    OGGM model output\n",
       "    oggm_version:   1.6.1.dev26+gf8a1745\n",
       "    calendar:       365-day no leap\n",
       "    creation_date:  2023-07-27 12:45:22
" ], "text/plain": [ " Size: 46MB\n", "Dimensions: (time: 101, rgi_id: 1000, month_2d: 12)\n", "Coordinates:\n", " * time (time) float64 808B 2e+03 ... 2.1e+03\n", " * rgi_id (rgi_id) \n", " volume_bsl (time, rgi_id) float32 404kB dask.array\n", " volume_bwl (time, rgi_id) float32 404kB dask.array\n", " area (time, rgi_id) float32 404kB dask.array\n", " length (time, rgi_id) float32 404kB dask.array\n", " calving (time, rgi_id) float32 404kB dask.array\n", " ... ...\n", " snowfall_on_glacier_monthly (time, month_2d, rgi_id) float32 5MB dask.array\n", " snow_bucket_monthly (time, month_2d, rgi_id) float32 5MB dask.array\n", " residual_mb_monthly (time, month_2d, rgi_id) float32 5MB dask.array\n", " water_level (rgi_id) float32 4kB dask.array\n", " glen_a (rgi_id) float32 4kB dask.array\n", " fs (rgi_id) float32 4kB dask.array\n", "Attributes:\n", " description: OGGM model output\n", " oggm_version: 1.6.1.dev26+gf8a1745\n", " calendar: 365-day no leap\n", " creation_date: 2023-07-27 12:45:22" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "code", "execution_count": 27, "id": "74267ea6-1ca4-4bbf-a314-0fd6263af340", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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descriptionunitcoords
variable
volumeTotal glacier volumem 3['time', 'rgi_id']
volume_bslGlacier volume below sea-levelm 3['time', 'rgi_id']
volume_bwlGlacier volume below water-levelm 3['time', 'rgi_id']
areaTotal glacier aream 2['time', 'rgi_id']
lengthGlacier lengthm['time', 'rgi_id']
calvingTotal accumulated calving fluxm 3['time', 'rgi_id']
calving_rateCalving ratem yr-1['time', 'rgi_id']
off_areaOff-glacier aream 2['time', 'rgi_id']
on_areaOn-glacier aream 2['time', 'rgi_id']
melt_off_glacierOff-glacier meltkg yr-1['time', 'rgi_id']
melt_on_glacierOn-glacier meltkg yr-1['time', 'rgi_id']
liq_prcp_off_glacierOff-glacier liquid precipitationkg yr-1['time', 'rgi_id']
liq_prcp_on_glacierOn-glacier liquid precipitationkg yr-1['time', 'rgi_id']
snowfall_off_glacierOff-glacier solid precipitationkg yr-1['time', 'rgi_id']
snowfall_on_glacierOn-glacier solid precipitationkg yr-1['time', 'rgi_id']
snow_bucketOff-glacier snow reservoir (state variable)kg['time', 'rgi_id']
model_mbAnnual mass balance from dynamical modelkg yr-1['time', 'rgi_id']
residual_mbDifference (before correction) between mb mode...kg yr-1['time', 'rgi_id']
melt_off_glacier_monthlykg month-1['time', 'rgi_id', 'month_2d']
melt_on_glacier_monthlykg month-1['time', 'rgi_id', 'month_2d']
liq_prcp_off_glacier_monthlykg month-1['time', 'rgi_id', 'month_2d']
liq_prcp_on_glacier_monthlykg month-1['time', 'rgi_id', 'month_2d']
snowfall_off_glacier_monthlykg month-1['time', 'rgi_id', 'month_2d']
snowfall_on_glacier_monthlykg month-1['time', 'rgi_id', 'month_2d']
snow_bucket_monthlykg month-1['time', 'rgi_id', 'month_2d']
residual_mb_monthlykg month-1['time', 'rgi_id', 'month_2d']
water_levelCalving water level['rgi_id']
glen_aSimulation Glen A['rgi_id']
fsSimulation sliding parameter['rgi_id']
\n", "
" ], "text/plain": [ " description \\\n", "variable \n", "volume Total glacier volume \n", "volume_bsl Glacier volume below sea-level \n", "volume_bwl Glacier volume below water-level \n", "area Total glacier area \n", "length Glacier length \n", "calving Total accumulated calving flux \n", "calving_rate Calving rate \n", "off_area Off-glacier area \n", "on_area On-glacier area \n", "melt_off_glacier Off-glacier melt \n", "melt_on_glacier On-glacier melt \n", "liq_prcp_off_glacier Off-glacier liquid precipitation \n", "liq_prcp_on_glacier On-glacier liquid precipitation \n", "snowfall_off_glacier Off-glacier solid precipitation \n", "snowfall_on_glacier On-glacier solid precipitation \n", "snow_bucket Off-glacier snow reservoir (state variable) \n", "model_mb Annual mass balance from dynamical model \n", "residual_mb Difference (before correction) between mb mode... \n", "melt_off_glacier_monthly \n", "melt_on_glacier_monthly \n", "liq_prcp_off_glacier_monthly \n", "liq_prcp_on_glacier_monthly \n", "snowfall_off_glacier_monthly \n", "snowfall_on_glacier_monthly \n", "snow_bucket_monthly \n", "residual_mb_monthly \n", "water_level Calving water level \n", "glen_a Simulation Glen A \n", "fs Simulation sliding parameter \n", "\n", " unit coords \n", "variable \n", "volume m 3 ['time', 'rgi_id'] \n", "volume_bsl m 3 ['time', 'rgi_id'] \n", "volume_bwl m 3 ['time', 'rgi_id'] \n", "area m 2 ['time', 'rgi_id'] \n", "length m ['time', 'rgi_id'] \n", "calving m 3 ['time', 'rgi_id'] \n", "calving_rate m yr-1 ['time', 'rgi_id'] \n", "off_area m 2 ['time', 'rgi_id'] \n", "on_area m 2 ['time', 'rgi_id'] \n", "melt_off_glacier kg yr-1 ['time', 'rgi_id'] \n", "melt_on_glacier kg yr-1 ['time', 'rgi_id'] \n", "liq_prcp_off_glacier kg yr-1 ['time', 'rgi_id'] \n", "liq_prcp_on_glacier kg yr-1 ['time', 'rgi_id'] \n", "snowfall_off_glacier kg yr-1 ['time', 'rgi_id'] \n", "snowfall_on_glacier kg yr-1 ['time', 'rgi_id'] \n", "snow_bucket kg ['time', 'rgi_id'] \n", "model_mb kg yr-1 ['time', 'rgi_id'] \n", "residual_mb kg yr-1 ['time', 'rgi_id'] \n", "melt_off_glacier_monthly kg month-1 ['time', 'rgi_id', 'month_2d'] \n", "melt_on_glacier_monthly kg month-1 ['time', 'rgi_id', 'month_2d'] \n", "liq_prcp_off_glacier_monthly kg month-1 ['time', 'rgi_id', 'month_2d'] \n", "liq_prcp_on_glacier_monthly kg month-1 ['time', 'rgi_id', 'month_2d'] \n", "snowfall_off_glacier_monthly kg month-1 ['time', 'rgi_id', 'month_2d'] \n", "snowfall_on_glacier_monthly