From 8df1b7b8ab8111f37b91a5a7142cc5f8c0585ac1 Mon Sep 17 00:00:00 2001 From: Marin Kneib Date: Thu, 26 Mar 2026 09:27:10 +0100 Subject: [PATCH] Added tutorial on running OGGM with avalanches --- .../running_oggm_with_avalanches.ipynb | 1114 +++++++++++++++++ 1 file changed, 1114 insertions(+) create mode 100644 notebooks/tutorials/running_oggm_with_avalanches.ipynb diff --git a/notebooks/tutorials/running_oggm_with_avalanches.ipynb b/notebooks/tutorials/running_oggm_with_avalanches.ipynb new file mode 100644 index 00000000..7a1e09fc --- /dev/null +++ b/notebooks/tutorials/running_oggm_with_avalanches.ipynb @@ -0,0 +1,1114 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e7bf0137", + "metadata": {}, + "source": [ + "# Avalanches in OGGM" + ] + }, + { + "cell_type": "markdown", + "id": "183960a3", + "metadata": {}, + "source": [ + "In this series of 2 notebooks, we showcase a recent addition to OGGM: a representation of avalanche contribution to glacier mass balance, based on the work by [Kneib et al. (2025)](https://doi.org/10.1038/s41467-025-65608-z). Here, based on an example for a specific glacier, we explain how one can use these maps to account for the avalanche contribution to glacier mass balance for future projections of glacier volume change." + ] + }, + { + "cell_type": "markdown", + "id": "ce874edb", + "metadata": {}, + "source": [ + "# Notes on dependencies" + ] + }, + { + "cell_type": "markdown", + "id": "34fd2d65", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "As aready stated in the previous notebook, we use the small python package which can be run with the latest version of OGGM (v1.6.3):\n", + "\n", + "https://github.com/OGGM/Snowslide\n", + "\n", + "You will need to install this package using pip install.\n", + "\n", + "Dependencies are explained in the README. Note that this package addresses 2 important functionalities:\n", + "- snowslide_main.py (which calls functions.py) has all the tools to run the Snowslide avalanche routing on any DEM, competely independently from OGGM\n", + "- oggm_snowslide_compat_minimal.py contains a list of functions and objects to link OGGM with Snowslide, starting with snowslide_to_gdir and snowslide_to_gdir_meanmonthly which OGGM calls to produce maps of snow redistribution for its DEMs. \n", + "\n", + "Snowslide currently runs with pysheds v0.5, which is not part of the default OGGM environment and will need to be installed accordingly in your environment if you wish to produce your own avalanche maps (for example to run this notebook). This can be done easily:\n", + "\n", + "```bash\n", + "pip install pysheds=0.5\n", + "```\n", + "\n", + "In the most recent OGGM glacier directories we provide maps of snow redistribution by avalanches representative of the calibration period 2000-2019 for all glaciers in the world (level L3, glacier boundaries 80), which can then be ingested in the classic OGGM workflow without the need to re-run Snowslide, therefore with a very limited number of dependencies. \n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4ef0be3c", + "metadata": {}, + "source": [ + "# Snow redistribution from avalanches maps with OGGM-Snowslide" + ] + }, + { + "cell_type": "markdown", + "id": "3d097ea9", + "metadata": {}, + "source": [ + "In the previous notebook we generated maps of avalanche correction factors for a specific glacier. These maps are available in the most recent OGGM glacier directories. Let's quickly check these maps. First, we load the glacier directory containing the avalanche map." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a322d5df", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-20 12:02:11: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.\n", + "2026-03-20 12:02:11: oggm.cfg: Multiprocessing switched OFF according to the parameter file.\n", + "2026-03-20 12:02:11: oggm.cfg: Multiprocessing: using all available processors (N=8)\n", + "2026-03-20 12:02:11: oggm.cfg: PARAMS['store_model_geometry'] changed from `False` to `True`.\n", + "2026-03-20 12:02:11: oggm.cfg: PARAMS['min_ice_thick_for_length'] changed from `0.0` to `1`.\n" + ] + } + ], + "source": [ + "# Libs\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import cftime\n", + "import time\n", + "\n", + "# Locals\n", + "from oggm import cfg, utils, workflow, tasks, DEFAULT_BASE_URL\n", + "\n", + "# Initialize OGGM and set up the default run parameters\n", + "cfg.initialize(logging_level='INFO')\n", + "\n", + "# Local working directory (where OGGM will write its output)\n", + "dir_path = utils.get_temp_dir('snowslide')\n", + "cfg.PATHS['working_dir'] = utils.mkdir(dir_path)\n", + "\n", + "cfg.PARAMS['store_model_geometry'] = True\n", + "cfg.PARAMS['min_ice_thick_for_length'] = 1 # a glacier is when ice thicker than 1m\n", + "\n", + "rgi_ids = ['RGI60-11.03638'] # Argentiere Glacier" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3f9cffe0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-20 12:02:15: oggm.workflow: init_glacier_directories from prepro level 3 on 1 glaciers.\n", + "2026-03-20 12:02:15: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers\n" + ] + } + ], + "source": [ + "base_url = 'https://cluster.klima.uni-bremen.de/~mkneib/global_snowslide_maps_oggm_2025.6/global_whypso/' # gdirs with avalanche maps\n", + "gdirs = workflow.init_glacier_directories(rgi_ids, prepro_base_url=base_url, from_prepro_level=3, prepro_border=80)\n", + "gdir = gdirs[0]" + ] + }, + { + "cell_type": "markdown", + "id": "e2ada718", + "metadata": {}, + "source": [ + "Now we access the gridded information contained in the glacier directory" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cae73b7a", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the glacier DEM (to compute the initial distributed snow height)\n", + "gridded_data_path = gdir.get_filepath(\"gridded_data\")\n", + "with xr.open_dataset(gridded_data_path) as ds:\n", + " ds = ds.load()\n", + "\n", + "pfact = ds.snowslide_1m" + ] + }, + { + "cell_type": "markdown", + "id": "0b6e2e96", + "metadata": {}, + "source": [ + "Let's now have a look at these snow depth maps!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2e5287b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img = pfact.where(ds.glacier_mask).plot.imshow(cmap=\"RdBu\", vmin=0, vmax=2, add_colorbar=True)\n", + "img.colorbar.set_label(\"Avalanche correction factor (-)\", fontsize=12)\n", + "img.colorbar.ax.tick_params(labelsize=10)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "541f15e5", + "metadata": {}, + "source": [ + "OK we have our maps of avalanche correction factors for our glacier - just as a reminder, these correspond to the ratio of snow depth with and without avalanches for the period 2000-2020. Now let's look into how we account for this avalanche contribution to glacier mass balance." + ] + }, + { + "cell_type": "markdown", + "id": "8bf25866", + "metadata": {}, + "source": [ + "# Influence of avalanches on glacier mass balance" + ] + }, + { + "cell_type": "markdown", + "id": "18c3c0c7", + "metadata": {}, + "source": [ + "OGGM being a flowline model, the avalanche correction factors need to be aggregated in 1D along the flowline. This of course introduces a very strong simplification - 2 tributaries, one with a strong avalanche input, and one without, will be attributed the same contribution. In an ideal world, one would be able to run a fully distributed model to properly account for this contribution, but as a first step, already having snow redistributed in 1D enables to get a first order estimate of the avalanche effect :)\n", + "\n", + "So for now, let's bin these avalanche correction factors along our flowlines.\n", + "\n", + "More documentation here:\n", + "\n", + "- https://docs.oggm.org/en/stable/generated/oggm.tasks.elevation_band_flowline.html\n", + "- https://docs.oggm.org/en/stable/generated/oggm.tasks.fixed_dx_elevation_band_flowline.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dad43e8f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-20 12:02:24: oggm.workflow: Execute entity tasks [elevation_band_flowline] on 1 glaciers\n", + "2026-03-20 12:02:24: oggm.core.centerlines: (RGI60-11.03638) elevation_band_flowline\n", + "2026-03-20 12:02:24: oggm.workflow: Execute entity tasks [fixed_dx_elevation_band_flowline] on 1 glaciers\n", + "2026-03-20 12:02:24: oggm.core.centerlines: (RGI60-11.03638) fixed_dx_elevation_band_flowline\n" + ] + }, + { + "data": { + "text/plain": [ + "[None]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "workflow.execute_entity_task(tasks.elevation_band_flowline, gdir, bin_variables=['snowslide_1m'])\n", + "workflow.execute_entity_task(tasks.fixed_dx_elevation_band_flowline, gdir, bin_variables=['snowslide_1m'], preserve_totals=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e001b2bb", + "metadata": {}, + "source": [ + "OK so now that we have avalanche correction factors along the flowline we can actually update our mass balance model from the OGGM TI model:\n", + "\n", + "$$B_i(z) = k_p \\times P_i^{Solid}(z) - d_f \\times \\max(t_b + T_i(z) - T_{Melt}, 0)$$\n", + "\n", + "To a model that accounts for the avalanche contribution:\n", + "\n", + "$$B_i(z) = k_{ava, i}(z) \\times k_p \\times P_i^{Solid}(z) - d_f \\times \\max(t_b + T_i(z) - T_{Melt}, 0)$$\n", + "\n", + "And of course, the parameters of the model now need to be recalibrated against the geodetic mass balance.\n", + "\n", + "At this stage, Snowslide has not been adapted to the spinup recalibration scheme, so we simply use the informed 3 steps calibration strategy introduced in OGGM v1.6 and described in Schuster et al. (2023). " + ] + }, + { + "cell_type": "markdown", + "id": "f113c382", + "metadata": {}, + "source": [ + "First we check the pre-calibrated parameters from the glacier directory originally obtained with model spinup" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6eb829d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'rgi_id': 'RGI60-11.03638', 'bias': 0, 'melt_f': 4.8469078329007225, 'prcp_fac': 2.967819925599159, 'temp_bias': -0.6326349913941922, 'reference_mb': -1049.4, 'reference_mb_err': 166.8, 'reference_period': '2000-01-01_2020-01-01', 'mb_global_params': {'temp_default_gradient': -0.0065, 'temp_all_solid': 0.0, 'temp_all_liq': 2.0, 'temp_melt': -1.0}, 'baseline_climate_source': 'GSWP3_W5E5'}\n" + ] + } + ], + "source": [ + "print(gdir.read_json(\"mb_calib\"))" + ] + }, + { + "cell_type": "markdown", + "id": "8a5bba7e", + "metadata": {}, + "source": [ + "Next, we recalibrate WITHOUT avalanches (informed 3 steps, Schuster et al., 2023) for reference." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "61b2b174", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-20 12:02:30: oggm.core.massbalance: (RGI60-11.03638) mb_calibration_from_geodetic_mb\n", + "2026-03-20 12:02:31: oggm.core.massbalance: (RGI60-11.03638) mb_calibration_from_scalar_mb\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'rgi_id': 'RGI60-11.03638', 'bias': 0, 'melt_f': 4.894659932282654, 'prcp_fac': 2.967819925599159, 'temp_bias': -0.6712514064382084, 'reference_mb': -1049.4, 'reference_mb_err': 166.8, 'reference_period': '2000-01-01_2020-01-01', 'mb_global_params': {'temp_default_gradient': -0.0065, 'temp_all_solid': 0.0, 'temp_all_liq': 2.0, 'temp_melt': -1.0}, 'baseline_climate_source': 'GSWP3_W5E5'}\n" + ] + } + ], + "source": [ + "from oggm.core.massbalance import mb_calibration_from_geodetic_mb\n", + "\n", + "mb_calibration_from_geodetic_mb(gdir, write_to_gdir=True, overwrite_gdir=True, \n", + " informed_threestep=True)\n", + "\n", + "print(gdir.read_json(\"mb_calib\"))" + ] + }, + { + "cell_type": "markdown", + "id": "fb66b2a8", + "metadata": {}, + "source": [ + "Finally, we recalibrate WITH avalanches (informed 3 steps, Schuster et al., 2023), using the adapted functionaties from oggm_snowslide_compat" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c7b0763e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-20 12:02:40: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) mb_calibration_from_geodetic_mb_with_avalanches\n", + "2026-03-20 12:02:40: oggm.core.massbalance: (RGI60-11.03638) mb_calibration_from_scalar_mb_with_ava\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'rgi_id': 'RGI60-11.03638', 'bias': 0, 'melt_f': 5.0, 'prcp_fac': 2.5919504914856173, 'temp_bias': -0.6712514064382084, 'reference_mb': -1049.4, 'reference_mb_err': 166.8, 'reference_period': '2000-01-01_2020-01-01', 'mb_global_params': {'temp_default_gradient': -0.0065, 'temp_all_solid': 0.0, 'temp_all_liq': 2.0, 'temp_melt': -1.0}, 'baseline_climate_source': 