{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import netCDF4 as nc\n", "import matplotlib.pylab as plt\n", "import imp\n", "import csv\n", "import pandas as pd\n", "from io import StringIO" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# AISM_VUB\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_AISM_VUB_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_AISM_VUB_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_AISM_VUB_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_AISM_VUB_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_AISM_VUB_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_AISM_VUB_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_AISM_VUB_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_AISM_VUB_SU_RCP26 = SL_wTd_nos_base_AISM_VUB_R1_RCP26+SL_wTd_nos_base_AISM_VUB_R2_RCP26+SL_wTd_nos_base_AISM_VUB_R3_RCP26+SL_wTd_nos_base_AISM_VUB_R4_RCP26+SL_wTd_nos_base_AISM_VUB_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# BISI_LBL\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_BISI_LBL_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_BISI_LBL_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_BISI_LBL_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_BISI_LBL_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_BISI_LBL_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_BISI_LBL_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_BISI_LBL_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_BISI_LBL_SU_RCP26 = SL_wTd_nos_base_BISI_LBL_R1_RCP26+SL_wTd_nos_base_BISI_LBL_R2_RCP26+SL_wTd_nos_base_BISI_LBL_R3_RCP26+SL_wTd_nos_base_BISI_LBL_R4_RCP26+SL_wTd_nos_base_BISI_LBL_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# CISM_NCA\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_CISM_NCA_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_CISM_NCA_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_CISM_NCA_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_CISM_NCA_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_CISM_NCA_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_CISM_NCA_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_CISM_NCA_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_CISM_NCA_SU_RCP26 = SL_wTd_nos_base_CISM_NCA_R1_RCP26+SL_wTd_nos_base_CISM_NCA_R2_RCP26+SL_wTd_nos_base_CISM_NCA_R3_RCP26+SL_wTd_nos_base_CISM_NCA_R4_RCP26+SL_wTd_nos_base_CISM_NCA_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# FETI_VUB\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_FETI_VUB_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_FETI_VUB_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_FETI_VUB_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_FETI_VUB_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_FETI_VUB_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_FETI_VUB_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_FETI_VUB_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_FETI_VUB_SU_RCP26 = SL_wTd_nos_base_FETI_VUB_R1_RCP26+SL_wTd_nos_base_FETI_VUB_R2_RCP26+SL_wTd_nos_base_FETI_VUB_R3_RCP26+SL_wTd_nos_base_FETI_VUB_R4_RCP26+SL_wTd_nos_base_FETI_VUB_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# GRIS_LSC\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_GRIS_LSC_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_GRIS_LSC_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_GRIS_LSC_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_GRIS_LSC_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_GRIS_LSC_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_GRIS_LSC_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_GRIS_LSC_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_GRIS_LSC_SU_RCP26 = SL_wTd_nos_base_GRIS_LSC_R1_RCP26+SL_wTd_nos_base_GRIS_LSC_R2_RCP26+SL_wTd_nos_base_GRIS_LSC_R3_RCP26+SL_wTd_nos_base_GRIS_LSC_R4_RCP26+SL_wTd_nos_base_GRIS_LSC_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# IMAU_VUB\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_IMAU_VUB_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_IMAU_VUB_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_IMAU_VUB_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_IMAU_VUB_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_IMAU_VUB_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_IMAU_VUB_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_IMAU_VUB_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_IMAU_VUB_SU_RCP26 = SL_wTd_nos_base_IMAU_VUB_R1_RCP26+SL_wTd_nos_base_IMAU_VUB_R2_RCP26+SL_wTd_nos_base_IMAU_VUB_R3_RCP26+SL_wTd_nos_base_IMAU_VUB_R4_RCP26+SL_wTd_nos_base_IMAU_VUB_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# ISSM_JPL\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_ISSM_JPL_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_ISSM_JPL_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_ISSM_JPL_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_ISSM_JPL_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_ISSM_JPL_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_ISSM_JPL_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_ISSM_JPL_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_ISSM_JPL_SU_RCP26 = SL_wTd_nos_base_ISSM_JPL_R1_RCP26+SL_wTd_nos_base_ISSM_JPL_R2_RCP26+SL_wTd_nos_base_ISSM_JPL_R3_RCP26+SL_wTd_nos_base_ISSM_JPL_R4_RCP26+SL_wTd_nos_base_ISSM_JPL_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# ISSM_UCI\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_ISSM_UCI_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_ISSM_UCI_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_ISSM_UCI_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_ISSM_UCI_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_ISSM_UCI_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_ISSM_UCI_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_ISSM_UCI_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_ISSM_UCI_SU_RCP26 = SL_wTd_nos_base_ISSM_UCI_R1_RCP26+SL_wTd_nos_base_ISSM_UCI_R2_RCP26+SL_wTd_nos_base_ISSM_UCI_R3_RCP26+SL_wTd_nos_base_ISSM_UCI_R4_RCP26+SL_wTd_nos_base_ISSM_UCI_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# MALI_LAN\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_MALI_LAN_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_MALI_LAN_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_MALI_LAN_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_MALI_LAN_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_MALI_LAN_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_MALI_LAN_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_MALI_LAN_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_MALI_LAN_SU_RCP26 = SL_wTd_nos_base_MALI_LAN_R1_RCP26+SL_wTd_nos_base_MALI_LAN_R2_RCP26+SL_wTd_nos_base_MALI_LAN_R3_RCP26+SL_wTd_nos_base_MALI_LAN_R4_RCP26+SL_wTd_nos_base_MALI_LAN_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# PISM_AWI\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_PISM_AWI_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_PISM_AWI_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_PISM_AWI_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_PISM_AWI_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_PISM_AWI_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_PISM_AWI_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_PISM_AWI_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_PISM_AWI_SU_RCP26 = SL_wTd_nos_base_PISM_AWI_R1_RCP26+SL_wTd_nos_base_PISM_AWI_R2_RCP26+SL_wTd_nos_base_PISM_AWI_R3_RCP26+SL_wTd_nos_base_PISM_AWI_R4_RCP26+SL_wTd_nos_base_PISM_AWI_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# PISM_DMI\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_PISM_DMI_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_PISM_DMI_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_PISM_DMI_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_PISM_DMI_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_PISM_DMI_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_PISM_DMI_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_PISM_DMI_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_PISM_DMI_SU_RCP26 = SL_wTd_nos_base_PISM_DMI_R1_RCP26+SL_wTd_nos_base_PISM_DMI_R2_RCP26+SL_wTd_nos_base_PISM_DMI_R3_RCP26+SL_wTd_nos_base_PISM_DMI_R4_RCP26+SL_wTd_nos_base_PISM_DMI_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# PISM_PIK\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_PISM_PIK_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_PISM_PIK_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_PISM_PIK_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_PISM_PIK_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_PISM_PIK_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_PISM_PIK_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_PISM_PIK_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_PISM_PIK_SU_RCP26 = SL_wTd_nos_base_PISM_PIK_R1_RCP26+SL_wTd_nos_base_PISM_PIK_R2_RCP26+SL_wTd_nos_base_PISM_PIK_R3_RCP26+SL_wTd_nos_base_PISM_PIK_R4_RCP26+SL_wTd_nos_base_PISM_PIK_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# PISM_VUW\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_PISM_VUW_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_PISM_VUW_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_PISM_VUW_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_PISM_VUW_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_PISM_VUW_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_PISM_VUW_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_PISM_VUW_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_PISM_VUW_SU_RCP26 = SL_wTd_nos_base_PISM_VUW_R1_RCP26+SL_wTd_nos_base_PISM_VUW_R2_RCP26+SL_wTd_nos_base_PISM_VUW_R3_RCP26+SL_wTd_nos_base_PISM_VUW_R4_RCP26+SL_wTd_nos_base_PISM_VUW_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# PS3D_PSU\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_PS3D_PSU_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_PS3D_PSU_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_PS3D_PSU_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_PS3D_PSU_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_PS3D_PSU_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_PS3D_PSU_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_PS3D_PSU_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_PS3D_PSU_SU_RCP26 = SL_wTd_nos_base_PS3D_PSU_R1_RCP26+SL_wTd_nos_base_PS3D_PSU_R2_RCP26+SL_wTd_nos_base_PS3D_PSU_R3_RCP26+SL_wTd_nos_base_PS3D_PSU_R4_RCP26+SL_wTd_nos_base_PS3D_PSU_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# SICO_UHO\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_SICO_UHO_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_SICO_UHO_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_SICO_UHO_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_SICO_UHO_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_SICO_UHO_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_SICO_UHO_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_SICO_UHO_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_SICO_UHO_SU_RCP26 = SL_wTd_nos_base_SICO_UHO_R1_RCP26+SL_wTd_nos_base_SICO_UHO_R2_RCP26+SL_wTd_nos_base_SICO_UHO_R3_RCP26+SL_wTd_nos_base_SICO_UHO_R4_RCP26+SL_wTd_nos_base_SICO_UHO_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Read data\n", "# UA_UNN\n", "\n", "fname=\"../ComputeProjections4OneIceModel_SSPs/EnsembleSingleModelProjections/SL_wTd_nos_base_UA_UNN_RCP26.nc\"\n", "ncf = nc.Dataset(fname, \"r\")\n", "\n", "Time = ncf.variables[\"Time\"][:]\n", "SL_wTd_nos_base_UA_UNN_R0_RCP26 = ncf.variables[\"Antarctica\"][:]\n", "SL_wTd_nos_base_UA_UNN_R1_RCP26 = ncf.variables[\"EAIS\"][:]\n", "SL_wTd_nos_base_UA_UNN_R2_RCP26 = ncf.variables[\"Ross\"][:]\n", "SL_wTd_nos_base_UA_UNN_R3_RCP26 = ncf.variables[\"Amundsen\"][:]\n", "SL_wTd_nos_base_UA_UNN_R4_RCP26 = ncf.variables[\"Weddell\"][:]\n", "SL_wTd_nos_base_UA_UNN_R5_RCP26 = ncf.variables[\"Peninsula\"][:]\n", "SL_wTd_nos_base_UA_UNN_SU_RCP26 = SL_wTd_nos_base_UA_UNN_R1_RCP26+SL_wTd_nos_base_UA_UNN_R2_RCP26+SL_wTd_nos_base_UA_UNN_R3_RCP26+SL_wTd_nos_base_UA_UNN_R4_RCP26+SL_wTd_nos_base_UA_UNN_R5_RCP26\n", "ncf.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "SL_wTd_nos_base_RCP26 =np.vstack([SL_wTd_nos_base_AISM_VUB_SU_RCP26,SL_wTd_nos_base_BISI_LBL_SU_RCP26,SL_wTd_nos_base_CISM_NCA_SU_RCP26,SL_wTd_nos_base_FETI_VUB_SU_RCP26,SL_wTd_nos_base_GRIS_LSC_SU_RCP26,SL_wTd_nos_base_IMAU_VUB_SU_RCP26,SL_wTd_nos_base_ISSM_JPL_SU_RCP26,SL_wTd_nos_base_ISSM_UCI_SU_RCP26,SL_wTd_nos_base_MALI_LAN_SU_RCP26,SL_wTd_nos_base_PISM_AWI_SU_RCP26,SL_wTd_nos_base_PISM_DMI_SU_RCP26,SL_wTd_nos_base_PISM_PIK_SU_RCP26,SL_wTd_nos_base_PISM_VUW_SU_RCP26,SL_wTd_nos_base_PS3D_PSU_SU_RCP26,SL_wTd_nos_base_SICO_UHO_SU_RCP26,SL_wTd_nos_base_UA_UNN_SU_RCP26])\n", "\n", "SL_wTd_nos_base_R1_RCP26 =np.vstack([SL_wTd_nos_base_AISM_VUB_R1_RCP26,SL_wTd_nos_base_BISI_LBL_R1_RCP26,SL_wTd_nos_base_CISM_NCA_R1_RCP26,SL_wTd_nos_base_FETI_VUB_R1_RCP26,SL_wTd_nos_base_GRIS_LSC_R1_RCP26,SL_wTd_nos_base_IMAU_VUB_R1_RCP26,SL_wTd_nos_base_ISSM_JPL_R1_RCP26,SL_wTd_nos_base_ISSM_UCI_R1_RCP26,SL_wTd_nos_base_MALI_LAN_R1_RCP26,SL_wTd_nos_base_PISM_AWI_R1_RCP26,SL_wTd_nos_base_PISM_DMI_R1_RCP26,SL_wTd_nos_base_PISM_PIK_R1_RCP26,SL_wTd_nos_base_PISM_VUW_R1_RCP26,SL_wTd_nos_base_PS3D_PSU_R1_RCP26,SL_wTd_nos_base_SICO_UHO_R1_RCP26,SL_wTd_nos_base_UA_UNN_R1_RCP26])\n", "\n", "SL_wTd_nos_base_R2_RCP26 =np.vstack([SL_wTd_nos_base_AISM_VUB_R2_RCP26,SL_wTd_nos_base_BISI_LBL_R2_RCP26,SL_wTd_nos_base_CISM_NCA_R2_RCP26,SL_wTd_nos_base_FETI_VUB_R2_RCP26,SL_wTd_nos_base_GRIS_LSC_R2_RCP26,SL_wTd_nos_base_IMAU_VUB_R2_RCP26,SL_wTd_nos_base_ISSM_JPL_R2_RCP26,SL_wTd_nos_base_ISSM_UCI_R2_RCP26,SL_wTd_nos_base_MALI_LAN_R2_RCP26,SL_wTd_nos_base_PISM_AWI_R2_RCP26,SL_wTd_nos_base_PISM_DMI_R2_RCP26,SL_wTd_nos_base_PISM_PIK_R2_RCP26,SL_wTd_nos_base_PISM_VUW_R2_RCP26,SL_wTd_nos_base_PS3D_PSU_R2_RCP26,SL_wTd_nos_base_SICO_UHO_R2_RCP26,SL_wTd_nos_base_UA_UNN_R2_RCP26])\n", "\n", "SL_wTd_nos_base_R3_RCP26 =np.vstack([SL_wTd_nos_base_AISM_VUB_R3_RCP26,SL_wTd_nos_base_BISI_LBL_R3_RCP26,SL_wTd_nos_base_CISM_NCA_R3_RCP26,SL_wTd_nos_base_FETI_VUB_R3_RCP26,SL_wTd_nos_base_GRIS_LSC_R3_RCP26,SL_wTd_nos_base_IMAU_VUB_R3_RCP26,SL_wTd_nos_base_ISSM_JPL_R3_RCP26,SL_wTd_nos_base_ISSM_UCI_R3_RCP26,SL_wTd_nos_base_MALI_LAN_R3_RCP26,SL_wTd_nos_base_PISM_AWI_R3_RCP26,SL_wTd_nos_base_PISM_DMI_R3_RCP26,SL_wTd_nos_base_PISM_PIK_R3_RCP26,SL_wTd_nos_base_PISM_VUW_R3_RCP26,SL_wTd_nos_base_PS3D_PSU_R3_RCP26,SL_wTd_nos_base_SICO_UHO_R3_RCP26,SL_wTd_nos_base_UA_UNN_R3_RCP26])\n", "\n", "SL_wTd_nos_base_R4_RCP26 =np.vstack([SL_wTd_nos_base_AISM_VUB_R4_RCP26,SL_wTd_nos_base_BISI_LBL_R4_RCP26,SL_wTd_nos_base_CISM_NCA_R4_RCP26,SL_wTd_nos_base_FETI_VUB_R4_RCP26,SL_wTd_nos_base_GRIS_LSC_R4_RCP26,SL_wTd_nos_base_IMAU_VUB_R4_RCP26,SL_wTd_nos_base_ISSM_JPL_R4_RCP26,SL_wTd_nos_base_ISSM_UCI_R4_RCP26,SL_wTd_nos_base_MALI_LAN_R4_RCP26,SL_wTd_nos_base_PISM_AWI_R4_RCP26,SL_wTd_nos_base_PISM_DMI_R4_RCP26,SL_wTd_nos_base_PISM_PIK_R4_RCP26,SL_wTd_nos_base_PISM_VUW_R4_RCP26,SL_wTd_nos_base_PS3D_PSU_R4_RCP26,SL_wTd_nos_base_SICO_UHO_R4_RCP26,SL_wTd_nos_base_UA_UNN_R4_RCP26])\n", "\n", "SL_wTd_nos_base_R5_RCP26 =np.vstack([SL_wTd_nos_base_AISM_VUB_R5_RCP26,SL_wTd_nos_base_BISI_LBL_R5_RCP26,SL_wTd_nos_base_CISM_NCA_R5_RCP26,SL_wTd_nos_base_FETI_VUB_R5_RCP26,SL_wTd_nos_base_GRIS_LSC_R5_RCP26,SL_wTd_nos_base_IMAU_VUB_R5_RCP26,SL_wTd_nos_base_ISSM_JPL_R5_RCP26,SL_wTd_nos_base_ISSM_UCI_R5_RCP26,SL_wTd_nos_base_MALI_LAN_R5_RCP26,SL_wTd_nos_base_PISM_AWI_R5_RCP26,SL_wTd_nos_base_PISM_DMI_R5_RCP26,SL_wTd_nos_base_PISM_PIK_R5_RCP26,SL_wTd_nos_base_PISM_VUW_R5_RCP26,SL_wTd_nos_base_PS3D_PSU_R5_RCP26,SL_wTd_nos_base_SICO_UHO_R5_RCP26,SL_wTd_nos_base_UA_UNN_R5_RCP26])\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "320\n", "R0: 0\n", "R0: 1\n", "R0: 2\n", "R0: 3\n", "R0: 4\n", "R0: 5\n", "R0: 6\n", "R0: 7\n", "R0: 8\n", "R0: 9\n", "R0: 10\n", "R0: 11\n", "R0: 12\n", "R0: 13\n", "R0: 14\n", "R0: 15\n", "R0: 16\n", "R0: 17\n", "R0: 18\n", "R0: 19\n", "R0: 20\n", "R0: 21\n", "R0: 22\n", "R0: 23\n", "R0: 24\n", "R0: 25\n", "R0: 26\n", "R0: 27\n", "R0: 28\n", "R0: 29\n", "R0: 30\n", "R0: 31\n", "R0: 32\n", "R0: 33\n", "R0: 34\n", "R0: 35\n", "R0: 36\n", "R0: 37\n", "R0: 38\n", "R0: 39\n", "R0: 40\n", "R0: 41\n", "R0: 42\n", "R0: 43\n", "R0: 44\n", "R0: 45\n", "R0: 46\n", "R0: 47\n", "R0: 48\n", "R0: 49\n", "R0: 50\n", "R0: 51\n", "R0: 52\n", "R0: 53\n", "R0: 54\n", "R0: 55\n", "R0: 56\n", "R0: 57\n", "R0: 58\n", "R0: 59\n", "R0: 60\n", "R0: 61\n", "R0: 62\n", "R0: 63\n", "R0: 64\n", "R0: 65\n", "R0: 66\n", "R0: 67\n", "R0: 68\n", "R0: 69\n", "R0: 70\n", "R0: 71\n", "R0: 72\n", "R0: 73\n", "R0: 74\n", "R0: 75\n", "R0: 76\n", "R0: 77\n", "R0: 78\n", "R0: 79\n", "R0: 80\n", "R0: 81\n", "R0: 82\n", "R0: 83\n", "R0: 84\n", "R0: 85\n", "R0: 86\n", "R0: 87\n", "R0: 88\n", "R0: 89\n", "R0: 90\n", "R0: 91\n", "R0: 92\n", "R0: 93\n", "R0: 94\n", "R0: 95\n", "R0: 96\n", "R0: 97\n", "R0: 98\n", "R0: 99\n", "R0: 100\n", "R0: 101\n", "R0: 102\n", "R0: 103\n", "R0: 104\n", "R0: 105\n", "R0: 106\n", "R0: 107\n", "R0: 108\n", "R0: 109\n", "R0: 110\n", "R0: 111\n", "R0: 112\n", "R0: 113\n", "R0: 114\n", "R0: 115\n", "R0: 116\n", "R0: 117\n", "R0: 118\n", "R0: 119\n", "R0: 120\n", "R0: 121\n", "R0: 122\n", "R0: 123\n", "R0: 124\n", "R0: 125\n", "R0: 126\n", "R0: 127\n", "R0: 128\n", "R0: 129\n", "R0: 130\n", "R0: 131\n", "R0: 132\n", "R0: 133\n", "R0: 134\n", "R0: 135\n", "R0: 136\n", "R0: 137\n", "R0: 138\n", "R0: 139\n", "R0: 140\n", "R0: 141\n", "R0: 142\n", "R0: 143\n", "R0: 144\n", "R0: 145\n", "R0: 146\n", "R0: 147\n", "R0: 148\n", "R0: 149\n", "R0: 150\n", "R0: 151\n", "R0: 152\n", "R0: 153\n", "R0: 154\n", "R0: 155\n", "R0: 156\n", "R0: 157\n", "R0: 158\n", "R0: 159\n", "R0: 160\n", "R0: 161\n", "R0: 162\n", "R0: 163\n", "R0: 164\n", "R0: 165\n", "R0: 166\n", "R0: 167\n", "R0: 168\n", "R0: 169\n", "R0: 170\n", "R0: 171\n", "R0: 172\n", "R0: 173\n", "R0: 174\n", "R0: 175\n", "R0: 176\n", "R0: 177\n", "R0: 178\n", "R0: 179\n", "R0: 180\n", "R0: 181\n", "R0: 182\n", "R0: 183\n", "R0: 184\n", "R0: 185\n", "R0: 186\n", "R0: 187\n", "R0: 188\n", "R0: 189\n", "R0: 190\n", "R0: 191\n", "R0: 192\n", "R0: 193\n", "R0: 194\n", "R0: 195\n", "R0: 196\n", "R0: 197\n", "R0: 198\n", "R0: 199\n", "R1: 0\n", "R1: 1\n", "R1: 2\n", "R1: 3\n", "R1: 4\n", "R1: 5\n", "R1: 6\n", "R1: 7\n", "R1: 8\n", "R1: 9\n", "R1: 10\n", "R1: 11\n", "R1: 12\n", "R1: 13\n", "R1: 14\n", "R1: 15\n", "R1: 16\n", "R1: 17\n", "R1: 18\n", "R1: 19\n", "R1: 20\n", "R1: 21\n", "R1: 22\n", "R1: 23\n", "R1: 24\n", "R1: 25\n", "R1: 26\n", "R1: 27\n", "R1: 28\n", "R1: 29\n", "R1: 30\n", "R1: 31\n", "R1: 32\n", "R1: 33\n", "R1: 34\n", "R1: 35\n", "R1: 36\n", "R1: 37\n", "R1: 38\n", "R1: 39\n", "R1: 40\n", "R1: 41\n", "R1: 42\n", "R1: 43\n", "R1: 44\n", "R1: 45\n", "R1: 46\n", "R1: 47\n", "R1: 48\n", "R1: 49\n", "R1: 50\n", "R1: 51\n", "R1: 52\n", "R1: 53\n", "R1: 54\n", "R1: 55\n", "R1: 56\n", "R1: 57\n", "R1: 58\n", "R1: 59\n", "R1: 60\n", "R1: 61\n", "R1: 62\n", "R1: 63\n", "R1: 64\n", "R1: 65\n", "R1: 66\n", "R1: 67\n", "R1: 68\n", "R1: 69\n", "R1: 70\n", "R1: 71\n", "R1: 72\n", "R1: 73\n", "R1: 74\n", "R1: 75\n", "R1: 76\n", "R1: 77\n", "R1: 78\n", "R1: 79\n", "R1: 80\n", "R1: 81\n", "R1: 82\n", "R1: 83\n", "R1: 84\n", "R1: 85\n", "R1: 86\n", "R1: 87\n", "R1: 88\n", "R1: 89\n", "R1: 90\n", "R1: 91\n", "R1: 92\n", "R1: 93\n", "R1: 94\n", "R1: 95\n", "R1: 96\n", "R1: 97\n", "R1: 98\n", "R1: 99\n", "R1: 100\n", "R1: 101\n", "R1: 102\n", "R1: 103\n", "R1: 104\n", "R1: 105\n", "R1: 106\n", "R1: 107\n", "R1: 108\n", "R1: 109\n", "R1: 110\n", "R1: 111\n", "R1: 112\n", "R1: 113\n", "R1: 114\n", "R1: 115\n", "R1: 116\n", "R1: 117\n", "R1: 118\n", "R1: 119\n", "R1: 120\n", "R1: 121\n", "R1: 122\n", "R1: 123\n", "R1: 124\n", "R1: 125\n", "R1: 126\n", "R1: 127\n", "R1: 128\n", "R1: 129\n", "R1: 130\n", "R1: 131\n", "R1: 132\n", "R1: 133\n", "R1: 134\n", "R1: 135\n", "R1: 136\n", "R1: 137\n", "R1: 138\n", "R1: 139\n", "R1: 140\n", "R1: 141\n", "R1: 142\n", "R1: 143\n", "R1: 144\n", "R1: 145\n", "R1: 146\n", "R1: 147\n", "R1: 148\n", "R1: 149\n", "R1: 150\n", "R1: 151\n", "R1: 152\n", "R1: 153\n", "R1: 154\n", "R1: 155\n", "R1: 156\n", "R1: 157\n", "R1: 158\n", "R1: 159\n", "R1: 160\n", "R1: 161\n", "R1: 162\n", "R1: 163\n", "R1: 164\n", "R1: 165\n", "R1: 166\n", "R1: 167\n", "R1: 168\n", "R1: 169\n", "R1: 170\n", "R1: 171\n", "R1: 172\n", "R1: 173\n", "R1: 174\n", "R1: 175\n", "R1: 176\n", "R1: 177\n", "R1: 178\n", "R1: 179\n", "R1: 180\n", "R1: 181\n", "R1: 182\n", "R1: 183\n", "R1: 184\n", "R1: 185\n", "R1: 186\n", "R1: 187\n", "R1: 188\n", "R1: 189\n", "R1: 190\n", "R1: 191\n", "R1: 192\n", "R1: 193\n", "R1: 194\n", "R1: 195\n", "R1: 196\n", "R1: 197\n", "R1: 198\n", "R1: 199\n", "R2: 0\n", "R2: 1\n", "R2: 2\n", "R2: 3\n", "R2: 4\n", "R2: 5\n", "R2: 6\n", "R2: 7\n", "R2: 8\n", "R2: 9\n", "R2: 10\n", "R2: 11\n", "R2: 12\n", "R2: 13\n", "R2: 14\n", "R2: 15\n", "R2: 16\n", "R2: 17\n", "R2: 18\n", "R2: 19\n", "R2: 20\n", "R2: 21\n", "R2: 22\n", "R2: 23\n", "R2: 24\n", "R2: 25\n", "R2: 26\n", "R2: 27\n", "R2: 28\n", "R2: 29\n", "R2: 30\n", "R2: 31\n", "R2: 32\n", "R2: 33\n", "R2: 34\n", "R2: 35\n", "R2: 36\n", "R2: 37\n", "R2: 38\n", "R2: 39\n", "R2: 40\n", "R2: 41\n", "R2: 42\n", "R2: 43\n", "R2: 44\n", "R2: 45\n", "R2: 46\n", "R2: 47\n", "R2: 48\n", "R2: 49\n", "R2: 50\n", "R2: 51\n", "R2: 52\n", "R2: 53\n", "R2: 54\n", "R2: 55\n", "R2: 56\n", "R2: 57\n", "R2: 58\n", "R2: 59\n", "R2: 60\n", "R2: 61\n", "R2: 62\n", "R2: 63\n", "R2: 64\n", "R2: 65\n", "R2: 66\n", "R2: 67\n", "R2: 68\n", "R2: 69\n", "R2: 70\n", "R2: 71\n", "R2: 72\n", "R2: 73\n", "R2: 74\n", "R2: 75\n", "R2: 76\n", "R2: 77\n", "R2: 78\n", "R2: 79\n", "R2: 80\n", "R2: 81\n", "R2: 82\n", "R2: 83\n", "R2: 84\n", "R2: 85\n", "R2: 86\n", "R2: 87\n", "R2: 88\n", "R2: 89\n", "R2: 90\n", "R2: 91\n", "R2: 92\n", "R2: 93\n", "R2: 94\n", "R2: 95\n", "R2: 96\n", "R2: 97\n", "R2: 98\n", "R2: 99\n", "R2: 100\n", "R2: 101\n", "R2: 102\n", "R2: 103\n", "R2: 104\n", "R2: 105\n", "R2: 106\n", "R2: 107\n", "R2: 108\n", "R2: 109\n", "R2: 110\n", "R2: 111\n", "R2: 112\n", "R2: 113\n", "R2: 114\n", "R2: 115\n", "R2: 116\n", "R2: 117\n", "R2: 118\n", "R2: 119\n", "R2: 120\n", "R2: 121\n", "R2: 122\n", "R2: 123\n", "R2: 124\n", "R2: 125\n", "R2: 126\n", "R2: 127\n", "R2: 128\n", "R2: 129\n", "R2: 130\n", "R2: 131\n", "R2: 132\n", "R2: 133\n", "R2: 134\n", "R2: 135\n", "R2: 136\n", "R2: 137\n", "R2: 138\n", "R2: 139\n", "R2: 140\n", "R2: 141\n", "R2: 142\n", "R2: 143\n", "R2: 144\n", "R2: 145\n", "R2: 146\n", "R2: 147\n", "R2: 148\n", "R2: 149\n", "R2: 150\n", "R2: 151\n", "R2: 152\n", "R2: 153\n", "R2: 154\n", "R2: 155\n", "R2: 156\n", "R2: 157\n", "R2: 158\n", "R2: 159\n", "R2: 160\n", "R2: 161\n", "R2: 162\n", "R2: 163\n", "R2: 164\n", "R2: 165\n", "R2: 166\n", "R2: 167\n", "R2: 168\n", "R2: 169\n", "R2: 170\n", "R2: 171\n", "R2: 172\n", "R2: 173\n", "R2: 174\n", "R2: 175\n", "R2: 176\n", "R2: 177\n", "R2: 178\n", "R2: 179\n", "R2: 180\n", "R2: 181\n", "R2: 182\n", "R2: 183\n", "R2: 184\n", "R2: 185\n", "R2: 186\n", "R2: 187\n", "R2: 188\n", "R2: 189\n", "R2: 190\n", "R2: 191\n", "R2: 192\n", "R2: 193\n", "R2: 194\n", "R2: 195\n", "R2: 196\n", "R2: 197\n", "R2: 198\n", "R2: 199\n", "R3: 0\n", "R3: 1\n", "R3: 2\n", "R3: 3\n", "R3: 4\n", "R3: 5\n", "R3: 6\n", "R3: 7\n", "R3: 8\n", "R3: 9\n", "R3: 10\n", "R3: 11\n", "R3: 12\n", "R3: 13\n", "R3: 14\n", "R3: 15\n", "R3: 16\n", "R3: 17\n", "R3: 18\n", "R3: 19\n", "R3: 20\n", "R3: 21\n", "R3: 22\n", "R3: 23\n", "R3: 24\n", "R3: 25\n", "R3: 26\n", "R3: 27\n", "R3: 28\n", "R3: 29\n", "R3: 30\n", "R3: 31\n", "R3: 32\n", "R3: 33\n", "R3: 34\n", "R3: 35\n", "R3: 36\n", "R3: 37\n", "R3: 38\n", "R3: 39\n", "R3: 40\n", "R3: 41\n", "R3: 42\n", "R3: 43\n", "R3: 44\n", "R3: 45\n", "R3: 46\n", "R3: 47\n", "R3: 48\n", "R3: 49\n", "R3: 50\n", "R3: 51\n", "R3: 52\n", "R3: 53\n", "R3: 54\n", "R3: 55\n", "R3: 56\n", "R3: 57\n", "R3: 58\n", "R3: 59\n", "R3: 60\n", "R3: 61\n", "R3: 62\n", "R3: 63\n", "R3: 64\n", "R3: 65\n", "R3: 66\n", "R3: 67\n", "R3: 68\n", "R3: 69\n", "R3: 70\n", "R3: 71\n", "R3: 72\n", "R3: 73\n", "R3: 74\n", "R3: 75\n", "R3: 76\n", "R3: 77\n", "R3: 78\n", "R3: 79\n", "R3: 80\n", "R3: 81\n", "R3: 82\n", "R3: 83\n", "R3: 84\n", "R3: 85\n", "R3: 86\n", "R3: 87\n", "R3: 88\n", "R3: 89\n", "R3: 90\n", "R3: 91\n", "R3: 92\n", "R3: 93\n", "R3: 94\n", "R3: 95\n", "R3: 96\n", "R3: 97\n", "R3: 98\n", "R3: 99\n", "R3: 100\n", "R3: 101\n", "R3: 102\n", "R3: 103\n", "R3: 104\n", "R3: 105\n", "R3: 106\n", "R3: 107\n", "R3: 108\n", "R3: 109\n", "R3: 110\n", "R3: 111\n", "R3: 112\n", "R3: 113\n", "R3: 114\n", "R3: 115\n", "R3: 116\n", "R3: 117\n", "R3: 118\n", "R3: 119\n", "R3: 120\n", "R3: 121\n", "R3: 122\n", "R3: 123\n", "R3: 124\n", "R3: 125\n", "R3: 126\n", "R3: 127\n", "R3: 128\n", "R3: 129\n", "R3: 130\n", "R3: 131\n", "R3: 132\n", "R3: 133\n", "R3: 134\n", "R3: 135\n", "R3: 136\n", "R3: 137\n", "R3: 138\n", "R3: 139\n", "R3: 140\n", "R3: 141\n", "R3: 142\n", "R3: 143\n", "R3: 144\n", "R3: 145\n", "R3: 146\n", "R3: 147\n", "R3: 148\n", "R3: 149\n", "R3: 150\n", "R3: 151\n", "R3: 152\n", "R3: 153\n", "R3: 154\n", "R3: 155\n", "R3: 156\n", "R3: 157\n", "R3: 158\n", "R3: 159\n", "R3: 160\n", "R3: 161\n", "R3: 162\n", "R3: 163\n", "R3: 164\n", "R3: 165\n", "R3: 166\n", "R3: 167\n", "R3: 168\n", "R3: 169\n", "R3: 170\n", "R3: 171\n", "R3: 172\n", "R3: 173\n", "R3: 174\n", "R3: 175\n", "R3: 176\n", "R3: 177\n", "R3: 178\n", "R3: 179\n", "R3: 180\n", "R3: 181\n", "R3: 182\n", "R3: 183\n", "R3: 184\n", "R3: 185\n", "R3: 186\n", "R3: 187\n", "R3: 188\n", "R3: 189\n", "R3: 190\n", "R3: 191\n", "R3: 192\n", "R3: 193\n", "R3: 194\n", "R3: 195\n", "R3: 196\n", "R3: 197\n", "R3: 198\n", "R3: 199\n", "R4: 0\n", "R4: 1\n", "R4: 2\n", "R4: 3\n", "R4: 4\n", "R4: 5\n", "R4: 6\n", "R4: 7\n", "R4: 8\n", "R4: 9\n", "R4: 10\n", "R4: 11\n", "R4: 12\n", "R4: 13\n", "R4: 14\n", "R4: 15\n", "R4: 16\n", "R4: 17\n", "R4: 18\n", "R4: 19\n", "R4: 20\n", "R4: 21\n", "R4: 22\n", "R4: 23\n", "R4: 24\n", "R4: 25\n", "R4: 26\n", "R4: 27\n", "R4: 28\n", "R4: 29\n", "R4: 30\n", "R4: 31\n", "R4: 32\n", "R4: 33\n", "R4: 34\n", "R4: 35\n", "R4: 36\n", "R4: 37\n", "R4: 38\n", "R4: 39\n", "R4: 40\n", "R4: 41\n", "R4: 42\n", "R4: 43\n", "R4: 44\n", "R4: 45\n", "R4: 46\n", "R4: 47\n", "R4: 48\n", "R4: 49\n", "R4: 50\n", "R4: 51\n", "R4: 52\n", "R4: 53\n", "R4: 54\n", "R4: 55\n", "R4: 56\n", "R4: 57\n", "R4: 58\n", "R4: 59\n", "R4: 60\n", "R4: 61\n", "R4: 62\n", "R4: 63\n", "R4: 64\n", "R4: 65\n", "R4: 66\n", "R4: 67\n", "R4: 68\n", "R4: 69\n", "R4: 70\n", "R4: 71\n", "R4: 72\n", "R4: 73\n", "R4: 74\n", "R4: 75\n", "R4: 76\n", "R4: 77\n", "R4: 78\n", "R4: 79\n", "R4: 80\n", "R4: 81\n", "R4: 82\n", "R4: 83\n", "R4: 84\n", "R4: 85\n", "R4: 86\n", "R4: 87\n", "R4: 88\n", "R4: 89\n", "R4: 90\n", "R4: 91\n", "R4: 92\n", "R4: 93\n", "R4: 94\n", "R4: 95\n", "R4: 96\n", "R4: 97\n", "R4: 98\n", "R4: 99\n", "R4: 100\n", "R4: 101\n", "R4: 102\n", "R4: 103\n", "R4: 104\n", "R4: 105\n", "R4: 106\n", "R4: 107\n", "R4: 108\n", "R4: 109\n", "R4: 110\n", "R4: 111\n", "R4: 112\n", "R4: 113\n", "R4: 114\n", "R4: 115\n", "R4: 116\n", "R4: 117\n", "R4: 118\n", "R4: 119\n", "R4: 120\n", "R4: 121\n", "R4: 122\n", "R4: 123\n", "R4: 124\n", "R4: 125\n", "R4: 126\n", "R4: 127\n", "R4: 128\n", "R4: 129\n", "R4: 130\n", "R4: 131\n", "R4: 132\n", "R4: 133\n", "R4: 134\n", "R4: 135\n", "R4: 136\n", "R4: 137\n", "R4: 138\n", "R4: 139\n", "R4: 140\n", "R4: 141\n", "R4: 142\n", "R4: 143\n", "R4: 144\n", "R4: 145\n", "R4: 146\n", "R4: 147\n", "R4: 148\n", "R4: 149\n", "R4: 150\n", "R4: 151\n", "R4: 152\n", "R4: 153\n", "R4: 154\n", "R4: 155\n", "R4: 156\n", "R4: 157\n", "R4: 158\n", "R4: 159\n", "R4: 160\n", "R4: 161\n", "R4: 162\n", "R4: 163\n", "R4: 164\n", "R4: 165\n", "R4: 166\n", "R4: 167\n", "R4: 168\n", "R4: 169\n", "R4: 170\n", "R4: 171\n", "R4: 172\n", "R4: 173\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "R4: 174\n", "R4: 175\n", "R4: 176\n", "R4: 177\n", "R4: 178\n", "R4: 179\n", "R4: 180\n", "R4: 181\n", "R4: 182\n", "R4: 183\n", "R4: 184\n", "R4: 185\n", "R4: 186\n", "R4: 187\n", "R4: 188\n", "R4: 189\n", "R4: 190\n", "R4: 191\n", "R4: 192\n", "R4: 193\n", "R4: 194\n", "R4: 195\n", "R4: 196\n", "R4: 197\n", "R4: 198\n", "R4: 199\n", "R5: 0\n", "R5: 1\n", "R5: 2\n", "R5: 3\n", "R5: 4\n", "R5: 5\n", "R5: 6\n", "R5: 7\n", "R5: 8\n", "R5: 9\n", "R5: 10\n", "R5: 11\n", "R5: 12\n", "R5: 13\n", "R5: 14\n", "R5: 15\n", "R5: 16\n", "R5: 17\n", "R5: 18\n", "R5: 19\n", "R5: 20\n", "R5: 21\n", "R5: 22\n", "R5: 23\n", "R5: 24\n", "R5: 25\n", "R5: 26\n", "R5: 27\n", "R5: 28\n", "R5: 29\n", "R5: 30\n", "R5: 31\n", "R5: 32\n", "R5: 33\n", "R5: 34\n", "R5: 35\n", "R5: 36\n", "R5: 37\n", "R5: 38\n", "R5: 39\n", "R5: 40\n", "R5: 41\n", "R5: 42\n", "R5: 43\n", "R5: 44\n", "R5: 45\n", "R5: 46\n", "R5: 47\n", "R5: 48\n", "R5: 49\n", "R5: 50\n", "R5: 51\n", "R5: 52\n", "R5: 53\n", "R5: 54\n", "R5: 55\n", "R5: 56\n", "R5: 57\n", "R5: 58\n", "R5: 59\n", "R5: 60\n", "R5: 61\n", "R5: 62\n", "R5: 63\n", "R5: 64\n", "R5: 65\n", "R5: 66\n", "R5: 67\n", "R5: 68\n", "R5: 69\n", "R5: 70\n", "R5: 71\n", "R5: 72\n", "R5: 73\n", "R5: 74\n", "R5: 75\n", "R5: 76\n", "R5: 77\n", "R5: 78\n", "R5: 79\n", "R5: 80\n", "R5: 81\n", "R5: 82\n", "R5: 83\n", "R5: 84\n", "R5: 85\n", "R5: 86\n", "R5: 87\n", "R5: 88\n", "R5: 89\n", "R5: 90\n", "R5: 91\n", "R5: 92\n", "R5: 93\n", "R5: 94\n", "R5: 95\n", "R5: 96\n", "R5: 97\n", "R5: 98\n", "R5: 99\n", "R5: 100\n", "R5: 101\n", "R5: 102\n", "R5: 103\n", "R5: 104\n", "R5: 105\n", "R5: 106\n", "R5: 107\n", "R5: 108\n", "R5: 109\n", "R5: 110\n", "R5: 111\n", "R5: 112\n", "R5: 113\n", "R5: 114\n", "R5: 115\n", "R5: 116\n", "R5: 117\n", "R5: 118\n", "R5: 119\n", "R5: 120\n", "R5: 121\n", "R5: 122\n", "R5: 123\n", "R5: 124\n", "R5: 125\n", "R5: 126\n", "R5: 127\n", "R5: 128\n", "R5: 129\n", "R5: 130\n", "R5: 131\n", "R5: 132\n", "R5: 133\n", "R5: 134\n", "R5: 135\n", "R5: 136\n", "R5: 137\n", "R5: 138\n", "R5: 139\n", "R5: 140\n", "R5: 141\n", "R5: 142\n", "R5: 143\n", "R5: 144\n", "R5: 145\n", "R5: 146\n", "R5: 147\n", "R5: 148\n", "R5: 149\n", "R5: 150\n", "R5: 151\n", "R5: 152\n", "R5: 153\n", "R5: 154\n", "R5: 155\n", "R5: 156\n", "R5: 157\n", "R5: 158\n", "R5: 159\n", "R5: 160\n", "R5: 161\n", "R5: 162\n", "R5: 163\n", "R5: 164\n", "R5: 165\n", "R5: 166\n", "R5: 167\n", "R5: 168\n", "R5: 169\n", "R5: 170\n", "R5: 171\n", "R5: 172\n", "R5: 173\n", "R5: 174\n", "R5: 175\n", "R5: 176\n", "R5: 177\n", "R5: 178\n", "R5: 179\n", "R5: 180\n", "R5: 181\n", "R5: 182\n", "R5: 183\n", "R5: 184\n", "R5: 185\n", "R5: 186\n", "R5: 187\n", "R5: 188\n", "R5: 189\n", "R5: 190\n", "R5: 191\n", "R5: 192\n", "R5: 193\n", "R5: 194\n", "R5: 195\n", "R5: 196\n", "R5: 197\n", "R5: 198\n", "R5: 199\n" ] } ], "source": [ "# compute cumulative probability distributions\n", "cdfnum = 1000\n", "cdfstep = int(len(SL_wTd_nos_base_RCP26[:,0])/cdfnum)\n", "print(cdfstep)\n", "\n", "SL_wTd_nos_base_R0_RCP26_cdf = [0] * (cdfnum+1)\n", "for t in range(len(SL_wTd_nos_base_RCP26[1,:])):\n", " print(\"R0: \",t)\n", " sortind = np.argsort(SL_wTd_nos_base_RCP26[:,t])\n", "\n", " slcdf = []\n", " slcdf.append(0)\n", " for i in range(1,cdfnum+1):\n", " cdfval = float(i/cdfnum)\n", " slval = SL_wTd_nos_base_RCP26[sortind[i*cdfstep],t]\n", " slcdf.append(slval)\n", " SL_wTd_nos_base_R0_RCP26_cdf=np.vstack([SL_wTd_nos_base_R0_RCP26_cdf, slcdf])\n", "\n", "\n", "SL_wTd_nos_base_R1_RCP26_cdf = [0] * (cdfnum+1)\n", "for t in range(len(SL_wTd_nos_base_R1_RCP26[1,:])):\n", " print(\"R1: \",t)\n", " sortind = np.argsort(SL_wTd_nos_base_R1_RCP26[:,t])\n", "\n", " slcdf = []\n", " slcdf.append(0)\n", " for i in range(1,cdfnum+1):\n", " cdfval = float(i/cdfnum)\n", " slval = SL_wTd_nos_base_R1_RCP26[sortind[i*cdfstep],t]\n", " slcdf.append(slval)\n", " SL_wTd_nos_base_R1_RCP26_cdf=np.vstack([SL_wTd_nos_base_R1_RCP26_cdf, slcdf])\n", "\n", "SL_wTd_nos_base_R2_RCP26_cdf = [0] * (cdfnum+1)\n", "for t in range(len(SL_wTd_nos_base_R2_RCP26[1,:])):\n", " print(\"R2: \",t)\n", " sortind = np.argsort(SL_wTd_nos_base_R2_RCP26[:,t])\n", "\n", " slcdf = []\n", " slcdf.append(0)\n", " for i in range(1,cdfnum+1):\n", " cdfval = float(i/cdfnum)\n", " slval = SL_wTd_nos_base_R2_RCP26[sortind[i*cdfstep],t]\n", " slcdf.append(slval)\n", " SL_wTd_nos_base_R2_RCP26_cdf=np.vstack([SL_wTd_nos_base_R2_RCP26_cdf, slcdf])\n", "\n", "SL_wTd_nos_base_R3_RCP26_cdf = [0] * (cdfnum+1)\n", "for t in range(len(SL_wTd_nos_base_R3_RCP26[1,:])):\n", " print(\"R3: \",t)\n", " sortind = np.argsort(SL_wTd_nos_base_R3_RCP26[:,t])\n", "\n", " slcdf = []\n", " slcdf.append(0)\n", " for i in range(1,cdfnum+1):\n", " cdfval = float(i/cdfnum)\n", " slval = SL_wTd_nos_base_R3_RCP26[sortind[i*cdfstep],t]\n", " slcdf.append(slval)\n", " SL_wTd_nos_base_R3_RCP26_cdf=np.vstack([SL_wTd_nos_base_R3_RCP26_cdf, slcdf])\n", "\n", "SL_wTd_nos_base_R4_RCP26_cdf = [0] * (cdfnum+1)\n", "for t in range(len(SL_wTd_nos_base_R4_RCP26[1,:])):\n", " print(\"R4: \",t)\n", " sortind = np.argsort(SL_wTd_nos_base_R4_RCP26[:,t])\n", "\n", " slcdf = []\n", " slcdf.append(0)\n", " for i in range(1,cdfnum+1):\n", " cdfval = float(i/cdfnum)\n", " slval = SL_wTd_nos_base_R4_RCP26[sortind[i*cdfstep],t]\n", " slcdf.append(slval)\n", " SL_wTd_nos_base_R4_RCP26_cdf=np.vstack([SL_wTd_nos_base_R4_RCP26_cdf, slcdf])\n", "\n", "SL_wTd_nos_base_R5_RCP26_cdf = [0] * (cdfnum+1)\n", "for t in range(len(SL_wTd_nos_base_R5_RCP26[1,:])):\n", " print(\"R5: \",t)\n", " sortind = np.argsort(SL_wTd_nos_base_R5_RCP26[:,t])\n", "\n", " slcdf = []\n", " slcdf.append(0)\n", " for i in range(1,cdfnum+1):\n", " cdfval = float(i/cdfnum)\n", " slval = SL_wTd_nos_base_R5_RCP26[sortind[i*cdfstep],t]\n", " slcdf.append(slval)\n", " SL_wTd_nos_base_R5_RCP26_cdf=np.vstack([SL_wTd_nos_base_R5_RCP26_cdf, slcdf])\n", "\n", "\n", "Percentile = np.arange(0,float((cdfnum+1)/cdfnum),float(1/cdfnum))\n", "\n", "# write cdfs\n", "ncfile = nc.Dataset('Cdfs/SL_wTd_nos_base_RCP26_cdf.nc','w', format='NETCDF4')\n", "ncfile.createDimension('Time', None)\n", "ncfile.createDimension('Percentile', None)\n", "\n", "SL_wTd_weighted_base_R0 = ncfile.createVariable('Antarctica', 'f4', ('Time','Percentile'))\n", "SL_wTd_weighted_base_R1 = ncfile.createVariable('EAIS', 'f4', ('Time','Percentile'))\n", "SL_wTd_weighted_base_R2 = ncfile.createVariable('Ross', 'f4', ('Time','Percentile'))\n", "SL_wTd_weighted_base_R3 = ncfile.createVariable('Amundsen', 'f4', ('Time','Percentile'))\n", "SL_wTd_weighted_base_R4 = ncfile.createVariable('Weddell', 'f4', ('Time','Percentile'))\n", "SL_wTd_weighted_base_R5 = ncfile.createVariable('Peninsula', 'f4', ('Time','Percentile'))\n", "p = ncfile.createVariable('Percentile', 'f4', 'Percentile')\n", "t = ncfile.createVariable('Time', 'f4', 'Time')\n", "\n", "t[:] = Time\n", "t.units = 'years'\n", "SL_wTd_weighted_base_R0[:,:] = SL_wTd_nos_base_R0_RCP26_cdf\n", "SL_wTd_weighted_base_R1[:,:] = SL_wTd_nos_base_R1_RCP26_cdf\n", "SL_wTd_weighted_base_R2[:,:] = SL_wTd_nos_base_R2_RCP26_cdf\n", "SL_wTd_weighted_base_R3[:,:] = SL_wTd_nos_base_R3_RCP26_cdf\n", "SL_wTd_weighted_base_R4[:,:] = SL_wTd_nos_base_R4_RCP26_cdf\n", "SL_wTd_weighted_base_R5[:,:] = SL_wTd_nos_base_R5_RCP26_cdf\n", "p[:] = Percentile\n", "\n", "SL_wTd_weighted_base_R0.units = 'meter'\n", "SL_wTd_weighted_base_R1.units = 'meter'\n", "SL_wTd_weighted_base_R2.units = 'meter'\n", "SL_wTd_weighted_base_R3.units = 'meter'\n", "SL_wTd_weighted_base_R4.units = 'meter'\n", "SL_wTd_weighted_base_R5.units = 'meter'\n", "\n", "p.units = 'percent'\n", "\n", "ncfile.close()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200\n", "200\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "print(len(Time))\n", "print(len(SL_wTd_nos_base_R0_RCP26_cdf[0:-1,500]))\n", "plt.plot(Time,SL_wTd_nos_base_R0_RCP26_cdf[0:-1,10])\n", "plt.plot(Time,SL_wTd_nos_base_R0_RCP26_cdf[0:-1,50])\n", "plt.plot(Time,SL_wTd_nos_base_R0_RCP26_cdf[0:-1,166])\n", "plt.plot(Time,SL_wTd_nos_base_R0_RCP26_cdf[0:-1,500])\n", "plt.plot(Time,SL_wTd_nos_base_R0_RCP26_cdf[0:-1,833])\n", "plt.plot(Time,SL_wTd_nos_base_R0_RCP26_cdf[0:-1,950])\n", "plt.plot(Time,SL_wTd_nos_base_R0_RCP26_cdf[0:-1,990])\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.26417553424835205\n", "0.40920981764793396\n", "0.7070159316062927\n" ] } ], "source": [ "print(SL_wTd_nos_base_R0_RCP26_cdf[-1,833])\n", "print(SL_wTd_nos_base_R0_RCP26_cdf[-1,950])\n", "print(SL_wTd_nos_base_R0_RCP26_cdf[-1,990])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }