{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Observed and simulated internal variability climate feedbacks comparison. \n", "Geocomputation and Machine learning\n", "\n", "Alejandro Uribe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Project description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the presence of radiative forcings ($F$), the climate system reestablishes its energy balance by increasing the mean global temperature ($T$). This increase of temperature produces secondary forcing fluxes which are proportional to the initial response ($\\lambda T$). The additional feedback fluxes are added to the initial forcing, leading to a final response (Lindzen et al, 2001). Examples of such a mechanism are cloud feedbacks; clouds are important in the energetic balance of the earth since they both reflect shortwave radiation coming from the sun back to space and longwave to the surface. For example, if high cloud cover from deep convective clouds in the tropics decreases with warming (Iris effect), more longwave would escape to space (negative longwave feedback), but if stratocumulus clouds from shallow convection in the subtropics decrease more shortwave would reach the surface (positive shortwave feedback). \n", "\n", "A typical approach to quantify the feedback mechanism is to consider the planetary energy balance at the top of the atmosphere (TOA), represented as:\n", "\n", "\\begin{equation}\\label{R}\\tag{1}\n", "R=F+\\lambda T\n", "\\end{equation}\n", "\n", "where $R$ is the net TOA radiative flux anomaly, $\\lambda$ is the radiative feedback parameter and $T$ is the global mean surface air temperature anomaly (Zelinka et al, 2020). Generally, to diagnose climate feedbacks, $T$ and $R$ are estimated using coupled ocean-atmosphere General Circulation Models (GCM's) in which atmospheric concentrations of carbon dioxide are abruptly quadruplet from their preindustrial concentration and held fixed. However, those diagnosed feedbacks are not easily comparable with observations and thus their validity is uncertain. \n", "\n", "In this project, I used a different approach in which, instead of utilizing $CO_2$ forcings, I estimated the feedback parameter produced by climate variations over time resulting from natural causes (internal variability) using observations. Similar ideas have been used before (Mauritsen & Stevens, 2015 for example); however, their approach focussed particularly on the tropics and the extent of their observational dataset is small compared to today. As the second step in this project, I diagnosed the same feedbacks from a set of model simulations of historical climate from the last generation of the Coupled Model Intercomparison Project (CMIP6) and compared them with those from observations. Finally, I wondered about potential relations between simulated feedbacks due to natural variability and $CO_2$ forcings. This project is framed within the GeoCompuation component of the lecture.\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Data set and Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.1 Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Diagnosis of internal variability feedbacks were produced using observations, whereas simulated feedbacks from model simulations. The data description is as follows." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.1.1 Observations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To estimate observed internal variability feedbacks and cloud properties, I used:\n", "\n", "* TOA shortwave and longwave fluxes (clear sky and all sky) at monthly in 1° × 1° regions from satellite data of the Clouds and the Earth’s Radiant Energy System (CERES) instruments and “Energy Balanced and Filled” (EBAF) updated to version Ed4.1.\n", "* Cloud properties stratified by cloud optical depth and pressure from CERES-MODIS/VIIRS and GEO.\n", "* Gridded temperatuere anolimalies datase from the latest version of HadCRUT(5) analysis\n", "\n", "Observations were obtained from https://ceres.larc.nasa.gov/data/ and https://crudata.uea.ac.uk/cru/data/temperature/, respectvely." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.1.2 Simulations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To estimate simulated internal variability feedbacks, I used TOA shortwave and longwave fluxes (clear sky (CS) and all sky) and air temperature from historical simulations of global coupled climate models produced for the sixth phase of Coupled Model Intercomparison Project (CMIP) and Atmospheric Model Intercomparison Project (AMIP) (Table 1.). In these simulations, boundary conditions (e.g irradiation, aerosols, orbital parameters, and green house concentration in the atmosphere) represent those estimated for the historical period starting in 1850 and ending in 2014 (Fiedler et al, 2020).\n", "\n", "To investigate if the simulated internal variability feedbacks relate to simulated $CO_2$ forcing feedbacks, I used outputs from CMIP6 in which atmospheric $CO_2$ concentrations were abruptly quadruplet from their preindustrial concentration and held fixed (abrupt-4xCO2) (Zelinka et al 2020).\n", "\n", "All simulated data was downloaded from the Deutsches KlimaRechenZentrum (DKRZ) data repository, using the DKRZ CMIP data pool (https://www.dkrz.de/up/services/data-management/cmip-data-pool). I constrained both observational and simulations datasets to their overlaping period, 2001 to 2014. Finally, Cloud Radiative Effects (CRE - the amount of energy that would return to space if there were no clouds, minus the amount that escapes with clouds present.) fluxes were calculated in both observations and simulations by substracting CS from all-sky fluxes. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| Model | CMIP6 historical | AMIP | abrupt-4xCO2 |\n", "| --- | :-: | :-: | :-: |\n", "| ACCESS-CM2 | X | X | X |\n", "| ACCESS-ESM1-5 | X | X | X |\n", "| AWI-CM-1-1-MR | X | | |\n", "| AWI-ESM-1-1-LR | X | | |\n", "| BCC-CSM2-MR | X | X | X |\n", "| CAMS-CSM1-0 | X | X | X |\n", "| CanESM5 | X | X | |\n", "| CESM2 | X | X | X |\n", "| CESM2-FV2 | X | X | X |\n", "| CESM2-WACCM | X | X | X | |\n", "| CESM2-WACCM-FV2 | X | X | X |\n", "| CMCC-CM2-HR4 | X | | |\n", "| CMCC-CM2-SR5 | X | X | X |\n", "| CMCC-ESM2 | X | | |\n", "| FGOALS-g3 | X | X | |\n", "| GISS-E2-1-G | X | X | X |\n", "| GISS-E2-1-G-CC | X | | |\n", "| GISS-E2-1-H | X | | X |\n", "| IITM-ESM | X | X | X |\n", "| MIROC6 | X | X | X |\n", "| MPI-ESM-1-2-HAM | X | X | X |\n", "| MPI-ESM1-2-HR | X | X | X |\n", "| MPI-ESM1-2-LR | X | X | X |\n", "| MRI-ESM2-0 | X | X | X |\n", "| NESM3 | X | X | X |\n", "| NorCPM1 | X | X | |\n", "| NorESM2-LM | X | X | |\n", "| NorESM2-MM | X | | |\n", "| SAM0-UNICON | X | X | X |\n", "| TaiESM1 | X | | |\n", "| BCC-ESM | | X | |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Table 1. Models used.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2 Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2.1 Preprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 1. shows the global mean temperature anomaly time series from 1850 to 2021 (even though the period extension is larger than the one I used to diagnose internal variability feedbacks, it helps us to visualize the most important fluctuations in the time series as example). The large time scale positive trend (red line) is related to anthropogenic warming and thus is does not belong to internal variability. On the othere side, the high-frequency oscillation is a print of seasonality. Both trends do not correspond to monthly climate variability and thus it is important to remove them before performing the feedbacks diagnosis. To that end, I used Climate Data Operators (CDO) within a bash pre-analysis script as showed in the cell after the figure." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import xarray as xr\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "input_data='/home/alejandro/CL_feedbacks/data/'\n", "DATA= xr.open_dataset(input_data+'HadCRUT/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc')\n", "#os.system('cdo -trend -fldmean '+input_data+'HadCRUT/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc afile.nc bfile.nc')\n", "a=xr.open_dataset('afile.nc')\n", "b=xr.open_dataset('bfile.nc')\n", "trend= (a.tas_mean.values+b.tas_mean.values*np.arange(len(DATA.time)))[0][0]\n", "fig, ax = plt.subplots(figsize=(15,5))\n", "ax.plot(DATA.time.squeeze(),trend, color='red',label='trend')\n", "weights = np.cos(np.deg2rad(DATA.latitude))\n", "weights.name = \"weights\"\n", "DATA.tas_mean.weighted(weights).mean(dim=('latitude','longitude')).plot(color='black', lw=0.5)\n", "ax.set_title('')\n", "ax.set_ylabel('Anomaly [K]')\n", "ax.set_xlabel('Time [Year]')\n", "ax.set_xlim(DATA.time.min(),DATA.time.max())\n", "ax.legend()\n", "plt.gca().spines[\"right\"].set_visible(False)\n", "plt.gca().spines[\"top\"].set_visible(False)\n", "plt.tight_layout()\n", "plt.savefig('T_timeseries.pdf', format='pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Figure 1. Global mean temperature anomaly time series. Values were weighted by the cosine of their latitude before taking the mean. Trend is displayed as the red line.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bash script to preprocess observations (detrend and deseasonalize)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting observations_preprocessing.sh\n" ] } ], "source": [ "%%writefile observations_preprocessing.sh\n", "\n", "#!/bin/bash\n", "##################################################################\n", "# 1. HadCRUT #\n", "##################################################################\n", "\n", "dir='/home/alejandro/CL_feedbacks/data/HadCRUT/'\n", "\n", "######### Selecting overlaping period ######################################\n", "\n", "cdo selyear,2001/2014 ${dir}HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc ${dir}HadCRUT.5.0.1.0.mean_2001_2014_mon.nc\n", "\n", "######### Removing seasonality #############################################\n", "\n", "cdo -sub ${dir}HadCRUT.5.0.1.0.mean_2001_2014_mon.nc -ymonmean ${dir}HadCRUT.5.0.1.0.mean_2001_2014_mon.nc ${dir}desea_HadCRUT.5.0.1.0.mean_2001_2014_mon.nc \n", "\n", "######### Removing trend ###################################################\n", "\n", "cdo -detrend ${dir}desea_HadCRUT.5.0.1.0.mean_2001_2014_mon.nc ${dir}detre_desea_HadCRUT.5.0.1.0.mean_2001_2014_mon.nc\n", "\n", "######### Removing temporary files #########################################\n", "\n", "rm ${dir}HadCRUT.5.0.1.0.mean_2001_2014_mon.nc ${dir}desea_HadCRUT.5.0.1.0.mean_2001_2014_mon.nc\n", "\n", "##################################################################\n", "# 2. CERES #\n", "##################################################################\n", "\n", "dir='/home/alejandro/CL_feedbacks/data/CERES/'\n", "\n", "######### Selecting overlaping period ######################################\n", "\n", "cdo selyear,2001/2014 ${dir}CERES_EBAF_Ed4.1_Subset_200101-201912.nc ${dir}CERES_EBAF-TOA_Ed4.1_Subset_2001-2014.nc\n", "\n", "######### Calculating anomalies ############################################\n", "\n", "cdo sub ${dir}CERES_EBAF-TOA_Ed4.1_Subset_2001-2014.nc -timmean ${dir}CERES_EBAF-TOA_Ed4.1_Subset_2001-2014.nc ${dir}flux_anom_200101-201412.nc\n", "\n", "######### Removing seasonality #############################################\n", "\n", "cdo -sub ${dir}flux_anom_200101-201412.nc -ymonmean ${dir}flux_anom_200101-201412.nc ${dir}desea_flux_anom_200101-201412.nc\n", "\n", "######### Removing trend ###################################################\n", "\n", "cdo -detrend ${dir}desea_flux_anom_200101-201412.nc ${dir}tmp.nc \n", "\n", "##################################################################\n", "# 2. Cloud properties #\n", "##################################################################\n", "\n", "# TOP quantitites\n", "\n", "######### Calculating anomalies ############################################\n", "\n", "cdo sub ${dir}CERES_CldTypHist-MON_GEO-MODIS_Ed4.1_Subset_200101-201412.nc -timmean ${dir}CERES_CldTypHist-MON_GEO-MODIS_Ed4.1_Subset_200101-201412.nc ${dir}CldTypHist_anom_200101-201412.nc\n", "\n", "######### Removing seasonality #############################################\n", "\n", "cdo -sub ${dir}CldTypHist_anom_200101-201412.nc -ymonmean ${dir}CldTypHist_anom_200101-201412.nc ${dir}desea_CldTypHist_anom_200101-201412.nc\n", "\n", "######### Removing trend ###################################################\n", "\n", "cdo -detrend ${dir}desea_CldTypHist_anom_200101-201412.nc ${dir}detre_desea_CldTypHist_anom_200101-201412.nc\n", "\n", "######### Removing temporary files #########################################\n", "\n", "rm ${dir}desea_CldTypHist_anom_200101-201412.nc ${dir}CldTypHist_anom_200101-201412.nc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bash script to preprocess simulations CMIP6 historical (detrend and deseasonalize)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting models_historical_preprocessing.sh\n" ] } ], "source": [ "%%writefile models_historical_preprocessing.sh\n", "#!/bin/bash\n", "dir='/home/alejandro/CL_feedbacks/data/CMIP6_historical/'\n", "input=\"models\"\n", "while IFS= read -r line\n", "do\n", "\tfor var in rlut_ rlutcs_ rsut_ rsutcs_ ts_\n", "\tdo\n", "\t\tmkdir /home/alejandro/CL_feedbacks/data/CMIP6_historical/${line}\n", "\t\tdir_out=/home/alejandro/CL_feedbacks/data/CMIP6_historical/${line}\n", " rm ${dir_out}/${var}2001_2014.nc\n", "\t\tif test -f \"${dir_out}/${var}2001_2014.nc\"\n", "\t\tthen\n", "\t\t\techo ${var}185001-201412.nc 'exist!'\n", "\t\telse\n", "\t\t\tcd ${dir_out}\n", "\t\t\tcdo -selyear,2001/2014 ${var}185001-201412.nc ${var}2001_2014.nc\n", "\t\t\tcdo -sub ${var}2001_2014.nc -timmean ${var}2001_2014.nc ${var}anom.nc\n", "\t\t\tcdo -ymonmean ${var}anom.nc ${var}seasons.nc\n", "\t\t\tcdo -sub ${var}anom.nc ${var}seasons.nc desea_${var}anom.nc\n", "\t\t\tcdo -detrend desea_${var}anom.nc detre_desea_${var}anom.nc\n", "\t\t\trm ${var}anom.nc ${var}seasons.nc desea_${var}anom.nc\n", "\t\t\techo ${line} desea_${var}anom.nc 'Created!'\t\n", "\t\tfi\n", "\tdone\n", "done < \"$input\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bash script to preprocess simulations AMIP (detrend and deseasonalize)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting models_amip_preprocessing.sh\n" ] } ], "source": [ "%%writefile models_amip_preprocessing.sh\n", "#!/bin/bash\n", "dir='/home/alejandro/CL_feedbacks/data/down_amip/'\n", "input=\"models_amip\"\n", "mkdir /home/alejandro/CL_feedbacks/data/AMIP_alejandro\n", "while IFS= read -r line\n", "do\n", "\tfor var in rlut_ rlutcs_ rsut_ rsutcs_ ts_\n", "\tdo\n", "\t\tmkdir /home/alejandro/CL_feedbacks/data/AMIP/${line}\n", "\t\tdir_out=/home/alejandro/CL_feedbacks/data/AMIP/${line}\n", "\t\tif test -f \"${dir_out}/${var}2001_2014.nc\"\n", "\t\tthen\n", "\t\t\techo detre_desea_${var}anom.nc 'exist!'\n", "\t\telse\n", "\t\t\tcd ${dir_out}\n", "\t\t\tcdo -selyear,2001/2014 ${var}197901-201412.nc ${var}2001_2014.nc\n", "\t\t\tcdo -sub ${var}2001_2014.nc -timmean ${var}2001_2014.nc ${var}anom.nc\n", "\t\t\tcdo -ymonmean ${var}anom.nc ${var}seasons.nc\n", "\t\t\tcdo -sub ${var}anom.nc ${var}seasons.nc desea_${var}anom.nc\n", "\t\t\tcdo -detrend desea_${var}anom.nc detre_desea_${var}anom.nc\n", "\t\t\trm ${var}anom.nc ${var}seasons.nc desea_${var}anom.nc\n", "\t\t\techo ${line} desea_${var}anom.nc 'Created!'\t\n", "\t\tfi\n", "\tdone\n", "done < \"$input\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bash script to preprocess simulations CMIP6 piControl and Abrupt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting models_pi_ab_preprocessing.sh\n" ] } ], "source": [ "%%writefile models_pi_ab_preprocessing.sh\n", "#!/bin/bash\n", "dir='/home/alejandro/CL_feedbacks/data/CMIP6_piControl-Abrupt/'\n", "input=\"models_pi_ab\"\n", "while IFS= read -r line\n", "do\n", "\tfor exp in abrupt-4xCO2 piControl\n", "\tdo\n", "\t\tfor var in rlut_ rlutcs_ rsut_ rsutcs_ ts_\n", "\t\tdo\n", "\t\t\tmkdir /home/alejandro/CL_feedbacks/data/CMIP6_piControl-Abrupt_ale/${line}\n", "\t\t\tdir_out=/home/alejandro/CL_feedbacks/data/CMIP6_piControl-Abrupt_ale/${line}\n", "\t\t\tif test -f \"${dir_out}/ymean_${var}${exp}.nc\"\n", "\t\t\tthen\n", "\t\t\t\techo ${var}${exp}.nc 'exist!'\n", "\t\t\telse\n", "\t\t\t\tcd ${dir}${line}/${exp}\n", "\t\t\t\tcdo -yearmean ${dir_out}/${var}${exp}.nc ${dir_out}/ymean_${var}${exp}.nc\n", "\t\t\t\trm ${dir_out}/${var}${exp}.nc # remove later\n", "\t\t\tfi\n", "\t\tdone\n", "\tdone\n", "done < \"$input\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 2. shows an example of the deseasonalized and detrended data to diagnose the feedbacks for the overlaping period." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import xarray as xr\n", "import matplotlib.pyplot as plt\n", "input_data='/home/alejandro/CL_feedbacks/data/'\n", "DATA= xr.open_dataset(input_data+'HadCRUT/detre_desea_HadCRUT.5.0.1.0.mean_2001_2014_mon.nc')\n", "fig, ax = plt.subplots(figsize=(15,5))\n", "DATA.tas_mean.mean(dim=('latitude','longitude')).plot(color='black', lw=0.5)\n", "ax.set_title('')\n", "ax.set_ylabel('Anomaly [K]')\n", "plt.gca().spines[\"right\"].set_visible(False)\n", "plt.gca().spines[\"top\"].set_visible(False)\n", "plt.tight_layout()\n", "plt.savefig('T_timeseries_detre_desea.pdf', format='pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Figure 2. Global mean detrended and deseasonalized air temperature anomaly time series. Values were weighted by the cosine of their latitude before taking the mean.