{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import copy\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import netCDF4 as nc\n", "import sys\n", "PFTname=['tropical evergreen','tropical deciduous','extra-tropical evergreen','extra-tropical deciduous',\n", " 'raingreen shrubs','deciduous shrubs','C3 grass','C4 grass','C3 pasture','C4 pasture','C3/C4 crop',\n", " 'Tree','Shrub','Grass','Pasture']" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2015\n", "2020\n", "2025\n", "2030\n", "2035\n", "2040\n", "2045\n", "2050\n", "2055\n", "2060\n", "2065\n", "2070\n", "2075\n", "2080\n", "2085\n", "2090\n", "2095\n", "(3, 15, 85)\n" ] } ], "source": [ "gridarea_T63 = nc.Dataset('/home/shkhwwey/OAE/gridarea_T63.nc').variables['cell_area'][:]\n", "cover_type = nc.Dataset ('/home/shkhwwey/OAE/jsbach_T63ORCA05_11tiles_5layers_1850_dynveg.nc').variables['cover_type'][:]\n", "\n", "startyear = 2015\n", "endyear = 2100\n", "flux = False\n", "mass = False\n", "save_to_csv = False\n", "DIFF = False\n", "var_name = 'pft_area'\n", "dirlist = []\n", "expset = 'SynTraAR'\n", "if expset =='SynTraREF':\n", " expname = ['HW013','HW012','HW011']\n", " dirlist.append('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW013_esm_ssp370_constLu/nc/')\n", " dirlist.append('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW012_esm_ssp370_constLu/nc/')\n", " dirlist.append('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW011_esm_ssp370_constLu/nc/')\n", "if expset == 'SynTraAR':\n", " expname = ['HW022','HW023','HW024']\n", " dirlist.append('/scratch/usr/shkhwwey/models/foci2.0_maps_wh/experiments/FOCI20.0-HW022_esm_ssp370_AR/nc/')\n", " dirlist.append('/scratch/usr/shkhwwey/models/foci2.0_maps_wh/experiments/FOCI20.0-HW023_esm_ssp370_AR/nc/')\n", " dirlist.append('/scratch/usr/shkhwwey/models/foci2.0_maps_wh/experiments/FOCI20.0-HW024_esm_ssp370_AR/nc/')\n", "# expname = ['CC104','CC105','CC106','HW002','HW005','HW006','HW003','HW007','HW008']\n", "# expname = ['HW002','HW005','HW006']\n", "# dirlist.append ('/scratch/usr/shkhwwey/output_others/FOCI1.20.0-CC104_RCP_ESM_spinup2089/nc/')\n", "# dirlist.append ('/scratch/usr/shkhwwey/output_others/FOCI1.20.0-CC105_RCP_ESM_spinup2099/nc/')\n", "# dirlist.append ('/scratch/usr/shkhwwey/output_others/FOCI1.20.0-CC106_RCP_ESM_spinup2079/nc/')\n", "# dirlist.append ('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW002_esm_ssp585_ocn_alk/nc/')\n", "# dirlist.append ('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW005_esm_ssp585_ocn_alk/nc/')\n", "# dirlist.append ('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW006_esm_ssp585_ocn_alk/nc/')\n", "# dirlist.append ('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW003_esm_ssp585_ssp126Lu/nc/')\n", "# dirlist.append ('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW007_esm_ssp585_ssp126Lu/nc/')\n", "# dirlist.append ('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW008_esm_ssp585_ssp126Lu/nc/')\n", "#dirlist.append ('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW011_esm_ssp370_constLu/nc/')\n", "# dirlist.append ('/scratch/usr/shkhwwey/models/foci2.0/experiments/FOCI20.0-HW014_esm_ssp585_AR_stop/nc/')\n", "#########\n", "# (nexp, npft, nyear)\n", "Var_timeseries = []\n", "for i in range(len(dirlist)):\n", " Var_timeseries.append([])\n", " for j in range(11):\n", " Var_timeseries[-1].append([])\n", "###########\n", "for year in range(startyear,endyear):\n", " arealist = []\n", " for iexp in range(len(dirlist)):\n", " veg_ratio_max = nc.Dataset(dirlist[iexp]+'jsbach_' + str(year)+'.nc').variables['veg_ratio_max'][:] \n", " cover_fract = nc.Dataset(dirlist[iexp]+'jsbach_' + str(year)+'.nc').variables['cover_fract'][:] \n", " veg_ratio_max[veg_ratio_max<5e-5] = 0\n", " cover_fract[cover_fract<5e-5] = 0\n", " glac = copy.deepcopy(cover_type[0])\n", " glac[cover_type[0]!=1] = 0\n", " bare = 1 - veg_ratio_max - glac\n", " for ipft in range(11):\n", " annual_mean = np.ma.average(veg_ratio_max * cover_fract[:,ipft,:,:],axis=0)\n", " global_sum = np.ma.sum(gridarea_T63 * annual_mean) * 0.0001 /1000000. # m2 to million ha\n", " Var_timeseries[iexp][ipft].append(global_sum)\n", " if year%5==0:\n", " print(f'{year}')\n", "###############\n", "# cover_fract_nat = nc.Dataset('/scratch/usr/shkifmsw/foci_input2/ECHAM6_GENERAL/JSBACH/input/T63/jsbach_T63GR15_11tiles_5layers_natural-veg.nc').variables['cover_fract'][:]\n", "# veg_ratio_max_nat = nc.Dataset('/scratch/usr/shkifmsw/foci_input2/ECHAM6_GENERAL/JSBACH/input/T63/jsbach_T63GR15_11tiles_5layers_natural-veg.nc').variables['veg_ratio_max'][:]\n", "# cover_type_nat = nc.Dataset('/scratch/usr/shkifmsw/foci_input2/ECHAM6_GENERAL/JSBACH/input/T63/jsbach_T63GR15_11tiles_5layers_natural-veg.nc').variables['cover_type'][:]\n", "# #(ntiles,lat,lon) and (lat,lon)\n", "# veg_ratio_max_nat[veg_ratio_max_nat<5e-5] = 0\n", "# cover_fract_nat[cover_fract_nat<5e-5] = 0\n", "# glac_nat = copy.deepcopy(cover_type_nat[0])\n", "# glac_nat[cover_type_nat[0]!=1] = 0\n", "# natural_veg_area = []\n", "# for ipft in range(11):\n", "# global_sum = np.ma.sum(gridarea_T63 * veg_ratio_max_nat * cover_fract_nat[ipft,:,:]) * 0.0001 /1000000. # m2 to million ha\n", "# natural_veg_area.append(global_sum)\n", "# tree = natural_veg_area[0] + natural_veg_area[1] + natural_veg_area[2] + natural_veg_area[3]\n", "# natural_veg_area.append(tree)\n", "# shrub = natural_veg_area[4] + natural_veg_area[5]\n", "# natural_veg_area.append(shrub)\n", "# grass = natural_veg_area[6] + natural_veg_area[7]\n", "# natural_veg_area.append(grass)\n", "# pasture = natural_veg_area[8] + natural_veg_area[9]\n", "# natural_veg_area.append(pasture)\n", "##############\n", "# add tree, shrub, grass, pasture\n", "for iexp in range(len(dirlist)):\n", " tree = np.array(Var_timeseries[iexp][0]) + np.array(Var_timeseries[iexp][1]) + np.array(Var_timeseries[iexp][2]) + np.array(Var_timeseries[iexp][3])\n", " Var_timeseries[iexp].append(tree)\n", " shrub = np.array(Var_timeseries[iexp][4]) + np.array(Var_timeseries[iexp][5])\n", " Var_timeseries[iexp].append(shrub)\n", " grass = np.array(Var_timeseries[iexp][6]) + np.array(Var_timeseries[iexp][7])\n", " Var_timeseries[iexp].append(grass)\n", " pasture = np.array(Var_timeseries[iexp][8]) + np.array(Var_timeseries[iexp][9])\n", " Var_timeseries[iexp].append(pasture)\n", "Var_timeseries = np.array(Var_timeseries) \n", "print(Var_timeseries.shape) # (nexp, npft+4, nyear)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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dz9xbRmshawkLCneJWLU+Pw//YyNPLd5MRs8OPH1NJl3axntdlkizULhLRPp81wHueHUV63eWc9VpafzqksEkxGqqXgkfCneJKHX1fp7/ZCsPvLeRtgkxPHNtJudpfF3CkMJdIkJdvZ83VuzgTws3Ubi/ivMGdeX+y06hcxsNw0h4UrhLWHPOMX91EQ8t2Mj2fZWcktKe+8YP5tsnJ+v+dQlrCncJW2sLy/jV39aRs30/A7u345lrMzl3oEJdIoPCXcJOWVUdv3snl1eWF5CUGMcfLj+VK0akEhWlUJfIoXCXsLKzrIrrnltOXvFBbhjTm9vH9qN9K827LpFH4S5hY+PuA0x5bhkHqn3MuiGL0X07e12SiGcU7hIWlm8rYeoLy0mIjWbOzaP0lKlEPIW7hLy31hTx4zmrSe3YipnXZ5GWlOh1SSKeU7hLyHLO8fRHW/jdOxvI7NWRZ67NpKMW1BABFO4Sonz1fn45fx2zl+Yz7tTuPDBhqKYPEDmEwl1CTnVdPbfNXsEHG/Zw67dO4n++c7JucxT5CoW7hJQD1XXcODObZdtK+PX4wVwzKt3rkkSCksJdQkZJRS3XPb+M9UXlPHLlMMYPS/G6JJGgpXCXkLCnvJpJzy4lv6SS6deO4JwBmslR5HAU7hL0ig/UcPWzSykqrWLmDVmM7NPJ65JEgp7CXYJaSUUtk59dyo79Vbxw/WmcrmAXaZIorwsQ+SallbVMenYp2/ZVMGNKpoJd5Cio5y5BqayyjskzlrK5+CDPXpupeWJEjpJ67hJ0yirrmDRjCRt3HeTpySM4q38Xr0sSCTkKdwkqXwr2a0bw7QHJXpckEpIU7hI0FOwizUfhLkGhvLqOa55bqmAXaSZNDnczizazlWb2VmC7t5ktNbNNZvaqmcUF9scHtvMCx9NbpnQJFweq65jy3DJyd5bz1OQMBbtIMzianvsPgdxDtn8PPOyc6wfsB6YG9k8F9jvn+gIPB9qJNKqixscNLyxnbWEZj1+dwdiBevJUpDk0KdzNLBW4CHg2sG3AOcDcQJOZwKWB1+MD2wSOjzUtNy+NqKjxMXXmcnK27+fRq4bz3cHdvC5JJGw0tef+CHAX4A9sdwJKnXO+wHYh8MUsTilAAUDgeFmg/ZeY2TQzyzaz7OLi4mMsX0LVF/exL9tawsNXDuOiU7t7XZJIWDliuJvZOGCPcy7n0N2NNHVNOPafHc5Nd85lOucyu3TRfcyRZM+Baq6c/inrdpTz5KQRmt1RpAU05QnVMcAlZnYhkAC0o6En38HMYgK981SgKNC+EEgDCs0sBmgPlDR75RKSCvdXMvnZpewur+G5607jjH568lSkJRyx5+6cu8c5l+qcSweuAhY65yYBi4ArAs2mAG8GXs8PbBM4vtA597Weu0QW5xzzVxdx8WP/pKSilpduPF3BLtKCjmdumZ8Cr5jZb4CVwIzA/hnALDPLo6HHftXxlSihbu/BGn7x189457NdDE3rwIMThtI3uY3XZYmEtaMKd+fcYmBx4PUWIKuRNtXAhGaoTcLAwg27+clrazhY7eOn5w/gpjN7ExOtZ+dEWppmhZQW4av38+CCjTy1eDODurfjkWnD6N+1rddliUQMhbs0uz3l1fzg5ZUs3VrCxKye/PLiQSTERntdlkhEUbhLs1qRv5+bZ+VwoLqOBycM5fIRqV6XJBKRFO7SbF7PKeSeeWvp1j6BWVOzGNCtndcliUQshbsct3q/4w/vbuDpj7Ywqk8nnpyUQcfWcV6XJRLRFO5yXD7dvI/fv7uBVQWlXDOyF/dePIhY3Q0j4jmFuxyTNYWl/PG9z/l40166t0/goe8N5bIMja+LBAuFuxyVz3cd4OEFG3l33S46Jsby84sGMnlkL90NIxJkFO7SJNv2VvDwPzYyf3URbeJi+NG5/Zh6Rm/aJsR6XZqINELhLodV73c898+t/PH9z4k245azT2LamX30galIkFO4yzfK31fJT15bzbJtJZw3qCu/vXQIye0SvC5LRJpA4S6N+tvqIn76+hqizXhgwlAuz0hBC2qJhA6Fu3zNc//cyn1vrSezV0cenTiclA6tvC5JRI6Swl3+zTnHH977nKcWb+Y7g7ryp4nDdReMSIhSuAsAdfV+fjZvLa/lFDIxqye/uXQI0VEahhEJVQp3oby6jttmr+DjTXu5fWw/7ji3n8bXRUKcwj3CFe6v5IYXlrOluII/XH4q3zstzeuSRKQZKNwj2KqCUm6cmU2Nr56ZN2Qxpq/WNBUJFwr3COSc4/lPtnH/O7l0bZfAyzedTj+tkiQSVhTuEWZ/RS3/M3cN/8jdzbkDk/njFUP1tKlIGFK4R5A1haXcPCuHvQdruHfcIK4fk64PTkXClMI9QnyQu5vv/2UlndrEMe/WMZyS2t7rkkSkBSncI8Dspdv5xV8/Y3CP9sy4LpPktpofRiTcKdzDmN/veOD9z3ly8Wa+fXIXHr86g9bx+iMXiQT6mx6mKmt9/PjV1by7bhcTs3ry6/GDidHydyIRQ+EehnaWVXHTi9msKyrn5xcNZOoZvfXBqUiEUbiHmRX5+7llVg6VtfXMmJLJOQO6el2SiHhA4R4mfPV+nly8mUc/2ESPDgnMmno6J3fTg0kikUrhHgYKSiq549VVZG/fz/hhPbhv/BDat9LapiKRTOEewpxzzMku4Ndv5WLAo1cNY/ywFK/LEpEgoHAPUTvLqrj79bV8uLGYkX2SeGDCUFI7JnpdlogECYV7CHpjZSH3vrkOX73jvvGDmXx6L6K0sIaIHELhHkJqfX7ue2sdLy3JJys9iT9OOJVenVp7XZaIBKEjPtViZmlmtsjMcs1snZn9MLA/ycwWmNmmwNeOgf1mZn8yszwzW2NmGS39Q0SCPeXVTHxmCS8tyefms/vwl5tOV7CLyDdqyiOLPuBO59xAYCRwm5kNAu4GPnDO9QM+CGwDXAD0C/yaBjzV7FVHmJX5+xn32D9ZX1TO41cP554LBuppUxE5rCMmhHNup3NuReD1ASAXSAHGAzMDzWYClwZejwdedA2WAB3MrHuzVx4hFm7YzcRnlhAfG8Ubt41m3Kk9vC5JRELAUXX/zCwdGA4sBbo653ZCwz8AQHKgWQpQcMjbCgP7vvp7TTOzbDPLLi4uPvrKI8Cc7AJuejGHvsltmHfrGAZ0a+d1SSISIpoc7mbWBngd+JFzrvxwTRvZ5762w7npzrlM51xmly5dmlpGRHDO8cSiPO6au4bRJ3XilWmj6NI23uuyRCSENOluGTOLpSHYZzvn5gV27zaz7s65nYFhlz2B/YVA2iFvTwWKmqvgcFddV8/P3ljLvBU7uHRYD/5wxVDiYjS+LiJHpyl3yxgwA8h1zj10yKH5wJTA6ynAm4fsvzZw18xIoOyL4Rs5vN3l1Vw1fQnzVuzgjnP789D3hinYReSYNKXnPga4BlhrZqsC+34G/A6YY2ZTgXxgQuDY28CFQB5QCVzfrBWHqdUFpUyblc2Bah9/npzB+UP0GbSIHLsjhrtz7p80Po4OMLaR9g647TjriigL1u/mBy+voFPreF6/dTQDu+uDUxE5PnpC1WMvLdnOvW9+xpCU9syYcpo+OBWRZqFw94hzjj++17C+6TkDknn86uEkxumPQ0Sah9LEA845fv1WLs99slXrm4pIi1C4e+DxhXk898lWrh+Tzr3jBml9UxFpduounmCzlmznwQUbuWx4Cr+4SMEuIi1D4X4CzV9dxL1vfsbYAcn8/opTNQe7iLQYhfsJsnxbCXfOWcVpvZJ4YlIGsRpjF5EWpIQ5AQpKKrllVg6pHRN55tpMEmKjvS5JRMKcwr2FVdT4uOnFbGrr/TxzbSbtE2O9LklEIoDCvQX5/Y47Xl3Fxt0HeOLqDPomt/G6JBGJEAr3FvTIB5t4f/1ufjFuEGf117TGInLiKNxbyEcbi3ls4SauGJHKdaPTvS5HRCKMwr0F7Cqr5kevrqJ/clt+PX6I7mUXkRNO4d7MfPV+bn95JdV19TwxKYNWcbozRkROPE0/0MweWrCRZdtKeOTKYfoAVUQ8o557M1qyZR9PLt7MxKw0Lh3+tTXBRUROGIV7M6nxNax92jMpkXvHDfa6HBGJcBqWaSZ/XryFLcUVzLwhS+PsIuI59dybwZbigzyxKI+Lh/bgbN3PLiJBQOF+nJxz/O8bnxEfG8Uvxg30uhwREUDhftzmrdjBp1v2cfcFA0hum+B1OSIigML9uBys8XH/O7lk9OzAxNN6el2OiMi/KdyPw9MfbmbvwVp+efFgLbwhIkFF4X6MdpdX88zHW7h4aA+GpnXwuhwRkS9RuB+jh97fSL3fcdd3T/a6FBGRr1G4H4PPdx3gtZwCrh2VTlpSotfliIh8jcL9GPzunVzaxMfwg3P6el2KiEijFO5H6ZO8vSz6vJjvn9OXDolxXpcjItIohftRqKjxcfe8NfTqlMi1o9K9LkdE5BtpbpmjcP87uRTur2LOzaNIiNX8MSISvNRzb6KPNxXz0pJ8po7pzWnpSV6XIyJyWAr3JiivruOuuWs4qUtrfqJbH0UkBGhY5gicc9z3t/XsLq9m3n+P0XCMiIQEhfth7DlQzc/mfcY/cnfz/W/3ZZieRBWRENEiwzJmdr6ZfW5meWZ2d0t8j5b21poivvvwR3y0qZj/vXAgd5zX3+uSRESarNl77mYWDTwBnAcUAsvNbL5zbn1zf6+mqqv3U1VXT2VNPQdr6jhYU8/Bah/VdfXU+PzU+OqpqPFRuL+K/JJKtu6tYMOuAwxNbc+D3xtK3+S2XpUuInJMWmJYJgvIc85tATCzV4DxQLOH+5zlBTz90Wbq/Y66eke93+HzO/yu4XW931FdV4/P75r0+8VFR5Ga1Iq0jolcnpHK9WPSiYnWZ84iEnpaItxTgIJDtguB07/ayMymAdMAevY8trnQOyTGMqBbO2KijegoIybKiI6KIjoKYqKiiDKjVVwUrWKjSYiNJjEuhjYJMbSNj6F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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "for ii in [1]:\n", " ax.plot(Var_timeseries[ii,11] - Var_timeseries[ii-1,11])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "save_to_csv = True\n", "if save_to_csv:\n", " for iexp in range(len(Var_timeseries)):\n", " # combine variables\n", " #df = pd.read_csv('csv/' + expname[iexp] + '.csv',index_col='year')\n", " #df[var_name] = Var_timeseries[iexp]\n", " #df.to_csv(expname[iexp]+'.csv')\n", " #print(df)\n", " # separate file for the current variables\n", " df = pd.DataFrame(data=Var_timeseries[iexp,11], index=range(startyear,endyear), columns=['tree'])\n", " df.index.name = 'year'\n", " df.to_csv(expname[iexp] + '_' + 'tree' + '.csv')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " tree\n", "2015 4158.170519\n", "2016 4161.918605\n", "2017 4160.361283\n", "2018 4156.313791\n", "2019 4157.533132\n", "... ...\n", "2095 4384.844056\n", "2096 4395.061709\n", "2097 4399.836329\n", "2098 4400.973737\n", "2099 4407.263154\n", "\n", "[85 rows x 1 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "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": "3.7.15" } }, "nbformat": 4, "nbformat_minor": 4 }