{ "cells": [ { "cell_type": "markdown", "id": "cf16edc4-e893-40c2-b5fc-45bbfcd26007", "metadata": {}, "source": [ "# Check how well the preprocessed gdirs match the regional volume community estimate " ] }, { "cell_type": "markdown", "id": "c67dad5e-9648-4626-a7fe-dc76963c607a", "metadata": {}, "source": [ "four options that I checked:\n", "- regional \"inversion volume\" (as in the summary statistics)\n", "- regional volume from spinup_historical from the year 2000\n", "- regional volume from the area-weighted median year\n", "- regional volume directly extracted from the oggm standard projections" ] }, { "cell_type": "code", "execution_count": 1, "id": "bc10d3de-cc54-4ed3-8ad5-6370b9d5d914", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import xarray as xr\n", "import glob\n", "import numpy as np\n", "from oggm import utils\n", "rgi_meta = pd.read_hdf('/home/www/oggm/rgi/rgi62_stats.h5')\n", "rgi_meta = rgi_meta.loc[rgi_meta.Connect != 2]\n", "rdf = pd.read_hdf(utils.get_demo_file('rgi62_itmix_df.h5'))\n", "rdf = rdf.loc[rgi_meta.index] # remove connectivity 2\n", "rdf.loc[rgi_meta.index, 'region'] = rgi_meta['O1Region'].values\n", "pd_gmip3_area_weighted_median = pd.read_csv('/home/www/lschuster/GlacierMIP3/data/table_S3.csv', index_col='rgi_reg')\n", "\n", "pd_vol_km3_reg = pd.DataFrame()\n", "pd_perc_dev_farinotti = pd.DataFrame()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "97e3e1a9-ecfc-48af-8bf6-de295fc8a49f", "metadata": {}, "outputs": [], "source": [ "rgi_meta = pd.read_hdf('/home/www/oggm/rgi/rgi70')\n" ] }, { "cell_type": "code", "execution_count": null, "id": "4a6bbbba-943e-4c80-9103-a60bb33a29c0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "id": "ce46c295-7063-4639-8015-99a1316dbc2a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0Year$^a$ (glacier-area weighted median)Glacier area$^a$ (km²)Glacier surface slope$^a$ (glacier-area weighted average, °)Glacier mass$^a$ (Gt)Glacier mass in 2020$^b$ (Gt)2000-2019 observed glacier mass loss$^b$ (rel. to 2000, %)Simulation time (years)
rgi_reg
globalGlobal200070573911.41423411374913.9NaN
19Sub- & Antarctic Islands (19)19861328673.641820413771.15000.0
03Arctic Canada N (03)19991051119.625498248512.55000.0
01Alaska (01)20108672513.917081162468.05000.0
05Greenland Periphery (05)20018971710.314123134105.35000.0
09Russian Arctic (09)20015159211.913176129651.75000.0
04Arctic Canada S (04)20014088811.5775072127.25000.0
07Svalbard (07)2008339598.8672365663.35000.0
17Southern Andes (17)20002942914.9480643689.15000.0
06Iceland (06)2000110606.7339331945.85000.0
13Central Asia (13)20074930319.5294427716.82000.0
14South Asia W (14)20013356822.3257924853.72000.0
02W Canada & US (02)20041452418.394279516.82000.0
15South Asia E (15)20021473421.179065618.22000.0
08Scandinavia (08)2002294911.926923712.92000.0
10North Asia (10)2011241018.412210919.62000.0
11Central Europe (11)2003209220.91158529.72000.0
16Low Latitudes (16)2000234125.3896922.32000.0
18New Zealand (18)1978116225.6665320.82000.0
12Caucasus & Middle East (12)2001130724.2574325.02000.0
\n", "
" ], "text/plain": [ " Unnamed: 0 \\\n", "rgi_reg \n", "global Global \n", "19 Sub- & Antarctic Islands (19) \n", "03 Arctic Canada N (03) \n", "01 Alaska (01) \n", "05 Greenland Periphery (05) \n", "09 Russian Arctic (09) \n", "04 Arctic Canada S (04) \n", "07 Svalbard (07) \n", "17 Southern Andes (17) \n", "06 Iceland (06) \n", "13 Central Asia (13) \n", "14 South Asia W (14) \n", "02 W Canada & US (02) \n", "15 South Asia E (15) \n", "08 Scandinavia (08) \n", "10 North Asia (10) \n", "11 Central Europe (11) \n", "16 Low Latitudes (16) \n", "18 New Zealand (18) \n", "12 Caucasus & Middle East (12) \n", "\n", " Year$^a$ (glacier-area weighted median) Glacier area$^a$ (km²) \\\n", "rgi_reg \n", "global 2000 705739 \n", "19 1986 132867 \n", "03 1999 105111 \n", "01 2010 86725 \n", "05 2001 89717 \n", "09 2001 51592 \n", "04 2001 40888 \n", "07 2008 33959 \n", "17 2000 29429 \n", "06 2000 11060 \n", "13 2007 49303 \n", "14 2001 33568 \n", "02 2004 14524 \n", "15 2002 14734 \n", "08 2002 2949 \n", "10 2011 2410 \n", "11 2003 2092 \n", "16 2000 2341 \n", "18 1978 1162 \n", "12 2001 1307 \n", "\n", " Glacier surface slope$^a$ (glacier-area weighted average, °) \\\n", "rgi_reg \n", "global 11.4 \n", "19 3.6 \n", "03 9.6 \n", "01 13.9 \n", "05 10.3 \n", "09 11.9 \n", "04 11.5 \n", "07 8.8 \n", "17 14.9 \n", "06 6.7 \n", "13 19.5 \n", "14 22.3 \n", "02 18.3 \n", "15 21.1 \n", "08 11.9 \n", "10 18.4 \n", "11 20.9 \n", "16 25.3 \n", "18 25.6 \n", "12 24.2 \n", "\n", " Glacier mass$^a$ (Gt) Glacier mass in 2020$^b$ (Gt) \\\n", "rgi_reg \n", "global 142341 137491 \n", "19 41820 41377 \n", "03 25498 24851 \n", "01 17081 16246 \n", "05 14123 13410 \n", "09 13176 12965 \n", "04 7750 7212 \n", "07 6723 6566 \n", "17 4806 4368 \n", "06 3393 3194 \n", "13 2944 2771 \n", "14 2579 2485 \n", "02 942 795 \n", "15 790 656 \n", "08 269 237 \n", "10 122 109 \n", "11 115 85 \n", "16 89 69 \n", "18 66 53 \n", "12 57 43 \n", "\n", " 2000-2019 observed glacier mass loss$^b$ (rel. to 2000, %) \\\n", "rgi_reg \n", "global 3.9 \n", "19 1.1 \n", "03 2.5 \n", "01 8.0 \n", "05 5.3 \n", "09 1.7 \n", "04 7.2 \n", "07 3.3 \n", "17 9.1 \n", "06 5.8 \n", "13 6.8 \n", "14 3.7 \n", "02 16.8 \n", "15 18.2 \n", "08 12.9 \n", "10 19.6 \n", "11 29.7 \n", "16 22.3 \n", "18 20.8 \n", "12 25.0 \n", "\n", " Simulation time (years) \n", "rgi_reg \n", "global NaN \n", "19 5000.0 \n", "03 5000.0 \n", "01 5000.0 \n", "05 5000.0 \n", "09 5000.0 \n", "04 5000.0 \n", "07 5000.0 \n", "17 5000.0 \n", "06 5000.0 \n", "13 2000.0 \n", "14 2000.0 \n", "02 2000.0 \n", "15 2000.0 \n", "08 2000.0 \n", "10 2000.0 \n", "11 2000.0 \n", "16 2000.0 \n", "18 2000.0 \n", "12 2000.0 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd_gmip3_area_weighted_median" ] }, { "cell_type": "code", "execution_count": 56, "id": "7e853e68-e8cb-4f3d-abad-21db52431739", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "87.870554" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv('/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.1/common_running_2100/volume/CMIP6/2100/RGI16/ssp126.csv').iloc[0].mean()/1e9" ] }, { "cell_type": "code", "execution_count": null, "id": "40317068-2dde-4ae9-b63f-9913b3cb1a7a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 53, "id": "41481169-4630-4d46-8261-8b25690d79fb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "85.98287777777777" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 2, "id": "ce1b10de-168f-42df-a9e1-9730b93a9839", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name '_p' is not defined", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m _test= (xr.open_dataset(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43m_p\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m/historical_run_output_11.nc\u001b[39m\u001b[33m'\u001b[39m).volume.load()/\u001b[32m1e9\u001b[39m)\n\u001b[32m 2\u001b[39m _test_e= (xr.open_dataset(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_p\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m/historical_run_output_extended_11.nc\u001b[39m\u001b[33m'\u001b[39m).volume.load()/\u001b[32m1e9\u001b[39m)\n\u001b[32m 