'GSWP3_W5E5'}\n" + ] + } + ], + "source": [ + "from snowslide.oggm_snowslide_compat_minimal import mb_calibration_from_geodetic_mb_with_avalanches\n", + "\n", + "mb_calibration_from_geodetic_mb_with_avalanches(gdir, write_to_gdir=True, overwrite_gdir=True, \n", + " informed_threestep=True)\n", + "\n", + "print(gdir.read_json(\"mb_calib\", filesuffix='_with_ava'))\n" + ] + }, + { + "cell_type": "markdown", + "id": "4f80ed26", + "metadata": {}, + "source": [ + "You can notice that both the precipitation correction and the degree-day factor are influenced by the introduction of the snow redistribution by avalanches. But let's now compare directly the altitudinal mass balances from our 'control' and 'avalanches' scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eb78eb4b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# import TI model classes from oggm and oggm_snowslide_compat\n", + "from oggm.core.massbalance import MonthlyTIModel\n", + "from snowslide.oggm_snowslide_compat_minimal import MonthlyTIAvalancheModel\n", + "\n", + "# Get flowline\n", + "flowline = gdir.read_pickle('inversion_flowlines')[0]\n", + "\n", + "# get mass balance model\n", + "mb_control = MonthlyTIModel(gdir)\n", + "mb_ava = MonthlyTIAvalancheModel(gdir)\n", + "\n", + "# SMB 2000-2020 (control & ava models)\n", + "\n", + "# get annual mass balance at bin elevation\n", + "df_control = pd.DataFrame(index=flowline.dx_meter * np.arange(flowline.nx))\n", + "df_ava = pd.DataFrame(index=flowline.dx_meter * np.arange(flowline.nx))\n", + "for year in range(2000, 2020):\n", + " df_control[year] = mb_control.get_annual_mb(flowline.surface_h, year=year) * cfg.SEC_IN_YEAR * mb_control.rho\n", + " df_ava[year] = mb_ava.get_annual_mb(flowline.surface_h, year=year) * cfg.SEC_IN_YEAR * mb_control.rho\n", + "\n", + "df_control = df_control.mean(axis=1)\n", + "df_ava = df_ava.mean(axis=1)\n", + "\n", + "plt.plot(flowline.surface_h,df_control,label='Control')\n", + "plt.plot(flowline.surface_h,df_ava,label='Avalanches')\n", + "plt.legend(fontsize = 14)\n", + "plt.title('2000-2020 SMB', fontsize = 20)\n", + "plt.xlabel('Elevation [m a.s.l]', fontsize = 16)\n", + "plt.ylabel('Annual SMB [mm w.eq/yr]', fontsize = 16)\n", + "plt.xticks(fontsize=12) \n", + "plt.yticks(fontsize=12) \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "aaeaec43", + "metadata": {}, + "source": [ + "The 2 models were calibrated following the same procedure (informed 3 steps) and against the same geodetic mass balance from Hugonnet et al. (2021). The glacier-wide mass balance is therefore the same. However, you can see that introducing avalanches introduced more spatial variability in the SMB patterns, even in 1D. \n", + "\n", + "IMPORTANT NOTE: All the steps above were included as such in the processing of the most recent glacier directories, so the different maps, parameter values & figures can be easily extracted for each glacier, even without having Snowslide installed!! \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "055799f4", + "metadata": {}, + "source": [ + "# Influence of avalanches on future projections of glacier volumes and runoff\n", + "\n", + "This part now discusses how to make future projections of glacier & runoff changes that include these avalanche effects. For this, we still need a few functions from oggm_snowslide_compat, but which have much fewer 'problematic' dependencies. While for now you still need to install Snowslide entirely to run these, hopefully this'll change in the future.\n", + "\n", + "For the future projections we use ISIMIP3b GCMs since these are already bias-corrected with W5E5. It's also a smaller subset so takes less time to download. Note that it would still work with the CMIP6 GCMs." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b673fe7d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-20 12:03:43: oggm.cfg: PARAMS['store_fl_diagnostics'] changed from `False` to `True`.\n", + "2026-03-20 12:03:43: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:03:43: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_control_2000-2020\n", + "2026-03-20 12:03:43: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_control_2000-2020\n", + "2026-03-20 12:03:43: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_control_2000-2020\n", + "2026-03-20 12:03:44: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:03:44: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ava_2000-2020\n", + "2026-03-20 12:03:44: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ava_2000-2020\n", + "2026-03-20 12:03:44: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ava_2000-2020\n", + "2026-03-20 12:03:44: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:03:44: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp126\n", + "2026-03-20 12:03:46: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp126\n", + "2026-03-20 12:03:46: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:03:46: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:03:46: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:03:46: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:03:46: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:03:46: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:03:46: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:03:46: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:03:47: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:03:47: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp370\n", + "2026-03-20 12:03:48: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp370\n", + "2026-03-20 12:03:49: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:03:49: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:03:49: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:03:49: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:03:49: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:03:49: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:03:49: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:03:49: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:03:50: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:03:50: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp585\n", + "2026-03-20 12:03:51: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp585\n", + "2026-03-20 12:03:51: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:03:51: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:03:51: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:03:51: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:03:52: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:03:52: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:03:52: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:03:52: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ipsl-cm6a-lr_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:03:53: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:03:53: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp126\n", + "2026-03-20 12:03:54: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp126\n", + "2026-03-20 12:03:54: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:03:54: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:03:54: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:03:54: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:03:55: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:03:55: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:03:55: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:03:55: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:03:56: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:03:56: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp370\n", + "2026-03-20 12:03:57: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp370\n", + "2026-03-20 12:03:57: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:03:57: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:03:57: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:03:57: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:03:58: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:03:58: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:03:58: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:03:58: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:03:59: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:03:59: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp585\n", + "2026-03-20 12:04:00: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp585\n", + "2026-03-20 12:04:01: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:01: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:04:01: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:04:01: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:04:01: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:01: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:01: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:02: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_gfdl-esm4_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:02: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:04:02: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp126\n", + "2026-03-20 12:04:04: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp126\n", + "2026-03-20 12:04:04: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:04: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:04:04: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:04:04: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:04:05: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:05: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:04:05: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:04:05: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:04:06: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:04:06: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp370\n", + "2026-03-20 12:04:07: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp370\n", + "2026-03-20 12:04:07: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:07: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:04:07: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:04:07: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:04:08: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:08: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:04:08: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:04:08: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:04:09: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:04:09: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp585\n", + "2026-03-20 12:04:10: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp585\n", + "2026-03-20 12:04:10: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:10: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:04:10: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:04:10: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:04:11: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:11: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:11: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:11: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mpi-esm1-2-hr_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:12: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:04:12: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp126\n", + "2026-03-20 12:04:13: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp126\n", + "2026-03-20 12:04:14: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:14: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:04:14: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:04:14: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp126_control_2020-2100\n", + "2026-03-20 12:04:14: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:14: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:04:14: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:04:14: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp126_ava_2020-2100\n", + "2026-03-20 12:04:15: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:04:15: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp370\n", + "2026-03-20 12:04:16: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp370\n", + "2026-03-20 12:04:16: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:16: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:04:16: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:04:16: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp370_control_2020-2100\n", + "2026-03-20 12:04:17: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:17: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:04:17: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:04:17: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp370_ava_2020-2100\n", + "2026-03-20 12:04:18: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:04:18: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp585\n", + "2026-03-20 12:04:19: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp585\n", + "2026-03-20 12:04:19: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:19: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:04:19: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:04:19: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp585_control_2020-2100\n", + "2026-03-20 12:04:20: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:20: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:20: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:20: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_mri-esm2-0_r1i1p1f1_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:21: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:04:21: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp126\n", + "2026-03-20 12:04:22: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp126\n", + "2026-03-20 12:04:22: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:22: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp126_control_2020-2100\n", + "2026-03-20 12:04:22: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp126_control_2020-2100\n", + "2026-03-20 12:04:22: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp126_control_2020-2100\n", + "2026-03-20 12:04:23: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:23: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp126_ava_2020-2100\n", + "2026-03-20 12:04:23: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp126_ava_2020-2100\n", + "2026-03-20 12:04:23: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp126_ava_2020-2100\n", + "2026-03-20 12:04:23: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:04:23: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp370\n", + "2026-03-20 12:04:25: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp370\n", + "2026-03-20 12:04:25: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:25: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp370_control_2020-2100\n", + "2026-03-20 12:04:25: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp370_control_2020-2100\n", + "2026-03-20 12:04:25: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp370_control_2020-2100\n", + "2026-03-20 12:04:25: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:25: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp370_ava_2020-2100\n", + "2026-03-20 12:04:25: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp370_ava_2020-2100\n", + "2026-03-20 12:04:26: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp370_ava_2020-2100\n", + "2026-03-20 12:04:26: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers\n", + "2026-03-20 12:04:26: oggm.shop.gcm_climate: (RGI60-11.03638) process_monthly_isimip_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp585\n", + "2026-03-20 12:04:28: oggm.shop.gcm_climate: (RGI60-11.03638) process_gcm_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp585\n", + "2026-03-20 12:04:28: oggm.workflow: Execute entity tasks [run_with_hydro] on 1 glaciers\n", + "2026-03-20 12:04:28: oggm.core.flowline: (RGI60-11.03638) run_with_hydro_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp585_control_2020-2100\n", + "2026-03-20 12:04:28: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp585_control_2020-2100\n", + "2026-03-20 12:04:28: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp585_control_2020-2100\n", + "2026-03-20 12:04:28: oggm.workflow: Execute entity tasks [run_with_hydro_ava] on 1 glaciers\n", + "2026-03-20 12:04:28: snowslide.oggm_snowslide_compat_minimal: (RGI60-11.03638) run_with_hydro_ava_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:28: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp585_ava_2020-2100\n", + "2026-03-20 12:04:28: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_ISIMIP3b_ukesm1-0-ll_r1i1p1f2_ssp585_ava_2020-2100\n" + ] + } + ], + "source": [ + "# oggm & oggm_snowslide_compat functions\n", + "from oggm.core import massbalance\n", + "from oggm.shop import gcm_climate\n", + "from snowslide.oggm_snowslide_compat_minimal import run_with_hydro_ava\n", + "\n", + "# required configuration\n", + "cfg.PARAMS['store_fl_diagnostics'] = True\n", + "\n", + "### FORWARD SIMULATIONS 2000-2020\n", + "\n", + "# control scenario\n", + "workflow.execute_entity_task(tasks.run_with_hydro,gdir,\n", + " run_task=tasks.run_from_climate_data,\n", + " climate_filename='climate_historical', # use climate_historical for 2000-2020 period,\n", + " mb_model_class=massbalance.MonthlyTIModel,\n", + " output_filesuffix=f'_control_2000-2020', # recognize run for later\n", + " fixed_geometry_spinup_yr=2000, # start simulation year 2000 (adds volume but not area)\n", + " ye=2020 # for 2000-2020 use W5E5 simulation\n", + " );\n", + "\n", + "# avalanches scenario\n", + "workflow.execute_entity_task(run_with_hydro_ava,gdir,\n", + " run_task=tasks.run_from_climate_data,\n", + " climate_filename='climate_historical', # use gcm_data, not climate_historical,\n", + " mb_model_class=MonthlyTIAvalancheModel,\n", + " output_filesuffix=f'_ava_2000-2020', # recognize run for later\n", + " fixed_geometry_spinup_yr=2000, # start simulation year 2000 (adds volume but not area)\n", + " ye=2020\n", + " );\n", + "\n", + "\n", + "### FORWARD SIMULATIONS 2020-2100\n", + "\n", + "# you can choose one of these 5 different GCMs:\n", + "# 'gfdl-esm4_r1i1p1f1', 'mpi-esm1-2-hr_r1i1p1f1', 'mri-esm2-0_r1i1p1f1' (\"low sensitivity\" models, within typical ranges from AR6)\n", + "# 'ipsl-cm6a-lr_r1i1p1f1', 'ukesm1-0-ll_r1i1p1f2' (\"hotter\" models, especially ukesm1-0-ll)\n", + "members = ['ipsl-cm6a-lr_r1i1p1f1','gfdl-esm4_r1i1p1f1', 'mpi-esm1-2-hr_r1i1p1f1', 'mri-esm2-0_r1i1p1f1','ukesm1-0-ll_r1i1p1f2']\n", + "ssps = ['ssp126', 'ssp370','ssp585']\n", + "\n", + "for gcm in members: \n", + " for ssp in ssps:\n", + " rid = f'_ISIMIP3b_{gcm}_{ssp}'\n", + " workflow.execute_entity_task(gcm_climate.process_monthly_isimip_data, gdir, \n", + " ssp = ssp,\n", + " member=gcm,\n", + " # recognize the climate file for later\n", + " output_filesuffix=rid\n", + " );\n", + "\n", + " # control\n", + " workflow.execute_entity_task(tasks.run_with_hydro,gdir,\n", + " run_task=tasks.run_from_climate_data,\n", + " climate_filename='gcm_data', # use gcm_data for 2020-2100 period,\n", + " climate_input_filesuffix=rid, # use the chosen scenario\n", + " mb_model_class=massbalance.MonthlyTIModel,\n", + " init_model_filesuffix=f'_control_2000-2020', # we start from last simulation year year\n", + " output_filesuffix=f'{rid}_control_2020-2100', # recognize run for later\n", + " ys=2020, ye=2100 # for 2000-2020 use W5E5 simulation\n", + " );\n", + "\n", + " # avalanches\n", + " workflow.execute_entity_task(run_with_hydro_ava,gdir,\n", + " run_task=tasks.run_from_climate_data,\n", + " climate_filename='gcm_data', # use gcm_data for 2020-2100 period,\n", + " climate_input_filesuffix=rid, # use the chosen scenario\n", + " mb_model_class=MonthlyTIAvalancheModel,\n", + " init_model_filesuffix=f'_ava_2000-2020', # we start from last simulation year year\n", + " output_filesuffix=f'{rid}_ava_2020-2100', # recognize run for later\n", + " ys=2020, ye=2100 # for 2000-2020 use W5E5 simulation\n", + " );\n" + ] + }, + { + "cell_type": "markdown", + "id": "8fb77dec", + "metadata": {}, + "source": [ + "OK, let's plot the results!!" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5f8ce15a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors=['blue', 'orange', 'red']\n", + "\n", + "def compute_stats(data):\n", + " return {\n", + " \"median\": np.median(data, axis=0),\n", + " \"p25\": np.percentile(data, 25, axis=0),\n", + " \"p75\": np.percentile(data, 75, axis=0),\n", + " }\n", + "\n", + "plt.rcParams.update({\n", + " 'font.size': 16, # Base font size\n", + " 'axes.titlesize': 20, # Title font size\n", + " 'axes.labelsize': 16, # Axis label font size\n", + " 'xtick.labelsize': 12, # X-tick label font size\n", + " 'ytick.labelsize': 12, # Y-tick label font size\n", + " 'legend.fontsize': 16 # Legend font size\n", + "})\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(12,6))\n", + "\n", + "ii = 0\n", + "for ssp in ssps:\n", + " control_volumes = []\n", + " ava_volumes = []\n", + " control_hist_volumes = []\n", + " ava_hist_volumes = []\n", + " for gcm in members:\n", + " rid = f'_ISIMIP3b_{gcm}_{ssp}'\n", + " with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix=f'{rid}_control_2020-2100')) as ds:\n", + " control_volumes.append(ds.volume_m3.values)\n", + " with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix=f'{rid}_ava_2020-2100')) as ds:\n", + " ava_volumes.append(ds.volume_m3.values)\n", + "\n", + " time = ds.time.values\n", + "\n", + " with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix=f'_control_2000-2020')) as ds:\n", + " control_hist_volumes.append(ds.volume_m3.values)\n", + " with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix=f'_ava_2000-2020')) as ds:\n", + " ava_hist_volumes.append(ds.volume_m3.values)\n", + "\n", + " time_hist = ds.time.values\n", + "\n", + " control_stats = compute_stats(control_volumes)\n", + " ava_stats = compute_stats(ava_volumes)\n", + " control_hist_stats = compute_stats(control_hist_volumes)\n", + " ava_hist_stats = compute_stats(ava_hist_volumes)\n", + "\n", + " init_diff = control_hist_stats[\"median\"][0]-ava_hist_stats[\"median\"][0]\n", + " \n", + " axs[1].plot(time, control_stats[\"median\"], color=colors[ii], label = ssp)\n", + " axs[1].fill_between(time, control_stats[\"p25\"], control_stats[\"p75\"], color=colors[ii], alpha=0.1, edgecolor=None)\n", + "\n", + " axs[1].plot(time, ava_stats[\"median\"]+init_diff, color=colors[ii], linestyle=\"dashed\")\n", + " axs[1].fill_between(time, ava_stats[\"p25\"]+init_diff, ava_stats[\"p75\"]+init_diff, color=colors[ii], alpha=0.1, edgecolor=None)\n", + "\n", + " axs[1].plot(time_hist, control_hist_stats[\"median\"], color=\"black\")\n", + " axs[1].plot(time_hist, ava_hist_stats[\"median\"]+init_diff, color=\"black\")\n", + "\n", + " axs[1].set_xlabel(\"Time [yrs]\", fontsize=16)\n", + " axs[1].set_ylabel(\"Volume [m³]\", fontsize=16)\n", + " axs[1].legend()\n", + " \n", + " ii += 1\n", + "\n", + "axs[0].plot(flowline.surface_h,df_control,label='Control',color = 'black')\n", + "axs[0].plot(flowline.surface_h,df_ava,label='Avalanches', color = 'black', linestyle = '--')\n", + "axs[0].legend(fontsize = 14)\n", + "axs[0].set_xlabel('Elevation [m a.s.l]', fontsize = 16)\n", + "axs[0].set_ylabel('Annual 2000-2020 SMB [mm w.eq/yr]', fontsize = 16) \n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b453a09b", + "metadata": {}, + "source": [ + "As you can see for Argentière, including avalanches does not seem to strongly affect the future evolution of that glacier, despite an avalanche contribution to accumulation of around 20%. Feel free to test this for your own favorite glacier just by changing the RGI ID at the start of the notebook! Example - Bazhin Glacier in the Karakorum (RGI60-18.02066)." + ] + }, + { + "cell_type": "markdown", + "id": "74a5c8a4", + "metadata": {}, + "source": [ + "The calibration of the MonthlyTIAvalanche model above was conducted with the informed three-steps approach described in [Schuster et al. (2023)](https://doi.org/10.1017/aog.2023.57), as was done in [Kneib et al. (2025)](https://doi.org/10.1038/s41467-025-65608-z) to allow for a direct comparison to the OGGM standard runs without avalanches and avoid the additional stochasticity from the dynamicc spinup. One can however also use this model with the OGGM dynamical spinup - just keep in mind though that the avalanche correction factors are fixed and correspond to the period 2000-2020 though! The example below follows the [advanced dynamical spinup notebook](https://tutorials.oggm.org/stable/notebooks/tutorials/dynamical_spinup.html). It would be similarly straightforward to use it for the daily model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5f9ae112", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-20 13:12:14: oggm.core.inversion: (RGI60-11.03638) get_inversion_volume\n", + "2026-03-20 13:12:14: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_hist_fixed_geom\n", + "2026-03-20 13:12:14: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_hist_fixed_geom\n", + "2026-03-20 13:12:14: oggm.core.flowline: (RGI60-11.03638) run_from_climate_data_hist_fixed_geom_ava\n", + "2026-03-20 13:12:14: oggm.core.flowline: (RGI60-11.03638) flowline_model_run_hist_fixed_geom_ava\n", + "2026-03-20 13:12:15: oggm.core.dynamic_spinup: (RGI60-11.03638) run_dynamic_spinup_spinup_dynamic_area\n", + "2026-03-20 13:12:18: oggm.core.dynamic_spinup: (RGI60-11.03638) run_dynamic_spinup_spinup_dynamic_area_ava\n", + "2026-03-20 13:12:22: oggm.core.dynamic_spinup: (RGI60-11.03638) run_dynamic_spinup_spinup_dynamic_volume\n", + "2026-03-20 13:12:23: oggm.core.dynamic_spinup: (RGI60-11.03638) run_dynamic_spinup_spinup_dynamic_volume_ava\n", + "2026-03-20 13:12:25: oggm.core.dynamic_spinup: (RGI60-11.03638) run_dynamic_melt_f_calibration_dynamic_melt_f\n", + "2026-03-20 13:12:25: oggm.core.flowline: (RGI60-11.03638) init_present_time_glacier_dyn_melt_f_calib\n", + "2026-03-20 13:12:25: oggm.core.dynamic_spinup: (RGI60-11.03638) run_dynamic_spinup_dynamic_melt_f\n", + "2026-03-20 13:12:29: oggm.core.dynamic_spinup: Dynamic melt_f calibration worked for RGI60-11.03638!\n", + "2026-03-20 13:12:29: oggm.core.dynamic_spinup: (RGI60-11.03638) run_dynamic_melt_f_calibration_dynamic_melt_f_ava\n", + "2026-03-20 13:12:29: oggm.core.flowline: (RGI60-11.03638) init_present_time_glacier_dyn_melt_f_calib\n", + "2026-03-20 13:12:29: oggm.core.dynamic_spinup: (RGI60-11.03638) run_dynamic_spinup_dynamic_melt_f_ava\n", + "2026-03-20 13:12:33: oggm.core.dynamic_spinup: Dynamic melt_f calibration worked for RGI60-11.03638!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We will \"reconstruct\" a possible glacier evolution from this year onwards\n", + "spinup_start_yr = 1979\n", + "\n", + "# here we save the original melt_f for later\n", + "melt_f_original = gdir.read_json('mb_calib')['melt_f']\n", + "\n", + "# and save some reference values we can compare to later\n", + "area_reference = gdir.rgi_area_m2 # RGI area\n", + "volume_reference = tasks.get_inversion_volume(gdir) # volume after regional calibration to consensus\n", + "ref_period = cfg.PARAMS['geodetic_mb_period'] # get the reference period we want to use, 2000 to 2020\n", + "df_ref_dmdtda = utils.get_geodetic_mb_dataframe().loc[gdir.rgi_id] # get the data from Hugonnet 2021\n", + "df_ref_dmdtda = df_ref_dmdtda.loc[df_ref_dmdtda['period'] == ref_period] # only select the desired period\n", + "dmdtda_reference = df_ref_dmdtda['dmdtda'].values[0] * 1000 # get the reference dmdtda and convert into kg m-2 yr-1\n", + "dmdtda_reference_error = df_ref_dmdtda['err_dmdtda'].values[0] * 1000 # corresponding uncertainty\n", + "\n", + "# ---- First do the fixed geometry spinup ----\n", + "tasks.run_from_climate_data(gdir,\n", + " climate_filename='climate_historical',\n", + " mb_model_class=massbalance.MonthlyTIModel,\n", + " fixed_geometry_spinup_yr=spinup_start_yr, # Start the run at the RGI date but retro-actively correct the data with fixed geometry\n", + " output_filesuffix='_hist_fixed_geom', # where to write the output\n", + " );\n", + "\n", + "# Read the output\n", + "with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix='_hist_fixed_geom')) as ds:\n", + " ds_hist = ds.load()\n", + "\n", + "# with avalanches\n", + "tasks.run_from_climate_data(gdir,\n", + " climate_filename='climate_historical',\n", + " mb_model_class=MonthlyTIAvalancheModel,\n", + " fixed_geometry_spinup_yr=spinup_start_yr, # Start the run at the RGI date but retro-actively correct the data with fixed geometry\n", + " output_filesuffix='_hist_fixed_geom_ava', # where to write the output\n", + " );\n", + "with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix='_hist_fixed_geom_ava')) as ds:\n", + " ds_hist_ava = ds.load()\n", + "\n", + "# ---- Second the dynamic spinup alone, matching area ----\n", + "tasks.run_dynamic_spinup(gdir,\n", + " spinup_start_yr=spinup_start_yr, # When to start the spinup\n", + " minimise_for='area', # what target to match at the RGI date\n", + " output_filesuffix='_spinup_dynamic_area', # Where to write the output\n", + " ye=2020, # When the simulation should stop\n", + " );\n", + "# Read the output\n", + "with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix='_spinup_dynamic_area')) as ds:\n", + " ds_dynamic_spinup_area = ds.load()\n", + "\n", + "# with avalanches\n", + "mb_ava = MonthlyTIAvalancheModel(gdir)\n", + "if hasattr(mb_ava, 'repeat'):\n", + " mb_ava.repeat = int(mb_ava.repeat)\n", + "tasks.run_dynamic_spinup(gdir,\n", + " spinup_start_yr=spinup_start_yr, # When to start the spinup\n", + " mb_model_historical=mb_ava,\n", + " minimise_for='area', # what target to match at the RGI date\n", + " output_filesuffix='_spinup_dynamic_area_ava', # Where to write the output\n", + " ye=2020, # When the simulation should stop\n", + " );\n", + "with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix='_spinup_dynamic_area_ava')) as ds:\n", + " ds_dynamic_spinup_area_ava = ds.load()\n", + " \n", + "# ---- Third the dynamic spinup alone, matching volume ----\n", + "tasks.run_dynamic_spinup(gdir,\n", + " spinup_start_yr=spinup_start_yr, # When to start the spinup\n", + " minimise_for='volume', # what target to match at the RGI date\n", + " output_filesuffix='_spinup_dynamic_volume', # Where to write the output\n", + " ye=2020, # When the simulation should stop\n", + " );\n", + "# Read the output\n", + "with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix='_spinup_dynamic_volume')) as ds:\n", + " ds_dynamic_spinup_volume = ds.load()\n", + "\n", + "# with avalanches\n", + "tasks.run_dynamic_spinup(gdir,\n", + " spinup_start_yr=spinup_start_yr, # When to start the spinup\n", + " mb_model_historical=mb_ava,\n", + " minimise_for='volume', # what target to match at the RGI date\n", + " output_filesuffix='_spinup_dynamic_volume_ava', # Where to write the output\n", + " ye=2020, # When the simulation should stop\n", + " );\n", + "with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix='_spinup_dynamic_volume_ava')) as ds:\n", + " ds_dynamic_spinup_volume_ava = ds.load()\n", + "\n", + "# ---- Fourth the dynamic melt_f calibration (includes a dynamic spinup matching area) ----\n", + "tasks.run_dynamic_melt_f_calibration(gdir,\n", + " ys=spinup_start_yr, # When to start the spinup\n", + " ye=2020, # When the simulation should stop\n", + " output_filesuffix='_dynamic_melt_f', # Where to write the output\n", + " );\n", + "\n", + "with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix='_dynamic_melt_f')) as ds:\n", + " ds_dynamic_melt_f = ds.load()\n", + "\n", + "# with avalanches\n", + "run_kwargs = {'mb_model_historical': mb_ava}\n", + "tasks.run_dynamic_melt_f_calibration(gdir,\n", + " ys=spinup_start_yr, # When to start the spinup\n", + " kwargs_run_function=run_kwargs,\n", + " ye=2020, # When the simulation should stop\n", + " output_filesuffix='_dynamic_melt_f_ava', # Where to write the output\n", + " );\n", + "\n", + "with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix='_dynamic_melt_f_ava')) as ds:\n", + " ds_dynamic_melt_f_ava = ds.load()\n", + "\n", + "# Now make a plot for comparision\n", + "y0 = gdir.rgi_date + 1\n", + "\n", + "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(16, 5))\n", + "colors = ['C0', 'C1', 'C2', 'C3']\n", + "\n", + "ds_hist.volume_m3.plot(ax=ax1, color=colors[0]);\n", + "ds_hist_ava.volume_m3.plot(ax=ax1, color=colors[0], linestyle='--');\n", + "ds_dynamic_melt_f.volume_m3.plot(ax=ax1, color=colors[1]);\n", + "ds_dynamic_melt_f_ava.volume_m3.plot(ax=ax1, color=colors[1], linestyle='--');\n", + "ds_dynamic_spinup_area.volume_m3.plot(ax=ax1, color=colors[2]);\n", + "ds_dynamic_spinup_area_ava.volume_m3.plot(ax=ax1, color=colors[2], linestyle='--');\n", + "ds_dynamic_spinup_volume.volume_m3.plot(ax=ax1, color=colors[3]);\n", + "ds_dynamic_spinup_volume_ava.volume_m3.plot(ax=ax1, color=colors[3], linestyle='--');\n", + "ax1.set_title('Volume');\n", + "ax1.scatter(y0, volume_reference, c='C3');\n", + "\n", + "ds_hist.area_m2.plot(ax=ax2, color=colors[0]);\n", + "ds_hist_ava.area_m2.plot(ax=ax2, color=colors[0], linestyle='--');\n", + "ds_dynamic_melt_f.area_m2.plot(ax=ax2, color=colors[1]);\n", + "ds_dynamic_melt_f_ava.area_m2.plot(ax=ax2, color=colors[1], linestyle='--');\n", + "ds_dynamic_spinup_area.area_m2.plot(ax=ax2, color=colors[2]);\n", + "ds_dynamic_spinup_area_ava.area_m2.plot(ax=ax2, color=colors[2], linestyle='--');\n", + "ds_dynamic_spinup_volume.area_m2.plot(ax=ax2, color=colors[3]);\n", + "ds_dynamic_spinup_volume_ava.area_m2.plot(ax=ax2, color=colors[3], linestyle='--');\n", + "ax2.set_title('Area');\n", + "ax2.scatter(y0, area_reference, c='C3')\n", + "\n", + "ds_hist.length_m.plot(ax=ax3, color=colors[0], label='Fixed geometry spinup');\n", + "ds_hist_ava.length_m.plot(ax=ax3, color=colors[0], linestyle='--', label='Avalanches');\n", + "ds_dynamic_melt_f.length_m.plot(ax=ax3, color=colors[1], label='Dynamical melt_f calibration');\n", + "ds_dynamic_melt_f_ava.length_m.plot(ax=ax3, color=colors[1], linestyle='--');\n", + "ds_dynamic_spinup_area.length_m.plot(ax=ax3, color=colors[2], label='Dynamical spinup match area');\n", + "ds_dynamic_spinup_area_ava.length_m.plot(ax=ax3, color=colors[2], linestyle='--');\n", + "ds_dynamic_spinup_volume.length_m.plot(ax=ax3, color=colors[3], label='Dynamical spinup match volume');\n", + "ds_dynamic_spinup_volume_ava.length_m.plot(ax=ax3, color=colors[3], linestyle='--');\n", + "ax3.set_title('Length');\n", + "ax3.scatter(y0, ds_hist.sel(time=y0).length_m, c='C3', label='Reference values')\n", + "handles, labels = ax3.get_legend_handles_labels()\n", + "f.legend(handles, labels, \n", + " loc='lower center', \n", + " bbox_to_anchor=(0.5, -0.15), # Slightly below the figure bottom\n", + " ncol=3, # Spread items horizontally\n", + " frameon=True)\n", + "f.subplots_adjust(bottom=0.2)\n", + "\n", + "plt.tight_layout()\n", + "plt.show();" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a1f6c30a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reference dmdtda 2000 to 2020 (Hugonnet 2021): -1049.40 +/- 166.80 kg m-2 yr-1\n", + "Fixed geometry spinup dmdtda 2000 to 2020: -1005.96 kg m-2 yr-1\n", + "Fixed geometry spinup dmdtda 2000 to 2020 (avalanches): -1009.40 kg m-2 yr-1\n", + "Dynamical spinup match area dmdtda 2000 to 2020: -1062.32 kg m-2 yr-1\n", + "Dynamical spinup match area dmdtda 2000 to 2020 (avalanches): -1035.92 kg m-2 yr-1\n", + "Dynamical spinup match volume dmdtda 2000 to 2020: -1268.63 kg m-2 yr-1\n", + "Dynamical spinup match volume dmdtda 2000 to 2020 (avalanches): -1293.18 kg m-2 yr-1\n", + "Dynamical melt_f calibration dmdtda 2000 to 2020: -1062.32 kg m-2 yr-1\n", + "Dynamical melt_f calibration dmdtda 2000 to 2020 (avalanches): -1035.92 kg m-2 yr-1\n", + "Original melt_f: 4.9 kg m-2 day-1 °C-1\n", + "Dynamic melt_f: 4.9 kg m-2 day-1 °C-1\n" + ] + } + ], + "source": [ + "# and print out the modeled geodetic mass balances for comparison\n", + "def get_dmdtda(ds):\n", + " yr0_ref_mb, yr1_ref_mb = ref_period.split('_')\n", + " yr0_ref_mb = int(yr0_ref_mb.split('-')[0])\n", + " yr1_ref_mb = int(yr1_ref_mb.split('-')[0])\n", + "\n", + " return ((ds.volume_m3.loc[yr1_ref_mb].values -\n", + " ds.volume_m3.loc[yr0_ref_mb].values) /\n", + " gdir.rgi_area_m2 /\n", + " (yr1_ref_mb - yr0_ref_mb) *\n", + " cfg.PARAMS['ice_density'])\n", + "\n", + "print(f'Reference dmdtda 2000 to 2020 (Hugonnet 2021): {dmdtda_reference:.2f} +/- {dmdtda_reference_error:6.2f} kg m-2 yr-1')\n", + "print(f'Fixed geometry spinup dmdtda 2000 to 2020: {get_dmdtda(ds_hist):.2f} kg m-2 yr-1')\n", + "print(f'Fixed geometry spinup dmdtda 2000 to 2020 (avalanches): {get_dmdtda(ds_hist_ava):.2f} kg m-2 yr-1')\n", + "print(f'Dynamical spinup match area dmdtda 2000 to 2020: {get_dmdtda(ds_dynamic_spinup_area):.2f} kg m-2 yr-1')\n", + "print(f'Dynamical spinup match area dmdtda 2000 to 2020 (avalanches): {get_dmdtda(ds_dynamic_spinup_area_ava):.2f} kg m-2 yr-1')\n", + "print(f'Dynamical spinup match volume dmdtda 2000 to 2020: {get_dmdtda(ds_dynamic_spinup_volume):.2f} kg m-2 yr-1')\n", + "print(f'Dynamical spinup match volume dmdtda 2000 to 2020 (avalanches): {get_dmdtda(ds_dynamic_spinup_volume_ava):.2f} kg m-2 yr-1')\n", + "print(f'Dynamical melt_f calibration dmdtda 2000 to 2020: {get_dmdtda(ds_dynamic_melt_f):.2f} kg m-2 yr-1')\n", + "print(f'Dynamical melt_f calibration dmdtda 2000 to 2020 (avalanches): {get_dmdtda(ds_dynamic_melt_f_ava):.2f} kg m-2 yr-1')\n", + "print(f'Original melt_f: {melt_f_original:.1f} kg m-2 day-1 °C-1')\n", + "print(f\"Dynamic melt_f: {gdir.read_json('mb_calib')['melt_f']:.1f} kg m-2 day-1 °C-1\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3703b0bf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "oggm_dev_v1.6.3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}