{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Meridional Heat Transport\n", "\n", "## (MOM5) \n", "\n", "This recipe calculates the model's meridional heat transport (MHT) using two methods based on distinct MOM5 diagnostics. The methods and the caveats associated with them are listed below:\n", "\n", "### Method 1: Using online diagnostics\n", "\n", "This is the recommended method. In MOM5 the meridional heat transported associated to resolved advection is given by the `temp_yflux_adv_int_z`. The heat fluxes associated to parametrised processes are provided in separate diagnostics (like `temp_yflux_gm` and `temp_yflux_ndiffuse` for the mesoscale parametrisations and `temp_yflux_submeso` for the submesoscale parametrisations).\n", "\n", "This recipe uses ACCESS-OM2-01, which does not have mesoscale parametrisations (and therefore no `temp_yflux_gm` or `temp_yflux_ndiffuse`). We further assume the heat flux due to submesoscale parametrisation (`temp_yflux_submeso`) is small and that the bulk of the meridional heat flux is due to resolved advection `temp_yflux_adv_int_z`. \n", "\n", "### Method 2: Using surface and frazil heat fluxes\n", "\n", "This is an alternative method that approximates the meridional heat transport from surface heat fluxes for the simulations in which the online diagnostics needed in Method 1 are not available. Note that this method relies on a steady state assumption. We use two diagnostics: `net_sfc_heating` (net surface heat flux) and `frazil_3d_int_z` which is the heat flux due to frazil formation at higher latitudes.\n", " \n", "The recipe calculates the total (all basins) MHT, and it also includes comparisons to a few observational products. Basin-specific MHT can be calculated by defining relevant masks.\n", "\n", "## Information needed to adapt to MOM6\n", "\n", "The diagnostics for meridional heat transports are called `T_ady_2d` (from resolved advection), `T_diffy_2d` (from diffusion) and `hfds` for the surface heat flux (includes frazil contribution)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MOM5 recipe" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "import intake\n", "\n", "import numpy as np\n", "import xarray as xr\n", "\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cmocean\n", "\n", "from dask.distributed import Client\n", "import warnings\n", "warnings.filterwarnings(\"ignore\", category = FutureWarning)\n", "warnings.filterwarnings(\"ignore\", category = UserWarning)\n", "warnings.filterwarnings(\"ignore\", category = RuntimeWarning)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start dask cluster." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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Client

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Client-31265ac2-b9f1-11f0-b98f-0000008ffe80

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Connection method: Cluster objectCluster type: distributed.LocalCluster
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Cluster Info

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LocalCluster

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adee10a3

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Status: runningUsing processes: True
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Scheduler Info

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Scheduler

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Scheduler-c16c8b7d-a094-48a4-a6b5-a166eaf10915

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Workers

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Worker: 0

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\n", " Nanny: tcp://127.0.0.1:40125\n", "
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Worker: 1

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Worker: 2

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\n", " Nanny: tcp://127.0.0.1:46203\n", "
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Worker: 3

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Worker: 4

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Worker: 5

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Worker: 6

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Worker: 7

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Worker: 8

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Worker: 9

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Worker: 10

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Worker: 11

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Worker: 12

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Worker: 13

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Worker: 14

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Worker: 15

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Worker: 16

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Worker: 17

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Worker: 18

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Worker: 19

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\n", " Nanny: tcp://127.0.0.1:36087\n", "
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Worker: 20

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Worker: 21

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\n", " Nanny: tcp://127.0.0.1:41071\n", "
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Worker: 22

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\n", " Nanny: tcp://127.0.0.1:39779\n", "
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Worker: 23

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45831\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44795/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44033\n", "
\n", " Local directory: /jobfs/154001283.gadi-pbs/dask-scratch-space/worker-rv9erdys\n", "
\n", "
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Worker: 24

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45941\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41641/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41907\n", "
\n", " Local directory: /jobfs/154001283.gadi-pbs/dask-scratch-space/worker-ldiggwmv\n", "
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Worker: 25

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:46237\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:41675\n", "
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Worker: 26

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\n", " Comm: tcp://127.0.0.1:34381\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:45083\n", "
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Worker: 27

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\n", " Comm: tcp://127.0.0.1:33645\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:36199\n", "
\n", " Local directory: /jobfs/154001283.gadi-pbs/dask-scratch-space/worker-ds6__mrv\n", "
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Worker: 28

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\n", " Comm: tcp://127.0.0.1:34691\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:39281\n", "
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Worker: 29

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\n", " Comm: tcp://127.0.0.1:44585\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:33521\n", "
\n", " Local directory: /jobfs/154001283.gadi-pbs/dask-scratch-space/worker-enuh6cv1\n", "
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Worker: 30

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\n", " Nanny: tcp://127.0.0.1:37305\n", "
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Worker: 31

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\n", " Comm: tcp://127.0.0.1:43301\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:38917\n", "
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Worker: 32

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\n", " Comm: tcp://127.0.0.1:46113\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:39619\n", "
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Worker: 33

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\n", " Nanny: tcp://127.0.0.1:40987\n", "
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Worker: 34

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\n", " Comm: tcp://127.0.0.1:45905\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:44603\n", "
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Worker: 35

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\n", " Nanny: tcp://127.0.0.1:39011\n", "
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Worker: 36

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\n", " Nanny: tcp://127.0.0.1:40355\n", "
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Worker: 37

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\n", " Nanny: tcp://127.0.0.1:39351\n", "
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Worker: 38

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\n", " Nanny: tcp://127.0.0.1:40657\n", "
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Worker: 39

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\n", " Nanny: tcp://127.0.0.1:36779\n", "
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Worker: 40

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\n", " Nanny: tcp://127.0.0.1:44477\n", "
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Worker: 41

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\n", " Nanny: tcp://127.0.0.1:34585\n", "
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Worker: 42

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Worker: 43

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Worker: 44

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Worker: 45

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Worker: 46

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\n", " Nanny: tcp://127.0.0.1:37387\n", "
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Worker: 47