3\u001b[39m _test_spinup= (xr.open_dataset(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_p\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m/spinup_historical_run_output_11.nc\u001b[39m\u001b[33m'\u001b[39m).volume.load()/\u001b[32m1e9\u001b[39m)\n", "\u001b[31mNameError\u001b[39m: name '_p' is not defined" ] } ], "source": [ "_test= (xr.open_dataset(f'{_p}/historical_run_output_11.nc').volume.load()/1e9)\n", "_test_e= (xr.open_dataset(f'{_p}/historical_run_output_extended_11.nc').volume.load()/1e9)\n", "_test_spinup= (xr.open_dataset(f'{_p}/spinup_historical_run_output_11.nc').volume.load()/1e9)\n" ] }, { "cell_type": "code", "execution_count": 47, "id": "41a31893-6b8d-4f38-92d9-a73b7013433c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'volume' ()> Size: 4B\n",
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<xarray.DataArray 'volume' ()> Size: 4B\n",
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timeMRI-ESM2-0
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<xarray.DataArray 'volume' (time: 42, rgi_id: 274531)> Size: 46MB\n",
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       "    calendar_year   (time) int64 336B 1979 1980 1981 1982 ... 2018 2019 2020\n",
       "    calendar_month  (time) int64 336B 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
" ], "text/plain": [ " Size: 46MB\n", "array([[1.0920017e-02, 5.2883616e-03, 8.8707032e-03, ..., 7.9476957e+00,\n", " 1.0417383e+02, 8.1643784e+01],\n", " [1.0776889e-02, 5.1795896e-03, 8.7293163e-03, ..., 8.0040674e+00,\n", " 1.0473831e+02, 8.2097763e+01],\n", " [1.0725359e-02, 5.0517698e-03, 8.6200526e-03, ..., 8.0257778e+00,\n", " 1.0496028e+02, 8.2290237e+01],\n", " ...,\n", " [3.8544778e-03, 9.2803227e-04, 1.3435486e-03, ..., 8.7298136e+00,\n", " 1.1220128e+02, 8.7375999e+01],\n", " [3.7534910e-03, 8.8357349e-04, 1.2291332e-03, ..., 8.7352800e+00,\n", " 1.1223716e+02, 8.7297005e+01],\n", " [3.5115520e-03, 8.1046781e-04, 1.0392953e-03, ..., 8.6669083e+00,\n", " 1.1155939e+02, 8.6742813e+01]], dtype=float32)\n", "Coordinates:\n", " * time (time) int64 336B 1979 1980 1981 1982 ... 2018 2019 2020\n", " * rgi_id (rgi_id) \u001b[39m\u001b[32m15\u001b[39m _vol_2000 = \u001b[43mds_vol_reg_km3_spinup_hist\u001b[49m\u001b[43m.\u001b[49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrgi_id\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mrdf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrdf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mregion\u001b[49m\u001b[43m==\u001b[49m\u001b[43mregion\u001b[49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m.sel(time=\u001b[32m2000\u001b[39m).sum().values\n\u001b[32m 16\u001b[39m pd_vol_km3_reg.loc[region, \u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mopt\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m-vol_spinup_hist_yr2000_km3\u001b[39m\u001b[33m'\u001b[39m] = _vol_2000\n\u001b[32m 17\u001b[39m t_itmix = pd_gmip3_area_weighted_median.loc[region,\u001b[33m'\u001b[39m\u001b[33mYear$^a$ (glacier-area weighted median)\u001b[39m\u001b[33m'\u001b[39m]\n", "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/oggm_env_2025/lib/python3.11/site-packages/xarray/core/dataarray.py:1716\u001b[39m, in \u001b[36mDataArray.sel\u001b[39m\u001b[34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[39m\n\u001b[32m 1600\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msel\u001b[39m(\n\u001b[32m 1601\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1602\u001b[39m indexers: Mapping[Any, Any] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1606\u001b[39m **indexers_kwargs: Any,\n\u001b[32m 1607\u001b[39m ) -> Self:\n\u001b[32m 1608\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Return a new DataArray whose data is given by selecting index\u001b[39;00m\n\u001b[32m 1609\u001b[39m \u001b[33;03m labels along the specified dimension(s).