kg month-1 ['time', 'rgi_id', 'month_2d'] \n", "snow_bucket_monthly kg month-1 ['time', 'rgi_id', 'month_2d'] \n", "residual_mb_monthly kg month-1 ['time', 'rgi_id', 'month_2d'] \n", "water_level ['rgi_id'] \n", "glen_a ['rgi_id'] \n", "fs ['rgi_id'] " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a table to see all computed variables, their description, unit and coordinates\n", "pd_data_vars_attrs = pd.DataFrame(index=ds.data_vars)\n", "pd_data_vars_attrs.index.name = 'variable'\n", "for v in list(ds.data_vars):\n", " try:\n", " pd_data_vars_attrs.loc[v,'description'] = ds[v].description\n", " except:\n", " pd_data_vars_attrs.loc[v,'description'] = ''\n", " try:\n", " pd_data_vars_attrs.loc[v,'unit'] = ds[v].unit\n", " except:\n", " pd_data_vars_attrs.loc[v,'unit'] = ''\n", " if '_monthly' in v:\n", " # the unit is missing in the raw OGGM output\n", " pd_data_vars_attrs.loc[v,'unit'] = 'kg month-1'\n", " pd_data_vars_attrs.loc[v,'coords'] = str(list(ds[v].coords))\n", "pd_data_vars_attrs \n", " #-> creates a markdown table for the README.md \n", "# print(pd_data_vars_attrs.to_markdown())" ] }, { "cell_type": "markdown", "id": "a438990b-1cb0-4e5e-bc70-3c0e14f6cb06", "metadata": {}, "source": [ "All variables are dependent on the `rgi_id`. The `*_monthly` variables have both `time` (i.e., the year) and `month_2d` (i.e., the calendar month) as coordinates. Most other variables are available on a yearly basis (`time`-dependent). Some variables are are constant over time (only `rgi_id`-dependent." ] }, { "cell_type": "code", "execution_count": 5, "id": "81a0c0a1-689b-44df-94b3-d6d7ecf04ec9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 46MB\n",
       "Dimensions:                       (time: 101, rgi_id: 999, month_2d: 12)\n",
       "Coordinates:\n",
       "  * time                          (time) float64 808B 2e+03 ... 2.1e+03\n",
       "  * rgi_id                        (rgi_id) <U14 56kB 'RGI60-14.00001' ... 'RG...\n",
       "  * month_2d                      (month_2d) int64 96B 1 2 3 4 5 ... 9 10 11 12\n",
       "Data variables: (12/29)\n",
       "    volume                        (time, rgi_id) float32 404kB dask.array<chunksize=(101, 999), meta=np.ndarray>\n",
       "    volume_bsl                    (time, rgi_id) float32 404kB dask.array<chunksize=(101, 999), meta=np.ndarray>\n",
       "    volume_bwl                    (time, rgi_id) float32 404kB dask.array<chunksize=(101, 999), meta=np.ndarray>\n",
       "    area                          (time, rgi_id) float32 404kB dask.array<chunksize=(101, 999), meta=np.ndarray>\n",
       "    length                        (time, rgi_id) float32 404kB dask.array<chunksize=(101, 999), meta=np.ndarray>\n",
       "    calving                       (time, rgi_id) float32 404kB dask.array<chunksize=(101, 999), meta=np.ndarray>\n",
       "    ...                            ...\n",
       "    snowfall_on_glacier_monthly   (time, month_2d, rgi_id) float32 5MB dask.array<chunksize=(101, 12, 999), meta=np.ndarray>\n",
       "    snow_bucket_monthly           (time, month_2d, rgi_id) float32 5MB dask.array<chunksize=(101, 12, 999), meta=np.ndarray>\n",
       "    residual_mb_monthly           (time, month_2d, rgi_id) float32 5MB dask.array<chunksize=(101, 12, 999), meta=np.ndarray>\n",
       "    water_level                   (rgi_id) float32 4kB dask.array<chunksize=(999,), meta=np.ndarray>\n",
       "    glen_a                        (rgi_id) float32 4kB dask.array<chunksize=(999,), meta=np.ndarray>\n",
       "    fs                            (rgi_id) float32 4kB dask.array<chunksize=(999,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    description:    OGGM model output\n",
       "    oggm_version:   1.6.1.dev26+gf8a1745\n",
       "    calendar:       365-day no leap\n",
       "    creation_date:  2023-07-27 12:45:22
" ], "text/plain": [ " Size: 46MB\n", "Dimensions: (time: 101, rgi_id: 999, month_2d: 12)\n", "Coordinates:\n", " * time (time) float64 808B 2e+03 ... 2.1e+03\n", " * rgi_id (rgi_id) \n", " volume_bsl (time, rgi_id) float32 404kB dask.array\n", " volume_bwl (time, rgi_id) float32 404kB dask.array\n", " area (time, rgi_id) float32 404kB dask.array\n", " length (time, rgi_id) float32 404kB dask.array\n", " calving (time, rgi_id) float32 404kB dask.array\n", " ... ...\n", " snowfall_on_glacier_monthly (time, month_2d, rgi_id) float32 5MB dask.array\n", " snow_bucket_monthly (time, month_2d, rgi_id) float32 5MB dask.array\n", " residual_mb_monthly (time, month_2d, rgi_id) float32 5MB dask.array\n", " water_level (rgi_id) float32 4kB dask.array\n", " glen_a (rgi_id) float32 4kB dask.array\n", " fs (rgi_id) float32 4kB dask.array\n", "Attributes:\n", " description: OGGM model output\n", " oggm_version: 1.6.1.dev26+gf8a1745\n", " calendar: 365-day no leap\n", " creation_date: 2023-07-27 12:45:22" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# in that case for this specific GCM and SSP 995 glaciers out of 1000 were running, thus 5 glaciers were failing \n", "ds.dropna(dim='rgi_id', how='all') " ] }, { "cell_type": "code", "execution_count": null, "id": "a9b0e22c-9029-48b9-bc8e-912f1be19e25", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "195336da-04b7-440f-88d2-5220ff43f1e3", "metadata": {}, "source": [ "### Example use case 1: \n", "You want to get the per-glacier `volume` and the volume below sea-level (`volume_bsl`) until 2300 for two RGI regions (here only RGI08 and RGI09, Scandinavia and Russian Arctic, to make the notebook run reasonably fast) and we want to use the GCMs from 2000 onwards (i.e., `gcm_from_2000`). You also