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To verify if the resulting data is in reality related to internal climate variability, I decided to decomposed the signal of the radiation fluxes and compared it with the sign of El Niño South Oscillation (ENSO), which perhaps is the most relevant sample of internal variability. To do that, I extracted the principal component (PC) of the leading mode of the empirical orthogonal functions (EOF's) of the global mean time series of TOA shortwave and longwave anomalies and compared it with sea surface temperature anomalies in the region NINO 3.4. The high correlation between shortwave and longwave with El NINO 3.4 index indicates that the preprocessing filters produced internal variability data (Figure 3)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "from eofs.xarray import Eof\n", "DATA_tmp=xr.open_dataset(input_data+'HadCRUT/detre_desea_HadCRUT.5.0.1.0.mean_2001_2014_mon.nc')\n", "DATA_flux=xr.open_dataset(input_data+'CERES/detre_desea_flux_anom_200101-201412.nc', decode_times=False)\n", "#LW\n", "lw_flux=DATA_flux.toa_lw_all_mon\n", "coslat = np.cos(np.deg2rad(DATA_flux.coords['lat'].values))\n", "wgts = np.sqrt(coslat)[..., np.newaxis]\n", "solver = Eof(lw_flux, weights=wgts)\n", "eof_flux_lw = solver.eofsAsCorrelation(neofs=1)\n", "pc_flux_lw = solver.pcs(npcs=1, pcscaling=1)\n", "var_lw=solver.varianceFraction(neigs=7)\n", "#SW\n", "sw_flux=DATA_flux.toa_sw_all_mon\n", "solver = Eof(sw_flux, weights=wgts)\n", "eof_flux_sw = solver.eofsAsCorrelation(neofs=1)\n", "pc_flux_sw = solver.pcs(npcs=1, pcscaling=1)\n", "var_sw=solver.varianceFraction(neigs=7)\n", "# Anomalies NINO 3.4\n", "sst_anom=DATA_tmp.tas_mean\n", "sst_anom_34=sst_anom.sel(latitude=slice(-5,5), longitude=slice(-170,-120)).mean(dim=('latitude','longitude'))\n", "# Plot\n", "fig, ax = plt.subplots(figsize=(12,4))\n", "R_LW=np.round(np.corrcoef(sst_anom_34,pc_flux_lw[:,0])[0,1],2)\n", "R_SW=np.round(np.corrcoef(sst_anom_34,pc_flux_sw[:,0])[0,1],2)\n", "sst_anom_34.plot(label='Nino 3.4',color='black')\n", "pc_flux_lw['time']=sst_anom_34.time\n", "pc_flux_lw.plot(label='LW flux, R='+str(R_LW),color='r')\n", "pc_flux_sw['time']=sst_anom_34.time\n", "pc_flux_sw.plot(label='SW flux, R='+str(R_SW),color='b')\n", "ax.set_title('')\n", "ax.set_ylabel('Amplitude')\n", "ax.set_xlabel('Year')\n", "ax.legend(frameon=False)\n", "plt.gca().spines[\"right\"].set_visible(False)\n", "plt.gca().spines[\"top\"].set_visible(False)\n", "plt.tight_layout()\n", "plt.savefig('PC_1EOF.pdf', format='pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Figure 3. PC of leading EOF of TOA fluxes and SST anomalies Niño 3.4.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2.2 Feedbacks " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Shortwave and longwave feedbacks were calculated from both observations and simulations using the linear regression coefficients between radiative fluxes anomalies and air temperature anomalies (equation 3 and 4). The following cell shows the python function used for that purpose, including the 95% confidence interval.\n", "\n", "\\begin{equation}\\label{Rl}\\tag{2}\n", "R_L=F_L+\\lambda_LT\n", "\\end{equation}\n", "\n", "\\begin{equation}\\label{Rs}\\tag{3}\n", "R_S=F_S+\\lambda_ST\n", "\\end{equation}\n", "\n", "where the subindexes $L$ and $S$ indicate longwave and shortwave, respectively." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def regression(sst_mean, y_values):\n", " x = sm.add_constant(sst_mean.values)\n", " y = y_values.values\n", " model = sm.OLS(y, x, missing='drop')\n", " fitted = model.fit()\n", " # Confidence intervals\n", " IC=fitted.conf_int(alpha=0.05,cols=[1])\n", " return(fitted.params[1],IC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of diagnosing global feedbacks, I decided to take a different approach. First, I divided the globe into \"boxes\" of 10° latitudinal width from 90°S to 90°N. Then, I calculated the feedbacks within each box and the mean feedback between the hemispherical equivalent boxes. I applied this methodology to differentiate potential feedbacks behaviors from the tropics to the poles. \n", "\n", "This method requires the calculation of 18 feedback-boxes for 2 radiative fluxes in observations times 30 models from CMIP6 and 23 models in AMIP for a total of 990 regressions. The process was repeated for clear sky, all-sky, and cloud radiative effects fluxes for a final total of 2970 regressions. To that end, the following python code was applied to produce npz files with NumPy arrays containing the results." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing feedbacks.py\n" ] } ], "source": [ "%%writefile feedbacks.py\n", "import xarray as xr\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import glob\n", "input='/home/alejandro/CL_feedbacks/data/'\n", "output='/home/alejandro/CL_feedbacks/data_prod/'\n", "North=np.arange(0,90,10)\n", "South=np.arange(0,-90,-10)\n", "full=[North,South]\n", "for Hem in full:\n", " if np.all(Hem == North):\n", " upp=10\n", " H='North'\n", " elif np.all(Hem == South):\n", " upp=-10\n", " H='South'\n", " for rad in ('all','cs','cre'):\n", " if rad == 'all':\n", " IR='rlut'\n", " IR_obs='toa_lw_all_mon'\n", " SR='rsut'\n", " SR_obs='toa_sw_all_mon'\n", " elif rad=='cs':\n", " IR='rlutcs'\n", " IR_obs='toa_lw_clr_c_mon'\n", " SR='rsutcs'\n", " SR_obs='toa_sw_clr_c_mon'\n", " else:\n", " IR='rlutcre'\n", " IR_obs='toa_lw_cre_mon'\n", " SR='rsutcre' \n", " SR_obs='toa_sw_cre_mon'\n", " savez_dict = dict()\n", " for l,domain in enumerate(('Global','Local')):\n", " feed_lw=np.zeros(9)\n", " feed_sw=np.zeros_like(feed_lw)\n", " top_lw_IC=np.zeros_like(feed_lw)\n", " bot_lw_IC=np.zeros_like(feed_lw)\n", " top_sw_IC=np.zeros_like(feed_lw)\n", " bot_sw_IC=np.zeros_like(feed_lw)\n", " for j,i, in enumerate(Hem):\n", " #-----------------------------------------------------------------\n", " #------------------------ OBS ------------------------------------\n", " #-----------------------------------------------------------------\n", " DATA_sst=xr.open_dataset(input+'HadCRUT/detre_desea_HadCRUT.5.0.1.0.mean_2001_2014_mon.nc')\n", " sst_anom=DATA_sst.tas_mean\n", " weights = np.cos(np.deg2rad(DATA_sst.latitude))\n", " weights.name = \"weights\"\n", " if domain=='Local': \n", " sst_anom=sst_anom.sel(latitude=slice(min(i,i+upp),max(i,i+upp)))\n", " weights = np.cos(np.deg2rad(sst_anom.latitude))\n", " weights.name = \"weights\"\n", " sst_mean=sst_anom.weighted(weights).mean(dim=('latitude','longitude'), skipna=True)\n", " DATA_flux=xr.open_dataset(input+'CERES/detre_desea_flux_anom_200101-201412.nc', decode_times=False)\n", " sw_flux=DATA_flux[SR_obs].sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " weights = np.cos(np.deg2rad(sw_flux.lat))\n", " weights.name = \"weights\"\n", " lw_flux=DATA_flux[IR_obs].sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " #\n", " sw_mean=sw_flux.weighted(weights).mean(dim=('lat','lon'))\n", " lw_mean=lw_flux.weighted(weights).mean(dim=('lat','lon'))\n", " #------------------------ SW FLUX ------------------------------------\n", " m_sw,sw_IC=regression(sst_mean,-sw_mean)\n", " bot_sw_IC[j]=np.min(sw_IC)\n", " top_sw_IC[j]=np.max(sw_IC)\n", " feed_sw[j]=m_sw\n", " #------------------------ LW FLUX ------------------------------------\n", " m_lw,lw_IC=regression(sst_mean,-lw_mean)\n", " bot_lw_IC[j]=np.min(lw_IC)\n", " top_lw_IC[j]=np.max(lw_IC)\n", " feed_lw[j]=m_lw\n", " savez_dict['feed_lw_'+domain] = feed_lw\n", " savez_dict['top_lw_IC_'+domain] = top_lw_IC\n", " savez_dict['bot_lw_IC_'+domain] = bot_lw_IC\n", " savez_dict['feed_sw_'+domain] = feed_sw\n", " savez_dict['top_sw_IC_'+domain] = top_sw_IC\n", " savez_dict['bot_sw_IC_'+domain] = bot_sw_IC\n", " for exp in (('CMIP6_historical','AMIP','CMIP6_4xCO2')):\n", " models=[]\n", " for i in glob.glob(input+exp+'/*'):\n", " models.append(i.replace('/home/alejandro/CL_feedbacks/data/'+exp,''))\n", " mean_lw=np.zeros_like(feed_lw)\n", " mean_sw=np.zeros_like(feed_sw)\n", " mod_m_lw=np.zeros_like(feed_lw)\n", " mod_m_sw=np.zeros_like(feed_lw)\n", " mod_lw_IC_min=np.zeros_like(feed_lw)\n", " mod_sw_IC_min=np.zeros_like(feed_lw)\n", " mod_lw_IC_max=np.zeros_like(feed_lw)\n", " mod_sw_IC_max=np.zeros_like(feed_lw)\n", " mean_mod_lw=[]\n", " mean_mod_sw=[]\n", " for mod in models:\n", " #-----------------------------------------------------------------\n", " #------------------------ MODELS ---------------------------------\n", " #-----------------------------------------------------------------\n", " for j,i, in enumerate(Hem):\n", " LW=xr.open_dataset(input+exp+mod+'/detre_desea_'+IR+'_anom.nc')\n", " rlut=LW[IR.replace('cre','')].sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " weights_mod = np.cos(np.deg2rad(rlut.lat))\n", " weights_mod.name = \"weights\"\n", " rlut=rlut.weighted(weights_mod).mean(dim=('lat','lon'))\n", " SW=xr.open_dataset(input+exp+mod+'/detre_desea_'+SR+'_anom.nc')\n", " rsut=SW[SR.replace('cre','')].sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " rsut=rsut.weighted(weights_mod).mean(dim=('lat','lon'))\n", " TS=xr.open_dataset(input+exp+mod+'/detre_desea_ts_anom.nc')\n", " if domain=='Global':\n", " weights_glob = np.cos(np.deg2rad(TS.lat))\n", " weights_glob.name = \"weights\"\n", " ts=TS.ts.weighted(weights_glob).mean(dim=('lat','lon'))\n", " #------------------------ SW FLUX ------------------------------------\n", " m_sw,sw_IC=regression(ts,-rsut)\n", " mod_m_sw[j]=m_sw\n", " mod_sw_IC_min[j]=np.min(sw_IC)\n", " mod_sw_IC_max[j]=np.max(sw_IC)\n", " #------------------------ LW FLUX ------------------------------------\n", " m_lw,lw_IC=regression(ts,-rlut)\n", " mod_m_lw[j]=m_lw\n", " mod_lw_IC_min[j]=np.min(lw_IC)\n", " mod_lw_IC_max[j]=np.max(lw_IC)\n", " else:\n", " LW_pi=xr.open_dataset(input+exp+mod+'/ymean_'+IR+'_piControl.nc',use_cftime=False,decode_times=False).isel(time=slice(None,150))\n", " LW_ab=xr.open_dataset(input+exp+mod+'/ymean_'+IR+'_abrupt-4xCO2.nc',use_cftime=False,decode_times=False).isel(time=slice(None,150))\n", " rlut_pi=LW_pi[IR.replace('cre','')].sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " rlut_ab=LW_ab[IR.replace('cre','')].sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " weights_mod = np.cos(np.deg2rad(rlut_pi.lat))\n", " weights_mod.name = \"weights\"\n", " rlut=rlut_ab.weighted(weights_mod).mean(dim=('lat','lon'))-rlut_pi.weighted(weights_mod).mean(dim=('lat','lon'))\n", " SW_pi=xr.open_dataset(input+exp+mod+'/ymean_'+SR+'_piControl.nc',use_cftime=False,decode_times=False).isel(time=slice(None,150))\n", " SW_ab=xr.open_dataset(input+exp+mod+'/ymean_'+SR+'_abrupt-4xCO2.nc',use_cftime=False,decode_times=False).isel(time=slice(None,150))\n", " rsut_pi=SW_pi[SR.replace('cre','')].sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " rsut_ab=SW_ab[SR.replace('cre','')].sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " rsut=rsut_ab.weighted(weights_mod).mean(dim=('lat','lon'))-rsut_pi.weighted(weights_mod).mean(dim=('lat','lon'))\n", " TS_pi=xr.open_dataset(input+exp+mod+'/ymean_ts_piControl.nc',use_cftime=False,decode_times=False).isel(time=slice(None,150))\n", " TS_ab=xr.open_dataset(input+exp+mod+'/ymean_ts_abrupt-4xCO2.nc',use_cftime=False,decode_times=False).isel(time=slice(None,150))\n", " if domain=='Global':\n", " weights_glob = np.cos(np.deg2rad(TS_pi.lat))\n", " weights_glob.name = \"weights\"\n", " ts=TS_ab.ts.weighted(weights_glob).mean(dim=('lat','lon'))-TS_pi.ts.weighted(weights_glob).mean(dim=('lat','lon'))\n", " else:\n", " ts_pi=TS_pi.ts.sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " ts_ab=TS_ab.ts.sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " ts=ts_ab.weighted(weights_mod).mean(dim=('lat','lon'))-ts_pi.weighted(weights_mod).mean(dim=('lat','lon'))\n", " #------------------------ SW FLUX ------------------------------------\n", " m_sw,sw_IC=regression(ts,-rsut)\n", " mod_m_sw[j]=m_sw\n", " mod_sw_IC_min[j]=np.min(sw_IC)\n", " mod_sw_IC_max[j]=np.max(sw_IC)\n", " #------------------------ LW FLUX ------------------------------------\n", " m_lw,lw_IC=regression(ts,-rlut)\n", " mod_m_lw[j]=m_lw\n", " mod_lw_IC_min[j]=np.min(lw_IC)\n", " mod_lw_IC_max[j]=np.max(lw_IC)\n", " mean_mod_lw.append(np.copy(mod_m_lw))\n", " mean_mod_sw.append(np.copy(mod_m_sw))\n", " savez_dict[exp+'_'+mod.replace('/','')+'_m_lw_'+domain] = np.copy(mod_m_lw)\n", " savez_dict[exp+'_'+mod.replace('/','')+'_lw_IC_min_'+domain] = np.copy(mod_lw_IC_min)\n", " savez_dict[exp+'_'+mod.replace('/','')+'_lw_IC_max_'+domain] = np.copy(mod_lw_IC_max)\n", " savez_dict[exp+'_'+mod.replace('/','')+'_m_sw_'+domain] = np.copy(mod_m_sw)\n", " savez_dict[exp+'_'+mod.replace('/','')+'_sw_IC_min_'+domain] = np.copy(mod_sw_IC_min)\n", " savez_dict[exp+'_'+mod.replace('/','')+'_sw_IC_max_'+domain] = np.copy(mod_sw_IC_max)\n", " savez_dict['mean_mod_lw_'+exp+'_'+domain] = np.mean(mean_mod_lw,axis=0) \n", " savez_dict['mean_mod_sw_'+exp+'_'+domain] = np.mean(mean_mod_sw,axis=0)\n", " np.savez(output+'Feed_Ftropics_'+rad+'_NOSI_NOI_'+H+'_Had5.npz', **savez_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additionally, changes in observed cloud cover, optical depth, cloud top height, and cloud top temperature anomalies with air temperature were calculated to understand the feedbacks results. Those changes were estimated using the same regression function with the following scripts." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing optical_properties.py\n" ] } ], "source": [ "%%writefile optical_properties.py\n", "import xarray as xr\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import glob\n", "input='/home/alejandro/CL_feedbacks/data/'\n", "output='/home/alejandro/CL_feedbacks/data_prod/'\n", "North=np.arange(0,90,10)\n", "South=np.arange(0,-90,-10)\n", "full=[North,South]\n", "savez_dict = dict()\n", "for l,domain in enumerate(('Global','Local')):\n", " North=np.arange(0,90,10)\n", " South=np.arange(0,-90,-10)\n", " full=[North,South]\n", " for Hem in full:\n", " reg_clda=np.zeros(9)\n", " reg_cldt=np.zeros_like(reg_clda)\n", " top_clda_IC=np.zeros_like(reg_clda)\n", " bot_clda_IC=np.zeros_like(reg_clda)\n", " top_cldt_IC=np.zeros_like(reg_clda)\n", " bot_cldt_IC=np.zeros_like(reg_clda)\n", " #\n", " for j,i, in enumerate(Hem):\n", " #print(i,i-10)\n", " if np.all(Hem == North):\n", " upp=10\n", " H='North'\n", " elif np.all(Hem == South):\n", " upp=-10\n", " H='South'\n", " #-----------------------------------------------------------------\n", " #------------------------ OBS ------------------------------------\n", " #-----------------------------------------------------------------\n", " DATA_sst=xr.open_dataset(input+'HadCRUT/detre_desea_HadCRUT.5.0.1.0.mean_2001_2014_mon.nc')\n", " sst_anom=DATA_sst.tas_mean\n", " weights = np.cos(np.deg2rad(DATA_sst.latitude))\n", " weights.name = \"weights\"\n", " if domain=='Local': \n", " sst_anom=sst_anom.sel(latitude=slice(min(i,i+upp),max(i,i+upp)))\n", " weights = np.cos(np.deg2rad(sst_anom.latitude))\n", " weights.name = \"weights\"\n", " sst_mean=sst_anom.weighted(weights).mean(dim=('latitude','longitude'), skipna=True)\n", " DATA=xr.open_dataset(input+'CERES/detre_desea_flux_anom_200101-201412.nc', decode_times=False)\n", " C_area=DATA['cldarea_total_daynight_mon'].sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " weights = np.cos(np.deg2rad(C_area.lat))\n", " weights.name = \"weights\"\n", " C_tau=DATA['cldtau_total_day_mon'].sel(lat=slice(min(i,i+upp),max(i,i+upp))) #\n", " cld_area=C_area.mean(dim=('lat','lon'))\n", " cld_tau=C_tau.mean(dim=('lat','lon'))\n", " #------------------------ Cloud area FLUX ----------------------------\n", " m_clda,clda_IC=regression(sst_mean,cld_area)\n", " bot_clda_IC[j]=np.min(clda_IC)\n", " top_clda_IC[j]=np.max(clda_IC)\n", " reg_clda[j]=m_clda\n", " #------------------------ Cloud thickness FLUX -----------------------\n", " m_cldt,cldt_IC=regression(sst_mean,cld_tau)\n", " bot_cldt_IC[j]=np.min(cldt_IC)\n", " top_cldt_IC[j]=np.max(cldt_IC)\n", " reg_cldt[j]=m_cldt\n", " savez_dict['reg_clda_'+domain] = reg_clda\n", " savez_dict['top_clda_IC_'+domain] = top_clda_IC\n", " savez_dict['bot_clda_IC_'+domain] = bot_clda_IC\n", " savez_dict['reg_cldt_'+domain] = reg_cldt\n", " savez_dict['top_cldt_IC_'+domain] = top_cldt_IC\n", " savez_dict['bot_cldt_IC_'+domain] = bot_cldt_IC\n", " np.savez(output+'optical_Ftropics_NOI_NOSI_'+H+'_Had5.npz', **savez_dict)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing Height_temperature.py\n" ] } ], "source": [ "%%writefile Height_temperature.py\n", "import xarray as xr\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import glob\n", "input='/home/alejandro/CL_feedbacks/data/'\n", "output='/home/alejandro/CL_feedbacks/data_prod/'\n", "North=np.arange(0,90,10)\n", "South=np.arange(0,-90,-10)\n", "full=[North,South]\n", "savez_dict = dict()\n", "for l,domain in enumerate(('Global','Local')):\n", " North=np.arange(0,90,10)\n", " South=np.arange(0,-90,-10)\n", " full=[North,South]\n", " for Hem in full:\n", " reg_cth=np.zeros(9)\n", " top_cth_IC=np.zeros_like(reg_cth)\n", " bot_cth_IC=np.zeros_like(reg_cth)\n", " reg_cod=np.zeros_like(reg_cth)\n", " top_cod_IC=np.zeros_like(reg_cth)\n", " bot_cod_IC=np.zeros_like(reg_cth)\n", " reg_ctt=np.zeros_like(reg_cth)\n", " top_ctt_IC=np.zeros_like(reg_cth)\n", " bot_ctt_IC=np.zeros_like(reg_cth)\n", " #\n", " for j,i, in enumerate(Hem):\n", " #print(i,i-10)\n", " if np.all(Hem == North):\n", " upp=10\n", " H='North'\n", " elif np.all(Hem == South):\n", " upp=-10\n", " H='South'\n", " #-----------------------------------------------------------------\n", " #------------------------ OBS ------------------------------------\n", " #-----------------------------------------------------------------\n", " DATA_sst=xr.open_dataset(input+'HadCRUT/detre_desea_HadCRUT.5.0.1.0.mean_2001_2014_mon.nc')\n", " sst_anom=DATA_sst.tas_mean\n", " weights = np.cos(np.deg2rad(DATA_sst.latitude))\n", " weights.name = \"weights\"\n", " if domain=='Local': \n", " sst_anom=sst_anom.sel(latitude=slice(min(i,i+upp),max(i,i+upp)))\n", " weights = np.cos(np.deg2rad(sst_anom.latitude))\n", " weights.name = \"weights\"\n", " sst_mean=sst_anom.weighted(weights).mean(dim=('latitude','longitude'), skipna=True)\n", " DATA=xr.open_dataset(input+'CERES/detre_desea_CldTypHist_anom_200101-201412.nc', decode_times=False)\n", " cth=DATA.cldtyp_cldhght_top_tot_daynite_mon.sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " weights = np.cos(np.deg2rad(cth.lat))\n", " weights.name = \"weights\"\n", " cod=DATA.cldtyp_cldtau_tot_daynite_mon.sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " ctt=DATA.cldtyp_cldtemp_top_tot_daynite_mon.sel(lat=slice(min(i,i+upp),max(i,i+upp)))\n", " cth=cth.mean(dim=('lat','lon'))\n", " cod=cod.mean(dim=('lat','lon'))\n", " ctt=ctt.mean(dim=('lat','lon'))\n", " #------------------------ ----------------------------\n", " m_cth,cth_IC=regression(sst_mean,cth)\n", " bot_cth_IC[j]=np.min(cth_IC)\n", " top_cth_IC[j]=np.max(cth_IC)\n", " reg_cth[j]=m_cth\n", " #------------------------ ----------------------------\n", " m_cod,cod_IC=regression(sst_mean,cod)\n", " bot_cod_IC[j]=np.min(cod_IC)\n", " top_cod_IC[j]=np.max(cod_IC)\n", " reg_cod[j]=m_cod\n", " #------------------------ ----------------------------\n", " m_ctt,ctt_IC=regression(sst_mean,ctt)\n", " bot_ctt_IC[j]=np.min(ctt_IC)\n", " top_ctt_IC[j]=np.max(ctt_IC)\n", " reg_ctt[j]=m_ctt\n", " savez_dict['reg_cth_'+domain] = reg_cth\n", " savez_dict['top_cth_IC_'+domain] = top_cth_IC\n", " savez_dict['bot_cth_IC_'+domain] = bot_cth_IC\n", " savez_dict['reg_cod_'+domain] = reg_cod\n", " savez_dict['top_cod_IC_'+domain] = top_cod_IC\n", " savez_dict['bot_cod_IC_'+domain] = bot_cod_IC\n", " savez_dict['reg_ctt_'+domain] = reg_ctt\n", " savez_dict['top_ctt_IC_'+domain] = top_ctt_IC\n", " savez_dict['bot_ctt_IC_'+domain] = bot_ctt_IC\n", " np.savez(output+'CTH_NOSI_'+H+'_Had5.npz', **savez_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even though the analysis was performed using global and local (box) change of temperature anomalies, for practical purposes of this project, I present here the local results. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.1 Observed feedbacks" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import glob\n", "input='/home/alejandro/CL_feedbacks/data/'\n", "input_data='/home/alejandro/CL_feedbacks/data_prod/'\n", "%matplotlib inline\n", "Ty_sky='all'\n", "fig, ax = plt.subplots(1,2, figsize=(17,6), sharey=True, sharex=True)\n", "fig.add_subplot(111, frameon=False)\n", "plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n", "plt.tick_params(axis='y', which='both', labelleft=False, labelright=True)\n", "plt.ylabel('[W $m^{-2} K^{-1}$]', labelpad=35)\n", "plt.xlabel('Latitude [Degree]', labelpad=10)\n", "intervals=['(10S,10N)','(10,20)S/N','(20,30)S/N','(30,40)S/N','(40,50)S/N','(50,60)S/N',\n", " '(60,70)S/N','(70,80)S/N','(80,90)S/N']\n", "domain='Local'\n", "feed_lw=[];top_lw_IC=[];bot_lw_IC=[];feed_sw=[];top_sw_IC=[];bot_sw_IC=[]\n", "for H in ('North', 'South'):\n", " DATA = np.load(input_data+'Feed_Ftropics_'+Ty_sky+'_NOSI_NOI_'+H+'_Had5.npz')\n", " #-----------------------------------------------------------------\n", " #------------------------ OBS ------------------------------------\n", " #-----------------------------------------------------------------\n", " c='blue'\n", " style='solid'\n", " sh=0\n", " feed_lw.append(DATA['feed_lw_'+domain])\n", " top_lw_IC.append(DATA['top_lw_IC_'+domain])\n", " bot_lw_IC.append(DATA['bot_lw_IC_'+domain])\n", " feed_sw.append(DATA['feed_sw_'+domain])\n", " top_sw_IC.append(DATA['top_sw_IC_'+domain])\n", " bot_sw_IC.append(DATA['bot_sw_IC_'+domain])\n", "feed_lw=np.mean(feed_lw,axis=0)\n", "top_lw_IC=np.mean(top_lw_IC,axis=0)\n", "bot_lw_IC=np.mean(bot_lw_IC,axis=0)\n", "feed_sw=np.mean(feed_sw,axis=0)\n", "top_sw_IC=np.mean(top_sw_IC,axis=0)\n", "bot_sw_IC=np.mean(bot_sw_IC,axis=0)\n", "for i in range(len(intervals)):\n", " ax[0].scatter(i+sh,feed_lw[i],marker='P',color=c)\n", " ax[1].scatter(i+sh,feed_sw[i],marker='P',color=c)\n", " ax[0].plot([i+sh,i+sh],[bot_lw_IC[i],top_lw_IC[i]], color=c,linestyle=style, linewidth=1)\n", " ax[1].plot([i+sh,i+sh],[bot_sw_IC[i],top_sw_IC[i]], color=c,linestyle=style, linewidth=1)\n", " ax[0].vlines(x=i-0.5,ymin=-10,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[0].vlines(x=i+0.5,ymin=-10,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[1].vlines(x=i-0.5,ymin=-10,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[1].vlines(x=i+0.5,ymin=-10,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", "ax[0].hlines(y=0, xmin=-0.5, xmax=9,linestyle='--', color='black', lw=1)\n", "ax[1].hlines(y=0, xmin=-0.5, xmax=9,linestyle='--', color='black', lw=1)\n", "ax[0].spines['right'].set_color('none')\n", "ax[0].spines['top'].set_color('none')\n", "ax[1].spines['right'].set_color('none')\n", "ax[1].spines['top'].set_color('none')\n", "ax[0].set_xlim(-0.5,8.5)\n", "ax[0].set_ylim(-10,7)\n", "ax[0].set_title('Longwave')\n", "ax[1].set_title('Shortwave')\n", "ax[0].xaxis.set_ticks(np.arange(0, 9, 1))\n", "ax[0].set_xticklabels(intervals)\n", "fig.tight_layout()\n", "plt.savefig('obs_feed.pdf', format='pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Figure 4. All sky Longwave (left) and shortwave (right) feedbacks. Bars indicate the 95% confidence intervals.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 4. shows longwave and shortwave feedbacks. Longwave feedbacks are negative across all latitudes with three distinctive features, first small negative feedback in the deep tropics, larger feedback in the subtropics, and nearly constant feedback in the extratropics. Shortwave feedbacks also exhibit three trends, a negative value in the deep tropics, positive feedback in the extratropics, and approximately no feedback in the extratropics. To try to attribute those trends to cloud-related and not related feedbacks, I split the total feedbacks into clear-sky and cloud radiative effects as follows." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import glob\n", "input='/home/alejandro/CL_feedbacks/data/'\n", "input_data='/home/alejandro/CL_feedbacks/data_prod/'\n", "%matplotlib inline\n", "fig, ax = plt.subplots(1,2, figsize=(17,6), sharey=True, sharex=True)\n", "fig.add_subplot(111, frameon=False)\n", "plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n", "plt.tick_params(axis='y', which='both', labelleft=False, labelright=True)\n", "plt.ylabel('[W $m^{-2} K^{-1}$]', labelpad=35)\n", "plt.xlabel('Latitude [Degree]', labelpad=10)\n", "intervals=['(10S,10N)','(10,20)S/N','(20,30)S/N','(30,40)S/N','(40,50)S/N','(50,60)S/N',\n", " '(60,70)S/N','(70,80)S/N','(80,90)S/N']\n", "domain='Local'\n", "for Ty_sky,tit in zip(('cs','cre'),('Clear sky','Cloud radiative effects')):\n", " feed_lw=[];top_lw_IC=[];bot_lw_IC=[];feed_sw=[];top_sw_IC=[];bot_sw_IC=[]\n", " for H in ('North', 'South'):\n", " DATA = np.load(input_data+'Feed_Ftropics_'+Ty_sky+'_NOSI_NOI_'+H+'_Had5.npz')\n", " #-----------------------------------------------------------------\n", " #------------------------ OBS ------------------------------------\n", " #-----------------------------------------------------------------\n", " if Ty_sky=='cs': \n", " c='blue'\n", " else:\n", " c='red'\n", " style='solid'\n", " sh=0\n", " feed_lw.append(DATA['feed_lw_'+domain])\n", " top_lw_IC.append(DATA['top_lw_IC_'+domain])\n", " bot_lw_IC.append(DATA['bot_lw_IC_'+domain])\n", " feed_sw.append(DATA['feed_sw_'+domain])\n", " top_sw_IC.append(DATA['top_sw_IC_'+domain])\n", " bot_sw_IC.append(DATA['bot_sw_IC_'+domain])\n", " feed_lw=np.mean(feed_lw,axis=0)\n", " top_lw_IC=np.mean(top_lw_IC,axis=0)\n", " bot_lw_IC=np.mean(bot_lw_IC,axis=0)\n", " feed_sw=np.mean(feed_sw,axis=0)\n", " top_sw_IC=np.mean(top_sw_IC,axis=0)\n", " bot_sw_IC=np.mean(bot_sw_IC,axis=0)\n", " for i in range(len(intervals)):\n", " ax[0].scatter(i+sh,feed_lw[i],marker='P',color=c,label=tit)\n", " ax[1].scatter(i+sh,feed_sw[i],marker='P',color=c)\n", " ax[0].plot([i+sh,i+sh],[bot_lw_IC[i],top_lw_IC[i]], color=c,linestyle=style, linewidth=1)\n", " ax[1].plot([i+sh,i+sh],[bot_sw_IC[i],top_sw_IC[i]], color=c,linestyle=style, linewidth=1)\n", " ax[0].vlines(x=i-0.5,ymin=-10,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[0].vlines(x=i+0.5,ymin=-10,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[1].vlines(x=i-0.5,ymin=-10,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[1].vlines(x=i+0.5,ymin=-10,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[0].hlines(y=0, xmin=-0.5, xmax=9,linestyle='--', color='black', lw=1)\n", " ax[1].hlines(y=0, xmin=-0.5, xmax=9,linestyle='--', color='black', lw=1)\n", " ax[0].spines['right'].set_color('none')\n", " ax[0].spines['top'].set_color('none')\n", " ax[1].spines['right'].set_color('none')\n", " ax[1].spines['top'].set_color('none')\n", " ax[0].legend(*[*zip(*{l:h for h,l in zip(*ax[0].get_legend_handles_labels())}.items())][::-1],loc=1,frameon=False)\n", " ax[0].set_xlim(-0.5,8.5)\n", " ax[0].set_ylim(-10,7)\n", " ax[0].set_title('Longwave')\n", " ax[1].set_title('Shortwave')\n", " ax[0].xaxis.set_ticks(np.arange(0, 9, 1))\n", " ax[0].set_xticklabels(intervals)\n", " fig.tight_layout()\n", "plt.savefig('CS_CRE_feedbacks.pdf', format='pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Figure 5. Clear-sky and Cloud radiative effects longwave (left) and shortwave (right) feedbacks. Bars indicate the 95% confidence intervals.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since variations in the tropical feedbacks relate strongly to CRE, changes in clouds might explain the behavior in that area. Extratropical feedbacks are produced mostly by clear-sky feedbacks (Planck, water vapor, lapse rate, sea ice albedo, etc). In the deep tropics, the positive longwave CRE feedback that counteracts the negative CS comes from rising clouds while remaining at nearly constant temperature (FAT hypothesis - Figure 6.) and increasing cloud cover (Figure 7.), whereas the negative shortwave feedback is related to the thickening of clouds. In the subtropics, longwave CRE negative feedbacks relate to decreasing cloud cover (potentially an IRIS effect) and thinning clouds, while the positive shortwave CRE feedbacks is a response of thinning clouds and reductions of cloud cover. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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BVWLDUACwd0wbpEbExzLziRFxqaTTM/NXIuLjmfn4o/7wkDA/Gj3mC3Xx2ALAfbB5/Ci5LOBJ0sGDB1sPoVNOvU6tklcvrXU59Tq1DsnaiHiopGdLek+LATA/qsupl9a6nHpprcuttwUWtgrat29f6yF0yqnXqVXy6qW1Lqdep9YhuVLS+yXdnJkfiYhvlnRTlwNgflSXUy+tdTn10lqXW28LvCsiAABAA5n5p5L+dNHlWyT9x3YjAgAAmDw2Z2wBAACMk4h4dX/z+OMjYkdEzEbE81qPCwAAYJLYLGxFNNnDrIlNmza1HkKnnHqdWiWvXlrrcup1ah2S78nM/ZKeIek2SY+R9OIuB8D8qC6nXlrrcuqltS633hZY2Cpoamqq9RA65dTr1Cp59dJal1OvU+uQHN///H2S3p6Ze7seAPOjupx6aa3LqZfWutx6W7BZ2Jqfn289hM7s2bOn9RA65dTr1Cp59dJal1OvU+uQXBMRn5Y0LWlHRJws6VCXA2B+VFeL3lbrpE6PrVOr5NVLa11uvS3YLGxh9Ix+6QsAwDHLzMslnStpOjO/JumgpIvbjgoAAGCysLAFAADQQESsl/RfJP3P/lWnqXf2FgAAAAZks7DltIfE+vXrWw+hU069Tq2SVy+tdTn1OrUOyZslzUk6r3/5Nkm/1uUAmB/V5dRLa11OvbTW5dbbAgtbBW3cuLH1EDrl1OvUKnn10lqXU69T65A8KjNfLelrkpSZd0vqdMLC/Kgup15a63LqpbUut94WbBa2nDZHnZ2dbT2ETjn1OrVKXr201uXU69Q6JHMRcYKklKSIeJSkr3Y5AOZHdTn10lqXUy+tdbn1trC29QAwfHNzc62H0CmnXqdWyauX1tGLkDK7v18eW6zgVyS9T9LpEfFHkp4s6flNR1SY2/PTqZfWupx6aa3LrbeFgRa2ImLTAIfNZ+a+YxwPAKBDrRZ7AEiZeV1EfFTSt6v3EsSfzcxV/1o3Ik6X9IeSTpU0L2l7Zv7WUAYLAAAwpgY9Y2t3/2OljRiOk/TwYx4RjtmaNTavMJXk1evUKnn10lqXU69T67GIiLOWXHV7//PDI+LhmfnRVd70PZJ+PjM/GhEPlHR9RDUKRbkAACAASURBVFyXmTeserCFuD0/nXpprcupl9a63HpbiBzgV/UR8bHMfOKxHtPS9PR0zszMtB5GaW5nfrj1oian5zGtGIFV7bweEX+9wrczM797leNZej/vlvSGzLzuSMcwPxo9p/8enVolv14AGECTd6UZdOnw3CEd08wgC3hV7N+/v/UQOuXU69QqefXSWpdTr1PrscjM71rhY1iLWo+Q9ERJHz7KWIZxdxOhxfNz167DP3fJ6b9Hp8fW6XGVvHpprcutt4WBFrYy89AwjhlURFwQETdGxM0Rcfky339ARPxJ//sf7k/ejja+YQ1v7B04cKD1EDrl1OvUKnn10jpaLf/nksd29KLJ7waHIyLOi4gfjogfXfgYwm1ukPRnkl6YmfeZTUfEtoiYiYiZO++881jvbmJ0/fzctUvaurX39dat3f/9w989o9PysXV6XCWvXlrrcutt4X6/2DMiXjqKgSy6/eMkvVHS0yWdKem5EXHmksN+QtIXM/PRkl4n6b+PckwAgMnV+n8uu9ZyEQ/3T0S8VdJvSPoOSU/qf0wf420er96i1h9l5ruWOyYzt2fmdGZOb968+VjuDivYuVNaeCOsubneZdTg+NhO8i8QANR31M3jI+Idiy9KeoJGu5B0jqSbM/OW/v3/saSLJS3e+PRiSVf0v36npDdERKTTaVkAgIEs9z8g5471i+dXb+ki3o4ddVuLmJZ05rDmLxERkt4k6VOZ+dph3CZW7/zzpakp6e67e5/PP7/1iDAsPLYAMF4GOWNrf2Y+u//xg5I+OOIxbZF066LLt/WvW/aYzLxH0pcknbT0hhafan/LLbcoIu79uP7663X99dcfdt0VV1whSTrttNPuve7ss8+WJG3btu2wY3fv3q1rrrnmsOu2b9++cL/3flx44YWSpAsvvPCw6yVp+/bth113zTXXaPfu3Yddt23bNknS2Weffe91p512miTpiiuuWLZpy5Yt5ZpWepy2bNnSpGnxbQy76UiP05YtWyb2cVrNc2/z5s0NmjTSpiM9Tj/5kz/Z5HFa0OXfEYv/juriufdLvxSanz+7X7pNv/RL3f69t2XLls7+ezrvvNBXv9r7++nuu3uXR9F0pMdpenq6078jFpoWPnf9994QfELSqcO4ob4nS/oRSd8dEf/Q//i+lX7A6Z2auj477dxze4vLUptFZqez8ZweW6fHVfLqpbUut94WjvquiBHxyMz87KLLmzJz78gGFPGDkr43My/tX/4RSedk5s8sOuaT/WNu61/+TP+Yu450u07v+jM3N6epqanO7zcavTOMU2+r1lZa9PI8Hr0Wrbt2SeedJ/3d33X/P5dd9i6csXX33dIJJ3T/P1xuj62O8Z1/ovfuiE+Q9PeSvrpwfWZedIzjGhjzo9Fz+nfFqVXy+TdU8ntsW6C1LrPe8XxXxIVFrYjY3L88skWtvtsknb7o8sMk7T7SMRGxVtKDJK04rvn5+SEOcbzNzs62HkKnnHqdWiWvXlpHa2HBo8XL8rrsbX2GSNePbYH9066Q9ExJvy7pNxd9dIb5UV1OvbTW5dRLa11uvS3cn/PPf39kozjcRySdERGPjIgpSc+RdPWSY66WdEn/62dJ+iv21wIAoO0iXtcmfQPnzPzfkj4t6YH9j0/1rwMAAMCA7s/CVienlPX3zLpM0vslfUrSOzLzkxFxZUQsnJr/JkknRcTNkl4k6fIuxgYAlfDueZh0Cxs4S5O5gXNEPFu9lyH+oKRnS/pwRDyr7agAAAAmy1HfFXGRzs6IysxrJV275LqXL/r6kHqTwIEt3iS5ug0bNrQeQqecep1aJa/erltbvnue0+MqefV23brw0svzzpvYd4B8maQnZeYdkhQRJ6v3Jj3v7GoAzI/qcuqltS6nXlrrcuttYezO2BoVp4nbiSee2HoInXLqbdXa6j8fHtvRafkSLqfHVfLqbdE64S+9XLOwqNV3l+7f3OyYMT+qy6mX1rqcemmty623hYEmTxFxnKRfHPFYRsppc9Q9e/a0HkKnnHqdWiWv3q5bW76Eq9Xj2monxha9Tq0T7n0R8f6IeH5EPF/SX2rJGeujxvyoLqdeWuty6qW1LrfeFgb9reBVmfmJkY4EQ+M0SZW8ep1aJa/erltbvnue0+MqefU6tQ5DZr5Y0nZJj5f07yVtz8yXth1VXW7PT6deWuty6qW1LrfeFgbdY+u4iLhy8T5XkhQRD5T0+5l5v/a7AgC0N+Ev4QJKyMw/k/RnrccBAAAwqQY9Y+vHJZ0bEZcuXBER/17SjHrvXIgxMrXw+iITTr1OrZJXL611OfU6tR6LiPib/ucvR8T+RR9fjoj9rcdXldvz06mX1rqcemmty623hcgBN+Lon521U9IvS9qi3jv5XJqZHxzZ6IZoeno6Z2ZmWg+jtIh2+7q04NRLa11uvcCQTfzO68yPRs/p71mnVsmr16kVwDFpMjcadPP4/yHpP0l6taQ3S/ohSd82KYtakjToAl4F+/btaz2ETjn1OrVKXr201uXU69Q6DBHxqIh4QP/r8yPiBRGxscsxMD+qy6mX1tHatevwz13isa3JqVXy621h0Jci/oOkb5X005KOl/QYSb8XEa+IiOeManDD5DRxO3jwYOshdKpFb6t/4Hls66K1Lqdep9Yh+TNJX4+IR0t6k6RHSvpfXQ6A+VFdTr20js6uXdLWrb2vt25l7jtKtNbl1tvCQAtbmbk9My/LzKdk5iZJ50n6XUn7JX3/KAcIjJvW/8ADAMqYz8x7JP2ApNdn5s9JemjjMQHAvXbulObmel/PzfUuA8C4GfSMraW2SPoFSRdJetfwhgOMP/6Br6vlqfYALH0tIp4r6RJJ7+lfd3zD8QDAYc4/X1rY93pqqncZAMbNoHtsnbrkqp9Xb1HrAklXDntQo7BmzWrX8CbPqacufbhq67q35T/wPLaj0/pMPKfH1qlV8up1ah2SH5N0rqRXZOZnI+KRkt7W5QCYH9XVdW/LXw45PbZdt557rrRjR+/rHTt6l7vEY1uTU6vk19vCoLOZ342IX46Idf3L+yT9sHqbyE/E21I77SExt3A6kYmue1v+A89jOzqtz8RzemydWiWvXqfWYcjMGzLzBZn59v7lz2bmqzoeQ5d315Tb87PL3ta/HHJ6bFu0Lsx1u17Uktr0RqP3u+V5XJdbbwuD7rH1TPU2kH9PRPyIpBdKmpe0XtIzRze84XGauO3du7f1EDrVorfVP/A8tqPT+lR7p8fWqVXy6nVqHYaIeHJEXBcR/xwRt0TEZyPili7HwPyori57W/9yyOmxdWqVvHpprcutt4WBzz/PzGskfa+kjertq3VjZv52Zt45qsEBQFdan2oPwNKbJL1W0ndIepKk6f5nYKK0/uUQAMDboHtsXRQRfyPpryR9QtJzJP1ARLw9Ih41ygECQFdanmrfitHJGsA4+lJmvjcz78jMuxY+Wg8KuL/45RAAoKW1Ax73a+ptbnqCpGsz8xxJL4qIMyS9Qr2FrrEWrV4s3cDGjRs7v8/FG4Z2PZlp0duKU6vk1UtrXU69Tq1D8tcR8Rr1zoT/6sKVmfnRrgbA/Kiurntb/nLI6bF1apW8emmty623hUEXtr6k3uLVCZLuWLgyM2/SBCxqSV4Tt/Xr13d6f0s3DO36N3Vd97bk1Cp59dJal1OvU+uQfFv/8/Si61LSd3c1AOZHdTn10lqXUy+tdbn1tjDoHls/oN5G8feo926IE2d+fr71EDqze/fuTu+v9YahXfe25NQqefXSWpdTr1PrMGTmdy3z0dmilsT8qDKnXlrrcuqltS633hYGOmMrM2cl/c6Ix4IJtbBh6N13s2EoAACDioiHSPp1Sadl5tMj4kxJ52bmmxoPDQA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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import glob\n", "input='/home/alejandro/CL_feedbacks/data/'\n", "input_data='/home/alejandro/CL_feedbacks/data_prod/'\n", "%matplotlib inline\n", "fig, ax = plt.subplots(1,2, figsize=(17,6), sharey=False, sharex=True)\n", "fig.add_subplot(111, frameon=False)\n", "plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n", "plt.tick_params(axis='y', which='both', labelleft=False, labelright=True)\n", "plt.xlabel('Latitude [Degree]', labelpad=10)\n", "intervals=['(10S,10N)','(10,20)S/N','(20,30)S/N','(30,40)S/N','(40,50)S/N','(50,60)S/N',\n", " '(60,70)S/N','(70,80)S/N','(80,90)S/N']\n", "domain='Local'\n", "m_cth=[];top_cth_IC=[];bot_cth_IC=[];m_ctt=[];top_ctt_IC=[];bot_ctt_IC=[]\n", "for H in ('North', 'South'):\n", " DATA = np.load(input_data+'CTH_NOSI_'+H+'_Had5.npz')\n", " m_cth.append(DATA['reg_cth_'+domain])\n", " top_cth_IC.append(DATA['top_cth_IC_'+domain]) \n", " bot_cth_IC.append(DATA['bot_cth_IC_'+domain])\n", " m_ctt.append(DATA['reg_ctt_'+domain])\n", " top_ctt_IC.append(DATA['top_ctt_IC_'+domain]) \n", " bot_ctt_IC.append(DATA['bot_ctt_IC_'+domain])\n", "if domain=='Global':\n", " c='red'\n", "else:\n", " c='blue'\n", "m_cth=np.mean(m_cth,axis=0)\n", "top_cth_IC=np.mean(top_cth_IC,axis=0)\n", "bot_cth_IC=np.mean(bot_cth_IC,axis=0)\n", "m_ctt=np.mean(m_ctt,axis=0)\n", "top_ctt_IC=np.mean(top_ctt_IC,axis=0)\n", "bot_ctt_IC=np.mean(bot_ctt_IC,axis=0)\n", "for i in range(len(intervals)):\n", " ax[0].scatter(i,m_cth[i],marker='.',color=c)\n", " ax[1].scatter(i,m_ctt[i],marker='.',color=c)\n", " ax[0].plot([i,i],[bot_cth_IC[i],top_cth_IC[i]], color=c, linewidth=1)\n", " ax[1].plot([i,i],[bot_ctt_IC[i],top_ctt_IC[i]], color=c, linewidth=1)\n", " ax[0].vlines(x=i-0.5,ymin=-0.8,ymax=0.7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[0].vlines(x=i+0.5,ymin=-0.8,ymax=0.7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[1].vlines(x=i-0.5,ymin=-4,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[1].vlines(x=i+0.5,ymin=-4,ymax=7,linestyle='--', color='black', lw=1,alpha=0.05)\n", "ax[0].hlines(y=0, xmin=-0.5, xmax=9,linestyle='--', color='black', lw=1)\n", "ax[1].hlines(y=0, xmin=-0.5, xmax=9,linestyle='--', color='black', lw=1)\n", "ax[0].spines['right'].set_color('none')\n", "ax[0].spines['top'].set_color('none')\n", "ax[1].spines['right'].set_color('none')\n", "ax[1].spines['top'].set_color('none')\n", "ax[1].set_xlim(-0.5,8.5)\n", "ax[0].set_title('Cloud top height')\n", "ax[1].set_title('Cloud top temperature')\n", "ax[0].set_ylabel('[ $\\% K^{-1}$]')\n", "ax[1].set_ylabel('[Dimensionless]')\n", "ax[0].xaxis.set_ticks(np.arange(0, 9, 1))\n", "ax[0].set_xticklabels(intervals)\n", "fig.tight_layout() \n", "plt.savefig('cloud_H_and_T.pdf', format='pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Figure 6. Cloud top height (left) and Cloud top temperature (right) regresion coefficients. Bars indicate the 95% confidence intervals.*" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import glob\n", "input='/home/alejandro/CL_feedbacks/data/'\n", "input_data='/home/alejandro/CL_feedbacks/data_prod/'\n", "%matplotlib inline\n", "fig, ax = plt.subplots(1,2, figsize=(17,6), sharey=False, sharex=True)\n", "fig.add_subplot(111, frameon=False)\n", "plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n", "plt.tick_params(axis='y', which='both', labelleft=False, labelright=True)\n", "plt.xlabel('Latitude [Degree]', labelpad=10)\n", "intervals=['(10S,10N)','(10,20)S/N','(20,30)S/N','(30,40)S/N','(40,50)S/N','(50,60)S/N',\n", " '(60,70)S/N','(70,80)S/N','(80,90)S/N']\n", "domain='Local'\n", "m_cth=[];top_cth_IC=[];bot_cth_IC=[];m_ctt=[];top_ctt_IC=[];bot_ctt_IC=[]\n", "for H in ('North', 'South'):\n", " DATA = np.load(input_data+'optical_Ftropics_NOI_NOSI_'+H+'_Had5.npz')\n", " m_cth.append(DATA['reg_clda_'+domain])\n", " top_cth_IC.append(DATA['top_clda_IC_'+domain]) \n", " bot_cth_IC.append(DATA['bot_clda_IC_'+domain])\n", " m_ctt.append(DATA['reg_cldt_'+domain])\n", " top_ctt_IC.append(DATA['top_cldt_IC_'+domain]) \n", " bot_ctt_IC.append(DATA['bot_cldt_IC_'+domain])\n", "c='blue'\n", "m_cth=np.mean(m_cth,axis=0)\n", "top_cth_IC=np.mean(top_cth_IC,axis=0)\n", "bot_cth_IC=np.mean(bot_cth_IC,axis=0)\n", "m_ctt=np.mean(m_ctt,axis=0)\n", "top_ctt_IC=np.mean(top_ctt_IC,axis=0)\n", "bot_ctt_IC=np.mean(bot_ctt_IC,axis=0)\n", "for i in range(len(intervals)):\n", " ax[0].scatter(i,m_cth[i],marker='.',color=c)\n", " ax[1].scatter(i,m_ctt[i],marker='.',color=c)\n", " ax[0].plot([i,i],[bot_cth_IC[i],top_cth_IC[i]], color=c, linewidth=1)\n", " ax[1].plot([i,i],[bot_ctt_IC[i],top_ctt_IC[i]], color=c, linewidth=1)\n", " ax[0].vlines(x=i-0.5,ymin=-10,ymax=16,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[0].vlines(x=i+0.5,ymin=-10,ymax=16,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[1].vlines(x=i-0.5,ymin=-1,ymax=0.9,linestyle='--', color='black', lw=1,alpha=0.05)\n", " ax[1].vlines(x=i+0.5,ymin=-1,ymax=0.9,linestyle='--', color='black', lw=1,alpha=0.05)\n", "ax[0].hlines(y=0, xmin=-0.5, xmax=9,linestyle='--', color='black', lw=1)\n", "ax[1].hlines(y=0, xmin=-0.5, xmax=9,linestyle='--', color='black', lw=1)\n", "ax[0].spines['right'].set_color('none')\n", "ax[0].spines['top'].set_color('none')\n", "ax[1].spines['right'].set_color('none')\n", "ax[1].spines['top'].set_color('none')\n", "ax[1].set_xlim(-0.5,8.5)\n", "ax[0].set_ylim(-5,5)\n", "ax[0].set_title('Cloud fraction')\n", "ax[1].set_title('Optical Depth')\n", "ax[0].set_ylabel('[ $\\% K^{-1}$]')\n", "ax[1].set_ylabel('[ $K^{-1}$]')\n", "ax[0].xaxis.set_ticks(np.arange(0, 9, 1))\n", "ax[0].set_xticklabels(intervals)\n", "fig.tight_layout()\n", "plt.savefig('cloud_properties.pdf', format='pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Figure 7. Cloud fraction (left) and optical depth (right) regression coefficients. Bars indicate the 95% confidence intervals.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.2 Simulated feeedbacks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observed feedbacks (blue boxes) and simulated feedbacks are shown in Figure 8. Tropical longwave feedbacks are on average biased from observations, for both CMIP6 and AMIP datasets (top). In CMIP6 (black) the difference locates in the deep tropics (10S to 10N), whereas for AMIP (green) it comes from 10S/N to 30S/N. For shortwave feedbacks, CMIP6 models exhibit a remarkable agreement with observation across all latitudes. This is not the case for AMIP, particularly within the tropics. By decomposing the all-sky feedback into CS and CRE, it is possible to attribute those biases to the simulation of tropical clouds (middle and bottom). " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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yrCo/+vXr5/e8fv36VXlefUyfPp1Vq1axaNEibrrppiqPffDBB4mLi+PVV1/ljTfeAKCsrIyTJ0/y9ddf88ILLzBnzpwq+zRt2jQ6duxIaGgow4YNIy0trcL+2NhYVq9ezcMPP8wXX3xBx44dK7WRmJjI5MmT6dq1K0FBQcydO5eNGzcCYLfbueGGGyqd8/XXX3PppZfSv39/ALp06QI4A1w33ngjI0aM4Fe/+hV79+6t8jUQERERkdpTAEpERKQFefbZZwkLC6uwLSwsjGeffbbObQ4fPpxt27YF3B8SEsKYMWP44x//6Ddw481dA2rVqlWMGDECgN69ezNr1iyMMYwfPx6bzUZWVlbANtxT9cAZLCorK6uwf8iQIWzbto3Y2FgeffTRSplMQJUBrtDQUOx2u99z/E1XfPzxx5kyZQp79uxhxYoVFBcXB2xbREREROpGASgREZEWZO7cubz66qv069cPYwz9+vXj1VdfZe7cuXVuc+rUqZw5c4bXXnvNsy0xMbFC5tH999/P888/T0RERK3bv+6661i7di3gnI5XUlJCZGRknfubmZlJWFgY8+bN44EHHmD79u0AtG/fnlOnTgEwYcIENmzYQFZWFg6Hg0WLFnHZZZdV2e7EiRPZsGEDhw4dAvBMwcvLyyMqKgpwrtInIiIiIg1PNaBERERamLlz59Yr4OTLGMOyZcv45S9/yR/+8AdCQ0OJjo7mpZde8hwzfPjwKle/q8rtt9/O7bffzogRIwgJCeGtt96qtjB6VXbv3s2DDz6IzWYjODiYV155BYD58+czY8YMevbsybp16/j973/PlClTsCyLK6+8kmuvvbbKdrt27cqrr77KrFmzKC8vp1u3bqxatYqHHnqI2267jT/96U9MnTq1zv0WEQkkPS+dPh37VH+giMg5zNS3bkRrMXbsWMu9Ao6IiIhII6h71M2LxixNZ+XKlcyYMaO5uyHngaSsJGIiY5q7GyIi3hpk3FIbmoInIiIiIiIiIiKNSgEoERERaXCfffYZcXFxFT6uv/765u6WiF9btmxp7i6IiIic81QDSkRERBrcFVdcwRVXXNHc3RCpEXdBehEREWk8yoASEREREREREZFGpQCUiIiIiIiIiIg0KgWgRERERERERESkUSkAJSIicp5YtmwZxhj2798PwOHDhzHG8Pjjj3uOycrKIjg4mHvuuQeAp556ihdffBGA+Ph4+vfvT1xcHKNHj2bz5s1N/yRE5LyVnpfe3F0QEZF6UABKRESkhVm4eyHRL0Vje9pG9EvRLNy9sEHaXbRoERdffDHvvPOOZ9uAAQP46KOPPI/fffddhg8fHrCNF154gZ07d/KHP/yBO++8s0H6JSJSE0WlRc3dBRERqQcFoERERFqQhbsXMn/FfNLy0rCwSMtLY/6K+fUOQhUUFPDVV1/xxhtvVAhAtW3blqFDh7J161YAFi9ezJw5c6pt79JLL+XgwYP16pOIiIiInD+CmrsDIiIi5xPztKn1OUWlRcxbOo95S+cFPMZ60qqyjQ8++IDp06czZMgQunTpwvbt2+nSpQsAN998M++88w49evTAbrfTq1cvMjMzq2xvxYoVxMbG1vq5iIiIiMj5SRlQIiIi54FFixZx8803A86A06JFizz7pk+fzqpVq1i0aBE33XRTle08+OCDxMXF8eqrr/LGG280ap9FRERE5NyhDCgREZEmVF2mUvRL0aTlpVXa3q9jPw7/8nCdrpmdnc3atWvZs2cPxhgcDgfGGH7+858DEBISwpgxY/jjH//I3r17WbFiRcC2XnjhBWbPnl2nfoiIiIjI+UsZUCIiIi3Is9OeJSw4rMK2sOAwnp32bJ3bfO+997j11ltJS0vj8OHDpKen079/f44ePeo55v777+f5558nIiKiztcREREREQlEASgREZEWZG7sXF69+lX6deyHwdCvYz9evfpV5sbOrXObixYt4vrrr6+w7YYbbuC5557zPB4+fDi33XZbna8hIiIiIlIVY1lVTwU4V4wdO9Zyr/AjIiIi0ghqX2HeD41Zms7KlSuZMWOG57O0bElZScRExjR3N+qkNfddYMuWLUyYMKG5uyHS0Bpk3FIbyoASERERERERCSAnJ6e5uyByTlAASkREREREWr0tW7Y0dxdqJT0vvbm7ICLSpBSAEhERERFp5Vpb8KUxtLYslaLSoubugohIk1IASkRERESklWttwRcRETn/KAAlIiIiIuclh8PR3F1ocMqEanh6TUVEGkZQc3dARERERKSpJSQk8Mknn5CVlUX37t1rdE5rWAlLmVANT6+piEjDUAaUiIjIeeDo0aNce+21DB48mIEDB3LfffdRUlJCQkIC99xzT3N3jw8++IB9+/Z5Hj/xxBOsXr26GXsk5xrvLJaysjKSkpKw2+0kJSXVOBNKgQgREZG6UwBKRETkHGdZFrNmzeK6667jwIEDJCcnU1BQwG9/+9tGuV5ZWVmtz/ENQD3zzDP88Ic/bMhuyXnOO3gUFBRETEwMDoeDmJgY7HZ7M/asepoC1njOhdf2XHgOInJ+UABKRETkHLd27VpCQ0P5yU9+AoDdbuf//u//ePPNNykqKiI9PZ3p06cTExPD008/DUBhYSFXXXUVI0eOZMSIESxevBiAbdu2cdlllzFmzBiuuOIKvvvuOwAmT57Mb37zGy677DKeffZZoqOjKS8vB6CoqIg+ffpQWlrKa6+9xrhx4xg5ciQ33HADRUVFbNq0iQ8//JAHH3yQuLg4UlJSiI+P57333gNgzZo1jBo1itjYWG6//XbOnDkDQHR0NE8++SSjR48mNjaW/fv3A7Bhwwbi4uKIi4tj1KhRnDp1qulebGk14uPjufXWW4mPj2/ya9c2YKDMq8ZzLry258JzEJHzg2pAiYiItECbN8P69TB5MkycWL+29u7dy5gxYyps69ChA3379qWsrIxvvvmGPXv2EBYWxrhx47jqqqtIS0ujV69efPzxxwDk5eVRWlrKvffey/Lly+natSuLFy/mt7/9LW+++SYAubm5bNiwAYDt27ezYcMGpkyZwooVK7jiiisIDg5m1qxZ3HHHHQA89thjvPHGG9x7771cc801zJw5k9mzZ1foZ3FxMfHx8axZs4YhQ4Zw66238sorr/DLX/4SgMjISLZv387f//53XnzxRV5//XVefPFF/va3vzFp0iQKCgoIDQ2t3wso56zmynxqjIDB8eOQnAwZGc7P56KMYoiJrN05raFul4jI+UIBKBERkRZm82aYNg1KSiAkBNasqV8QyrIsjDEBt19++eVEREQAMGvWLL788kuuvPJKHnjgAR5++GFmzpzJJZdcwp49e9izZw+XX3454FxBrGfPnp72brrppgpfL168mClTpvDOO+/w85//HIA9e/bw2GOPkZubS0FBAVdccUWVfU9KSqJ///4MGTIEgNtuu42//e1vngDUrFmzABgzZgxLly4FYNKkSfz6179m7ty5zJo1i969e9fpdRNpTUpLITwcQkOdn5vb9u1bGD26YQM/Jfm1P6e2wT4FrKQm9HMiUjeagiciItLCrF/vDD45HM7P69fXr73hw4ezdevWCtvy8/NJT0/HbrdXCk4ZYxgyZAjbtm0jNjaWRx99lGeeeQbLshg+fDg7d+5k586d7N69m88//9xzXrt27TxfX3PNNaxcuZKcnBy2bdvG1KlTAee0p7/+9a/s3r2bJ598kuLi4ir7bllWlfvbtGkDODNZ3LWnHnnkEV5//XVOnz7NhRde6JmaJxJITYuQS83l5rbOaWGaziY1oZ8TkbpRAEpERKSFmTzZmflktzs/T55cv/amTZtGUVERCxYsAJxvtu+//37i4+MJCwtj1apV5OTkcPr0aT744AMmTZpEZmYmYWFhzJs3jwceeIDt27cTExPDiRMn2Lx5MwClpaXs3bvX7zXDw8MZP3489913HzNnzvRMdTp16hQ9e/aktLSUhQsXeo5v376931pNF1xwAYcPH+bgwYMA/Otf/+Kyyy6r8vmmpKQQGxvLww8/zNixYxWAEg9/tZcSEhJYsGABCQkJTd+hBlDTelLbt6tQdSDpeenN3QURkfOCAlAiIiItzMSJzml3v/td/affgTOjadmyZbz77rsMHjyYIUOGEBoaynPPPQfAxRdfzI9//GPi4uK44YYbGDt2LLt372b8+PHExcXx7LPP8thjjxESEsJ7773Hww8/zMiRI4mLi2PTpk0Br3vTTTfx9ttvV5ia97vf/Y4JEyZw+eWXc8EFF3i233zzzbzwwguMGjWKlJQUz/bQ0FD++c9/cuONNxIbG4vNZuOuu+6q8vm+9NJLjBgxgpEjR9K2bVtmzJhR15dOzjG+WQsOh4OkpCTsdjtJSUl1WsGxMdQ0qLRly5YaZ2K01oykplBUWtTcXRAROS+oBpSIiEgLNHFi/QNP3vr06cOKFSsqbY+Pj/e7CtgVV1zhtz5TXFwcGzdurLR9vZ95grNnz640he7uu+/m7rvvrnTspEmT2Ldvn+exdzbKtGnT2LFjR6VzDh8+7Pl67Nixnj68/PLLlY4V8cdutxMTE0NKSgoxMTEEBdV8aNyYNWBqGlTSNKDWo9wqb+4uSC2oxpNI41AASkRERETOW/Hx8URGRjJz5sxanafgj9RUYkYi3xz9hoKSAsZFjWvu7kgN6PdbpHFoCp6IiIiInNfcNcqaQk2n1zV2G9I0yq1yMvIzsNlsZORneDKh9D0UkfORAlAiIiIiIk2kITIrzpfsjOoKp7eGII7N2IjqEEV5eTlRHaKwGefbr4b4HraG5y8i4k0BKBERERERaTHcgafqCqc3ZCCuNsGc2gZ+xkWNY+qAqQ0+/e58CUSKyLlDASgREREREWkxmmPFvtoEc3JyckjPSwfwfK6OO/PJW0MUJq/p9UVEWgIFoM4TStEVEREREWkYRaVFFT7XVnJ2MmtT15KYkdgg/QhEAaqG4XA4mrsLIucEBaDOE0rRFREREalaU9+wc1+vpQQJ3FPfqqu9JPVTbpWTXZRdqTB5Y6hrgEzOSkhIYMGCBSQkJDR3V0RaPQWgRERERERo+ht27uu1lCCBe+pbc0yBO5/YjI2IsIhKhcml5SkrKyMpKQm73U5SUhJlZWXN3SWRVk1/7URERERERLx4Z8OdOHOiwdsfEjGkUQqTS8MKCgoiJiYGh8NBTEwMQUFBzd0lkVZNASgRERERkXpSvc1zi3c23JnyM1Ue6/u9935c1fTKlpj5pJ/jyuLj47n11luJj49v7q6ItHoK4YqIiIjIOe/bzBN0sLqSnHx2W0YGJCdX/hyI937fY3fvzqFz5+r7kZHh/Ox9vUP5YHIqHuOvn/767W4rK8v5dWMWS84oSCcqvE+jtd9a+U7d9H68eetm+kxrPa+Z6sb6Z7fbm7sLIucEBaCa2JYtW5gwYUJzd0NERETkvFJUcoZu7SE8/Oy20FDnY9/PgXjv9z22unO9j4OK1wsrq7ot38eFQSc857vbKiuDJUsSWLPmE06ezGLOnPjqO1NLp8tqX6tq+/YtjB59/o598/LymrsLfuk9iYg0h5aX91lDxpjDxpjdxpidxpitzd2fmtJdBRERERGpjxI/U8IcDgcHDzqLJR88eLZYcnOvaNdYBc2bcuVAR7mjya/Z2JrzPYmm+Ymcv1ptAMplimVZcZZljW3ujoiIiIiINBe73c6gQc5iyYMGnS2W3NQr2jVVwKuxVg70LTiekJDA8g+Wk5CQUK9r7tq1q75dO2fohrzI+au1B6BERERERFqcxgzEuDNyKmxzOJgzJ57Zs29tlOl3NdWYAS/LKq9yf0PUv/IuOF5WVkZSUhI2u42kpCTKHGV1brelTsUTEWlKrTkAZQGfG2O2GWPm+zvAGDPfGLPVGLP1xImGXz5VREREpCFozHLuaaxAzJIlCXz26XKWLEmosG316gUsWZLQoMWSqwqibd0KL7/s/NwUdh5P5Otja0nMSPS7PyEhgQULFpCQkFCrdqsKWgUFBRETE0O5o5yYmBiC7C2zfG5jFp6Xis6laZgizaE1B6AmWZY1GpgB/Lcx5lLfAyzLetWyrLGWZY3t2rVr0/dQREREpAY0Zmk9mrOmUpQtgBoAACAASURBVFlZGQcPOjNyDh5MwuFwVKr91JDBiEBBtK1b4aab4H//1/m5sYNQ27Zv5lhRBsZmIyM/g3KfTCh3ppLdbndmKpXVLFPJHbR6f+n7AY+Jj4/n2uuuJT4+vj5PoUr1CWosXba0ToE3qZvGmvopcr5otQEoy7IyXZ+PA8uA8c3bIxERERE517mDMs2RdRIUFMSgQc6MnEGDYrDb7ZVqPzXFcvGbN0NJCZSXOz9v3ty418vLzaVHWBRWeTlRHaKwmYpvYdyZSg6Hw5mpFFR9ppI7aJVnz2N92nr2H99fYb93LSi7rXFf07oGNRwOB4cOHap14E3qT1lnInXTKgNQxph2xpj27q+BHwF7mrdXLZN7lQn9kRQRERFpGEuWJPDeewsqTIMLpKEzpubMieeK6ddWqPM0Z048P/xh09V+mjgRQkIAthAS4nzsy+FwkFHQcNOV4rqN48IeUxkXNc7v/vj4eG699dYaZyoFBQUxeMhgCqwCenbvyckzJytkVp3xs9JgS2O324mOjq428KZV5xpWXad7igi0zInM1esOLDPGgPM5/NuyrE+bt0stU05ODgkJCXzyySdkZWU1avqwiIiIyLnOd8pbWVlZlRk3vtPYHA5HrbOUfG8k+svIqUvmU0ZBOlHhfSptq87YsbB4Mbz9dg7z5jkfe1uyJIE1az4hNmsUw7rH+W1j+/YtjB49oVb9Nabqe+f+XoNAN2G3bNnC7T+5nTxbHiEXhFB2oAybsfkt8N5SJWcn03FkRya2mUj8bfEBj9Oqcw2nzFF5umdQUBBbtmxhwoTa/TyLnI9aZQDKsqxUYGRz96M1cDgcfv9IioiIiEjtuae8paWlMGhQ5ayTqgIr7sBMr169mTp1RqX9/s51n3PyZJbfDKfaZrl7X+N0WeWpX/62+Wtj7NgJ5OefDT652/UO0KUdOURMZKzfNupSpP34cUhOdn59KB8yMs4+hsqPly1LYP36T9i7N4vrr4+v0Nbu3Tl07gztSy8gyhZNRvFhnn8+gY++Xk5eSiilHc62dex7i+Rk5zVNjvM6pqvz6+r68f33zq/dn72P8W7DuF6ONm2gX7/qX4tyq5zjp47T29bbk73lOzWxqaXnpdOnY5/qD2zFguzO6Z4pKSkVss4U5BOpGUUiznF2u93vH0kRERERqR139tKcOfF07hzJ5ZfPrHRMoMCKu4C43W4nM/Oo33o9KVlJjOZsACox8asqs63Wr/+M77/P5OTJLJwTBM7yDWa5A1UNsTqfvzbc27wDdP369m/QmlQlJRAe7nxuHQZ0IjTU+djN+3FZWRnp6Um0aWMnPT2J0NCKr5372NBQaNfOht1eSnp6EiEhNtLTk+gd14/wcOfqewcLdnKwKJHwsA6ec8LCnOeHlVFlP9q0cX7t/ux9TFgYJCc7n4t7X0FBzV6LBW8t4Ov1X9OrpBdjI8Y2e/AJzp8C3fHx8URGRjJzZuXffxGpWvP/pZJGV9s58SIiIiKtTWMsj+5dv8ld9+mLL1YBtZ/y5i4g7nA46NWrt9+bgiU+dYdOncqvUGDc+5x///sN1q1bSUbGEb+r33kHiWpTs6ohzJkTz+zZtzJ9xqxGab8mQTTv19tfppq3T1cuZenSt8nOPnG2wLvNTrlV7lx9z9g4VpSB5bP6Xk1YWPV+Lr7cBdS72boR+n0oAzsNrHUbgTR33djWUq+qKYr9i5yLlA5zntAfSRERETmX1SX7olOnLlXudwcHapK9VBPuzKnajMv8ZVs5yh0cPZxKhw6dycs7Sf/+gzh92n+bvjWrOneOrHRMoGwpX/v27WLQRTE16nd9xp51CYL4q11VVaaa97XSjjhXkouI6ErsD0YzZ2Y8Cz99DZuxOVffs8rpERbFt9/uZvBFQ2vcp53HE9mbvZOdxxNr/Xyq4l71LyUlhQH9B1T5WpfXImhW37qxu3btImZazX4+AmkpU9maOxAncq5SBlQTq80fs9ZyB0BERESkpfMtLp1RkF7jItg1yV6qjjubqi6BGd9z7DbnNLfevfsyZcoMbrnlp1We650J5O/63lk4n65cGjBb6lR+Xo36W5/V7+qarRWodpX7+br75Lsqod1up1/f/p7XJzgouML+uG7jGB4RR1y3cTV+/kDl7KlqMqECCfR+wD3DYdb1gbPMEjMSWZu6luTs5IDHuLmzqrzrxtZWXl7NX5+WTKvciTQeBaCaUG3/mLWUOwAiIiIirVlCQgKrVy/3TJ/bvn1LhYBFdZlQcHZa2SWXXF5hu29AI5DaTLUKdMPSO7Dj7s/kyVdU2577WH9FzH2v684G8jetr6ZqUsjcm/t5eWea+bt+auquSv2tbZ/8fR+mz5hV5etjMDW6rvfPgm/2lG8bNVXV+4HqMp8y8jOw2WxkF2VXyoTyfQ7urCqHw3Fe141tiECciASmAFQT0R8zERERkabnHoPZbDaOHj1CWVlZpSBETTOhqsoeyi47Ueu++QtkVJUB5BvY8e5PnlX19QMFK7yDJt7ZQBEDajdVsD7cz8u3bpOvwsKzGTZVZWrVhe9z9c2Yc3N/fz5duRSgQl0o358r7+wpXw6Hg7ZBYZXaaAhbtmzBZmxEdYiivLyciLCICkXKA90Ub+i6sa1xNocCcSKNS79RTcR7rrb+mImIiIg0DfcY7P31HxF2OpSlS98mMrJ79SfWkm8B8eosWZLAmjWfcPJkFqN/NBFwBj3SDqZWWa8p4PWp3fXdfIMm02fMIiYylv7jBnNke2qN2wkUgPMNsvnWm/Llrtt08mQW7723gJMnsyp9v3wztRr6xu6SJQl89uVyQgtCK2RFeX9/0o4cYtt3X7M7ZxtleWVMZYbftvxlPrm/99NOXkl+cB5HMlMpLC3wG6iqC3fW1LiocYSHhHM457Bnn7+b4t7vSxoy6Bgoe+vY6WMNdo2Glp6XrlXuRBqRMqAaib+If2tfja413sUQERGRxtGaxgXz5s2jW7deREcP8ju1qz41i+qitLS0wlSzPbu3A2drOzV1BpKvulzXHYDzfm39ZSkFmoroO5Wxqql4vnWbGvLGrqPcWbTdZrdVCm55f3/69InmxJljGGPjZHHlKW6BeE8zPHBwP9lFxz11ompTMLymvDOfwBmQjRwY2WwZPgkJCSxZuqTF1ldyL2agBZxEGofScBpJoIh/a/5jpppUIiIi4taaxgVBQUF0796Tku+K/Bbirm3Novr44otVZGYeJTy8gyeAUlhY4NnvzgCqbQZSQ6iuplJ1hbTdmT2xWaOIiYytkKWUmPgV48ZNCniud2DKXTg9LS0lYOF0d6aWvxXuqsuyqoo7yLT32E6/wS3v709BST7HCjPoGNy5UqAnEPc0w7S0FAYPuoD84DxPnaiatlFfM6+bSUyPmCbJ8EnPS6dPxz6A13RYu81v9pWInPuUASUiIiIi57xRoyZVW4i7Pkuvu2sGbdq0jvHjo/nxj69k/PhoNm1a5zmmrKyMzMyj2O12IiK6MmvWPL/98Q641Dc7q6bPybe2ka+dxxPZm72TnccT/V/HlTnknp4GVMhSOnUq3+95gYq4exdODzS9L9CN3doUfA907R9Nvzrgz4r7unHdxpG/K49tqzbXqhaV93Mb0HEIk3pNbbDpdzXVVDfF3RlFcHY6bLmjXCVJRM5T+q1vJlu2bGHChAkV7grUtQ0RERERqV5Vb7q9azJVt1qcv3M/+3I5e9bvYMmSBEpLSwDIyEjj9ddfYujQH1DevpygoCB69epNZuZRBg2KITg4uNq2/WVnhXdoX+N+uZ9TVXWvHI6KwaOYyNgK+8utco4VZXimigUTUqkNd+ZQWloK/fr2x263V8hSWrt2pd9r5+bm0IFOfve5v1+1ra9VlZoUi995PJH9J/ew83hilYGhsrIy0tMP16lml/fPommizKeWID4+nuLwYuJnxzd3V2qlS5fqV8oUkeqdP3/tWhh32rr3XYG6ttEYWlNdBxEREZH6cDjKKtQcSkz8qhbnnq0ZtHz5O57gk1tpaSm/+MWPefyZXzBjxli++eYrTp3K58iRQ6xZ8wmffbacI+mHyMhIp8xRWu31dh5P5GTH7ICZSL79ClRHyZt7ypvD4fAEj7zZjI0eYVGeqWL+CmvD2cye6TNmVWi7vgKtSFcX1QWzLKwKwbaq6jIFBQVVyPJyP9eqglyfff2h3+2BMsHOJe73F3Zb6ytJopv+Ig1DGVDnuLqmkremug4iIiIi9WG3B1WoORRoupj/c8/WDCooCHzeqfw8/nNom+fxhg2fAZCQ8FeIgL8+9XuIgBcfeZKwsHB6joiiTUEo/cYMwGTbKCwsIDlnL5EDu1MaVubJRAoUDKqujtL27VvoMOBs5lF1tafiuo1jT8QO4rqNYy3+s5nc121I3ivSNTR/0xsNpkKwrbq6TP6yvPwFueyW821XVu73ftup77TB1uBcfn9Rn+m7IucTBaDOUel56axZtoZPPvmErKwshg4d6vc4TeMTERERORuAqWq6WFXnHi5IYcPSz7CsyoW6e/Xqw22//jkTh0xm9eqPyc3NoXPnLuzcmYjdbmf/id2U2Us5Xn6MnOwscnKyOHr6MGTDl9+ugWx4++3/BxFANhABbYvb0bF9Jzp16sx77y0greAEHUpyyco6Th65FGTkc/XVc+jcOYLLL7+60nPKzc2hvdWhwjbf4JHvm+pAwa7GkFGQXiG77ODBJEzXfp79VgOsGOdveqOFVSHY5rvPn6qCbo5yB0uWJLBuzUpspwx0CHhoBQ05Rvf+Pm7ZsoVOA/1PeZS6SUhI8Lznaq2rnYs0FQWgzlH5xfkkJTnTrpOSkpg3b57f487lOxEiIiIitVHX7J2jR9N4882XXcEnA+5ARQcIKgrh0Ud/z+mwIsaMuZC8vJMATJ06g7VrVzJ16gwWfvoac6ffwYJP/h9XjLmGjz9+j+AeIRz4eh9WFzi8LYWQkGD2fr8Lq20535dlcjq7kNOFhRw7lsH+/XsgAnauWeO87rtANjzy3F0Enw6mR48o2rQJZdGi1ykpKWH37u0kHd1Dx+860y6nHcN7j6a8vGJA59OVS9m9foffmlibNq3jkUfuJiMjjaiofjzyyLMQVqeXLqDTZUUVsssGDYrh0CnnFMXUvGSOZKZSllfGVGbUqL2aBKzchdb/b9vveD3zJR557S56hfdlSpvpDD8eV6EIu2WVV8oi87VkSQIrv1hGcF4Q4eEdOHgwid5x/QIe762mY/Sw4Kpf+KXLlrJj1Q5PcCQnJ0cBqAbkXtnP/Z5LK/uJVE2/HS1YfVI5g+zOVSZSUlK0yoSIiIic86qq1dOY8vPz+a//uoJT+XkMHBhDeHh7kpK+pbi4kHadOnHr3DuYNWsuCz99rdq27DYb3bv3JDp6EH1HDyAqtC99Rw/gyA9SKwSqDp7cTwTdWLFiCbm5OfTq1Ycla5YztFsfjh//jrTCFI7tySQ/OJfT+UWkpx8G4ODB/QCsXv2R84Kf4cyoAu6778e0b9+RbsN60rYwjILQfOwngzh1Ko927drTs2dvTuZms2TJW7z++p8rFFp/6KH5XPvTW5g7/Y4Kz6chspTmzHEWrZ5zZTzPv/Ea5VY5J4uz6WZ6crI4m3KrPOA0OfcUu53HE9l54hsKSwsCXsdd++lQ2UHe2fmmZxpdRkEaSwoTCE1p66kLdSTvsDMAllbG5AHT/bZXVuasKxYUFIRlOWuBDRoUQ6mt+jpftdGnYx/2sCdgHw4dOuQJjnz1Vc1rm0nNuFf203sukZrRb0gjOXGm+hU2quIvlbMmqbjex8THxxMZGcnMmTPr1RcRERGRliwxI5Fvjn5DQUkB46Kabjn7wsIC/vjHJ0hNTab78CimTJzBiRPHuPjiHzJmzEQGTojhUOKBOrcf8GakMXTu1IXevfvRu3c/pk6dQUpeMQ//1BkAOpibxJHtqfQdPYDkTXu54IJYPv30A9LTU9m6dTM9evQiu+AEBWGnOJWUR9GpQgoKTpGbm0NuUo5nmh/ZkJy8lw0bPnde1z0F0Mfp00UsW/ZvRvUZT0ZGOvnBuSQl7yEpfA9BuSE1zlIKxLtotc3Y6BwagWWV0zk0osoaTafLiioVFQ80jQ4LCksL+bpkPaVUDBKVWGdYevBtZtpupFvbnuwu3u4JgAUKsgUFBXmyt669+ibP9M6aBCL9qcuUvKCgIPr378+OgzuIiYkhP7/mtc3EP3+Bbr3nEqk5BaAayZkaLhe7a9cuYqbFVNgWKJWzJqm4vsfY7XbS89Lp07FPzTsvIiIi0kqUW+Vk5Gdgs9nIyM9gTK8x1RaOrg93UMjhKGP+/NmkpibTp080t/7s50TSlTVrMhg0KIbg4OAK09jctX+yy04QEdS1Qpv+giLuc2OzRjGse1yd+xsa2pYBAwYzePBQcnNzGDx4KL17RzNu3CSixw4kfcdhACZNmsqHH75Dm6i27P9yD7ZuNlK2JBEeHs6+fbtwOBwcLjjIqWz/QYwzxad5+OE7nQ8W4gli2U7aeOV//pc2bdoQHT2I0tJSNmz4nNzcHI4dy3AGrELyKDpWyKlT+YSHt6/2OQ3oOIQ+vaJJ/97Z96qmwrmLih8rzKBHWBTZVLxJfLQkjVV7PuLz9OUcS80IeM2c4iz+E7aV24J/XiEAZqr4WRs0aChXt5/DnBnxta4rVun6dSybMev6WcT2jGXmzJmsXFm/PtRFc2UmNgbvQLevhi6+L3KuUgCqmeXm5VbaVtNUzmOnj9XoGkWllQss1oVWd6gbBQBFREQaj83YiOoQRUZ+BlEdql+1rD6++GIVmZlHyc4+zooVS1i//jPCwzuwcOFnfH1gA3Omny1kvmrVR6QdcU5/8q7947tCmrvukLu2EDjHXO5z044cIiYyttZ99R23+VsVzx08cTgctGnThoiIbvQdNoDw4vbOqX99UyvVqvr9A49y8mTlNCib3c64CRdRUlLCmfBiMvcdpahtASXZZ/j++0wAjhw5BMDmzesBeO+9Bc6T/wVkw4MP/oyQkBDatetAZExXQgvbsmzZvzlefoyTB7PZm/otn3/eg8OHUwjqEUxxcRGWZZGbm0MHAtc1ius2jnbB4QzuPJS97CQ17wDLsv/Njs+2cCj/bIZal9BICs6cosTyfyN5Z1Ei096PZXTYhcwP/RXtOlYdLMvNzaFbmx5VHtOY3AXHmys4kpydTGpqapNnJjYG30B3CCHN3SWRVkkBqEbiKK86WONwOEjMSGTnsZ0kZiRW+qNcXSpnQkICS1YvweSbBlttoapAiVZ3qLuGCgCKiIiIf+OixhEeEs7Qrv5X/a2v7du38IMfjCEz8yg2m50FC/7Brl2JhIW144EHnmHgwCF8fWADcDYTwm63069vf3an7ghY+8d3epjDclQ6t1/f/p42A04fA4KDoMCVmPHh8qXs3biD4ZmjuKBrHAUFUFwMV14ZT9u2kUyZMpONG1dSVARr167iu++OcuxYFhER3Skqch7r/uw+t6AAik47iIu7iA0bPqHca6wbFBTEwMHDGTl8EqNHT6LfmMEc3ZlK1MhoDiUeYPjwUaxatYzo6CFs2rSabt16smPHZsLDO3DgwB7OtC8mO+l7Tp8upKiokJKSLE4eyIJs2L17O0TAlx+shghYs9gVtHJNBwwNDSUsrD2RF3SjzalQli9/h8LCAvbv342jUxmlx0rp3DmCQyTz0cH3+Pex13hoyXxP38Ns4VwzaA59Tw3gv698mIeX3cX7Of+ilJKzry0hPD7xBT7d/QHbT29me9HX3LX2Jka0HcXPev+SLlZk7X+ovLQNqrqQeHpeep3arUvB8S5dutTpWgCljrM/4+VWOdlF2fS09fRkJjaHhroR7BvoziKrAXoncv5RAKoRJCQksHz1ckILQv0GaxISEvj4k4/pmdazynTxQHcr3FP0bHZbg662EChQ0pirOyg7SERERBpCdZlPVQVvqpObm0NQUBC9evVm48bV7N+/m+DgYF5/fanfDHF38evpM2YRExlbofZP+w4dodh5nHt6mGWVk38ml29z/uPJhHKf23/cYI5sT62QKRUe0qHSNTt1giFDnOO2/FOH6NTJTv6pQ/SIjWXIEEhJwfXZ7nncp08ZBQVH6dTJTm5uEsOHR9I/GqwTeD57n9trj51bb51FUdFxDh48SHZ2Nv369WPkyJGUtC9h4sQYune3e84d0N8GWcFcdlkfiooGM2PGdKKiLL7//ntSU21M+NFwfvazGxkwbgCpianMmDGDoqIi3n33Xdr1bce+L/bRt29fPvr6I/q378/qHV/SxerCoUMHKAorJCc/i+LiYoqLi8lJOuEKWG0DYOXKpdAXiII/5j0Fvb1erDMQnBJC+5MduKA4ls7fRXDk5CGWFi0kY2M65YfLYQrQHsgDNlp0jojgus638PdrF/HYx/eypuBj9pzewS833Mbg0KFwBM/ih9XdiPYVFd6HpACFxKFpb2bWts6U25P/eJIVm1bwffb3PH3X085aXW06U15eXqvMxIaecdGQr513oHvlf5p+OqPIuUABqAbmGxz66quvmDRpkmd/hw4d2LhxI0H2IPIy8mjbs221f5R9iw66p+jtTN/ZJKstNObqDsoOEhERkcaWmJHItyd3YpWfrmdBbMP+/bsBeOmlt7jssh/5re1zuuzs+MZ9Q9EdlBg2bCRHtqd69sd1G8d/umyjQ5tOnkyoYNf0Hu/MJ+9MqYHB4Z7zs8sq1jTyLjzdv3//KqdfBdmD6N27N0ePHiUmJqZGU7W8s/RXrlzJjBkzWLlyJWmFacTPjq+2zpDD4Th7YzM1iZgeFWuhhoWF0a1bNwbEDqBdcTtmzJhBSYcS5s+az/+88hq3X3sHa9eudE4R3J7KhRdeyt8WPk9xt9Pk78pjyOBhvJe6gMywo+SEe702ZQZbsqH8UDnsgNKyEnIistiUvY5NX6wD4J133jhbaP0InoLrpZTyh+9/w33PPEbXsO7cFPET7rrsAd7c+DKrC1dwoPhbbv3sKvqFDGTNwo85vCWF0IJQIiO7V/t61obvjVt3tlJdspbqk+nkq6S0hD3pezDGsCd9DyWlJfx74b/Z/Mlm4s7EMW5CzabftYYZF405xVfkfKAAVAPzDQ7l5+d7/lmk56UzadIkDhw4QEpKClOGTKGkQwnjosZVubKFv6KD8fHOJWnjZ8fXeFWM9Lx0Mvdn1unOhlZ3EBERkdbIXbvFYONkcXadiyKvXv0xr7/+EgBPP/0S1113S43PXbIkgc++dGbHj/7RRM92d/DIbuyeTKgeYVHk+Ezv8c6U6hEWVaHwtW9NKThbeHrwhYNJTUytsM93pebLL78ch8NRqUh1Va+Tv0CV90p1Vckpy/Hc2KwuQAZUKFnhT2jbtpxuV8Sx9pls672ZdwrfoLybs+8htjaMbDuWn154H/07DGZ4xEj++eHLTH3pKj79dBlt+4QRkt+GrKzj7Nv3H4KC7CzdsNDvdTIyjrBmzcf8cJRzLNw+pCPXd/kv7rjsl7z1xSusLfqYtNMpvFXyd8KGtCMoPYhbOv2MNkFtavS61ITvjVv3mN53bF+TLKK6Zjr5ExIcwog+IziUfogRfUZgMzZPkPHw4cM1mj3RmDMuGkNDBvBEzict97e6FfMODq1cudLzz8L92TuY89pSZzp2TVa2cP8zcQec3P/oa7oqRlFpUaVjA/2D8t2enpfuGSDUZRlYERERkbqo73R9d+0WC+eqZTZjq/U0nwMHvuV///cxysvLufrqG/nZz+6r8bkOh4ODB53Z8QcPJjFy2tlsEHfwyFHuIK7bOPZE7CCu2zjWUjmLyHv/wdykaq8bKLDju1Kzw+GodOwxxzFSU1Mpyy5jhlfGWG2nlgW6/h3xdxAZGVkhQObve2JhOQs/G2fJCu9plKdK8tiQ/zmvr3yJLwpXU57iDDoF24KJaTOCyR2u4JqLbiJr93GmDpjBwdwkjDG0adOW6OiBDBkynL6jBzCoU8UMrLWXriQ32//Y+rPPlrNmyceMGjWBm++5nUh6EBYczlWdbuDZa/7Kw8vvZM3JjznZJps1bT5mV8ZWruo0m4vKptT49fmuMJ0h1P3n3TuLqHv3+mdg1XTc//RdT9M9ojs/v/HnABWCjDUJJDXmjIvGoPdCInXTsn+zW7Hq7gLVdjWKhv5n4tumd5qre/uWLVv43e9+B1S841LXZWBFREREaqshpuuPixrH0M476N++D0uWJLBmzSfEZo3inlserfbc5OR9/PGPT1JcfJpLLrmcG2/8Sa2u7V59bu+xna7V5yoOv72zo0wHU2VbhsD727fvWOW5vplPAEuXLWXHqh307t2bGTOcgaZyq5zOAztTXFBMdtHZjDHvGqfVjUX9ZU/5rt7sPRZ298N3TGowRHWIotxy1hHaWn6E9w+8zZvf/YU9qTsps5xFr+3YGd/9YkaWj+NXM57go3Xv0jOkN2HB4cDxKvvqa/qM63nvH4so4+zPXVBQGPPn38uGPZ+z/6v/kJj4FYm//opu9p7MumMusb1GExoUyvh2F/Pi9a/zwPs/ZYf1DSl5SbyV9Tc+XbyUqW2vYkLpJdXWIjvtqPjzXptgqW8WUWRkJGHBVRc4r05txv3B9mDP1+4b7oMvHFzpuI4d/f+stoQZF7XNkNSNeZHaUQCqibiXQa3uGH9/wCrMlXf9M6kvf2mugba39DsQIiIi0nRa6xsug/FkI9ntdtKOHKp2nJOdfYKHHppPYWEBP/zhTObNuxPjEwOqSYBgzhxndvycK+MrZC85yh2kHUz1ZEf1jutX5+cXc8HIKvf7y3w6dOgQdrudo0ePesaC3qt9RYSdzRjzrnFa1Vg0OTuZ1NRUwhxhDGAAUHH1Znzqp3v3w3tM6jas6zBKQ0v5/Ze/Z0XWCsrWu/qJjeFt45gxbBYDsocwfFwcR7an0rFN7VZ983XZZT+iV8gUn+dm4wAAIABJREFU/vTWL4AcoB+/+MWz3H//XKI/Hci0/7uK5557hC/2reL4t9/xj3+8iD03iM8+W06PEb3Zc2IHYSXh/N8PE/h8y4esOLmYtKJUFhW9zkcLlzCAGPodHVijvrhvCo/6bhRxveOqPd43i8hut9OnYx/2BChw7p1d2Bi/14FuuMf+ILbW5zSFxIxEvjn6DQUlBTU+RzfmRWpHkYUmUpNlUAP9AbPb7ZX+mdRXoDRX7+29e/dW8ElEREQqaM1vuNzZSGlpKfTrW/XUoJMnc3jhhcf57rujDB48lH/8YzGbN2+ocIw7m+rkyaxKgZVK1/aTHW+3VcyOKrWV+jkzsE6dulBQfKpW53iubbd7ipX7jvncq30dzjnsOda7xqm/saiFRblVzpmgM4TYQug8sDPlVrlnAR538KrfmIpBNu9+uMekYR3D+Dzlc949+C6zd8z2ZMEZDBf2uJSYshFcN/G/KNiXT98BAziSm+p5Pfy9RgD79u1i0EUxlfb7GjZsJNdcFENot/Yc2BbMvHkzGDvWuc9R7qBHj15cd90t3PPUI3yU8C6bktfz9Ucb+PDDxfAVfPj2O/SeEM2h2AOMD7+Y8e0uhsHwuw0PcKD4W3aRSPznVzOt/VXEFo0O2A/vG9CHDh0itmfgoI033yLx/rizfGozw6GhAlTeQZ5xUTUrTt6Y/XFz14pzr1Ae4loIQEQalqILrURV/0zWrVvH3XffzZEjR+jatSt/+tOfCO4RTMy0qv/JBkpzdW9vzjsQIiIiIo1hzpx4OneOpP+4ylOD3M6cOUN8/NVkZBxhyJBh/OpXT9K2bcWpTGVlZZ5sqvpkL3lnRy345B+1Onf06Al0yK1Zxo/vtCeHw+EpVu5vzOe72pdvjVNvydnJJOUnsS1zG1MumcI3R7/xrPLsXoDHHbzyDbK5+3FB9wsIigli3tJ5LE9aXiELZUDbAfzi0l+QsfUMv7z6IdauXUmX0EgKyK/0evh7jQBO5ec5r1fDOlb//eNrWBu10hN88p4qGRnZHZvNTlzceK65/WZ2TPuatLRUXv/gzxw7lMGxwgx2LU9k3NiLmTbtSu6c+mvoBRk9j/CXzc/yneMoH+e+x5p3PuKSdpcTUzC80vW9b0DXpFi777mBuDPUCkoK6NCmmqipl4YIPHtqermCPGN6janzqnJ16Y9v0Mo7A8w78y+qQxRZPgsBiEjDUACqhfBXANKXv/0LFy7kz3/+MyUlJQAcP36c+fPnc8s9tzBn2pxqrxvomgo+iYiICAQuAl7bQt4tie84x/Kq+1JaWsrLLz/Lrl1bCQ1tyy23/JTw8PaV2ggKCvJkUwXKXrKqqSfTvoMzKGS32SsFOLz5y+yprZEjz07Rq+3ULjd/WVzlVjnZRdmeQuHXXHAN4SHhDO06lKQs53RD7+CVewEegHeXvsvnmz+ncF8hSSVJFO4o9Owb3nU4E0In8MSsJ/h87efcceEd/M8O57l1/dmr6jWuijvY6J4q2blzxSmIERHduPHG2+g2rCfdbD14/vXfcuDrb9m0aS2bNq1lyZJ/Mn78JTz66O+xRdkYPjqOJ1bdx/air1mVv4J176xkYrspROcN8rTpcDgq1FHyXc2wLiwssouy6WnrSUZ+BuGR4fVuszbKHeUVgjx1DT7VlW/Qyre+nDvzb2jXoaz8j//sMRGpn6b9rZdK2tjakJCQwIIFC1i1ahXgjM57q+qf7H333ecJPrkVFRWx7INlteqH7zVFREREwH8RcPfYJSEhoek71MB2Hk/kq8y17DyeiGXBnDlT2bVrK0FBwVx00RSysk4EHIvNmRPP7Nm3MmdOfKV9qXnJfJW5ltS85IDXHjbMGRRylFdcKc/3ev4ye+rKUV5xalddgjneBc1txkZEWISnULjN2PwGFtzBq3KrnLWH1vLn1D/zP5n/wxfRX7D99HYKHYX8oPsPuLnfzRy49wDv3vgus3vOpl+nipll/5+98w6Pqkr/+GdK2mTSC+m9UKWEEBAEDSuKAipSVJSNHd11192fq67u6q5ld3V3LauudQUVGwKWRWDpKD1AEgglIYWUSZ/USZ32+2NyJzOTmcmkQcD5Pk+emdx77rnn3rn3nPd8z/t+33Xr1rB+/cds3bKx39dteY/7IggFCGSjTqvrFpK3sYArlnD99Tdz332PcuBAIQsXLsPHx5e8vFN88sk7TJkSxrp1H6Ev1/NoyDPsuPUEM+Rz0Oq1/Kjazux1yfwt67c8/97zxvdrKBeFRYgMv5VOZ0YAlTWVDdk5bEHoM05tP0V6XPqgwu/6C0fmOUKZgZJi/v6DJ4mdcOKnAKcH1EWGv9Sf/Xn7zQQgTdl5e9nvtDotSqXSoDlg7oFMfXf6WEc7Q0c0qpwYOgw2pbQTTjjhhBNOXCxYJke51BKWKFQ9k22dXkdVmwKRSExVm4INX67lyJF9iMUSJk6ciru7h13CAXq8qUyzm+n1Oho6lASLQmnoUPaZWctSC2o4PdEl4oGHdgmwFDRPCkjCQ+Zhk1TQ6XWcaz3HLzf/kk/OfkJzbrfhKgKvTi+mBU5jWcwyHlj8AO9vfJ+G9gaOlB9Bo9Qwn/nGegwkUpFRRD450L4ukinB5CGVmd3jElUhiooSWtUqJgX3TYaYhkoeP95DaHhIe0IzTcP7oqJiWbYsg8WLV9DZ2ckbb/yFU6eyOXb0AAsXTicqKo6HHvodP/f7BXGlyfyo20F5wHn2VmxmL5sZFTMKj3wPlgYu7bNt/UG8bzzxcfFmHmpDkWnSHiyTHKXOv3Dkk06vcyhcz7JMfwmlSzExgxNOXAxcOtbCJQpHVpVsiX4Loo22st/5+vkSEBCAEmWvOv38/QBDZygMLo62x4nhx3AP9E444YQTTjgxXLBMjnIpkU8A7ZqeMVgsEhMiC6eqVcGhr39g8/cbEIvFpKTM4I477sXPL5Brr13Arl32w3GyazI5pcwmuyYTAJFIjJ97AHq9Dj/3AIe8KkwJjr7O1x9Ym0gPNLTLTexmc58I8/SAer2enKoc3jn6Dl+d/gpFiwK6TxUsDeaaoGu4Y/YdiM6JSJqRZGyHqU6Qss1A3gn6VQJRJ4jIBwQEWW2LVqelqCmf0ooiI8EUIA0ifdl8OuQdLJm/kn9+8SeCRaFUtSnQ6R3TIhK8uKZMSTNmMwyXR5JHrll4n6kgvVTqwrx5i/D0lBMTk8DTr/6Sk7uPUVpaxO9//xBubu5MmZJGlCSOjFseJjsgmy0lX1LtWc3HfMyJcyd4NOJRgvTWr7U/2L59O+Xl5UyunMyY+8dYLdOfRVJHhMBlLrLeyY8kffcZQyEyLgieWxKZjsBJKDnhxPDg0rIYLjHYi6/39/c37r/hhhtYuXJlrxUoQbTRWva7TEUmSrmSX//p1zz/4vOoMdcdiIzsPXBs/HojWduzqKurIyMjY+gu1AknfgJweq05DkeNxks1lbsTTjhhO5HJpYhJwakc2LOb9//xKgCvvLIaX19/h4gnMBAmpl5ULt3Zs+J8kogMi6Gs+rzDbbGmsTRY2OpnJRIJMheZ1X22EORmnwTR6/VkVWXxdubb7CjewfnG88Z9/i7+JLYnIq2SMitwFrMmziIxMJGiYnMCTITIqBMUGxaLWCQ2068yFZFP8E02EkEC1q1bw9Z93+Dq48o1yfONBJPxusUSxCYEYYhs8FpEWq15eJ8tQfq4uEQW3LiENf/4jr///Rmysg5x6NAPxuyKKlUTc269ibxVeTz01UMcaD5AdnM2Gd9mMNpzNL8O+jVJAUmIRCKr9duCv78/xfXFlJeXG0MvNRqN1bL9WSS15VlkajMJn6Z9hunieH/rFtDXorppVjuByHTCCScuPpwaUMMEtVZtN74+JSXFzBXVFjIyMli5cqUZYWS6MjRh7gTuuusuoqMNA52Pjw8ikYgTOSd4552eTCoajYbi4mIzd3lL9NUxO2Obnfgpw+m15jgczUxzKadyd8IJJ3pCz4ZCR3KotSj7o2mzd+82/vbXpwBYeOdyli41LAqahurZgwgRIbJwI5nh5xvQs28QxIaj+kQDLQ84vLBiaccebjhMzGsx3HjkRqJfi+bLii/5puobkt5MIuW9FD7I+oDzjecJ9gzm0bRHOXjvQf6a/FdWxa/Cq92L0cmj7Yb+pYankh6Xzt0L7ra639axppkJ2+ta0WjUNgmmOJ8kZoalOxR+1xcCAoLMNaL6IBLd3Ny44YbFbNiwl7/97V3uvffXyGSeZGdn8vprf2BS4iQ6d3eyZd4W5vvNx1XkytnWszy0+SGS30xm3al1VrP5WT73wu+WlpZGl66LiIgItFotsbGx/fJc7K9GlC2baahCSx3RoBOy2ul0OgJkBi9EZySIE05cfDg9oIYBmYpMituLkUXL0BYaOnlLWLqiOqItIBBAlhkkpk+fzn9e/o8xJW5FRQX3PXEfv/jFLwgPDydhegJSqZTY2FiyCrKsusubpmQNkYcAvY0NYQXNVHjSCSeccMKad5jTY8wJJ346GAoyeSjqMPXmcXTRIDs7k/vuW4zWQ8uDD/4f8dOSjftMQ/UE+Pr609jYu62TglMpiS00kBnB9PLKAXONqL4ghI9pmjREEddrv6nuEBhCAA9XHSFQoSI1PHVIvT0s9UjXnljLx4qPUesN3velTaV81PSRsXywZzA/i/sZq1JWESgLZEyQIdTr5JGTZtnwtmzZYmyntfYOxCtJEAvPrcpibMhEZkdeS6Kf9VAzGBxBaIopU9KYMiXNGEL56db3HToGICQkjLvueoArr7yGlpYm3t3wKmd+zGbPnj3M2jCL8Nhw4lLjUPoraR7VzLn6cyxfv5zkgGSenPUkKyaswEXiApg/96a/m7CQfe2116LVakmcntiv6xtJi3CWelL2NOiErHbn689bvR9OOOHEhYeTgBpiCO6evqPi8JOGEkIKhXWneGvjx4w5MYlVS54ylr3yygw6OwO58soF/PDDFrRaLfn5oFBAvknCFOF/P780XnppDXv2bObqq2/givR0fFrHUF2dbTwOwG/cFYyfnkLu98dYsnQpd730CPOnLWPy5MX46yZw5ZUL2JtVRqhnJAoFnM3TkV+mJMgvlEyVglhvOV99tZ3KynJOnarjllsyKG4GUbe9VaxoIz8f3Nwg2rqHsRNOOPETgjXDdCQZq0444cRPA/0lvUtKC3n0rp/T1tbKtTct5A9/eJnPt/3H7jFTpqTZDMsbN3ay2f++vj2e46YaUXJXb8tDzaCzEDDX6np7rYfLe65VLwipi8UomhXo9DqOKo6iUWqIs0JeCXpKwrlaNC00qZs4UHaAU5pTZJ3JwkXswrn6c5wvOc/5mvM0xDawq3gXaoWa5iPNveoEkOqlbF25lTkxcyisLyQ5MNlqqJXgHSQsfhYWFRLvH++QTo+ryLYGlYCkq8cR1RlDuDx6yAgmR9HfEMp169awc+dmGhrqCAwcxdKlK0memUa4Wxe/+vOvOL79OIpiBTSDpFlC/Lx4Zt8xmx21O8hT5nH3t3fz7J5nefzKx7ln8j3Geq0lChC2my56D5SovJgh9L30pPrw5BI8ny7lxAlOOHE5wfnmDTEEd8+sLgUxo8KpLa+m4kwRbe5NHK3fQ15LOimh043lPT0lyOWQmbmdiopy2tsNA5Bc3lOnuzvI5QbG/7QiEzc3CWVleUxzT0UuNxBBcjm4uGgRS8Q06RVMuWoaPnof9m/exRevv0P83+KYPna28XxnThwk8cpI3N3B20vMKO8ApK46YgLCcZfoqK0tN57nzJn9+CUGIpcbBso9+77FR+3ODTdkXPgbbANObwsnnHDCCSecuDAYqWEs/ZkU55Rm8sELr9FY38A111zPk0/+BbG4b7LC0bA86BGqttSIineR2z1OLBITGRxLh74Nvbueg5V7aNe02QwVE3ULqZ+vK0PmKuNoxVGKm4opry+n5XQLbhI3lO1KCusLUevUlDWX8ezZZ6lorECVqeohIU7aaJAEEByurMsGde/SMCdqDlJxz/TCmsaUHj06vQ5lm5IQUQjlTeUkiBIc0unxl9rXoBKyGoolUho6lAMKS7xQMA0XLCjIw8+vJ9nQhAkTuOOOO9j00SaefvppdmbvJHdvLvmH8sn/Pp9Zc2Zx/e3Xs1u9mzxlHr/c8kue/+F5Vl6xkj/O+aPVRAGCFmxERAQPpj5oFOhWdanwdrNPilpiIB6L/Q3js4f+atBd6okTnHDicoLz7RsGpIanUlchZ2LwGD5QvU5HSDs15ZWMC5pMbWcVOr3OzK1Yo9FQUVFudQAyhVQqJTw6gvri2u4UvT0/n7CCEhYWgazKi2NZh0iffQPtjW0czzvEK7/9E/e//hvcmzxIZz4tzU1Az+qcIJSZ6DeGgsY8wsIiqKgoJyEhmZaWZnz1/saBUhBYNKymjIxHyOltcXHhJAAdw+V+n5yi4k44cfnDMhxrJMHRSXFzSyOr33iLRnUDsWMTeefddVSpKwDw8vaxeoxAulkLy7PExoJPeeHw49S0VRIsC+UW+QoWypYaNaIc8cpZlv5zjlUfgqQsylrOU9VWwZmGE+xu2kLJkULqO+qo76ijoUNJVVsFKnUzjR316LLMCZcvd3/Z57k8JZ54S72JCowiUBaIVCwl3i8ePXpGB44mUBbI/iP7uWf+PeQcyuH3Rb+ntKm0dz16z14Te8sxL1ORSXZVNscqjhEgC0Cv1xPhE4Ferzfq9FiDo2LpQlZDvV6Hn0fAgDygTMMbh5JsNfWIg55wwZKSwm67vrf3lKenJ9dddx0P/eEhtn2+jX1n9vH9x9+zb+8+9u3dR0BgAIvuXURBWAGnG07z94N/54OsD7gh4Ab+teJfRpLGVAu2vLycLk2XUU9W0axAFtD3/R3svRhqW72/elKOklY5OTkkz022W8YJJ5wYOEYGe3AZQiQSG12oU1Nn4trpyoTJk60KIUqlUuLjkykszLM5AAm4fv5ikgMncO21C4z6Ap6eck4fP9E9qJQSLYljlD4UXZmWT97fzE33XknBobN88vd3uemm28xWl4T4c6HNAhYuXI5SWcu11y7gg69fN6SxDVKRkJDMqapsEhKGb/VgJE7STdvU3/b9FLJuOAlAx3Ah7tPFfH+Eyd9I9Y5wwgknBgdL7ZXAQOsLZiNxHAeQugB48vsnfoGyqoaA8cH88eW3QO9FWxuopBATMxGVCjo6oK3N8PnJJwb5g6qqOibOnUFHB6hUPfVWqGpRqQzlPy//lD8ceYAOraG/r26r4MP2fxGqSMDXPZSWNg1HFLsoUeZzMvMUxS356PU6cquP8uU3H1NSX8BfP36Kpq562/ZDju1r9HHzIVAWiKeLJ+1N7fgH+jPKcxRpEWmotWra1G2odWpifGIIqAlg2Y3L2LFtBwDz5883esVMi5iGt5s3yYGGibjyjJJxweModSnlL3P/wj3f3EOXrst4XjeRG7dF3Wb3/huT6IgMpEdiQCKxcbHE1ccRExfD+frzNo/t63kyHXcmBaeSG5BFmKv1Y/rS4hLCG03D45Yty7B7jL12CXa9qc0tQMjo50jGxYSEBObdPo/3//Y+n332Ge+88w45OTl899J3AExcMpGa8TVUdlTyqeJTvn3tW64PuJ6pqqmEyEOMWrARERG4Sl2NerKNnY3sKd5DV02XzRDIyyWTtiOkVVNTU7/qdGrjOuFE/+AkoIYRpilexwZNZFb4z2wKIT7++PNs377JoQHIsvMcP2EKXZWdlJQUEhERRVLSWM7sO0lCQjK+vn68/PJ7rFq+nPKC8+z4bBNP3PmCzbqPHz+Md5yvUePAVIegqk3BkqUrjQKLpsbXUGIkkhmmbepP+0zdm1PDB59lxRQj1cB34uLiYr8/gnfE5MrJ/P7+31/UtjjhhBMDhzWPRkcTqFzsfsgWfH21bPj0VXJzj+Hv78+Xaz9j7sS5AOjrICmw57OwEGJjQFOlpbAwD19fCY2NeURGpqKvBY9RPWOwX24nSUmGY1fte9pIPgno0nfy7NEHAfjX9md6dmRbNFCYx3ZzKV6uXgR7BuPp6kmEVwSleaW0NbcRKY9k+aLljPIaRW1rLWWNZcyKnkWEdwTjR40HIK8uj61btyJNlDItYhqp4ank1eXR3NlsJJjqGuqM4tX+/v5maesVzQrkgdZDBVdMWEFlSyVvHnmT0qZSonyiWBawjERP+8LWIkSGrGR6QzKdOuqMi7IDERwXsHXLRk7uyWJC3WR+efvvjeeyBlMtLnuwDI8TNINcxX1rUAkwjU5IT7etbdVfTx5vb29WrVrFgw8+yJEjR3j33Xf54osvyFmfA7vBL9IP15+5Ui2vZn3leja9vol7J9/LrT+7lQmhE4znSw1PReYi45zyHLt/2E3BgQJ8dD7MXDgT6LEztVptn5m0beFSyqA90MWzTl3nELfECScubzgJqGGGENpWVn2+TzdgywFIoSojXB7Zy2XXEnq9zriCIpFISE+fbySJAAICgln3+U4WZEwnLzeXP/zhEcbNNhfKFDrdxsZ6vPE1bheLxPi4+BndxsUicb8FFi9X9EUAWRpyKWEpgzKwLDFSDXwnfnoQVulNRT6Li4uNBrswkbV8Z5yeUk44MXJhK5zNNIxFyL57KUCv1/PVuq/Y9c0uvGK9eOF3LxARHtHncb20Y7rlD6yNwRUtFZQ0ldisK8g1iLigONyl7khVUkaFjqKls4Xxo8bTUNrA7em3s3v/blYtWoW/hz9FDUVGIe/kwGQjwX/DDTeQMS0DnV7Hd2e/Q6fX0aHp6GWfJQUkERMXY8xCBz1ZwcYEjWHLiZ7fTyAbTbMs27NZbky8kceufAww2EO5B3JpcW3p836mhqeSFZJFaniq2fkHAg+pDI1GQ0mpgRwpKS22Ky5tqcXlgqvNui3D44Q6A/rQoBKg1WkpKShCIpFQUVE+LKLXIpGItLQ00tLSeOWVV/jkk0/463t/pTK70kBuhoPvAl8aQxt5K/Mt3j32Ltf4X8PS0KVGcXqpWEqIZwiV1ZXIRXLO5Z8jTWt4FkyfcXuZtO3hUgnLNw0tpn9yWE444UQ/4SSgLgCsEU8CuWQNljoD1lx2BWTXZJJde4RWtcqMwLI0QhITx/C3v77Nb1Zm8PHHbzNfv5g7598P9KzQTKibzNhRk8yOW7duDcd2HmRc1yQm3TG0HjyXOvoigARBekcMucHC6Q3lxFCiP8+TqZef6UQtJibGaKQKE1lr6aEPHz7M888/P/QX4YQTTgwb+uOxMVK04Z599ll27dqFu7s777z9Dn46P4ePNSXdrGV1U2lUPLbtMd448obNOqJ9onk66mnuX3w/eXV5FGYW0hXTRaWqklB5KDVNNcyOnk3esTxGya1ra1lq2PRlZ/j7+1u1PezZI6YElbVrhd7e3ULfPnHiRJv1msKWd5IAeyFypgsXgh0dHRXLyaIsoqNi7ZIjIkRGfagQWTj11Nlth2l4XH/hIZUZCaywsAizdvXHi8oStvSJfH19mb54OjeLb2Zy2GS+ePML9u/fT+O7jRAMHtd60JHQwfa67eyo28H17ddzVdRVvHnkTSpVlfi4+5DkmWRGsoKJV/O1k1m5ciUTr7L/G5tmWBwsLtQilWVocXRK/1J8a3XOxTQnnOgPLmxuUieMsCZiqdTUsm7dGtav/5h169b0WYcx7W73So4ePUqN7TjkCVeksGrVY4hEIrZs3sjGjZ+auRiXlBabdfZarda4r6zsvEMut0OZ4eJyQGp4Kulx6UMefmcJpzeUE9Zw+PDhAe1z9Hmy9PJTa9RkZGQw48YZ+Ez0IVOR2as8GIy9zLxMq+789trlhBNOjEzY00AZSLasocZbb73F888/j0gs4ssvvyQlJaXfdVgj3drV7by07yWeznuafx78J13aLmZEzMBD6mFWTuYi48W5L5pNzo3haDpDOJqvj69l9Q61w56dMVDiTyCobGWwM+33h1rnUhAptxw/wECGfPNNbxv5+vmLWbJkJbcsvLPP+icFpzIuYBKTglP7jDCA/ofHCQiQBrFsWQZLlqxk4cLlvfYNFLb0iUzH46DRQfzfY/+HQqHgvvvuI8k3ifZP29F/oodjgA62FGzhqV1PUaGqQI+eRl0jxyOO45LiYqxTo9WYeTVD3/aBQEIO5rnw9/dnzZo1fPzxx6xZs6bX/qG2E4TQYq1Wawgt7iPSw/S9WLNmDd9+863VdjrhhBPW4SSgRhDaNW1m8eZarX3CR2SS6SNEFo4IEV19xCHPm3cTzz77CgC//e3dHDr0AwkJhk43OirW3ItKIjHb54jLrZMI6Q1rK40/BWHyweBSITKHq539rddWeWHiJxhrpkbbUEwKhdV3nU5H1t4s1n6ylg9Xf0hDZ0OvyUmmIpNdRbvIVGQilUqJiItAq9USEWG+MjwSJqtOOOFE/zCSNVDWrVvHI488AsDzzz3PokWL+l2HZR/r7evN+tPrSXwjkSd3Pkm7rp1r465lw7INHLj3AO8vep8weRgiRITJw3hv4XusmLCil4eQKXkk7HMbgHeMox7W/bU9rHnCWhJn/fHutvSOsdQHshQpN22v4KUiFgs2cs+CqV6vQyKR9IossOVBI3hg2YswGCpIJJILch7jeNytrxXgH0BAQACLFy/m7Nmz7N69m/kz5yPdIkX/mh66etehEWlY9c0qlG1KAKSSHmImNjbWITJO5iIzG+8HgpSUFDOPJMsFcMFOGIy+lOU7nZGRwcqVKx0SWBfeC+MzKRH3WxvLCSd+ynCG4I0gSMQSi3Ssff88k4JTOVC5hwd2LkWhKsVX4odHge1UqlOmpFFQcIbwiGgUOSXcd98tfP31Pvz8AolNTaQ485xZecH9ODbVtrDk5Rz+NRzXNpzC5EOBsqYyKs5WXNSQieEiMof69xyudva3XtPy1iYYgrFmjdyxdk+EOmxlfzQNqUkNTyU/N5/yknIkEgm1PYqpAAAgAElEQVTH8o+RMDrBbHJiuWKeEpbC4lsWm4mhWmKkhO3Yw+Xc913ucP52/cNIWTSx1S/Y+j137tzJnXfeiV6v58UXX+TWJbcO6LxCH6vX69lVvIs3s97kdO1pACaHTGaO+xxevetVY8jaigkrmBo61Uy/yRYsCZwgt4F7x9iDqe3h7TY4kRvTML3+wJKAS0tLMwvzC/APQOwtNpIopvdG8FLJzi40yxgtSFFomjSkm2RwO3p0P21lbWbC5Jc7TPW1CO/ZLhKJuPrqqwkdH8rx+cfZvn07q11WW61DpVMxc/VM4j3iSQlN4cFZD7IiYAWjZ4ymKLOozzaEe4dzrOJYv/RPLd9ry2QHthbAB2IjCKSVNTurv95uxmeyLLvf2lhOOPFThtMDapjhiHuvKZYty+Cam+c7nO51Y8Gn/OPYMyhUJYCeRm09j//4AMdbrbunCiF3yUnjiI8fTUtLM3feOZ/Gxga2btnI+vUfs3XLRrNjLDtky9j8y9nraTDXZs0rxTJkyVGj/kJ6BLWp2y5bL5SR8qwO1++5etNq46qjrXNYuq5b3hPTlUtb2R8tn4+21jbjKmlEXASjg0ebhYRYWzHXda9Y24Kl99ZIw+HDh0fM8+RE/6HqGqY0rpcwbPUZjngzXCgNFFtjk7V38dTpUyxatAi1Ws2vfvUrfv/7wZEQp1pOcdXqq3h488Ocrj1NhHcEny3+jKMPHGWs19hB1T3c6Mv2GIgnyXDoWqalpZEansqkkElWF+gyMjK4+eaVRhtZZyJF0dChNAvzrqmpNhMmh5GT+KKvdugHQfj2pa/l6+vL0qVLwZZevBrQQGF7IeuK1jH307nccvAWZr44k6fWP8U7n77D7t27USqV6PW9tbpMPaMtSURbNq+197o/HkmmsNaPmYbM2SOt7IUS20JGRgY33XxTv9vpxMAwUm1CJ/oHJwE1zBiI261W5LgL598yn6ZT22G2rV3Txtamr62WF7J66HQ6Hnzwt0ybNouqKgV///sfOVdwttdgbQlH09c6ipGyqjocsGYQ2xuYBVgbPAcz0XV21iMP1n7PwZJSOr2O4opi4wRD1aXqZeTWdtb2MvRycnLM6tj94267BKnp82TaZsFYXHzLYqD35MQ01ESY0OYr8/u8rpFKhtbW1l7W/dfljMGGh1yusNYvObJoMlQaKEJ/ZdnHDGQMKyws5P7776etrY3ly5fz6quvIhLZn5jbQml7Kb/Y/At+d+Z37C/bj5+7H/+6/l9svmMzt0+4vU8ixpqO0oWGLdtDIJ5GmrepPRLFdOFCbCJF4eceYLwuqVRKcPAoMwmJ/misDif6akd2TSaHqhzrnwZjN/ie8e0dhtcFnjs9WdG+gtS8VOSFcqgDvZsepa+S7KhsXmt8jfT16dz1yV14XuHJLx/9JStWrOC5557jyy+/JCcnh3F+43rpkvVn3BcwEP0ta/2Yo96uAw0ldmYHv3AYqTahE/2D01fwAqG/nlCOwEMqo0JVanVfo7aezcUbuS76pl77li3LoEPewYob7mfy1dN56I5lFBSc5ciRHxk3agpjuwdryzabrjRVtSmI80hhMBymZSjacIdEXKyQC8uMJX25rg+1V4Wzsx4aDDfZMNjfXSwSEyALME4wvvnmG7K2Z5FXlcfYcMPqfJumDdwM74Iw2TMVNBWLxLhp3OwSpKbPk2Wb+zIWT544SXJ6snFCq2xTog8wrKDaej9Hyoq1KdasWcMnWz4hrDCMXy35FanhqQMOGdTpdcOaIfNyxkDunSmhsmPvDlJu7zs85KeMvrKsabXaXhooAwlDMU2BPmpUTwY4wSPXN94xgW6Aqqoq5s2bR31TPZMnT+ajjz5CLO79G/fl9VPXVcfq3avZcHoDOnS4id14fNbj3JR8EylhKTYzxFnClt0xGP2agcBadruRRjwNBJOCU/F0kVNWfd5s+9SpMwlOCyU2NdEs4U5BQR4Rk/qX5cwe+jNGWbbD8n3RozfY2d2Er5ePV686TDXCBmM3PLX0KZ5+62nUc9TgBbSAZJeEdx97l6nX9YSPxnjF8I8P/sFJ8UkOnD9ApXslmmANBEM77RSpiygqK4LvgdeBbhMhdHwo40PHk5ycTFJSEkqZEtcQV9pUbYZ+uAJE3WUVCsi3wkvZ2l5aqrW6HaC42VBvZWsZoZ49755lXUI54ROgutpQpqHGzWb91iAcZwo3N4geusfMCScuKzgJqAuEKVPSKGi0b6zYSztrLKPXGYmhcHkkYfKo7vC73rh/x63EeCdwS8IKfjHxcbN9Alvv6unK2rVbuO66KZSWFBPuFsV1191sbDP0kGemK00hsv4JT1rCclU1JSxl2MNZLla4jLWMJY7eu/5Oai83XZPhup7+1jvSdbvKFTpE9eDZkUS4OIaCH+rJziqmtVXCkeN5yLXJvPTSGjYd+oZEv7HszM7m1A9ZnDpVR5e3nvz8HmNNqMOndQxFlXlGw2zrkRw2BR/lhaO/plFXT1huFOnyhfxx7htUVBqMQYUCREGgUvlTfL63cZmf30RBpBhtYzhVdQq0qgDKSnXk50NRcxvt3j1lS0u1/P5v/yR772FOnapj1oJrmDnOujVX0lhCtK9hnykpIbw/pvv7wqcnP+XpnU9T2lRKmFcYL137EismrDDuL64v5mzeWbrEXdTU1FDaUEpKWEovotf0nNbeY51ex7GKY+RU5TAxZOKIfK7279/PzJkzR2S9mYrMAd07gVDZsG0DFQcqCO0KdYZO9AF7iyYSiWTQGiiWKdADAwMH3Na2tjbmz59PUVER42aP4+lfPY2bm3VRb0v9IQHNnc08tfMpXjnxCp26TsSIuW3cbSxwWcCKa1Y4TDz1hYtB/gwF2TocxNlg6xTZuC5hUUTw/hc0VtVi9aDOJ2Drlo2c3JNFQ0OdQ9IZlu2wfF9EiAiRhVOiNBC+3kG9tbqGSiPstsW3EeYaxuMvPU5lbiVhY8K448Y7WLFiBWdqzxjLubm5MSVuCounLub80fNETIlA2a7k39v+zRn1GXLrciEOwx/g0uKCJk9DZWUllXsr2b59e89JA8Cl2YVN72zCOy6UlOhU4uOTqa+vA2Yhl5sTbu7uIJebt3vdujXs3LmZ9nbr91ymMRwj0rSZHWtZl1BO+DRcq+F7mDyo13ntQTjOFKphjPK+kLa+YL9cCrqclrgU2/xTgZOAusgQyB1HQtsEocVJEdOM255MfZHf7rkPtb4nDM9V5MEUr1Qq9GWcby7g1eN/Zs2pN1kUv5zfTnmWQI9gs3ojI2N47LHn+Mu7T3Dw4F7e/+BVXn78PeN+U/JsUnAquQFZTApOHVTn2teq6qWAofaIsTagOOq9JLTlctOkcfR6+jvI9Oc+WSNLB/q8OvrMWBMBt4VMRSY/lB4hLVKFuzt4eopRapuJjYvhVFk28fGxuLrqOFqxmyaPWvLrcoj0jMXNTcLRit246dwpaMtE7uaNXI6xjvz8w3jH+RqNqoMN/2Nj4VrU3T77FW0lfNb6No0bq6g+WUVHRwP+foHIdBJiJyRQ2lJMEx6IENMuVdImrgLXTrqkdXRVNzAtJpX133/M8cq9NLVVMHluGm4yNRJpF2v/+w+O7TpEvbiWUW5hHK3YzdltJ3D1vR1FvoKbr7nZ7PqPlB9hWne/mFOVA+Vw3033UVtba7bflKiwRkp9evJTHvjvA8bnQ9Gi4J6v7wEwklAd2g5GJ48msyiT4OBgovyigJ5V8JLGEmpaazhSfgRZrYy7F9zNacVp0ugx4FZvWo06RE1TZxNyV/mgn6uhRllTGTu/3snmzZs5d+7ckBI0gqeLo/WWNJZQfqrcjLAS3km1Tj2gezd51GTcq90JFgcPymvnpwR79zcjw+BVnbEkw7jNtK/rS1fFUnDYmielI31nZ2cnzz33HCdOnCAhIYH33nuPpiLrKeutoUvbxSsHX+G5vc/R1Gk4bl78PJbIljB79myHBJh/CrA31pqGG/Yn9NAWGTgU4YseUkMdQlKda69dwKdb3x90vRqNBkV38g1r3ky2YNoOa5gUnIpILSc1vMdTbThIv0ifSFasWMHsBbONdsaWLVusitXnK/MpKipCo9QQJ4nj6piraY9sp7q6mg37N+Ce5o40Qsq2wm3UUw9TDeeQLpISI4rBr94PbZ6W4sJiGpQNhtD/4hx+bN5qbM+zzz7KqFGhxMUlEx9v+GttbSEuLonIyBgkEkmfHmQXC5YOBMePH0YuHz7iQ1iws0ROzmEmThza8548WY+fX8+nKS6Gl1d/PA6dESAjFxf/rf2JY8qUNGNom6dcTlWbwqonlGX4m05vMLgXJ6xg47ZKdre+CT6l0BLKde4v89iSqcR6J7D5/EZeO/48ZxtO8tHpf/Nl3ocsSfw5Iepws/pjYuJ57vnXeeLeB/l07fuMDZ5ERsbDVtvcl8ChIyhrKhtwFpWRgOHwiOkveWSa3n6oMttcqrA3yJSUQGd3WL81l+u+0eOxExwYTsG5nolYf+o5qczkRN0RrghU4eni3es4oS7LcoKRIRgWpm7lClUJZxoUNDUb+gUXXAEoasrHe6IPKa4z6OhqZ8PGT2ga1YBep8fdX0Z0dBy557NQttbSmFOPq8SNn9/4EGAY3DU6DXm1uajd1ewo3cSRqn1sq/sWvdi8b9KJtGzqXA9JkKnaByrAliRFt6PmHz95pPuCgFDD39bmr8FStm4aoAeRXoRILELULOLTD98EHbgedMVV4opYJEatU4Pe4NUpEono6OhA26nlN0d/g16rxyXLBTcPN1wkLvi4+SAWienQdNCuacfL1QsvNy/EIjFikZhjFcfo1JprQHTpu1i1aRVZlVk0dTRxruQc08dMZ+bSmQRFBnG4/DB/+vJP1GXXseaHNUxZOAX04OvuS0FZAR+u/pB1O9chahYRPCrYqNUVOiqU0txSJqRMoC6/DvGYnueqPx5CjpTtr5He2N5o9Ej5du+33HnnnYM28gVdQcvU2vbqzVRk8q/1/6LiQAV3nbvLSFgNdgFDKpUSFxtHVkHWgLx2RsqkZ7hhz9C3nBCbaqBYjo+O6KpkZGQQGBjIggUL2LJli9k+YfJrTzheq9Ny5513cuLECUJCQti2bRtdXl02CShTQkur0/LZyc94cueTVLRUAIbMdo9Me4S2rjbazvU9Nvv4+PRZxtb5LyeYLpgMxkNDELW3VoeLi8G7pKOjx8ukrc38fzCM+x068CHSuF2tlqBSGfaZHquy8zpb1tvzv5Sx4ansObeZyMhkOjqkxvOats/yeNN2WNbp5ubf/Wnep6WlpTms92QaoucITO+xZcZaeaAcnV6Hsk1JqDgUZVuP0LsQfivXy/Gq9uLfv/g3IrGIoxVH+V/h//j67NecqD5Bga4AfIE0CE4PZkX0Csa4jEGd50e9QsGxYwcoKSmmrq6K6upKqqsrOXhwj7FN//jHs7i6uhITk0B8fDJdXZ3U1dUSExNPS0szfn72ybnjxw8PSI/XUZg6EEwKNswHGhvradSVkZQ0PF5Kpl5bpujsrO+X55YjEDzHrHmjDaeXlzWYhmo7PZcvbVz+FtQlACG0LSIhmhBZOEp6rxZWtioIkYVT1f1panAvuWIhB3/+GF1dIA3J4763k4E8JGIJC+OWMtpvAsqOGv557E8cqNzN2rPvArD/vztZMeZBEnwN+kRzrryOe+75FR98/Rp//OMjhIaGc911vTWkvLz7Z2hZg0C2jJQV//5gKD1iBgrBwG/ubKaypRKxWMzuH3ezcO7CXmUHktXjYmE43GU7O3sGTWsu1wIUqjLC5daNhRnyVAK95ST6mZOltowA0/oUqjJCPcNpqlPg7iGmSa8g0EPe6zgPtQ6Zp85mOcGwMHUrl2g6iAkIJ7fc0C/UU4der6OhQ0mwKJSz9SdRqMoRS0WEeIajRkNnreHdu3XxXXyT8xmVunJO1h7jL7uepLK5nOKWAtrOt6JDC6YSc7YecT0GzQc9IAIk3d+lIkTd2/SAWCICEYjEYgPJLgG9TodeBCKxUE5vfi4R6EV643ZhUqJWq2lVt9poEOAKnXQaRjgd0F20nHLbx9iBqkvFPw/+0/j/3v17DV9OmRSKhyMcYcOuDd1NFyHWihHrxIijxRwsPoinwpPYilham1vxPOGJuFVM0Y9FlJ4rZVfJLsZPGk/ZyTJOHDvBVSevYsmiJchd5Xi6eCJ3lSN3leMicTGGGX64+kO2btnKwdMHeez+x8zaLJRZs2YNu3btIj093WiwPfz2w7xX9B5aTy2SVgkPxD3Avx/6N9DTt7hGu5KzLweVXMXzHzzPPbfd43AYo+n5AT5c/SF7du9hzpw5DqXWFiY4pQ2l1NTU0CXu4mzeWTPiJzU8lWDPYJttMrvGNgkPxPZcI8Cdy+5kQugEJsya0C/Cz9r9vBzRl6Fvq58WxseS/BLCvcNJCUtx+Jy2PJ+Eya+iWYFrN8luCr1ezwsvvMD69euRyWRs3bqV2NhYm6FywjPe0tnCieoTLFu/jBPVJwCI8oliQeIC0mPSDYS2psNs0m0LEydOdPg6R2pI92DCevrr3WsPa9as4dsd3+Kucrf67AUHQ1ISFBYaPgH0daCv7fkfIOq0G0Fu5tuEY0bl9mz3aJIRacesNT2P5f9PPJHBuHEG4lSAULdQzvJ4e3UmJaUZr8cSkT6R5JJru6HdsAzRs+Y9ZcujSshYK5D7J0+cZMzcMUZ9yQBZj9C7EH5bWFhIbGyssW9Oi0gjLSKN5eOWE+QZxI6iHWwt2MrWgq1Uqir59PSngMH+nzwmjeiEeObJbuLn1z5MVaWCwsI849/Roweor6+jqkpBfv5p8vNPG9uak5PJ66tfxF8aSHx8Mj4+/kyffhWyKE9IBo2vIcxy8+YNrFq1HIWihPDwaJ588kUWL+4Jq3c046A1G9GWcwBAh+7iRSQ4Qrr979B3XDd90aDquJCwDNW+UAtBpovYFwLD4cXWF5KTidLrzSz+YYeTgBohEEQUE/3GcIrsXvvbNW1mZUwxfjx8+SUcPAgRk3RMnQoFjT37RSIR00Nn89dZb6PTa3nm4K/Zr9jFwaq9HKzay+pTbzBTlM4c2TzmzJmHzk/Lh39/g4cfvp1163aRkjLd7HxjxzpuaF2uuJjhg6YEWGVLJaFeBsPcTeNm1hbBmDRdfR6ogTmc8eampNPFdJdt19g3FqxpTNgzXIT62jVtRpJZIJAt6xLCa1vVKrvlrGFScCoKbzmTgsewiy2IRGL83APQ6jSIxCKCAkeRX3aK2JBEaksqaYtu5UTTcTRtGtr8VCC4VAu33sPwIRd5EeAeREdxB83nGmlPaQNr0RBN4LXGB9DT0dmB2qsLlIC3Hn1zdxlv0DUL3lPd9ywAQzkMBJXp/8bvgd3tEpn8BQCN3d/9Tb4Lf14YCCc/Q9uEba5qNySBEtw63EEqQuerxaPLE7W0i9baFkRiER03t1u/xnYIrByFTq+jDRVyDx/8A4LQiDW06VtoVNfT1dkBroCbCCR6dGIdWokWrcRAmnXSSbOmmcrySkOdpvzZKMhtyIXd3f9Hw4HqA7z0/ku9miIVueAidkWik6DTaNHHwLaSHXzx1reEekYik8ppVbfQqm4hTBZNSc45WkTNVGa10BkVxIZDH7C97RvoJjG1ci1vK96m4i8tpIffRHNgA8rORnzCQ5BGeOEulvBd/naU20WMD0hBXd7FhAm9J817j3/PnCk3cvJkJi4RrhQ2nSbeZyw6rZaPTnxMdWkFP7x5kIdX3c/yB5YzdepUCpWFSCVSNFoNUomULk0XZ+rOcKb2DGOCxiAWi5EFymgpacE/zp+SphKkEnOzpaTR4Fon1HH06FG+OfsNa6vW9lyjp+EaeRsjCRXpE8kOdvCHj/5g9LBa+fOViEViq4RUSWMJo9xHkZeXh1qtJi8vjy51F2KRGKlUSpe6C1eX3uTIpQhrhr4tCBNZgSQWvNNULaohGR8tkyvU0Xtm/sILL/D5Z5/j5ubGs88+aySDrJEdwvhZ2lzKO8ffIbfGMKGP9I7k4dSHuTr6ao5VHCPK1xBaW9lSaTbpHiysLWCNFAw0hF8g1GRa++FypsSbKUzD7IRnb7Ci9uC4VtJgbZuBZGpzFJbPsL+/f7/tJFOyWKjPkkA2tcNMoxOyDxvmI0kBScTExXC+/rzZcYLnYuL0RKvn9vfwZ9m4ZSwbtwy9Xs9/8/7LWeVZthZsZV/pPo7VHOQYB9nYsJbXPn+eORHzuDriehalLSdYFsKuXVtIT59Pa6uKw2d+4MDWvXh4eLB//25aW1soaDxLfXldt4YU7NjxX6P9IAmW4K8Poq6uGr3e0D8pFCX87nf3A3BF+lQz22tScKrdBXZrNqI9bdyiohyYltzrmKGErYXTxkbrz4hp+brGart126rjYsEyVNuRfmEoktiYLmJfCPTXi22IiML+uU0OAZwE1AWAEH8ufIL1iasjk01rZTykMhKmgjTK0JFm16iQu1oPxUryG8uTU//Clx2r6QzoYHPxRuMA8N1XXzLf9xaWr7ibDkU7n332ARkZC/n22wOGiZ4TZnoy6XHpwxI+2NcKoWX4SWp4Kt5u3r0MA2vG5EANTFvHDYXHkmBMWXMvt8wgOJJgabj0BVMC2TQhgeUK2rzoRWblHBlciotPUj8xmLOtueQWZ3FKk01DiZI8cS4NXUq0Y7Wc6soyhLwJ0Bv6Dj9RAKlRM1HVtBCmieTcoTOEuoRxNPMQJc1FPaRQK5AOZg4IanDb584bb6xl7s9uYFvJd1S1VhDoGsTskHns2L6JurpqtmZ+g78+iJaWZlJSpiOTyQgbHYlc58Px4wdJShrHntz/kRI9g72Z/0Mfrkd5tgZ5tDcoQKvScu5cER4eEtqkLQQFhtLS1USzayPqdg0SjQSZzJOWlkZc29zo1HSi0+rQtKjRaXXolDq66IQOaFd2P8ve0NzcaE58/WDlGruAzVBXUd1NrEFHczt1VPWU8QaaLb6LAZfuuoKBNgxhCJrubaZ/Qjkvw+8iECe4Au4YRurushqxGo22W0BXAnhCO60cq9uHlbm54ZzdCcR2/PiN4YuLRRkX+Fa9lm/Pr4XzJttH93zNPn2w559iK+cBjA5mlvt9gWsMX5+oegKqgP026rCGRDhYcZAn3nzC8WOsXOO7Be+yYv8KZs6ciU6vwzvGm5qDBg+rXfm76MzspOJ4BWd2neHcuXMkJiYyc+ZMDpUe4q2v30J5XEmYSxgarQbXaFcefPtBqo9V4yJzobqrmutSr+PPq/7cjwsbmahsrXTY0E9LSyNTkUl2VTaZikxSw1NJDU8lKyRryLx7hMnvmKAxbDlhHp733nvv8cwzz0AgfPbZZ7i6Gl5eYazWKDXMZ76xfGlTKW9mvsnO4p0A+Lj58MfZf+QX035BbWstkT6R+Lj7GMd2uau819g6GNgLHx0KvSNHMVTnMiXU/OL9bGamtCTeTKUmTAkgYZI5GFH74caFyl5ozVMuLS2tV4iqJWxNum29E9B78c/abygWia1eu6MEnEgkIjkwmUWjF/H4zMfJOqUip3k3aw+9y4m2Y9R2VvFt4Rd8W/gFAOMCJhGrTcSjUkZK8AyiEuLhShHp6fOZPDmN9PT5FDScRdYup7Awj82bN+LiIuVkxXEqc8spaz9PbV1Vr3Z0dLTz6KMZzFgwGz+/AOTx3ujidIz1m+jwArupXWaqjWuK1lb7+nO2bLv+EAqmpJhCZTtEUyCe+lpodaSuiwnTUO2+cKmG6/WXNBtpRKGjGHk9+2UIgW0WPvs7cXWkfstJbLxLD31qSnxB90T4GsME9+5xj3C0ej9vHP0r55sLeLv573xW9QF3L/sls5Rz2fe/ndx113ze/fwr4yRmuDEYbYSh9tQxJUAsDajkQPvEyECuw1HXfEv9rMGszg6GRBpKjyVrJJe1DIL9RWVrGYnd75414tde6J0t2HO7tgeBQDZthxBem5mzn4kzpiIWic2IZmFwUWpqUevUlDQXUtSUR2FjHsdrDlHbXs3Jlhz++kn3qrJgb5l4QQZIgxg3ahL6Zj1yjTchsWH4VQbwm1uf4fP//QcqYc1/36KmsZLTp3OMA6B0lAtTp1/JhAmT+eH0DvK/P41+rs5AlDQBR2BJ8kokEomZl1eETwxbt3zNzp2bmTv3BhbddBsVJ8ooLz/PyZPHcXf3IDg4lHnLFiKVSklPn0+7rI3br7uXGtdKgieGUhNTyaLrlpPsPw6Azz/fz+23z2T79k00NNSxYfcntIW20ZzdhKxdRlDQKORybyIiYoiZGk9XeCfuVTLEShHbtm8iYEYQGr2a1IlX4l3jQ1LSOD77/j/kdeZybvsZ0OmoLa+h67+dMBfw6b7GneCS70rAlCAkTVI8NV7Ez0xk9OgJqNVqtFoNGo2G4uICxGIR5eUltLQ04+Ehw9fXj8DAYBSqMrpqO6mtrUKr1SISidFo1IhEIJd7I5FIaGlvRiVS4dLlQpdHByKlCK1Oh95fh14J+OkNBJMUw4JAJ9aJLW8MTmYCmeUKeHY/CDIgGqzK+OmBFnpCKYX5ocjBbeLu89orLwa0FuWFT1vbsNhn+d1yv7j72q1co85Lx3PPvUp6+jluuSUDWqKQe4TR1FIKnr6UVnRy9HQuklYX1qz5Cjc3D7x8/kOJrpBGVyXebX64hnfhlxZDW5CMYxtykba6Ul9di5fcl0OncxG5n4jVd1xhi6K7JNCmbnPY0DeOiyLzkHRHtSIdHbOt9a9ff/01Dz1k0K579tlnaW5uZvPmzdTU1uA/3R+xWGwMn6ttreW5vc/x3vH30Og0uEnceHT6o9w65lbjWCu0w/Rcw+HhbEv/8kJmsB3Iuazpkzqqx2ZZrlncbLUcWBe1H2r0V7PLFBciq9ZApR5sTbpN6xPeiYE827bE4gcCTxc586IXIi2UotfriZ2ayJ6yrewu3w+SQE4AACAASURBVMqBit2cUmZzimw2bfoKuYsXk4LSSOwaTWKLyXsjEhEWFkFYWARqdRfp6fP57sA6Fl25jK92r+U3d600ej+ZQqvVsG/fLsOiUvci1AtuvyNyUiwTI1Jx6fZmHT16AqGh4YhE5v2Z5aTfen/Xc15rdqZQhyXh5AihYM2WtSSXFKoyqvMrmDIlzSHiybQdjhJVFwOOEJ4XK1xvsHjppTVs3bqZ48freOKJDIeOGQovr4uBkf9rXKLwkFhfXXJk4mptkO8L9kJ8hE7PlIgS9rtL3Ll73C+5a8wqtpz/mndO/J3s2kxey34et1nu+I8K5Pz3hTzx0IN89dVuZDJPhhOD1UYQSIyhIqIEAkQYrG0ZWpbns+Vqbq9d/XXN78t4EMgzQTfHFi73LBHtWsMzYUr8mnoIDmSgtXzfsrMymTIlzczIsDQQjh83ZJWzJKCF8Npct54VtMbOBjKr9nOgci+5yuN8su0dshSH+b8P70Grt/57SvUueLbLcVe5493lx9j4K3j4xifQ6/XUnqgiPX0+u3YZVk4jx8ZQ0l7M4UM/su6rj8jZdQS1lxqUhlXKyMgYAgNHkTB9NPNTF3PddYv4ePM71J7x4pWPHgRlKxBA+tJFvPzyu8Z6BS+vWK9ENhdsNGaqiZgUTUJCMsXF5xCJwMXFxZjBRoCr2BBC6ucegF6vw889AIm4Z4gaP94QEuXr60dm5n4CRMHESH2o86vFLcSVlpZm1Go1CQnJLJuXQXlLCSHJ4bz88h9xdXGho7YNtxB3RkdNoLahmqPHDnA0/wAx0xOY9sgs2ltaOZT7I5lr98PJnj5YKnVl2fIMKtvKWbhsKbGxiaSm9oRnCUab8CmkiJ479wYSEsaYGZYCeSbst6zr2PEuUqa48vTff0VtQSVNoxpQqVvQyXTolHpCZeHIm72RSCV0dXaRNG0MP7/pIdZv+IQf9m6nTlJNp6yDGK8ERgdO5OTJY3jGeBKaFMH+/btpq1Bx3q/AQK5Zohkis2ORi73wbvVj3NgrmDVrLiKRiH37dpKTcxR3Dxk1+kpUHs3oWnWoJWr81QHo9Xq0Xjr0rnr0nTrkUi886j3RqLvw8PAkIDCY1OtmcryskKkJCTR21nM4bx+FuWeQB/ogbZTi4eJJg0aJvzQQWYcnUokL7e1tpKSkETU5lu+Pfs2ZnTmI3SW4Rrji1eFFiDiSjvY21D5q6nTVuLvJUOs7KUg4a/Maq/1KySzfxe3udzIjOpW0u//DsQkH2XNuG1s2fIaLhytdvp1IXaRo6eRM1zH8RIGIEeGuc8E/2pfEiXEcyNyDsqYGT5UnSaPH0NLZxNio8WzrvOKysascMfSFcVGn1w0o5G6gXrl79+7l9ttvR6fT8ac//YklS5ew9rW1SCQSzuWf46a5N6FoVuDp7smf9/yZfx78J63qVsQiMfdOvpe7rriLOTFzhmwy3V9cavqXmYpMzqnOGb3cTOFoQhnTcnUnrLlr9sBU1H440B/NroFiMJ5SA0m0YG/SbVqfvZDS/rbZUWH0viASiYjzSSTOJ5F7xj9Ch6aDI1U/8uXxNZzpyiGv4RT7Knawjx2s/uJNQl0iuOHgrST7jSdcHkV9R8/z1NJssNu7OtsJC4tCoSjpdb7g4FAeeOo3NBY0cKzkAGVZ5ykvL6Gg4CwFh88C8MUX/wEMNkdy8nhCJ0SQFj+b0aPH09ZmR4MSQyKYgtY8ozC5YGfaI6JswfIYW7Ys9JARWq2Wdk2b3bot55q2ytojOCoq7DZ9SCEk4FEoID+/r9JSfH2TaWwsxNc3maKi3sOyo1pLCgUkX4BAjEOHNLzxRh56vYRz5/KYM0fD9On2zQnB3mxoqGPZsozhb+QQ4pI1lEQi0fXA6xiCED7Q6/V/u8hNMoOQpcoSlhNXy0HANJuCacfiiEierRAfAZadnuVEeGHcUiI8o/mm8HNOKI9xpOpHOuM64JeQfTaT2x+fx4bX9jpy+QOCNQJG0awYEJFkatR+t+c7Fl1tW2jPHvToe5FiggFlarianu/goYNU+1RbdTXvy9gWDISGwgazrFj2cPjwYXzje7unNTU19Snm6Sj6CoUTvL2Giviz9B6z5qXl6LnyzuYwPjTRjPiNlQ4+/MD0fduVayBgBCPDmoHQ2FiPl97b2I5y1XlkLp58cXg1ASGBbKv/jq+++4iCxrM0dloYAt3eTCJE+Lr5E+YZSYhnOHIXLxJ9x9J4zINxPoHsyPyezs525HJvPGVejPYZz3lVobEaX19/Tp48zpbMjWz77L/U1VUbPGa6wN3LgwnTU7jzugfQabXs3LmZ1lYVGzeupampHom3BKl0ObAOgytNDFJptLFeAaJuTZyEhGRKSgpJSEimS9xlTD0tEDAJCYbwCqXGIJIfIDXodcT5JBEZFkNLZ3Mv702A1NSZFBefo6SkkJSE6ZyXFlJYmMfChebkUISXoW1COzxqXChXlPDJiXdpbm6ks7MdTy85NbWV/PxnD5F3MpfsoKMERI5DWVKKIY4umunT7+bll59l+/ZNVtNmC0abQDTZS7EtkUjs7hc0hF783b/IzNyPS7QrJ2uPMyFoCp3nO0ibdpVxUmH6WVdbg5e3D7Wu1biIXGl1V7Fly3rknt4ESAIJT4omvjWRouwC/IoCaBin7BVmeIPfrYTeHEFTYQPqEgNxNHfujQAcPXqQ8PAoIiJi+NnKBXy26wOKcvIQe4uRN3iz6Mpl3HzLHZxuyOG/P66jrqAGdUkXLS3N+Pr6cdVVP2PZLRnknNAx8Qox2TWZ+I8NpGpsGlFjY0j2GU9K6HQOHNjLnj1bKS8/T0REDNdcM58ZM2YDcMf19/Nlwmo2HVpPhVsp/qogGkrrCI+IQpug5YqYFNwjPQhyD+E/n75OmUdxr2tMVo2nrqGaltom1m/4mNuW32MQwp2cxvbtm4hoiUETpGbGLXMoOnGOM9knCfYNgyq4a+qDJMaPMf4m7lXuTJVfidRHyqvPrUan1+Hq4sprv+v1s1726Cvkrq9FEEdgOgbl5OSwaNEiOjs7ufHGG3nmmWfIV+abhQ1OCZ/CpoJNvFn2Jk3FhgnpouRF/CX9L4wLHtcv4snf3/+yzVrnCATbLG5MnJk3jmkYn6OE2qVGvA0Gg/GUysnJYdncZX0Se6bPZV8aOYL9ai+ktL9tNrVpLe20wYR5ukvdmR1xLbMjrgUMJMxX+R+x5+xWznadpFJdzn9yXwfgmQOPcEXgVBI1Y4lqjDXzeHryyRf57W/vQa3uMm7z8JCx8tcP4TFWxow5V7PU9eck+CbT0tLMzpzNqM43s337Jlpbmzlz5iSNjfUcPvwjFMA3ys+N9YSFRTJ69ATGjJmAklomRU4jIWE0Lq4uKNtqkJg4GggYyIKn6TGWTgydBYUkXGnoFzdv3sCpvdls3foNKlUzE+omM3bUJKt1WsvcJ+D48cN4xRrsVlOCIzBwVK96hkIfSSDY+go9FBLwhIT4O3TOu+7KICTEuq0FPVpLfZ23q8vmriFFZqYUvT4ZKESvTyYzU8r06bbLazQaCgryjAu9l4qXl4BLp6UmEIlEEuAt4FoMqhOZIpHoO71ef9r+kSMDtsTE9d0di6dcbjZJtsd2W8IRHSnTc4ksOsjq9grifJOYETaHv818mw9yX+Or/I9Qj1FzlAOk/DuCX6c/Rax3Agbuz3GUNZVRcbbC7gBnueIz0NVRU1TX2xfas4VMRaYh9fr5JuSuchTNCjRlGspayxgz17ZB0NjQSHhkz3XUWslqaEnoPL/3eV499CqNHY2EyEO4M/hOoCeDnT3jt76+3khAWaaWLsorMhPztAbLLHnWziV4glkjgkwJOm83+8+nPeTk5OCFl1XvMWteWo4+Gy0tTWbEb3NnIwcr9xg9jwRy99TpLONAbgnBe8kSlu/b9pL/cseWeShUZfi6+bEkYSUL4pZwuLKGPc1baT2hoqj5HIWNZ2norEcneDMJi4jdi2oeUhmJvmMI9gjBR+XHzyYvJO9ULldNvZazDSeYFDQNgOzaI9Tm1JC3M5Pxt97AsmUZPQRPtMEA9ZDKqK9X8s47/2DDhrWcPp1jdDkPD49i8eI7GTvnCg4U7Oa26+6lPreO9PT5+Pj4syN7E61NLUYvppkzpYjfn42Ow0gkyYxJNFy/tcFbIFqCJo7iy+2rya7JtErAdFlJ0S4Sie0aBJZ1ZGbuN/Mksizr4+PPsWMHqausJi8vl4iIaPR6kHd5MSV0Oindf3V1NegisjlU/n9otWNwd0/jd92EQkCAdUFba6uDtsoKRJ0tzxI3t560xmPGGK4nLqrbS3ZsT+pvA3o+IyMNk46rpy2g2a2J5qIGWtxbaWxsYPKo2cwI/hnjr76STe6fI2/3Yt2BD6gZXQFyEDWLuS34Qf60+N8oVCUcad/NwZJdZmnFhfojI5MZ6zuT526ZgW6RDrFEjE6rM4hwd0KCLJVfz0vh27aP2ZO/mXnzlrJw4Z1IpVJUKvD2EqNSGcp5BAQTPi/a6F2qUsEV/8/emcdHVZ3//31nJttkGzLZ95WwEwIBgmyygwhKlaog4IKtdtHaunyr/qrVttZ++63aaqu4UJbWFatigiDIJntIgEAIJCH7vu/JLOf3x+RO5mYmG4tb83m98prMmbPfe8957nOe5/OMm0Vu7iWKi0sIC4tn7NiZNmGeVdx44z0sXryWf378ItlHTxMU60xnZyeRvlEExoahUWkwmo0sG7maLYfeoGFEOXgKaFAzX3Uf9965ht+9+iCeLTqyz1+kvl4W2LrnMNZ3DCqhZenMNSyeZuJiUyYUwrI591qvU3T4RAh24XDBHiZPnmOdp07Lrfw1xsu5uvjii1NIifFcagSpFsVJs5zWGyorJMWpdEWFpexHH21k+5GPach1tbg99oBtvXJ7PdstKYFyQwMXwiAjo4K1a++msbGRhQt/wI033sXFixKXGmHatHW0tftQF9JK7IsjKWrOA2DUsAk8MfFlJvlNx6Vt8POiClWxJ2+PQ+6c/wb0Zo3zdboMfpMYrFXQ5Xgx9IQsc/WlsHPkMdCf6+y1VAD2lNMc3R+Xq5QK8QhjafStjKufyIzZ80ivPMqXRTv4vOBjsusyOVZxkGMcZOv7rzNMrefMgZOoWlX8eunzzMybz5dSKmZPM6omFbcErWHktLFUtJYpKEs8Pb0YM2YCsdPjCQ4OY86cxQghqKws59NP36fcWEL1+QrOnz/D+fNnKC0torS0iD17UkAP77zyFhqNBr3eH5PaiDrMnfH+kRRJ+QhPe7laPnzrDT0VI7KixtaI4WyjhSQ+rewIO7L3Ul9ixqWlkPj4CPIL84j3HQt0GzEIYe7TE6ekuYiTBUfQuGjorO2kOqfSquAwudvLOlUdRUD3dR4Ir1XPPLKCrTcLLDm/PAbbsrZ1ObIuk2UtR/2SZbeamqo++z4Y9CWLOoJtm52dW4GngQLgGJ2dTsCqXsv2POj9Limf4DuqgAImAzlCiDwASZLeAZYD3wkFFDhWFGmdPAjUhhAaG2F9SW4xNFPZVuaQ2+lK23dkiWWbFu8zmj/N3MAjk57l9188zvv5/6TarYKnDj/Im2f/ytrhD/No1NoBt9lqaLXboGRFzNdB7j0YyCd+apUaBBjNRksEnvxqGtr65yVSRBFxENXQlttoy+kt/O7A7+gwWd5byprLeLn1ZcafGU+HuWPAbokXai6Ql5dnzadWqQdE5tlh7rAqlvoiqQSLgGFreSQQCqs1D9/B35+yAqyhoQEPNw9Ffb25m10OzMVmkiNnc6nxIm2mNspbSzhZaeZ01QnSK4+SfukYEbExDnnZ6utr8eqHBG3zudf544knMJgtxyX1HbW8cfZF3jj7os1gu/+VkAjziMTN6M6M2HnUllRxW/I9xOjiae60RDHLqDqG8YKRZTEr2XKxnqnBM9C7+VqV1y4qVz7K/5dVQEhK8rUqZ6ZOncm7725k27YtHDy429quu7sHc25cwl3LfkpS0nWoVJZnv7GhgQT/JE7qjgIWN7mI8CjO5KUTGxuPpFExaRJ8tPGXbNq0nTVrlqJSHe1zTlRqlULQceoySbkaUYNs6+hvw/fzC7Bu1PHxY2hubrSzmAL4wbI7SY6bhSRdx+HDkJwMkyZZfnMkmPRm/tybECOn21qM2SI42D5ct4XUqG889tg6pk+3EGZnVWVx9LOjpFS1ERc3l1//+p6uXBFMGulPhC6CW/fPJSU1hYLsAiIjI3n2V8+i0cBwIrg+cR1G42rFemFbf3efVDb/K/s7wi6/BbZjG06Ew/KPPWbfvhIa/jjqV3aWYLIiyyzMqJaoWF24hL0nyynKDGH1yiSSky2lm3N+xN4v95KcHM+oUY7HaMuPYhaTHby0WcboqJ9fdyjjq4m6ugY8PEBrtJxqu7p2n27LaeBY0HdxUZ6Eu7iAq6uRoqJsnJ1VFBVl4+pqf0JrW6/cXs92XV3BRQUdHVW89NITVFWVk5w8m1df3cKhQ19a+5zesJs/1f+SS4dyAAj3jOZm9zu4efoq4oZZGPWblV7x/cIRd861xtUiub6apOYDdbO7WnBRXXlQpsuJGOcIg7EK6knIfy1gMpn65Ijqb38dzP01UGXaQJ+Lq6G0rGwtZ3LgdCYHTucHcXeicxnG+xc2sfvsZ1wwnaWmvYot518DYPM//wHe3eMwe5l5r/1tTIVGdC4+eDl5U9B0CSe1M04qJ6pbK/Bx1dNmbqXN2IaTygl//0Cio+OYnbiQWJ3lkHLXrs+Ijo7j0NkvqbxYxs60T2gpbCY/P4eKii6/tA547uFHeI5HcAvVMsJvLCHjwkiKns6IEWOpbbS4Dfbm4lZfX6uwRmoztloVFrIRQybpmIWZP33yIqd8DsGyFmhwp620iaSgRAqac61GDOmVR2k1tlijLcuR+2QKCYBWQzN17TX4S0HUdVQTHRNHQUEuNTVVnP08A3WLWiHrnL34FXMm3QZY9oWeSiS5v7ZKnp7cV7JiyXYebPeY3OpsVJUqnvnPwxxvOoTwNKNqUrM66D7GD0sikSkcP/4Vulgfu7LVxkrrXNrW/VXKbrbv/8BqLdabhRfAgdMfMHbsLVa5wNbSyCzMZKQfZ9y4iWzbtoXdu1O4dOmilX6hZ3RcWUF18uRRAoYHW+dl27atvPTlWnjY1MWxWsBf995FVBSEhUWSlHQdRqORivYyK++zSlL1exib8tU2lly3gqPHDjBl8gxFPyZOSrYeIH4jEEJ85/6AW7C43cnf7wT+5iDffcAJ4ISLi4vAwggnALF582axefNmRdqsWbOEEEKoVCprmpubmxBCiBEjRijypqWliSeffFKRtmrVKiEsDVv/fH39RUmJEL6+/or0fWfPixUrVinSHnzwSbFjR5pA35WmRwRGhIhP048IpyAna5pKpRIlJUJMnTpLUf7llzeLJ/7ygjUfIKZOnSVKSpRjcnV1E/vOnhexsSO62wKxY0eaePDBJxVpK1asEiUlXWNyQjAZwS8QPN319wiCxQi0CCGEuHHdjYo+PfnkkyItLU3RpxEjRojzVeeFm5ubNU1SSeLVY6+K0ZNGK9p/4c0XBnSdXEJchBBCRCVFKdpKS0sTS9YsUaQ5uk7+/v5CCCH8/ZXX6f5X7heLbl6k6NOSNUssY7JJi0qKEkIIy5i60iQ/SQghRNzUuO469ZZ7b9UjqwSxCGYjeMJmPm3/fo34wT9+IHyTfQXB3W19dvAzy71n0/7SdUvFU689pUjziPQQQgjhEemhaF8IIVatWqVIu+2228SnBz5VzFN8fHz3mGzqPV91Xsyapbz31vx+jXj6pacV+RxeJ38X++dJ3+M6daUlL0kW979yf+/XyaatlJQU5ZhArFu3zu46hSdGic/Sj3U/T11t/WTb48J/fJDd8/Tww79R5JOfJ8ldEsQgWIBQ3aYSPs/6Or6GTyP4DYL1CG5GxPwkXvzjwHuC4Qg0lnrdwz1ESYkQ7uE218kL8eZXH4mEZUmK9n94791iw0cfKtL8/YPE9Om3CicnZ0X/XV1dFd8Bcc89D4o///ktgVd3mjwm23lydXUTmzeniPDEKEV56xphk6ZYI7r6JK97np7eintq8+YUu3Vv0eqbLeueTVrEuBhRUiKEq6vN8yT1vu69/LJyjeht3du48VPLujfYMdH7Wr58+W1i+PDRijTrWm6TFhs7wm5Mva3lV7Q/eVmep7Vr1yrKO1r3nJ2dxdtvv2237tmtEfRYy7v+RowYYbfuqVQqIYSwWyO+rj23t7X86xrTFco236zM0vXs7jt7vm+ZRe/gPtcr73NNgEZR71/+8rbyPvfqIbPYPLsvvv3P7jHpEU5OTuLZV18SLi7KNW3HjjSxfPltgkAEd3WvuYEvBAsSEfha8ulifeyfXX3vMsuH+5RrbERMhPjbkb8JJ2elHHau8lz3PdE1/hfffrH7Onk5uE5d+WSZZcQIpRxmd530lut0vup83/d5V1vnq87b3ef3P3m/QzmsN5ll1qxZij5Z772+9nf9AO+9rjoWLFhg9+z6DPcZ1LO7ceNGxTyFhITYjUku3/PZHahsOZjn6Y5Vd4iPzn2kmCd/f3+RkpJiNyZHMosjedmRbAmIt99+W0yYOsHxdeprTIO8TnHXx4ljxccUafJ18vb2VryvvLj5xUGtsbZ7IaDcC7vuZ8X+3tXW+LlJ3ft7V5omwEksXXqr8PMPFIxAMAPBfVjkr6evwt+TCH6N4DGE2//TCq+nvQUPYvl7AKF5UCMSXkkS0r2SYB2COxGsQ7jf4ylYKQluR3ATglsR3IBgEYJlCOYiWIhwme8qbvj9LZZ3gskIJiLUE9Vixf+uFp6LvQQjLWnEIJ5/9+9i4a+WC0KwjHUagiclZX+fQDgtdhJ3vfAz4RrkJghH4IkgHPHnPW+KxbfeLAhF4G75W//QL8Tjv/29pU43BMEIVx9X8fv/e0VoPDQCFyzrrAviXE6dSLpuuuWdMACBBvHnl94Uj/z5OYEagQ6BCjEleaZ4YcPrAqn7ejo5OYnNm1Mscpgsh+oRtz+4Ximb6RHJybPEwYMXLdd4LPbvSr9GMNWSP2ZcvNCN9um+TyTEuMmTxPCFoy190lj+PIZ5iR8+dLdlPEEIXC3jHZ00XngF6Szz4WGZq8TZySJgdLAgomtMoQiXEFcRMDLY0oYeQSQCP4R+pK+QAlWW+Qi01K0b5yPUkRrLnMZbyquj1EI/wc9S59iu8lFY3qOftB+fNE5SyOvJy2aLnz/9ZHeaFyIoKMTyPAxTKZ6dv/zlbeEVrVOkPfTQk+L2O+5VPHcxMfECRNyVyC6X8ycJB9EBvu2QJOlWYKEQ4t6u73cCk4UQP+utzKRJk8SJEye+ri5y4ULffrE59dlWTbqj35o7G8moOkaC32QS/JO4WJdl5XbqrZxtvf1F9pLzOaqvrzbeeuuvPPXSz1EFqQi5LZIig8XE3VXjytrxa7lpxE0sil3ksGx2dTZ5x/NYvHgx2dXZxPvGs2HbBtavWK+wgPJy8bJGmJPz9QVHZW3LyW0MpK6eeH3b69y34j5r2dTUVIrbiu3qy67Opj63nuARwWQeyrSO8fD2w7zzxTvMmDmDgEkB7MjZwbmqc2RVZw2qHwDBnsEkhyYT7h1OhCECd4079y66l9TUVKKTotmxYweaOA3aKi13Lb2LDds2kDAlgbc/fpu7lt9FUkgSWVVZdieYz7+xAV9CCU2I5sD+HWS2nqDySCk3zbjT6jLx7u7X+eHc+9i/P5XQhGiaLtUzfvwU9u9PZebMxeQ2ZBHjPZK8xmyivSxzIhMG2sL2dxnv7d7AyrnreW/3BgKdQpk5czEX689S0HSRr45+wXVT5uGU58ysWUvs6irOyGPmzMXWfthi//5Uho32ZdP2t/nRrXdRm1lNeGI0sbp46/OUUXmcd3e9zYRJk0k/cYwfzr9LYQG1Z08qCdMms/mLf1Cnr6GkpZAzVWkUNecP+LpJSBSvN7NnT6q1fdtnbMuO11m96D7e37mJWxessZZ7Z/fbtAe20pltIDwxkv2HvmDmtHnEeMdbLaBy6rOJ8R7Ob37zv5hMBXzwwSaam5usdUydOpMVK1Zzww23oNMN67OfcuQYGfIJTfaxTObMUc7tnj2pdmkybMcmn3DJY5S/25bPqc/m5M7DVmJuX98Ah3VfuGCxELoSyPfJQMkn+8NHH21k794UZs9e4tC9aLBwcYGIiCuuZsD46quv7KyUhnDFGFj4t37wdcssAM/9fQN3L19vfYZtn1Pb9cGRnNFz/di6YwOrFq0HYFPKP1iz5MeKthzJIHJ7tmX/9u8/kLE7jbSCQ1SeK8PPL4CdOzPw9w+kqCmfh7ffzaHmLwHwdPJikefN/GH5q7hptOTUZ1N4Ms+67oLFAmr48G7ZwpFc0HNvj/eNV+yd2dXZNHY0WuWOpJCkAcsXjtrtq6yj/KmpqSxe7Hj9Heh4+ivbXz0DaXew+QY6Rke/2cqXfeWVv1+OPNgbbNs4XnKc93a/x8q5K60WUI7621u/ZDiSW41GI0899RT5+fkWy9Vnn+Vi3UU7mW6w94ejMllVWWRXZ/PF3i+YN3sezvnOxCTFKO7F1NRUIidF8u+P/k3QhCDK0st4ev3TA3bzk9+T5CAmtvt+b+8mPZ/nnPpsIj1i+J8/P0BLQROhoZGEJkQwbtJE3vn8TTa3vdbrihynG4VaUtFqbAEkmjobMJgNCMyYjCaEyozBbPiv5n+7ZpDVH7bfe/5vm6ah9+i9Zpvfvm8Uc/UwqnQCxspO/L0DKfcoRd2ixiPQi46GduLGjmB66DwKSnPZkrIBd8kd11A3fBv8Mag7aXNto9ZYjd7JD0zg0eZJrWc16g41LcYm/FXBTA6Zzquv/GyEECFfa0SO76oLXjG2TqcQCnyNXPzXHj15ogbK7SRjsGHlB4q77/4Z2WWZbHn1darqY4fU1QAAIABJREFUynnyL0/yn8r/kFmVyWtpr/F62ussH7GcR6Y9wrSwab3W03NB743cuz/0NEO+HBewvtBXKOme5u21tbXoDDqajc18nvM5r+97nfSCdIqii/i87HP4tDuvk+REUmgS7m3upLemU91qHxHGXeWOT60PTv5OlJhKKG0q5cOsDxV5nsl6hlBVKNNU03DHnbkBc2mpsRAJWd3jusJjm4WZEyUn7Fz5jAZw9QStFobrY7hUkEmAxt/qMpFZm05OcwY5rcdxdbXkq+4i75NdJdyNKi5csPAkyYpXmTBQMWdGe8Ws7Lrh4gKuzpb/PY0aIp1DOOpkJlIfQm1ZtaKczMnkyGXDWq+roEGUIMxK9y9bPqcE/yQy9emsjF9H56VOwr2i2Fv0OQdL91DQmMOx4oNU51pMeCnrrttV7cZofQLhXtHMCJlH4flLbGp4VRGJRYa/NsguTYYtEaROUprF+0uBhAVHUlSRj4/W12ouLa8FubkXeOvjl9n34efk53cTjcfFjSQhYTK/+tUzhIYOXJsxapQyKpBskp1N5oDr6Am5Dvk5cuSaZjIpiRSjouIc1uXYPW1wqKvzYfhwGD786oTRfuyxdYwe3X+o+m8rhpRPQ+gPspJJjixlC1ty3J7rhy36i2jWk3tDJi03Go3kF+Rx+vRxKtvL0Ov9ePTR36Hx0vCbw79g07lX6TR34qxy5qbYO3hqyv+SceiYw+AFVwrbl+rBRqy1hSO3uKvpKjeEbxZJIUl4LL42roo9ScbT0tIcBqC5GugZ3dLsYx8YRs6n1+oxm819RtfrD7L7laPD8/4O1DUajYIuoFPVSXlrCSq1BneVJy2iya5MiEcEb8zfplCCy65qCX6TrVyYYHneX//oL0ixMMpnPCN8xrLv4E6Spl5HbkM2we5hfHLwXeZNXYrRbMBgNrBx09/IrsxnRmIyleYy3MM9uXD+HJFxsbRVtzAsUM/FvCw62jqoLC7Dy8WbDkM7Ne2VtDW0WeK8GLBQ7boBRkAr4ax2xlunQ6VV09LWjEHqpMOtvVfljKvBDZVahdbdHbVKjcFsQIWalpYmjGYDKkmNpJHADAZjJ6glVEgIBGaTGUmlQkgCSYAZs0XhI0lYjRdt23X0f89P29+vxlGNRO90xLaKLKvCS7L8IyxSqUatwWwWCGFGmAFJIJklUElIJsu7t1mYkSQJhEAl1AizQKgFmLDMjdEyHy5aV1SoMJqNSJ1YLH1UAoGwPBdmUJnVmNRGJLMKVAKVUY2QBO3erY7nwxsoBn1UAE1VLWBWIVBRVlKCh9qbqopqSHAj63gWbq5u1LfV429yo0NtoE3TBkaBr28AxkoTLp2udGpMmAFhNOPrH4ymzIXwsBFAyGWwI14ZvqsKqONAnCRJUUAJcBtwxzfbpauPwSqdvi784X/+TmNxA5989S6v/uRVfvn2L/Gc6MmpilNsOrWJ/5z/D/85/x+SQ5P51bRfsTx+uUIAdUQyDZdHjng5oWqvFsK8LX6456vP81HWR+y/tJ+8i3nk1uUiTnatfF3DHqYexmjv0cwYNYPl8cupOFXBshuWsWHbBtbGreW+T+9TkGq7SC7MaZ+DqdbEONdx3Pbz21Cr1Wz8fCOVHpWczDtJTmsOxY3FFFPMkSNHAHg291mGOQ1jat1UpAaJWQGzaDA2EOgRSFlTmUO+AFuo1Woiw6OtG3lvHD6OMBCeJBg40Z+sHErwT2IPqX225ciHvtZYTZx2JKYuxU0t1Yqyte3VnK5K48vGVPbu2sHh0n08tvlHdvW4abSEaiIYGzqRGSHzGOc7kVjdCDQqjVVw2Zq3gbtH/ZyXMp7FYDYoyq4f8wuH47Mlgvxg52YqD5TZcQlJkgqTyUSCfxKnhh0nVBXBmx++zIGPdpGefsxKJu7hMYw77riLsLAo7rrrJ3z55Y5BKZ/6Qm98RVerTpVKpSBSHAyB42BxJVGJesPV4LMawhC+TTh37pQ1IMPlRG0aLGR+jhpjFe+9t5HPD1oit95661ouXjxHSUkhmgANb7y5jc05r/H0O7+gxdCEhMR1HnP4v6Vv0W5qx8dVf837Co7ljivhwLnWZN4DVXB91y09rhZ31pXiWsqhtiTjqamp10wBBT2iW4ZgPRw2C7OCW2u4fjiR0ZF9RtfrCwcO7KK0tJi6umoSFyTb/S6vQX1FA1+0eAXxvmOZP38pW3dsIFAbgtlk5P4Jj/CXE7/DpOqODaERWh5P+p1dHYrIxj1kzqaOBvw1QbQYm9G7+eGj8SXCKxqD2UCsLp4LvudI9O+WLzomt3Myt5xfrb6LNz56CY2fhmGFPkQExmBsNFJXUkvmmQzGTkjEaaSG2xbeQ9WpCj4/+THNzY1cqslBXatBCDNu4VrOHzpNg1RPZ00HVVRYZT/0WEhoHN0GTeD+T08Mhk5cotxoLWym3b2NtuKuNV0P1Bi66+pKM8n11gCYlW3VAAhL9OTG7jRJpUIKkFA3qpD8VTg1OOPq5kandyd+Wn+cfZzxNHhz9sIpnPw1OGldcHZyxd2gxdPLixjdSJzDnMltzaaurBqfyABmhc6jydDI7rTPOB9yxtJmD0jNEh/dfpDq05VkV2Vy6OAeWoNacS9z5wfX38n4eUlsTnmNY2kHUQkJnUFPuXsJbs2ueAR488T9f+TEV4fIaDzOuWOnEO4CdZuaRtFAlDmW8aFJ+MZG0lGRz9mSs4wOHU3U5CgOFB+g5mQNnq6eiDqBe4Q7yTcn09DRQGZaJjmHcrhn4T3ExcXxReYXpKalsihxESZMFBuKyTicwZipY5ikm0R9Wz3PXXoOs9b+/taiZf4NM1k1fxWnTpxiX/4+ak7WEOQ0iTJDGfooPTPGxdBQMIba2gIS/MfgF+lHRVoFSclzaXJt4vb5t9N+qZ3cnFze+/I9RiVGMityFncvu5vDhw5z3XXX8dhjXz935XdSASWEMEqS9FPgcyyv+G8JIc5+w936r4FKpeLFF/9J5m2nyTuWxV+f+itbN2/lZ1N+xprxa9iZu5NXj7/K4eLD/OC9HxDrE8vDUx8mOTTZapVTcKGAEK8QeiM4dBRtrTcM1HrqcoSrmlqLmbAcqediUQsnW89y5sNnSK8+zOmaozQa6hVlNGgYo5+EttWDlYn3UZZVyN0Lf2lxX/OOZlhrPGfKUrlwwRIpaOWYVfx2EryQ8SiVbWX4uwVxo8sq4tpHsb3xXXSj4ikrcSXaK54RHXOZNzKamTV5lLoVMnnybD44+AbHS/ZTYs6n0aOeOkMdqTmWzTPlixQAtm9JIdwjFg8nL6YGXE9LtZ6J/tehklTU1UGkZ3f/bTfykuYiBVmhrMS5EvQW6cIR+rJAk+GICPrkyaN0mjtI8E8i1j2dMM9I9rbuYF/WTjLKj1H0WT4VrTZGk12H+24arcWyyTOamSHzactrZdWC9ezbu5PwCdG9uqaazCZ+MfEpXDSubDz3CiXNhQRqg3liyh8Zq09U5JUtsOTIfGaTkZbSNjRqjV0o1R2p20j/4gSvvfZnsmsyeTLnZ5g9zNBoIROfvWARq5f9iJoaLfPnT2P//lRaWiTa2wdPttvaCs0OdoThw6fY1dVX/aJDa/ebs7O3Ik2u80zNcU5XH2PclMksdfPl+uuX9lqvy5Vz0l4TfFteeoYwhKsFR9ZOg4VsxWSL/iwZ2oytFOcUoFKryMnJ5q9//QMnjh9CpVEx9d5ZrE27kUZh2W/nhC3m8aQ/UHqyiFDPCHLqv1bvAYXcMdBAId8UBqLg+raPYSC4FgcMVwOXs0f0lD1tUV6utkaLlPzsf7eNItkTvUWz7FlGzmcb3fJSI2RetOzZxotGoryGI/nJ/VD12a4jlJRATIyR0tJiqwX0+LmO7z3ZOsnYYCScaGu6rVLK9jDowt6zFB7PJ9FlKsv4OR81vAdehdAcxM/HvMCK2FUO1wz54N/2UFMlqRjmqrfKwY4UjLYWoCXNlrDGPupAAKK9hxMWHEl4RTRhwZHkl+ZSmVWOvwikoaIOv8AAyltLKGzOpyOonYKCXDqD2xFtEqZKA4tm3sSi+TexM+dT2jJa8FYNo92jHalGcKr4OG272+BGUJwPdwKHoKbGYsHfWFwPdVhd1LRad1SeKnw9A+jwaEOr86CmqRqjvwEPD0+GBekZPn0U+ig/zlZkEBc1kpKqQsJdo/HR6DmY9SVLpqygot0JqbGI0tJCyo0lqCQ1Nd6VBLaEMjYkkYhxMaxaei87jvyHRVNv4v4nbye3NZv5k25kVPhYWkqb2b7/A6ZNnkWjUwNtTa1oCtV4Vg0jMzuduXOXEDIvjO0nPuCQ25fgZDNGA0wzzyYpcBonS48ybtpERvsnEDkphvwTudZ3mFsW3IlRdKJXB5CzL4tZExYQ6zuCVvdmEvyTMEeYWRR3Eynu2zjTepLKw6XEeo1A1AtiY+MJCRlJi87Fanm47gfrWFi/kJCfhKDRaKxUBmZhJq00Da1GS4J3AnevvRuAGdNnsKZmDTH6GN7e/jZRQVHMiJjB/FnzKc8uZ8qUKdT/q56/X/g7Bqn7AFuNmsevf5w149cQoYsgKSSJ66quI+4ncdYALCXNJZbffpzEvDHzmDF9BmZhtpKLF9QXEKGLgBBLP1avXm0tA9+sFfyAFFCSJA1k9TQLIer7z3Z1IIRIAVK+rva+LbgWpuWXAxcXF374wx/xcfPrnCs4x28f+C25P8+lM6ST5fHLuXPcnezM3cn/Hfk/cmpzeCDlAYa5DmOxz2LWxq2luamZEK8QqnAchrRn5BLbyGsybJVU/Z04XY5wZRZmStvK2F7yFnuKdpBTf44LdecsSjMb+dxbPYwwXSQh7eEsm3IbbvlaFs5dxtYdG7h11K3sKU+1uom5uZkVLmOy+9ntY1aRFDqJwpN5xE8ew7nDp5g/fylC386dS9aRU59tLafVWj69NW6MC46n2ncOYxjN7t0pjJmVgJ8+EI84L9459hZmLxMnyg5R31lLfe0xAA5VfAFY+DLG+E6gzeBEa9MEzI0CujZzeSNvM7b2aYnUF2xP0a8VTCaTwn3LaDRSb6hlX8nnnGk9yZ6dKRw07+PlLV2WTTZudG4aLWP0E3BuceHWiWspzL7EQzc8iVqlJqc+m8a8enJV2QrrPdl6S/48d+4UJ4sPW0/sFyxYxgPjH7FymWRUHuer0j20GJqtbRbW51mttxL8kzilP46bi5bdeSnWUKomk4lTp07w4d7NFKZfwmQygh4kITFn0hISE5P58Y8fJivrDImJU6y8Jrm5lk/Z1WwwcGvQEuY9sLxyO44wHPuXHbeAZLu6zcLMOVMJgRoVao8SwsKdr9jF7pvAt/WlZwhDuJpwHkRUsvfe28gX+7fj2uyqsOjsaU1l6w4NFstI11hXzpZnUFNTySuvPA+TwWuBjoOa3SBA1+LDRFMyP9P/D2d3p1sPHxxZTkDvEaauBmTLp96ikV0NDNY973Lc+a7EpXAI/eNy9gijAUVESlvYUg9otb3/7giO6nNURs5nG93SzWCmtLMEVzcVlaIGF1dhlUflz764b3uis9PiPucV6k1jcQOxsfFkZ58lftpoRT5hYy1e116DyWwEupVSLYZmPJy7zWNMZhMFOXlWuXB50kzWDXuBLVtSmX1LNDdN71sudXSoKSuRZFoU276B8t2s5zpnMpmsii1JUqFWq4mNjedseQbxwWOoFVX4uwVxqvU4Eycm097UhrO7C4fO7yXYO5Ti4gIWJC5jZvg8zl7KYO7cJRi8DKxatJ4tqRv4/S8fo+HTOpiLxWWrCdgFukYf4qeMxsXFjWGReq5PXMjwaWOoPFfO/Pk38P7OTeRX57LrxKfMn3QjCX5JZFWd5uw+Sxt6X38M0Z2YDpmZOW0elVnlBGlC2X7yfdqHtXK2LoOp4fehDvSgqOgSC+csJ1ofb60jNjae2GDLfC2aehMA61c8xJnyNNYuecA6P3IUt5MnjzIragHVcVVs27+FloYmcnKySZiVxPv/bw/3vLSCXe2fYPIwoWpVs9r/Pm6duhbopneo0JeidfJAr/cDLHQ0IR5hlMeVsGjqTRyP645CN2r4eEXZWJ8RLFm4gqygM8yfv5Rdu7Yzf/7SLvl6CnFx3ZF9ZQUOdCtxVJKKpJAk/N39KadccQ/E6GMAuGvpXZYIdhMt90PEFEs9L93xEpPPTOaJ3U9Q2FBIuHc4z815jtXjVivq8XD2sB5QazQaRT9mTJ9h7YdKo7Lrp6My3yQGagFV2vXXlzmCGgi/4h59T+Di0rcFQm/WBra/2eaRrQq8CbvsevtrY7B1eHlpSU1NZdLCSezbt4/qlmoe+NsDVi6mn035Gfcn3c+2rG386dCfOFF6gn+V/ott725jstdkdK4DNx22dU+TMdDwulYupH4ExMaORo4WH+Vw8WEOFx/maPFR6trrYH93HjUaQpzDWDB8ORP9k0n0n8K/t7+J/4ggKk+VMdpnPEUF+Q77kddwgcLSPKsyojd8lbLbuvmpvXp379FrLAusyWSyhuKMSoqj8GQec6IXU3O+krVLHmBL6utcP2MxmdUn2Veyk+KmAjKqjlHTXsXhsn0AZFR+wT8+/xPOkgvDG0YRZAil6kIFOmcfIryiB2SJBJbNWFbOXI1T9P7QLJowRHRwvvMMuUHZbHtvC2Utxd0ZGi0fWo07oeoIxoVPQt/gx7QJ13N96CLUKrWF/Hv4GrbmbVAom+rra+k0d5tty8oj+TeA1oYW8nIuWE/sbU/vbF3syltLUJlUhHiEKTiV3ntvIzsPfsry6T/kllvuJCgolGee+SUff/xvKirKLObNJtDr/RgW68uaG37MPff8nD17UnFz0/ZqTXY5Au+1dANxVHdPN5bqq2BdN4QhDOHqwdZVVt5veoP8AmY0WjjdPMye1kOB3tDTlXrUqPHETosnvfAYWz56De4BwqCeWvQaf5Y630L1iUrGzU6kvr5OcfjgyHJiR+o2zuxNZ2z1BH56+/8McvQDw7WmARjsunw56/g3SWVwtTDEo3XtIXVZbZe3lDDMVd+vXDhguoWkyfhPDbK6z/XVbquhmcNle2kxNFPZVmaVr2KcujVfalW3gic2Nh61Ws2kSdDYCOFj+u5LT05KW4v0nrQotgow2+A1tpCVWWOrJzAqIKGrDYvMbvYU3Lb4LrbseJ2cfVmcPHgEf1MwoZ7hREyMwZxjxlxlIjbCMoYlS37ASL9xzJ+/lPd3burqEzz33F95+OG7MZzptDSqB5cWV57908ucOZNGaWkxsycsQKfT4+vtT43aYhXlLenILs1ErZLILs1kvN9ERRt79qQqPCBqpCpUahWeId6ICoFniDejRiczftx1ViXSnj2p/GDZnSTHzXJIp5CYOIXcnUrLM1lZlJg4hZLmIpKTR3K64Dgn844SGxvPkutWAPDmg9v6vnh0K5NCEpXroKwAk/vk6L7U6XwI9YygUl+u6JeMgVoLRegirIolR+htfV01dhWrxq7qs+5r7a79dWKgCqgsIcSEvjJIkpR+FfrzvUF/kYxENQz37fs32zyOrAocYaBWDI7aGEjfbKHPhPDwcF5//XXW3LCGsyfOsu2tbfzh//3B+oBpVBpWjl7JraNuZcvpLfxt7984Vn+M/bX7if9bPAleCYwuGt0nYfnVQE/hSghBdk02W/dtpcK5gsPFhzlbedbOJdBT5c2MiHlEesawIHI5NWeqqDFWsip5vTWPbJo7zFWv2KBsT4zNwkxdew3+UlCvXEpuGq2dRU9oQt83ku1Jja9vgDX9T//6DbtOfEplfQXBPqHWU4AY3Qga8+rxmqojPzsHKUjilc//gcm9hiJhcUvLrEknk3R27bOwpruoXfBXB3H6QBpOjc5MrZ2Jk7Dvv6xgMxYYexU6+vLh7wuVHeVUtVZwujqN09Vp7ClLoWh7PlVtXacMIVhIGo1dyiZNJP6aQCJM0Zw8lMVYtzham5uJcxrBqAALefipjBMkJk6xI//urZ89lUcA/i6BqGIlG0Gne0lV2QhNZw6lk7Mvi/q6Wut1kl/UOg0dvPPO2xQVXaK0tKi7bv8glqxZwUi/caxefZ/DSFI98V1zB7N1Y0k9PXDruiEMYQjXHomJUwbs2ia71mk0GgWnm/zy1ht6rrUf7N/C5rp/INZZ9uJhLnqWed5GnPNI7rrhJ+zy3249aLFtx3btBcv6WlB4CbVaTUHhJcWL5NWG7Tp2rXC1FCy91fN1jOFa4vv0YvZthsyTVFSR32/ewdAt9MelmOCfhJtGS4W6FElSWegqtEGUt5QogrPIWLlyHe0e7axcss4aZW9g/RjY+iUQigNGs7A/2LaV5wsKLxHnM5rdO7Zz8suj1NVV4+9rcdHzcPfk3MnT6CV/XMpcmZY4h8jgGMLnRGMymayKHdt5sj0QWLFiFefOneKTT96lpLQIna+OW366lptuvh2dzgeTyURdXTUffLCJuXVLrDKoRu1EfPAYCksvER88Bo3aSdGGPO+yB8RFVRYSktWl7ZYFd+JWr6K5GbRaP5qbLfQM3oThPbJ3Y4lg93iHlAzQbWSxaukvCXbf3ictw9WG3I+en99W+ofvOga6Gzu2bR58niFcY3wTm/DIkSP58MMPWbx4Mbs/3M2RMUdYcPsCRZ4TpSdobG/kDv0dbLxjI/f9+z6ONR4jvTGd6966juTQZO4YewexPrFXtW/HS46TUZ5BXGccpcWl7GjfwW/2/oavCr6i0dCoyOukcmJC0ATG+I1hQcwCio8U89Fnu5jvuRRXrZakgGnsOWu/kcmmufKGLJv8224QPX3IHXEphXiEka3OVGx+BpXBLp+MnsqqYcMsWkOjyaA41fDXBSjKyafOqhYV1UcqMBwyMClyCn9+8C3OHk+nNbyV45kHadW1cLLiCCUthRSZ8tly/jUA3t79V1SoGNkwDn2nP4Vn89C56KlsKydECqeuvUbBtyXzftj68M+hO9yuI66QmvYq8gtz2Fn/CZvefpULbVl0FnfY5XOV3BgfMAmnZmdWTlrHON+JRHsPZ9/enRS1X6Ikowi3Ng8uFp4jNDSCgsJLxPuOtVoz2Srw1J6W5dC2n25u9sK67N8vw1bQ6flbgn8SLipXPsr/l+I6GVo7eWf7W6SmbiOv4YKVANLHx5fk5Ov50Y8epr6+joiJ0RSetFhdDcT99rvoDvZdPG0fwhCG0Dtki9z58y3RIXvjerQeWjQYiW8ew5N7fsqBqi8gEjRmJ344ch0xuhGc+uAEX9Rvx73FXXHQYttOT0WZbWSsiPCoa6Z8knGt1zFb2e5KDhr6khGvZAxXQ0H2dVgxfdcOaa4E/XGuDQS28pksgzjiSRoIBmoR1Rt2ff6J1UronkU/J8E/CU8nL+KGjVQ8/8auQDCOom8O5BC05/rlCBKS9YDRES+UTueDTgfe3hZ5vr6+lg8+2ERHRxseHl4KmT1pwnQ6yzooKMglMjwatVptnWNbZZCtDCjPvW+XfD9t2vU8+eQLikh+tn1y9K4A8Mgdz+CvC2DtkgcslvUO5EwvL4uVql7jZw2Kk+lnUUo1a1FEFh4I/cNAoxDn5qq/k7QMQxgYBrQjCyHa5f8lSXpMCPHHvvIMoX98W8yFr1Y/5s+fz0MPPcSfN/6ZBx98kJd9Xyb+douftS2/QE1rDfG+8awJXcP7C97nnk33cLjpsNXt7aWjL7Fq7CrGm3oP6dwfhBDk1eXxVdFXvJv5Lmm1afxj0z/shOAgjyCinKK4edLNJIcm4+HswfjA8WRXZxOji+GprU9Z+IBysgn16NsSSd4sUlI+5Oy+DDuLJFD6kPfFpWS7+TkyR7aF8gTYslFJqBSnGsN0js3ZZAWWSqWitLQYk8mIp9qb0QETCCgJYs6cxeTUZ+PnFsBLqc/hHx3E+2kbqXGqoqqtgrM1GQDsP7Szq10Jv5OBBJlD0GX6UNFRSnNnE23GVszCzEe5/+KDi5uo76jl9X//H48n/R7jSQOfHHmP3LpsCjR5VBsqeORf6ylvKVF21gmczM5Eu8UxM24B6koNyQmzIAfmzb2BrTs28IO4bl/pvIYL5LZm4xPpBxchPn4Mzc2NRIRHoVarCfEI45zplHVT/vTT93F1dYNGgW6Kj5VrwNXNzTpXMhxFhZIFnYoLpYQkhinyO2mciY2NJy/vIq2tTfz1r3/gzJkTdHZazKU1ARpuWH4LsbEj+NnPfs2BA18wceJUu1M7vcbvigW4IQxhCEO4EvTkbeoN8n6UUXmcszUZZFQet/K0bMvZynNHH6GitQzvRh3DjL787t1H6TR3goBhRXp2/iqD07UnKG8qobimAG9nnd3Lk207jiAH1IhKiruCEX/78G08aLgah59fxwHqt3HurhWuJIKlm0ariEi5cuU6hTJLjlw3uiqBMYF9OshYMRiLKFsIYbZaNMpWQmN8LG3KsresWOpp/W8LWxqM3lzmZAwkuq1t1LyeSEycgpMTjB07BW9vH/amf05DQy3t7W0YDAaFzB7iEWZHo+Gm0aLT+SjmLMQjjApdqUOOKtld2lGf9Ho/xbtCT2hU3czejiz9J46z2JfYRiv0CnW8B1zN5+u/SVn834h+FVCSJL1n+xVIAOwUUEMYHPraaL9O5dTV3PDnzp2LUWfkpade4pe/+iWJEYlMmzbNEmkttw6znxm9Vm/Vygd6BHJT4E28e++7vJ3+Nn/86o/k1ObwzL5n8NJ4ccrjFAtjFrL1zFYe3fkoZc1lhHuH87u5v1P4yTZ1NHG46DDvn3ufXbm7yKzKpLZNudGpJTVhrmEsG7uM5LBk8jPzeeyHj7Fjxw4WT7NY42RXZ3P06FF0MTo0Gg3x8fEcPJ9hZ4kknwj1VAZcqDnHofK9SBrJoaAM9j7kMrZt28rzzz9BSUkBISERLFu20kJC3svJsbwRzJ27hFtuWWM10ZXNe+fOXcKqefeydskDvbpQqNVqnCKdqS0qI8Tt8EtWAAAgAElEQVRvhJ0LgwxvFx2RzrGsGfdjNLkaNMM1tGS3MHnadbz/1SYMvp3sv7SLSkMZlW1lVFLGqSMnAPjHP/+XUI8I/LSBnKo6gUlY+EBKmgt5cO9aNCYNnSM7ONz6paJNrcYdXasP6iYNnpVeaFUe3J50N/5+QcxJXswbH71EcXM+xkZ7fhHZ3REBY6+bgKYjnPW33sOXX25nxOTxnD92iuZmMBjUhIXFc/HiRSz6Iieyz19k7qTlFLSVoJX0tLYa2bx5I3v3plBeXo1eH0Bra3cUOOtnh6C5Gf7znw+5884bqK+vQafT8+tfv0hhaw6V50rZu3cHbW0tXfeBxLRp81i2bDXtLu3cvuRH7N+fSkeHk6Ju0aFVfC8vr1W22/X5dZsIDwkGQxjCfyd68jb1hZ48eDFOHmzL2cqjB+6zvhw3dNbTQFf8mgswrnQiP7ztboK9QqlsL6O8pYRQfQQN9XWKl7aBQs4/GOW9rQz2bTksHMLlYWivujwEuAZZDii7+C1tXVhNJkvkukZNA4fK9+Lq7KaITHc1YeVZ8mu2WjRGhkcrLBrlPB017Q6t/3U6HzsaDLPoJtrvL2BOSXNRr9Zevcn00M0FbDI5MSokib0XU1iw4FZ0OkvE3/37UxXuZQaD2ipfehOG9/Awax5Z1ouOnsinn/4Hs1lNVlY29fWW66KIVtzpYf2/vd1icTR8+BTc3Hypr6/mnXc2WeXZ5mbo6KAXudLHyj1cX2+koqLa2u7K6fddc9nzv0lZ/N+IgVhANQoh7pW/SJL092vYnyHw9ZwCXSuh6sc/+jFtRW28/uHr3HjjjRw6dAhtoJZAdSCR0ZHk1+bblfFwthCWz4maw9mqszy7/1kyKzN5Zt8zPLf/OSQkjF2Ki4KGAu75+B725++nqKiIx//xOJmVmQqXLwA/rR/Tw6eTHJpMVV4Vz9z2DFs+3cL6xRbupg0XNiBJ9gSKtbW16GIswvW6desoaGln5c3rrJZIBw7sIrfqAq7NrgoLp5SUD/niq8/A4tJNdExcr4LyyZNHrZuZ0Wxg27atPProfbS1WQTykpIC3njjJdxDPal0KiOj8jiFOZesG6TJZKS4uBAnJydycrJJSvLtSjdRk1dtxx/lyKRWCDMCwdhpE7hQWUVUgG+vpsm2p2DRvt3uhpf2X6Rhdx1z5y5hUuA09Bo/DNEGjpzah8G3k725O6kylVHUnE9Rc75dvWZholNlQm1SE6gJob26Db+mQBK9ppKcPAtjuYGPs99l+fQf0u7Rzm1L7mbPnlSFINGbu9+lrBxyms/jo/JjdMDUrghxai6dshC8u7hUExISwL33rmP0aF+qq6tJSUkhOTmelTOmklXlzbOfP0tWfRbaDi3hunDq67MZPdqXqEgwlpusUeca3I9TJjJ4d+ezbN78ktWyqb6+hkcfvdNCJN7lYpeQkMDEiRN55plnKC4uZsqUKaSmpioi2MmfFy6YmDUhjNbyTGs64DDv140hwWAIQ/jvxUC5/GQePNn1XJJUPH/8CceWGU0QdTGOf2/cxWdHPgC6T/PDZ0dT3F7QJ59Lfy+Rg7G+sJXBhriFvtvoba/6vigWr1VkbJnLTea3tFX4qNUaAoNCKKsqIdIvFqOb4bL5PXuDEGY7BfbCRTfZWTTaRsgzaU0MDxrVzWnUZdkjK55taTBs3dP6C5jz0adbBhXMQJ6L4OBuOU2WNZcuXWon88nIzYXRcVpEVXe67NJ24YKpK01DcnI8KSm5JCfHM2qU/Wu8LWewrUvchQuQn5+NTqe2yrPDh0P4OReHcqXSVU7DTTfNscrJcydF9TsPQxhCXxiIAup3Pb4/cS06MoSvF1dDqKqptSxolxpBqoWSEpD8JB588BUyirI5lrqPefMW88Jbm2kpAclPRUmJpUxFhfIToKhRQ4LXSl6YNI4vj33CCecDfFm63a7dDlMHr5983fpdI2mI8R5FsDYCvas/CfqpTPSbTqxuFADvnd5A0SU3RVvy/3J/wDIOyxgs4wFoa+ze3I1Gi+LHw8lTYeFkMpkoKsrHHQ+Eu2D27IXcduvdvQrKn376LqWlxWz9cgOl7UUUfp5nVT7JMBg6efX5F0i8eTKfGT6go6mDMLdISkuLOZq7n8LCPFQqFbNnL7QquuTQrj35o3pyANjybkzSTgMVeDsNc3iSYzB2Kk7BvL0t7mmOyNKdVS7E+ozE3duDOTMWs7VlA7fOX8uGvX/h93mPO5wLCYmnQ18keEwob258mY76DiaOm0pbayurVq63civZuiLa8mkNc9UrBIk2YytGoxFThZEAbRCG/E5MY03W65SdbelzdnY2vr6+1nlbt24dvr4W4QDAaDKSX5UPamg0NFLdUE1MTAwZGRls3bGVC4cvsHnzZiRJokndRE5FDvkH863KJ8UYVRKPPf4Y4eHh3H///Wzfvp2QkBBOnz7tcE4ANm7cSEpKCtXV1QQEBPSa7/uEoZPqIQzhm4Vscdub5S0o3VjcnfqPuW5LYptTn01pc6HjjB7wpz+9gU43TJFs5UJxwOdiy7nX2Fjfb18c4fuiiBjC4PBtVyw6SwMzLblSjqe+YMtv2ROzZi4gpDYc55HOJPhN7tMSaDAQCEV0OVueJUlS2R3s2kbIm5Z0PQn+SQQMC2LtkgfsKCxsaTAGisEGM7Dte6xW6eY3EOvNMO8wSn1Krd+nTJmikAfXrVtnJ6/2BVsFrFqtJj4+ntzcXOLj4/Hzs/DU+rlYPmUZrDdZbDDtDmEI/aFfBZQQ4hKAJEm+QohqIcTlOfEO4XsHowE8PEBrtHy6uoJWCzqdhmef+wuPlt7HqVMnePrpH/HIXb9FqwUnJxMeHhazTdtPAD1aPDzA3SgxznssD815lJANKuhFGF6qW8nd03/KOL+JlDQX0dzZSEbVMVzLnfCIUlvr1fRoE7r/b9FUWdO0xu4xyGkepm4i8W3btlBcXIAkoVD8AESER9GYV8/owASG60f1Omey6TISXCw/h9lgpqne8elLa1MzBz/bw8FNe0APbz7/kjxRVoua3/zmITQaZ/z8/DGZTISGRtDS0sKxYwcpasun/GwJHh5eeHp6UVlZQfDIEM5mpRPoFkpzaRMrnFbTfqmZtLOH6TS0W8PEQvdG6hTpjLncTE1NFdu2bWFs7QTifcf2S5YuEDirndEZfAjxiKCkucAuj69bABcbznH2QAYNjfXEDIvnth/ebQ0x6+h0r6S5yI743RYajYbAwGDOV53Bx0dPWVkhBw4c4MSJE9TW1pKdbTEnz83NJSUlhaysLF555RXy8/N54oknaGhooFZVS9OlJutcZ5LJF7u+UMz/yZMnuxvVA/Yc6ZZ5MAv+8Ic/kJqaOiDFUm+Ksp55vi+QXwCHrKqGMIRvDsdLjpNVl8F72RutnE09eVJsrU8Pl+3FVe06ID4V23DtOpUPdeYauzze6PDzG5yyXbaksuWZkjFQrqpvuyJiCP8d6PnS76Px6yVn/5D5gK4GHCl+ZcT6jLAqdAYaLbMvZFQeJ7MmnabSBrTOHpS3lrAgYpmV06i3NnryHtlyGvWEraJsINZjgwlm0NNiK9ptItC7Ys7umnd9t5WFjEajQh6UFWCDdUWW61+8eHGvSiS53b5ksctpdwhDcITBhAV5C1h2rToyhO8XXF21/POf21m+fBoXss/yt789z8K65Zzbf4q6umrwsi8jn+TIm0JG5XF0LjrqO+oc5I1gpuc8pgTNsKbZhoeVTWBt3ccctdlp7kVr0AMms4mCnDyiomIxGAysWLGa/ft3WckAx86ewC23rLESCDqsw2RCrdbg6upGWtoRSsuK6HTvvX13d0/iJ47CXxVEblU22lB3issKaHZtwlDXidlsprHLdLi2tgqAnJzzABw/fhD0cPA/ux1X3qVEeev3L4Me1PVqvsjYjrdhGD5RvkiN4OLrBnoYptIjmSUKCnJpaWki6/3T6Iw+TJ06k0mTriMpaTo70z6m08MAwqIs7PlCMFcsYVPn38HZpg8GGC0SQAsNFXX4i0DMVSba2tpwbnOhoCCPpuJGDh3aS+bZdN5v2kRa2mH2ntlB6ZliNLs1VGaV8tZbL5PbkM0bf3yRWqma9pI2mpstyqODH+8B4JlnlMNPT0/v/WLL8wM4OTvhG+SLWq3G2dmZ5uZmnAKdcPZyZvjw4ej1eiZPnszJopOkbkqlqqrKriofvUWo6E+xJAsfPU+pem74tkosR8qp7xqGXgCHMIRvFnKgEJC6XvIkO54U6LY+NZmNIECl0vQagrw3iC+A6Sj3gk6QDqph5eD73jMculNXxbZcVY7cgwZr+TRkKTWEa4mreQBzOYFKLvdQS1boXKkroPwcqyQ1SGA2G62ucpKk6lehfDkWWHLQmP4gBzMYldwdHMnRfKlsrLEcRcaT4UjR5Og7YOWjleXBK4nmKdffU6Ycsj4fwjeBwdzJ9oQ5QxhCH/DzC2Dz5lSWrp3CqVPHaXZtZHTABAU/kSOEeIRxnjOUt5YwP3w5H+f+W6EoclG78njS72jLseeRkCSV1UWgsb1B4T7WV5v9Qa3qdm8LDQ1Ho9Eo3NBKCoqJ9x3ba/mNG1/h3/9+kxoqKcvsjvDmF+nPtOnXk5KyDYOh24rIycmZ55//O23aVlYtWs+ePanMvn4hf/rXUwROCKU8vZgJ/lOYnDSDnTs/YezYRNLSDhMcHMaRIweIjIxm75nPifcdTVNTI83NjeTmZqPVulNcXIBRZ6RRNGA0dtJIAyaTCVNbG+01bVRUdJv/2lpb9UxLSzsMwNNPP2RN0wRocGlxw81Li8nbyOGt+1B5qihMy4NYYD7gCTQBu+Cr7D2cGa7DXGGiWd2IuUqwffv79hOoh001f1f2ybZvtmld5IvOLi7og/yQJDWRkSF0dnYSGxtLQ0MDY8aMoaKigqSkJAoKCpgxYwbnz59nwYIFpKenM3LGSMozy6kyV7F+xXq2b9/OV199RX5+Ph5RHtw49UZqa2tJSUnB29ubG8bcwIIJC7j77rsVbnjOzs7cfNPNlnuoH8WSrfBha+qcmtrtymmrxHr//fdxc3OzmmUPYQhDGMLlQCWpCPEKAQSxunjSOWb3AiW/cEV7DycsJJIWQzMZVfb5+kPDgVqow24vaMi8PON6ORy6zO1i1imVTbYu53NYbE0frOJ7SFE+hO8rHEVVGywcRU8bDGyf4+Sg2XaR3GwVygNVdnl6eff5+2B44eToyaCcr8QFyYp8ttZYMhF4TwXPYJWN19r1bcj6fAjfBAajgOqdFGAI31sUNRRdkeAVEzOcPzz/dx5avYbcnGzUdRquv36h1WVLjijXE/JmlOA3iQjPaDaf/zuVreUEe4Rz16ifsiJ2FVtzNtiVEzYuAlUd5UTHxFlJFB25iQ0GcphUR5xLE6KT7BQKJpOZvXs/57XX/szhw3stArwe3NzcSU6eyS9+8RvOVZxm9eL1PPfco3zyyXuKKHgrVqxS+LB/8P4mTh48gr8pmPGek9CondDphuHr68/IkWMZObJbATZnzmLEDsGqReutaXv2pDJnzmL27EklPDGaS8cvMn/+Uv74xgbcXKvYm78D0wUTkcNiiYqKY/jwUeTUn8ej3ZNTp07g7x9IVtZpKoxlVGdX4hbghmgASYLSzmKMkgGj0YixuYmW5ibogLqaGotSqA04A5SiUB4Z6KSmrLI7zQQqlQo3Ny0+Pr6AhKuPG62uLYRNjEBrdKfcUIa5ykSdupbg8FBU4Sr8wgNITprNiJAxjAuexNGjB6k0lbJq0XpSUlK5997FpKamsnhx759OTk4kJCRQVlbGl19+SfqudLThWtavWG9VHh3LO4YmQENuXS6V2ZVWa6bVi1dzvOg4EyZMICcnh5qaGiIiIli5ciVxU7oJMx0plnoKJ/KLnnw/2f4u9+PixYsAODk5KcyyhzCEIQzhcpAUksTIYemsjF9H56VOhVud7QuXr28AkqTqMwR5XwgODqPkTKHdXhAcEm6XdyBudDqdj4JnCv/u31zVrg4CVlwdrpohDOH7AKPRqODz/CZliZ58cb1hoLxXE/9/e3ceJ1V15n/8+3Q1a7BZugFlFQQ6riyCCMYVUTGokbgko9E2i5PJMtlMYhIzcSbJZCaLmSy/TGIc0zFmJhLjQhRR1JC4IkRAUGwCKNqgkWa1Ze3q8/vj3lt9u7rW7rrdVd2f9+vVr+6uunXuOVW36p567nPOOWlW9o3ylPx8nXremW22Sc7GKkSAh6Fv6G7IgEJG+w6nWK0mB+GU9xNOmKpPfOJG/ei339SGDS8Go7S0cGGtHv3LA+rb2LfNVZd4PJ7o4L5zuFGD+w7RlKGntDoxpQpeWWiC6iP7j9QFV75Ph4441GYi6/ZKPgkEQam5c+cnJh1/442t+s53vqo777xVu3fv8OtlqqoaptPnn6v5p1+h3r17a9q0mVq/xJuIevbss3XTTd9tFSRKfj42bqxTpQ1Tnzf6auxRx3SoHUseukdrl63yhkOa08HYAY0aO06vvrFRs2afqWFDj9I558zToqcX6uLZV6QMXgXDDc84Y66u/fxFGtg0SP1G9depE8/U9u1v6qEV92rWxDPV2LdRi27/nXbt2iElxQArK4fpyk9ep8vPvkZLVz2ocUcco3nzLtWf/rQksb8hJ1TprqW/0pVzr9P2NX9XXcNarV22Ss1DmlW2s0wT3nOsdrrtmn7ibA3oXaGKioGSnHqXtZ7EM12gJ1k8Hlf95nrFYjH9/c2/q6nJW4Hxmmuv0d/2/02jpo/SG6ve0MRJExPZTJJUV1encePG6eyzz9Zpp52WCDJteaf13FeZMp9SzRGV3HkJgljByn0dTcsGAKllrqbwnE3xeOsvXMHiG1Luw17CmQg33vjv+vwXPqrDOpC4rby8t268MXm9m9ZZD+kEw40sRRd11BFj0y5Ygfx15jBEhgVFIzl7KFjtLpjPsyv6EuG+fHNz4VbTa88E7dmyq5KfrzGDWA0OaI98Pmmyrz2JgirVOQfCq0AEV1GnT5+lzw79uv7rpm/qS1/6mD78uX9Wr329NaD5iDZXXcJXW6fOndlqfodgTorw3E7JwavklS4yTaKYq+RgRlgsFlNj49tatuxhLf+fv2jdspb5haqqhum66z6tyy+/Ri+99EKrQFWylSulRYukioqWSSSDjns42+roMeM7dDXk8OHDiVU9Nm6skw0dq8q+lRo6abhO6DdFH7isZQW/VMvT9ivv32r/5eXlGjZsuPa9vk97396jRx5ZpIMH92tQ5RBJpne/+wRN/7cf6bOf/bDie1uGp5XFeunmm2/R/v77NGnScVqx+Sn17d1PZq2/SARXxTYse7HNfFtBFtedS25NBCeD42fEiFHSedckykkX6Dn22NZX8GOxmGZUz9DiTYs1fOTwxHFZZmUadsQwNTc3q7J/pT589Yc1bOgwzZ8/X3UNdSmH1y1dulTr31qvvo1920w6nhwAS55sMtPk46lW7gOAQgh/IYzFWn/has+557jjWuZOWbDgKj38cLMeeOafJL0jqbeqqiarqSl9hvKgQUP0xlv1ee9XUsYFK9Batj5nZw5DZFhQNFIFZcaPr9GTT1Zp/PjO7Uv06SP95je1evDZ+zVx8LHauvXvevDZ+2U7+mrynFlq9L+h7tsnHTgguYP9deCAEkPbgvsay1t+S9LBg623CQRlBPcFZYW3H6jRamxUq/0E+w/+v/DCGvXrV6Wzz56fcj9BXfokfW0oZFC1qwK0BIZRKDkFoMws5pxr/+BetEuxzzmQvFRzPB6XS1oFIjyJ6aWXXqWNz76sBx74vX7945/p05/+iuLxeKurLsnprZPnzGgzqV9wRTaY2ynIUAkr1JKwgUHW9kO3udnpqaf+pJ///Pu6/vrLtH//PqlS6t27j8aPn6SqqqE66aQZ+tSnblR5ebnq6l5MPE/Jnfi//U367ne9k9wf/yjddZfX+Qp33INsq+SJzvNZ8SQIzjQPaVZZvEwTJlTrlbcPt+mkZyoz1Vj/2aeco4oZg7Rs1cPas2enDhzYr6amJk2YUK1DZYe0YMFVeuyxNbrvvrskvSaVD9aM887U+y79oP7v4f/JWu/m5ubEcbHltVcS820Fz2Nw9ftw06HEdtu21ac8NpInA7/66qvbbBMEd9449Ear2ydVTtLR44/WqztfbbX/8GOCzKempia99tpr6tWvV5uAUvKyulLbySaTj5GlS5eqvr6+VXYUadkACiXeHE95cSdVpm9HfOxjH9Li5+rVrL9K2q4JE0YkzvephLOF26PQ/YHuqtj7nD1RRyf3zmblSunKK6UDB2J68EHprruk6dMj3WXCsGFN2r27ThUVZdq79zXt2FGuiooy7d5dp9GjZ2iSf82u357+ctulM6eO1r4312nSpJYyXIM0qarltyQNX6dW2wQ2bfLKCP8/aVLL9suXL08EPoP7gn247a3L3LQplnIf4TolK2RQtasCtASGUSi5ZkDdJum6KCuC0hIs2bz6rRUa0LuiZTW4hql6z5xz064CcfnlNerVq5fuvfd/9Zvf/Fxf/OK3WmUwJae3xmLlmlLZeq6J4IpsMLdToVOGk1fMSZ7/Yvv2v+uWW/5Vd9zx39q+/e+J7aqrT9ClH/0HHTOoWo2Ne/XYY4s1ceK7W9UvnJ1zzjktE6KuXy8Fc1cfOiQ980zqTkCqgEO6FU+SJ2AMT5peMWiQzj1rvs4//2L95/94QxPDnfR8V1Gp9JcMnjp+hh57ZbEuuuhyHRhwQJOmH6+7lv5Kq99aofe//2xdd9139aPf/kgT55ZrxMGmnIdEhCeBHz366JTPw+q3VuiPzyxU36P7K74lrhEjRqm8vFwDB7YOpiVPBp7u+InFYuqTIvMtU53D8zbdeeed2rLFG363YM6CxH3JAbBw9l+6ycebmprU0NCQMTsKANqrtrZWSx+5V33fKVdZZduLO/kGuzNdxCgrW66f/GSBHl94ovqPXqtdG1clzvfpZMpCTqW9q3oBxSRVxlI+Fx2zeeaZ3Pqeucj3PRdcdFv9+mqNGTNGxx13nNZuW+v1y0KfBaMHjta6DkxwnqudO1NPSt6cYhXNUkYWE4pBrt/cY2b2b865fwnfaGZHSLrdOXd54auGrpItDTtYstnkZTmNLevbKjvl40OmppyctF95f5WVmX7wg9u1fv1avfzyWt1yy82aN+9Sbd68IRH0CF9tDeZ7Sr6CecUVNTow4ICuuLAma3uCjmu6Cc/DghVzguGDQdBGcnrwwT+ovn6LXn55bWL7wYMrdcYZ5+lLX/qmNm/eoOpTTlDdc+t04YULEm0IhANAydk5kybF1bu3lwHVu7c0K2nuxFQn9nSdkOD2cPaU1HoY39gx49SrV6+sz0e+wq/dnUtubZUNVxYv0/Tp0nV7J2n01KP1+qpX05aT3N4jKgbq4guu0O6yXep9bG9t3rBBYzQ+cf+AiiP05r6t2v/Ofs2YfZomDz1Z5TGvfZMntw2mpRq+luqkPLTP0HyfAknSySefrPvuu08TJkzQ4cOHdfXVV+uvf/2rpOwBsFRf9MrLy3XOOeck5nwi8wlAoSSG/5aXyzklslfbc3Hn4WcX6fxTL055ESO4uLN7906dMG2QKg7FNGbaAr2y4sRW5/tUq1cdU1Wdcx2SLxoVWnf7QorSku8FwkxmzVLGvme6wG9yJn97V9KrqfH68mPfNVbz5s3TgQEHVHNZjeoa0k9CHlaIqUrKM3wVXrF1hZ6rf05NO5o0L7SKZikjiwnFINfexYclPWRmH3XO3SZJZjZZ0kJJd0VVOXSNbGnYwZLNTt5E3xV9B7UKbJSXl7cJGMXj8cTQrfvv/z+NHTtee/bs1LZtr+sjH3mfPvaxL7TaPtMX7KATm+vcTpXlQ9POGRUeRhhvbkqsmPPmvq2KN09TfX2d1q17WitWPK143AsY9erVS0cfPVFVVcM1deopOuWU9+joo4/R5s0bWg1PS25DOAAUZOdI3ol7zZrFuv76Br3xxnBdfXXrK1DpOtPpOiGZOidBgOi4WZNV91w0V5TCw+KCZXXXPr1KG/+8Xrt37UysopROqvYed9xkNbtm7Tm8K7GiUThT7fjjpqrx0N7E5PM7Yw051zOQz0k52xWk8HC6MWPGqLy8vFX57Zm/KV12FAB0RPB59eTLq3XRRVfmfHEnlYbdf095e3huyGTJn8XJF0+k3L90Jw/jD0+aXgjBF9LGQ42q6FNR0LKLSanOQYr8TJ/uDbu780616XtKLdntYcEiNiNGjNJV0/6xwyvphfvy+c7Z2tFho7W1tXpo8UOyvdZmns7gYntZWZl27PNW0eyqhQzIWkJ3k9MnhHOuycwWSFpmZtskjZT0NUkfdc49GmUFUZyCJZuDScbD8xOFPf/8cj1d93hixbXBg6u0cWOd+vbtp7lzL9GiRXfp2Wf/IueczjtvvsrKMn+4Z+rEphNkHvW2Xnryyce1YMHVijfHtfqtFXpxhzeMUJJWb39O+w43aveOnaq7f52+vfjL2rx5Q6KcadNO1eTJMzRz5hnav78