\u001b[39;00m\n\u001b[32m 1610\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 1714\u001b[39m \u001b[33;03m Dimensions without coordinates: points\u001b[39;00m\n\u001b[32m 1715\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1716\u001b[39m ds = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_to_temp_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1717\u001b[39m \u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1718\u001b[39m \u001b[43m \u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1719\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1720\u001b[39m \u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1721\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mindexers_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1722\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1723\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._from_temp_dataset(ds)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/oggm_env_2025/lib/python3.11/site-packages/xarray/core/dataset.py:2982\u001b[39m, in \u001b[36mDataset.sel\u001b[39m\u001b[34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[39m\n\u001b[32m 2914\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Returns a new dataset with each array indexed by tick labels\u001b[39;00m\n\u001b[32m 2915\u001b[39m \u001b[33;03malong the specified dimension(s).\u001b[39;00m\n\u001b[32m 2916\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 2979\u001b[39m \n\u001b[32m 2980\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 2981\u001b[39m indexers = either_dict_or_kwargs(indexers, indexers_kwargs, \u001b[33m\"\u001b[39m\u001b[33msel\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m2982\u001b[39m query_results = \u001b[43mmap_index_queries\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2983\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtolerance\u001b[49m\n\u001b[32m 2984\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2986\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m drop:\n\u001b[32m 2987\u001b[39m no_scalar_variables = {}\n", "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/oggm_env_2025/lib/python3.11/site-packages/xarray/core/indexing.py:201\u001b[39m, in \u001b[36mmap_index_queries\u001b[39m\u001b[34m(obj, indexers, method, tolerance, **indexers_kwargs)\u001b[39m\n\u001b[32m 199\u001b[39m results.append(IndexSelResult(labels))\n\u001b[32m 200\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m201\u001b[39m results.append(\u001b[43mindex\u001b[49m\u001b[43m.\u001b[49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 203\u001b[39m merged = merge_sel_results(results)\n\u001b[32m 205\u001b[39m \u001b[38;5;66;03m# drop dimension coordinates found in dimension indexers\u001b[39;00m\n\u001b[32m 206\u001b[39m \u001b[38;5;66;03m# (also drop multi-index if any)\u001b[39;00m\n\u001b[32m 207\u001b[39m \u001b[38;5;66;03m# (.sel() already ensures alignment)\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/oggm_env_2025/lib/python3.11/site-packages/xarray/core/indexes.py:873\u001b[39m, in \u001b[36mPandasIndex.sel\u001b[39m\u001b[34m(self, labels, method, tolerance)\u001b[39m\n\u001b[32m 871\u001b[39m indexer = get_indexer_nd(\u001b[38;5;28mself\u001b[39m.index, label_array, method, tolerance)\n\u001b[32m 872\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m np.any(indexer < \u001b[32m0\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mnot all values found in index \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 875\u001b[39m \u001b[38;5;66;03m# attach dimension names and/or coordinates to positional indexer\u001b[39;00m\n\u001b[32m 876\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, Variable):\n", "\u001b[31mKeyError\u001b[39m: \"not all values found in index 'rgi_id'\"" ] } ], "source": [ "for opt in ['oggm_v161-per_glacier_spinup', 'oggm_v163-per_glacier_spinup', 'oggm_v163-regional_spinup']: #,'oggm_v163-regional_spinup_rgi70G']:\n", " opt1,opt2 = opt.split('-')\n", " if opt == 'oggm_v161-per_glacier_spinup':\n", " _p = '/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2023.3/elev_bands/W5E5_spinup/RGI62/b_160/L5/summary'\n", " elif opt=='oggm_v163-regional_spinup_rgi70G':\n", " _p= f'/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2025.6/elev_bands/W5E5/regional_spinup/RGI70G/b_160/L5/summary'\n", " elif 'oggm_v163' in opt: \n", " _p= f'/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2025.6/elev_bands/W5E5/{opt2}/RGI62/b_160/L5/summary'\n", "\n", " # less glaciers are included here ... \n", " ds_vol_reg_km3_spinup_hist = (xr.open_mfdataset(f'{_p}/spinup_historical_run_output_*.nc').volume.load()/1e9)\n", "\n", " for region in rdf.region.unique():\n", " pd_vol_km3_reg.loc[region, 'vol_itmix_km3'] = rdf.loc[rdf.region==region,'vol_itmix_m3'].sum()/1e9\n", " _vol_2000 = ds_vol_reg_km3_spinup_hist.sel(rgi_id = rdf.loc[rdf.region==region].index).sel(time=2000).sum().values\n", " pd_vol_km3_reg.loc[region, f'{opt}-vol_spinup_hist_yr2000_km3'] = _vol_2000\n", " t_itmix = pd_gmip3_area_weighted_median.loc[region,'Year$^a$ (glacier-area weighted median)']\n", " if t_itmix<1979:\n", " print(region)\n", " t_itmix = 1979\n", " _vol_rgi_date = ds_vol_reg_km3_spinup_hist.sel(rgi_id = rdf.loc[rdf.region==region].index).sel(time=t_itmix).sum().values\n", " pd_vol_km3_reg.loc[region, f'{opt}-vol_spinup_hist_yrRGIdate_km3'] = _vol_rgi_date\n", " if opt == 'oggm_v163-per_glacier_spinup':\n", " _t = pd.read_csv(f'/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.3/w5e5/per_glacier_spinup/common_running_2100/volume/CMIP6/2100/RGI{region}/ssp126.csv', index_col='time')\n", " elif opt == 'oggm_v161-per_glacier_spinup':\n", " _t = pd.read_csv(f'/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.1/common_running_2100/volume/CMIP6/2100/RGI{region}/ssp126.csv', index_col='time')\n", " elif opt == 'oggm_v163-regional_spinup':\n", " _t = pd.read_csv(f'/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.3/w5e5/regional_spinup/common_running_2100/volume/CMIP6/2100/RGI{region}/ssp126.csv', index_col='time')\n", " elif opt == 'oggm_v163-regional_spinup_rgi70G':\n", " _t = pd.read_csv(f'/home/www/oggm/oggm-standard-projections/oggm-standard-projections-csv-files/1.6.3/w5e5/regional_spinup/common_running_2100/volume/CMIP6/2100/RGI2000-v7.0-G-{region}/ssp126.csv', index_col='time')\n", " pd_vol_km3_reg.loc[region, f'{opt}-vol_standard_proj_yr2000_km3'] = _t.loc[2000].mean()/1e9\n", " \n", " \n", " files = glob.glob(f'{_p}/glacier_statistics_*.csv')\n", " assert len(files) == 19\n", " \n", " df = []\n", " for f in files:\n", " df.append(pd.read_csv(f, index_col=0, low_memory=False))\n", " df_rgi6 = pd.concat(df)\n", " \n", " df_rgi6['vol_farinotti'] = rdf.vol_itmix_m3 * 1e-9\n", " vol_reg = df_rgi6[['inv_volume_km3']].groupby(df_rgi6.rgi_region).sum()\n", " vol_reg.index = vol_reg.index.astype(str).str.zfill(2)\n", " pd_vol_km3_reg.loc[vol_reg.index, f'{opt}-inv_volume_km3'] = vol_reg['inv_volume_km3'].values\n", " \n", " for t in ['vol_spinup_hist_yr2000_km3','vol_spinup_hist_yrRGIdate_km3', 'inv_volume_km3','vol_standard_proj_yr2000_km3']:\n", " pd_perc_dev_farinotti[f'{opt}-{t}'] = (100*(pd_vol_km3_reg[f'{opt}-{t}'] -pd_vol_km3_reg['vol_itmix_km3'])/ pd_vol_km3_reg['vol_itmix_km3']).round(3)\n", " #pd_perc_dev_farinotti[opt]= 100*np.abs(vol_reg['inv_volume_km3'] -vol_reg['vol_farinotti'])/ vol_reg['vol_farinotti']" ] }, { "cell_type": "code", "execution_count": 8, "id": "5ed0bf8d-ea22-4c38-9f39-d60713c377d3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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vol_itmix_km3oggm_v161-per_glacier_spinup-vol_spinup_hist_yr2000_km3oggm_v161-per_glacier_spinup-vol_spinup_hist_yrRGIdate_km3oggm_v161-per_glacier_spinup-vol_standard_proj_yr2000_km3oggm_v161-per_glacier_spinup-inv_volume_km3oggm_v163-per_glacier_spinup-vol_spinup_hist_yr2000_km3oggm_v163-per_glacier_spinup-vol_spinup_hist_yrRGIdate_km3oggm_v163-per_glacier_spinup-vol_standard_proj_yr2000_km3oggm_v163-per_glacier_spinup-inv_volume_km3oggm_v163-regional_spinup-vol_spinup_hist_yr2000_km3oggm_v163-regional_spinup-vol_spinup_hist_yrRGIdate_km3oggm_v163-regional_spinup-vol_standard_proj_yr2000_km3oggm_v163-regional_spinup-inv_volume_km3
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" ], "text/plain": [ " oggm_v161-per_glacier_spinup-inv_volume_km3 \\\n", "01 0.040 \n", "02 0.000 \n", "03 -0.033 \n", "04 -0.005 \n", "05 -0.009 \n", "06 0.001 \n", "07 -0.035 \n", "08 0.000 \n", "09 0.031 \n", "10 -5.645 \n", "11 -0.002 \n", "12 -5.947 \n", "13 -0.040 \n", "14 -0.003 \n", "15 -0.046 \n", "16 -0.050 \n", "17 -0.048 \n", "18 0.029 \n", "19 -0.111 \n", "\n", " oggm_v161-per_glacier_spinup-vol_spinup_hist_yrRGIdate_km3 \\\n", "01 1.322 \n", "02 -4.412 \n", "03 -2.841 \n", "04 1.133 \n", "05 -1.166 \n", "06 0.667 \n", "07 -0.567 \n", "08 3.272 \n", "09 -4.506 \n", "10 -11.855 \n", "11 -0.295 \n", "12 -4.267 \n", "13 0.166 \n", "14 0.051 \n", "15 0.972 \n", "16 -5.407 \n", "17 -3.616 \n", "18 0.031 \n", "19 -1.029 \n", "\n", " oggm_v161-per_glacier_spinup-vol_spinup_hist_yr2000_km3 \\\n", "01 3.703 \n", "02 -2.128 \n", "03 -2.875 \n", "04 1.473 \n", "05 -0.951 \n", "06 0.667 \n", "07 1.155 \n", "08 3.420 \n", "09 -4.354 \n", "10 -1.137 \n", "11 -0.127 \n", "12 -2.508 \n", "13 1.286 \n", "14 0.631 \n", "15 1.316 \n", "16 -5.407 \n", "17 -3.616 \n", "18 -7.734 \n", "19 -2.183 \n", "\n", " oggm_v161-per_glacier_spinup-vol_standard_proj_yr2000_km3 \n", "01 3.688 \n", "02 -2.153 \n", "03 -2.957 \n", "04 1.469 \n", "05 -0.954 \n", "06 0.665 \n", "07 1.065 \n", "08 3.402 \n", "09 -4.402 \n", "10 -1.492 \n", "11 -0.166 \n", "12 -2.710 \n", "13 1.239 \n", "14 0.608 \n", "15 1.316 \n", "16 -5.412 \n", "17 -3.641 \n", "18 -7.734 \n", "19 -2.216 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v = 'oggm_v161'\n", "pd_perc_dev_farinotti[[f'{v}-per_glacier_spinup-inv_volume_km3', # inversion volume as in glacier_statistics_summary files\n", " f'{v}-per_glacier_spinup-vol_spinup_hist_yrRGIdate_km3', # glacier volume as in spinup_historical_run_output summary files at regional median glacier-area weighted RGI date\n", " f'{v}-per_glacier_spinup-vol_spinup_hist_yr2000_km3', # same as above but for year 2000\n", " f'{v}-per_glacier_spinup-vol_standard_proj_yr2000_km3', # glacier volume from start of OGGM standard projections (2000 is first year) \n", " ]]\n", "# 100*(V-V_farinotti)/V_farinotti" ] }, { "cell_type": "code", "execution_count": 14, "id": "b56854b7-a5d1-4b8f-870a-a6cfcc229642", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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oggm_v163-per_glacier_spinup-inv_volume_km3oggm_v163-per_glacier_spinup-vol_spinup_hist_yrRGIdate_km3oggm_v163-per_glacier_spinup-vol_spinup_hist_yr2000_km3oggm_v163-per_glacier_spinup-vol_standard_proj_yr2000_km3
010.0281.0743.4573.336
020.003-5.054-2.764-2.783
03-0.028-2.900-2.934-3.194
04-0.0051.0461.3861.380
05-0.033-1.308-1.094-1.092
060.0020.4740.474-0.755
07-0.035-0.6121.1071.068
08-0.0012.4772.6292.620
090.035-4.846-4.689-4.736
10-5.646-13.430-3.045-3.200
11-0.002-0.928-0.660-0.672
12-5.913-5.071-3.312-3.485
13-0.067-0.6760.4380.421
14-0.004-0.1650.4080.385
15-0.0740.2790.5440.536
160.039-7.397-7.397-7.444
17-0.050-3.611-3.611-3.610
18-0.009-0.001-7.993-7.993
19-0.106-1.722-2.927-2.952
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" ], "text/plain": [ " oggm_v163-per_glacier_spinup-inv_volume_km3 \\\n", "01 0.028 \n", "02 0.003 \n", "03 -0.028 \n", "04 -0.005 \n", "05 -0.033 \n", "06 0.002 \n", "07 -0.035 \n", "08 -0.001 \n", "09 0.035 \n", "10 -5.646 \n", "11 -0.002 \n", "12 -5.913 \n", "13 -0.067 \n", "14 -0.004 \n", "15 -0.074 \n", "16 0.039 \n", "17 -0.050 \n", "18 -0.009 \n", "19 -0.106 \n", "\n", " oggm_v163-per_glacier_spinup-vol_spinup_hist_yrRGIdate_km3 \\\n", "01 1.074 \n", "02 -5.054 \n", "03 -2.900 \n", "04 1.046 \n", "05 -1.308 \n", "06 0.474 \n", "07 -0.612 \n", "08 2.477 \n", "09 -4.846 \n", "10 -13.430 \n", "11 -0.928 \n", "12 -5.071 \n", "13 -0.676 \n", "14 -0.165 \n", "15 0.279 \n", "16 -7.397 \n", "17 -3.611 \n", "18 -0.001 \n", "19 -1.722 \n", "\n", " oggm_v163-per_glacier_spinup-vol_spinup_hist_yr2000_km3 \\\n", "01 3.457 \n", "02 -2.764 \n", "03 -2.934 \n", "04 1.386 \n", "05 -1.094 \n", "06 0.474 \n", "07 1.107 \n", "08 2.629 \n", "09 -4.689 \n", "10 -3.045 \n", "11 -0.660 \n", "12 -3.312 \n", "13 0.438 \n", "14 0.408 \n", "15 0.544 \n", "16 -7.397 \n", "17 -3.611 \n", "18 -7.993 \n", "19 -2.927 \n", "\n", " oggm_v163-per_glacier_spinup-vol_standard_proj_yr2000_km3 \n", "01 3.336 \n", "02 -2.783 \n", "03 -3.194 \n", "04 1.380 \n", "05 -1.092 \n", "06 -0.755 \n", "07 1.068 \n", "08 2.620 \n", "09 -4.736 \n", "10 -3.200 \n", "11 -0.672 \n", "12 -3.485 \n", "13 0.421 \n", "14 0.385 \n", "15 0.536 \n", "16 -7.444 \n", "17 -3.610 \n", "18 -7.993 \n", "19 -2.952 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v = 'oggm_v163'\n", "pd_perc_dev_farinotti[[f'{v}-per_glacier_spinup-inv_volume_km3', # inversion volume as in glacier_statistics_summary files\n", " f'{v}-per_glacier_spinup-vol_spinup_hist_yrRGIdate_km3', # glacier volume as in spinup_historical_run_output summary files at regional median glacier-area weighted RGI date\n", " f'{v}-per_glacier_spinup-vol_spinup_hist_yr2000_km3', # same as above but for year 2000\n", " f'{v}-per_glacier_spinup-vol_standard_proj_yr2000_km3', # glacier volume from start of OGGM standard projections (2000 is first year) \n", " ]]\n", "# 100*(V-V_farinotti)/V_farinotti" ] }, { "cell_type": "code", "execution_count": null, "id": "b44b3ba6-3e43-45c7-b23e-00027be27020", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:oggm_env_2025]", "language": "python", "name": "conda-env-oggm_env_2025-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": "3.11.14" } }, "nbformat": 4, "nbformat_minor": 5 }