do not want to compare your data to CMIP5, so we only use the common running glaciers within CMIP6." ] }, { "cell_type": "code", "execution_count": 6, "id": "1fb478f1-0776-44f1-a0b1-0a3480004014", "metadata": {}, "outputs": [], "source": [ "### get the GCMs and SSPs until 2100 (those \n", "# can download it like that, or do it manually ... \n", "import oggm\n", "fp_metadata = oggm.utils.file_downloader('/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.1/common_running_2100/metadata.csv')\n", "pd_meta_2100 = pd.read_csv(fp_metadata, index_col=0)\n", "pd_meta_2100 = pd_meta_2100.loc[pd_meta_2100.end_year == 2100]\n", "pd_meta_2100['gcm_scenario'] = pd_meta_2100.gcm_original + '_' + pd_meta_2100.scenario # we use here gcm_original, as some GCMs have upper and lower cases \n", "gcms_scenarios = pd_meta_2100['gcm_scenario'].unique()" ] }, { "cell_type": "code", "execution_count": 7, "id": "2ba3dbd6-e3ee-4b47-aa56-df1e2fc9195c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cmipgcm_originalgcmscenariorgi_regend_yearbias_correctionperc_area_missingfpathexphistorical_future_optionoggm_versiongcm_scenario
ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11ISIMIP3b_CMIP6mri-esm2-0MRI-ESM2-0ssp585RGI112100ISIMIP3b:1979-20140.000495/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3mri-esm2-0_ssp585
ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11ISIMIP3b_CMIP6ukesm1-0-llUKESM1-0-LLssp585RGI112100ISIMIP3b:1979-20140.000495/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3ukesm1-0-ll_ssp585
ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11ISIMIP3b_CMIP6mri-esm2-0MRI-ESM2-0ssp126RGI112100ISIMIP3b:1979-20140.000495/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3mri-esm2-0_ssp126
ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11ISIMIP3b_CMIP6ukesm1-0-llUKESM1-0-LLssp370RGI112100ISIMIP3b:1979-20140.000495/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3ukesm1-0-ll_ssp370
ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11ISIMIP3b_CMIP6mri-esm2-0MRI-ESM2-0ssp370RGI112100ISIMIP3b:1979-20140.000495/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3mri-esm2-0_ssp370
..........................................
CMIP5:2100:GFDL-CM3:rcp45:RGI04CMIP5GFDL-CM3GFDL-CM3rcp45RGI0421002000-20190.000053/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3GFDL-CM3_rcp45
CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04CMIP5CSIRO-Mk3-6-0CSIRO-MK3-6-0rcp26RGI0421002000-20190.000073/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3CSIRO-Mk3-6-0_rcp26
CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04CMIP5IPSL-CM5A-LRIPSL-CM5A-LRrcp45RGI0421002000-20190.000063/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3IPSL-CM5A-LR_rcp45
CMIP5:2100:GFDL-CM3:rcp85:RGI04CMIP5GFDL-CM3GFDL-CM3rcp85RGI0421002000-20190.000053/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3GFDL-CM3_rcp85
CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04CMIP5IPSL-CM5A-LRIPSL-CM5A-LRrcp60RGI0421002000-20190.000057/home/www/oggm/oggm-standard-projections/oggm_...match_geod_perglaw5e5_gcm_mergedoggm_v1.6.1_2023.3IPSL-CM5A-LR_rcp60
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2432 rows × 13 columns

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" ], "text/plain": [ " cmip gcm_original \\\n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11 ISIMIP3b_CMIP6 mri-esm2-0 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11 ISIMIP3b_CMIP6 ukesm1-0-ll \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11 ISIMIP3b_CMIP6 mri-esm2-0 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11 ISIMIP3b_CMIP6 ukesm1-0-ll \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11 ISIMIP3b_CMIP6 mri-esm2-0 \n", "... ... ... \n", "CMIP5:2100:GFDL-CM3:rcp45:RGI04 CMIP5 GFDL-CM3 \n", "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04 CMIP5 CSIRO-Mk3-6-0 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04 CMIP5 IPSL-CM5A-LR \n", "CMIP5:2100:GFDL-CM3:rcp85:RGI04 CMIP5 GFDL-CM3 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04 CMIP5 IPSL-CM5A-LR \n", "\n", " gcm scenario rgi_reg \\\n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11 MRI-ESM2-0 ssp585 RGI11 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11 UKESM1-0-LL ssp585 RGI11 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11 MRI-ESM2-0 ssp126 RGI11 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11 UKESM1-0-LL ssp370 RGI11 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11 MRI-ESM2-0 ssp370 RGI11 \n", "... ... ... ... \n", "CMIP5:2100:GFDL-CM3:rcp45:RGI04 GFDL-CM3 rcp45 RGI04 \n", "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04 CSIRO-MK3-6-0 rcp26 RGI04 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04 IPSL-CM5A-LR rcp45 RGI04 \n", "CMIP5:2100:GFDL-CM3:rcp85:RGI04 GFDL-CM3 rcp85 RGI04 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04 IPSL-CM5A-LR rcp60 RGI04 \n", "\n", " end_year bias_correction \\\n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11 2100 ISIMIP3b:1979-2014 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11 2100 ISIMIP3b:1979-2014 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11 2100 ISIMIP3b:1979-2014 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11 2100 ISIMIP3b:1979-2014 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11 2100 ISIMIP3b:1979-2014 \n", "... ... ... \n", "CMIP5:2100:GFDL-CM3:rcp45:RGI04 2100 2000-2019 \n", "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04 2100 2000-2019 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04 2100 2000-2019 \n", "CMIP5:2100:GFDL-CM3:rcp85:RGI04 2100 2000-2019 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04 2100 2000-2019 \n", "\n", " perc_area_missing \\\n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11 0.000495 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11 0.000495 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11 0.000495 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11 0.000495 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11 0.000495 \n", "... ... \n", "CMIP5:2100:GFDL-CM3:rcp45:RGI04 0.000053 \n", "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04 0.000073 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04 0.000063 \n", "CMIP5:2100:GFDL-CM3:rcp85:RGI04 0.000053 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04 0.000057 \n", "\n", " fpath \\\n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11 /home/www/oggm/oggm-standard-projections/oggm_... \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11 /home/www/oggm/oggm-standard-projections/oggm_... \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11 /home/www/oggm/oggm-standard-projections/oggm_... \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11 /home/www/oggm/oggm-standard-projections/oggm_... \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11 /home/www/oggm/oggm-standard-projections/oggm_... \n", "... ... \n", "CMIP5:2100:GFDL-CM3:rcp45:RGI04 /home/www/oggm/oggm-standard-projections/oggm_... \n", "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04 /home/www/oggm/oggm-standard-projections/oggm_... \n", "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04 /home/www/oggm/oggm-standard-projections/oggm_... \n", "CMIP5:2100:GFDL-CM3:rcp85:RGI04 /home/www/oggm/oggm-standard-projections/oggm_... \n", "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04 /home/www/oggm/oggm-standard-projections/oggm_... \n", "\n", " exp \\\n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11 match_geod_pergla \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11 match_geod_pergla \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11 match_geod_pergla \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11 match_geod_pergla \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11 match_geod_pergla \n", "... ... \n", "CMIP5:2100:GFDL-CM3:rcp45:RGI04 match_geod_pergla \n", "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04 match_geod_pergla \n", "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04 match_geod_pergla \n", "CMIP5:2100:GFDL-CM3:rcp85:RGI04 match_geod_pergla \n", "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04 match_geod_pergla \n", "\n", " historical_future_option \\\n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11 w5e5_gcm_merged \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11 w5e5_gcm_merged \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11 w5e5_gcm_merged \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11 w5e5_gcm_merged \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11 w5e5_gcm_merged \n", "... ... \n", "CMIP5:2100:GFDL-CM3:rcp45:RGI04 w5e5_gcm_merged \n", "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04 w5e5_gcm_merged \n", "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04 w5e5_gcm_merged \n", "CMIP5:2100:GFDL-CM3:rcp85:RGI04 w5e5_gcm_merged \n", "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04 w5e5_gcm_merged \n", "\n", " oggm_version \\\n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11 oggm_v1.6.1_2023.3 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11 oggm_v1.6.1_2023.3 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11 oggm_v1.6.1_2023.3 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11 oggm_v1.6.1_2023.3 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11 oggm_v1.6.1_2023.3 \n", "... ... \n", "CMIP5:2100:GFDL-CM3:rcp45:RGI04 oggm_v1.6.1_2023.3 \n", "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04 oggm_v1.6.1_2023.3 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04 oggm_v1.6.1_2023.3 \n", "CMIP5:2100:GFDL-CM3:rcp85:RGI04 oggm_v1.6.1_2023.3 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04 oggm_v1.6.1_2023.3 \n", "\n", " gcm_scenario \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp585:RGI11 mri-esm2-0_ssp585 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp585:RGI11 ukesm1-0-ll_ssp585 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp126:RGI11 mri-esm2-0_ssp126 \n", "ISIMIP3b_CMIP6:2100:UKESM1-0-LL:ssp370:RGI11 ukesm1-0-ll_ssp370 \n", "ISIMIP3b_CMIP6:2100:MRI-ESM2-0:ssp370:RGI11 mri-esm2-0_ssp370 \n", "... ... \n", "CMIP5:2100:GFDL-CM3:rcp45:RGI04 GFDL-CM3_rcp45 \n", "CMIP5:2100:CSIRO-MK3-6-0:rcp26:RGI04 CSIRO-Mk3-6-0_rcp26 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp45:RGI04 IPSL-CM5A-LR_rcp45 \n", "CMIP5:2100:GFDL-CM3:rcp85:RGI04 GFDL-CM3_rcp85 \n", "CMIP5:2100:IPSL-CM5A-LR:rcp60:RGI04 IPSL-CM5A-LR_rcp60 \n", "\n", "[2432 rows x 13 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd_meta_2100" ] }, { "cell_type": "code", "execution_count": 8, "id": "a557795d-e59a-4802-a0ad-5dee209ed0e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mri-esm2-0_ssp585\n", "ukesm1-0-ll_ssp585\n", "mri-esm2-0_ssp126\n", "ukesm1-0-ll_ssp370\n", "mri-esm2-0_ssp370\n", "ukesm1-0-ll_ssp126\n", "gfdl-esm4_ssp370\n", "mpi-esm1-2-hr_ssp370\n", "mpi-esm1-2-hr_ssp126\n", "ipsl-cm6a-lr_ssp585\n", "gfdl-esm4_ssp126\n", "ipsl-cm6a-lr_ssp370\n", "gfdl-esm4_ssp585\n", "ipsl-cm6a-lr_ssp126\n", "mpi-esm1-2-hr_ssp585\n", "CCSM4_rcp60\n", "GFDL-CM3_rcp85\n", "CCSM4_rcp45\n", "GFDL-CM3_rcp45\n", "CanESM2_rcp26\n", "CCSM4_rcp85\n", "NorESM1-M_rcp26\n", "GFDL-CM3_rcp60\n", "NorESM1-M_rcp60\n", "CanESM2_rcp45\n", "GFDL-CM3_rcp26\n", "CESM1-CAM5_rcp60\n", "NorESM1-M_rcp45\n", "NorESM1-M_rcp85\n", "CanESM2_rcp85\n", "CCSM4_rcp26\n", "IPSL-CM5A-LR_rcp45\n", "IPSL-CM5A-LR_rcp60\n", "CSIRO-Mk3-6-0_rcp85\n", "CSIRO-Mk3-6-0_rcp45\n", "CNRM-CM5_rcp26\n", "GFDL-ESM2G_rcp26\n", "CESM1-CAM5_rcp26\n", "CSIRO-Mk3-6-0_rcp60\n", "MPI-ESM-LR_rcp26\n", "IPSL-CM5A-LR_rcp85\n", "GISS-E2-R_rcp26\n", "GFDL-ESM2G_rcp45\n", "CNRM-CM5_rcp45\n", "CSIRO-Mk3-6-0_rcp26\n", "GISS-E2-R_rcp60\n", "GISS-E2-R_rcp45\n", "CESM1-CAM5_rcp45\n", "MPI-ESM-LR_rcp45\n", "GFDL-ESM2G_rcp60\n", "GISS-E2-R_rcp85\n", "IPSL-CM5A-LR_rcp26\n", "MPI-ESM-LR_rcp85\n", "CNRM-CM5_rcp85\n", "GFDL-ESM2G_rcp85\n", "mri-esm2-0_ssp585\n", "ukesm1-0-ll_ssp585\n", "mri-esm2-0_ssp126\n", "ukesm1-0-ll_ssp370\n", "mri-esm2-0_ssp370\n", "ukesm1-0-ll_ssp126\n", "gfdl-esm4_ssp370\n", "mpi-esm1-2-hr_ssp370\n", "mpi-esm1-2-hr_ssp126\n", "ipsl-cm6a-lr_ssp585\n", "gfdl-esm4_ssp126\n", "ipsl-cm6a-lr_ssp370\n", "gfdl-esm4_ssp585\n", "ipsl-cm6a-lr_ssp126\n", "mpi-esm1-2-hr_ssp585\n", "CCSM4_rcp60\n", "GFDL-CM3_rcp85\n", "CCSM4_rcp45\n", "GFDL-CM3_rcp45\n", "CanESM2_rcp26\n", "CCSM4_rcp85\n", "NorESM1-M_rcp26\n", "GFDL-CM3_rcp60\n", "NorESM1-M_rcp60\n", "CanESM2_rcp45\n", "GFDL-CM3_rcp26\n", "CESM1-CAM5_rcp60\n", "NorESM1-M_rcp45\n", "NorESM1-M_rcp85\n", "CanESM2_rcp85\n", "CCSM4_rcp26\n", "IPSL-CM5A-LR_rcp45\n", "IPSL-CM5A-LR_rcp60\n", "CSIRO-Mk3-6-0_rcp85\n", "CSIRO-Mk3-6-0_rcp45\n", "CNRM-CM5_rcp26\n", "GFDL-ESM2G_rcp26\n", "CESM1-CAM5_rcp26\n", "CSIRO-Mk3-6-0_rcp60\n", "MPI-ESM-LR_rcp26\n", "IPSL-CM5A-LR_rcp85\n", "GISS-E2-R_rcp26\n", "GFDL-ESM2G_rcp45\n", "CNRM-CM5_rcp45\n", "CSIRO-Mk3-6-0_rcp26\n", "GISS-E2-R_rcp60\n", "GISS-E2-R_rcp45\n", "CESM1-CAM5_rcp45\n", "MPI-ESM-LR_rcp45\n", "GFDL-ESM2G_rcp60\n", "GISS-E2-R_rcp85\n", "IPSL-CM5A-LR_rcp26\n", "MPI-ESM-LR_rcp85\n", "CNRM-CM5_rcp85\n", "GFDL-ESM2G_rcp85\n" ] } ], "source": [ "bc = '_bc_2000_2019' # bias correction from 2000 to 2019 (at the moment, this is the only option that has been simulated globally)\n", "cmip = 'CMIP6' \n", "endyr = 2100 # take the simulations that go until 2300\n", "hist = 'gcm_from_2000' # take the simulations where we use the GCMs from 2000 onwards\n", "\n", "dfs = []\n", "for rgi_reg in [14,15]: # all RGI regions would be np.arange(1,20,1)\n", " df = []\n", " for gcm_scen in gcms_scenarios: \n", " try:\n", " # need to download the data first, if you are not on the oggm cluster and then adapt the path here:\n", " dpath = f'{dirpath}/{cmip}/{endyr}/RGI{rgi_reg:02}'\n", " with xr.open_mfdataset(f'{dpath}/run_hydro_{hist}_{gcm_scen}{bc}*.nc') as ds:\n", " ds = ds[['volume','volume_bsl']].load()\n", " # could also instead select the common running glaciers if they have been computed already\n", " # ds = ds.sel(rgi_id=rgi_reg_glaciers_working)\n", " # and then directly do the sum or anything\n", " n_running_rgis = len(ds.rgi_id)\n", " #ds = ds.sum(dim='rgi_id')\n", " ds.coords['rgi_reg'] = f'{rgi_reg:02}'\n", " ds.coords['gcm_scenario'] = gcm_scen\n", " ds.coords['gcm'] = gcm_scen.split('_')[0]\n", " ds.coords['scenario'] = gcm_scen.split('_')[1]\n", " df.append(ds)\n", "\n", " except:\n", " print(gcm_scen)\n", " pass\n", " dff = xr.concat(df, dim='gcm_scenario', fill_value=np.NaN)\n", " #df = df.expand_dims('rgi_reg')\n", " dfs.append(dff)\n", "\n", "df_sel_1 = xr.concat(dfs, dim='rgi_id')\n", "df_sel_1.coords['bias_correction'] = bc[1:]\n", "df_sel_1.coords['hist'] = hist" ] }, { "cell_type": "code", "execution_count": 9, "id": "ebe2b7f4-4b05-4c91-b7a1-d31ec4b02837", "metadata": {}, "outputs": [], "source": [ "working_rgis = df_sel_1.volume.dropna(dim='rgi_id', how='any').rgi_id\n", "# select commont running glaciers\n", "df_sel_1 = df_sel_1.sel(rgi_id=working_rgis)\n", "df_sel_1_sum = df_sel_1.sel(rgi_id=working_rgis).sum(dim='rgi_id')" ] }, { "cell_type": "code", "execution_count": 10, "id": "606dd6ad-fe8f-489b-83ad-8f161e63d235", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 54kB\n",
       "Dimensions:          (gcm_scenario: 16, time: 301)\n",
       "Coordinates:\n",
       "  * time             (time) float64 2kB 2e+03 2.001e+03 ... 2.299e+03 2.3e+03\n",
       "    hydro_year       (time) int64 2kB 2000 2001 2002 2003 ... 2298 2299 2300\n",
       "    hydro_month      (time) int64 2kB 4 4 4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4 4\n",
       "    calendar_year    (time) int64 2kB 2000 2001 2002 2003 ... 2298 2299 2300\n",
       "    calendar_month   (time) int64 2kB 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1\n",
       "  * gcm_scenario     (gcm_scenario) <U24 2kB 'CanESM5_ssp585' ... 'ACCESS-ESM...\n",
       "    gcm              (gcm_scenario) <U13 832B 'CanESM5' ... 'ACCESS-ESM1-5'\n",
       "    scenario         (gcm_scenario) <U11 704B 'ssp585' ... 'ssp585'\n",
       "    bias_correction  <U12 48B 'bc_2000_2019'\n",
       "    hist             <U13 52B 'gcm_from_2000'\n",
       "Data variables:\n",
       "    volume           (gcm_scenario, time) float32 19kB 1.429e+13 ... 2.508e+07\n",
       "    volume_bsl       (gcm_scenario, time) float32 19kB 1.613e+12 ... 8.174e+06
" ], "text/plain": [ " Size: 54kB\n", "Dimensions: (gcm_scenario: 16, time: 301)\n", "Coordinates:\n", " * time (time) float64 2kB 2e+03 2.001e+03 ... 2.299e+03 2.3e+03\n", " hydro_year (time) int64 2kB 2000 2001 2002 2003 ... 2298 2299 2300\n", " hydro_month (time) int64 2kB 4 4 4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4 4\n", " calendar_year (time) int64 2kB 2000 2001 2002 2003 ... 2298 2299 2300\n", " calendar_month (time) int64 2kB 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1\n", " * gcm_scenario (gcm_scenario) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 19MB\n",
       "Dimensions:          (gcm_scenario: 16, time: 301, rgi_id: 498)\n",
       "Coordinates:\n",
       "  * time             (time) float64 2kB 2e+03 2.001e+03 ... 2.299e+03 2.3e+03\n",
       "  * rgi_id           (rgi_id) <U14 28kB 'RGI60-09.00027' ... 'RGI60-09.01069'\n",
       "    hydro_month      (time) int64 2kB 4 4 4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4 4\n",
       "    calendar_month   (time) int64 2kB 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1\n",
       "    rgi_reg          (rgi_id) <U2 4kB '09' '09' '09' '09' ... '09' '09' '09'\n",
       "  * gcm_scenario     (gcm_scenario) <U24 2kB 'CanESM5_ssp585' ... 'ACCESS-ESM...\n",
       "    gcm              (gcm_scenario) <U13 832B 'CanESM5' ... 'ACCESS-ESM1-5'\n",
       "    scenario         (gcm_scenario) <U11 704B 'ssp585' ... 'ssp585'\n",
       "    bias_correction  <U12 48B 'bc_2000_2019'\n",
       "    hist             <U13 52B 'gcm_from_2000'\n",
       "Data variables:\n",
       "    volume           (gcm_scenario, time, rgi_id) float32 10MB 9.405e+08 ... 0.0\n",
       "    volume_bsl       (gcm_scenario, time, rgi_id) float32 10MB 3.694e+07 ... 0.0\n",
       "Attributes:\n",
       "    description:    OGGM model output\n",
       "    oggm_version:   1.6.1.dev26+gf8a1745\n",
       "    calendar:       365-day no leap\n",
       "    creation_date:  2023-07-26 14:41:40
" ], "text/plain": [ " Size: 19MB\n", "Dimensions: (gcm_scenario: 16, time: 301, rgi_id: 498)\n", "Coordinates:\n", " * time (time) float64 2kB 2e+03 2.001e+03 ... 2.299e+03 2.3e+03\n", " * rgi_id (rgi_id) 0).dropna(dim='rgi_id')" ] }, { "cell_type": "markdown", "id": "94f2792c-8d12-4f46-b2a7-745240a0b019", "metadata": {}, "source": [ "### Example use case 2: Estimate glacier runoff for an entire RGI region \n", "\n", "You want to get the annual glacier runoff for Central Europe (RGI 11) until 2100 using `ISIMIP3b_CMIP6`. You want that the projections use W5E5 from 2000-2019 (i.e., `w5e5_gcm_merged`). You also want to compare your data later to the other CMIP options, i.e., CMIP5 and CMIP6, thus, we can use the already computed common running glacier file until 2100. \n" ] }, { "cell_type": "code", "execution_count": 13, "id": "7df2230b-2d93-4570-a90b-84b1819777df", "metadata": {}, "outputs": [], "source": [ "### get the GCMs and SSPs until 2100\n", "fp_metadata = oggm.utils.file_downloader('/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.1/common_running_2100/metadata.csv')\n", "pd_meta_2100 = pd.read_csv(fp_metadata, index_col=0)\n", "pd_meta_2100['gcm_scenario'] = pd_meta_2100.gcm_original + '_' + pd_meta_2100.scenario # we use here gcm_original, as some GCMs have upper and lower cases " ] }, { "cell_type": "code", "execution_count": 14, "id": "efc9160b-2caa-48aa-b6bc-b26035438d19", "metadata": {}, "outputs": [], "source": [ "# these are the glaciers that are failing for one of the GCMs or SSPs \n", "# so we have to remove them from the projections of all GCMs\n", "fp_missing_rgis = oggm.utils.file_downloader('/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.1/common_running_2100/rgi_ids_missing.json')\n", "with open(fp_missing_rgis, 'r') as f:\n", " invalid_per_reg = json.load(f)" ] }, { "cell_type": "code", "execution_count": 15, "id": "fe0148c0-a4c4-4458-81ac-014b700102b7", "metadata": {}, "outputs": [], "source": [ "bc = '' # internal bias corretion done in ISIMIP3b from 1979-2014, no additional one from OGGM\n", "cmip = 'ISIMIP3b_CMIP6' \n", "endyr = 2100 # take the simulations that go until 2300\n", "hist = 'w5e5_gcm_merged' # take the simulations where we use W5E5 from 2000 until 2019, and the GCMs from 2020 onwards\n", "scenarios = pd_meta_2100.loc[pd_meta_2100.cmip == cmip]['scenario'].unique()\n", "gcms = pd_meta_2100.loc[pd_meta_2100.cmip == cmip]['gcm_original'].unique()\n", "\n", "dfs = []\n", "for rgi_reg in [11]: # all RGI regions would be np.arange(1,20,1)\n", " dgs = []\n", " invalid_glac = invalid_per_reg[f'RGI{rgi_reg:02}']\n", " for scen in scenarios:\n", " df = []\n", " for gcm in gcms: \n", " try:\n", " dpath = f'{dirpath}/{cmip}/{endyr}/RGI{rgi_reg:02}'\n", " # attention, ISIMIP3b_CMIP6 has the realisation included in the path, but not in the metadata of the aggregated files\n", " # thus we added the * after {gcm}\n", " with xr.open_mfdataset(f'{dpath}/run_hydro_{hist}_{gcm}*{scen}{bc}*.nc') as ds:\n", " # drop the failing glaciers, to only look into the common running glaciers ... \n", " ds = ds.sel(rgi_id=~ds.rgi_id.isin(invalid_glac))\n", " ds['runoff'] = ds['melt_off_glacier']+ds['melt_on_glacier']+ ds['liq_prcp_off_glacier'] +ds['liq_prcp_on_glacier']\n", " ds['runoff_monthly'] = ds['melt_off_glacier_monthly']+ds['melt_on_glacier_monthly']+ ds['liq_prcp_off_glacier_monthly'] +ds['liq_prcp_on_glacier_monthly']\n", " # we don't want to load all, just the total runoff\n", " n_running_rgis = len(ds.rgi_id)\n", " # in the last year, no runoff data is available, let's remove that year\n", " ds = ds.isel(time=slice(0,-1))\n", " ds = ds[['runoff','runoff_monthly']].sum(dim='rgi_id', skipna=True).load()\n", " #ds = ds.sum(dim='rgi_id')\n", " ds.coords['rgi_reg'] = f'{rgi_reg:02}'\n", " ds.coords['gcm_scenario'] = f'{gcm}_{scen}'\n", " ds.coords['gcm'] = gcm\n", " df.append(ds)\n", "\n", " except:\n", " print(gcm,scen)\n", " pass\n", " dg = xr.concat(df, dim='gcm', fill_value=np.NaN)\n", " #df = df.expand_dims('rgi_reg')\n", " dg.coords['scenario'] = scen\n", " dg = dg.expand_dims('scenario')\n", " dgs.append(dg)\n", " dgs = xr.concat(dgs, dim='scenario', fill_value=np.NaN)\n", " dgs = dgs.expand_dims('rgi_reg')\n", " dfs.append(dgs)\n", "\n", "df_sel_2 = xr.concat(dfs, dim='rgi_reg')\n", "df_sel_2.coords['bias_correction'] = bc[1:]\n", "df_sel_2.coords['hist'] = hist" ] }, { "cell_type": "code", "execution_count": 16, "id": "4cf39abf-df30-42a1-8251-2699d18d21c1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 84kB\n",
       "Dimensions:            (rgi_reg: 1, scenario: 3, gcm: 5, time: 100, month_2d: 12)\n",
       "Coordinates: (12/13)\n",
       "  * time               (time) float64 800B 2e+03 2.001e+03 ... 2.099e+03\n",
       "    hydro_year         (time) int64 800B 2000 2001 2002 2003 ... 2097 2098 2099\n",
       "    hydro_month        (time) int64 800B 4 4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4 4\n",
       "    calendar_year      (time) int64 800B 2000 2001 2002 2003 ... 2097 2098 2099\n",
       "    calendar_month     (time) int64 800B 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1\n",
       "  * month_2d           (month_2d) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    ...                 ...\n",
       "  * rgi_reg            (rgi_reg) <U2 8B '11'\n",
       "    gcm_scenario       (scenario, gcm) <U20 1kB 'mri-esm2-0_ssp585' ... 'ipsl...\n",
       "  * gcm                (gcm) <U13 260B 'mri-esm2-0' ... 'ipsl-cm6a-lr'\n",
       "  * scenario           (scenario) <U6 72B 'ssp585' 'ssp126' 'ssp370'\n",
       "    bias_correction    <U1 4B ''\n",
       "    hist               <U15 60B 'w5e5_gcm_merged'\n",
       "Data variables:\n",
       "    runoff             (rgi_reg, scenario, gcm, time) float32 6kB 8.553e+12 ....\n",
       "    runoff_monthly     (rgi_reg, scenario, gcm, time, month_2d) float32 72kB ...
" ], "text/plain": [ " Size: 84kB\n", "Dimensions: (rgi_reg: 1, scenario: 3, gcm: 5, time: 100, month_2d: 12)\n", "Coordinates: (12/13)\n", " * time (time) float64 800B 2e+03 2.001e+03 ... 2.099e+03\n", " hydro_year (time) int64 800B 2000 2001 2002 2003 ... 2097 2098 2099\n", " hydro_month (time) int64 800B 4 4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4 4\n", " calendar_year (time) int64 800B 2000 2001 2002 2003 ... 2097 2098 2099\n", " calendar_month (time) int64 800B 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1\n", " * month_2d (month_2d) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n", " ... ...\n", " * rgi_reg (rgi_reg) " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# IPCC colors from: https://pyam-iamc.readthedocs.io/en/stable/tutorials/ipcc_colors.html\n", "colors_scenario = {'ssp126': '#003466',\n", " 'ssp370':'#df0000',\n", " 'ssp585':'#980002'}\n", "\n", "import seaborn as sns\n", "# create a pandas dataframe which facilitates plotting with seaborn\n", "pd_runoff = df_sel_2.runoff.to_dataframe().reset_index().dropna()\n", "for scen in ['ssp126', 'ssp370', 'ssp585']:\n", " pd_sel = pd_runoff.loc[pd_runoff.scenario==scen]\n", " n_gcm = len(pd_sel.gcm.unique())\n", " sns.lineplot(data=pd_sel,\n", " x='time', y= 'runoff',\n", " errorbar=('pi', 100), estimator='median',\n", " color= colors_scenario[scen],\n", " label=f'{scen}, n={n_gcm} GCMs'\n", " )\n", "plt.ylabel('runoff (kg year-1)')\n", "plt.legend(title='median and total range')" ] }, { "cell_type": "markdown", "id": "d7f16ddd-42f2-4445-af20-19b2578704f8", "metadata": {}, "source": [ "*Now you could compare that to other cmip options, and look into the monthly runoff*" ] }, { "cell_type": "code", "execution_count": null, "id": "72dcc223-c9b1-412f-824d-4324674cc486", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "87a7680c-1ba6-4caa-930c-e77a018ef73b", "metadata": {}, "source": [ "### Example use case 3: Estimate glacier runoff of an entire basin \n", "-> the per-glacier runoff files were aggregated in [this notebook](https://nbviewer.org/urls/cluster.klima.uni-bremen.de/~oggm/oggm-standard-projections/oggm_v16/2023.3/_run_scripts/compute_runoff_for_basins.ipynb), and are available here https://cluster.klima.uni-bremen.de/~oggm/oggm-standard-projections/oggm_v16/2023.3/CMIP6/2100/basins/ (at the moment only for CMIP6 until 2100). All glaciers of a basin are aggregated in one file, and there are different files for the GCM, Scenario and historical projection option. The files of each basin are in one subfolder, with the subfolder name being the MRBID of that basin. \n", "- The basin files contain the variables: volume (m3), area (m2), runoff (kg year-1) and runoff_monthly (kg month-1)" ] }, { "cell_type": "code", "execution_count": 17, "id": "59a5ddb9-9615-49d6-b2fb-213e825b90ce", "metadata": {}, "outputs": [], "source": [ "# You can get the MRBID from: \n", "import geopandas as gpd\n", "fp_basin = oggm.utils.file_downloader('/home/www/fmaussion/misc/magicc/basins_shape/glacier_basins.shp')\n", "pd_basin_num = gpd.read_file(fp_basin)" ] }, { "cell_type": "code", "execution_count": 18, "id": "e52e81c5-7fc0-4f92-97ae-480b6ae37236", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MRBIDRIVER_BASICONTINENTOCEANSEAAREA_CALCShape_LengShape_AreaRGI_AREAgeometry
02103INDIGIRKAAsiaArctic OceanEast Siberian Sea341234.085.60405170.212499171.941POLYGON ((151.72500 70.97500, 151.72500 70.970...
12108OBAsiaArctic OceanKara Sea3040606.1168.355250448.342075763.493POLYGON ((91.75000 57.70417, 91.74542 57.70299...
22302BRAHMAPUTRAAsiaIndian OceanBay of Bengal540782.566.05444049.68661110528.496POLYGON ((97.76667 28.77083, 97.76335 28.77168...
32306GANGESAsiaIndian OceanBay of Bengal1006558.6122.34998390.9468807906.081MULTIPOLYGON (((88.02555 21.57150, 88.02361 21...
42309INDUSAsiaIndian OceanArabian Sea865012.681.28629182.85227527206.546MULTIPOLYGON (((67.44167 23.97006, 67.44028 23...
.................................
706241POEuropeAtlantic OceanAdriatic Sea73289.521.5760988.425867313.417POLYGON ((9.68333 46.42500, 9.68418 46.42168, ...
716242RHINEEuropeAtlantic OceanNorth Sea163122.348.24761220.200160336.922POLYGON ((4.45638 52.14527, 4.46175 52.14110, ...
726243RHONEEuropeAtlantic OceanMediterranean Sea96637.628.00491611.197342917.302POLYGON ((6.84167 47.82083, 6.84167 47.81667, ...
736254THJORSAEuropeAtlantic OceanNorth Atlantic8277.89.2708471.545336972.419POLYGON ((-17.50000 64.62500, -17.50035 64.605...
746255TORNEALVEN (also TORNIONJOKI, also TORNIONVAYLA)EuropeAtlantic OceanBaltic Sea40545.228.9190258.68565234.257POLYGON ((23.69167 68.84167, 23.70214 68.84102...
\n", "

75 rows × 10 columns

\n", "
" ], "text/plain": [ " MRBID RIVER_BASI CONTINENT \\\n", "0 2103 INDIGIRKA Asia \n", "1 2108 OB Asia \n", "2 2302 BRAHMAPUTRA Asia \n", "3 2306 GANGES Asia \n", "4 2309 INDUS Asia \n", ".. ... ... ... \n", "70 6241 PO Europe \n", "71 6242 RHINE Europe \n", "72 6243 RHONE Europe \n", "73 6254 THJORSA Europe \n", "74 6255 TORNEALVEN (also TORNIONJOKI, also TORNIONVAYLA) Europe \n", "\n", " OCEAN SEA AREA_CALC Shape_Leng Shape_Area \\\n", "0 Arctic Ocean East Siberian Sea 341234.0 85.604051 70.212499 \n", "1 Arctic Ocean Kara Sea 3040606.1 168.355250 448.342075 \n", "2 Indian Ocean Bay of Bengal 540782.5 66.054440 49.686611 \n", "3 Indian Ocean Bay of Bengal 1006558.6 122.349983 90.946880 \n", "4 Indian Ocean Arabian Sea 865012.6 81.286291 82.852275 \n", ".. ... ... ... ... ... \n", "70 Atlantic Ocean Adriatic Sea 73289.5 21.576098 8.425867 \n", "71 Atlantic Ocean North Sea 163122.3 48.247612 20.200160 \n", "72 Atlantic Ocean Mediterranean Sea 96637.6 28.004916 11.197342 \n", "73 Atlantic Ocean North Atlantic 8277.8 9.270847 1.545336 \n", "74 Atlantic Ocean Baltic Sea 40545.2 28.919025 8.685652 \n", "\n", " RGI_AREA geometry \n", "0 171.941 POLYGON ((151.72500 70.97500, 151.72500 70.970... \n", "1 763.493 POLYGON ((91.75000 57.70417, 91.74542 57.70299... \n", "2 10528.496 POLYGON ((97.76667 28.77083, 97.76335 28.77168... \n", "3 7906.081 MULTIPOLYGON (((88.02555 21.57150, 88.02361 21... \n", "4 27206.546 MULTIPOLYGON (((67.44167 23.97006, 67.44028 23... \n", ".. ... ... \n", "70 313.417 POLYGON ((9.68333 46.42500, 9.68418 46.42168, ... \n", "71 336.922 POLYGON ((4.45638 52.14527, 4.46175 52.14110, ... \n", "72 917.302 POLYGON ((6.84167 47.82083, 6.84167 47.81667, ... \n", "73 972.419 POLYGON ((-17.50000 64.62500, -17.50035 64.605... \n", "74 34.257 POLYGON ((23.69167 68.84167, 23.70214 68.84102... \n", "\n", "[75 rows x 10 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd_basin_num" ] }, { "cell_type": "markdown", "id": "a9f4eeb7-e38a-4213-bf31-43c8233ac295", "metadata": {}, "source": [ "Let's look at the Indigirka basin:" ] }, { "cell_type": "code", "execution_count": 19, "id": "7deb4057-7929-4af3-8117-1e26ac286d9e", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basin_name = 'Indigirka'\n", "basin_idx = pd_basin_num.loc[pd_basin_num.RIVER_BASI==basin_name]['MRBID']\n", "basin_idx = 2103 # this is the basin \"\"\n", "end_yr = '2100' # you could change that to '2300'\n", "cmip = 'CMIP6' # or change that to CMIP5\n", "path = f'/home/www/oggm/oggm-standard-projections/oggm_v16/2023.3/{cmip}/{end_yr}' \n", "# if you are not at the oggm cluster need to change the path \n", "# you could download it also directly via oggm.utils.file_downloader(path ... )\n", "# but attention, the files are large!!!\n", "pd_basin_num = gpd.read_file(fp_basin)\n", "hist = 'gcm_from_2000'\n", "bc = '_bc_2000_2019'\n", "ssps = ['ssp126', 'ssp585']\n", "# load all the GCMs and SSPs at once! \n", "with xr.open_mfdataset(f'{path}/basins/{basin_idx}/basin_{basin_idx}_run_hydro_{hist}{bc}*.nc') as ds:\n", " ds = ds.runoff.sel(scenario = ssps)\n", " ds = ds.isel(time=slice(0,-1)).dropna(dim='rgi_id', how='all')\n", " n_rgis= len(ds.rgi_id)\n", " # we need to make sure that GCMs that are only available for some scenarios, will keep to have nan values after doing the sum over the basin\n", " ds = ds.sum(dim='rgi_id', skipna=True, min_count=n_rgis).load()\n", "\n", "\n", "# create a pandas dataframe which facilitates plotting with seaborn\n", "pd_runoff = ds.to_dataframe().reset_index().dropna()\n", "for scen in ssps:\n", " pd_sel = pd_runoff.loc[pd_runoff.scenario==scen]\n", " n_gcm = len(pd_sel.gcm.unique())\n", " sns.lineplot(data=pd_sel,\n", " x='time', y= 'runoff',\n", " errorbar=('pi', 100), estimator='median',\n", " color= colors_scenario[scen],\n", " label=f'{scen}, n={n_gcm} GCMs'\n", " )\n", "plt.ylabel('runoff (kg year-1)')\n", "plt.legend(title='median and total range')\n", "plt.title(f'{ds.basin_name.values}: glacierised area = {ds.glacierised_area_perc.values:0.2f}%, glaciers in basin = {ds.glaciers_in_basin.values}')\n", "plt.tight_layout();" ] }, { "cell_type": "code", "execution_count": null, "id": "fda5cb4a-34f7-4335-a3f2-f2ce41c2f405", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "f8f3d0af-194d-410b-851e-149a14b11d1e", "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 }