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\n", " Comm: tcp://127.0.0.1:35173\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:43185\n", "
\n", " Local directory: /jobfs/154001283.gadi-pbs/dask-scratch-space/worker-wjkm_of9\n", "
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" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client = Client(threads_per_worker=1)\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load ACCESS-NRI default catalog" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "catalog = intake.cat.access_nri" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define experiment of interest" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true, "tags": [] }, "outputs": [], "source": [ "experiment = '01deg_jra55v140_iaf_cycle3'\n", "start_time = '2000-01-01'\n", "end_time = '2005-12-31'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now ready to load the data to start our analysis. We load `temp_yflux_adv_int_z`. For this example, we have chosen to use 6 years of output." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Method 1: Using online diagnostics" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'temp_yflux_adv_int_z' (time: 72, yu_ocean: 2700,\n",
       "                                          xt_ocean: 3600)> Size: 3GB\n",
       "dask.array<getitem, shape=(72, 2700, 3600), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
       "  * yu_ocean  (yu_ocean) float64 22kB -81.09 -81.05 -81.0 ... 89.92 89.96 90.0\n",
       "  * time      (time) datetime64[ns] 576B 2000-01-16T12:00:00 ... 2005-12-16T1...\n",
       "Attributes:\n",
       "    long_name:      z-integral of cp*rho*dxt*v*temp\n",
       "    units:          Watts\n",
       "    valid_range:    [-1.e+18  1.e+18]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT
" ], "text/plain": [ " Size: 3GB\n", "dask.array\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n", " * yu_ocean (yu_ocean) float64 22kB -81.09 -81.05 -81.0 ... 89.92 89.96 90.0\n", " * time (time) datetime64[ns] 576B 2000-01-16T12:00:00 ... 2005-12-16T1...\n", "Attributes:\n", " long_name: z-integral of cp*rho*dxt*v*temp\n", " units: Watts\n", " valid_range: [-1.e+18 1.e+18]\n", " cell_methods: time: mean\n", " time_avg_info: average_T1,average_T2,average_DT" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_adv = catalog[experiment].search(variable = ['temp_yflux_adv_int_z'], frequency='1mon').to_dask(xarray_open_kwargs={\"decode_timedelta\": True})\n", "ds_adv = ds_adv['temp_yflux_adv_int_z'].sel(time=slice(start_time, end_time))\n", "ds_adv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We convert the dataset from Watts (W) to PetaWatts (PW)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'temp_yflux_adv_int_z' (time: 72, yu_ocean: 2700,\n",
       "                                          xt_ocean: 3600)> Size: 3GB\n",
       "dask.array<mul, shape=(72, 2700, 3600), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
       "  * yu_ocean  (yu_ocean) float64 22kB -81.09 -81.05 -81.0 ... 89.92 89.96 90.0\n",
       "  * time      (time) datetime64[ns] 576B 2000-01-16T12:00:00 ... 2005-12-16T1...\n",
       "Attributes:\n",
       "    units:    PetaWatts
" ], "text/plain": [ " Size: 3GB\n", "dask.array\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n", " * yu_ocean (yu_ocean) float64 22kB -81.09 -81.05 -81.0 ... 89.92 89.96 90.0\n", " * time (time) datetime64[ns] 576B 2000-01-16T12:00:00 ... 2005-12-16T1...\n", "Attributes:\n", " units: PetaWatts" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_adv = ds_adv * 1e-15\n", "ds_adv.attrs['units'] = 'PetaWatts'\n", "ds_adv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then we compute the mean across `time` and sum over all longitudes." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "MHT_method_1 = ds_adv.mean('time').sum('xt_ocean')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Method 2: Using surface and frazil heat fluxes " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we load the surface heat flux and grid metrics:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'net_sfc_heating' (time: 72, yt_ocean: 2700, xt_ocean: 3600)> Size: 3GB\n",
       "dask.array<getitem, shape=(72, 2700, 3600), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
       "  * yt_ocean  (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.89 89.94 89.98\n",
       "  * time      (time) datetime64[ns] 576B 2000-01-16T12:00:00 ... 2005-12-16T1...\n",
       "Attributes:\n",
       "    long_name:      surface ocean heat flux coming through coupler and mass t...\n",
       "    units:          Watts/m^2\n",
       "    valid_range:    [-10000.  10000.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT
" ], "text/plain": [ " Size: 3GB\n", "dask.array\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n", " * yt_ocean (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.89 89.94 89.98\n", " * time (time) datetime64[ns] 576B 2000-01-16T12:00:00 ... 2005-12-16T1...\n", "Attributes:\n", " long_name: surface ocean heat flux coming through coupler and mass t...\n", " units: Watts/m^2\n", " valid_range: [-10000. 10000.]\n", " cell_methods: time: mean\n", " time_avg_info: average_T1,average_T2,average_DT" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_sfc = catalog[experiment].search(variable = ['net_sfc_heating'], frequency='1mon').to_dask(xarray_open_kwargs={\"decode_timedelta\": True})\n", "ds_sfc = ds_sfc['net_sfc_heating'].sel(time=slice(start_time, end_time))\n", "ds_sfc" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'frazil_3d_int_z' (time: 72, yt_ocean: 2700, xt_ocean: 3600)> Size: 3GB\n",
       "dask.array<getitem, shape=(72, 2700, 3600), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
       "  * yt_ocean  (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.89 89.94 89.98\n",
       "  * time      (time) datetime64[ns] 576B 2000-01-16T12:00:00 ... 2005-12-16T1...\n",
       "Attributes:\n",
       "    long_name:      Vertical sum of ocn frazil heat flux over time step\n",
       "    units:          W/m^2\n",
       "    valid_range:    [-1.e+10  1.e+10]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT
" ], "text/plain": [ " Size: 3GB\n", "dask.array\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n", " * yt_ocean (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.89 89.94 89.98\n", " * time (time) datetime64[ns] 576B 2000-01-16T12:00:00 ... 2005-12-16T1...\n", "Attributes:\n", " long_name: Vertical sum of ocn frazil heat flux over time step\n", " units: W/m^2\n", " valid_range: [-1.e+10 1.e+10]\n", " cell_methods: time: mean\n", " time_avg_info: average_T1,average_T2,average_DT" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_frz = catalog[experiment].search(variable = ['frazil_3d_int_z'], frequency='1mon').to_dask(xarray_open_kwargs={\"decode_timedelta\": True})\n", "ds_frz = ds_frz['frazil_3d_int_z'].sel(time=slice(start_time, end_time))\n", "ds_frz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add the heat fluxes and take the `time` mean:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [], "source": [ "shflux = (ds_sfc + ds_frz).mean('time').load()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Get the relevant grid_info\n", "area = catalog[experiment].search(variable='area_t', frequency='fx').to_dask()['area_t']\n", "geolat_t = catalog[experiment].search(variable='geolat_t', frequency='fx').to_dask()['geolat_t']\n", "geolon_t = catalog[experiment].search(variable='geolon_t', frequency='fx').to_dask()['geolon_t']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Add geolat_t and geolon_t coords\n", "area = area.assign_coords({'geolon_t': geolon_t, 'geolat_t': geolat_t})\n", "shflux = shflux.assign_coords({'geolon_t': geolon_t, 'geolat_t': geolat_t})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now calculate Meridional Heat Flux (MHF). This is done by calculating the total heat flux as the heat flux times the area, and then integrating in latitude space such that for each latitude:\n", "\n", "$$\n", "\\mathrm{MHF}(y) = \\int_{y_{0}}^{y} (\\mathrm{SHFLUX} \\times \\mathrm{AREA}) \\, \\mathrm{d}y\n", "$$" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Create left edge for bottom bin\n", "latv_bins = np.hstack(([-90], area['yt_ocean'].values))\n", "\n", "MHT = shflux * area\n", "MHT = MHT.groupby_bins('geolat_t', latv_bins)\n", "MHT = MHT.sum()\n", "MHT = MHT.cumsum()\n", "MHT = MHT.rename(geolat_t_bins='yt_ocean')\n", "MHT.coords['yt_ocean'] = area['yt_ocean']\n", "\n", "MHT_method_2 = MHT + (MHT.isel(yt_ocean=0) - MHT.isel(yt_ocean=-1)) / 2\n", "\n", "# Convert to petawatt\n", "MHT_method_2 = MHT_method_2 * 1e-15\n", "MHT_method_2.attrs['units'] = 'PetaWatts'" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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dezOpr2/RokVv6zg/P780x4PZTyB1BKfBgwdneWSPPXv2sG3bNpydnXn99dcz3S+18/Iff/xxW0Nvli5dmtq1a2MYxk3fD88//zzFixdPt75mzZrWNrF79+7N8uNmpmfPnjg4OLBgwQIiIyMBs5/K+vXr8fb2tr7HMmMYhrVvweuvv46TU8YjQj/00EN4eXlx5coVduzYkeE+Tz/9NAEBAbf9HPbu3WvtwzF8+HAcHNL/6XjuuecoXbr0bZ/7999/59q1a4DZZjkjL730knVI0sx+Fl1dXTMdArdLly5A9v4/O3XqBMDmzZvv2BCwERERXLp06aZLZn0ycvP9kRFHR0fat28PwIYNG27zmd15b775Zo6GGE39/73ZcwsMDLT+Pvqvzp07A+nfW6nDbYaGhmbrfePu7p7p+3nkyJGAOfzq77//bl2fWz9LDg4OvP3227ddsxRcCgNS4CQlJWX6BzOzDq7/tXPnTgzDAMyL9szcf//9gPmLPrM/xvfcc0+mx6du2759e7ptCQkJzJgxg7Zt21KqVCnc3NzSdMS6fPkyAGfPns3Sc7odtztjauprdaPt27dbX5MHH3wwy+dK/aObkpJC5cqVMw12qRcg0dHRaUJI6rHff/89nTt3pkyZMri7u6d57f766y/g5q9d48aNM92W2mk99Q9rTgQEBHD//fcTExNjDU+zZs3CMAy6d++Om5vbTY8/ePCgtY5evXpl+nr5+/tbO06fOnUqw3M1b948W88h9f3r5OREy5YtM9zHwcEhW50HU88dGBhIpUqVMtzH0dHxpj9LYA4i4OHhkeG2W/1/Xrp0iVGjRtG0aVOKFi2aZhK4atWqARATE8P169ez/sRuQ+r74WZL69atMzw2t94f69evp1evXlSpUgUPD480P0/vv/8+cGd+F+VUVt7Te/bs4aWXXqJWrVp4eXnh4OBgfW4vvfQScPPn1rBhwwwDMGT+3rrvvvtwc3Nj165dtGzZkq+//poTJ05k9WnRoEEDvLy8MtxWsWJFa6i/8echt36WKlSokOEHJXL30qRjkq/c+In1tWvXMpygqEqVKmkuTk+ePJlmdIOsSL3QBm76aWbqL9ykpCSuXbuW4QQ5Nzs+dduNj5f6/X333ce+ffus69zc3PDz87N+yhUaGkpKSgrR0dFZeEZZU7RoUc6dO8eVK1du67iM7ihcvHjR+nVQUFCWz5U6q3FycnKmowz9V0xMTJqvH3jgAdasWWNd5+Ligq+vL87OzsC/Exbd7LXz9PTMdFvqp6u5NaJTnz59WLFiBbNmzaJPnz7Wcfz79Olzy2NvnAU6NDQ0S4934+t1o+z+gU99/6aOFJWZ7Nx1SD33re4qpJ77vz9LqbLy/5k6wd6NNm/eTMeOHdOM/OXh4WEdaSk5Odn68xIdHW29S5Zf5Mb74+2337Ze8IN5wVikSBFcXFwAiIqKIjo6Old/F+WWW72nP/nkEwYOHGgd8cdiseDt7W19H8fGxhIREZHrvyvKly/PV199xYsvvsjmzZvZvHkzAMWKFaNNmzb06NGDzp07Z/rBzK1+HkqXLs3Zs2fT/Dzk1s+SgoD90Z0ByVdSP4UDcrV5TG643U/Tb+a1115j3759FC1alJkzZ3LhwgViY2MJDQ21TqiT+olTRp/KZ1fq67tr164sHxMREcHx48cB89PXnEq9ZZ4a6rKy3Dgh17vvvsuaNWtwd3dn8uTJnDp1iri4OK5evWp97VI/9c/N1y4nunTpgq+vL5s2beKTTz7hzJkzVK9enYYNG97y2BubGFy8eDFLr1dmw3fmdMbW3PwZyO65c7OGpKQkunfvTlhYGHXq1GHp0qVEREQQGRnJpUuXuHjxIlu2bLHun1/eTzfK6fvj999/twaBl156iX379lknIEz9eUqd2T0/Pv+bvacPHTrEq6++SkpKCo899hh//fUXcXFxXL9+3frcJk2aBNyZ5/bkk09y6tQpZsyYweOPP05gYCChoaH8+OOPPPTQQ7Ru3TrT4atz8j7P6c+SZna2PwoDkq+0adPG+gtqyZIld+xxbvzk42a3h1O3OTk5UaRIkQz3OXfuXKbHp2678fESExP56aefAPNTq969e1OyZMk0x934aWRuuvfeewHzk/61a9dm6ZhFixZZ/1De2CTqxrs2mTVLyUjqcz1+/Hi2PmlM7X8xcuRIXn31VcqUKZPuj9qNdy3yA1dXV7p37w6YbZwBevfunaVjb3xv3HgnKS+lvn9DQ0OJj4/PdL+b/Szc6txnzpy56X6pP4vFihW77cfIzObNmzl16hSOjo78+uuvdOjQId2nwPntvfRfOX1/pP48tWvXjk8//ZQaNWqkuxjM769BZhYsWEBycjJVq1Zl3rx5NGzY0Hq3I9Wdfm6+vr688MILzJs3j9OnT3Ps2DEGDx6MxWJh/fr1mbbtv1WTrIz+ttjyZ0kKNoUByVf8/f2tHSq//fbb22pjeTvq1atnbQO6atWqTPf7448/AKhdu7a1Ccp/3dhcJbNtDRo0sK4LDQ21TjRTt27dDI/bsGFDppMk5UTv3r2tbdTfeeedW34aFh8fz3vvvQdgnYQpVYMGDax/WH/55Zcs15DaxjchIYFFixbdTvnAv3/oMnvtTp48ybFjx277vHdaapOghIQEnJycePrpp7N0XI0aNaxth2+3I3puSX3/JiUlZdrRMiUlJcsBM6Nznz17lqNHj2a4T3JysvVnKSt3U7Iq9b1UrFixTJtWpP4OyK9y+v641c+TYRisXr060+NTf4/mx7sGqc+tdu3ambb5z+v/3+DgYCZMmECPHj0A0nQAvtH27dutAw7817Fjx6wX9Df+bcmrn6UbX8v8+P8ut09hQPKdcePGUbhwYaKjo3nooYfStInNLT4+PrRr1w6ADz74IMM21nv27GHhwoUA1k91M/Lhhx9meOG+Zs0aNm7cCMDjjz9uXe/l5WX9JHvPnj3pjktKSmLYsGG38Wyyrnjx4rz11lvW+oYOHZrpvikpKbzwwgscOnQIgPHjx6f5VK1QoUI88cQTAEycOPGWn0alatCggfXCY9iwYbds5/zfjnne3t5Axq8dmCMb5Uf16tVjzJgxvP7660yePDnL7XKdnJysQWLOnDm3HNElNzo9/1etWrWsM1G/++67Gc64OnPmzGx1ML3//vutfVEy+5T0888/t/4euNnP4u1KfS+lDkDwX2fPnmXatGm59nh3Qk7fH7f6eZoxY4a1mWBGUoNIZrOt21Lqc9u3b1+GF63Lli3LVoDNipvdQQOsI9Bl1iQnNjaWjz76KMNt48aNA8y7DqmDXEDe/Szd2LE5P/6/y+1TGJB8p0qVKnz33Xe4uLiwd+9eatWqxbhx4zhw4ECaX+gREREsX76cAQMGZOtx3n33XZydnTl27Bjt2rWz3mJPSUlh6dKldOzYkaSkJIKDg3nhhRcyPc+FCxfo1KkTR44cAcyL+QULFvDoo48C5kXgjcNHenh4WD8dHzRoEKtXr7ZeXO3fv5+OHTuyfft2ChcunK3ndSujRo3igQceAMyL+I4dO7J+/Xpr2+PExERWrlxJq1atmDNnDgD9+vWjZ8+e6c717rvv4ufnx9WrV2nevDk//vgjsbGxgPnHcO/evbz55pt8++231mMsFgszZszA1dWV06dP07hxYxYsWJAmkJ07d47vvvuO+++/P90Qd6mjDI0bN46ffvrJ2in0xIkT9OjRgx9//DHTJl22NnLkSD788EP69+9/W8eNGDGC4OBgkpKSaN++PZMmTUoTosLDw1m+fDk9e/bMdLSfnHr33XcBM0T26NHDeuEfFxfHjBkz6N+/v3U4xdvh7u5uvXD54YcfePHFF60X5jExMXz88ce8+uqrgBmq69evn+PnkqpFixYULlwYwzDo1q2b9dPU5ORkVqxYQUhIyB3tJ5FbcvL+SP15WrZsGWPHjrU23QsLC2P8+PEMGDDgpkMR16hRA4ClS5dmq5nYnZT63A4cOMDLL79sDULR0dF8/vnnPProo7c9zHJW9e/fn27durFw4cI0HXWjoqKYMWOGdRCBjh07Zni8t7c3Y8eOZcKECdY7BFeuXGHgwIHW38sjRoxIMxpZXv0s+fj4WO+kzZo1K8OO+VLA5N6UBSK5a/PmzdbZYFMXR0dHo2jRooaXl1ea9Z6ensbYsWPTTdB1s0nHDMMw5s2bZ50JF/6dzj71+8DAQOvMwje6cdKbxYsXWycE8vb2NlxdXa3bypQpYxw/fjzd8du3bzcKFy5s3c/V1dXw9PQ0wJx195tvvrFOXpXRZDepx93upGOpEhMTjddee806qVrqa+vr62s4ODhY17m5uRkTJ0686bl27Nhhncws9TxFihSxzloMGJMnT0533MqVK42iRYum+78tVKhQmv/b/07mc/LkSaNEiRLW7U5OToa3t7f1+/Hjx990IqSsvHY5mUjpv5OOZdXNJh0zDMM4fvy4Ubt27TSvjY+PT7qfhQoVKqQ7Nqvvl5tNOmYYaScFBHNm1tT3UMuWLY0hQ4bc9qRjqV577TXreS0WS5pzA0abNm2MiIiI267ZMG4+Odj06dPTPCcPDw/r7wA/Pz9jyZIlmU6wlF9mIDaM7L8/EhISjJYtW6Z77VN/D3Tq1MkYPnx4po999OhR6+vl4OBglChRwggKCjKCgoJuOsFdTp7v7bzuTzzxRLrXxNHR0QCM+vXrW2fszehvxK3es4aR+d+YGycPS31fpc7Qnbq0aNHCOityRo/5+OOPZ/o79Zlnnsl0hvQ7+bOUauzYsWn+fgUGBhpBQUEZzmws+Z/uDEi+1aRJEw4ePMjChQut4197eXkRHh6Og4MDVatW5cknn2TOnDlcuHCB4cOH33LM9v96/PHHOXDgAC+88ALBwcHEx8fj5OREnTp1GDNmDPv377c2j8hMly5d2LRpE4888ghubm4YhkG5cuV4/fXX2b17d4bDntavX5+//vqLbt264efnR0pKCp6ennTr1o1NmzZluT15djk5OTFp0iQOHjzIW2+9Rf369fHx8SEyMpKiRYvStGlTRo0axbFjx245+Uy9evU4dOgQEydOpEmTJnh6ehIdHU1AQAAhISFMmjTJ2j72Rvfffz/Hjh1jwoQJtGjRAm9vb8LCwnBwcKBatWo8++yzLFmyhI8//jjNcUFBQWzfvp1nn33WOuKSm5sbDzzwACtWrGDIkCG590LlI+XKlWP79u188803PPDAA/j7+xMdHU1CQgLlypWja9euzJw50zqE4Z0wbtw4fv31V+655x68vLyIj4+natWqTJw4kVWrVqXrnHk7Jk2axOrVq3nkkUcoUaIEUVFReHp60qZNG2bOnMnvv/9+0yEes+vFF1/kt99+IyQkBA8PD5KSkihdujQDBgxgz5491KxZM9cf807I7vvD2dmZlStXMmrUKCpVqoSzszOGYdCoUSOmT5/OkiVLbjq6TMWKFVmzZg2dO3emWLFiXL16lVOnTnHq1Kl88Ynx3LlzmTJlCrVq1cLV1ZXk5GRq1qzJhAkT2LhxY6ZzU+TUiBEjmDZtGl27dqVKlSo4OTkRFRVF8eLFuf/++5k5cyZr16696R3gH374genTp1O3bl2SkpIoXLgwTZs25ZtvvmHOnDmZ9oPIi5+loUOHMnXqVBo0aICzszNnz57l1KlTBbazub2zGIZ6f4iIiIjYWq9evZgzZw49e/Zk9uzZti5H7ITuDIiIiIiI2CmFARERERERO6UwICIiIiJipxQGRERERETslDoQi4iIiIjYKd0ZEBERERGxU062LsCWUlJSOH/+PJ6engVilkkRERERkawwDIPIyEhKlSqV6bwUYOdh4Pz58wQGBtq6DBERERGRO+LMmTMEBARkut2uw0DqDHxnzpzBy8vLxtWIiIiIiOSOiIgIAgMDbznjtF2HgdSmQV5eXgoDIiIiInLXuVVTeHUgFhERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERyROzZ8/GYrFgsVhYu3Ztuu2GYVChQgUsFgshISHZeozPPvuM2bNnp1u/du1aLBYLCxYsyNZ5b0evXr0oW7bsLffbsGEDffv2pX79+ri6umKxWDh58uQdr+9GCgMiIiIikqc8PT35+uuv061ft24d//zzD56entk+d2ZhID9atWoVf/zxB2XKlKFZs2Y2qUFhQERERETy1OOPP87ChQuJiIhIs/7rr7+madOmlClTxkaV5a0RI0Zw8uRJFi1aRKdOnWxSg8KAiIiIiOSp7t27A/DDDz9Y14WHh7Nw4UL69OmT4TEJCQmMGzeOKlWq4OrqSrFixejduzehoaHWfcqWLcuBAwdYt26dtTnSf5vrJCYmMmzYMEqVKoWXlxf33XcfR44cSfd4M2fOpHbt2ri5ueHr60vXrl05dOhQuv1mz55N5cqVcXV1pWrVqnzzzTdZfh0cHGx/Ke5k6wJERERE5OYMA2JibF3FvwoVAosl+8d7eXnx6KOPMnPmTF544QXADAYODg48/vjjTJkyJc3+KSkpdOnShfXr1/PWW2/RrFkzTp06xahRowgJCWH79u24u7uzaNEiHn30Uby9vfnss88AcHV1TXOuoUOH0rx5c7766isiIiJ4++23efDBBzl06BCOjo4ATJgwgaFDh9K9e3cmTJjA1atXGT16NE2bNmXbtm1UrFgRMINA79696dKlCx999BHh4eGMHj2a+Pj4fHGhnxUKAyIiIiL5XEwMeHjYuop/RUVB4cI5O0efPn1o06YNBw4coHr16sycOZPHHnssw/4CP/74I8uXL2fhwoU8/PDD1vW1a9emYcOGzJ49m379+lG3bl3c3d3x8vKiSZMmGT5utWrV+O6776zfOzo60q1bN7Zt20aTJk0ICwtj7NixdOzYke+//966X0hICBUrVmT06NHMnTuXlJQUhg0bRr169Vi0aBGW/09HLVq0oGLFipQqVSpnL1AeKRiRRURERETuKq1btyY4OJiZM2eyb98+tm3blmkToV9//RUfHx8efPBBkpKSrEudOnUoWbJkhiMTZaZz585pvq9VqxYAp06dAmDz5s3ExsbSq1evNPsFBgZyzz33sGrVKgCOHDnC+fPn6dGjhzUIAAQFBdmsM3B26M6AiIiISD5XqJD5aXx+UahQzs9hsVjo3bs306ZNIy4ujkqVKtGyZcsM97106RJhYWG4uLhkuP3KlStZftyiRYum+T61GVFsbCwAV69eBcDf3z/dsaVKleL3339Ps1/JkiXT7VeyZMk8HyI0uxQGRERERPI5iyXnzXLyo169ejFy5EhmzJjBu+++m+l+fn5+FC1alOXLl2e4PSdDkf5Xali4cOFCum3nz5/Hz88vzX4XL15Mt19G6/IrNRMSEREREZsoXbo0b775Jg8++CA9e/bMdL8HHniAq1evkpycTIMGDdItlStXtu7r6upq/ZQ/O5o2bYq7u3uafgUAZ8+eZfXq1dx7770AVK5cGX9/f3744QcMw7Dud+rUKTZt2pTtx89rujMgIiIiIjYzceLEW+7zxBNPMHfuXDp27MjAgQNp1KgRzs7OnD17ljVr1tClSxe6du0KQM2aNZk3bx7z58+nfPnyuLm5UbNmzSzX4+Pjw4gRIxg6dCjPPPMM3bt35+rVq4wZMwY3NzdGjRoFmMOCjh07lr59+9K1a1eee+45wsLCGD16dIZNhzISGhrKunXrANi3bx8Ay5Yto1ixYhQrVozWrVtnue7sUhgQERERkXzN0dGRJUuWMHXqVL799lsmTJiAk5MTAQEBtG7dOs3F/pgxY7hw4QLPPfcckZGRBAUF3Xb7/SFDhlC8eHGmTZvG/PnzcXd3JyQkhPHjx1uHFQV49tlnAXjvvfd4+OGHKVu2LEOHDmXdunVZ6tR84MABHnvssTTrXnrpJcDsYH07HaOzy2LceF/DzkRERODt7U14eDheXl62LkdEREREJFdk9TpXfQZEREREROyUwoCIiIiIiJ1SGBARERERsVMKAyIiIiIidkphQERERETETikMiIiIiIjYKYUBERERERE7pTAgIiIiImKnFAZEREREROyUwoCIiIiIiJ1SGBARERERsVMKAyIiIiKSJ2bPno3FYsFisbB27dp02w3DoEKFClgsFkJCQrL1GJ999hmzZ89Ot37t2rVYLBYWLFiQrfPejl69elG2bNmb7pOcnMykSZNo3749AQEBFCpUiKpVqzJ48GDCwsLueI2pFAZEREREJE95enry9ddfp1u/bt06/vnnHzw9PbN97szCQH4TGxvL6NGjCQoKYsqUKSxdupTnnnuOL774gubNmxMbG5sndTjlyaOIiIiIiPy/xx9/nLlz5/Lpp5/i5eVlXf/111/TtGlTIiIibFhd3nB3d+fEiRMULVrUui4kJIQyZcrw2GOPsXDhQp566qk7XofuDIiIiIjkc4ZhEJ0QnW8WwzBy9Hy6d+8OwA8//GBdFx4ezsKFC+nTp0+GxyQkJDBu3DiqVKmCq6srxYoVo3fv3oSGhlr3KVu2LAcOHGDdunXW5kj/ba6TmJjIsGHDKFWqFF5eXtx3330cOXIk3ePNnDmT2rVr4+bmhq+vL127duXQoUPp9ps9ezaVK1fG1dWVqlWr8s0332TpNXB0dEwTBFI1atQIgDNnzmTpPDmlOwMiIiIi+VxMYgweEzxsXYZV1JAoCrsUzvbxXl5ePProo8ycOZMXXngBMIOBg4MDjz/+OFOmTEmzf0pKCl26dGH9+vW89dZbNGvWjFOnTjFq1ChCQkLYvn077u7uLFq0iEcffRRvb28+++wzAFxdXdOca+jQoTRv3pyvvvqKiIgI3n77bR588EEOHTqEo6MjABMmTGDo0KF0796dCRMmcPXqVUaPHk3Tpk3Ztm0bFStWBMwg0Lt3b7p06cJHH31EeHg4o0ePJj4+HgeH7H3mvnr1agCqV6+ereNvl8KAiIiIiOS5Pn360KZNGw4cOED16tWZOXMmjz32WIb9BX788UeWL1/OwoULefjhh63ra9euTcOGDZk9ezb9+vWjbt26uLu74+XlRZMmTTJ83GrVqvHdd99Zv3d0dKRbt25s27aNJk2aEBYWxtixY+nYsSPff/+9db+QkBAqVqzI6NGjmTt3LikpKQwbNox69eqxaNEiLBYLAC1atKBixYqUKlXqtl+Tc+fOMXjwYBo0aMADDzxw28dnh8KAiIiISD5XyLkQUUOibF2GVSHnQjk+R+vWrQkODmbmzJn06tWLbdu28dFHH2W476+//oqPjw8PPvggSUlJ1vV16tShZMmSrF27ln79+mXpcTt37pzm+1q1agFw6tQpmjRpwubNm4mNjaVXr15p9gsMDOSee+5h1apVABw5coTz588zaNAgaxAACAoKolmzZpw8eTJL9aS6du0aHTt2xDAM5s+fn+07C7dLYUBEREQkn7NYLDlqlpMfWSwWevfuzbRp04iLi6NSpUq0bNkyw30vXbpEWFgYLi4uGW6/cuVKlh/3v+30U5sRpY7ec/XqVQD8/f3THVuqVCl+//33NPuVLFky3X4lS5a8rTBw/fp17r//fs6dO8fq1aspX758lo/NKYUBEREREbGJXr16MXLkSGbMmMG7776b6X5+fn4ULVqU5cuXZ7g9J0OR/ldqWLhw4UK6befPn8fPzy/NfhcvXky3X0brMnP9+nXuu+8+Tpw4wapVq6x3KvKKRhMSEREREZsoXbo0b775Jg8++CA9e/bMdL8HHniAq1evkpycTIMGDdItlStXtu7r6uqaozH6mzZtiru7e5p+BQBnz55l9erV3HvvvQBUrlwZf39/fvjhhzSjK506dYpNmzZl6bFSg8Dx48dZuXIldevWzXbd2aU7AyIiIiJiMxMnTrzlPk888QRz586lY8eODBw4kEaNGuHs7MzZs2dZs2YNXbp0oWvXrgDUrFmTefPmMX/+fMqXL4+bmxs1a9bMcj0+Pj6MGDGCoUOH8swzz9C9e3euXr3KmDFjcHNzY9SoUQA4ODgwduxY+vbtS9euXXnuuecICwtj9OjRGTYd+q/Y2FjatWvHrl27mDJlCklJSWzZssW6vVixYgQHB2e57uxSGBARERGRfM3R0ZElS5YwdepUvv32WyZMmICTkxMBAQG0bt06zcX+mDFjuHDhAs899xyRkZEEBQXddmfeIUOGULx4caZNm8b8+fNxd3cnJCSE8ePHW4cVBXj22WcBeO+993j44YcpW7YsQ4cOZd26daxdu/amj3Hp0iW2bdsGwMCBA9Nt79mzZ57MpGwxcjprRAEWERGBt7c34eHhaWa/ExEREREpyLJ6nas+AyIiIiIidkphQERERETETikMiIiIiIjYKYUBERERERE7pTAgIiIiImKnFAZEREREROyUwoCIiIiIiJ1SGBARERERsVMKAyIiIiIidkphQERERETETikMiIiIiIjYKYUBERERERE7pTAgIiIiImKnCnQYmDBhAg0bNsTT05PixYvz0EMPceTIEVuXJSIiIiJSIBToMLBu3TpefvlltmzZwu+//05SUhJt27YlOjra1qWJiIiIiOR7FsMwDFsXkVtCQ0MpXrw469ato1WrVrfcPyIiAm9vb8LDw/Hy8sqDCkVERERE7rysXuc65WFNd1x4eDgAvr6+GW6Pj48nPj7e+n1ERESe1CUiIiIikh8V6GZCNzIMg0GDBtGiRQtq1KiR4T4TJkzA29vbugQGBuZxlSIiIiIi+cdd00zo5Zdf5rfffmPDhg0EBARkuE9GdwYCAwPVTEhERERE7ip21UxowIABLFmyhD///DPTIADg6uqKq6trHlYmIiIiIpJ/FegwYBgGAwYMYNGiRaxdu5Zy5crZuiQRERERkQKjQIeBl19+me+//56ff/4ZT09PLl68CIC3tzfu7u42rk5EREREJH8r0H0GLBZLhutnzZpFr169bnm8hhYVERERkbuRXfQZKMA5RkRERETE5u6aoUVFREREROT2KAyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqecbF2AiIhIQWIYBldirnDs2jEiEyJxcnBKs7g7ueNXyA+/Qn44OzrbulwRkZtSGBAREbmFiPgIfv/nd377+zeWHVvGxaiLWTquqHtRgnyCKOlRkuKFi+Pu5E54fDibz2zmcvRlnB2dcXZwxsXRBU9XT6r6VaVm8ZrULFGTmsVrUsG3Ao4Ojnf42YmIPbMYhmHYughbiYiIwNvbm/DwcLy8vGxdjoiI5CNXY67yzZ5v+GbvN+y7tI9kIznN9lKepShWqBjJRjJJKUnWJTohmquxV0kxUnJcg5uTG1X9qtKyTEs6V+5Mm3JtcLCoha+I3FpWr3MVBhQGRETkBjvO72DylsksOLiA+OR46/qKvhXpVLETnSp1omWZlrg6uWZ6juSUZK7HXed85HlOh5/mcvRlLkVdIi4pDk9XTyr6VqRasWqkGCkkpiSSkJzA1Zir7L+8n32X97H/8n4OhB4gJjEmzXn9PfzpU7cPz9d/njLeZe7YayAiBZ/CQBYoDIiICJj9ANafXs/EDRNZdmyZdX3dknV5vv7ztK/QnrI+ZfO0puSUZE6EnWD3xd0sPLSQX478QnRiNAAOFgceqPQA/Rr0o21wW90tEJF0FAayQGFARES2nt3KyLUjWfnPSuu6ThU7Mar1KBqUaoDFYrFhdf8Kjwtn2bFlfLHjC9acXGNd7+niyTO1n6F7je40DWyqYCAigMJAligMiIjYr8vRlxm7biyfbPsEAGcHZ56u9TRP1nqSe8rdY+Pqbu7wlcPM2D6DmbtmEpkQaV3v7epNx4odaVmmJfX869GgVAN1QBaxUwoDWaAwICJifwzDYNrWaby64lXruvvK38f4e8bTsHRD2xWWDXFJcczfP5/v93/P6hOrSUpJSrPd192XtsFtaR/cnnYV2lHSo6SNKhWRvKYwkAUKAyIi9iU6IZrXV77O5zs+B6B44eKMaj2Kfg36YbFYCAuDv/+GAwfg2DH45x84cwbOn4e4OHBwAC8vKFMGAgOhShWoUwcaNABvb5s+NaIToll1YhVrT67l8JXDbDqzifD48DT71C1Zl04VO9G/UX9KeJSwUaUikhcUBrJAYUBExH4kJifS5Osm7LywE4DHq/bgicJfsmFNIbZuhcOH4cqV7J3bYoFq1aBJE2jc2AwH1auDi0suPoHblJicyNZzW1l+bDnLjy1nx4Ud1m1uTm48VfMphrUalucdo0UkbygMZIHCgIiIfThy5Qi9fu7FlrNbcMCBFlFT2fNlf8LD0+9bsiRUrWp+6h8cbN4FKF0a3N3BMODaNTh9Gk6dMu8g7NwJJ06kP4+LC9SsCTVqmOerVs28mxAVZd5puHwZUlLSL2CGi/Bw87FuXDw8oGxZc6lSBerWhUqVzP1v5VLUJRYdXsS0rdM4dOUQYPaT6NegHxPum0Ah50LZfn1FJP9RGMgChQERkbvfodBD1JhegxQjBQfDCWPBDxgHHgUgIADatoWQEPOT/EqVzAvu23XpEmzdClu2wF9/wY4dEBaWq08jU0WKmHckUu9KlC8PpUqBs7O5/DcoJKcks/KflXy0+SNWnVgFQKugVsx9eC4BXgF5U7SI3HEKA1mgMCAicnfbf3k/jb9qTExiDI4RZUn+fhFcrEPnztC/P9x7r9kPILcZhnm3YNcuOHgQDh0ylwsXwNMT/PzMuwSOjubjOziYF+0ODuaxKSng4wO+vv8uRYpARAScPGmee98+2LPH7MuQGS8v885EzZr/LvXrQ+HC5vaFBxfSfWF3ElMSAfh7wN9U8K2Q+y+IiOQ5hYEsUBgQEbl7nY04S4MvGnAp+hJEF4O5S6lYuAGffgr332/r6nJHQgLs3QubN5t3JXbtMpswRUdnfoybG7RrBw8/DA8+CH9dXUH7ue0BCC4SzKGXD+Hs6JxHz0BE7hSFgSxQGBARuTulGCl0/b4bS44thEs1YPY6+j7py9SpUOgubxpvGGYYSEyEc+fMOwipy65d5rpUrq7w1FPw5KA9PPJLG67HXefJmk8yq8ssBQKRAk5hIAsUBkRE7j6Xoy8T8lV7DoXtgmQnXOb+yVejm/L007auzPYMw7yT8NNP5rJ/v7nexwd6f/gjk88+DsA95e5h1TOrbFeoiORYVq9zNWe5iIjcNWISYwieXMUMAoaFouvnsPl/CgKpLBaoXRvGjDHvFGzcCPXqmZ2dJ/ftRiOHFwFYfWI1iw4tsm2xIpInFAZEROSu0fWjCUQlXweg5r6lHPqxB/Xq2biofKxZM3MUpIEDze//GvUphQ1zluJuC7rx99W/bVidiOQFhQERESnwEhMNqg3txcr4cQDUjR7MjnntKVbMxoUVAE5OMGUKTJ0KGA5ET96Gi6UQSSlJ9PipBwnJCbYuUUTuIIUBEREp0K5eS6b44BYccp0DQCvHN9nx3gSc1f/1trzyCowcCUQEkDj7NwC2n99Ox7kdbVuYiNxRCgMiIlJgHf47kaDBDxHmtQmA5t5PsHbYe1makVfSGz0aevUC40QITnufBWDViVV88tcnNq1LRO4chQERESmQNmxKpu6bg4ku/SsAvSq8zYZXf8CiJJBtFgt88QXccw8kLfoCl/MhALyx8g1OXD9h2+JE5I5QGBARkQJn9rwwWs1sTVzdSQCMb/4Zs56caOOq7g7OzrBgAVSt4kDCD3NxjC1JfHI8LWa1wI5HIxe5aykMiIhIgWEYMH5iEr03N8AI3AjAOy0nMvjeF21c2d2lSBFYuRLKFClF8o9zATgfeZ5JmyfZuDIRyW0KAyIiUiAYBgx8LZlhf4eA7z9YDAe2PrudEfe8raZBd0BAAPz+OxSPvgeulwPgjd/f4HT4aRtXJiK5SWFAREQKhPETkvk4IgTKmHcE3m/7Ho0C6tu2qLtcpUqwbBm4fLPFum702tG2K0hEcp3CgIiI5HvTp8PwP9+AoA0AvNLoFd5o9oaNq7IP9erBlHeLw//mA/Dj/oVcibli46pEJLcoDIiISL723Xfw0rSfoekUAF5v+jpT2k+xaU325sUXoWvlR+BibaKTImjwRQPikuJsXZaI5AKFARERybeWLIGeg3dBt0cAeLTqo3zY9kP1EchjFgt89aUj3hs/BuBU+Clm7Zpl46pEJDcoDIiISL60ahU8MvwnUp5pBQ7JFHYuzLQO02xdlt3y9YUPX2kJW/sDMHnzx6QYKTauSkRySmFARETynS1boNOrS0l6+FFwjaK8T3kO9z+Mv6e/rUuza336QK3ItyHJlb+vH6L/0v62LklEckhhQERE8pW9e+GeIVOJf7QTWAzqlazPnn57CPAKsHVpds/BAb74MAA2vgnA9O3T1ZlYpIBTGBARkXzj6FFo/tZ7xIa8CkCH8g+w9Mnf8HDxsG1hYtW4MfQMegciSgEwYOkAG1ckIjmhMCAiIvnCqVMGTV7+nKimgwFoX+5BfntqCSU8Sti4Mvmvse9YcNw8BID/HVzAybCTti1IRLJNYUBERGzu/IUUao7ozfUWLwJwX5mOLH36Z40alE8FBsKAxv3h+D0kG0l8sOlDW5ckItmkMCAiIjZ19apBnUHDiAyeA0CX8t35qcc8BYF8bsgQcN35OgA/7l6CYRg2rkhEskNhQEREbCY8IoXyo+8jtMpEAB4N7sP/eszB09XTxpXJrRQvDq90aQ2JblxJPMOULRr2VaQgUhgQERGbiIuDegM+IMJvNQDtArox/8kvcXZ0tnFlklVD3yiMy8E+AAz+fQjXYq/ZuCIRuV0FOgz8+eefPPjgg5QqVQqLxcLixYttXZKIiGRBXHwyZYa253h5s7Nw6xIPsazPPBwsBfrPkt3x8YG3G4wDIMGIZdnR321bkIjctgL9Wzc6OpratWvzySef2LoUERHJIsOAhoOHEeq9AoAupfqx+oWF6iNQQL09sAjue18B4Nmf+5KQnGDjikTkdjjZuoCc6NChAx06dMjy/vHx8cTHx1u/j4iIuBNliYjITXT5cCL7fd4DIMS3B4uf+8zGFUlOFC4Mr7R4lvciphFvRLHm2CbaVQ6xdVkikkUF+s7A7ZowYQLe3t7WJTAw0NYliYjYlc9XrOGXGHN8+rpOT7C6/3c2rkhyw5iXauF6/h4AHpn/GClGio0rEpGssqswMGTIEMLDw63LmTNnbF2SiIjdOHX1Ii9uMS8Yy4Q9xY6hP6hp0F3C1RXerGOOCBVtXGHnqSM2rkhEssquwoCrqyteXl5pFhERyRstP37U+vX8l4ajHHB3GfVcQ9wvtQKg85wnbVyNiGSVXYUBERGxjQ9+W8AZy0YAXg2YR5OKlW1ckeQ2Jyd4oW5/AC4YuzlzOdLGFYlIVigMiIjIHRUTl8SwPwcBUCS2Ph/0fsTGFcmd8tGzj+EcEwgWg94zptq6HBHJggIdBqKioti9eze7d+8G4MSJE+zevZvTp0/btjAREbFq/f7LJBY6gyXWl12vrMfJoUAPZCc34eAAXQKfA2CVMYKzV8JsW5CI3FKBDgPbt2+nbt261K1bF4BBgwZRt25dRo4caePKREQEYNexC2xP/hqAe0s+QVApdxtXJHfa571fsX49duZfNqxERLKiQIeBkJAQDMNIt8yePdvWpYmICNDr08/AIRnXmHIsf+VjW5cjecC3sDfNC/UG4OsLA4iNNWxckYjcTIEOAyIikn8t/PMge33GAdCn9nM4OuhPjr0Y/4jZVCjZ5yhDvlxj42pE5Gb0m1lERO6I4d/+bP36vUf627ASyWutyjfFFU8APjn9LImJNi5IRDKlMCAiIrlu89ZEDhf+AoB3Gn2Op6unjSuSvPZpp08BSPY8yeQ5x2xcjYhkRmFARERy3WPf9YEiJ3FLKs7r9z1l63LEBvrUfwo3fAAYv/ZDkpNtW4+IZExhQEREctWs37dyzu87APo3GEQh50I2rkhswWKxMPHe9wAIL7KWn36ycUEikiGFARERyVXjF/8PAPekUrz7wGs2rkZs6cl6D4NhAb8jDJu+GUMDC4nkOwoDIiKSa/4+nsAxb7OvwMhmE3FxdLFxRWJLfoX8aFuuEwB/13iG336zcUEiko7CgIiI5JpeM98F10gsKU70anm/rcuRfGBYyJvmF0WPMeyDk7o7IJLPKAyIiEiuiI2Fvy6vBaCWdwglPUratiDJF1oFtaJ60doA7A1+mhUrbFyQiKShMCAiIrninVmbSCr9JwDf9Jhi22IkXxl9z3Dzi6ANDPnwqO4OiOQjCgMiIpIrph19FYDilirULFHNtsVIvvJotUdpVdpsNrY7ajmrV9u4IBGxUhgQEZEcW/XXBWKKbANg1sNfYbFYbFyR5DftKoeYX1RdyDvv2LQUEbmBwoCIiOTYJwt3AeAZV42ONZrbuBrJj56p/QxOFico+yd/ntjEmjW2rkhEQGFARERyKDHRYEnKCwA0KFXfxtVIfhXgFUCPWj3Mb/q0YODrsSQl2bYmEVEYEBGRHPpk0XZSPM4CMOj+HjauRvKzYS2HmV9YDPYl/ML06batR0QUBkREJIe+X70HAEfDjQeqtLdxNZKfVSpaiYGNB5rftB/IsJEJnDlj25pE7J3CgIiIZFt0TArbvYcA0D24v42rkYKgR83/v3vkeZHIct/RowdqLiRiQ062LkBERAqud35cDIWuAPB8qy62LUYKhEalG9GjZg++3/c9lvsHs+Gjp3jhBRe++gru1CBUKSlw8CBs3gynTsG5c3D+PFy/Du7u4OkJvr5QpMi//wYGQt26EBR05+oSyQ8UBkREJNt+3L8QPME3uQotg1rYuhwpIIa3HM73+77HKBQKD/Vm5sy5FC8OEybkzvmTk2H/fli37t/l6tX/3+iQCP67IHAjBOyCwpfB/RoYDhDhCtfcINkVNrjBLB9cDV8CinoTHOBFtWBv6lXzoUpACYJ9gyniVkTD6EqBpzAgIiLZEhmTwEnn3wB4t8lXNq5GCpKqxarSqWInfvv7Nyw152FseIuJE2sTHg5Tp4Kzc9bPZRhw5Ij5qf/+/bBrF2zfDpGR/7+DUxyUWY9T/XV4VNtIlPdWkiyxWT5/PPDP/y8rTwOn/91W2NGbhgH1ubd8G7rX6E6wb3DWCxfJJyyGYb+TgkdERODt7U14eDheXl62LkdEpEAZ98NKRhxth0NMCeLHn8fJUd3QJOtiE2Np/FVj9l3eR3GHKlweuxMS3QkJgf/9D/z8Mj82KQnWr4fFi+GXX+DEif/s4BKFW5W1+Lb8kSvFFpFAVJrNRdyK0LxMc5qUbkKAVwBFCxXFMAzikuKIT44nLimO2MRYQqOu88+565y8GM65q+FciQwnOjkMPC6A1/l0ddUoXoOuVboyoNEAihUultOXSCRHsnqdqzCgMCAiki3V3nyJQx7TqRb7PAcmfm7rcqQA2np2K02+bgLAA8UGsG7wNCIjoWxZ+Phj6NgRHP4/Y16+DCtWwPLl5nLt2r/ncXGBZs2gVL3dnCg5ma0x35FCinW7r7svnSp2okWZFrQo04IqflVwsGQvvEZEmHchFvwcw+I//+aK+2aouhDKrQGHZOt+rzR6hUFNBxHkE5StxxHJKYWBLFAYEBHJHsMA14G1SSy6lxEVF/FOj4dsXZIUUB9t+og3fn8DgOeqDGH1sPH884+5rWRJKFMGTp+GixfTHle0KHTuDO0fjOGa/3y+3vcZ289vt24v5VmKrlW68mTNJ2kS0OSOtO1PSYFNm+Dzz2H+kmskVv4BWr8DHpcBcHF0YWiLoYxoPSLb4UMkuxQGskBhQEQke/btM6g1zwNcYtjb9yg1S1e0dUlSgD3787PM3D0TgDcaDSNlzUi+nOHyb7v//1e3LrRvD/e2i+da0d+Yf/B7fj36K/HJ8YB58d25cmcGNRl0xwJAZs6cgVGjYNZsA6ouxCHkXVKK7wagdona/NrjVwK8AvKsHhGFgSxQGBARyZ4h751kYlw5LIYj8SNicXa8jR6fIv9hGAZtv2vLH8f/AKCcTznebDqEotfbkRjlRSG/KyR5/83uqxvYcWEHm85sIjLh36RQ1qcs/Rr0o0/dPvgVuklngzywdy+8+CJs3mxAva+wPNAfwyEBb1dvpneaTvea3W1an9gPhYEsUBgQEcke79ebEuG1hSDHxpwcvsXW5chdwDAMxq8fz0ebP+J63PVb7u9XyI+etXvSqWInWpdtna+a4aSkwPTpMHgwRDmdwKH3vaR4m72cmwY0ZdUzq3B3drdxlXK3y7MwEBsbi2EYFCpUCIBTp06xaNEiqlWrRtu2bXNy6jtOYUBE5PadvxpJ6am+4JjE23U/YGLnN2xdktxFohOi+eSvT/jp8E9sP7+dFCMFdyd3yniXoWlgUxqXbkyDUg2oW7Iujg6Oti73pk6dgsceg2074+Gxx6HKzwAUdS/Khj4bqOJXxcYVyt0sz8JA27Ztefjhh3nxxRcJCwujSpUqODs7c+XKFSZNmkS/fv1ycvo7SmFAROT2DZvzC+NPdsYxtgRJEy/e+gCRbIpPisdiseDs4FxgJ/eKj4eBA81OxjT6GOf2w0l0iMDTxZM/e/9JnZJ1bF2i3KWyep2b43tqO3fupGXLlgAsWLCAEiVKcOrUKb755humTZuW09OLiEg+s/TwagAq85BtC5G7nquTKy6OLgU2CAC4usKMGeZkapZtA0j8Yh0YFiITImn4ZUPe3/i+rUsUO5fjMBATE4OnpycAK1eu5OGHH8bBwYEmTZpw6tSpHBcoIiL5y/GwowC0KFffxpWIFByvvGJOklYoog58eAHv661ISkni7T/eZsgfQ2xdntixHIeBChUqsHjxYs6cOcOKFSus/QQuX76spjciIneZsDCIKLkUgHvqlbNtMSIFTOfO8Mcf4OVYgvBpa/C91BWAiRsn8ulfn9q4OrFXOQ4DI0eO5I033qBs2bI0btyYpk2bAuZdgrp16+a4QBERyT8+XbbK+nX94LK2K0SkgGra1AwEPt4OXJuxgKLnngKg/7L+9P65t42rE3uU4zDw6KOPcvr0abZv387y5cut6++9914mT56c09OLiEg+8svBFdavg4sE27ASkYKrYUNYswZ8izhw9avZBJx/GYDZu2fz3ob3bFyd2Jsch4HTp09TokQJ6tati4PDv6dr2LChdbhRERG5O/wduRuAp72/KtCdOkVsrU4dWLYMChdy5OwXH+Mb0wSAwasGs/DgQtsWJ3Ylx2GgXLlyhIaGplt/7do1ypVTe1IRkbuFYUB4yjkAmlQpa9tiRO4CjRqZnYqdnS1ce38TpePvBeCpRU+x9exW2xYndiPHYcAwjAw/HYqKisLNzS2npxcRkXzi7FlILnQegKY1/G1cjcjd4b77YO5csFgsnPtwCcUcKhKXFEeLWS04F3HO1uWJHXDK7oGDBg0CzDfviBEj0jQJSk5OZuvWrdSpUyfHBYqISP4wb/1f4B4GQDm/UrYtRuQu8thjZtgeNKgQV97bhOvbZYhPiSVgcgBxw+JwdXK1dYlyF8t2GNi1axdg3hnYt28fLi4u1m0uLi7Url2bN97QFPUiIneLn47Oh/+/Eezt6m3bYkTuMq++Cvv2waxZfjh9/wfxTzQHoOGXDdnz4h710ZE7JtthYM2aNQD07t2badOmWSceS2UYBmfOnMlZdSIikm8ci9wDXvBIoam6MBHJZRYLTJ8OR47Apk3N8D34Fteqvc++y/t4ZvEzfNv1W1uXKHepHPcZ+Oabb4iNjU23Xh2IRUTuLtctxwC4v1pDG1cicndydYWffoKAALj243tUujAcgO/2fseULVNsW5zctXKlA3FG1IFYROTucfGiQbL7RQBa1FHnYZE7pUQJMxC4usLRz9+hPPcB8NqK15i+bbqNq5O7Ua50IB45cqQ6EIuI3MU2bA8Hp3gAgkuUtHE1Ine3hg1hxgzo3dvC8XeW0uCjrmwP/42Xlr7E9bjrDG051NYlyl1EHYhFROSWZu+aA4Bzsg9uTrrrK3Kn9eoF27fDp586c+SdBfi9VZ4r8RcYtnoYQd5BPFnrSVuXKHeJXOlAPHXqVLy8vHKtKBERyV/2X90FRcDBMdnWpYjYjcmT4cABWLvWDZdPDsFzPoA5KZlfIT/aVWhn2wLlrpDjPgOzZs1SEBARuctdijb7CwysPNXGlYjYD2dnc4biunXh6jlvAr6JxL9wAAAdv+/IkiNLbFug3BWyfWfgvw4ePMjp06dJSEhIs75z58659RAiImID585BnNMFAJrW0GRjInnJ2xuWL4eWLeHoUQ+CZ++n+cCubDy/hq7zuzK1/VT6N+pv6zKlAMtxGDh+/Dhdu3Zl3759WCwW6+hCqWNQJyfrlrKISEG2dSvgaYaBcn7qPCyS14oXh99/h1at4J8D3jh+/DMPvtmHX44vYMCyAZy4foIP2n6AgyXHDT7EDuX4XTNw4EDKlSvHpUuXKFSoEAcOHODPP/+kQYMGrF27NhdKFBERW/p56x4oHIrFcKB8kfK2LkfELpUpA6tXm3MQHN3nyfH3f2RY4/EATNoyiUd/fJSohCgbVykFUY7DwObNm3nnnXcoVqwYDg4OODg40KJFCyZMmMArr7ySGzWKiIgN/Ro9AoDizsF4unreYm8RuVPKlzcDgb8/HNhv4X8DhzC5xVxcHF1YdHgRTb9uyrFrx2xdphQwOQ4DycnJeHh4AODn58f58+cBCAoK4siRIzk9vYiI2FBiIlxPNDsPdwxWHzARW6tYEdasMe8UHD0KE5/swedN1uLv4c/+y/tp+GVDlh9bbusypQDJcRioUaMGe/fuBaBx48a8//77bNy4kXfeeYfy5XU7WUSkINu7FwzPswC81LKHjasREYDKlWHzZqhVCy5dgv4PNWV0qe00DWhKWFwYHeZ2YNCKQcQlxdm6VCkAchwGhg8fTkpKCgDjxo3j1KlTtGzZkqVLlzJt2rQcFygiIrazYVMieJh3Bsr4BNi4GhFJVaoU/Pkn3H8/REfDCz1K0eKfNbxYvx8Ak7dMpv4X9dlweoONK5X8zmKkDv+Ti65du0aRIkWsIwrlVxEREXh7exMeHq65EkREMnD/c2v5I6ANrngRM/K6RisRyWeSkmDIEPjwQ/P7Dh2g17tLGbj6WS5GmUG+e43uvH//+wR4KdDbk6xe52b7t/qxY5l3UPH19c33QUBERG7tz0IDAajiVV9BQCQfcnKCDz6AuXPB3R2WLYNh3TrybdP9vFD/BSxY+GH/D1T+pDJj140lMj7S1iVLPpPt3+yVKlUiMDCQZ555hlmzZnHy5MlcLEtERGzt8mVIcLoKwKO1O9m4GhG5mR49YONGs2PxsWPQvlVRfDfNYHOvHbQo04KYxBhGrh1J+WnlmbR5ErGJsbYuWfKJbDcTWr9+PevWrWPt2rVs3ryZuLg4ypQpwz333EObNm1o06YNpUuXzu16c5WaCYmIZG7xkiS67nAFhxTODzqPv6e/rUsSkVu4cgUGDIB588zvq1aFjz82uFLiR0asGcHf1/4GoJRnKYa2GMoztZ/RkMF3qaxe5+ZKn4HExEQ2b97M2rVrWbt2LVu2bCE+Pp4KFSrk6+FFFQZERDI3YNhZPnEJxGI4kjgyHkcHR1uXJCJZtGgR9OtnjjYE0LUrvDshic0x3zBm3RhOh58GwMPFg+41uvN8/eep719fzbzvInkaBlLFxsayYcMGVqxYwZdffklUVBTJycm5dfpcpzAgIpK52g+vZG/tdhR3rMCl4X/buhwRuU3XrsGoUTB9OiQng6Mj9OwJbw6JZ9W1r5j21zSOXj1q3b9OyTo8X+95etTsgbebtw0rl9xwxzsQA8TFxbF69WpGjBhBy5YtKVKkCK+88gpRUVFMnz6d06dP5+T0IiJiIwkJcDBiCwANSzeycTUikh2+vvDxx7BnD3TsaAaCmTOhZlVXtn32MnObHGZdr3U8WfNJXB1d2X1xNy8tfYmSH5Xkge8f4PPtn3M24qytn4bcYdm+M9C6dWu2bdtGcHAwrVq1onXr1rRu3ZoSJUrkdo13jO4MiIhkbNMmg+a/m58XTW0/jVcaD7BxRSKSU1u2mHcKVq78d13jxvDyy9Cm01UWHP2WL3d+ycHQg2mOa1iqIQ9XfZjHqj1GsG9wHlct2XXHmwk5Ozvj7+/PQw89REhICK1atcLPzy/bBduCwoCISMaenbCCmQntAdj23DYalGpg44pEJLds2WLeMfjf/yAx0Vzn4wNPPQXPPmvg6L+fX4/+yi9Hf2HL2S0Y/Hup2KFCBwY2Hkjb4LbqX5DP3fEwEB0dzfr161m7di1r1qxh9+7dVKpUidatWxMSEkLr1q0pVqxYtp9AXlAYEBHJWIUXh/CP/0Tc8CJmZJj+6IvchS5dgi+/NJcbW3bXqwcPPmg2LQqscoklRxez4NACVh1fZQ0GDUo14L373uOecvfYqHq5lTzvQBwZGcmGDRtYs2YNa9euZc+ePVSsWJH9+/fnxunvCIUBEZH0UlLAtfeDJJX/lbeqf8Z7j/azdUkicgclJ8OqVfDVV7B48b93CwCKFoU2beCeeyC44TGWXvmUr3d9RVRCFABtg9vy/n3vU7tkbdsUL5nKkw7ENypcuDC+vr74+vpSpEgRnJycOHToUG6dXkRE8sj+/ZBUyPyYsFXNcjauRkTuNEdHaNsWfvwRzp2Dr7+GRx4BT0+4ehUWLICXXoJ2DSuwoO9kHjn7Dw+XHoCzgzMr/1lJvS/qMWjFIM1uXEBl+85ASkoK27dvtzYT2rhxI9HR0ZQuXdo66VibNm0ICgrK7Zpzje4MiIik9+mn0P9MEXAP48BLB6hWrJqtSxIRG0hMhG3bYPVqc9m40RxpLFXJqsfx7TaYg5b/AVDaszTTOkyja5WualqYD9zxZkJeXl5ER0fj7+9PSEgIISEhtGnThuDggtPLXGFARCS9zk9e4JdKpbDgQMSQcDxcPGxdkojkA7Gx8OefMH8+/PQThIeb6x0qrsD90ZeIdj0OQKeKnfi4w8eUK6I7i7Z0x8PA559/Tps2bahUqVK2i7Q1hQERkbQMA3xbzSPsvu5U8qjHkdd32LokEcmH4uLg55/hs8/MgIBTLLQcj6XlexgOibg7uTOy9UgGNR2Ei6OLrcu1S3e8z8ALL7xQoIOAiIikd/AghPmsBaB9lRCb1iIi+ZebGzz+OKxbB/v2wbM93XFaPxbjs71wIoTYpFiGrBpC3c/rsvbkWluXKzeRax2IRUSk4Pv9d6DsWgDurdDaprWISMFQo4Y5EtGxY9DvsSo4/7AafvoGootxMPQgbea0of137dl5YaetS5UMKAyIiIjVL2svgN8RLFhoWaalrcsRkQIkKMhsNnT8HwuvhDyN65eH4a+XINmJFf+soP4X9Xn0x8c4fOWwrUuVGygMiIgIAPHxsM5tEACVvGtTxL2IjSsSkYIoIACmToUTB315o9qnuM88DHufBMPCwkMLqPZpdXovfpbT4advfTK54xQGREQEgPUbE0ku/yug/gIiknP+/vDBB3B6dzDDq32Hx7d74MiDGKQwe89MgqdU5OVfX+FC5AVbl2rXchQGoqKi2LlzJ2FhYblUjoiI2Mq3q3aCqzmr6IR7x9u4GhG5W/j5wdixcHZnTd6tsQSv/22Ek61JIoHPdnxM0KRgBi17iysxV2xdql26rTDw/PPPW7/+888/qVGjBsOHD6du3br88ssvuV6ciIjknT8PHAGgqts9uDu727gaEbnbeHvD0KFwfmszPqq5Bt9ff4czTUgklsl/fUDpD8rx1vIRhMWF2bpUu3JbYWD79u3Wr4cPH85vv/3G0qVLWb9+PaNGjcr14kREJG+cPw8nS30AQP1yBWfySBEpeAoXhkGDLJxbfx+f1ttEsd9/hQt1SSCKD7aOw39iOYYuf5fI+Ehbl2oXst1MKDY2lurVqwMQEBBANucuExGRfGD+kutQYj8ADcpUt3E1ImIP3NzgpZcsnFvTiZmNd1Bq/QK4XJ04SxgTtg6n2PggXvzpLU5cP2HrUu9qtxUG9u7dS/HixSlWrBj79u3j4sWLACQkJJCcnHxHChQRkTtv7sa11q+fr/985juKiOQyZ2fo3dvC6RWPML/NHsrumAtXKxLvcJ3P931A+anBtPj0QX4+vIT4pHhbl3vXua0wkJSUxOXLlwkNDSUuLo6SJUsCEBMTw+eff35HChQRkTsrJgb2nDsEwIOBz6i/gIjYhKMjdHvUkeM/92Bpp0NU3/szHGsLFoONV37lofldKDK+OE8ueIZv93zLqbBTti75ruCUGyfx8fHBxcUlN04lIiJ5bPVqSPL6G4AGwRVsXI2I2DuLBTq0c6RDu87s3t2ZcdOPsPjsDJKr/Eis13m+P/At3x/4FoBAr0DalGtDm7LmEuQTZOPqCx6LkUuN/cuUKcPp0wVr8oiIiAi8vb0JDw/Hy8vL1uWIiNjEsy/EMrNUIQD+99j/eLTaozauSEQkrStX4IsvU5j60yYuF/kZgv4E/53gmJRmvyDvIJoFNqNZYDOaBDShql9VCrsUtlHVtpXV69zbCgPdunXLcL1hGCxbtoyoqKjbr9SGFAZExN6lpIDPg+OJbDQMCxauvHUFX3dfW5clIpKh5GRYuRK+/BKWLI8m2X8TlFsDZddA6W3gkL4PaxnvMlQqWokShUvg6+6Lr7svbk5uuDq64uLogquT+a+LowsOFgcsWHCwOKRbLJZM1mNJ95gWy7/rGpZqaJPml1m9zr2tZkJ//PEH3377LR4eHmnWG4bBn3/+mb1Kc8Fnn33GBx98wIULF6hevTpTpkyhZcuWNqtHRKSg2LoVIv3NWYcfqNhFQUBE8jVHR+jQwVyuXSvMb7/dz88/38/yeRCdGAkBWyFwE45lN+EQsI1E52ucDj/N6XDbtV45NuAYwb75d8jm2woDISEheHh40Lp163Tb6tatm2tF3Y758+fz6quv8tlnn9G8eXM+//xzOnTowMGDBylTpoxNahIRKSh+/J8BJfYC8N79mnVYRAoOX194+mlziYuD1as9+fnn+1iy5D4uroNkgEJXoOgRKPo3Dh5XKFL6Gl7Fr1PYKw7Xwgm4uMfj5JaAo0s8OCRgYJBipFj/TV0M4z/f///25JT0dyIM0ja6cXZ0zpsXJJtyrc+ArTRu3Jh69eoxffp067qqVavy0EMPMWHChJsea+tmQjt2QGIiODikXyyWjNenLk5OUKQIFCqU52WLyF0iJQWKt/6Jq/c9ggOOxA6PwcVRg0GISMGWkgK7dsG2bbB9O+zcCYcPQ2zszY9zcgIvL/DwAE9Pc0n9+r/rChUCV1dzcXFJ+2/q146O5vVcjRrmcXntjjQTym8SEhLYsWMHgwcPTrO+bdu2bNq0Kd3+8fHxxMf/Oz5tREQEALt3707X9CkvdOoEly7l7BwuLuDjYy4lSoC/P5QsaS4BAVC2rDnTn4jIf+3dC1c9JsF5KOZRjP179tu6JBGRXGGxQKNG5gJmQLh0CU6cMJcLF+DiRXPdpUtw9SokJcG1a+aSm2bPhpo1c/ecWZHVvrw5CgNnz55l+vTpbNq0iYsXL2KxWChRogTNmjXjxRdfJDAwMCenv6UrV66QnJxMiRIl0qwvUaKEdUK0G02YMIExY8akW59Rs6eCIiEBLl82l6NHbV2NiBRUl7hI/Un1bV2GiMhdp1cvW1dwc9kOAxs2bKBDhw4EBgbStm1b2rZti2EYXL58mcWLF/Pxxx+zbNkymjdvnpv1ZujGHttgdmj+7zqAIUOGMGjQIOv3ERERBAYGsm7dOpvcGTgUak7yk9pr3WKxWHuqW9dhSdN7PfV7V0c3HJM8iYhwIDzcTLGXLpkpNzXtnj5tJt3MlCkDtWtDrVpQp455F8HhtqahE5GCKjkZ2nc7w7W2D+FkcWZT3404OjjauiwREcklUVFRWfrAO9th4LXXXqNv375Mnjw50+2vvvoq27Zty+5D3JKfnx+Ojo7p7gJcvnw53d0CAFdXV1xdXdOtr1Onjk36DLR4twWxSbdowHYTDhYHfN19KepeFD8PP0qWLElgy0CqeQVQwbcC5YqUo6ilIif/dufgQdIsZ8+aYeH0afjlF/N8RYpA06bQvDm0awd16yociNyt/vgDrpVcB6WgWvFqNGzQ0NYliYhILkptDn8r2e5A7O7uzu7du6lcuXKG2w8fPkzdunWJvVVvjRxq3Lgx9evX57PPPrOuq1atGl26dMn3HYgrf1KZ2MTYDHun/3e5sRd7spFMUkrSrR8A865DuSLlqOpXlap+ValZoib1/etTwrEqf211YONG2LTJHF7wv/9VJUqYQ3c98oj5r6M+NBS5azzRK5z55XwAeL3p63zY9kPbFiQiIrnqjncg9vf3Z9OmTZmGgc2bN+Pv75/d02fZoEGDePrpp2nQoAFNmzbliy++4PTp07z44ot3/LFz6kj/I9k+NiE5gWux17gSc4WrMVcJjQnlQuQFToef5kzEGY5dO8bx68e5Hned49ePc/z6cX77+zfr8Z4unjQNbEqz+5sx7Nnm1CvemOOHPdm4EdauNT81vHTJ7PQye7bZGblvX+jXD4oXz/FTFxEbioqCRQcXQznz+wGNBti0HhERsZ1sh4E33niDF198kR07dnD//fdTokQJLBYLFy9e5Pfff+err75iypQpuVhqxh5//HGuXr3KO++8w4ULF6hRowZLly4lKCjojj+2Lbk4ulDSoyQlPUpmuo9hGITGhHIo9BAHQw9y6Mohdl7Yye6Lu4lMiGTlPytZ+c9KABwtjjQo1YB7a9zL61078H3xpmze5MiSJfDdd2azotGjYeJE6NMH3ngDypXLoycrIrnqp58godI8AMaEvEOQz939+1JERDKXo3kG5s+fz+TJk9mxYwfJyeakC46OjtSvX59BgwbRrVu3XCv0TrB1MyFbSUxO5EDoATae3sjGMxvZcHoDZyLOpNnH192XByo9QLdq3WhVui2//OzMlCnmmL1gNhnq1g3eftvshCwiBUerdldZ36QEOCRzpP8RKhWtZOuSREQkl2X1OjdXJh1LTEzkypUrgNmp19k5f8+0lspew0BGToefZvWJ1az4ZwXLjy0nLC7Muq20Z2lebvgyLzbox+4tPkycCCtX/ntshw5mKGjVyhzXV0Tyr5Mnodz9K+Cp9pT3qsQ/r2W/uaKIiORfWb3OzZWxYpydnfH398ff398aBM6cOUOfPn1y4/SSB8p4l6FXnV788MgPXH7jMmt6rmFg44EUL1ycc5HnGLp6KGWmBLLRcRw//hzOzp3w+OPmaEPLlkFICDRrBosXmxN7iEj+9MUXwEM9AWgQWMemtYiIiO3dsYEjr127xpw5c+7U6eUOcnZ0JqRsCFPaT+HkwJN88cAXVCpaiaiEKEasGUHZqWVZHDaKz2Zd4+hRePFFc+rtLVuga1dzlr1Vq2z9LETkvxIS4PP/HQMPc+rzThU72bgiERGxtWw3E1qyZMlNtx8/fpzXX3/d2pcgP1IzoaxLMVKYt38e4/4cx6Er5mRpni6evNzwZQY1HURKVDGmToXPPoPwcPOYp56Cjz8GHx/b1S0i/5o/H56YOAse6kN9/wZsf/7OzQMjIiK2dcf7DDg4mDPm3uxwi8WiMHCXSU5J5qdDPzFu/Tj2XtoLgJODE4OaDGJYq2GkxHoxciR8+qnZXKhMGXM0opYtbVy4iBASAusKvwyNPuPVxq8yuX3Gk0aKiEjBd8f7DPj7+7Nw4UJSUlIyXHbu3JndU0s+5ujgyGPVH2PXC7tY/PhiapeoTVJKEu9veh//j/yZfXgKU6amsHEjlC9vznAcEgITJkDOu6qLSHYdPAjrjuyGRuYEjc3LNLdtQSIiki9kOwzUr1//phf8t7prIAWbg8WBLlW6sPOFnXzS4RO8XL2ISYzhtRWvETwtmNgSa9i9G3r2NO8QDB0KvXtDfLytKxexT5MnA1UWWb9/oNIDtitGRETyjWyHgTfffJNmzZplur1ChQqsWbMmu6eXAsLB4sDLjV7m3KBzvN38bSxYOBl2knu+uYdBa55j5qwUPvvMnJdgzhx49FGzE6Pc2okTsHQpzJ0LO3dCUpKtK5KC6uJF+OYbIGALANPaT8PNyc22RYmISL6QK/MMFFTqM5D7/rn2D/d9ex8nw04CEOgVyPre6zm0JYiuXSEuDh55BObNA6dsz399dzt8GAYMgD/+SLu+cGFo2tScz6F1a2jUCNx0PSdZMHQoTPh2C/RtCsChlw9Rxa+KjasSEZE7KU/nGRBJFewbzPFXjvNyw5cBOBNxhrJTy5JY7hcWLwYXF1i40GwypPkI0jIMmDgRatUyg4Cjozm7c6tW4O0N0dHm+pEjzTDg4wPt25vzPNhvpJdbiYiA6dOBrs8A0CqolYKAiIhYKQxIrrNYLHzS8RNWPLXCuq7zvM4c9p7KggXmHYHvvoOXXtJFbKozZ6BzZxgyBBIT4YEH4OhR2L0b1q2Da9dg71745BPo1g1KlDD7X6xYAR07QsOG5v4i//X++xAWdx2K/g3AiFYjbFyRiIjkJ2ompGZCd9Tp8NO0mtWKU+GnAJh470SCzr5Njx5mEHj9dfjgA7BYbFyojSQkwJQpMGYMxMSYd04++QSee+7mxxmG2Zzoyy/hq68gMhI8PMzZZbt3z5PSpQA4exYqVYLYB7pB9f9R0bciRwcoNYqI2AM1E5J8oYx3Gf4e8DddKncBYPCqwZwr8xFffWVu/+gjeOcdGxZoQ8ePm30A3n7bDAItWsCOHbcOAmCGp6pVYdIkMxSEhEBUFPToAc8/D7Gxd7x8KQBGjIBYIwyqLQCgT90+ti1IRETyHd0Z0J2BPJGUkkTtGbU5GHoQgHfveRePXUMZONDc/uGH5l0Ce/Hrr+YMzeHh4OtrXtQ/88y/d0iiEqJY+vdSdpzfwbHrx/Bz96N68epUL1adBqUa4O3mneZ8yclmqBo71rxrULMmLF5szvUg9mnPHqhbF4yKv0KPBwFIGpGEo4OjjSsTEZG8cMdnIL4bKAzkrcTkRJp83YSdF3bi7ODM4f6H+eGz8gwfbm6fMQNeeMG2NeaFhQvh8cfNC/hmzcyRlQIDzW37L+/nw00fMnffXJJSMh5L1N3JnU6VOvFWs7doWLphmm2rVsGTT8KlS+bsz+vXm/+K/WnXDlaujcRxcAmSHWLp37A/H3f82NZliYhIHlEYyAKFgbx3NeYqfh/4AVDBtwLre21g6vgSTJxofio+ffrdHQjWrYO2bc2+Ak8/DV9/Dc7OcDbiLOPXj+fzHZ+TYpjDLJUvUp72we2pWLQiodGhHAg9wJ5Le6zDtgK0DmrNwm4LKVqoqHXd+fPQpo3ZoTg4GP78E0qVyutnKra0YoU50pRDx1dJaTQVB4sDfw/4m/JFdKtIRMReKAxkgcKAbZyPPE/zmc05GXYSHzcf/uy1ni/G1eCTT8zt/fqZnWpdXGxaZq47dMi8ExAWZs61MH8+xKfE8MWOLxixZgRRCVEAVPGrwvRO02kd1BrLf3pWG4bBpjOb+GLnF3yz5xvAnMvhq85f0Ta4rXW/s2fNIUlPnIAqVcwQUrx4nj1VsaHkZLN50L6D8TDCnIhiaIuhvHvvuzauTERE8pLCQBYoDNjOsWvHaPZ1M0JjQinlWYo9L+zl8ylFGTHCbPPevDksWAAlS9q60txx6RI0aQInT5qdhletgoV/f8ezS54lIdmckrmMdxkmt5tM1ypd04WAjCz7exk9F/ckNCYUR4sjM7vM5OlaT1uPPXHCDARnz5pzF6xdC0WK3MEnKfnCrFnQpw+4dhpCfMOJuDm5EfZ2GK5OrrYuTURE8pBGE5J8rYJvBVb3XI23qzfnI89T+/NadO67j19+AS8v2LgRGjSATZtsXWnORUfDgw+aQSA4GGbPv0bHH9vw9KKnSUhOwNvVm2ntp3FswDEervpwloIAQIeKHdjXbx/Vi1Un2Uim5+KePLXoKVLzfblyZugoWdKco+DBB81Ri+TuFROD2QfH4wLxDScC0C64nYKAiIhkSmFAbKZG8Rqs6bmGQs6FOB95nlozalGl6T9s22YOm3nunPnJ9oQJBXe24uRkc9SgbdugaFFY+Es0LeZVYe3JtQA8X+95Lr95mQGNB+Ds6Hzb5y/hUYItfbfwbN1nAfh+3/f0+rmXdXulSmb7cW9vM2A9/jgkZdwvWe4Co0ebfUY8Og+3rpvxwAzbFSQiIvmewoDYVF3/uvzU7Sfr923mtCHGazdbt5qj4iQnw9ChcP/95iy9Bc3bb5tDfLq6wtc/XqD90gqExoQCsOqZVXz+4Oe4OOasc4SHiwdfPvglT9d6GoBv9nzDp399ar1DUKsW/PILuLmZQ5o+95xmfr4bbdtmztuB+zWiKs0EYPHjiynpcZe0tRMRkTtCYUBsrl2Fdhx6+RD+Hv6ciThDwy8b8tme95jzTQpffw2FCsHq1eZF7bffFpwL2c8++/+LM2DiF8d4YnN5LkZdBGB97/XcU+6eXHssi8XCrC6zCCkbAkD/Zf0Z9+c46/aWLc0Oy46OMHs2vPVWwXkd5daioszRqVJSoGR/cwrqasWq0aVKFxtXJiIi+Z3CgOQLVfyqcOClA7QNbktSShKDVw2m9exWdOx2kV27oFEjcxSeZ56Be+81R+bJz377DQYMML8eMu4Sr52oSFxSHABbnt1CizItcv0xHR0c+ePpP3ix/osAjFw7kl6Le1nvEHTuDF98Ye774YfmHRcFgoLPMMzheI8cgeJVjnGx8EoAHqr8kG0LExGRAkFhQPKNIu5FWNpjKZ92/JRCzoXYeGYjdT+vy1nn1WzYAOPGmU1d1qyB2rVh2DCIjbV11ent3Gm2zU9JgZ59Elju18G6bU3PNTQOaHzHHtvRwZHPOn1G9xrmp8Nz9sxh5JqR1u19+sDUqebXEyfCm28W3P4YYvr8c/j+e/OuT9BLLwPgV8iPd9q8Y+PKRESkIFAYkHzF0cGRlxq+xPre66lUtBIXoy5y3zf38fofr9D/9XAOHoROnSAxEcaPN5sOrV5t66r/dfSoOdlTdLR5ByPoyYnsurgLgC8f/NLajOdOslgsfP/I93Sr3g2AcevH8e6f/44x/8or/waCjz6CBx4wO51KwbNpEwwcaH799vjTbLtm3hWY3WU2jg6ONqxMREQKCoUByZfq+ddj1wu76Fu3LwYGH//1McHTgjmQ+Cu//AI//WTOqnvsmHnR3bcvREbatuazZ82OzqGh5qRPj437lnfWjwJgSIsh9K3XN0/r+f7h73my5pMADF8znEmbJ1m3vfKK2f/C3R2WLTOHPH3rLbh2LU9LlBzYsQM6dDBns36wczKzHJsAULdkXTpV6mTj6kREpKBQGJB8q5BzIb7s/CU/dfuJIO8grsZe5cEfHqTvkme5r2MkBw/CSy+BxQJff202HdqwwTa1XrkCbdvC6dNQsSI8OelLXlzxDABvNH2D8feOz/OaHB0c+bbrtzxR4wkAXl/5unXWYjCHPN282ZzgLS4OPvjAnJtg3DizQ6rkX/v2me+3iAhz+N06A8ZzIeoCAFPbT7VxdSIiUpAoDEi+17VqV3a9sIt2we0AmLl7JkFTglh0YjYff5LC2rUQFPTvjLuDB0N8fN7VFxkJHTuanZpLl4ZnPp7OG+ueB+CJGk/w3v3v5V0x/2GxWPjhkR94pdErAPRc3JMpW6ZYt9euDevXm0OO1qplXlyOGAHly5udjKOjbVS4ZOrIEfMO1LVrZsf6wdPXMXaj2S/k5YYv0zKopY0rFBGRgsRiGPY7nkhWp2mW/OO3o7/x8tKXORV+CoDGpRsztf1Uqno1ZuBAc9hMMC9yv/sOatS4s/XEx5t9GFatAl9f+GTxFp5e04JkI5n2Fdrz8xM/53gegdyQYqTw5E9PMm//PACerfssnz/weZp25Skp5vCjI0bAP/+Y64oXN+dKePFFc4hXsa0TJ8xhYs+dgzp14NcVMbRb0IgDoQeoVqwaW57dgqerp63LFBGRfCCr17m6MyAFSqdKnTg64Cjj2ozDw8WDree20uTrJgxY3ZN3p53np5/Azw/27IH69c1Pt5OT70wtSUnQo4cZBDw84K1ZP9FjdVOSjWQq+lZk8eOL80UQAHCwOPBt12/pXac3AF/v+ppuC7qRmJz47z4O0L07HD4MM2eaTYYuX4bXXzf7FEyaZF6M2u/HB7Z19izcc48ZBKpWhZUrYdKuERwIPYAFC98//L2CgIiI3DbdGdCdgQLrQuQFhq4eyuzdswEo7FyYYS2H0b3cawzo58avv5r7Va4MI0eaw3065tIAKykp8PzzZl8FFxeY+uNO+u2uD0AZ7zL81fcvSniUyJ0Hy0WGYfDu+ncZsWYEAD5uPux6YRdlfcqm2zcxEebMMfsQnDr173onJ/DyMhdPT/D2Nhd/f7O/RIUK5r/BwbqbkFsOHTLvQJ04Yb6u69fDhmv/o9sCc8Sofg368Vmnz2xcpYiI5CdZvc5VGFAYKPC2ndvGK8tfYcvZLQCU8ynHh20/4sr6hxgyxGIdIadqVRg1Ch59NGehICEBXn4ZvvrK/DR90ncHeOdMK67Fmg90pP8RKhWtlNOndccYhsHQVUOZuHGidd3PT/xM58qdM9w/IcEMBV99Bbt2mSEhq+rWNTu6tmtndlR2yR83SgqUFSvMIBsebvblWL0aThrrCJkTAsD95e9n+VPLcbDoRq+IiPxLYSALFAbuHoZh8P2+73nrj7c4H2kOmt+mbBtGN/uQP+fXZdIkC9evm/tWrGg2fXnmGXNozdtx8CA8+STs3m0GgTc/W82ky+1JTEnE3cmdbc9to3rx6rn75O6QrWe30m1BN06Hnwbg4aoP813X73B3zvxFSUw0mw6Fh5sdpyMizK/DwuDMGXOo17//NpewsLTHenqaw8CGhECLFmYzpCJFzNGgciouznzsY8fMuxgnT/67JCX9e/fC1xfKljUvqlOXUqVy745RboqPNyfW++gj8/tmzWDxYjiduIMOczsQGhNK88Dm/NbjN7zdvG1aq4iI5D8KA1mgMHD3iUqI4r0N7/HBpg+ITzaHFGpcujED6g7mn9+6MGXKv6EgIMCcuOzJJ80L+5sxDPj0U3PG3rg4KFoUBk79gzH/tCfZSMbB4sChlw/l6zsCGbkQeYGBywfyv4P/A8ymVgu7LaRdhXY5Oq9hwMWLZn+KlSvNT7cvX06/n7u7+f9QqpR518DV1WxuVKrUv/+mfl28uNn/48IF2L8f9u79dzl6NPt9Q1xczNGobgwI5cubYaV8eTNE5LVff4VXX/23I3e/fmafjfOxx6n0cSWSjWS8Xb3Z9cIuyhUpl/cFiohIvqcwkAUKA3evk2EnGbJqCAsPLiQxxWzXUsqzFAMbvAU7+/LJpMKcOWPuW7u2+QnsQw+Bs3Pa8xgG/PEHDB0K27eb69q2M2gxaBqjNr+GgUGAVwB/PP0Hlf0q590TzEUpRgofb/2Y4WuGE5VgTjDQKqgVn3T4hJolaubOY6SYk2StWgXr1pmv5ZUrt3cOi+XmnZe9vc3+CuXKmZ/+ly1rXuS7uZl3L8LDzQnhTp6E48fNJfXOwc2k3k3w94eSJf/9t3Rpsz9KcHDuNX/atQuGD4elS83v/f1h+nTo0gXOR56n3NRyJCQnEFwkmDU91xDoHZg7DywiIncdhYEsUBi4+12Musj49eOZsX2GNRSUKFyCt5oMI3bD87w/wZWICHPfEiWgTRvzwi86Gq5fh7/+Mj91BihcGIa+e5m9pV9h/oH5gNlee96j8/B197XBs8tdYXFh9Frci5+P/GxdV7tEbXrX6U2vOr1yvSlKXJw5Ms7Zs+an/cnJ5ut+4YK5nD//778XL5qBAszAVrWqOS9CzZr//luq1O03OUpONh//xIl/A8KNS2jorc/h6GiGgtq1zaVSpX/vLnhmYXCfyEj4+Wf45hv4/fd/n+Nrr5nBwNMTrsRc4YkFT7DqxCqcHZz5s/efNAlocntPVkRE7IrCQBYoDNiP67HXmbZ1GrP3zOZk2EkAAr0C6V/nba6ueZrZn3tl2IwFzE99n38pliLtP+bL/ZO5GHURgKdrPc3Xnb/G2dE54wMLqBPXT/DG72+w6NAiDMxfDx4uHjxR/Ql61elF7ZK18XDxyNOakpPNOwmuruYoRrdq1pVboqLMoHDqlBlILlww/7140Vx35MjNZ2suVsy8c5AaDoKDzQv98HAzbOzcac4CHRdn7u/gAE88YY5+Vfn/bzSduH6C4GnBGBg4WhxZ03ONJhYTEZFbUhjIAoUB+5OQnMDMXTMZ++dYa0djZwdnOld6iKo8QsTh+kRfL4xD4eukFLqEo/8+LrquZ/3ZVVyPMzsbBHgFMLvLbO4tf68tn8oddzr8NN/v+57v9n7HgdADabaV9ChJPf961ChWg1ZBrWhUuhHFChezUaW2Yxjm3Y29e825LfbtMzsxHz8OV69m/TyVK5sh4JlnzNCQ6q9zf/HQvIe4EHUBgJ+6/UTXql1z+VmIiMjdSGEgCxQG7FdsYixf7PiCqVunciLsRJaO8ffw563mb9GvQT9cnVzvcIX5h2EY/HnqT2btnsXCQwut/Qr+K9ArkHr+9ahdoja1StSirn9dgryD0sxybE9SP/0/ftzsCJz6dUqK2b+hVClz6NVGjaB69bRNnJJSkvhh3w+8vvJ1QmPMtkpLeyylQ8UONno2IiJS0CgMZIHCgKQYKaz8ZyWLDy9m2/ltHAo9RHxyPD5uPhR1L0rVYlWpXaI295a7lxZlWtjthW0qwzCIiI/gQOgB/jr3F/sv72fdqXUcu3Ysw/193HyoXaI21YtVp3FAYzpU6GCXdxBuR3hcOB2/78imM5sA8y7M2p5rC2wHdRERsQ2FgSxQGJD/MgwDA0MTON2m67HX2X95P3+d+4udF3dy5MoR9lzaQ1JK+qF6KvpWpHmZ5jQLaEazwGZULVZVrzfm3YDx68czbes0rsZexc3JjZcavMQ7bd6hsEthW5cnIiIFjMJAFigMiNw5SSlJ7L20l/2X97Pv0j5+P/47ey7tSbeft6s3TQOb0iygGR0rdqSefz0suTETWQEyb/88+v3Wj7C4MMBskrak+xIalGpg28JERKTAUhjIAoUBkbx1LfYaW85uYdOZTWw6s4mt57YSkxiTZp/SnqVpU64NHSp0oEOFDhRxL2Kjau+ssxFnmbVrFvMOzONg6EEA3JzceK7ec7x333s3nQlaRETkVhQGskBhQMS2Uu8ebDqziV+P/sqKf1ak26eib0WqFatG5aKVqVS0EqW9SlO8cHF83Hwo7FyYwi6FKeRcKN83NboSc4VDoYfYeGYjK/9ZyYbTG6xzX7g6ujK4xWBeb/o6nq5ZmJxARETkFhQGskBhQCR/CYsLY93JdSz9eynrT6/n0JVDWT62qHtRPFw8cHNyw8XRBTcnN1ydXHFzcqOwc2E8XT3xcPbAy9ULbzdvvFy98HDxwMnBKc3i5uSGm5Mb7k7uuDq54uroioujC65O5r8OFgeiE6KJTIgkKiHKukTGm99HxEcQGhPK5ejLXI6+TGhMKKfCTnE1Nv1Yoy3KtKBv3b50qdIFHzefXHwlRUTE3ikMZIHCgEj+Fhodyu6Luzly9QiHrxzmn+v/cD7yPFdirhAeF05MYox1YrSCIMg7iNola9O2fFvaBrelYtGKti5JRETuUgoDWaAwIFKwGYZBbFIskfGRXIm5QnRiNHFJccQnxROfHE98UjyxSbHWT/Ij4yOJTIgkIj6CiPgIohKiSEpJIikliWQjmcTkROKS4ohNiiU2MZaE5AQSkhOIT463fg3g7uSOh4uHdfF09fz3axdPihUqRvHCxa2Lv6c/lYpWopBzIRu/YiIiYi+yep3rlIc1iYjkKovFQiHnQhRyLkQJjxJ3/PEMwyDFSLH7+SZEROTuoTAgIpJFFosFR4uCgIiI3D3y9/AbIiIiIiJyxygMiIiIiIjYKYUBERERERE7pTAgIiIiImKnFAZEREREROyUwoCIiIiIiJ1SGBARERERsVMKAyIiIiIidkphQERERETETikMiIiIiIjYKYUBERERERE7pTAgIiIiImKnFAZEREREROyUwoCIiIiIiJ1SGBARERERsVMKAyIiIiIidkphQERERETETikMiIiIiIjYKYUBERERERE7pTAgIiIiImKnFAZEREREROyUwoCIiIiIiJ1SGBARERERsVMKAyIiIiIidkphQERERETETikMiIiIiIjYKYUBERERERE7pTAgIiIiImKnFAZEREREROyUwoCIiIiIiJ1SGBARERERsVMKAyIiIiIidqpAh4F3332XZs2aUahQIXx8fGxdjoiIiIhIgVKgw0BCQgKPPfYY/fr1s3UpIiIiIiIFjpOtC8iJMWPGADB79mzbFiIiIiIiUgAV6DBwu+Lj44mPj7d+HxERYcNqRERERERsq0A3E7pdEyZMwNvb27oEBgbauiQREREREZvJd2Fg9OjRWCyWmy7bt2/P1rmHDBlCeHi4dTlz5kwuVy8iIiIiUnDku2ZC/fv354knnrjpPmXLls3WuV1dXXF1dc3WsSIiIiIid5t8Fwb8/Pzw8/OzdRkiIiIiIne9fBcGbsfp06e5du0ap0+fJjk5md27dwNQoUIFPDw8bFuciIiIiEg+V6DDwMiRI5kzZ471+7p16wKwZs0aQkJCbFSViIiIiEjBYDEMw7B1EbYSERGBt7c34eHheHl52bocEREREZFckdXr3Hw3mpCIiIiIiOQNhQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETulMCAiIiIiYqcUBkRERERE7JTCgIiIiIiInVIYEBERERGxUwoDIiIiIiJ2SmFARERERMROKQyIiIiIiNgphQERERERETtVYMPAyZMnefbZZylXrhzu7u4EBwczatQoEhISbF2aiIiIiEiB4GTrArLr8OHDpKSk8Pnnn1OhQgX279/Pc889R3R0NB9++KGtyxMRERERyfcshmEYti4it3zwwQdMnz6d48ePZ2n/iIgIvL29CQ8Px8vL6w5XJyIiIiKSN7J6nVtg7wxkJDw8HF9f30y3x8fHEx8fb/0+IiIiL8oSEREREcmXCmyfgf/6559/+Pjjj3nxxRcz3WfChAl4e3tbl8DAwDysUEREREQkf8l3YWD06NFYLJabLtu3b09zzPnz52nfvj2PPfYYffv2zfTcQ4YMITw83LqcOXPmTj8dEREREZF8K9/1Gbhy5QpXrly56T5ly5bFzc0NMINAmzZtaNy4MbNnz8bBIev5Rn0GRERERORuVGD7DPj5+eHn55elfc+dO0ebNm2oX78+s2bNuq0gICIiIiJi7/JdGMiq8+fPExISQpkyZfjwww8JDQ21bitZsqQNKxMRERERKRgKbBhYuXIlx44d49ixYwQEBKTZls9aPomIiIiI5EsFtl1Nr169MAwjw0VERERERG6twIYBERERERHJGYUBERERERE7pTAgIiIiImKnFAZEREREROyUwoCIiIiIiJ1SGBARERERsVMKAyIiIiIidkphQERERETETikMiIiIiIjYKYUBERERERE7pTAgIiIiImKnFAZEREREROyUwoCIiIiIiJ1SGBARERERsVMKAyIiIiIidsrJ1gXYkmEYAERERNi4EhERERGR3JN6fZt6vZsZuw4DkZGRAAQGBtq4EhERERGR3BcZGYm3t3em2y3GreLCXSwlJYXz58/j6emJxWKxdTm5KiIigsDAQM6cOYOXl5ety7nr6fXOe3rN855e87yl1zvv6TXPe3rN7xzDMIiMjKRUqVI4OGTeM8Cu7ww4ODgQEBBg6zLuKC8vL/1w5SG93nlPr3ne02uet/R65z295nlPr/mdcbM7AqnUgVhERERExE4pDIiIiIiI2Kn/a+/eY6qs/ziAvw/8DkgHRLkJyO1MB23hEokU8m6CpqG2OXVtgBENFZWFmq3Ycd4xIReLoWVHraY2U+cfVgJ5KCUNEReXMhRERZDhDAWH3D6/P5pnPYGXEHl2znm/trPxfJ7P8/XDd58NPzzP4XAYsFKOjo4wGAxwdHRUuxSbwP0eeNzzgcc9H1jc74HHPR943HP12fQbiImIiIiIbBnvDBARERER2SgOA0RERERENorDABERERGRjeIwQERERERkozgMWBmTyQSNRtPrq7i42JzX2/nc3FwVK7dcQUFBPfZy7dq1ipyrV6/i9ddfh06ng4eHB1asWIH29naVKrZsV65cQWJiIvR6PZycnDBixAgYDIYe+8ke7185OTnQ6/UYNGgQwsPD8fPPP6tdklXYsmULIiIi4OLiAi8vL8ydOxcXL15U5CQkJPTo5XHjxqlUseVbt25dj/309vY2nxcRrFu3Dr6+vnBycsLkyZNRUVGhYsWWr7efkxqNBsuWLQPAHlebTX8CsTWKiopCfX29Ipaeno78/Hy89NJLirjRaMSMGTPMx0/yKXXUu/Xr1yMpKcl87OzsbP66q6sLs2bNgqenJ06dOoVbt24hPj4eIoLs7Gw1yrVof/zxB7q7u7Fz506MHDkS5eXlSEpKQmtrK7Zv367IZY/3j4MHDyI1NRU5OTl45ZVXsHPnTsycOROVlZUICAhQuzyLVlhYiGXLliEiIgKdnZ344IMPEB0djcrKSuh0OnPejBkzYDQazccODg5qlGs1XnjhBeTn55uP7e3tzV9v27YNWVlZ2LNnD4KDg7Fx40ZMnz4dFy9ehIuLixrlWrzi4mJ0dXWZj8vLyzF9+nTMnz/fHGOPq0jIqrW3t4uXl5esX79eEQcgR44cUacoKxMYGCgff/zxQ88fP35c7OzspK6uzhzbv3+/ODo6SnNz8wBUaP22bdsmer1eEWOP95+XX35ZkpOTFbHnn39e1q5dq1JF1quxsVEASGFhoTkWHx8vc+bMUa8oK2MwGOTFF1/s9Vx3d7d4e3vL1q1bzbG2tjZxdXWV3NzcAarQ+q1cuVJGjBgh3d3dIsIeVxsfE7Jyx44dQ1NTExISEnqcS0lJgYeHByIiIpCbm4vu7u6BL9BKZGRkwN3dHaNHj8amTZsUj6z88ssvCA0Nha+vrzkWExOD+/fvo6SkRI1yrU5zczPc3Nx6xNnjT6+9vR0lJSWIjo5WxKOjo1FUVKRSVdarubkZAHr0s8lkgpeXF4KDg5GUlITGxkY1yrMaVVVV8PX1hV6vx8KFC1FdXQ0AqKmpQUNDg6LfHR0dMWnSJPZ7P2lvb8dXX32Ft956CxqNxhxnj6uHjwlZud27dyMmJgb+/v6K+IYNGzBt2jQ4OTmhoKAAaWlpaGpqwocffqhSpZZr5cqVGDNmDIYOHYpff/0V77//PmpqavD5558DABoaGjBs2DDFNUOHDoWDgwMaGhrUKNmqXL58GdnZ2cjMzFTE2eP9o6mpCV1dXT16eNiwYezffiYiePfddzF+/HiEhoaa4zNnzsT8+fMRGBiImpoapKenY+rUqSgpKeGntvbB2LFjsW/fPgQHB+PmzZvYuHEjoqKiUFFRYe7p3vq9trZWjXKtztGjR/HXX38pfknJHleZ2rcm6MkYDAYB8MhXcXGx4ppr166JnZ2dHDp06LHrb9++XQYPHvysyrc4fdnvBw4dOiQApKmpSUREkpKSJDo6ukeeVquV/fv3P9Pvw5L0Zc/r6upk5MiRkpiY+Nj12eN9U1dXJwCkqKhIEd+4caOEhISoVJV1Wrp0qQQGBsq1a9cemXfjxg3RarXy7bffDlBl1q2lpUWGDRsmmZmZcvr0aQEgN27cUOS8/fbbEhMTo1KF1iU6Olpmz579yBz2+MDinQELkZKSgoULFz4yJygoSHFsNBrh7u6O2NjYx64/btw43LlzBzdv3uzxGxFb1Jf9fuDBX0C4dOkS3N3d4e3tjbNnzypybt++jY6ODu71P/zXPb9x4wamTJmCyMhI7Nq167Hrs8f7xsPDA/b29j3uAjQ2NnIf+9Hy5ctx7Ngx/PTTT/Dz83tkro+PDwIDA1FVVTVA1Vk3nU6HUaNGoaqqCnPnzgXw9x1dHx8fcw77vX/U1tYiPz8fhw8ffmQee3xgcRiwEB4eHvDw8HjifBGB0WhEXFwctFrtY/NLS0sxaNAgDBky5CmqtB7/db//qbS0FADMP0giIyOxadMm1NfXm2MnTpyAo6MjwsPD+6dgK/Bf9ryurg5TpkxBeHg4jEYj7Owe//Yn9njfODg4IDw8HHl5eZg3b545npeXhzlz5qhYmXUQESxfvhxHjhyByWSCXq9/7DW3bt3CtWvXFP9Zpb67f/8+fv/9d0yYMAF6vR7e3t7Iy8tDWFgYgL+fcS8sLERGRobKlVo+o9EILy8vzJo165F57PEBpvatCXo28vPzBYBUVlb2OHfs2DHZtWuXlJWVyaVLl+Szzz6TwYMHy4oVK1So1LIVFRVJVlaWlJaWSnV1tRw8eFB8fX0lNjbWnNPZ2SmhoaEybdo0OX/+vOTn54ufn5+kpKSoWLnlevBo0NSpU+X69etSX19vfj3AHu9fBw4cEK1WK7t375bKykpJTU0VnU4nV65cUbs0i7dkyRJxdXUVk8mk6OV79+6JiMjdu3clLS1NioqKpKamRk6ePCmRkZEyfPhwuXPnjsrVW6a0tDQxmUxSXV0tZ86ckdmzZ4uLi4u5n7du3Squrq5y+PBhKSsrk0WLFomPjw/3+yl1dXVJQECAvPfee4o4e1x9HAas1KJFiyQqKqrXc999952MHj1anJ2d5bnnnpPQ0FDZsWOHdHR0DHCVlq+kpETGjh0rrq6uMmjQIAkJCRGDwSCtra2KvNraWpk1a5Y4OTmJm5ubpKSkSFtbm0pVWzaj0fjQ9xQ8wB7vf59++qkEBgaKg4ODjBkzRvGnL6nvHtbLRqNRRETu3bsn0dHR4unpKVqtVgICAiQ+Pl6uXr2qbuEWbMGCBeLj4yNarVZ8fX3ljTfekIqKCvP57u5uMRgM4u3tLY6OjjJx4kQpKytTsWLr8MMPPwgAuXjxoiLOHlefRkREjTsSRERERESkLn7OABERERGRjeIwQERERERkozgMEBERERHZKA4DREREREQ2isMAEREREZGN4jBARERERGSjOAwQEREREdkoDgNERERERDaKwwAREfW7oKAg7Nix45msPXnyZKSmpj6TtYmIbA2HASIiG5eQkIC5c+f26do9e/ZgyJAhPeLFxcV45513zMcajQZHjx7tW4FERPTM/E/tAoiIyPp4enqqXQIRET0B3hkgIqKHysrKwqhRo6DT6eDv74+lS5eipaUFAGAymbB48WI0NzdDo9FAo9Fg3bp1AJSPCQUFBQEA5s2bB41GYz7u7Y5EamoqJk+ebD5ubW1FXFwcnJ2d4ePjg8zMzB41tre3Y82aNRg+fDh0Oh3Gjh0Lk8nUj7tARGS9OAwQEdFD2dnZ4ZNPPkF5eTn27t2LH3/8EWvWrAEAREVFYceOHRg8eDDq6+tRX1+PVatW9VijuLgYAGA0GlFfX28+fhKrV6/GyZMnceTIEZw4cQImkwklJSWKnMWLF+P06dM4cOAAfvvtN8yfPx8zZsxAVVXVU3znRES2gY8JERHRQ/3zjbp6vR4bNmzAkiVLkJOTAwcHB7i6ukKj0cDb2/uhazx4ZGjIkCGPzPu3lpYW7N69G/v27cP06dMBAHv37oWfn5855/Lly9i/fz+uX78OX19fAMCqVavw/fffw2g0YvPmzf/l2yUisjkcBoiI6KFOnjyJzZs3o7KyEnfu3EFnZyfa2trQ2toKnU73TP/ty5cvo729HZGRkeaYm5sbQkJCzMfnz5+HiCA4OFhx7f379+Hu7v5M6yMisgYcBoiIqFe1tbV47bXXkJycjA0bNsDNzQ2nTp1CYmIiOjo6nnp9Ozs7iIgi9s91/32uN93d3bC3t0dJSQns7e0V55ydnZ+6RiIia8dhgIiIenXu3Dl0dnYiMzMTdnZ/v8Xsm2++UeQ4ODigq6vrsWtptdoeeZ6enigvL1fELly4AK1WCwAYOXIktFotzpw5g4CAAADA7du38eeff2LSpEkAgLCwMHR1daGxsRETJkzo2zdKRGTD+AZiIiJCc3MzLly4oHh5enqis7MT2dnZqK6uxpdffonc3FzFdUFBQWhpaUFBQQGamppw7969XtcPCgpCQUEBGhoacPv2bQDA1KlTce7cOezbtw9VVVUwGAyK4cDZ2RmJiYlYvXo1CgoKUF5ejoSEBPNgAgDBwcF48803ERcXh8OHD6OmpgbFxcXIyMjA8ePHn8FOERFZFw4DREQEk8mEsLAwxeuLL75AVlYWMjIyEBoaiq+//hpbtmxRXBcVFYXk5GQsWLAAnp6e2LZtW6/rZ2ZmIi8vD/7+/ggLCwMAxMTEID09HWvWrEFERATu3r2LuLg4xXUfffQRJk6ciNjYWLz66qsYP348wsPDFTlGoxFxcXFIS0tDSEgIYmNjcfbsWfj7+/fjDhERWSeNPMlDmUREREREZHV4Z4CIiIiIyEZxGCAiIiIislEcBoiIiIiIbBSHASIiIiIiG8VhgIiIiIjIRnEYICIiIiKyURwGiIiIiIhsFIcBIiIiIiIbxWGAiIiIiMhGcRggIiIiIrJRHAaIiIiIiGzU/wE5CogwVb7oggAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(9, 6))\n", "ax = fig.add_subplot()\n", "\n", "MHT_method_1.plot(ax = ax, color = 'blue', label = 'Method 1')\n", "MHT_method_2.plot(ax = ax, color = 'green', label = 'Method 2')\n", "\n", "# add legend\n", "plt.legend(frameon=False, fontsize=12)\n", "plt.axhline(y=0, linewidth=1, color='black')\n", "\n", "# limits along the y axis\n", "plt.ylim(-2.25, 2.75)\n", "\n", "# add titles and labels\n", "plt.title('Global Ocean Meridional Heat Transport', fontsize=18)\n", "plt.xlabel('Latitude')\n", "plt.ylabel('$10^{15}$ Watts');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison between model output and observations\n", "\n", "The following section compares the model's heat transport to observations. These observations are derived using various methods, in particular using surface flux observations a la method 2 (which assumes a steady state)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read ERBE Period Ocean and Atmospheric Heat Transport\n", "\n", "This data comes (annoyingly) in a text file. The cell below opens and saves latitudes and heat transport into two separate lists: " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Path to the file containing observations\n", "filename = '/g/data3/ik11/from_hh5_tmp/cosima/observations/original/MHT/obs_vq_am_estimates.txt'\n", "\n", "# Create empty variables to store our observations\n", "erbe_MHT = []\n", "erbe_lat = []\n", "\n", "# Open data and save it to empty variables above\n", "with open(filename) as f:\n", " #Open each line from rows 1 to 96\n", " for line in f.readlines()[1:96]:\n", " #Separating each line to extract data\n", " line = line.strip()\n", " sline = line.split()\n", " #Extracting latitude and MHT and saving to empty variables\n", " erbe_lat.append(float(sline[0]))\n", " erbe_MHT.append(float(sline[3]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read NCEP and ECMWF Oceanic and Atmospheric Transport Products\n", "\n", "These datasets are available at https://climatedataguide.ucar.edu/climate-data. We use a climatological mean of surface fluxes or vertically integrated total energy divergence for oceanic and atmospheric transports respectively for the period between February 1985 - April 1989.\n", "\n", "This also comes as a text file and again, we will save it into lists. There is also an estimate of the observational error:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "#Path to the file containing observations\n", "filename = '/g/data/ik11/observations/ANNUAL_TRANSPORTS_1985_1989.ascii'\n", "\n", "#Creating empty variables to store our observations\n", "ncep_g_mht = []\n", "ecwmf_g_mht = []\n", "ncep_g_err = []\n", "ecwmf_g_err = []\n", "ncep_a_mht = []\n", "ecwmf_a_mht = []\n", "ncep_a_err = []\n", "ecwmf_a_err = []\n", "ncep_p_mht = []\n", "ecwmf_p_mht = []\n", "ncep_p_err = []\n", "ecwmf_p_err = []\n", "ncep_i_mht = []\n", "ecwmf_i_mht = []\n", "ncep_i_err = []\n", "ecwmf_i_err = []\n", "ncep_ip_mht = []\n", "ecwmf_ip_mht = []\n", "ncep_ip_err = []\n", "ecwmf_ip_err = []\n", "o_lat = []\n", "\n", "#Opening data and saving it to empty variables above\n", "with open(filename) as f:\n", "#Open each line in file (ignoring the first row)\n", " for line in f.readlines()[1:]:\n", " #Separating each line to extract data\n", " line = line.strip()\n", " sline = line.split()\n", " #Extracting values and saving to correct variable defined above\n", " o_lat.append(float(sline[0]) * 0.01) # T42 latitudes (north to south)\n", " ncep_g_mht.append(float(sline[4]) * 0.01) # Residual Ocean Transport - NCEP\n", " ecwmf_g_mht.append(float(sline[5]) * 0.01) # Residual Ocean Transport - ECWMF\n", " ncep_a_mht.append(float(sline[7]) * 0.01) # Atlantic Ocean Basin Transport - NCEP\n", " ncep_p_mht.append(float(sline[8]) * 0.01) # Pacific Ocean Basin Transport - NCEP\n", " ncep_i_mht.append(float(sline[9]) * 0.01) # Indian Ocean Basin Transport - NCEP\n", " ncep_g_err.append(float(sline[10]) * 0.01) # Error Bars for NCEP Total Transports\n", " ncep_a_err.append(float(sline[11]) * 0.01) # Error Bars for NCEP Atlantic Transports \n", " ncep_p_err.append(float(sline[12]) * 0.01) # Error Bars for NCEP Pacific Transports \n", " ncep_i_err.append(float(sline[13]) * 0.01) # Error Bars for NCEP Indian Transports \n", " ecwmf_a_mht.append(float(sline[15]) * 0.01) # Atlantic Ocean Basin Transport - ECWMF\n", " ecwmf_p_mht.append(float(sline[16]) * 0.01) # Pacific Ocean Basin Transport - ECWMF\n", " ecwmf_i_mht.append(float(sline[17]) * 0.01) # Indian Ocean Basin Transport - ECWMF\n", " ecwmf_g_err.append(float(sline[18]) * 0.01) # Error Bars for ECWMF Total Transports\n", " ecwmf_a_err.append(float(sline[19]) * 0.01) # Error Bars for NCEP Atlantic Transports\n", " ecwmf_p_err.append(float(sline[20]) * 0.01) # Error Bars for NCEP Pacific Transports\n", " ecwmf_i_err.append(float(sline[21]) * 0.01) # Error Bars for NCEP Indian Transports\n", "\n", "#Calculating MHT\n", "ncep_ip_mht = [a+b for a, b in zip(ncep_p_mht,ncep_i_mht)]\n", "ecwmf_ip_mht = [a+b for a, b in zip(ecwmf_p_mht,ecwmf_i_mht)]\n", "ncep_ip_err = [max(a, b) for a, b in zip(ncep_p_err, ncep_i_err)]\n", "ecwmf_ip_err = [max(a, b) for a, b in zip(ecwmf_p_err, ecwmf_i_err)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting model outputs against observations\n", "\n", "We plot the global meridional heat transport as calculated from model outputs (blue line) and observations." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(9, 6))\n", "ax = fig.add_subplot(1, 1, 1)\n", "\n", "#Plotting MHT from model outputs\n", "MHT_method_1.plot(ax = ax, color = \"blue\", label = \"ACCESS-OM2-025 (MHF diagnostic)\")\n", "MHT_method_2.plot(ax = ax, color = \"green\", label = \"ACCESS-OM2-025 (shflux)\")\n", "\n", "#Adding observations and error bars for observations\n", "ax.plot(erbe_lat, erbe_MHT, 'k--', linewidth=1, label=\"ERBE, JRA-25, NCEP/NCAR, and ERA40\")\n", "plt.errorbar(o_lat[::-1], ncep_g_mht[::-1], yerr=ncep_g_err[::-1], c='gray', fmt='s', \n", " markerfacecolor='k', markersize=3, capsize=2, linewidth=1, label=\"NCEP\")\n", "plt.errorbar(o_lat[::-1], ecwmf_g_mht[::-1], yerr=ecwmf_g_err[::-1], c='gray', fmt='D', \n", " markerfacecolor='white', markersize=3, capsize=2, linewidth=1, label=\"ECWMF\")\n", "plt.errorbar( 24, 1.5, yerr=0.3, fmt='o', c='black', markersize=3, capsize=2, linewidth=1,\n", " label=\"Macdonald and Wunsch 1996\")\n", "plt.errorbar(-30, -0.9, yerr=0.3, fmt='o', c='black', markersize=3, capsize=2, linewidth=1)\n", "plt.errorbar( 24, 2.0, yerr=0.3, fmt='x', c='green', markersize=3, capsize=2, linewidth=1,\n", " label=\"Lavin et al. and Bryden et al.\")\n", "plt.errorbar( 24, 1.83, yerr=0.28, fmt='^', c='red', markersize=4, capsize=2, linewidth=1,\n", " label=\"Ganachaud and Wunsch 2003\")\n", "plt.errorbar(-30, -0.6, yerr=0.3, fmt='^', c='red', markersize=4, capsize=2, linewidth=1)\n", "plt.errorbar(-19, -0.8, yerr=0.3, fmt='^', c='red', markersize=4, capsize=2, linewidth=1)\n", "plt.errorbar( 47, 0.6, yerr=0.1, fmt='^', c='red', markersize=4, capsize=2, linewidth=1)\n", "\n", "# add legend\n", "plt.legend(frameon=False, fontsize=10)\n", "plt.axhline(y=0, linewidth=1, color='black')\n", "\n", "# limits along the y axis\n", "plt.ylim(-2.25, 2.75)\n", "\n", "# add titles and labels\n", "plt.title('Global Ocean Meridional Heat Transport', fontsize=18)\n", "plt.xlabel('Latitude')\n", "plt.ylabel('$10^{15}$ Watts');" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "client.close()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:analysis3-25.09]", "language": "python", "name": "conda-env-analysis3-25.09-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 4 }