From fa79bab06045048881c25ea7ca86430eca09984a Mon Sep 17 00:00:00 2001 From: Martin Dix Date: Thu, 26 Feb 2026 13:12:58 +1100 Subject: [PATCH] Interpolate GHG to Jan 1 --- CMIP7/esm1p6/atmosphere/cmip7_HI.py | 3 +- .../atmosphere/ghg/cmip7_HI_ghg_generate.py | 21 +- .../ozone/esm_historical_ozone_check.ipynb | 1900 +++++++++++++++++ 3 files changed, 1922 insertions(+), 2 deletions(-) create mode 100644 examples/esm1p6/historical/atmosphere/ozone/esm_historical_ozone_check.ipynb diff --git a/CMIP7/esm1p6/atmosphere/cmip7_HI.py b/CMIP7/esm1p6/atmosphere/cmip7_HI.py index b8d103c..e729d82 100644 --- a/CMIP7/esm1p6/atmosphere/cmip7_HI.py +++ b/CMIP7/esm1p6/atmosphere/cmip7_HI.py @@ -9,7 +9,8 @@ from cmip7_PI import CMIP7_PI_YEAR CMIP7_HI_BEG_YEAR = CMIP7_PI_YEAR -CMIP7_HI_END_YEAR = 2022 +# Model time interpolation requires an extra year +CMIP7_HI_END_YEAR = 2023 CMIP7_HI_NBR_YEARS = CMIP7_HI_END_YEAR + 1 - CMIP7_HI_BEG_YEAR diff --git a/CMIP7/esm1p6/atmosphere/ghg/cmip7_HI_ghg_generate.py b/CMIP7/esm1p6/atmosphere/ghg/cmip7_HI_ghg_generate.py index 7ead72f..2562824 100644 --- a/CMIP7/esm1p6/atmosphere/ghg/cmip7_HI_ghg_generate.py +++ b/CMIP7/esm1p6/atmosphere/ghg/cmip7_HI_ghg_generate.py @@ -5,6 +5,7 @@ import f90nml import iris import numpy as np +import cftime from cmip7_ancil_argparse import dataset_parser, path_parser from cmip7_HI import ( CMIP7_HI_BEG_YEAR, @@ -44,11 +45,28 @@ def load_cmip7_hi_ghg_mmr(args, ghg): variable_id = full_cube.metadata.attributes["variable_id"] assert ghg == variable_id + new_cube = full_cube.copy() + # Linearly interpolate to Jan 1 so that UM time interpolation reproduces annual means + new_cube.data[:-1] = 0.5*(full_cube.data[:-1] + full_cube.data[1:]) + # Extrapolate the last year + new_cube.data[-1] = full_cube.data[-1] + 0.5*(full_cube.data[-1]-full_cube.data[-2]) + cube_time = full_cube.coord('time') + units = cube_time.units + new_cube_time = new_cube.coord('time') + # Can't assign to elements of time.points so create a temporary array + tvals = np.array(new_cube_time.points) + for i in range(len(tvals)): + date = units.num2date(cube_time.points[i]) + # Interpolated data is Jan 1 of the next year + newdate = cftime.DatetimeProlepticGregorian(date.year+1, 1, 1, 0, 0, 0) + tvals[i] = units.date2num(newdate) + new_cube_time.points = tvals + # Extract the historical years. date_constraint = cmip7_pro_greg_date_constraint_from_years( CMIP7_HI_BEG_YEAR, CMIP7_HI_END_YEAR ) - hi_cube = full_cube.extract(date_constraint) + hi_cube = new_cube.extract(date_constraint) # Determine the mass mixing ratio. ghg_mmr_list = [] @@ -56,6 +74,7 @@ def load_cmip7_hi_ghg_mmr(args, ghg): year_constraint = cmip7_pro_greg_date_constraint_from_years(year, year) year_cube = hi_cube.extract(year_constraint) ghg_mmr_list.append(cmip7_ghg_mmr(year_cube, ghg)) + return ghg_mmr_list diff --git a/examples/esm1p6/historical/atmosphere/ozone/esm_historical_ozone_check.ipynb b/examples/esm1p6/historical/atmosphere/ozone/esm_historical_ozone_check.ipynb new file mode 100644 index 0000000..0b4f7ef --- /dev/null +++ b/examples/esm1p6/historical/atmosphere/ozone/esm_historical_ozone_check.ipynb @@ -0,0 +1,1900 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5c8737e9", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import iris\n", + "import cftime\n", + "import numpy as np\n", + "import xarray_tools as xrt\n", + "from notebook_metadata import create_savefig\n", + "from pathlib import Path\n", + "\n", + "savefig = create_savefig(dir='/scratch/tm70/mrd599/plots', nb='esm_historical_ozone_check.ipynb')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "298538a3", + "metadata": {}, + "outputs": [], + "source": [ + "from dask.distributed import Client\n", + "client = Client(threads_per_worker = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c9dcf15e", + "metadata": {}, + "outputs": [], + "source": [ + "area_esm15 = xr.open_dataset('/g/data/fs38/publications/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/piControl/r1i1p1f1/fx/areacella/gn/latest/areacella_fx_ACCESS-ESM1-5_piControl_r1i1p1f1_gn.nc').areacella\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "546123f5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/jobfs/161382074.gadi-pbs/ipykernel_3975997/1774121848.py:8: FutureWarning: In a future version, xarray will not decode the variable 'forecast_period' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.\n", + "To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.\n", + "To opt-in to future behavior, set `decode_timedelta=False`.\n", + " ozone_esm16 = xr.open_dataset('/scratch/tm70/mrd599/ozone_esm16.nc').field60.squeeze()\n" + ] + } + ], + "source": [ + "# ozone_esm15 = iris.load_cube('/g/data/vk83/configurations/inputs/access-esm1p5/modern/historical/atmosphere/forcing/global.N96/2021.06.22/ozone_1849_2015_ESM1.anc')\n", + "# ozone_esm15 = xr.DataArray.from_iris(ozone_esm15).squeeze().rename({'latitude':'lat'})\n", + "\n", + "# ozone_esm16 = iris.load_cube('/g/data/tm70/pcl851/CMIP7/esm1p6_ancil/2026.02.20/modern/historical/forcing/global.N96/2026.02.20/ozone_1849_2023_cmip7.anc')\n", + "# ozone_esm16 = xr.DataArray.from_iris(ozone_esm16).squeeze().rename({'latitude':'lat'})\n", + "\n", + "ozone_esm15 = xr.open_dataset('/scratch/tm70/mrd599/ozone_esm15.nc').field60.squeeze()\n", + "ozone_esm16 = xr.open_dataset('/scratch/tm70/mrd599/ozone_esm16.nc').field60.squeeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1966be24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DimCoord : time / (hours since 1970-01-01 00:00:00, 360_day calendar)\n", + " points: [\n", + " 1849-01-15 00:00:00, 1849-02-15 00:00:00, 1849-03-15 00:00:00,\n", + " 1849-04-15 00:00:00, 1849-05-15 00:00:00, 1849-06-15 00:00:00,\n", + " 1849-07-15 00:00:00, 1849-08-15 00:00:00, 1849-09-15 00:00:00,\n", + " 1849-10-15 00:00:00, 1849-11-15 00:00:00, 1849-12-15 00:00:00]\n", + " bounds: [\n", + " [1848-12-30 00:00:00, 1849-01-30 00:00:00],\n", + " [1849-01-30 00:00:00, 1849-02-30 00:00:00],\n", + " ...,\n", + " [1849-10-30 00:00:00, 1849-11-30 00:00:00],\n", + " [1849-11-30 00:00:00, 1849-12-30 00:00:00]]\n", + " shape: (12,) bounds(12, 2)\n", + " dtype: float64\n", + " standard_name: 'time'\n" + ] + } + ], + "source": [ + "print(otmp.coord('time'))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1612dd53", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/xp65/public/apps/med_conda/envs/analysis3-26.01/lib/python3.11/site-packages/iris/fileformats/_ff.py:737: _WarnComboLoadingDefaulting: The stash code m01s00i060 is on a grid 22 which has not been explicitly handled by the fieldsfile loader. Assuming the data is on a P grid.\n", + " warnings.warn(\n", + "/g/data/xp65/public/apps/med_conda/envs/analysis3-26.01/lib/python3.11/site-packages/iris/fileformats/rules.py:371: IrisUserWarning: Unable to create instance of HybridHeightFactory. The source data contains no field(s) for 'orography'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "otmp = iris.load_cube('/scratch/tm70/mrd599/ozone_subset')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "691e437f", + "metadata": {}, + "outputs": [], + "source": [ + "ozone_esm15_a = xrt.annual_mean(ozone_esm15).load()\n", + "ozone_esm16_a = xrt.annual_mean(ozone_esm16).load()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cdd79d7f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'ESM1.6 annual mean ozone at 80S')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,4))\n", + "# axes = axes.flat()\n", + "vmin = -7.25\n", + "vmax = -5\n", + "ax = axes[0]\n", + "np.log10(ozone_esm15_a)[:,15:].sel(lat=-80).plot(x='year',ax=ax, vmin=vmin, vmax=vmax, cmap='afmhot_r')\n", + "ax.set_title('ESM1.5 annual mean ozone at 80S')\n", + "ax = axes[1]\n", + "np.log10(ozone_esm16_a)[:,15:].sel(lat=-80).plot(x='year',ax=ax, vmin=vmin, vmax=vmax, cmap='afmhot_r')\n", + "ax.set_title('ESM1.6 annual mean ozone at 80S')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61f8baaf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'ESM1.6 September ozone at 80S')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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<xarray.DataArray 'time' ()> Size: 8B\n",
+       "array('1849-01-16T00:00:00.000000000', dtype='datetime64[ns]')\n",
+       "Coordinates:\n",
+       "    time          datetime64[ns] 8B 1849-01-16\n",
+       "    lon           float32 4B 360.0\n",
+       "    pseudo_level  int32 4B ...\n",
+       "    realization   int32 4B ...\n",
+       "Attributes:\n",
+       "    axis:           T\n",
+       "    standard_name:  time
" + ], + "text/plain": [ + " Size: 8B\n", + "array('1849-01-16T00:00:00.000000000', dtype='datetime64[ns]')\n", + "Coordinates:\n", + " time datetime64[ns] 8B 1849-01-16\n", + " lon float32 4B 360.0\n", + " pseudo_level int32 4B ...\n", + " realization int32 4B ...\n", + "Attributes:\n", + " axis: T\n", + " standard_name: time" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone_esm15.time[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "89c6c6d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'time' ()> Size: 8B\n",
+       "array(cftime.Datetime360Day(1849, 1, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "      dtype=object)\n",
+       "Coordinates:\n",
+       "    time                     object 8B 1849-01-15 00:00:00\n",
+       "    lon                      float32 4B 180.0\n",
+       "    forecast_period          timedelta64[ns] 8B ...\n",
+       "    forecast_reference_time  object 8B ...\n",
+       "Attributes:\n",
+       "    axis:           T\n",
+       "    bounds:         time_bnds\n",
+       "    standard_name:  time
" + ], + "text/plain": [ + " Size: 8B\n", + "array(cftime.Datetime360Day(1849, 1, 15, 0, 0, 0, 0, has_year_zero=True),\n", + " dtype=object)\n", + "Coordinates:\n", + " time object 8B 1849-01-15 00:00:00\n", + " lon float32 4B 180.0\n", + " forecast_period timedelta64[ns] 8B ...\n", + " forecast_reference_time object 8B ...\n", + "Attributes:\n", + " axis: T\n", + " bounds: time_bnds\n", + " standard_name: time" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ozone_esm16.time[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d4721676", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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l7JgaKzWF/g0WFRVBCBH1Ni0tnccDBjpxKjMzE8ePHw+7/NixYwCArKwsbbtvv/02bLvQZmS5/fz586MOzdTXpk9Weno6zGYzbrzxRsybNy/iNgUFBdr/b7nlFvzhD3/Qeobee+893HXXXYY+gqysLIwePRq/+93vIu5PBoFtzeFwRBwa25qBop4MUvXk6ytPBllZWcjMzMSyZcsi7kOfpQKaF5QsWLAgpmbl/Px8HDhwIOb96k2cOBHLli3DiRMn4r4P4Z133kFtbS3efvttQwZh48aNJ73vH/7wh3jppZea3G7q1KmNZgguueQSrF27NuJ1GzduxPXXXx/W43PqqafC7/dj+/bt6N+/vzb/0+bNm8OG8W/evLnJ+aHkiLfNmzdj+PDh2uVerxc7duxoNFvSGPk3E2nARmFhoeELZlZWFhISErS+qVD6z2Gv1xsWgEUbFAKE/w1mZWVBURR88cUXEUf5deeRfwx04tQ555yDhQsXYv369TjllFO0y19++WUoioLp06cDUL9JvfHGG3jvvfcM5Y1XX33VsL8hQ4Zg0KBB2LRpEx5//PE2P36n04np06djw4YNGD16dNRMgzRs2DBMmjQJixYtgs/ng8vlCvtGOXPmTHz44YcYMGAA0tPT2/LwG9WvXz8UFxejqKhIOym73W58/PHHbXJ/1dXVEV9fk8mEKVOmAFCfm9dffx0+nw+TJk1q1fv/6U9/GtNkeS39IBZCYNWqVUhLS4uYUYs38gSnf76EENoQar3mZhMfeeQR3HbbbU1uFxr4hsrMzIz6WuTl5WHdunXw+XyGYGf16tUAoGWUevXqhYkTJ+Lf//437r33Xm3bNWvWYOfOnbjrrrsaPYZJkyahZ8+eWLx4sWFwxX//+1/U1NQ0ay4dvdNOOw0OhwOvvPKKoXz09ddf4+DBg4ZAZ+bMmXj88ceRmZlp+GIWaurUqXjyySfxn//8xzBk/vXXX4/5uGbOnInf//73OHr0aFgJrLtjoBOn7r77brz88su4+OKLsWDBAuTn5+ODDz7Ac889h1tvvVXrr7jpppvw5z//GTfddBN+97vfYdCgQfjwww8jnnRfeOEFXHjhhTj//PNx8803o1evXigrK8P27duxfv16vPnmm636GJ5++mmceeaZOOuss3DrrbeiX79+qK6uxp49e7Tavt4Pf/hDzJ07F8eOHcPkyZPDMkwLFizAJ598gsmTJ+OOO+7AkCFD0NDQgAMHDuDDDz/E3//+95jS9idr1qxZeOihh3DdddfhvvvuQ0NDQ9RZYVtDZmYmbr31Vhw6dAiDBw/Ghx9+iBdffBG33nor+vbtCwC47rrr8Morr+Ciiy7CnXfeiYkTJ8JqteLIkSNYsWIFLrvsMlxxxRUtuv+8vLxWy5ZddtllGDNmDMaOHYvMzEwcO3YMixcvxqpVq/Dss88ahiEfPHhQyyrs3bsXgHqSA9Rgc8KECS0+DrkfOWx33bp1SEpKAgBcffXVMe1DnhCbm8U699xzYbPZcP311+P+++9HQ0MDnn/+eZSXl4dtO2rUKLz99tt4/vnnMX78eJhMpkYfd79+/dp8zpi7774bd9xxBy655BLMnTsXTqcTn376KZ566inMmDHDMLnjE088gXPPPRfXXHMNfv7zn6O4uBi//OUvMXLkSMMXmYMHD2LAgAGYM2cO/t//+38AALPZjCeffBI33ngj5s6di+uvvx67d+/G/fffj3PPPbfREU2NSU9Px7333ovHHnsMP/7xj3HNNdfg8OHDeOSRR8JKV3fddRfeeustTJkyBXfffTdGjx4Nv9+PQ4cO4X//+x9+8YtfYNKkSbjgggtwxhln4Be/+AWqqqowfvx4rF69Gi+//DKA2KY0OOOMM/DTn/4Ut9xyC9atW4cpU6YgMTERx48fx5dffolRo0ZpQdSCBQuwYMECfPrpp5g6dSoAtU/onHPOwUMPPYSHHnpI26/FYsHUqVPx6aeftuj56hQ6shOaopOd9SdOnDBcPmfOHJGYmBi2/dSpU8WIESMMlx08eFD84Ac/EJmZmcJqtYohQ4aIP/zhD9roKOnIkSPiqquuEklJSSI5OVlcddVV4uuvv444mmTTpk3i2muvFdnZ2cJqtYrc3Fxx9tlni7///e/aNq016kpe/sMf/lD06tVLWK1W0aNHDzF58mTx2GOPhe2jsrJSJCQkCADixRdfjHg/J06cEHfccYcoKCgQVqtVZGRkiPHjx4tf//rXhpFNaOGoq3nz5oVdnp+fL+bMmWO47MMPPxRjx44VCQkJon///uKZZ56JOuoqdJ/yufrDH/5guFw+72+++aZ2mXxfrFy5UkyYMEHY7XbRs2dP8atf/Sps9JzH4xF//OMfxZgxY4TD4RBJSUli6NChYu7cuWL37t2Gx9PSEUQn64knnhCnnnqqSE9PF2azWWRmZorzzz9fLF26NGxbOWom0k/o6xGqqfdwtP1G+0iN9JxlZWWJ0047rcnHHGnU1fvvv6+9Tr169RL33Xef+Oijj8KOuaysTFx99dUiLS1NKIrSrJFnbemtt94SZ555psjKyhKJiYlixIgR4re//a3hb1D63//+J0477TThcDhERkaGuOmmm0RRUZFhG/k3Eel1ffXVV8Xo0aOFzWYTubm54o477hDV1dVNHmO0UVdCCOH3+8XChQtFnz59hM1mE6NHjxbvv/++mDp1atiItJqaGvHggw+KIUOGCJvNJlJTU8WoUaPE3XffLQoLC7XtysrKxC233CLS0tKE0+kU5557rlizZo0AIJ5++mltu2jnBumf//ynmDRpkkhMTBQJCQliwIAB4qabbhLr1q0L24f+vSLf86Gfe2iFUXYdTREi0CFGREQA1LWupk+fjuXLl2Pq1KlhkxHGyufzQQiBgQMHYuTIkdrkkdu2bcOIESOwdOlSXHzxxa156NRKFi9ejFtuuQV79uxBfn5+i98DJ+PVV1/F7Nmz8dVXX2Hy5Mntfv/xgqUrIqIoZsyYAQBYu3Zti0pd48ePx6ZNmwDA0Dy7YsUKnH766QxyuoCBAwcCUCdhlc3DbeG1117D0aNHMWrUKJhMJqxZswZ/+MMfMGXKFAY5J4kZHWpTQogme0/MZnOnmJo+mqaWhzCZTHE/pLm7qa6uNswLNXz48BatJbRt2zbU1dUBUJc4kCdN6vxKS0uxf/9+7fexY8e2aVZn6dKleOSRR7Bnzx7U1taiZ8+euPzyy/HYY48hJSWlze63O2CgQ21KlgAas2jRolZfPqI1NRWEzZkzB4sXL26fgyEiomZhoENtKvSbcSQFBQWdeljwunXrGr0+Kyur069uTETUXTHQISIiorjFxgIiIiKKWwx0iIiIKG4x0CEiIqK4xUAn4PPPP8cll1yCvLw8KIqCd955p83v8+jRo7jhhhuQmZkJp9OJsWPH4rvvvmvz+yUiIuouGOgE1NbWYsyYMXjmmWfa5f7Ky8txxhlnwGq14qOPPsK2bdvw1FNPIS0trV3un4iIqDvgqKsIFEXBkiVLcPnll2uXud1uPPjgg3jllVdQUVGBkSNH4oknnsC0adNadB+//OUv8dVXX+GLL75onYMmIiKiMMzoxOiWW27BV199hddffx3ff/89rrnmGlxwwQXYvXt3i/b33nvvYcKECbjmmmuQnZ2NcePG4cUXX2zloyYiIuremNGJIDSjs3fvXgwaNAhHjhxBXl6ett2MGTMwceJEPP74482+D4fDAQC45557cM011+Dbb7/FXXfdhRdeeAE33XRTqzwOIiKi7o6LesZg/fr1EEJg8ODBhstdLpc2o++BAwdQUFDQ6H7mzZun9QD5/X5MmDBBC5LGjRuHrVu34vnnn2egQ0RE1EoY6MTA7/fDbDbju+++g9lsNlyXlJQEAOjVqxe2b9/e6H7S09O1//fs2RPDhw83XD9s2DC89dZbrXTURERExEAnBuPGjYPP50NxcTHOOuusiNtYrVYMHTo05n2eccYZYWtA7dq1C/n5+Sd1rERERBTEQCegpqYGe/bs0X7fv38/Nm7ciIyMDAwePBizZ8/GTTfdhKeeegrjxo1DSUkJPvvsM4waNQoXXXRRs+/v7rvvxuTJk/H444/j2muvxbfffot//OMf+Mc//tGaD4uIiKhbYzNywMqVKzF9+vSwy+fMmYPFixfD4/Hgsccew8svv4yjR48iMzMTp59+Oh599FGMGjWqRfe5dOlSzJ8/H7t370ZBQQHuuece/OQnPznZh0JEREQBDHSIiIgobnEeHSIiIopbDHSIiIgobnXrZmS/349jx44hOTkZiqJ09OEQERFRDIQQqK6uRl5eHkymxnM23TrQOXbsGPr06dPRh0FEREQtcPjwYfTu3bvRbbp1oJOcnAxAfaJSUlI6+GiIiIgoFlVVVejTp492Hm9Mtw50ZLkqJSWFgQ4REVEXE0vbCZuRiYiIKG4x0CEiIqK4xUCHiIiI4hYDHSIiIopbXT7QOXr0KG644QZkZmbC6XRi7Nix+O677zr6sIiIiKgT6NKjrsrLy3HGGWdg+vTp+Oijj5CdnY29e/ciLS2tow+NiIiIOoEuHeg88cQT6NOnDxYtWqRd1q9fv447ICIiIupUunTp6r333sOECRNwzTXXIDs7G+PGjcOLL74YdXuXy4WqqirDDxEREcWvLh3o7Nu3D88//zwGDRqEjz/+GD/72c9wxx134OWXX464/cKFC5Gamqr9cPkHIiKi+KYIIURHH0RL2Ww2TJgwAV9//bV22R133IG1a9di9erVYdu7XC64XC7tdzmFdGVlJWdGJiIi6iKqqqqQmpoa0/m7S2d0evbsieHDhxsuGzZsGA4dOhRxe7vdri33wGUfiIiI4l+XDnTOOOMM7Ny503DZrl27kJ+f30FHRERERJ1Jlw507r77bqxZswaPP/449uzZg1dffRX/+Mc/MG/evI4+NKJOw+8XaPD4OvowiIg6RJcOdE499VQsWbIEr732GkaOHInf/va3+Mtf/oLZs2d39KERdRq3vvIdJj3+KSrq3B19KERE7a5Lz6MDADNnzsTMmTM7+jCIOq3vDpajst6DfSW1OKWvraMPh4ioXXXpjA4RNa3B4w/8y/IVEXU/DHSI4pgQAvWBAMfl9Xfw0RARtT8GOkRxzOMT8PnVqbJczOgQUTfEQIcojjV4g8ENMzpE1B0x0CGKYw3uYKDDHh0i6o4Y6BDFsXoPMzpE1L0x0CGKY/pAhxkdIuqOGOgQxTE5tBwAXB5mdIio+2GgQxTH6vU9Ol5mdIio+2GgQxTH9OUqZnSIqDtioEMUx/SBDjM6RNQdMdAhimPGZmRmdIio+2GgQxTHOLyciLo7BjpEcUyfxeHwciLqjhjoEMWxBmZ0iKibY6BDFMfquQQEEXVzDHSI4hh7dIiou2OgQxTHjPPoMKNDRN0PAx2iOMaMDhF1dwx0iOJYAxf1JKJujoEOURwzLOrJjA4RdUMMdKhLO1Jeh7n/Wod1B8o6+lA6JY66IqLuztLRB0B0Mj74/jg+3loEm8WMCf0yOvpwOh326BBRd8eMDnVpZbXuwL+uDj6SzkmfxfH5BTw+BjtE1L0w0IkTQgiU1HS/k31FnQcAUF7r6eAj6ZxCy1UsXxFRd8NAJ078ftkOTHhsOVbuLO7oQ2lXFfVqRqeizt3BR9I51YcENixfEVF3w0AnDhytqMc/v9wPANhytLKDj6Z9lcuMTh0zOpHom5EBZnSIqPthoBMHnl2xBx6fAABUu7wdfDTtqzIQ4NR7fDyJR9AQksFhRoeIuhsGOl3ckfI6vLnusPZ7TUP3CnRk6QoAylm+MvD5BdyBwMZqVgAwo0NE3Q8DnS7uuZV74fEJKOp5DDXdKKMjhDCUrNqyIbm4qgFP/W8njlfWt9l9tDZ9UJOaYAPAjA4RdT8MdLqw4uoGLZtz5bjeALpXRqfB49cyFkDbNiQv/voA/vbZHrz09cFW37cQAn/+ZBc+3V7Uqvs1BjqWsMuIiLoDBjpd2J7iGnh8AgVZiTh7aDaA7tWjoy9bAW3bkHy4XM3klNe2fjC19VgVnv50Nx56d2ur7leOuLJbTHDa1ECHGR0i6m4Y6HRhdS71RJaSYEWSQz2RdaeMTmipqi17dIoqGwAAte7Wf35LA8FTUVUD/H7RavuV2RuH1Qy7Rf1TdzGjQ0TdDAOdLkyedBNtZiTZA4FON87otGXp6niVmtGpbYPnt7pBDdi8ftGqwVq9W83eJFjNcFjNAJjRIaLuh4FOF1YbyOgk2i1IdnS/QKeyLjSj0zalKyEEiqrUWaflc96aqnVZuOLq1pvdusGrHmuCLZjRYY8OEXU3DHS6sLpIGZ3uVLoKC3TaJqNTXufRmp7bonQlMzpA6wY6crJAhy6j0+AJz+i89u0hfLy1sNXul4ioM2Gg04XJ7ILTbtF6dNw+P1ze7vGtXZauTIGh9W3RKAwAhYH+HKBtSldV9cF9nmjNQEfr0TEFe3RC3htFVQ2Y//Zm3P7ahm7zviGi7oWBThem79FJDIyqAdqmvNIZydJV73QngLYrXRVWBefOqXW3RelKn9FpaGTL5pFlqgSrGfYoGZ1jFepjc3v92FVY02r3TUTUWTDQ6cJkdiHRboHZpMBpU09m3aV8JUtVBVmJANquGbmwMphlaZtm5LbJ6BgCnSgZnZKa4HO29Vj3WieNiLoHBjpdWF0guyCzObJPp9rVPRa4rAhkcGSg03YZnWCWpc7ta9Uh4ABQ1UbNyLH06JTUBO9v67GqVrtvIqLOgoFOFyazC067ehLrbnPpVNQbA52qBg98rRyEAEBhyLIPda08cklfujpR1ZqjrtSgxtFYRkcXWG1hRoeI4hADnS4s2KOjBjjJ3WwuHVmqys9Ue3SEACrrWz+rUxgSfLR2+Uqf0TlR0/oZnQSbKaaMzo7j1W0SKBIRdSQGOl2Yfh4dQJfRaadAp7LO06HzssjSVVaSXZtHqC2GmBdVGhuEWzvQMTQjV7VNM7LDKjM6oYFO8Pmq9/iwv4QNyUQUXxjodGH6eXQAXY9OO5SualxenPXkZ7j671+3+X1FIoTQSldpTivSnerq3G3RkBy6Ynlrj2rTv161bl+rBVL1hiUgZEbHeOyhGaQtR9mnQ0TxhYFOF6afRwcAkuxWAO2T0TlYWouqBi+2HK2Cx9f+ywrUe3zaJH5pThvSnepjL6tt3dJVvdunlZayk+0AWvf5FUIYMjpA64280q91FTWjE7ivwTlJADjyiojij6XpTai5SmpceOazPdrvTpsZt5xRgB6BE2VrqQ3J6CS3YzNymW5yvtIaN3JTHW1+n3qybGU1K0i0mZEWyOi0dulKjrhy2szITXWguNqlZdJaQ63bB9kWk5NiR1GVC8XVLvQLNFifjHpPcK2rpjI604ZkY1dRDUdedUJenx8WM7+TErUU/3raQHWDF4u/PqD9PLdyL15efaDV76cutEenHZuR9YFOa879EisZ6KQm2KAoCjIS26Z0JctWuakOrem7NZ9fmc2xmBT0zVCbqltr0kDj8PLwjE6Dx6eVzaYN7gEA2HK0EkK0X0PynuIa7ChkcBXN6r2lGPHwx/jXmoMdfShEXRYDnTaQlmDF7WcPxO1nD8RZg7IAAEcr6pu4VfO4vX64AyUjbR4dR/v16OiXWzhR03oNtLGSAU1aoGQl/23tuXSKAhmd3BQHEgPD+OtacXZk+VolOyzITlazYq0VOLq8wVFXMqPj0mV0SgOvoc1swvh+6bCaFVQ1eHGkvHXfq9F4fH5c+8JqXP7sV606I3Q8WXegDC6vH6v3lnT0oRB1WQx02kB6og2/OG8IfnHeEFw2theA5p28Vu4sxiV/+xLbj0f/pluvO9kmhDQj17TDhIFluoCiQzI6gUZk2ZvTVs3IclZkNdBRn9+mmoVX7y3FnuLqmPYvMzrJDqtW2mytSQO14eW6jI6+dCX7czKTbLBbzBickwyg/SYOPFZRj7JaNxo8fnzw/fEW7aOrr8/V4PFh9v+twd9X7Y14fXXgvdYW0yYQdRddOtB55JFHoCiK4Sc3N7ejD8sgK0k9AeuH8TbG6/PjwXe2YPPRykY//GsCfSI2swm2wGRwskenPda6KqsNnow7unQFBAOe8lZuRpYZnZxUB5wxlK72l9TiB/+3Bj9+aV1M+6/SZXS0QKeVJg2MNOpKX7qSc+hkJan3OyIvBQCwrZ0akg+V1Wn/f2/TsWbf/ovdJzDy4Y/xyHtb27Xc1po2HKrAV3tK8dLXByJeLzN+DHSIWq7LNyOPGDECy5cv1343m80deDTh5Mkr1mDg/e+PaaWDxj7c6rR1roKPN7gERHuUrjo2o1MeVrpqm2Zk2aPTM9UBb6BU2Fjp6svdJyAEcLi8Hn6/gEkurR5FVb3M6Fi0UV2tNWlgfYRRV4aMjhboqM+dbIA+Vtk+ZaTDZcES2YZDFThUWoe+gckfIxFCQFGCz+eafaXw+AQWf30ANosJ8y8cari+K6gKZPRKa9xhjw8IBtX6Fe6JqHm6dEYHACwWC3Jzc7WfHj16dPQhGfQIfFsuq3U1Oeus3y/w3IpgCruikUBHrqLt1K1arpWuGtqhdKXr0Yk1W9WaKqOWrlr3sctZkXN0pSt58jlR7cKlz3yJ1749pG2/Zn8ZAMDnFzH1SsltUhxWZKeoPTqtNWmgK8KoK31GRwaoMqOTlaj+W9qKszM3Rp/RAdQgX08Igbe+O4LbX9uAM37/GUY8/DE2HCrXrte/B//x+T78TTfS8WS0Z3ZIBrpunz9iMCNLm8zoELVclw90du/ejby8PBQUFOC6667Dvn37om7rcrlQVVVl+GlrGYk2KArgF8YP5kj+t60Iu4uDM9M29uFWGymj044zI+szJx1TupIZHVvgX9mM3LpBl5wVOTclOOpKZtM+21GE749U4qn/7YLPLyCEwDf7yrTblsVwLMFmZKsWFJe0ckYnwWbM6MgTuQxQswKZpMxAZqe0ifdpazkcCHSG5qq9Qe9tNAY66w+V4xdvbsL7m47haEU96tw+fLE72JQrj39snzQAwJ8+2YWDpbUndUzPrdyD8Y8tx74T7TNDtP5vvKQ2/HWXU0VUNXhafTFZou6iSwc6kyZNwssvv4yPP/4YL774IgoLCzF58mSUlpZG3H7hwoVITU3Vfvr06dPmx2gxm5DhlH066gdZvduHvyzfZWg2FkLguZXqN9LhPdVeicpGTpTagp66jE6ynDCwnefRac31mWJVrvXoBDI6icHSVWt9I3d5fdpooNxUfUZHDSBkQFBS48J3B8uxr6TWEKTEEnQFm5EtyE6xa/v1tsIkjNrwckswo+MXgDdwwjwR0qOTmSQzOu0T6MiMzk/O6g+b2YSdRdWGoebrDqjZmzF90nDxqJ4AjEGgfA/OndIfg7LVCQ/15bCWWL6tCGW1bu2+25p+nbOSCF8YZCAsRPuUpIniUZcOdC688EJcddVVGDVqFGbMmIEPPvgAAPDSSy9F3H7+/PmorKzUfg4fPtwuxxnap/Ph5uP4y/Ld+MPHO7VtdhfX4PsjlbBbTLhzxiAAjZeuZJ+ILFcBwexOrdsHn19gw6FynL7wU7y78WirPh4hRIdndCrrZOlKDXBkMOnxCa2sBwA7C6vxuw+2NWtZhY2HK/DDxWtxyoJP4BfqHDdZSfbg8xvYlz4gWLal0JDNAYxD8KORPRopDgsynDaYTQqEOPlyoBACDYERSQ6bCXZr8E9d9unIE6t8f2YmBgPy9ijfyEBnRK8UTBuilpz1WZ3vj6hN0ReMyMWp/dK1Y5NkiS0zya4FvFUnWbaVwVNTpSIhBL7cXaK9D6WNhyu0TFUsqnT3EymTps/Oym1dXh/mv70ZH28tjPl+iLqzLh3ohEpMTMSoUaOwe/fuiNfb7XakpKQYftpDVkhJYn+Jml4/pptbR/6/f48kDOihNoU21m8iZ0V22sJLV/L6j7cW4XhlA5Ztad0PxGqXFx5f8ERY4/K26mzBsaioNzYjJ9jMsAdGn+kDjCeW7cCLX+zHv6NMuPbksh348UvrDMP1f/POFny2oxi1bh8yE224a8YgmE1KsHQVeKz6rNbHWwuxZp8xk9hUqRIwlq5MJkVrDI4WPL76zSHM/de6iIupCiHw7Io9eOnrA3B5/ZCxitqjE/xTl306oc3IsnTl8vrbvPxZWe/Rgok+6U5cPFrN2KzceULbZtORCgDAmN6pWnmtpFo3I3fg+c1ItCElEOiELqfRXLEGOqt2ncAN/+8bPPDW99plu4uqcdXzX+MnL8c24g4wBmaRSpb66+Uxrd5bite+PYQ/f7Ir5vtpC7UuL5ZvK+rQhX2JYhFXgY7L5cL27dvRs2fPjj4Ug9CMzuFy9Ruf/lu71i+RFPzQbqwuH+zRCQY3dosZtsBU8TUNXq1foaUZl8p6D46Uh387lYFEgtWMBKsaaOlPQK2tsLIh7MM0tHQFBLM7MtskhMD3gZPlxsMVYfv9cPNxPLdyL5ZvL8IHm9Wh/IfL6rD5aCVMCrDk55Ox9tczcNvZaoYttBlZ/w38aEU9lgW+YecGmopjaYzWmpET1H3LSQOjTaD310934+OtRfh2f1nYdR9sPo4/fLwTD7+31TDpn8NqhqIoWrCjZXQC7znZG+S0WbTAua3LVzLrkZVkQ6LdgskD1Ik1tx2vQlmtG6U1Lu0xjOydqn1ZkOU2lzc4q3NWkg0pgSD/ZEYneXx+rZTUVKCzo1CdJ2nFzmLt+fzftiL4/AI7i6q1ddiaoj/e0CyeECJiRkf+PccSSLelv6/aix+/vM7QjE/UGXXpQOfee+/FqlWrsH//fnzzzTe4+uqrUVVVhTlz5nT0oRkE59IJBDqBD3n9SKwTujKCPHkLEX2WY21BT5txOL2+IflAqQyoWhbozPnntzj7qVXYfMQ4r0qZ7pt0VnIgA9FGsyMfq6jHmU98hh8uXqtdJoQIlq4C5RYA6JORAADYVaQ2khZXu7STx6aQQKe0xoXfvLNF+/2dDWp5T2a/JhVkYlzfdMPw8NCZkWXpRI78cnv9sJlNmD5ULcPE1owcnDAQQKOTBjZ4fNraW0UhI7Pq3F48/sF27fcvd6uZEYtJgTUQ/MpAx+X1w+X1aSdzGUQA+obkti1Hyr+BPoFlL3ok2zEkMGHh6r2lWtlqQI9EpDiswaxo4HmR0xtYTApSHFbDl4OW0pdjmwp05PPv8vqxOpDJWxXIRgmhBuexaCyjU+f2QV9BlMcUa9apre0JDJw41sqzvhO1ti4d6Bw5cgTXX389hgwZgiuvvBI2mw1r1qxBfn5+Rx+aQXhGR/1g0I/Ekh9yPZLssFvMWgAjSzShZPlE36Oj/726wXPSGZ3tx6vg9vrx4LtbDJklfaAjswEn2iijs6OwCl6/MMzWW+f2actfpOkyOuP6qn0c6wNDkPUrcR+rbDAM237o3a0orXUjPzBvy1d7S1BY2YAPt6iZnYtGhU88GZrRkc/D1eN7a9uM6ZOKnqlqwBWpR2dPcTXOevIzvPqN+i1YvwQEAPTLVMuWr3xzMCwroB+OHRoIPb9yr2H+m88Do5Nkxg1QMzuAGjDJjI3FpBiyYpmJsszattkC+Vj6pAfnzZk8MBOA+lrIDNyY3mkAglmnapcXDR6f9veSnmiDKRDsAMael+bSzw3VVBChn9Rx1c4TqKz34Dvd0PcjFbH16Rh6dEICndAvOaGBjsvr79CykXwPtscoT6KT0aUDnddffx3Hjh2D2+3G0aNH8dZbb2H48OEdfVhhgj06bjR4fIbAQ35gh85pIk/g0T5wI82jAwQDnf0ldVrmodbta3YPTb3bp/VybDpcgdfXBhu3DYFOK09yF0qeUCrrPdqJXz5nCVazIaM1LjDMeMOhCgDAlqPG6QM2BbIEn2xTS1UWk4Jnf3AKTu2XDiHUVPyGQxVQFOD8ERECHVtwCQghhBYsXD2+jzY79aSCTC3LFKm08PHWIhwuq8d/v1Ofz+CEgerrPXdqf6Q5rdhytAp//dTYa3awVBfo6IK2Q6V1eOFzdVoF2euyeq+aZXDYwgMdl9evPYeZSTZD1iqrnUZeyfKtXMgUAM4IlK++3lOilRzHBF7TlASLVpYtqXFpZUPZQC1Lf1XNGHG47kAZlm8r0n7Xv16xZnQAtXz15e4SwzxZR2NcL0wf6IQGl6FLucjsj367k83qFFY2tHgfsrzaHmvrEZ2MLh3odBX6jE5oz4s84WgZncC2MhUfrc8j0jw6QLB0teWosdzU3B6a0KHRT368QzsRyOsMgU4bjbzS7zc0+5WVbDPMJHtKvprR2VlYhVqXV8voyBOkLF/JnoIfnlmAkb1SccU4NSOzODAN/6n5GdrkfXryufYHRkXJrFLfDCeuGNsLFpOCC0bmaiPAIr12u4vU3o7dxTUQQoRldHJSHHj8ilEA1DldvjsY7MXRzxGjz+i88PleuL1+nDkwC7++aBgA/azIwT9xfY9O6PIPkiyztvWkgYcCw8D1gc6k/hkwmxQcKK3DmsAIttG9UwEAiqJoZbWSGre2BIk8/uZkdI5V1GPeK+tx9d9X48cvr9OVkoPv+ab2U6TroTpYWhe2hMOxilhLV8EgIfQ5Dw3aghmd4HYnM0FmZb0HZz+1Elc9/3WzbyuEQFHgS0hzRjQSdQQGOu1AP+rqcMg3PXkiD8voBPo+9EPM9eWjYI+OMaOTHMjobAtZmLG5GRd9MDM0NxkVdR78Zbk6yqOsNji0u0dS6664Hao4YvZLNm4bT9I5KQ7kpTrgF+rQZJnRuWCkmp3ZdKQClXUefBHoX7l2ghrgXDyqpxYM6bcPpX+uZenFaTMjwWbGY1eMxLoHZ2Bkr1StZydSj46cELK6wYvCqgZtzTJ5ogaAi0b1xJXjesEvgHve2KRlCvQZHX1GYXegJ+naU/sgLy0BvdIStOsila5cHr8W+IY+h+01aWBojw6gZrVG9VIDm3qPDxaTgmE9gyMj9X06MuOUoWV0YuvRWX+oHOc8tUprPgeAA4EAsizGHh39SV6WPr89oAZmE/tlAACOxlC68vqMo9vCMjpNlK6aOs6m7C+pRZ3bhz3FNc2et6mq3qtlWFm6os6OgU47kFmPsjo3DpQYZ26NltFJCyxWKT/INh2uwJgF/8Pir/YDCPboRMvobA1ZmLG5gYj8ppiZaMN95w8BEBz6K79RZiRa2zyjox99pAU6UbIRQLBPZ8XOYhwNNEleN1GdGHLT4Qp8tOU4PD6BobnJGJitNr+mOq04e2i2to9ogY7ZpGiBgzxRyxOt1WzSZmnWJi8MCRZ8fqE1cMrjkc2myQ5jwPrIZSOQaDPjYGmddpuDUXp05OPsna4GOBMLMrTr9IFOsBnZpz2H8vWTgj06zXs93V4/nl2xB3/6ZFeTM/j6/ELLbMoGcumMQJ8OAAzrmaIFZ/pjNZSuAoFZMKPT+En3pa8PoN7jw5jeqdqMzDJoKQ8JIKLNJaQvo16j689KsJpx+bheAIKvSWNCA4SaQP9RtOsr68NH+1WcxEzg+vJnc0p+gPHvkqUr6uwY6LSDdKcNJkUdjRE6+udEtQsen18bLi1LB7JBVM6O/OWeElQ3ePHJdrWnQPboJEbp0akNWXiyuScumdFJd9q0KfYPldWhxuXVMjoZifbgvC9N7L+l09cbMzqB0lV1Y4GOeqxvrlN7YPIznTi1XwbsFhOqGrz4+yp1LbGZo41TEMwKBEOn9c9AXprx5KsnG5JloJOpG/UlyeCnot44PcDR8nrDWlPrA71ENrPJcEIH1BP3iEB2Y3OgDGkoXVWpk/p5fX5tJFbvwHGf2i8Y6NgjNiP7o5autIxOM3p0DpTU4uq/f40/fLwTf/10N95pYoLKwqoGeHwCFpOiNW5Lsk8HCJatJP0cQ9pkgWE9OsEMxzsbjuKCv3yOvYHlHIQQ+GqP2rv0ywuHYWTg+ZXZMX2mxOsXURdvlYFRutOK83S9XJMHZKIgsDBqLD06MihLsJq1Hi/932nonECynFbaSj06Rbq/reYGTPq/y9p2nkOLqLkY6LQDs0nRptffEAh0eqaqJZ+SGrf2wWU2KdpcMFrpKhAAyVW0Ze1fWwIiSkZHamnGRQZeaU4rMpPs2srau4qqdWWtYEYn0vT10lP/24nxj31imDHW7xfYX1Ib9q059Hf96JbS0OxXUniQITM68vhH5KXAajZpJzU55H7m6DzD7aYPycabPzsdz/zglKiPAwhm0GR2JTNCsCVfu9CFPXcXVxu2W39QHaUTms2RZBlny9FKeH1+w8nT7fOjos6DwqoG+PwCNrNJC1omFqRr20XN6GjBovE51JqRYxxevulwBWb+7Ut8f6QS5kBT85PLdhomYAwl3we90xO020in5KdrxykbkUOPraTGpQUlmY306Ly1/gh2FFZj8VcHAKjTDpTUuOCwmnBKfhpyAktuRAp0gOhBhNw+J8WBQdlJyAv8LU8b0kPLqh2raGgyuJdBWWqCFVnarNTBY5DvHdlwX1nvQb3bp/VfNXaMsdBndMqb2eujz+i0x5IzRCeDgU47kcNjZZ+FzDyU1Li0k05mYnAETGpIj46cl+NoRT2EENrCkqEZneSQ4eZy6vzGMi5CCPxqyWY8uWyHdlmlLqMDAEMCaf4dx6u1FH+60zjqKlqq/5NtRSiv8xhmDv7HF/sw/Y8r8eZ3R7TLth6rxITHluPl1Qe044o0Qi3YjBweZKiBjaL7XQ0W5DBlQA0g+gW+eeud2i8jYpZITz7fh0JKV3p2ixmJgZOTvu9D9ufIE7nM1DQV6Hx/pALHKhrg9QvYLCYt21dc7dKCn7w0h/beGdAjSesTijy83K+Vw3rrhncDzc/o/HvNQdS4vBjbJw2f3jMVvdISUFjVgBe/iL647qEI/Tn6Y5w9KR/5mU5MH5JtuE4/elEGBJkhPTq1bp/WbyK3Wba1ED6/wFd71CH3aobPrE3sKAOX0Ab8pgKd7BQHFEXBI5eOwNXje+PKU3ojN9UBk6IGopEW6Yy0/5QEi/ZeLjVkdNS/cdlzVVXvCQtATyqjowt0KqNMYxGN/gtITWAUIlFnxUCnnYSelMf1CQQg1a6w/hxAV7qqlxkd9UPJ7fWjtNYdLF1FmUdHOiWQ4Wgs47KnuAavfnMIz63cC1dgfSQto5OoHodsCt1ZWKWdvDMSbdrJx+31R63zyzS3/oNVDh9edyA4quiz7cUorXVj6abj2mN365oktdKVNot0eFDisJoxPC9Y8hiRpx73mD7By0LLVs2RFFq6ipBVAoyLjEqyaXjKYHVCQVnGStHNY6Mns1Dbjldhb4l62/wMp+EELXtB9OU2RVEwIVC+SrCFZ3RKalzYGRj9dUp+muE+ZY9OWV1sC4vuCZSFfnxWAfplJeKBC4cCUIfqF1dFHnkUqRFZ76FLhmPVfdPD+oeydEF1WUiPjj5Y1Gau1k3d8N3Bci3QOWOgWh6TI+sKAyft0OAuWhAh3885geM5b0Qu/njNGCTaLbCaTcgJ7Lep8pXMPqU4rIZ1xkIfR69Alqiy3hOWdTqZUVdFVfrSVXMzOsHbenzCUJIl6mwY6LSTHiEn5bH6jE6EfgmtGbnOmNEBgOMVDY00IwdPmj1THdo39sYyOvrJ+GQGpTw0oxOYtXbLsSrtQzEj0QaH1axNvx+pPObx+bUP50LdiU8GbvtOBPtOZC+FHAUTOileWEYnSvZlnK7kITM6MrAEgnPNtIQsFcrHEqlHB9AtR6E7Me0JlK4uCJmjJ1pGp39WIhJtZjR4/PhsezEAtedIrnKuz+j0Cukrks3VfdKDl8t+nTX7SiGEOrRbLjkRPG4rlEA/WVPlDCEE9gYyQwN6qKuHXzK6J8b1TUOd24e/fbYn4m0+CcxdI1ccj5V+hvFgj476XFjNJq3EU1Xvhd8vDEHBe5uO4pvAshmyD0gGjMUhGR05Ai+W0lUk8rVoqiFZW9A1wWrIVkmyJNRbF+jEGozFori65YFO6MzcHGJOnRkDnXYil0oA1JOJHJZaVutGUeCkr/8GGxxerk4yqB9psb+0VltUM9qEgYB6UuyRHP5NMZR+hJb88KvQVgdXj2NoTzXQkZkYRQlmnbIa6QPSfzAXVrp0/1cf837dKLR9JcEAp87tNaTH1ccQ2owcOciQ8+nkpNi157RvphMPXjwMv7tiZFi5pjlkBk1m6uWJNlTopIFCCK10NaZPqnbyAoBke+SMjsmkaA3JHwVmbO6bkaidYPUZnV7pxkDn2gl9sOTnk3H7OYO0y2RGR846PD4/HaEsZpMWpJXWqq/Dr5ds1rIheiU1blQ1eKEo0JpwFUXBPecOBgC8//0xeEKyQit2FmNHYTUSbWZcERihFCv5ZeFYRb2W0czQvQe0Pp0GD6oaPPDqemT+s/YwalxepDmtGB7I8snnsbhaXYpFvlZ9A3+bTQc6kV97mV1rOqMjpxawaL1GhmbkwISBvdLU4/HqRqtJFa3Uo9Pc/YR+CeEQc+rMGOi0E31Gp0+GE5mJdpgUdfI5WUbQZyj0pavQE/6eomBTa2LIWlf67EC/zETDPDfR6uj6jI68L/ntVg6ZHpidBLNJ0QKs1AQrLIFvvtoyEBGCKX3TojxBeH1+7YOytNaNyjp1KK8+u3OorE67rQz6SmtcqHf7tJNcpB4dADhveA6uGNcLvwyUUaQfn9Ufsyed3PIgoc93RpRgKyOkmfxoRT3q3OrcMPmZiRioy2ZEy+gAwT4dGeT1y3JqjeEnql3BQCcko2M2KRjXN11b5woI9ujI1/CUCIEOEMxSlda48db6o3jlm0P49ZLNYe8f2efTJ91pGDV2ev9MZCbaUFHnwTf7gqVJdXV1ddTbDafla++tWMmgtcGjBk82s8nQk6aNvKr3aM9Xos2MZLtFe8yn98/UGqCzktTRkD6/wOGyOq38IpfhiDZpoCz5RJpUEggGnbFmdFITrLqJGsObkbOT7bAEjnl/iRroyC80kYKxnYXVuOH/vjGUhUPJErjU3FFXoV9qOMScOjMGOu1En63pk+6E2aRojaxyJeRIPToVdR5txJWkb2q1mI0vYaIho5OoZZIaPP6wIeeAevIxlq4atPsFgktR2C1m9Nc18OqbcBsb2aW/TJZ7Smrchuny95XU4ES1y/Ct8EBJnRYMDctVv4GX1rq1/dksprDGa8lhNePPs8ZqMx63ptCeqKwoGR15Epf9TPI1K8hKhNVsMpRtovXoAOFDrPtmOI0ZnfLIGZ1IZEZHOiVQPg2VqSsRfRNoID9QWqc9BkmWGgeGlKAsZhPOG5EDIJiJAoBv95fhu4PlsFlM+NGZBU0eb6jUBKuh0TwzyTgztj6jI0tbOSkOzBieo20zeWBw+LpFN1Jt+3H1b8BuMWkjIqP26MRYumpqsUutR8dQugpvRk52WLT3yP5Ar1b/HurfYmWEAOXt9Ufw5Z4SvLT6YNT7Dv1S0uwencBzIF8Plq6oM2Og0070GZ3egUnS5IfbvsAJQ1+KkVkMl9ev9axI8oQTetIFjKWrgiwnnDaLloWIFIgcKa83fKAXh/bo6AKaobpZajN038blB/7S74+FfeCFjpry+Pxhgdu+E7XYe8L4GA+W1mrZJVk28/kF9pwIBIVJdsNJrr2ENntHzeiETBq4J9CIPDjQ6xRrRkc2JEv9MhO1jI6+dNU7relynD7rkmgza31XoTJ1/SKyrwUAPg6s7C7JQGdAj/ARbBeMVPugPg6MeAKAZ1eq2ZxrxveOmg1pjKIohlJh6Ig3bXbkeq9hQsELdRNAnqkLdIDge3d74MtGRqItbCCAnt8vgs3IUUpXMug8Uq6OkHz0/a247dX1hukVgOAkffrV2fUZHRn4Jzus2jHJ8q780hHpGI8Egt/dRdVh10mhjeLNKV3Vurzalya5hAdLV9SZMdBpJ1khGR0gmAmRyQ19RifJbtFS7DLjIzMYcnZlZ0gZBTCeNPMDKXj9jLKA2owqy0hbQ5aKKK5S+xXkB6gMuABoM8kCxgBo1ql9kGy3YMOhCtyyaK0h2NHX8oVQAx99YzWg9unsKzFmCw6UBktXvdIStA96+VxE689pa6E9UVGbkUN6dOQcOjLAkbMyA8EFPSMpyEzUgiuzSUGv9AQtSNhZWA2X1w9FAXJTmw4c9BmdsX3TwrKBkpzTZf3BckOg+j/dAphAsHQlG5H1Jg/IRGqCFSU1bqw7UIaPtxbi810nYDYpmDtlQJPHGo2+1y10DiP53tdndDIT7ZgyuAfG56fj3OE56JdpDAi1QCeQ0Wkq0Cmrc8PrF1CU6M3wvXXNyJ/tKMairw5g6ffHceHTX+CNtYe1EqB+eLk+iybVRMjoyGCpf+A5rwyZlBKA1sez90RNWI+UVBRSDo+UGYpG/k07bWbtfcdAhzozBjrtJLRHBwj/oNRvoyhK8OR+XD1JypFasskydA4dQE3v2ywm2CwmreFZ3s+Jahe+3V+G6/6xBnP++S2EENgmF74MnARP1LhQ3eDRmm3l6C/AGOjoMzqDc5Lxrx9PQrLDgm8PlOFHL63VPnxDs0iFVQ3aiCtpX0mN1p8jA5iDpbXaB2p2ikM7Ecjnoqn5btpKkt2YFQmd0VgKXdhTZuEG5chAJ7aMjsmkaM2zvdISYDWbtIyO/Fadk+zQXr/G6I91fN/I/TlA8Ln9bIc60mtQdhJMijrvj74cI1+z0NIVoI6CmjFMLRk9v2ov7v7PRgDAzZP7ac2+LaF/3bNCMzq6SQO1eXaS1JGBb906GS/eNCEsCyizMrEGOkXaaDu7of9JTzYjVzd48bsPtgNQvzDUuLy4/63vtTmG9MPL5ePSD+uXMyMnOyzaMcmYRjZ/+wW09dIkmdHx+ETYkjOS/BIh77c5GR1tHqFku/YZxB4d6swY6LQTGYAA6lwoQPg6Q6G/p2lZDPVDOHSUTOjQckA9mb140wT8c86pWvZBn9FZHlhCYkdhNb7eW6pldE7rr64xVFzdoA0rTrJbDCfQIfpAJySjMrZPGv79o0mwW0xYs69Ma7AODXSKKhu0Xh2ZfldLV2ogMHWwOiz6YGmdNrqqR5Jd+0DeGaGfqT3pMzrRylYADAt7+vxCm0NnkFxfK8GqBSwpjQQ6ADA6UL6SgWt2SMkkLy22MpA+oxOtERkIZkrkDLwXjMzFhHx1Xp7/bVXLV3Vur1Y2i5TRAaCVjFbuPIE6tw9nDMzE/JAG8ebSBzrhpSuZ0fFqE+tFy7hJcoi5DA7SnTYteyIDnS1HKzH7/9Zg85FKrZwarWwFqCVlmQndV1KLZLsFn94zFT85S+1L+nhrUeA4gz06ocP6fX6hBbJJdkvYeyQvzaG9npW6/po6t9fQZLwzSvlKBivyy0voumyN0X8BkTOxs0eHOjMGOu3EZFKw4NIRuGvGIG1WXn35xWoOZnAkOTuyDDyG90wxnKwi9egAwNTBPXDmoGAvgj6j88Xu4DDhxV8f0AKd6UPUSeyKq1y6EVfG4+mVlqCVzzIijJgZ0ydN6+M5oA0VVz9QZcCkz+hMDizgeKC0ViuDyPlfjlXW41iglyc7xa5lu/Zq/UwdE+jon/NoQ8sB48Ke6w+Vo8blRYrDojWRAup8PmlOK0brZm2O5MJRPZHisGiLjdotZsNr0yvG4fL6jM64RjI6oZMgTizI0JqLZflKZnMyEm2GMqbemYOytLJb3wwnnrn+lKjlsljpX/fQ0pU+o1OqZXQaf5+ENhRHyuj8e81BfLWnFL9duq3JOXQk/Si4n0zpj8wkO648RW2O31NcAyGEbni5OoJR/k2V1Bgb85N0GR3tsSfatfeAPvMUOqR9V2G0QEcNVmSGsarBaxgg0JhiXUZHfh5EKl35QuYyIuooDHTa0XUT++KuGYO13w1p+AjNtaEfbnlpCYYP0Eg9OpHI7Mf249Vaih4Alm8vQmFVAxRFDY6AwDpCNcbJAiVFUTAsUEaJllEpCGQd9gcaqOXojmGBb46FVQ3avEET8jNgNSto8Pi1b9Sn9ktHst0CIYLDiLOT7drJV5btOqpHJ8kQ6EQ/Bv3CnjILMn1otqHc8fAlI/Ddg+c2uogooGbyNj18nmFofI5uor/QoeXRyPfT0NzksPeWnv65tZgUjM9Px3nD1SDrm/1lKK91B0dcRcnmAGpgdeu0ARiam4z/mzMhakDUHPpjC33+tWbkBn2g0/h95qRGD3RkaUk+1m8PlGF5YOLGxjI6QPA1yUi04YeBEWYFWYlQFDUwUecgCg4vVx9bsCFZBg42iwl2izns9cpIshlGZkpHQgKdaBkdmZUZrGtIjzacPpTM0mYnO7TAP1Lp6u7/bMTE3y2PWj4jai8MdDqQPliIlKFIC/lwy011GE6KkXp0IpH7XrVL/ZAekZeCMwdmaX04/TIT0TfDCSUwr4/8YA/N6ADALy8cip+cVYBzdUN29WQD9IHAgp3yQ3FUYJh0UWUDjlcFRgqlJ2jbA2oQ0SPZjvysYIYiwWpGkt0S9vxEm0OnrekXUW3sJKpf2PPdjccAIOJzFrqoZTShQbC+fBXL0HIAmNQ/Aw9cMBS/v2p0o9vpM1Uje6XCabOgb6YTw3qmwOcXeHn1weCMyNnhI6705k0fiGV3TTGcUE+G/m8m9PkPZnS82jpTjWXdgPCAJT3RpmVSK+vD53eSpd/QGaVDyVLwvecN0YJjh9WsDUTYWVitrY4uS276hmStETlwW32gYzUrSLZbgrOn1+sDnbrAc6HebleRsclfklmZXmkJ2vHF2qcTLF3ZGy1dbTxcAa9faJ8nRB2FgU4H0p+8I2VI9BOq2QKpbX0/RujK5dHIfctJ084clIU5k/tp1w/PS4HFbNJOCvJbYKQJ3U7pm45fXzw8atlMNkkeKKlDtcurZWXkxHfHKxtQFJghOTfVoW0PqHODKIpiCH6yU9RMV+hJreOakXU9Oo2cRPULexZXu2A1K1rWrDXoT7S9Y8zoWM0m3DptAMaGrAoeSv9cT+qfof3/1mnqaKlnV+zBip0nAETvz2krPRorXWk9OsGMTlOZv9zQ0pUzmCnx+AQKqxoMPS9SU6WrW87oh3UPzsAPJvU1XC4bt9cfKtcuk+8p+Z4uqmowNCKrjy0Y6GQkqvMHycsqdAtyyozO1MCCqAdKa9HgCZ8/S1+CS9NK5LGVmWQ5uqnSldyf/Awg6igMdDqQsXQV/oGs/xaXk2qHyaQYMzpRgo3w+zHu+6yBPXD20GxtGQK58KVsjt0VCHTSI2R0miL7jw6U1mrZnGS7RQteth+vgtunDonOTnYYelZkc3K+brFHeUxhGZ0OCnT05cKmTqL6Us1p/TMbHUbeXDktyOjESt+EPqkgGOhcMronpg7uAbfPr628PqCZ61WdLH0mL6x0FXh+S2vdWpajqR4d/SABQA0iEm1mLdO24VAFADUg0g8GaKp0pShKxPdoaKCTZLdofUsyaNxVVIPqQOAgMyb6zwL5hSRSj44MdMb0TkVGog1CBKcBkFxen9b3l5Oi6/WJcdJA2ZCtL12FBjpen18rZ8mFgok6CgOdDpSRqE5BD0TO6Og/3HqmqCezlpSu9Pu2W0yY0C8dZpOC3185GucNz8G1E/oACJZD5Aih5k7RD6jzvgBqFuNgoE+nR7Jd++YsJ0nLSrLDZjEZZluWH/T99BmdQOYiNKgIXSS1vRgzOk0EOrrnL1qpr6Wyda9prD06sVIUBVeM7YXRvVNxev8sw+WPXT4SCbqm5sZ6dNqCfNyKEqF0Ffh7kQG2SQkv/4ZSFMWQ1ZHZEvm3tyEQkPTvkYgfnhGczbmpjE408vlaf1Ddr340lZwYc0dhlRYkJEUoXcnHrTVN63t0AiPh+mQ4MTjQaLwzpCFZBio2iwmpCVatBCYzQ/9afQCPf7g9bH4eSRtenmLXji+0R0dfBmNGhzpabGdKahPqMhB2lNS4Ivfo6DIqcmKuljQj6/c9sSBDG31z5qAsw+gseRKRa/60JKOT6lSHypbXebD2gPph3iPZHjahnZxmv7/uRCn/n6+bZ6VHhIyOzWzSyhTtzTC8vKlAR3e9nFOmtcgTbZrTGnNmrzmeuDpyH0+fDCfuPncQHv9wBxKs5lYPspqS5rThlxcODaxWbnzcoUOwMxLVLGhTclLsOBSYiC89UX3PpyZYUVbr1jI6/Xsk4vwRORjeMwXF1S5DybU5ZE+TNiuyLoCRS53sLqrRJvCTWcDUkNIVEAzijKOu1MfROz0BQ3KSsWZfmZahlbQem2S1LJyqW5fN7fVjwdJt8PgEzh6arfUaSQ0en3bsOckObYLD0B4d/dpZzOhQR2Og08F6JMcW6MjAoKcuYIj1BOewmpHssKC6wRs2BX7oseiFjrqKVb+sRJQfqsC3geUDeiTb4bCqI0fkh7L8Fh3aoyNvL8ksk3FYsa1Dln8A1G/BNrMJbp+/yfKZXNhzRF5KkyOrmmt4XgpMCjCmiaHpbeGHZxSgos6DAT2SYgokWtvPpkaeWTm0NBjryDx9dka+52UAopXoeiTBYjbh7Z9PBoCoE0U2ZWAPY1O2PtDpnZ6ARJsZtW4fNh1R71f2wKQ4wgOd1JCFY+vdPm2ixN7pTgwOjHQMHXkVulZXmm701sHSWq2X77MdxVqgs/FwBV7++oC2L5tF/bKRbFdvG1q6Kq9jRoc6D5auOtgtk/vhtP4ZOGtQeACi/xYnMyIt6dEB1An97BZToyWU0JEkkUZdxUKWr74/UgEgGEDpSwQyYMtMtGFivwwMyk7SSlfZyXY4rMaV0Z02s3ZZR/XnSOPz05GVZG/yW7080Vw2Nq/VjyE/MxGf3z8dL9w4vtX33RSL2YT7LxiKq8a3/qKpJ8NmMRnKak0NLZfkCT/FYdGG/8u/PZndlNlGhzX6bNixSHVaDe9ffQBjMinapJxy5fHkCD068vah8/0crajTbpOaYNXWMgudSyfYiKzuJ12bxdtt6OeRM2MLIXDX6xvw9oaj2rxbZw7MgqIo2qSlNSGlK/0EhMzoUEdjRqeDXXtqH1x7ap+I16Xqll+QgYHDakZmog2ltW5tVE8snp19CirrPNryE5Fkt2JGBwiO8pIBVE6qQ/tGmJuqBmyKouA/c0+DENCyA4qiYEhOMjYdqdQWDZTNnUfK6ztsDh3p3z+eBI/P3+QJ74dnFODUfhmNLrdwMnrHOFFgd5KSYNFmdG5qaLkkA3B9KTJ03pr+LSxVRTIwO1Er+YSWYIf2TMH6QxU4UKoGLbIZOdlh0WZO1jI6IYHO4XI5bYP6vhgUCHSOVTZgw6FybZLIIt08OEDwC01FvcewQv2e4hocKq3D0Yp6HCitQ6LNjKeuHYOB2UkoyEoyHF+N2wshhJZp1c/tI4NFoo7CQKcTM/boBDM5o3unYsXOE4YST1NSHFbDt8dIQpcWaGmgkx+yllEwoxPcv74EpygKQitRT149BhsPl2OibtRPphbodGxGx2xSYDY1HWQ6rGac2i+jye2o9aQ4rNqsv7FmdHqmyYb34PsqVReA2C2mVu1FGpidhDX71IxN6N/ksFxjaSspUBoymdS5c6oavMEeHadxHp0jWqCTEHgMVhRkJWJ/SS2ueO5rTBncA6N6pWBFIFMj/971Ew/uDhmh9dmOImw8XAEAuHRsL21VekmWroQA6tw+LcusH6oeaXg7UXtioNOJpSZYYTYp8PmFYf6cZ2efghPVLsN8M60htHSV2tLSVUgAFql01dRq20Nykw1rawFAj8CJq6MmC6TOLyVCiacpZw/Nxg8m9cVFupO4PqNTkJXYqr1I+rmHUkIyR3IJFUm/4GuPZDuqGrzal4TQjI6cLFAflC26+VQ8/eluvLfpGD7fdQKf7zqhXSezpTJgqqj3aI3KEwsy8O3+Mryz8Ri2BWZTv35ieObZYTXBFJhotMbl1QU6zOhQ58FApxOzmk149NIRqHV5DUGI02ZBfmbrv3T6ZmSzSWlyscloQjNNss9GP91+zyYCnUiG9UzB8u3FGB5yMiCS9O/Zphb0lJw2Cx6/YpThMn2g09qTIupXew/9GwsN7vWBzsIrR2PL0Upt8k3ZRFzj8sLj84dldAD1b/HPs8bi7hmD8Z91h1Dr8sFmMSEnxaEt66EtQFvr0rJhP5vaH9/uL9OyOcN6pmj3q6coCpICmaYalxeyA7CCGR3qRBjodHI3nJbf9EatxGE1I8WhfmilJVhbPLIpxWHV+oiAYIpcH9y0ZB6SO88ZhEvH5BlOFER6KYb5Zlqe+dMHOvpJLVuDIdAJyeikOKzonZ6gBS36QGdiQYahlKu/bVW9RxfohPdu9c104r7zI68cL0vkh8vU29stJkwdnI0+GQnaZded2ifq50Gyw6oGOrqG5HLD8HJmdKhjcdQVGWSnGBsUW0pmdcwmRev1kev8ZAeGmzeXxWzCoJzkDhtaTp2fPjCItUcnkrYMdHJTHBEnApSG5gYzlrJHJxKzSdEeb0W9R1u5vHczZ8rWD3oA1AyW2aTgnKFqfsZuMeHysb2i3l4beeXSBzq60hUzOtTBGOiQgSwztbQRWZKzG2cm2rTp9AflJOORS4bjj9eMObmDJIpC39ybFeOoq4j70Qc6Wa2bQVQURVtOYkCEIGpYz2D5KrmJ8rEMlL7ZV6aN5OrTzNF4ocHWoMCMyleP7w2b2YQbT8tvtF8v0uzIFczoUCfC0hUZyDJTS5Z/0CsIrEAeOpLrZt00+kStzVi66pwZHQB4bvYpUWdYNmZ0Gv+ITnNacaS8Hr9ashkAMK5vWrNnDbdZTNpEhUBwmYqRvVKx5dHzYTU3nkFNCgSXtVEyOuzRoY7GQIcM5Fw6LVn+QW9sH/Ub65AcNg5T+5EZHbvFFPMSKZH0zXCqi9FmOVt1MVYp0W5BQZQgZmgLMjqAugDrCzeOb1FpN81pQ61bLX3JjA4Aw4Kn0SSFlK6EEMzoUKfCQIcMzh+Ri/9tK8IFI3NPaj9nDMzE0tvPbPURK0SNkdmMrCT7SfVyJTus+OKB6Sc1C3JL9ctMxLi+aVAQuYdHb1B2Mr7aU4orxvXC768aBbulZceb5rTiaGBB0IHZyU1sbZQUsoJ5rdunTRYKAC4uAUEdjIEOGUzol4FV900/6f0oioKREYajErWl/Ay1FNQa5aaTLd+2lNmk4O1b1TW1mgrW5l80FNdO6INhPU+uSV8OPrCYlLAJP5uSFLLelX75BwBo4BIQ1MEY6BBR3BjVOxX/+elp2tpUXVWsQYvdYsbwvJMvD6cFRl4VZCVq633FKilkvSv98g8AMzrU8RjoEFFcmRRYcZtiJ0dV6ftzYqWtdyUzOoH+HKtZgccnmNGhDsfh5URE3dywwGzjE1uwNpssXcnh5TLQkZOCMqNDHY0ZHSKibu6GSX0xZVBWs+fgAYITBsrh5bJ0lZviwJHyejR4fYaVzYnaGzM6RETdnKIoyM9s2eKlyVFKV3LhXiFgGIVF1N4Y6BARUYuFjrrSZ3QkF/t0qAMx0CEiohYLXetKZnT0s6I3sE+HOhADHSIiarFkmdHRmpHVjE660wZ7YGZlZnSoIzHQISKiFpPDy+s9Pnh9fm35h3SnTZtZmhkd6kgMdIiIqMVk6QpQy1eydJWeaGVGhzoFBjpERNRidotZazz+em8pKmrV0lVaExmdD74/jvc3HWu/A6Vui4EOERGdlKvG9wIA/HvNQVQHmpIb69Gpc3tx5+sbcNd/NqIyZMkIotbGQIeIiE7KtRP6AFAzOgCgKOrK6zKj4/IaMzpHy+vh9Qv4/AL7S2vb92Cp22GgQ0REJyU/MxFnDAyuMZbisMJsUoIZHY8xo3Okol77/0EGOtTGGOgQEdFJm3VqX+3/6YFFQu1WWboyZnSO6QKdAyV17XB01J3FTaCzcOFCKIqCu+66q6MPhYio2zlveA7SAgFOmtMGAHBYZDOyMaNztFwX6DCjQ20sLgKdtWvX4h//+AdGjx7d0YdCRNQtOaxmXDFObUrOTFQDnZgyOgx0qI11+UCnpqYGs2fPxosvvoj09PSOPhwiom5r3vSBuHJcL8ydOgBAIxkdQ48OS1fUtrp8oDNv3jxcfPHFmDFjRpPbulwuVFVVGX6IiKh1ZCXZ8adZYzGxIAOALqPjCc3oNGj/L6t1o7KeQ8yp7XTpQOf111/H+vXrsXDhwpi2X7hwIVJTU7WfPn36tPEREhF1X3aZ0dHNo+P1+VFYpQY6NrN6CuLIK2pLXTbQOXz4MO688078+9//hsPhiOk28+fPR2VlpfZz+PDhNj5KIqLuK1JGp6jaBZ9fwGpWMKp3KgDgAMtX1IYsHX0ALfXdd9+huLgY48eP1y7z+Xz4/PPP8cwzz8DlcsFsNhtuY7fbYbfb2/tQiYi6JZnR0TcjyxFXPVMT0D8rEd8dLMfBEmZ0qO102UDnnHPOwebNmw2X3XLLLRg6dCgeeOCBsCCHiIjalyOQ0dE3I8sRV73SEtAvKxEAODsytakuG+gkJydj5MiRhssSExORmZkZdjkREbW/iBmdQKCTl5aA/EwnAI68orbVZXt0iIioc4uU0ZGBTq/0BPTLVDM6bEamttRlMzqRrFy5sqMPgYiIAhrr0emV5tAyOiU1blQ3eJDssLb/QVLcY0aHiIjahFzUM3KPjhPJDiuyktRZlFm+orbCQIeIiNqEw2rM6AghdD066rQg+YHy1YHSWpTUuFBY2RBhT0QtF1elKyIi6jxkRkcGOpX1HtS51exOXloCAKBfpjrE/PEPtqOo2gWzomDFfdPQK3A90cliRoeIiNqEltEJlK6OBPpzspJs2nUFWWqfzrHKBvj8Am6fHzsLuTwPtR5mdIiIqE2EZnT0c+hIs07ti11FNSjISsTKncXYdKQSZbVc+4paDwMdIiJqEzJrI5uR9UPLpR7Jdvz1+nEA1GHmm45UorzW3c5HSvGMpSsiImoToRkdObQ8LzVy/016ojoCq5SBDrUiBjpERNQm7CETBspVy3tGaTTODAQ6zOhQa2KgQ0REbcIRmDDQ6xfw+vwornYBALKTIy+uLDM6ZXUMdKj1MNAhIqI2ITM6AOD2+VESCHR6RAl0MpyBQIcZHWpFDHSIiKhNyCUgAKDB48eJJjI6GS0sXfn9ooVHSN0BAx0iImoTZpMCq1kBAFTUuVHt8gJoJKPTgtLV3f/ZiDOf+AzVDRySTpEx0CEiojYj+3TkZIEOqwlJ9sgzm8gencp6D7w+f8RtQn2yrQjHKhuw/Xh1KxwtxSMGOkRE1GZkn87hcnXRzh7JdiiKEnHbtAR19XIhgIr6pjM09W4fagJZopIaV2scLsUhBjpERNRmZJ/O4TI1o5Od7Ii6rcVsQppTDXZi6dPRBzelDHQoCgY6RETUZsIyOkmR+3Ok5oy8OqELbk7UcKQWRcZAh4iI2ozM6BwpC5auGqPNpRNLRqeaGR1qGgMdIiJqM45ARkc2I0cbWi6lO2MfeXXCULpiRociY6BDRERtRq53Jdevaiqj05xlIEqqg9uwGZmiYaBDRERtRj9pINCc0lXTo64MzcicTZmiYKBDRERtxmE1nmaaCnQyEtVRV2W1ahBTXuvGx1sLI85+fELXo6Pv1yHSY6BDRERtJjSj09jwckDfo6NmdB7/cDvm/us7LNlwNGxbfUan2uXVVkkn0mOgQ0REbSY0o5OZZGt0e3m97NFZd7Dc8K9eaF8Oy1cUSbMCnTfeeANud/CNdODAAfh8wQi6rq4OTz75ZOsdHRERdWn6jE5Gog1Wc+OnnXTdPDo1Li8OlNYCALYdrwrbVpau5ETLHGJOkTQr0Ln++utRUVGh/T569GgcPHhQ+726uhrz589vtYMjIqKuTZ/RaWpoOaBb2LPWjZ2FVRCB1pydhVXw6fp06t0+1LrVL9r5GU4AHHlFkTUr0BFCNPo7ERGRnj6j01QjMhAcdVXv8WH9wQrt8gaPH/tLarXfZVBjt5jQNzMxcBlLVxSOPTpERNRm5Dw6QNPLPwBAst0Cq1mtRX21t8Rw3XZd+ao4ULbqkWxHVqCvhxkdioSBDhERtRmHtXkZHUVRtD6db/eXAQhOIqjv05FBTVaSXQugODsyRWJp7g0+/vhjpKamAgD8fj8+/fRTbNmyBQAM/Tt6VVXhTWRNSUlJafZtiIioc7HrenRiCXQAtU+nuNqFukAPzmVje+GfX+03ZHT0gY4cqcVmZIqk2YHOnDlzDL/PnTvX8Lsi29910tLSIl4ejaIo2LVrF/r379/cwyMiok7E0cweHSDYkAwACVYzLhqVi39+tR/bjgUDnRNa6cqGrEBGhz06FEmzAh2/39/iO/rvf/+LjIyMJrcTQuCiiy5q8f0QEVHn0ZKMTrou0BnaMxnDeqZAUdS+nJIaF7KS7FpGp0eSHZlaoMOMDoVrdkanJfLz8zFlyhRkZmbGtH3//v1htVrb+KiIiKit6UddNTUrspThDAY6w3qmINFuQb/MROwvqcX241U4a1APLaOTZWhGZkaHwjUr0Pn8889j2m7KlCmG3/fv39+cu9F6foiIqGs72YzO8J4p2r/6QEcGNVlJdq10VVbrgt8vYDLF3ipB8a9Zgc60adOiXid7cBRFgdfrPamDIiKi+CCHl9ssJqQ4YjvlZOoDnTw10BnWMxkfbD6u9enom5FlT49fAOV1bq2URQQ0c3h5eXl5xJ+jR4/ivvvug91ux9ChQ1t0IEVFRViwYEGLbktERJ2THCreOy0h5kEpMqOjKMDQ3GQAagkLALYfrwagb0a2w2o2Ic2ptjtwvSsK1axAJzU11fCTnJyMN998ExMnTsRrr72GZ599Ft9//32LDqSwsBCPPvpoi25LRESd09DcZCy4bASevHp0zLfpmar28gzokQSnTc0CjchTpzXZXVyNHYVV2tBz2Z+jjbyqZkMyGbW4Gfntt9/Gr371K5w4cQLz58/H7bffDrs9erqwqQBo586dLT0UIiLqpBRFwU2n92vWbcb3TceDFw/D+Px07bLcVAemDO6Bz3edwINL1D5Ou8WEJLt6GstMtGEPgBJmdChEswOdVatW4YEHHsDmzZtx55134oEHHtAmEGzM2LFjoShKxPWx5OXNmWuHiIjik8mk4Mdnhc+jdv/5Q/D5rhNYd7AcgFq2kueNrGRmdCiyZgU6F110ET799FPccssteOedd5CbmxvzbTMzM/HEE0/gnHPOiXj91q1bcckllzTncIiIqBsZ2SsVM0f3xNLvjwMIlqsAICvQ11Nay0CHjJoV6CxbtgwWiwX/+c9/8MYbb0TdrqysLOyy8ePH49ixY8jPz494m4qKCq6GTkREjbr3vCFYtqUQXr8wBjpc74qiaFags2jRohbf0dy5c1FbWxv1+r59+57U/omIKP71y0rEdRP74N9rDqFfplO7nLMjUzSK6MZplKqqKqSmpqKyspKLiBIRdREurw9LNx3H2UOztaHoH28txNx/fYdxfdOw5OdndPARUltrzvm7RaOu6uvr8cknn2DXrl1QFAWDBw/GjBkzkJCQ0KIDJiIiipXdYsZV43sbLpOjr+pcvo44JOrEmjWPDgC89957yM/Px+WXX477778f9913Hy677DLk5+fj/fffj3ibe+65p9GyVaj58+dH7PMhIiKKJMGmrqlV6+bM/GTUrEDn66+/xtVXX40pU6bgq6++QllZGcrKyvDll1/irLPOwtVXX43Vq1eH3e7pp59GXV1dzPfz7LPPoqKiojmHRkRE3ZgzEOjUu5nRIaNmla4ee+wx3HLLLXjhhRcMl0+ePBmTJ0/G3Llz8dvf/hYffvih4XohBAYPHhzzPDnNyf4QERElBmZQrmOgQyGaFeisXr0aTzzxRNTr582bh6lTp4Zd3pLRVDk5Oc2+DRERdU+ydFXv8XEFczJoVqDT0NDQaHdzamoqXK7woX1z5sxp/pERERHFSJauAKDB69PWyCJqVo/O4MGD8dlnn0W9/tNPP8XAgQNP+qCIiIiaw2EJBjosX5FeswKdm2++Gffee29YDw4AfPDBB7j//vtxyy23tNrBNeX555/H6NGjkZKSgpSUFJx++un46KOP2u3+iYioczCZFCRY1WCHQ8xJr1m5vTvvvBNff/01Zs6ciSFDhmDYsGEAgG3btmH37t24/PLLceedd7bJgUbSu3dv/P73v9eySC+99BIuu+wybNiwASNGjGi34yAioo7ntJlR7/GhzsMh5hTUrIyOyWTCm2++iddeew1DhgzBjh07sGPHDgwdOhSvvPIK3nrrLZhMzZ6ap8UuueQSXHTRRRg8eDAGDx6M3/3ud0hKSsKaNWva7RiIiKhzcNoDGR2WrkinRd1as2bNwqxZs5p9u9WrV6Nfv37o2bMnjh8/jgMHDuD0009vySGE8fl8ePPNN1FbWxt1ny6Xy9AsXVVV1Sr3TUREHc9pVU9pnEuH9JqVfvnRj36Eb775Jur15eXlOPvss6NeX1dXh1/84hcA1NmS6+vrm3P3EW3evBlJSUmw2+342c9+hiVLlmD48OERt124cCFSU1O1nz59+pz0/RMRUecgh5gzo0N6zQp0Fi1ahGnTpkWdF8ftdmPVqlVRb3/OOecgPT0dDz74IDIyMhoNimI1ZMgQbNy4EWvWrMGtt96KOXPmYNu2bRG3nT9/PiorK7Wfw4cPn/T9ExFR5+DUAh326FBQs0tX999/P+bOnYuNGzfiz3/+c8w9OdOnT4eiKKiqqsL69esxfvx47bLGhqw3xWazac3IEyZMwNq1a/H000+Hzd4MAHa7HXa7vcX3RUREnZeTGR2KoNmBzrx583D22Wfj2muvxdatW/HGG28gIyOjydutWLFCu/15552HyspKPPvss80/4iYIISJOWkhERPHNyWUgKIIWDZGaOnUqvv32W5SVleHUU0/Fli1bYrrdp59+ipKSEjz++OMoKys7qUwOAPzqV7/CF198gQMHDmDz5s349a9/jZUrV2L27NkntV8iIup6ggt7snRFQS0eC56fn4+vvvoKEydOxOmnn4633367ydskJCTgqaeeAgA89dRTcDgcLb17AEBRURFuvPFGDBkyBOeccw6++eYbLFu2DOeee+5J7ZeIiLoeNiNTJM0qXYWuPp6QkIDXXnsNTzzxBK677jr8+Mc/bnIfZrNZ21esq5lH8//+3/87qdsTEVH8YI8ORdKsjI4QIuLlDzzwAN577z28/vrrjd6+vr6+1YeXExERAfoeHZauKKjZw8tTU1O130tKSlBaWgoAuOCCC/DNN9/g4Ycfjnr7thheTkREBCC41hUzOqTTrNLVnDlzUFFRgXvuuQf/+c9/UF5eDgBIT0/Hddddh8ceewwPPfRQxNu21fByIiIiAEi0y2ZkBjoU1KxAp6ysDKeffjqOHj2K2bNnY9iwYRBCYPv27Vi8eDE+/fRTfP3110hPTw+7bXsNLyciou4pgcPLKYJmBToLFiyAzWbD3r17kZOTE3bdeeedhwULFuDPf/5zxNvL4eXPPvssrr/+enz22WcsXxERUatwytKVh4EOBTWrR+edd97BH//4x7AgBwByc3Px5JNPYsmSJVFv39rDy4mIiCTOo0ORNCujc/z4cYwYMSLq9SNHjkRhYWHU6ydPnqz9Py8vD3l5ec25eyIioqjkPDq1LmZ0KKhZGZ2srCwcOHAg6vX79+9HZmbmyR4TERFRsyXa1e/u9SxdkU6zAp0LLrgAv/71r+F2u8Ouc7lc+M1vfoMLLrig1Q6OiIgoVsHh5SxdUVCzSlePPvooJkyYgEGDBmHevHkYOnQoAGDbtm147rnn4HK58K9//atNDpSIiKgxskenweOH3y9gMp3c7PsUH5oV6PTu3RurV6/Gz3/+c8yfP1+bKVlRFJx77rl45pln0KdPnzY5UCIiosbImZEBtXwlS1nUvTX7XVBQUICPPvoI5eXl2L17NwBg4MCByMjIaPWDIyIiipXDaoKiAEIAtW4vAx0C0IJAR0pPT8fEiRNb81iIiIhaTFEUJFjNqHP7ODsyaZrVjExERNSZOTk7MoVgoENERHFDNiQz0CGJgQ4REcWN4OzIDHRIxUCHiIjiRoKNc+mQEQMdIiKKGyxdUSgGOkREFDcSrGxGJiMGOkREFDcS7SxdkREDHSIiihtsRqZQDHSIiChuaKUrrmBOAQx0iIgobjCjQ6EY6BARUdyQw8trXezRIRUDHSIiihuJcng5S1cUwECHiIjihlzriqUrkhjoEBFR3ODMyBSKgQ4REcUNNiNTKAY6REQUN7RmZAY6FMBAh4iI4gZ7dCgUAx0iIoobiS3s0Wnw+LCnuLotDok6GAMdIiKKGwktXL18/tubMeNPn2P9ofK2OCzqQAx0iIgobsjSlcvrh88vYr7d3hM1AIBDpXVtclzUcRjoEBFR3JCjrgCgvhmTBtYEZlJ2e/2tfkzUsRjoEBFR3LBbTFAU9f91zVgGQi4Z4fKyiTneMNAhIqK4oSgKEgPlq+b06dS61G1dzOjEHQY6REQUV5rbkCyEQK1bZnQiBzour69ZPT/UeTDQISKiuKLNjuyJrXRV5/ZBBGKYSIGOzy9w9fOrMeXJFSxtdUEMdIiIKK4kWJuX0anV9fJECmS+3luCzUcrcbSiHiU17tY5SGo3DHSIiCiuyIyO7LtpSo0+0PGEZ3SWrD+q/d/DHp4uh4EOERHFFW0ZiBhLV/qAKLR0Vevy4qMthdrvHh8Dna6GgQ4REcWV1AQrAKCizhPT9jWNlK6WbSk0zMfj8bEhuathoENERHElI9EGACivja2fRt+jEzph4JINRw2/M6PT9TDQISKiuCIDndJYAx23PqMTDGSOV9bjq70lAIAku1oOY6DT9TDQISKiuCIDnbIYAx1j6SoYyCzddBxCABP7ZSA31QEAcDPQ6XIY6BARUVxpbqBjGF6u68c5UFoLADhtQCasZvV06WWPTpfDQIeIiOJKZrMzOsHgRp+xaQgMNXfazLCZ1QW0WLrqehjoEBFRXEk/qYyOLtAJjMByWExaRoeBTtfDQIeIiOKKzOiU17nhj2F9qmgzIzcEZlZOsJlh0TI6LF11NQx0iIgorsiMjl8AlfVNz6VT6448YaCW0bGamdHpwrp0oLNw4UKceuqpSE5ORnZ2Ni6//HLs3Lmzow+LiIg6kNVsQrJDHQ4eyxDzaPPoyB4du8UMWysHOjUuLy5/9is8t3JPq+yPouvSgc6qVaswb948rFmzBp988gm8Xi/OO+881NbWdvShERFRB2pOQ3K04eUNHpnRMWmlK3crla6+P1yBjYcr8Oa6I62yP4rO0tEHcDKWLVtm+H3RokXIzs7Gd999hylTpnTQURERUUfLSLThQGldTIFOtB6dek946crbShkdGVA1eGJbeJRarksHOqEqKysBABkZGRGvd7lccLlc2u9VVVXtclxERNS+MhLtAGLL6NSGZHSEEFAURRuBlWBt/dKVDKjqGei0uS5dutITQuCee+7BmWeeiZEjR0bcZuHChUhNTdV++vTp085HSURE7SEjUV3Ys6zW1cSWxnl0hAiOrGqIkNFprVFXMqNT72ag09biJtC57bbb8P333+O1116Lus38+fNRWVmp/Rw+fLgdj5CIiNqLzOg0txkZCE4aGKlHp6mMzuGyOjy9fHeTC4rKQMfl9cc0BJ5aLi5KV7fffjvee+89fP755+jdu3fU7ex2O+x2ezseGRERdYTMGFcw9/lFWPnI5fEh0WZGQyAYac7w8r+v2otXvjmERLsZPz6rf9Tt9KO7XF4/EmzmRvdLLdelMzpCCNx22214++238dlnn6GgoKCjD4mIiDqB9BhXMNevXK6oSRu4vH54fAK+QKbFYTHDZomtdHWiWi2VVdQ1Pn+PfnRXV+7TeW7lHvzx4849rUuXzujMmzcPr776Kt59910kJyejsLAQAJCamoqEhIQOPjoiIuoojQ0v/+PHO7GzqBrPzz5FK1tZTAocVjNqXF64vH5tskAAcNhMsMrh5d7GMzpygsKmghd3HAQ6DR4f/vDxTggBzDq1D/pkODv6kCLq0oHO888/DwCYNm2a4fJFixbh5ptvbv8DIiKiTiEjSunK7xf4x+f74Pb5saOwGg6rmqlJtFtgNSuocalBiOzPURTAZjbBYgoML/c3HuhUNaiBU1PBi2EYexdtSK6s90AEElw7C6sZ6LQFIdjARURE4TJ0pSs5XBwAyurcWrNxYWUDspLVvs0ke/B06PL64PKoPTMOixmKogRLV97GzztVgYxOQxPBizvCxIRdTXldMIjcUViFGcNzOvBoouvSPTpERESRyEDH5fWjThd0FFY2aP8/XtWgla4S7cE+HJcuoyMzPtYYR13JQKeuGwQ6+j6kHYXVHXgkjWOgQ0REccdpM8MeCFz0fTpFVcFAp7CyXlv+IdFu0bZ3efyGWZEBaKUrTyNDwX1+gWpXrKWrrt+jU6HL6OxkoENERNR+FEWJ2JBcqAt0jlcGMzpJ+kDH69MW9EwIBDpWrXQVPaNT3RDMcDSrGbmL9ujoMzr7SmoNfUedCQMdIiKKS+kRAp2iSn1GR1e6sllgt6hBjb4Z2R4IdGwxlK6q6oND1ZsqR+mDgoYmRnJ1VhX1wUDH5xfYW9w5F9RmoENERHEpI8JcOkVVwSUhCqsatOUfEu0W2K2N9eio/7obCXQqdSf+prI0+v001bjcWembkQFgZ1HnXD+SgQ4REcWlSLMjF1ZFzugk2YMLd7q8vmCPTiDLY9FWL4/eo1OlK1011YwsFwwFum6PTmXIpIidtSGZgQ4REcWlSLMj65uR69w+HKusBxCe0dFWLrfFXrrSZ3SaKl0ZMjpdNNCRGZ2huckAOm9DMgMdIiKKS8FmZGO5Sm9vcQ0AOepK16PjjVy6arxHJ/Zm5HjI6Mhm5NP6ZwJgoENERNSu5Armshm5wePTTs55qQ4AwN4TagOtcdSVrkcnpHTV2FpXlSGBTmOT2rp8XT/QkY93YkEGAHUUW2g5qzNgoENERHEpI9EKIBjoyLKVw2rC4EC5RT+PjjZhoCc4vFyOuoplwkB9j44QxrlyQrl0wU1Xb0buk+5ErzR1fcmdRZ0vq8NAh4iI4lJoRkfOipyb4kDPVOPCz0l2syGjUx8y6soWU+nKa/i9sZFXxh6dLjq8PJC9SXNaMUTr0+l8I68Y6BARUVzShpfXqOtdyf6cnBQHegZKV5K+R0dfutImDGxm6QpovCTV1Xt06t0+LWOlD3Q648irLr2oJxERUTS90xNgNSuodnlxqKxOK13lpjqQGzHQCWZ0JG0JiGaWroDGAxh3F+/RqahXs2QWk4IkuwV90tWVy/XzFHUWzOgQEVFccljNGNUrFQDw7f4yFFaqJ+HcCBmdJH2Pjten9dA0p3QVltFprHTVxRf11JetFEVBskPNm1Q3sBmZiIio3ZwaGBG09kCZltGJXrrSjbrShpfHXrqqak7pSr8ERBcMdGQjcmqC2vCdFAh0ZHN3Z8JAh4iI4tYkLdAp13p01NJVSDOyzaKNsHJ5/Fo2Jji8PJYJA9WTvEndNGpGRwhhXNSzCwY6lVpGR+2DSrYz0CEiImp34/MzoCjA/pJa7AoMfc5JcSDJbtFOzgCQqBt15fb5tZFQDm1m5BhGXQXKNj2S1dFe0QIYr1/Ar0sMdcXVy8sDgU66MySj08BAh4iIqN2kJlgxJEcdEVQdOAnLRmT5r91igsVsCo668viCpSuLcWZkv1BX6g7V4PFpWZrcFId2WSTukPl1uuLwctmMnJoQyOg41ICnmhkdIiKi9iVn7gUARQGyAxkXGegkBTI7NsPMyIGMjuzRsQRPl5GyOrI/x6QAmUmBjE6UTE3oRIJdsUenMjSjE3gO3V6/of+oM2CgQ0REce3UfsFAJzPRrmVnZENyYuAkHXEJCDm8XDbeIHKgI0dcpSRY4QyUu6KtYB6a0emKPTqyGTktJNABOl/5ioEOERHFNX1GJzfVHvx/SuRAx+316QIdY+kKiDzySvbnpDiCgU60ACY049HUulidkRxenhpoRjabFO1xd7aGZAY6REQU13JSHMjPVCe0k8ENAG3kVZJdPUFro64izIxsNikwm6KPvJLLP6QmWLXbNNWjI9fPEsI4gWBXUBFSugKCWZ1qZnSIiIjalyxf6WdEnjI4C/mZTlw8qieA4MiqSD06QLB81XjpyqKN1GqqR0fOQQMADe4uFugEmpHTAs3IQOedS4dLQBARUdy7ddoAlNe6ccNp+dplvdOdWHXfdO13e6BM1aAbdSUvA9RAyOX1N1m6khmduqilKzWocdossJg88PoF6j0+pMJq2O5wWR16pSXApOsPiuSNtYeR5rTivBG5jW7XmvQzI0ty5BV7dIiIiNrZgB5J+H83n4qhuSlRt5E9OrUuL2TLjD6jI0deRczoyJ4VXTNyQ9SMjnq5zWLSgqLQfp4Pvj+Os55cgedW7mn0cRVXN+D+t77H3f/Z2Oh2rUkIETnQkaUrV+daBoKBDhEREaDNo6OfJkfOjAwEe2pCR00BuoyOrkcnWjOyvL3dYtLKXKH9PDsLqwCoMzo3pqRaLSHVun2NTmbYmuo9Pq2nSM6MDAR7dJjRISIi6oRsFuMp0WxStOAGACwm9XpvhAkDZY9OaoJVywI1FejYLCZtVFfotrWBbNDB0tpGj1m/Yrp+OPufPtmFH7+0Dt42CH7krMhWs4JEWzAQlD06nW3SQAY6RERECJauJIfFBEUJBjq2RkpXctRVisOChBibkW3mYOkqtMxV51b3d6S8vtFgRb9iuj4rtOir/Vi+vQg7Cquj3ralKuqCsyLrnx9mdIiIiDqxsEBH158DBEtXngilK/2EgTGXrqzmqNvWutTfvX6BYxUNUY9ZH+jIjI4QQvv/iRpX1Nu2VOisyFJKJx11xUCHiIgIgKIohvJVeKATyOhEKF0ZenSakdGxa3PuGIOnWl2wcKCR8lWVLtCR9+f2+bX1uEqqWz/QKY/QiAx03oU9ObyciIgowG4x6TIuxlyARQY6jWR0UhOskMWc6Bmd4ND1BF+UjI47GCwcLKuLeryGQMej3kYfYJXUuKPetqW0OXR0jcgAkGRXA5+qThboMKNDREQUoC9fJYRkdGzmxmZG1s2jE2UklSQzOnZz9OHl+sbigyXRMzqRSld1hkAn9ozOuxuP4v++2NfkdtrQ8oQoGZ1ONrycGR0iIqIAu244ebTSVehyDX6/0EYapSZYtUbiaKUrfcZIFsFcYT06+tJVIxkdXfakPkKgcyLG0pXfL/DAW9+jwePHxaN7omdgeYxIKkIW9JTkPDrs0SEiIuqk7IYencilK2/IzMjVugkGUxKCo67qoizWqe/R0YaXuyM3IwPAobLYMjoyK1QfJaOzp7gaL319QOvf0Surc2t9QmW1jZe7SgPXpyeGlK7Yo0NERNS5GZqRLbGVrmTZymE1wW4xI8GqXi+EGtSEZoZkRshuNWvLOzTao1NaB79fRFwKojJCM3Kd7rb6QOfR97fhi90lsFtMuG5iX8N+CiuDI7uaWpSzuErdZ06yw3B5MkddERERdW72WEZdhQQ6lbr+nNDbRerTkWUqW5QeHf3wcEANloqqIw8xj9ij44ncjLzvhJoZ+nhrYdh+iqtjD3QKq9Rtc1KMgQ5XLyciIurkYunRCV3UUwYJPZLt2nZyzp1II6+0jI5urSv98HKXNzg8PCtJLQ8djNKnU9VE6aq8zg2vzw+vz68FKF/tLTX0AAFAUVUw86PfZyRFgf3kptoNlycHRl25vP6Iy2R0FAY6REREAfoh5eE9OpFLV8cDZR99A68MkuoiNCS7PPolIMJHaOmDkGE91UVIoy0FEbl0pc8OqT03hVUNWvDk9vrxxe4Thv3I4AUAqhuiBzp1bq+WsckOyegk2oOBYWcqXzHQISIiCmisdGWLUroq1AKd4Inf2cikgS6fLtCJsJ0MVBKsZhRkJQKIPPKqwePTGpsBfUbHGGQUV7twtLzecNn/thUZftdndBorPcntnDazNspKsuhKcZ2pIZmBDhERUYCtkVFX0UpXcomGXF2gkxAhUyMFVy+PvASEbEROtJuRn6kGOociBDpVIZmXSPPoAGpD8tEKNdCRDcOf7Sg2rKGlz+iE7ldPBnW5KQ7DOldScGHPzjOXDgMdIiKiAEOPTsioq2ilq8IqNYjQZ3QaW8HcFWH1cmPpSv2/02ZBfoYTQORlIEJ7aeQ+wgMdt5bROXdYDtKcVlTUebDuYLm2jbF0FT0bI/uRslPsEa9P7oRDzBnoEBERBRhmRrbFVrqK1KOjX+/K7xfYdLhCC0S0JSAMzcjhPTqJdgv6ZamBzsHSurA5eSrrQzM6gYkKPdEzOn0ynDh7aDYA4BNd+SrW0pU+oxNJciccecVAh4iIKEAf6NhjGHUlhIjYo6MvSX245Tgue/Yr/OHjnQCMGZ1IpSsZsCTazOid7oSiqM29oRP5hQc64fPoAOrCnjLQ6ZWegPOG5wAAPt1eFHg8fpTW6kZdNVK6kgFR6NByKakTzqXDQIeIiChAH9w4LNF6dIIZnap6rxZg5EZpRt5wqAIAsPdEDQB9j46uGTlS6cpugcNqRp90Navz6fZiw/FU1RuDidDSlRyafqIm2IzcOz0Bpw/IAqA2OFc1eHCi2gV9sqixRTmLosyhI2lz6TDQISIi6nxkeQoIH3UVqUfneKA/J91pNWyv79HZH1iUUy6GaejRsYTPo6PP6ADAjaflAwCe/nQ3XN5gQCQzOnI7GeDIEVx9A/09J3QZnd5pTqQmWLXs067CakN/DgBUNzKPTtOBjjqXDnt0iIiIOqHGVy8PL11F6s/R37be48OBQKAjAxPDqCu50rmugbgmkNFJDGRHbjw9HzkpdhytqMdr3xzStpP7ywkELfUhGR0Z6OwqqobL64eiBLNOQ3KTAQA7Cqu1cpQ85sYyOoVRJguUkjvhCuYMdIiIiAKMEwaG9uiEZ3Qi9ecAwWbkmgYvDpWpQ8PLA6t+uyI0I0fr0ZHHccc5gwAAz6zYo10vR13JxuD6KBkduQxETrJDGz4vA52dhdXaSKoB2epQ9mgTBgohtHWuspOjNCNz1BUREVHnZVwCIvLq5YbSVaAklBsl0NlVVANvYEbiynoP/H5h6NGRgY7XL7T96nt0pGsn9EHfDCdKatxY9NUBbX+ALtCRGR2PGmT0CQQ6Uq/0YNZpqC7QkeWogT2SAKilNX2JTCqv82jLV0QbXt4Z17tioENERBRgi2lm5Eilq5BAJ3Db7certMuEUAMAfY+OPoMUbCY2ZnQAtRFaZnXe3XgUgC7QCdx36ISBvdIToJ/Tr1daMNAZkqMuLbGjsAqFlWqWZkAg0AEiByoyIMpItBkCQr3ghIEMdFrF559/jksuuQR5eXlQFAXvvPNORx8SERF1YfbGZka2RChdVTXeoyObgKWKerehR8duMWnBiMzI1Ojm0dE7fUAmAGB/SS08Pr82DFwGOu7AYqCydJVstyIz0abdXp/RGZCdCLNJQVWDF5uOVAAA8tISGs3IRFu1XE/enqWrVlJbW4sxY8bgmWee6ehDISKiOBDb6uW60lUTPTqhSmvdWinLZjFBUZTgpIFudb8yI5NoMwY6eakOJNrM8PgEDpbWojIwvFwfeNR7fMG1smxmZCUFS0z6jI7dElxHa09xjbYf2WMTaQXzYtmIHKVsBeibkTtPoGNpepPO68ILL8SFF17Y0YdBRERxorFFPS0mY+lKCBG1Ryf0tlKxbii3LJM5rGbUuX1oCPTFyJmRnXbjPhRFwcDsJGw6UondRTVaMJKdbIeiqKWxerdPy+g4tUCnGoAxowOoDckyyAGAnBQ7UhxWHK9siJzRqWx8skAASHYEhpd3okCnS2d0msvlcqGqqsrwQ0REJDXaoxNSuqp2eVEbCCpCS1fOkIyOzNrol1qQQZU28iqkxyY0owMAg3LUJuLdxTVaj06a06bto7oh2DCsBjrB0lXvNOMxDg3sS8rWZXQijbwqqo69dMVm5A6ycOFCpKamaj99+vTp6EMiIqJOxJDRiTozsprRkUPL05zWsFJV6Bw8o3unAgguiqkogMWkBk6yF0j26NRG6dEBgEHZasPwjsIqLWuSmmDV7q9Ut0xEWOkqQkZHf7wpDkuwdBUp0KlsTqDDeXQ6xPz581FZWan9HD58uKMPiYiIOhG5BITFpGjDyaVg6UrNmBxvZIFLfaCT4rCgf2BEk5yHxh7ozwF0C4DKQCcw6io0KwQAg3LU/XynW3k82WHR9lFao+7fbFJgM5uQlawGOulOK5whGaKhuSna/3NS7FAUBSkJaukp4qir6sYnC5THAqhD1GXTdUfr0j06zWW322G3R3+BiIioewstJ+mFlq5kf05oIzIAbQ0rACjISkS6Uw0giqrVQMSw1ESgAdolh5eHzIysNyhbzcLIEliizQyr2RSW0XFazVAUBT0CGZ3QbA6grnvltKn9QdmBYC2Y0YneoxNtssDQY651eWGz2KJu2166VUaHiIioMX0znOiRbMf4fulh12mlK29IRic1PIjQB0oFWYlICwQ6shlZv3ioPqMjhNAyOokRMjq90hIMw95TAxkYp5bRcRv2OWVwD0zsl4GbJxeE7ctkUrSeH1mOSgk0E4eOutKvcB7aeK1nNZu04+ssDcldOqNTU1ODPXv2aL/v378fGzduREZGBvr27duBR0ZERF1Rot2CLx+Ybsi4SFqg4zf26ORFOPE7DRmdJKQlqJkNOelepMVD691+NHj8COw+YkbHZFJHXm05qg6mkaUmuY8ymdEJ3H+PZDve+NnpUR/vsNxkbDpcoWWl5Kip0NKVXOHcalaQ4Ww8S5PssKLB48KGwxWoqPPAZjEZ+oHaW5cOdNatW4fp06drv99zzz0AgDlz5mDx4sUddFRERNSVRZv1N3Stq2OVkYeWA8aMTr8sp/Z7eWAFc/2MyMmBgKay3qNlc0L3oTcoOzks0JGBTUmgRychwoitSH50ZgHcPj+un6gmB6KNujoeeKzZyQ6YTAoak2y34ES1C3e8tgEA0D8rEZ/dOy2m42kLXTrQmTZtGoQQTW9IRER0kkJLVyeqo88ro+/R6Z+VZFi0EzBmdGT/zNGKOq0/x2kzRw0oZEMyECxdyVJVaEanKYNykvGna8dqv8vAKXTU1YESdWHSflnG9bMiueG0fPy/L/dr5+ceyR3bG9ulAx0iIqL2Elq6kquRZySGl3KcVjOSHRZ4fQIFPRJxLGQpCH2PTu9AoHOkvF434ir66Vk2JAO6QMeqbi97dGINdEIFMzrG0tXB0loAQH5mYpP7+OGZBfjhmeE9QR2FgQ4REVEM9EtACCFQXqtmPdIjBDoWswmv/eQ0+PwCSXYL0gIBiWTXZXR6p6tZksNlddocOkn26IGKnEsHiNCMHMjoRCt7NSUlSqCzvzSQ0clsOqPT2TDQISIiioHs0RECqKr3ajMQR2vOHdkrVft/qtMY6OhnYNZndORIpcYyOn0ynLBZTHB7/dooKVm6klmmlmZ0tFFXIaUrmdHpF0NGp7Ph8HIiIqIYWHVZGDnDsd1iirqAp57dYjYEH/oZmHumJkBR1En2DpepmZPERjI6ZpOCAYEJCFMT1IBIZnB8gbJarM3IofSjrmSPjRAC+0sCgU4WAx0iIqK4ZAx01EbkSP050ejLV/qMjs1i0mZX3lmkLsDZWEYHAM4e2gMWk4LRfdIAhK+WfrI9Oj6/0Bqoy+s8Wimrb0bXK10x0CEiIoqBLF0Bwflw0puYU0YvVbetPWQdLVm+2lmoBjpJEebQ0bvv/KHY9PB5OKWvOrFhaGDT0kDHaTPDHBjtVVWvBjcHAmWrvFRH1FXZOzMGOkRERDFQFEVbiFMuwZCeaG3sJgbpzsgZHSDYkCwDnVgCFf2EgqEBSCzltEgURQmbS+dASewjrjojBjpEREQxkuUr2aPTnIxOmi7QCZ2UUGZ05BpTkWZFbkxYRuckMi+h610dKI19Dp3OiIEOERFRjGT5qiU9OqkJwW3DMzrG9bIaa0aOJHQ4eVM9Po0JHXnVlUdcAQx0iIiIYqZldFrQo2PM6BhPv33SjdmS5gYqoaWqlpaugPBJA1m6IiIi6iZkoKP16Dhbt0dHirRyeWNCA6OWNiMD+iHmgR6dQOmqoAsOLQcY6BAREcXMapGlq0BGp1nDy/WjroyBSG6qA/qlrZzN7NEJLV2dTEZHK13Ve1FR50ZlvRrwdMWh5QADHSIiophZTepps8ETmBW5OT06jWR09HPpAE0PLw8VPry85T06+lFXcqLA3BTHSQVPHYmBDhERUYz0kwYCzezRiTJhoKQvXzW39BQ6vPxkSlf69a4OdvERVwADHSIiopjJ0pXUnNKVftvQZmTAOPLqZIeXt3RRTwBICQRkZXVubbLArjriCuCinkRERDELzehEW9AzEn1Gp6lApyMzOmmBx/TB98fhsKrH2VVHXAHM6BAREcVM9ugAsS/oKaU0GegEy0PN7dExmxTDPk+mR+fcYTm4aFQurGZF60UamJ3U4v11NGZ0iIiIYqQvXTWnERlQsy4JVjPqPb4oPTr6jE7zT88JNjNcXj8UBVompiVSnVY8N3s8ymrdWPr9MVTVe3D20OwW76+jMdAhIiKKkb501ZxG5OBtrKiv9IUNLweMGZ3mzowMqMs+VMADp9UMRVGavkETMhJtuOn0fie9n47G0hUREVGMLLrSVXMW9JROG5CJZLsFg3LCS0G90hNwar90nDUoq0XNxLKMlnASZat4xGeDiIgoRjZd6aolGZ2nrhkD95X+iBkds0nBmz+bDCFEizIyMtA5mUbkeMSMDhERUYz0pavm9ugAgKIoEYOc0G1awmlVcxcMdIwY6BAREcXoZHt02pJDK10x0NFjoENERBQjq1lfump+j05bclpZuoqEgQ4REVGMDBmdFpSu2pLWjGxl+60eAx0iIqIYnWyPTltiM3JkDHSIiIhiZDGf3KirtpTA0lVEDHSIiIhiZOvEpaueqQ4AQG7gX1KxkEdERBQjQ+mqk2V0Zk/KR6+0BJw1uEdHH0qnwkCHiIgoRjLQae6Cnu0hwWbGhaN6dvRhdDosXREREcVIDi/vbI3IFB0DHSIiohjJjE5na0Sm6BjoEBERxUgLdFqwoCd1DAY6REREMTqtfwb6ZTpxyei8jj4UihGbkYmIiGLUv0cSVt43vaMPg5qBGR0iIiKKWwx0iIiIKG4x0CEiIqK4xUCHiIiI4hYDHSIiIopbDHSIiIgobjHQISIiorjFQIeIiIjiFgMdIiIiilsMdIiIiChuMdAhIiKiuMVAh4iIiOIWAx0iIiKKWwx0iIiIKG5ZOvoAOpIQAgBQVVXVwUdCREREsZLnbXkeb0y3DnSqq6sBAH369OngIyEiIqLmqq6uRmpqaqPbKCKWcChO+f1+HDt2DMnJyVAUpVX3XVVVhT59+uDw4cNISUlp1X1T18H3AQF8HxDfA61NCIHq6mrk5eXBZGq8C6dbZ3RMJhN69+7dpveRkpLCNzXxfUAA+D4gvgdaU1OZHInNyERERBS3GOgQERFR3GKg00bsdjsefvhh2O32jj4U6kB8HxDA9wHxPdCRunUzMhEREcU3ZnSIiIgobjHQISIiorjFQIeIiIjiFgMdIiIiilsMdBrx+eef45JLLkFeXh4URcE777xjuL6mpga33XYbevfujYSEBAwbNgzPP/+8YZtp06ZBURTDz3XXXWfYpry8HDfeeCNSU1ORmpqKG2+8ERUVFW386ChWTb0PioqKcPPNNyMvLw9OpxMXXHABdu/ebdjG5XLh9ttvR1ZWFhITE3HppZfiyJEjhm34Pui8WuM9wM+Crm3hwoU49dRTkZycjOzsbFx++eXYuXOnYRshBB555BHk5eUhISEB06ZNw9atWw3b8LOg/THQaURtbS3GjBmDZ555JuL1d999N5YtW4Z///vf2L59O+6++27cfvvtePfddw3b/eQnP8Hx48e1nxdeeMFw/Q9+8ANs3LgRy5Ytw7Jly7Bx40bceOONbfa4qHkaex8IIXD55Zdj3759ePfdd7Fhwwbk5+djxowZqK2t1ba76667sGTJErz++uv48ssvUVNTg5kzZ8Ln82nb8H3QebXGewDgZ0FXtmrVKsybNw9r1qzBJ598Aq/Xi/POO8/wGj/55JP405/+hGeeeQZr165Fbm4uzj33XG1dRYCfBR1CUEwAiCVLlhguGzFihFiwYIHhslNOOUU8+OCD2u9Tp04Vd955Z9T9btu2TQAQa9as0S5bvXq1ACB27NjRKsdOrSf0fbBz504BQGzZskW7zOv1ioyMDPHiiy8KIYSoqKgQVqtVvP7669o2R48eFSaTSSxbtkwIwfdBV9KS94AQ/CyIN8XFxQKAWLVqlRBCCL/fL3Jzc8Xvf/97bZuGhgaRmpoq/v73vwsh+FnQUZjROQlnnnkm3nvvPRw9ehRCCKxYsQK7du3C+eefb9julVdeQVZWFkaMGIF7773XEN2vXr0aqampmDRpknbZaaedhtTUVHz99dft9lioZVwuFwDA4XBol5nNZthsNnz55ZcAgO+++w4ejwfnnXeetk1eXh5GjhypvcZ8H3RdsbwHJH4WxI/KykoAQEZGBgBg//79KCwsNPyd2+12TJ06VXv9+FnQMbr1op4n669//St+8pOfoHfv3rBYLDCZTPi///s/nHnmmdo2s2fPRkFBAXJzc7FlyxbMnz8fmzZtwieffAIAKCwsRHZ2dti+s7OzUVhY2G6PhVpm6NChyM/Px/z58/HCCy8gMTERf/rTn1BYWIjjx48DUF9jm82G9PR0w21zcnK015jvg64rlvcAwM+CeCKEwD333IMzzzwTI0eOBADtNcrJyTFsm5OTg4MHD2rb8LOg/THQOQl//etfsWbNGrz33nvIz8/H559/jp///Ofo2bMnZsyYAUCtyUsjR47EoEGDMGHCBKxfvx6nnHIKAEBRlLB9CyEiXk6di9VqxVtvvYUf/ehHyMjIgNlsxowZM3DhhRc2edvQ15jvg64p1vcAPwvix2233Ybvv/8+LGMHhL+Gsbx+/CxoWyxdtVB9fT1+9atf4U9/+hMuueQSjB49GrfddhtmzZqFP/7xj1Fvd8opp8BqtWojMnJzc1FUVBS23YkTJ8K+GVDnNH78eGzcuBEVFRU4fvw4li1bhtLSUhQUFABQX2O3243y8nLD7YqLi7XXmO+Drq2p90Ak/Czomm6//Xa89957WLFiBXr37q1dnpubCwBhWZfQv3N+FrQ/Bjot5PF44PF4YDIZn0Kz2Qy/3x/1dlu3boXH40HPnj0BAKeffjoqKyvx7bffatt88803qKysxOTJk9vm4KlNpKamokePHti9ezfWrVuHyy67DIB6ErRarVqJAgCOHz+OLVu2aK8x3wfxIdp7IBJ+FnQtQgjcdtttePvtt/HZZ5+FBbGyLKn/O3e73Vi1apX2+vGzoIN0VBd0V1BdXS02bNggNmzYIACIP/3pT2LDhg3i4MGDQgh1FMWIESPEihUrxL59+8SiRYuEw+EQzz33nBBCiD179ohHH31UrF27Vuzfv1988MEHYujQoWLcuHHC6/Vq93PBBReI0aNHi9WrV4vVq1eLUaNGiZkzZ3bIY6ZwTb0P3njjDbFixQqxd+9e8c4774j8/Hxx5ZVXGvbxs5/9TPTu3VssX75crF+/Xpx99tlizJgxfB90ESf7HuBnQdd36623itTUVLFy5Upx/Phx7aeurk7b5ve//71ITU0Vb7/9tti8ebO4/vrrRc+ePUVVVZW2DT8L2h8DnUasWLFCAAj7mTNnjhBCiOPHj4ubb75Z5OXlCYfDIYYMGSKeeuop4ff7hRBCHDp0SEyZMkVkZGQIm80mBgwYIO644w5RWlpquJ/S0lIxe/ZskZycLJKTk8Xs2bNFeXl5Oz9aiqap98HTTz8tevfuLaxWq+jbt6948MEHhcvlMuyjvr5e3HbbbSIjI0MkJCSImTNnikOHDhm24fug8zrZ9wA/C7q+SK8/ALFo0SJtG7/fLx5++GGRm5sr7Ha7mDJliti8ebNhP/wsaH+KEEK0X/6IiIiIqP2wR4eIiIjiFgMdIiIiilsMdIiIiChuMdAhIiKiuMVAh4iIiOIWAx0iIiKKWwx0iIiIKG4x0CGiLmflypVQFAUVFRUdfShE1MlxwkAi6vSmTZuGsWPH4i9/+QsAdQ2hsrIy5OTkcEVnImqUpaMPgIiouWw2m7ZaNBFRY1i6IqJO7eabb8aqVavw9NNPQ1EUKIqCxYsXG0pXixcvRlpaGpYuXYohQ4bA6XTi6quvRm1tLV566SX069cP6enpuP322+Hz+bR9u91u3H///ejVqxcSExMxadIkrFy5smMeKBG1CWZ0iKhTe/rpp7Fr1y6MHDkSCxYsAABs3bo1bLu6ujr89a9/xeuvv47q6mpceeWVuPLKK5GWloYPP/wQ+/btw1VXXYUzzzwTs2bNAgDccsstOHDgAF5//XXk5eVhyZIluOCCC7B582YMGjSoXR8nEbUNBjpE1KmlpqbCZrPB6XRq5aodO3aEbefxePD8889jwIABAICrr74a//rXv1BUVISkpCQMHz4c06dPx4oVKzBr1izs3bsXr732Go4cOYK8vDwAwL333otly5Zh0aJFePzxx9vvQRJRm2GgQ0Rxwel0akEOAOTk5KBfv35ISkoyXFZcXAwAWL9+PYQQGDx4sGE/LpcLmZmZ7XPQRNTmGOgQUVywWq2G3xVFiXiZ3+8HAPj9fpjNZnz33Xcwm82G7fTBERF1bQx0iKjTs9lshibi1jBu3Dj4fD4UFxfjrLPOatV9E1HnwVFXRNTp9evXD9988w0OHDiAkpISLStzMgYPHozZs2fjpptuwttvv439+/dj7dq1eOKJJ/Dhhx+2wlETUWfAQIeIOr17770XZrMZw4cPR48ePXDo0KFW2e+iRYtw00034Re/+AWGDBmCSy+9FN988w369OnTKvsnoo7HmZGJiIgobjGjQ0RERHGLgQ4RERHFLQY6REREFLcY6BAREVHcYqBDREREcYuBDhEREcUtBjpEREQUtxjoEBERUdxioENERERxi4EOERERxS0GOkRERBS3GOgQERFR3Pr/BDRJmadFyH8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ozone_esm15[9::12,30].sel(lat=-80).plot()\n", + "ozone_esm16[9::12,30].sel(lat=-80).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1569a678", + "metadata": {}, + "outputs": [], + "source": [ + "ozone_esm15[9::12,30].sel(lat=-80).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d196cc61", + "metadata": {}, + "outputs": [], + "source": [ + "ozone_cmip6 = xr.open_mfdataset('/g/data/qv56/replicas/input4MIPs/CMIP6/CMIP/UReading/UReading-CCMI-1-0/atmos/mon/vmro3/gn/v20160711/vmro3_input4MIPs_ozone_CMIP_UReading-CCMI-1-0_gn_*.nc', decode_times=False).vmro3\n", + "ozone_cmip7 = xr.open_mfdataset('/g/data/qv56/replicas/input4MIPs/CMIP7/CMIP/FZJ/FZJ-CMIP-ozone-1-2/atmos/mon/vmro3/gn/v20251025/vmro3_input4MIPs_ozone_CMIP_FZJ-CMIP-ozone-1-2_gn_*.nc').vmro3\n", + "# ozone_cmip7x = xr.open_mfdataset('/g/data/qv56/replicas/input4MIPs/CMIP7/CMIP/FZJ/FZJ-CMIP-ozone-1-2/atmos/mon/vmro3/gn/v20251010/vmro3_input4MIPs_ozone_CMIP_FZJ-CMIP-ozone-1-2_gn_*.nc').vmro3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ef3ac6b", + "metadata": {}, + "outputs": [], + "source": [ + "ozone_cmip7_v2 = xr.open_mfdataset('/scratch/tm70/mrd599/fixed_ozone/vmro3_input4MIPs_ozone_CMIP_FZJ-CMIP-ozone-2-0_gn_*.nc').vmro3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86aa05a1", + "metadata": {}, + "outputs": [], + "source": [ + "ozone_cmip7_clim = xr.open_dataset('/g/data/qv56/replicas/input4MIPs/CMIP7/CMIP/FZJ/FZJ-CMIP-ozone-1-2/atmos/monC/vmro3/gn/v20251010/vmro3_input4MIPs_ozone_CMIP_FZJ-CMIP-ozone-1-2_gn_185001-185012-clim.nc').vmro3\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a349aa76", + "metadata": {}, + "outputs": [], + "source": [ + "# Time coordinate is months which is non standard. CMIP7 data uses noleap calendar so match that\n", + "ny = len(ozone_cmip6) // 12\n", + "tvals = []\n", + "units = \"days since 1850-01-01 00:00\"\n", + "for y in range(1850, 1850+ny):\n", + " for m in range(1,13):\n", + " t1 = cftime.date2num(cftime.DatetimeNoLeap(y, m, 1), units, \"noleap\")\n", + " y2 = y\n", + " m2 = m + 1\n", + " if m2==13:\n", + " m2 = 1\n", + " y2 += 1\n", + " t2 = cftime.date2num(cftime.DatetimeNoLeap(y2, m2, 1), units, \"noleap\")\n", + " tvals.append((t1+t2)/2)\n", + "tvals = cftime.num2date(tvals, units, \"noleap\")\n", + "\n", + "ozone_cmip6 = ozone_cmip6.assign_coords({'time': tvals})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc2b5cbf", + "metadata": {}, + "outputs": [], + "source": [ + "# Ozone data is VMR. Conversion to MMR factor\n", + "conv_factor = 48.0 / 28.966\n", + "\n", + "# Factor for conversion of kg/m^2 to DU from https://sacs.aeronomie.be/info/dobson.php\n", + "du_factor = 2.1415e-5\n", + "\n", + "# Mass of each layer\n", + "# Midpoint pressures\n", + "plev = 100*ozone_cmip7.plev.values # Pa\n", + "pmid = 0.5*(plev[1:] + plev[:-1])\n", + "pmidx = np.concatenate(([1e5], pmid, [0.]))\n", + "dp = -np.diff(pmidx)\n", + "grav = 9.806\n", + "dmass = dp / grav" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6fc0bd0", + "metadata": {}, + "outputs": [], + "source": [ + "ozone_mass_cmip6 = conv_factor*(ozone_cmip6*dmass[np.newaxis,:, np.newaxis, np.newaxis]).sum('plev')\n", + "ozone_mass_cmip7 = conv_factor*(ozone_cmip7*dmass[np.newaxis,:, np.newaxis, np.newaxis]).sum('plev')\n", + "\n", + "ozone_mass_cmip6_z = ozone_mass_cmip6.mean('lon').compute()\n", + "ozone_mass_cmip7_z = ozone_mass_cmip7.mean('lon').compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c28f35f4", + "metadata": {}, + "outputs": [], + "source": [ + "ozone_mass_cmip7_v2 = conv_factor*(ozone_cmip7_v2*dmass[np.newaxis,:, np.newaxis, np.newaxis]).sum('plev')\n", + "ozone_mass_cmip7_v2_z = ozone_mass_cmip7_v2.mean('lon').compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "920ce270", + "metadata": {}, + "outputs": [], + "source": [ + "# ozone_mass_cmip7x = conv_factor*(ozone_cmip7x*dmass[np.newaxis,:, np.newaxis, np.newaxis]).sum('plev')\n", + "# ozone_mass_cmip7x_z = ozone_mass_cmip7x.mean('lon').compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "355c0c26", + "metadata": {}, + "outputs": [], + "source": [ + "ozone_mass_cmip7_clim = conv_factor*(ozone_cmip7_clim*dmass[np.newaxis,:, np.newaxis, np.newaxis]).sum('plev')\n", + "ozone_mass_cmip7_clim_z = ozone_mass_cmip7_clim.mean('lon').compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddf2ff49", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,4))\n", + "ax = axes[0]\n", + "# xrt.annual_mean(ozone_cmip7x[:12]).sel(lat=0,method='nearest').mean('lon').plot(y='plev',yscale='log')\n", + "xrt.annual_mean(ozone_cmip7_clim).sel(lat=0,method='nearest').mean('lon').plot(ax=ax, y='plev',yscale='log', label='1850 climatology')\n", + "# xrt.annual_mean(ozone_cmip7).sel(year=1849).sel(lat=0,method='nearest').mean('lon').plot(y='plev',yscale='log', label='1849 historical')\n", + "xrt.annual_mean(ozone_cmip7).sel(year=1850).sel(lat=0,method='nearest').mean('lon').plot(ax=ax, y='plev',yscale='log', label='1850 historical v1-2')\n", + "xrt.annual_mean(ozone_cmip7_v2).sel(year=1850).sel(lat=0,method='nearest').mean('lon').plot(ax=ax, y='plev',yscale='log', label='1850 historical v2-0')\n", + "ax.invert_yaxis()\n", + "ax.set_ylim(1000,0.01)\n", + "ax.legend()\n", + "ax.set_xlabel('Ozone VMR')\n", + "ax.set_title('CMIP7 1850 ozone at equator')\n", + "\n", + "\n", + "ax = axes[1]\n", + "# (xrt.annual_mean(ozone_mass_cmip6_z).sel(year=1850)/du_factor).plot(label='CMIP6')\n", + "(xrt.annual_mean(ozone_mass_cmip7_clim_z)/du_factor).plot(ax=ax, label='1850 climatology')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z).sel(year=1850)/du_factor).plot(ax=ax, label='1850 historical v1-2')\n", + "# (xrt.annual_mean(ozone_mass_cmip7_z).sel(year=slice(1829,1849)).mean('year')/du_factor).plot(ax=ax, label='1829-1849 mean v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z).sel(year=1850)/du_factor).plot(ax=ax, label='1850 historical v2-0')\n", + "ax.legend()\n", + "ax.set_title('1850 total column ozone')\n", + "ax.set_xlabel('Latitude')\n", + "ax.set_xlim(-90,90)\n", + "ax.set_xticks(np.linspace(-90,90,7))\n", + "\n", + "ax.set_ylabel('DU')\n", + "\n", + "savefig('cmip7_1850_ozone_v2.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "834f16a9", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,4))\n", + "ax = axes[0]\n", + "xrt.annual_mean(ozone_cmip7_clim).sel(lat=0,method='nearest').mean('lon').plot(ax=ax, y='plev',yscale='log', label='1850 climatology v1-2')\n", + "xrt.annual_mean(ozone_cmip7).sel(year=slice(1850,1870)).sel(lat=0,method='nearest').mean(('year','lon')).plot(ax=ax, y='plev',yscale='log', label='1850-1870 mean v1-2')\n", + "xrt.annual_mean(ozone_cmip7_v2).sel(year=slice(1850,1870)).sel(lat=0,method='nearest').mean(('year','lon')).plot(ax=ax, y='plev',yscale='log', label='1850-1870 mean v2-0')\n", + "ax.invert_yaxis()\n", + "ax.set_ylim(1000,0.01)\n", + "ax.legend()\n", + "ax.set_xlabel('Ozone VMR')\n", + "ax.set_title('CMIP7 1850-1870 ozone at equator')\n", + "\n", + "\n", + "ax = axes[1]\n", + "(xrt.annual_mean(ozone_mass_cmip7_clim_z)/du_factor).plot(ax=ax, label='1850 climatology v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z).sel(year=slice(1850,1870)).mean('year')/du_factor).plot(ax=ax, label='1850-1870 mean v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z).sel(year=slice(1850,1870)).mean('year')/du_factor).plot(ax=ax, label='1850-1870 mean v2-0')\n", + "ax.legend()\n", + "ax.set_title('1850-1870 total column ozone')\n", + "ax.set_xlabel('Latitude')\n", + "ax.set_xlim(-90,90)\n", + "ax.set_xticks(np.linspace(-90,90,7))\n", + "\n", + "ax.set_ylabel('DU')\n", + "\n", + "savefig('cmip7_1850_ozone_v2a.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37b5b235", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,4))\n", + "ax = axes[0]\n", + "(xrt.annual_mean(ozone_mass_cmip6_z).sel(year=slice(1960,1980)).mean('year')/du_factor).plot(ax=ax, label='CMIP6')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z).sel(year=slice(1960,1980)).mean('year')/du_factor).plot(ax=ax, label='CMIP7 v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z).sel(year=slice(1960,1980)).mean('year')/du_factor).plot(ax=ax, label='CMIP7 v2-0')\n", + "ax.legend()\n", + "ax.set_title('1960-1980 total column ozone')\n", + "ax.set_xlabel('Latitude')\n", + "ax.set_xlim(-90,90)\n", + "ax.set_xticks(np.linspace(-90,90,7))\n", + "ax.set_ylabel('DU')\n", + "\n", + "\n", + "ax = axes[1]\n", + "(xrt.annual_mean(ozone_mass_cmip6_z).sel(year=slice(2000,2020)).mean('year')/du_factor).plot(ax=ax, label='CMIP6')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z).sel(year=slice(2000,2020)).mean('year')/du_factor).plot(ax=ax, label='CMIP7 v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z).sel(year=slice(2000,2020)).mean('year')/du_factor).plot(ax=ax, label='CMIP7 v2-0')\n", + "ax.legend()\n", + "ax.set_title('2000-2020 total column ozone')\n", + "ax.set_xlabel('Latitude')\n", + "ax.set_xlim(-90,90)\n", + "ax.set_xticks(np.linspace(-90,90,7))\n", + "ax.set_ylabel('DU')\n", + "\n", + "savefig('cmip7_2000_ozone_v2.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bee19bb4", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,4))\n", + "ax = axes[0]\n", + "# xrt.annual_mean(ozone_cmip7x[:12]).sel(lat=0,method='nearest').mean('lon').plot(y='plev',yscale='log')\n", + "# xrt.annual_mean(ozone_cmip7).sel(year=1849).sel(lat=0,method='nearest').mean('lon').plot(y='plev',yscale='log', label='1849 historical')\n", + "xrt.annual_mean(ozone_cmip6).sel(year=1850).sel(lat=0,method='nearest').mean('lon').plot(ax=ax, y='plev',yscale='log', label='1850 CMIP6 historical')\n", + "xrt.annual_mean(ozone_cmip7).sel(year=1850).sel(lat=0,method='nearest').mean('lon').plot(ax=ax, y='plev',yscale='log', label='1850 historical v1-2')\n", + "xrt.annual_mean(ozone_cmip7_v2).sel(year=1850).sel(lat=0,method='nearest').mean('lon').plot(ax=ax, y='plev',yscale='log', label='1850 historical v2-0')\n", + "ax.invert_yaxis()\n", + "ax.set_ylim(1000,0.01)\n", + "ax.legend()\n", + "ax.set_xlabel('Ozone VMR')\n", + "ax.set_title('CMIP7 1850 ozone at equator')\n", + "\n", + "\n", + "ax = axes[1]\n", + "# (xrt.annual_mean(ozone_mass_cmip6_z).sel(year=1850)/du_factor).plot(label='CMIP6')\n", + "(xrt.annual_mean(ozone_mass_cmip6_z).sel(year=1850)/du_factor).plot(ax=ax, label='1850 CMIP6 historical')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z).sel(year=1850)/du_factor).plot(ax=ax, label='1850 historical v1-2')\n", + "# (xrt.annual_mean(ozone_mass_cmip7_z).sel(year=slice(1829,1849)).mean('year')/du_factor).plot(ax=ax, label='1829-1849 mean v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z).sel(year=1850)/du_factor).plot(ax=ax, label='1850 historical v2-0')\n", + "ax.legend()\n", + "ax.set_title('1850 total column ozone')\n", + "ax.set_xlabel('Latitude')\n", + "ax.set_ylabel('DU')\n", + "ax.set_xlim(-90,90)\n", + "ax.set_xticks(np.linspace(-90,90,7))\n", + "\n", + "savefig('cmip7_1850_ozone_v2x.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12f5ea54", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,4))\n", + "ax = axes[0]\n", + "(xrt.annual_mean(ozone_mass_cmip6_z).sel(lat=0,method='nearest')/du_factor).plot(ax=ax, label='CMIP6')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z).sel(lat=0,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z).sel(lat=0,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v2-0')\n", + "ax.legend()\n", + "ax.set_title('Annual total column ozone at equator')\n", + "ax.set_ylabel('DU')\n", + "\n", + "ax = axes[1]\n", + "(xrt.annual_mean(ozone_mass_cmip6_z).sel(lat=60,method='nearest')/du_factor).plot(ax=ax, label='CMIP6')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z).sel(lat=60,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z).sel(lat=60,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v2-0')\n", + "ax.legend()\n", + "ax.set_title('Annual total column ozone at 60N')\n", + "ax.set_ylabel('DU')\n", + "\n", + "savefig('ozone_60N_series.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b764d9c", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,4))\n", + "ax = axes[0]\n", + "(xrt.annual_mean(ozone_mass_cmip6_z).sel(lat=-60,method='nearest')/du_factor).plot(ax=ax, label='CMIP6')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z).sel(lat=-60,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z).sel(lat=-60,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v2-0')\n", + "ax.legend()\n", + "ax.set_title('Annual total column ozone at 60S')\n", + "ax.set_ylabel('DU')\n", + "\n", + "ax = axes[1]\n", + "(ozone_mass_cmip6_z[9::12].sel(lat=-60,method='nearest')/du_factor).plot(ax=ax, label='CMIP6')\n", + "(ozone_mass_cmip7_z[9::12].sel(lat=-60,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v1-2')\n", + "(ozone_mass_cmip7_v2_z[9::12].sel(lat=-60,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v2-0')\n", + "ax.legend()\n", + "ax.set_title('October total column ozone at 60S')\n", + "ax.set_ylabel('DU')\n", + "\n", + "savefig('ozone_60S_series.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "438a7c2a", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,4))\n", + "ax = axes[0]\n", + "(xrt.annual_mean(ozone_mass_cmip6_z).sel(lat=-80,method='nearest')/du_factor).plot(ax=ax, label='CMIP6')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z).sel(lat=-80,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v1-2')\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z).sel(lat=-80,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v2-0')\n", + "ax.legend()\n", + "ax.set_title('Annual total column ozone at 80S')\n", + "ax.set_ylabel('DU')\n", + "\n", + "ax = axes[1]\n", + "(ozone_mass_cmip6_z[9::12].sel(lat=-80,method='nearest')/du_factor).plot(ax=ax, label='CMIP6')\n", + "(ozone_mass_cmip7_z[9::12].sel(lat=-80,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v1-2')\n", + "(ozone_mass_cmip7_v2_z[9::12].sel(lat=-80,method='nearest')/du_factor).plot(ax=ax, label='CMIP7 v2-0')\n", + "ax.legend()\n", + "ax.set_title('October total column ozone at 80S')\n", + "ax.set_ylabel('DU')\n", + "\n", + "savefig('ozone_80S_series.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "450f03b5", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots()\n", + "ax = axes\n", + "lev1 = 26\n", + "xrt.annual_mean(ozone_cmip7_clim).sel(lat=0,method='nearest').mean('lon')[0,lev1:].plot(ax=ax, y='plev', label='1850 climatology')\n", + "xrt.annual_mean(ozone_cmip6).sel(year=1850).sel(lat=0,method='nearest').mean('lon')[lev1:].plot(ax=ax, y='plev', label='CMIP6 1850 historical')\n", + "xrt.annual_mean(ozone_cmip7).sel(year=1850).sel(lat=0,method='nearest').mean('lon')[lev1:].plot(ax=ax, y='plev', label='CMIP7 1850 historical v1-2')\n", + "xrt.annual_mean(ozone_cmip7_v2).sel(year=1850).sel(lat=0,method='nearest').mean('lon')[lev1:].plot(ax=ax, y='plev', label='CMIP7 1850 historical v2-0')\n", + "ax.invert_yaxis()\n", + "ax.set_ylim(50,0.01)\n", + "ax.legend()\n", + "ax.set_xlabel('Ozone VMR')\n", + "ax.set_title('1850 ozone at equator')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9a786dd", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,5))\n", + "fig.suptitle('1850 ozone VMR')\n", + "ax = axes[0]\n", + "# Don't plot to the top to better show interesting region\n", + "toplev = 43\n", + "botlev = 20\n", + "vmax = 1.1e-5\n", + "levels = np.linspace(0, 1.1e-5, 23)\n", + "levels = np.linspace(0, 1.1e-5, 12)\n", + "levels = np.linspace(0, 1.1e-5, 45)\n", + "cbar_kwargs = {'ticks':[2e-6,4e-6,6e-6,8e-6,1e-5], 'orientation':'horizontal', 'shrink':0.75}\n", + "ozone_cmip7_clim[:12,botlev:toplev].mean(('time','lon')).plot.contourf(ax=ax, yscale='log', cbar_kwargs=cbar_kwargs, levels=levels, vmax=vmax, cmap='gist_heat_r')\n", + "ax.set_title('CMIP7 climatology')\n", + "ax.set_xlabel('Latitude')\n", + "ax.set_ylabel('Pressure (hPa)')\n", + "ax.set_xticks(np.linspace(-90,90,7))\n", + "ax.invert_yaxis()\n", + "ax = axes[1]\n", + "ozone_cmip7.sel(time='1850')[:,botlev:toplev].mean(('time','lon')).plot.contourf(ax=ax,yscale='log', cbar_kwargs=cbar_kwargs, levels=levels, vmax=vmax, cmap='gist_heat_r')\n", + "ax.invert_yaxis()\n", + "ax.set_title('CMIP7')\n", + "ax.set_xlabel('Latitude')\n", + "ax.set_ylabel('Pressure (hPa)')\n", + "_ = ax.set_xticks(np.linspace(-90,90,7))\n", + "savefig('ozone_comparison.png')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af4998ab", + "metadata": {}, + "outputs": [], + "source": [ + "(ozone_mass_cmip7_z[:,48].sel(time=slice(None,'1860'))/du_factor).plot()\n", + "# (ozone_mass_cmip7_z[:,48]/du_factor).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c961c2f", + "metadata": {}, + "outputs": [], + "source": [ + "mosaic = [ ['a', 'b'], ['c', '.']]\n", + "fig, axes = plt.subplot_mosaic(mosaic, layout='constrained', figsize=(12,8))\n", + "fig.suptitle('Annual mean total column ozone (DU)')\n", + "vmin=220\n", + "vmax=370\n", + "ax = axes['a']\n", + "(xrt.annual_mean(ozone_mass_cmip6_z)/du_factor).plot(ax=ax,x='year',vmin=vmin,vmax=vmax)\n", + "ax.set_title('CMIP6')\n", + "ax.set_ylabel('Latitude')\n", + "ax.set_yticks(np.linspace(-90,90,7))\n", + "ax = axes['b']\n", + "(xrt.annual_mean(ozone_mass_cmip7_z)/du_factor).plot(ax=ax,x='year',vmin=vmin,vmax=vmax)\n", + "ax.set_title('CMIP7 FZJ-CMIP-ozone-1-2 v20251025')\n", + "ax.set_ylabel('Latitude')\n", + "ax.set_yticks(np.linspace(-90,90,7))\n", + "ax = axes['c']\n", + "(xrt.annual_mean(ozone_mass_cmip7_v2_z)/du_factor).plot(ax=ax,x='year',vmin=vmin,vmax=vmax)\n", + "ax.set_title('CMIP7 FZJ-CMIP-ozone-2-0')\n", + "ax.set_ylabel('Latitude')\n", + "ax.set_yticks(np.linspace(-90,90,7))\n", + "savefig('cmip7_ozone_v2.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90dca292", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots()\n", + "(xrt.annual_mean(ozone_mass_cmip7_clim_z)/du_factor).plot(label='1850 climatology')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z)[0]/du_factor).plot()\n", + "# (xrt.annual_mean(ozone_mass_cmip7x_z)[0]/du_factor).plot()\n", + "axes.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24191f2d", + "metadata": {}, + "outputs": [], + "source": [ + "(xrt.annual_mean(ozone_mass_cmip7_clim_z)/du_factor).plot(label='1850 climatology')\n", + "(xrt.annual_mean(ozone_mass_cmip7_z)[0]/du_factor).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e546890", + "metadata": {}, + "outputs": [], + "source": [ + "print((xrt.annual_mean(ozone_mass_cmip7_clim_z)/du_factor).sel(lat=0,method='nearest'))\n", + "(xrt.annual_mean(ozone_mass_cmip7_z)/du_factor).sel(lat=0,method='nearest').plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56442e74", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots()\n", + "(ozone_mass_cmip6.mean(('time', 'lon'))/du_factor).plot(label='CMIP6')\n", + "(ozone_mass_cmip7.mean(('time', 'lon'))/du_factor).plot(label='CMIP7')\n", + "axes.legend()\n", + "axes.set_title('1850 annual mean total column ozone')\n", + "axes.set_xlabel('Latitude')\n", + "axes.set_ylabel('DU')\n", + "axes.set_xlim(-90,90)\n", + "_ = axes.set_xticks(np.linspace(-90,90,7))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ddde5d0", + "metadata": {}, + "outputs": [], + "source": [ + "zmta = xr.open_dataset('/g/data/qv56/replicas/input4MIPs/CMIP7/CMIP/FZJ/FZJ-CMIP-ozone-1-2/atmos/mon/zmta/gn/v20251118/zmta_input4MIPs_ozone_CMIP_FZJ-CMIP-ozone-1-2_gn_185001-202212.nc').zmta\n", + "zmta_clim = xr.open_dataset('/g/data/qv56/replicas/input4MIPs/CMIP7/CMIP/FZJ/FZJ-CMIP-ozone-1-2/atmos/monC/zmta/gn/v20251118/zmta_input4MIPs_ozone_CMIP_FZJ-CMIP-ozone-1-2_gn_185001-185012-clim.nc').zmta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc17c930", + "metadata": {}, + "outputs": [], + "source": [ + "# Set missing\n", + "zmta_clim = xr.where(zmta_clim>1e36, np.nan, zmta_clim)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebea1143", + "metadata": {}, + "outputs": [], + "source": [ + "xrt.annual_mean(zmta_clim).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b82f2033", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1,2,layout='constrained',figsize=(10,4))\n", + "ax = axes[0]\n", + "xrt.annual_mean(zmta_clim).plot(ax=ax, y='plev',yscale='log')\n", + "ax.set_title('1850 climatology')\n", + "ax.invert_yaxis()\n", + "ax = axes[1]\n", + "# xrt.annual_mean(ozone_cmip7x[:12]).sel(lat=0,method='nearest').mean('lon').plot(y='plev',yscale='log')\n", + "xrt.annual_mean(zmta[:12]).plot(ax=ax, y='plev',yscale='log')\n", + "ax.set_title('1850 historical')\n", + "# # xrt.annual_mean(ozone_cmip7).sel(year=1849).sel(lat=0,method='nearest').mean('lon').plot(y='plev',yscale='log', label='1849 historical')\n", + "# xrt.annual_mean(ozone_cmip7).sel(year=1850).sel(lat=0,method='nearest').mean('lon').plot(ax=ax, y='plev',yscale='log', label='1850 historical')\n", + "ax.invert_yaxis()\n", + "# ax.set_ylim(1000,0.01)\n", + "# ax.legend()\n", + "# ax.set_xlabel('Ozone VMR')\n", + "# ax.set_title('CMIP7 1850 ozone at equator')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c40d9d1", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots()\n", + "xrt.annual_mean(zmta_clim)[0,:,48].plot(y='plev',yscale='log', label='Clim')\n", + "xrt.annual_mean(zmta[:12])[0,:,48].plot(y='plev',yscale='log', label='Hist')\n", + "axes.invert_yaxis()\n", + "axes.set_ylim(1000,0.01)\n", + "axes.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "304142af", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots()\n", + "plt.semilogy(dp, 0.01*plev)\n", + "axes.set_ylim(100,1)\n", + "axes.set_xlim(0,1200)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:analysis3-26.01] *", + "language": "python", + "name": "conda-env-analysis3-26.01-py" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}