xMcQuufOcKT06eJ4Sw7Ga49qycbNisZji8Tq9971VrToAmVYgyiZ57oDwBNap5nHqiFTzWkneBOKrKp7TG2vrE20YN25iihJayknX3rLQCoeD+1a2CWKFl/B9XOkDNIU4kScHq1J11oOAUbpganuymMh8AhCFmpoabXnngK64tDArxoYlzw3ZS70T97VnjpvkYfJhbYfxF+4zM/yFdOverRpQNaBgZRebYp4Pii/jhTV9urR3b25D75qamhKL2GzbVq94vKkgK+l1xWuaPB1C8tQGwcX2rXu3qrJ/7qtoRhG8JWsJ3U2uk5D/TNILkr4r6VeS1kqa6ZxLfakLPULyssepOno7dmzXq69tTgQUZsyoSpyopk6doerq4/Uf//EVLV/+hL71rS/pX9CiMIQAACAASURBVP7l+2n31+x3Yus3btGR7xrZZhL0559frorxbZdsDmceVY6o0j333KklT96nDTtfkiRte+d1NR08pLXPrtIzv12m13ZultvulT148JH64Aev0Zgxx+hDH7o+MQHrRRdd1mZS1iDwlO1Kbfh5Cs9tNGFCtSorhyZt2/7OdHjugExDEgYPSD9EIZd5N9LNaxXc9+iTD+jIXiMTE87PmHFaYqhFsvBrFW5v8CUlPFl6qi8uqZbiTpbtRN6euUPSddYJGAEoFYVYMTYVs7JWi4nsVEuGavhiSKbAUiAYJt+0p6nVMOywQk+aHgh/IR1Z0XaOS2RXiC/nxfZlvD3Bk1INopWXl2vsmHFau9nLgArmbQu/59qjM17T5Oc8eTqEVP21GSNnaEDvAYmFZ3JRzMFboFjkGqZeLekkSSdK6iVpkqRfmtlaSWudc7+LqH7oYpnmOshlTqUtjZt08KgD2vHqWzprwvmKxWJtOoe33Xav/uEfztcvfvEDjRgxWh/96GdSllXmd2LfaWzUkf1Haoe2t7p/9+6dqlDbAJTUcnKUpBUrnlIsFtOaJ1dq1+EGrb5vhTa/uEGNvfZKO6Ty4eU6/73v05VXXqfKyvM0ZUp5yk5scLLKNfCUStWg4Tr/gosznrhTdabzmYQy25CEWcddrO3bU3fSU6Vfh2Wa1yrI7iqLlamycqjOO+9inX/+xVnrm6q94WBakPkU/uKSKhjVnok6w6vTDR8+PNFhSf6dTi6dykJ0PEu18wqgZwkuDE0Z1rKYSKoM1XB287t6pc4sanbNOtT7oHpZ7zbDsJNFFfwPvpAeO/RYvb7n9Uj20Z11xy/n7QmeFFsQLR8XzFug6qoTU04z0R7JF5PbIxzYHDiw7RxyUurnPJepDQg0A4WX07vKOXerc+5TzrkznXNDJM2W9HNJeyW9N8oKouus2LpCj29+XCu2rmhzX21trR599H4tXFib9vHNrlm7DuzQ9OmzNe09p+qyy69J3Bc+Ub3nPefo+us/J0m6+ebP6cEH/5C476WX1iT+fv755ZoybIaOr5ySGPoXCAcg0mWwxGIxxWIxDR9+pF55ZaPqHl+rV1b8TS88s1KNe/dq4sRjddVV1+uee5/Qrbf+XnPmXJhyVZ7kwEZHJoQ8/9SLE3XLJPn+fPYZpEcHGUiF6pg715zIWIrH463mtZJasrua482aMKE6r0nPk4N72aRaKSbf1yUxEa+fjh2PxxMdlvDvcEcnORCUS6cy145npiBTKXdeAZS2fIbN7d7dsrLU+vVrU27j5BJD9J55Y5me3PpoYmh8WJmVafaMs9MOw+4swRfS7hhMQenoypUek7PT22vF1hVa/ebqxPeMYOXhdJly6fpF4ffi5Mlt55DLJJc+MRf9gMLKevY2s7lm9kt/0nGZ2fXOuXrn3GLn3H865z4UfTXR2ZLnOghnQgVf1MvKvKWaV6x4KmUZ4Tl7KvsPy3gVYfbss/WVr3xHzjl9+tNXacMGb3jc23v3JLYJOrKphlkFAYglD92ju+++Q0seuqfV/YcPH9bKlc/ollv+Vf/2bzfolc0b9M47jSov76UzzjhPjzyyWpdd9iHt2bNLzzz9p4zPTSFXIMmmoyf3wBVX1Oiyy67Ja3WSsORA0OY9G/TUtse1ec+GRNmnnz435X7Pv+CSNvvtV94/0XlKteJRoDOf62Ai3ng8nnGluXBHJ8pAUC5l0ykC0NmC822+56fw+TwsWLAi3twkOamsrFxv7tuaMjNiyrAZOm3EORo/cFJO+yzkkvWFxCTf6IiFC2t19913ZLwI3BlSXfxLJzlglvieYS3fM4KVh5ODu8Fju+riGxf9gMLKZQjeJyRdJ+kmM6uUNCXaKqEYZJrrILxizoQJ1Xr77b2J+5I7pOE5e7L55Ce/rK1bX9Mdd/y3brnlZp17bn7JdfF4PDE54pbXXlFTU5M2bHhJv/3trfrsZ6/Vjh3ekL2yWEwjJ4xVzVc/oX7936XrLv6kmpqaVPvA/2v12PZMpFho+Zzcs+lI5lM4EBRktgWr0TX7mVBp95tiXpGnFj+WmJPqtAvntLtehRbFSnP5Bony2Z5OEYAoJQ9zC3+JbO/5KVXmRjBE753DjVq9/bk2c0WF5ZP5NG3azLRzDnYlsqfQXh1ddS4fhQrghuchDS5IBt8zml1zxjnVwlMj1NTUFKQ+UtdmkAE9XS6fWNudc7sl3WBm/yFpRrYHoHsIz3WQLLxiTnh+pFQd0lw7i2amb33rJ3rzza165JFFuvrqebr2C5+UJK1bJy1bJFVkWPU4FotpwthqPfvyX7Rv+zuaP3+m1q59vqVuI8fq9DPO1Sn/8B6teXmlxp86SQ0vvyWp9cSKY8eMizz41JGTeqGyotpbhzIr0+hh43TA7dPgvulXBkk3gXly52lB36szbp+qzlFe1Q6CaYXKLkoXJEpXPkElAMUgedXZVF8i8xWUcWLDVB03vPX1TLOyrHNFAT1dIVady1WqLPR8+6CZAmYzRs7QqiNXacbI1F8tk6dGKFSwLXm+TwCdK5d38YPBH865G83s0xHWB0Um07C5KFbMicVi+tnP/k/nnz9NmzbV6Ve3/1gnHPlxff7T/XVwu/THP0of/pK3bTg9v6mpSWvWrNDLL6/T448vVtPAJmmHNHDgIJ188mn6whe+oR07GmQmDRlVpdXrn2szkXkwseK4GRPT1q8jgY/wYzsytKxQWVHhOqRrV/IVomC7D8y5Tn/btV5vH9yb6mGS0k9gnq7zlG3C86DOG3fXdcrQvEIEgpjHCUApCladNSvTm/u2qixe1u6si+ALa7AwRZBpXF11Ysrtc7lolW/2AtkO6E46uupcR4QXgMlFtoBZptWLgxEXwUp1hQg+JQe1qqqqsj8IQEFlfSc75+6XJDOrcs41OOd+En210JP169dfn//8zfre976uV7du1Fe/erkONSyStE6HDkmbN3lXZl/csVoPPHe3Xnh0pf7wh9/ozTe3Jco45ZT36MwTztfHP36Dnn76T5oyZUYiU2vKsBlaV7lKU4bN0Ita3Wrf2YapdSTw0ZnzGeUr1TCFVFe7w20wK0sZxMolSylV5ynX4F6mq2+5ZFEFOmP+pEIHmZjzCUBnCFadffOdrd5wuFhDu7MugosmwcIUW7Zs0oSxuS2IkerzfslD92jtslU6sWGqPvXBr2QtI9h+164GXXhhTc71BopZtvdPplUiO1tHAmbhqREKITmoFdWKmQDSyyeUfLuk7GuoAxnkGmSoqBioO+98SOd9cIo2b14iabCkd9TcPFb7D5yuu363Sg8t/oPuWPvficcMHz5CNTWf1FFHjdbUuafotec3q2/fvq3KDa6CJl9xCZaKDv8fVcAo1XNQLBOlBvVozxwDwfOV/Dud9q7sF84AS37ecsmiCpRiBlIp1hlAaQoPh3t+0HKdc868DmddhL+IPv/88qzbJ2f8NjU1tZnrMdW5KXwuC7YPzmX5dX1zw6TiKCbh4bPJq0Z3lY4EerI9dvny5Xn1j6KY7xNA7vI5C6fPkQSSpMtSySeoM27cBJ0xe66W3HWfpHf8W7foz8u2SH+QVCn1699fp8w4XZ/73Ne1d+9ezZkzr9WcVFJLR/SJJ5Zq27Z67drVICXNJbV7905VaFCr/7Npb9Ao1XOQ6/NSiEBVujL6lffXhGnVkqKZYyCKIFvy8xYEGIsloAcApSwYDhd81hYiWyAooz0XedLN1xhvbj3ELig7vH2U8+UwqTiKRfLw2WZ3csbpNIpJe7O8d+7M3mdPlumzjGxzIFr5nInbrocLpNHReYqCAMKKR59OeX+fPn105bUf1tf/6Xt69tm/aMaM09oEngLTps1UU1OTtm2rT1wFHTVlbIfqF5Tb2Qqxz+Qy+vSRGhulgRqtxsaW2y+8sEb9+lXp7LPnt7o9sG+f1Oh/ghw4oJTbSJI71EeNjdKkSTNbbZPpMZnuS+fee2u1bNlivflmgy69tCbx+D65j8oDgB4vn6HM2UQx91LyfI0LF9bq4SfvV9/GviknRw+2nzs39bkM6E6Sh8+WSvBJKkyWdyGyEcP1ILsRKDwyoFAUkjupQZBkx463Um5/6NAhfeQjn1H//u/Kqfzy8nKNGDFK27bVa8KEah0uO5xX/dqTUVPILJwoM3pGjJAmTUp936ZNsbT3uQZpUlWwXfoyTtw0NOV9mR6za9eQtPel0tTUpN276zRoUEy7d9dp/PjoliUGgO4sn6HMmeS6al6ui2CEBdkLwXDxslhZxuHizPOCUtLRwG14+GxPU+hsRLIbgcLL5xta9pkegZBc5neQMndSKyuHpQxCjRgxJmOZqYYAnn76XMXjcc2dO1+/XfJLSamv9IYfG2QHJWfv5KI9j4myrHRZRcWYIZTvVbAoVkoBALRPqnkE00mV2Ztr8CoYLv7im6vTDrHLd9l4oCM6mjFTW1urRYsWa/duL5s7nWyZ4vv3l6mxV277zDfr/MAB73emrPZs/xdj3xNA58j5W5pzLvc1N9EjJV/FzGUepXg8nnGy6yuuuFa33fYjHT58KHFbeXkv3XjjtzOWm26Z2OSroKmu9IYfmyk7qNRkyjjqDgq1Ugpj/wH0ZIXIuE01j2DyPE3pxOO5LYIRrPJ1xRU1OjDggK5Is8JdvsvGAx3RkYyZpqYm1dXVqaIiezZ3tj5dOEs9m3z7h5s2eb/Dj0kuI9v/xSLo80UxXBhAanmnCZhZL+dcfuOXULLyuZLTnvmJKiuHZpzsevbssyVJixYt1NatWzRy5Fid874LtGDBVdq4uy7v/fVkXRVY6cz9FmKYBSvNAeiJynu1zfgNz/UnZc+UOHiw5f7wPIK/+U2tHnz2ftmOvqqsHJ5xXsGDB8s1erSX0Tp6dLUOHChvs936tzbob5s3q6GqUSdWzlDT4VjGegX1JusCxaw7ZXOXSkBn5syZqq2t1eLFi9XQ0KCampqsj+FCJdAxeX2ymdltkhaY2TuStkl6QdILzrmfRFE5dL2Ojn3OtirZtGkzNW3azFbLMicHsmbPPls33fRdLV36gObOna+lSx9oV10GDRqSNisrHo93+zT9QgdWcg1OEtABgOJXOaRthkJyFkW2LIbh65KzHmIaP96bo6+ioky7d9fp+OOrUpYR7Ms1SBd8uUbHH586ozW+vVlPD9ihI488SrEBWzVh4slt9pusWLMvgGSFyubOR6EDKvkGdLpSkHUWi8VUV5c+4zKMfi3QMfmG1k+XNNw5d9jMRkqaLOmkwlcLpaC3Zb6U+MQTS7VtW72m7Zipf/zAFzJuG2SupAsQBfNBLFlynxob92rXrgZNO29WXvWdNm1mypXygrLn7LpQVVXD8yqzJ2NiRgDo3tasWaPqOdUdKiPI6lj9+mpVV1dr6NDcJjlPl9FaZmWq7F+p5uZmjaworVW+gFx09qT5hQyotCeg05W6U9YZUCryfZc9K2mwpLecc1slbZW0uOC1QkkYkmGlnKamJm3bVq9YLKYdmxvafQIKMqg2bqyTmamubp1GjRqrjRvrNHnOjJwem8oRFQMT24Tnmhg8OMcB82iFdGQA6H727NlTkHJqarx5mmouqylIeZMqJ+no8Ufr2KE9b5UvoJiVYkCnK7LOgJ4s30+FWyX92cz+R9JyecPvCtM7QbdSXl6uESNGadu2+pRzO+UyyWmQmTRixCgde+xJ2rJlk6qrT1Bj415NmFCtWCz14Tto0JCsK+gcd9xkSd5VpvAcVCzV3D6kIwMAMomVFfb8Gs586pNiRVsAXaMUAzr0/4HOk28A6k5Jd/iP+4Skk8ysr3PumILXDCXv9NPnKh6Pa+7ctiegbBOWh5dw3ratXl/72ncT80QFc0Glm4T8pJNO1pIl92VdQSdwxRU1ibJTDdFDNMiaAoDiMXxItEPQowwSDe2T27A+AJ2DgA6AdPINQNU7574RvsEsy0RA6HHCE4m39wQUXsJ5xIhRKi8vT5SVrcxUyz9nw4my85E1BQDF4+KzLo60fIJEQPfQ2RcQuWAJdC/5BqBWm9lnnHM/Cm5wzh0scJ1QYpLnWko3kXi+gsykjjw2VfZVJrkMDQQAAAB6os6+gMgFS6B7yTcANVzSuWb2ZUnPS1ojabVz7vcFrxlKQra5ljrq6acf17Zt9dq1qyHvFepSZTUlB5iS/882NLCn4aoTAAAAojBw4MAu2S/9W6Dr5BWAcs5dISWG3R0v6URJp0giANUDJa8gV+ilVsMr6eWyQl2mVe8CyQEmAk6ZcdUJANBeUXzJ69+rf8HLBNAx7X2vT548ucP7zqX/n4z+LdB1yrJv0pZz7qBz7nnn3K+dc18sdKVQGoIV5OLxeM5zLeUjWEkvKD/dPE3xeFwLF9bq7rvv0MKFtQXZd3tOZgAA9GTx5tbnzny+5OUaWBo9cHRedQIQvfYGdDoaUK6trdUdd9yh2traDpUDoPMUNmKAHmXgwCG68MJ57ZprKVfhlfRSrVC3cGGtli59QAcP7teAARXtzsQKD8ULDyu88MKajjYBAIBur7a2Vvc/er/6NvZVTU1N3o8nsAT0PB153zc1NamuzhuJUVdX+JEYAKLBuxTtdsopM9XYKB0+HFNjY8vtBw5IjY3eb0mt7ksn/JjksqRYq/uC32+/3aT16+sk9VJT037t339Yo0dX68CB8lZlpuIO9m9136RJXluamrwym5tjWr++Thdd1CTeJqWDMf0A0PmCL4JlsTK+CALoFOXl5aqurtamTZtUXV34kRgAopHTO9XMPivpKUmrnHNN0VYJpWLsWO/3pk3SpEkttwf/b9gQVywWa3VfOuHHJJclefcF2wS/XUO5Zs2q1uLFm3T55ZerqqpK8+fPb1NmKpOU7opLS5mzZlXrmGM4mZUSxvQDQHTSBfmDL4KrX1/NF0EAbUR1gbCmpqZN/x9Accu1hzBK0o8kvdvMXpD0tLyA1DPOuZ1RVQ6lq7a2VosXL9aoUaM0b968vB7T0NCQU/p+/179W514Hnqo7RC99uBkBgBAW5mC/DU1NTow4IBqLqtJeT8ZqkDPFeUFwnRzxAIoTjlNQu6cu8E5N1vSkZK+KmmnpA9LWmdmL0VYP5SgeDyeGJNdX1+vpqbsSXMVFRVtxnFnE4wbj+LEw8kMAID8lJWl71Z2ZYYqwS8AAIpDvqvg9ZNUIWmg/7NN0vJCVwqlLRaLqbraWx1v1KhROaXin3baaYnHkL4PAEBpWbF1hVa/uVortq7oUDkdXRUrFYZnAwBQHHKdA+pWScdLeltewOlpSbc453ZFWDeUsGAYWz6ZRNmGvnEFEwCA4tPsmrV171aVWZm27t2qk0ecrDLL9xqnh9XwgOzoEwMoVbn2DsZI6iPpTUlbJdVL2h1VpdA9tGcYW6bH5HsFk5MzAADRK7MyjawYqWbXrJEVI9sdfAKQG7L6AJSqnDKgnHMXmJnJy4KaLekLkk4ws53yJiL/RoR1BNqFkzMAAJ1jxsgZWnXkKs0YOaOrqwIAAIpUzhPtOOecvEnHd0va4//Ml3SKJAJQSCmKLCQymwAAiF6fsj55bW+ygu07n3M9/QKgtSjmUgOAQsh1DqjPSJol6TRJhyU9JekZSbdLWhtZ7VDyCpGFlNyxJLMJAIDoDe0ztMv2nelcT78AyIy51AAUq1wzoMZKulvS55xzb0RYH6CVIUOG0LEEAAAJM2fOVF1DXVdXA0A3QRYl0HlynSXyLOfc3ZmCT2b2fIHqBCQQfAIAAAAQFb5vAJ0n1wyod5vZCxnuN0kDC1CfrMzsZkkfk7Tdv+mrzrnFnbFvAAAAAAAA5C/XANSxOWwT70hF8vRD59z3O3F/yEM83pmHAgAAAAAAKHY5BaCcc1uirgi6h9raWi1evFgNDQ0aPnx4V1cHAAC0ExeUAABAIeU6B1Sx+ZSZvWBmt5vZ4HQbmdn1ZrbSzFZu37493WYokKamJtXV1SkWi6muro6OKwAAOSq2Pkttba3uuOMO1dbWdnVVAABAN1GUASgze9TM1qX4uUTSf0s6RtIUSW9I+kG6cpxztzrnpjvnpg8d2nVLCfcU5eXlqq6uVjweV3V1tWKxWFdXCQCAklBMfZbkC0pNTU1dWp9U+vfq39VVAFAEWMEOKC25zgHVqZxz5+aynZn9UtIDEVcHWYQ/+GtqalRVVaX58+froYce6vT9AwCAjgkuKG3atEnV1dUqLy++7uLogaO7ugoAigAr2AGlpfh6FFmY2VHOuTf8fy+VtK4r64O2H/w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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "for Ty_sky,tit in zip(('all','cs','cre'),('All sky','CS', 'CRE')):\n", " fig, ax = plt.subplots(1,2, figsize=(17,6), sharey=True, sharex=True)\n", " fig.add_subplot(111, frameon=False)\n", " plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n", " plt.tick_params(axis='y', which='both', labelleft=False, labelright=True)\n", " plt.ylabel('[W $m^{-2} K^{-1}$]', labelpad=35)\n", " plt.xlabel('Latitude [Degree]', labelpad=10)\n", " plt.title(tit)\n", " intervals=['(10S,10N)','(10,20)S/N','(20,30)S/N','(30,40)S/N','(40,50)S/N','(50,60)S/N',\n", " '(60,70)S/N','(70,80)S/N','(80,90)S/N']\n", " domain='Local'\n", " feed_lw=[];top_lw_IC=[];bot_lw_IC=[];feed_sw=[];top_sw_IC=[];bot_sw_IC=[]\n", " for H in ('North', 'South'):\n", " DATA = np.load(input_data+'Feed_Ftropics_'+Ty_sky+'_NOSI_NOI_'+H+'_Had5.npz')\n", " #-----------------------------------------------------------------\n", " #------------------------ OBS ------------------------------------\n", " #-----------------------------------------------------------------\n", " if domain=='Global':\n", " c='red'\n", " else:\n", " c='blue'\n", " feed_lw.append(DATA['feed_lw_'+domain])\n", " top_lw_IC.append(DATA['top_lw_IC_'+domain])\n", " bot_lw_IC.append(DATA['bot_lw_IC_'+domain])\n", " feed_sw.append(DATA['feed_sw_'+domain])\n", " top_sw_IC.append(DATA['top_sw_IC_'+domain])\n", " bot_sw_IC.append(DATA['bot_sw_IC_'+domain])\n", " feed_lw=np.mean(feed_lw,axis=0)\n", " top_lw_IC=np.mean(top_lw_IC,axis=0)\n", " bot_lw_IC=np.mean(bot_lw_IC,axis=0)\n", " feed_sw=np.mean(feed_sw,axis=0)\n", " top_sw_IC=np.mean(top_sw_IC,axis=0)\n", " bot_sw_IC=np.mean(bot_sw_IC,axis=0)\n", " ax[0].scatter(intervals,feed_lw,marker='.',color=c,label='Observations')\n", " ax[1].scatter(intervals,feed_sw,marker='.',color=c)\n", " for i in range(len(intervals)):\n", " ax[0].fill_between(np.linspace(i-0.5,i+0.5),bot_lw_IC[i],top_lw_IC[i], color=c, alpha=0.1)\n", " ax[1].fill_between(np.linspace(i-0.5,i+0.5),bot_sw_IC[i],top_sw_IC[i], color=c, alpha=0.1)\n", " exps=[]\n", " names=''\n", " for experiment,name in zip(('CMIP6_historical','AMIP'),('HIS-','AMIP-')):\n", " names=names+name\n", " exps.append(experiment)\n", " domain='Local' \n", " #-----------------------------------------------------------------\n", " #------------------------ CMIP6 ----------------------------------\n", " #-----------------------------------------------------------------\n", " for exp in exps:\n", " models=[]\n", " for i in glob.glob(input+exp+'/*'):\n", " models.append(i.replace('/home/alejandro/CL_feedbacks/data/'+exp+'/',''))\n", " if exp=='CMIP6_4xCO2':\n", " c='magenta'\n", " elif exp=='CMIP6_historical':\n", " c='black'\n", " elif exp=='AMIP':\n", " c='green'\n", " mean_mod_lw=[];mean_mod_sw=[]\n", " for k,mod in enumerate(models):\n", " m_lw=[];bot_lw_IC=[];top_lw_IC=[];m_sw=[];bot_sw_IC=[];top_sw_IC=[]\n", " for H in ('North', 'South'):\n", " DATA = np.load(input_data+'Feed_Ftropics_'+Ty_sky+'_NOSI_NOI_'+H+'.npz')\n", " m_lw.append(DATA[exp+'_'+mod+'_m_lw_'+domain])\n", " bot_lw_IC.append(DATA[exp+'_'+mod+'_lw_IC_min_'+domain])\n", " top_lw_IC.append(DATA[exp+'_'+mod+'_lw_IC_max_'+domain])\n", " m_sw.append(DATA[exp+'_'+mod+'_m_sw_'+domain])\n", " bot_sw_IC.append(DATA[exp+'_'+mod+'_sw_IC_min_'+domain])\n", " top_sw_IC.append(DATA[exp+'_'+mod+'_sw_IC_max_'+domain])\n", " m_lw=np.mean(m_lw,axis=0)\n", " bot_lw_IC=np.mean(bot_lw_IC,axis=0)\n", " top_lw_IC=np.mean(top_lw_IC,axis=0)\n", " m_sw=np.mean(m_sw,axis=0)\n", " bot_sw_IC=np.mean(bot_sw_IC,axis=0)\n", " top_sw_IC=np.mean(top_sw_IC,axis=0)\n", " mean_mod_lw.append(m_lw)\n", " mean_mod_sw.append(m_sw)\n", " for j in range(len(intervals)):\n", " xtick_mo=j-0.5+k/(len(models))\n", " ax[0].scatter(xtick_mo,m_lw[j],marker='.',color=c,linewidth=0.1, alpha=0.3)\n", " ax[1].scatter(xtick_mo,m_sw[j],marker='.',color=c,linewidth=0.1, alpha=0.3)\n", " ax[0].plot([xtick_mo,xtick_mo],[bot_lw_IC[j],top_lw_IC[j]],color=c, linewidth=0.5, alpha=0.3)\n", " ax[1].plot([xtick_mo,xtick_mo],[bot_sw_IC[j],top_sw_IC[j]],color=c, linewidth=0.5, alpha=0.3)\n", " mean_lw=np.mean(mean_mod_lw,axis=0)\n", " mean_sw=np.mean(mean_mod_sw,axis=0)\n", " ax[0].plot(intervals,mean_lw,marker='o',linewidth=2,color=c, label=exp)\n", " ax[1].plot(intervals,mean_sw,marker='o',linewidth=2,color=c)\n", " ax[0].hlines(y=0, xmin=-1, xmax=9,linestyle='--', color='black', lw=1)\n", " ax[1].hlines(y=0, xmin=-1, xmax=9,linestyle='--', color='black', lw=1)\n", " ax[0].spines['right'].set_color('none')\n", " ax[0].spines['top'].set_color('none')\n", " ax[1].spines['right'].set_color('none')\n", " ax[1].spines['top'].set_color('none')\n", " ax[0].set_title('LW feedback')\n", " ax[0].legend(*[*zip(*{l:h for h,l in zip(*ax[0].get_legend_handles_labels())}.items())][::-1],loc=1,frameon=False)\n", " ax[1].set_title('SW feedback')\n", " ax[0].set_xlim(-1,8.5)\n", " ax[0].set_ylim(-15,10)\n", " fig.tight_layout()\n", " plt.savefig('mod_feedbacks'+Ty_sky+'.pdf', format='pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Figure 8. Observed and simulated feedbacks for All-sky (top), CS (middle) and CRE (bottom). Bars and boxes indicate the 95% confidence intervals.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To investigate if the above internal variability feedbacks are related to $CO_2$ forcing feedbacks, I calculated anomalies of air temperature and TOA fluxes for a set of models from the abrupt increase of $CO_2$ experiments (TAble 1.). The anomalies were computed by taking the differences between pre-industrial concentration and increases $CO_2$ simulations, then feedbacks were diagnosed by performing the regressions. Figure 9. shows the one-to-one comparison between internal variability and $CO_2$ feedbacks, plus their lineal relation. The approximately one-to-one relation indicates a high probability that the internal variability features are also present with $CO_2$ forcings." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import os\n", "import statsmodels.api as sm\n", "import itertools\n", "from statsmodels.graphics.regressionplots import abline_plot\n", "equ_hist = os.popen(\"diff -q /home/alejandro/CL_feedbacks/data/CMIP6_historical/ /home/alejandro/CL_feedbacks/data/CMIP6_4xCO2/ | grep Common* | cut -d' ' -f 5 | cut -d'/' -f 7\").read().split('\\n')#.remove('')\n", "equ_hist.remove('')\n", "equ_amip = os.popen(\"diff -q /home/alejandro/CL_feedbacks/data/AMIP/ /home/alejandro/CL_feedbacks/data/CMIP6_4xCO2/ | grep Common* | cut -d' ' -f 5 | cut -d'/' -f 7\").read().split('\\n')#.remove('')\n", "equ_amip.remove('')\n", "%matplotlib inline\n", "Ty_sky='all'\n", "fig, ax = plt.subplots(1,2, figsize=(17,6), sharey=False, sharex=False)\n", "fig.add_subplot(111, frameon=False)\n", "plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n", "plt.tick_params(axis='y', which='both', labelleft=False, labelright=True)\n", "plt.ylabel('Internal variability feedback [W $m^{-2} K^{-1}$]', labelpad=35)\n", "plt.xlabel('abrupt-4xCO2 feedback [W $m^{-2} K^{-1}$]', labelpad=10)\n", "intervals=['(10S,10N)','(10,20)S/N','(20,30)S/N','(30,40)S/N','(40,50)S/N','(50,60)S/N',\n", " '(60,70)S/N','(70,80)S/N','(80,90)S/N']\n", "exps=[]\n", "names=''\n", "LW=[]\n", "SW=[]\n", "for exp,name in zip(('CMIP6_historical','AMIP'),('HIS-','AMIP-')):\n", " if exp=='CMIP6_historical':\n", " models=equ_hist\n", " c='black'\n", " else:\n", " models=equ_amip\n", " c='green'\n", " m_mod_lw=[];m_mod_sw=[];m_mod_ab_lw=[];m_mod_ab_sw=[]\n", " for k,mod in enumerate(models):\n", " DATA = np.load(input_data+'Feed_global_'+Ty_sky+'_Had5.npz')\n", " m_lw=DATA[exp+'_'+mod+'_m_lw']\n", " m_mod_lw.append(m_lw)\n", " m_sw=DATA[exp+'_'+mod+'_m_sw']\n", " m_mod_sw.append(m_sw)\n", " m_lw_ab=DATA['CMIP6_4xCO2_'+mod+'_m_lw']\n", " m_mod_ab_lw.append(m_lw_ab)\n", " m_sw_ab=DATA['CMIP6_4xCO2_'+mod+'_m_sw']\n", " m_mod_ab_sw.append(m_sw_ab)\n", " ax[0].scatter(m_lw_ab,m_lw,marker='.',color=c)\n", " ax[1].scatter(m_sw_ab,m_sw,marker='.',color=c)\n", " LW.append(m_mod_ab_lw)\n", " LW.append(m_mod_lw)\n", " SW.append(m_mod_ab_sw)\n", " SW.append(m_mod_sw)\n", " x = sm.add_constant(m_mod_ab_lw)\n", " y = m_mod_lw\n", " model = sm.OLS(y, x, missing='drop')\n", " abline_plot(model_results=model.fit(),linestyle='--',color=c, ax=ax[0], label=f\"{exp}\")\n", " #\n", " x = sm.add_constant(m_mod_ab_sw)\n", " y = m_mod_sw\n", " model = sm.OLS(y, x, missing='drop')\n", " abline_plot(model_results=model.fit(),linestyle='--',color=c, ax=ax[1], label=f\"{exp}\")\n", "min_lw=np.min(list(itertools.chain(*LW)))\n", "max_lw=np.max(list(itertools.chain(*LW)))\n", "min_sw=np.min(list(itertools.chain(*SW)))\n", "max_sw=np.max(list(itertools.chain(*SW)))\n", "ax[0].plot([min_lw-0.15, max_lw+0.15], [min_lw-0.15, max_lw+0.15],color='grey')\n", "ax[1].plot([min_sw-0.15, max_sw+0.15], [min_sw-0.15, max_sw+0.15],color='grey')\n", "ax[0].spines['right'].set_color('none')\n", "ax[0].spines['top'].set_color('none')\n", "ax[1].spines['right'].set_color('none')\n", "ax[1].spines['top'].set_color('none')\n", "ax[0].set_title('LW feedback')\n", "ax[0].legend(*[*zip(*{l:h for h,l in zip(*ax[0].get_legend_handles_labels())}.items())][::-1],loc='best',frameon=False)\n", "ax[1].set_title('SW feedback')\n", "ax[0].set_xlim(min_lw-0.15,max_lw+0.15)\n", "ax[0].set_ylim(min_lw-0.15,max_lw+0.15)\n", "ax[1].set_xlim(min_sw-0.15,max_sw+0.15)\n", "ax[1].set_ylim(min_sw-0.15,max_sw+0.15)\n", "fig.tight_layout() \n", "plt.savefig('abru_his.pdf', format='pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Figure 9. Internal variability vs abrupt-4xCO2 feedbacks. Discontinuous lines indicate the feedback linear relation and the solid line depicts a perfect relation.*" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }