{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 2, A \"zero-dimensional\" energy balance model of Earth's climate\n", "\n", "```{note} This lecture requires\n", "- **Programming knowledge**: Basic to intermediate Python programming skills\n", " - Understanding of variables, loops, conditionals, and functions\n", " - Experience with scientific Python libraries (NumPy, Matplotlib, Pandas)\n", " - Familiarity with Jupyter notebooks and markdown cells\n", "- **Mathematical background**: Calculus and differential equations\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Observed Global Energy Budget\n", "\n", "```{note} Reminder\n", "The global energy budget is the balance of energy coming into and going out of Earth, influenced mainly by the sun's radiation and Earth's reflective and absorptive properties.\n", "```\n", "\n", "### So, Why Does It Matter?\n", "\n", "- **Climate Change Connection**: Changes in Earth's energy budget directly drive global warming and alter weather patterns. Higher greenhouse gas concentrations trap more energy, resulting in planetary warming.\n", "\n", "- **Real-world Consequences**:\n", " ```{margin} Quick Fact\n", " Energy imbalances are responsible for unpredictable climate events.\n", " ```\n", " - **Extreme Weather**: More frequent and intense hurricanes, floods, droughts, and unpredictable precipitation patterns.\n", " - **Polar Ice Loss**: The warming accelerates ice sheet melting, raising sea levels and disrupting critical ocean circulation systems\n", " - **Biodiversity Risk**: Warmer regions put numerous species' habitats in danger.\n", "\n", "- **Human Influence**: Our activities—like burning fossil fuel and deforestation—significantly alter the energy budget by increasing atmospheric greenhouse gases and raising global temperatures. Understanding these energy dynamics helps us assess our environmental impact and develop sustainable alternatives.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{figure} https://brian-rose.github.io/ClimateLaboratoryBook/_images/GlobalEnergyBudget.png\n", ":height: 500px\n", ":name: energy-budget\n", "The global **annual mean** energy fluxes.\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this lecture, we'll build a basic climate model based on this energy budget. But first, we need a **simpler** version of this budget.\n", "\n", "```{prf:assumption} Climate Model Basics\n", "\n", "Our simple climate model can be written as:\n", "\n", "- **Heat Content Change** = \n", " - $+$ Energy from the Sun's rays\n", " - $-$ Blackbody cooling to space\n", " - $+$ Radiation trapped due to human activities\n", "\n", "These elements represent a global average, hence the term \"zero-dimensional.\"\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{figure} https://raw.githubusercontent.com/hdrake/hdrake.github.io/master/figures/planetary_energy_balance.png\n", ":height: 500px\n", ":name: energy-balance\n", "Planetary Energy Balance\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{note} How to build the model?\n", "To make this simple conceptual model quantitative, we need a mathematical formulation for each of these four processes.\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1 Absorbed solar radiation\n", "\n", "At Earth's orbital distance from the Sun, the power of the Sun's rays that intercept the Earth is equal to\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "S = 1368 # solar insolation [W/m^2] (energy per unit time per unit area)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A small fraction" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alpha = 0.3 # albedo, or planetary reflectivity [unitless]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "of this incoming solar radiation is reflected back out to space (by reflective surfaces like white clouds, snow, and ice), with the remaining fraction $(1-\\alpha)$ being absorbed.\n", "\n", "Since the incoming solar rays are all approximately parallel this far from the Sun, the cross-sectional area of the Earth that intercepts them is just a disc of area $\\pi R^{2}$. Since all of the other terms we will consider act on the entire surface area $4\\pi R^{2}$ of the spherical Earth, the absorbed solar radiation *per unit surface area* (averaged over the entire globe) is reduced by a factor of 4." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{figure} figures/earth-solar-radiation-diagram.png\n", ":height: 500px\n", ":name: energy-solar-radiation\n", "Earth Solar Radiation\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The absorbed solar radiation per unit area is thus\n", "```{math}\n", "\\text{absorbed solar radiation} \\equiv \\frac{S(1-\\alpha)}{4}\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def absorbed_solar_radiation(S=1368, alpha=0.3):\n", " return S*(1-alpha)/4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Outgoing Thermal Radiation\n", "Assume that the Earth acts similarly to a blackbody radiator. If we consider the Earth's effective global mean emission temperature as $T_e$ then the relation is given by:\n", "\n", "```{math}\n", "OLR = \\sigma T_e^4\n", "```\n", "\n", "In the above equation:\n", "- $OLR$ stands for Outgoing Longwave Radiation\n", "- $\\sigma$ represents the Stefan-Boltzmann constant and has a value of $5.67\\times 10^{-8} W m^{-2}K^{-4}$\n", "\n", "From observational data, the average global annual value for $OLR$ is 238.5 W $m^{-2}$.\n", "\n", "Given this value, we can rearrange the equation to find $T_e$:\n", "\n", "```{math}\n", "T_e = \\left(\\frac{OLR}{\\sigma}\\right)^{0.25}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{exercise} Earth's Emission Temperature as a Blackbody Radiator\n", ":label: earth-blackbody\n", "\n", "Determine the value of $T_e$.\n", "\n", "1. Compute the emission temperature $T_e$. \n", "2. Contrast this calculated value of $T_e$ with the real global mean surface temperature. Are they close?\n", "3. Evaluate the validity of representing Earth's emission to space using the simple blackbody radiator model.\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Earth's Emission temperature (Te): 254.67 K\n" ] } ], "source": [ "OLR = 238.5 # W/m²\n", "sigma = 5.67e-8 # W/m²/K⁴\n", "\n", "# Calculate Te using the Stefan-Boltzmann law\n", "Te = (OLR / sigma) ** 0.25\n", "\n", "print(f\"Earth's Emission temperature (Te): {Te:.2f} K\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The outgoing thermal radiation (blackbody cooling to space), denoted as $G(T)$, represents the combined effects of negative feedbacks that **dampen warming**, such as **blackbody radiation**, and positive feedbacks that **amplify warming**, such as the **water vapor feedback**.\n", "\n", "Since these physics are too complex to address in full detail here, we linearize the model by approximating the relationship between temperature and radiation.\n", "\n", "We begin with the assumption that the preindustrial climate existed in energy balance, meaning the equilibrium temperature equals the preindustrial temperature $T_0$.\n", "\n", "This allows us to simplify our approach by using just the first-order term of a Taylor series expansion:\n", "\n", "```{math}\n", "G(T) \\approx G(T_0) + G^{'}(T_0) (T-T_0)\n", "```\n", "\n", "around the pre-industrial equilibrium temperature $T_0$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "T0 = 14. # preindustrial temperature [°C]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By expanding: \n", "\n", "$$ G(T) \\approx G(T_0) + G'(T_0)T - G'(T_0)T_0 $$\n", "\n", "\n", "To simplify the expression, we define:\n", "\n", "$$ A \\equiv G(T_0) - G^{'}(T_0)T_0 $$\n", "$$B \\equiv - G^{'}(T_0) \\text{ (the climate feedback parameter),}$$\n", "\n", "which gives\n", "\n", "$$ \\text{Outgoing Thermal Radiation} \\equiv G(T) \\approx A - BT$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def outgoing_thermal_radiation(T, A, B):\n", " return A-B*T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{important} Value of the climate feedback parameter B\n", "$B$ comes from a bottom-up estimate based on the best understanding of the various climate feedbacks (read more [here](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019rg000678))\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "B = -1.3 # climate feedback parameter [W/m^2/°C]," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{note}\n", "Since $B<0$, this tells us that the overall climate feedback is negative (i.e., stabilizing). Positive feedbacks cause $B$ to become less negative, reducing the efficiency with which Earth cools itself by radiating thermal energy to space and thus amplifying warming.\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{prf:assumption}\n", "The value of $A$ is determined by the preindustrial equilibrium condition, which states that before human influence, Earth's energy budget was balanced:\n", "\n", "$$\\frac{S(1-\\alpha)}{4} = A - BT_0$$\n", "\n", "Where:\n", "- $\\frac{S(1-\\alpha)}{4}$ represents the absorbed solar radiation\n", "- $A - BT_0$ is the OLR during the preindustrial equilibrium\n", "\n", "```\n", "\n", "By rearanging this equation, we find that the value of $A$ is given by" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "A = absorbed_solar_radiation() + B*T0" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "221.2\n" ] } ], "source": [ "print(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Human-caused greenhouse effect\n", "\n", "The greenhouse effect from carbon dioxide has been empirically determined to follow a logarithmic relationship with atmospheric CO₂ concentrations:\n", "\n", "```{math}\n", "\\text{Human-caused greenhouse effect} = a \\ln\\left(\\frac{CO_2}{CO_{2,PI}}\\right)\n", "```\n", "Where:\n", "- $a$ is a constant representing climate sensitivity to $CO_2$ changes\n", "- $CO_2$ is the current atmospheric carbon dioxide concentration \n", "- $CO_{2,PI}$ is the preindustrial carbon dioxide concentration" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = 5 # CO2 forcing coefficient [W/m^2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "CO2_PI = 280 # preindustrial CO2 concentration [parts per million; ppm];" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def greenhouse_effect(CO2, a=5, CO2_PI=280):\n", " return a*np.log(CO2/CO2_PI)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "co2_present = 420\n", "co2_range = 280*2**np.linspace(-1,3,100)\n", "\n", "plt.plot(co2_range, greenhouse_effect(co2_range), color = \"black\")\n", "plt.ylabel('Radiative forcing [$W/m^2$]')\n", "plt.xlabel('$CO_2$ concentration [ppm]')\n", "plt.plot(CO2_PI, greenhouse_effect(CO2_PI), marker=\".\", markersize = 20, label = \"pre-industrial (PI)\", color = \"blue\")\n", "plt.plot(co2_present, greenhouse_effect(co2_present), marker=\".\", markersize = 20, label = \"present day (2020)\", color = \"red\")\n", "plt.xticks([280, 280*2, 280*4, 280*8])\n", "plt.legend(loc = 4)\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observations from Mauna Loa Volcano\n", "\n", "```{figure} https://i.pinimg.com/originals/df/1a/e7/df1ae72cfd5e6d0d535c0ec99e708f6f.jpg\n", ":height: 500px\n", ":name: volcano-mauna-loa\n", "Mauna Loa Volcano\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.4 Change in Heat Content\n", "\n", "The heat content of the climate system is determined by the temperature $T$ (in Kelvin) and the system's heat capacity. We can express this relationship as:\n", "\n", "$$C_{temp} = c_p \\cdot T$$\n", "\n", "Where:\n", "- $C_{temp}$ represents the total heat content of the climate system\n", "- $c_p$ is the effective heat capacity of the climate system\n", "\n", "While the atmosphere itself has a relatively small heat capacity, it is thermally coupled with the upper ocean, which possesses a much larger heat capacity. This coupling is critical because:\n", "\n", "1. The upper ocean absorbs and stores significantly more heat than the atmosphere.\n", "2. The exchange of heat between the atmosphere and upper ocean occurs on relatively short timescales.\n", "3. This thermal coupling effectively determines the response time of the climate system to radiative forcing.\n", "\n", "The effective heat capacity of the climate system is primarily dominated by the upper ocean's contribution, which is approximately 51 $\\frac{J}{m^2·K}$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "C = 51 # atmosphere and upper-ocean heat capacity [J/m^2/°K]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{prf:assumption} Constant heat capacity\n", "The rate of change in heat content over time is expressed as\n", "\n", "$$\\frac{d(C_{temp})}{dt}.$$\n", "\n", "Since the heat capacity of seawater remains essentially constant, we can simplify this expression in terms of temperature change:\n", "\n", "$$ \\frac{d(C_{temp})}{dt} = c_p\\frac{dT}{dt} $$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.5 \"Zero-Dimensional\" Climate Model Equation\n", "\n", "Combining all the subcomponent models, we can express the governing equation of our \"zero-dimensional\" energy balance climate model as an Ordinary Differential Equation (ODE):\n", "\n", "$$ c_p\\frac{dT}{dt} = \\frac{S(1-\\alpha)}{4} - (A - BT) + a \\ln\\left(\\frac{CO_2}{CO_{2,PI}}\\right) $$\n", "\n", "This equation determines the time evolution of Earth's globally averaged surface temperature, where:\n", "\n", "- $c_p\\frac{dT}{dt}$ represents the rate of heat content change\n", "- $\\frac{S(1-\\alpha)}{4}$ is the absorbed solar radiation\n", "- $(A - BT)$ is the outgoing longwave radiation\n", "- $a \\ln\\left(\\frac{CO_2}{CO_{2,PI}}\\right)$ accounts for the human-caused greenhouse effect\n", "\n", "Each term corresponds to a component of Earth's energy budget that collectively governs how global temperature changes over time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2) Numerical solution method and data structures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1) Discretization\n", "The energy balance model equation above can be discretized in time as\n", "\n", "$$ C\\frac{T(t+\\Delta t) - T(t)}{\\Delta t} = \\frac{S (1-\\alpha)}{4} - ( A - BT(t)) + a \\cdot ln \\frac{CO_2}{CO{_2}_{PI}} $$\n", "\n", "This finite difference equation approximates the exact ordinary differential equation through a first-order truncation of the Taylor series expansion. The approximation becomes more accurate as $\\Delta t$ $\\rightarrow 0$. In practice, we must select a sufficiently small $\\Delta t$ to ensure numerical stability and solution convergence while balancing computational efficiency. \n", "\n", "Using the notation $T_n \\equiv T(t_n)$ to represent the temperature at timestep $n$, and $T_{n+1} \\equiv T(t_{n+1})$ for the temperature at the next timestep $t_{n+1} = t_n + \\Delta t$, we can rearrange the equation to solve for $T_{n+1}$:\n", "\n", "$$ T_{n+1} = T_n + \\frac{\\Delta t}{C} \\left[\\frac{S(1-\\alpha)}{4} - (A - BT_n) + a \\cdot \\ln\\left(\\frac{CO_2}{CO_{2,PI}}\\right)\\right] $$\n", "\n", "### 2.2) Timestepping\n", "More generally, this equation follows the form of an explicit Euler method:\n", "\n", "$$ T_{n+1} = T_n + \\Delta t \\cdot \\text{tendency}(T_n; \\ldots) $$\n", "\n", "which we implement below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{note} Note\n", "Increasing $\\Delta t$ would:\n", "1. Reduce computational cost but at the expense of accuracy\n", "2. Potentially introduce numerical instability\n", "3. Violate the core assumption of the discretization that $\\Delta t$ should be small\n", "\n", "The explicit Euler method used here is conditionally stable, meaning that if $\\Delta t$ exceeds a certain threshold (determined by the system's characteristics), the numerical solution may become unstable and produce unrealistic oscillations or exponential growth of errors.\n", "\n", "For climate models, the appropriate timestep is typically determined through convergence testing, where $\\Delta t$ is progressively reduced until further reductions produce negligible changes in the solution.\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{exercise} Calculate the tendency of our Energy Balance Model\n", ":label: earth-blackbody\n", "\n", "Using the energy balance model discretization, calculate the temperature tendency when:\n", "\n", "- Current temperature $T_n$ = 15°C\n", "- Timestep $\\Delta t$ = 1 year\n", "- Heat capacity $C$ = 10 W·year/m²/K\n", "- Solar constant $S$ = 1366 W/m²\n", "- Albedo $\\alpha$ = 0.3\n", "- Outgoing radiation parameters: $A$ = 210 W/m², $B$ = 2 W/m²/K\n", "- CO₂ concentration = 280 ppm (pre-industrial level)\n", "- Greenhouse effect parameter $a$ = 5 W/m²\n", "\n", "Determine whether the temperature will increase, decrease, or remain stable in the next timestep.\n", "Explain what this result indicates about Earth's energy balance at this temperature.\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "T_n = 15\n", "deltat = 1\n", "t = 0\n", "\n", "tendency = 1. / C * (\n", " + absorbed_solar_radiation(alpha = alpha, S=S)\n", " - outgoing_thermal_radiation(T_n, A = A, B=B)\n", " + greenhouse_effect(280, a = a, CO2_PI=CO2_PI)\n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{important} Why is the tendency negative?\n", "\n", "The tendency is negative because at $T_n$ = 15°C, the outgoing thermal radiation exceeds the sum of absorbed solar radiation and greenhouse effect. This means the Earth is losing more energy than it's gaining at this temperature.\n", "\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the next timestep, using the equation $T_{n+1} = T_n + \\Delta t \\cdot$ tendency\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "T_n1 = T_n + deltat * tendency" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now think about how to calculate the temperature change for the next 150 years? What temperature do you expect after 150 years?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Initialize an array to store temperature values for 150 years\n", "T_n = np.zeros(150)\n", "\n", "# Set the initial temperature to 15°C\n", "T_n[0] = 15\n", "\n", "# Loop through each year (1 through 149)\n", "for year in range(1, 150):\n", " # Calculate the temperature tendency (rate of change) for the current year\n", " # tendency = (1/heat_capacity) * (energy_in - energy_out)\n", " tendency = 1. / C * (\n", " + absorbed_solar_radiation(alpha = alpha, S=S)\n", " - outgoing_thermal_radiation(T_n[year-1], A = A, B=B)\n", " + greenhouse_effect(280, a = a, CO2_PI=CO2_PI)\n", " )\n", " # Calculate the new temperature using the explicit Euler method:\n", " # T(next) = T(current) + (timestep * tendency)\n", " T_n[year] = T_n[year-1] + deltat*tendency" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Time [years]')" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(T_n)\n", "plt.ylabel(\"Temperature [°C]\")\n", "plt.xlabel(\"Time [years]\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have everything ready. In the next step we will put this into a class called ebm:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class EBM:\n", " \"\"\"\n", " Zero Order Energy Balance Model (EBM)\n", "\n", " The Energy Balance Model (EBM) represents the balance between incoming solar radiation and outgoing thermal radiation.\n", " It also considers the greenhouse effect caused by CO2 levels. This model can simulate the temporal evolution of temperature \n", " based on various parameters like albedo, solar constant, and greenhouse effect coefficients.\n", "\n", " Attributes:\n", " - T : Temperature (in Kelvin)\n", " - t : Time\n", " - deltat : Time step\n", " - CO2 : Carbon Dioxide function that returns CO2 levels in dependency of time t\n", " - C : Heat capacity\n", " - a : Greenhouse effect coefficient\n", " - A : Outgoing thermal radiation constant\n", " - B : Temperature sensitivity of outgoing radiation\n", " - CO2_PI : Pre-industrial CO2 concentration\n", " - alpha : Albedo\n", " - S : Solar constant\n", " \"\"\"\n", "\n", " def __init__(self, T, t, deltat, CO2, C, a, A, B, CO2_PI, alpha, S):\n", " self.T = np.array(T)\n", " self.t = t\n", " self.deltat = deltat\n", " self.C = C\n", " self.a = a\n", " self.A = A\n", " self.B = B\n", " self.co2_pi = CO2_PI\n", " self.alpha = alpha\n", " self.S = S\n", " self.co2 = CO2\n", "\n", " def absorbed_solar_radiation(self, S, alpha):\n", " return (S*(1-alpha)/4)\n", "\n", " def outgoing_thermal_radiation(self, T, A, B):\n", " return A - B*T\n", "\n", " def greenhouse_effect(self, CO2, a=5, CO2_PI = 280):\n", " return a*np.log(CO2/CO2_PI)\n", "\n", " def tendency(self):\n", " current_T = self.T[-1] if self.T.size > 1 else self.T\n", " current_t = self.t[-1] if self.T.size > 1 else self.t\n", " \n", " return 1. / self.C * (\n", " + self.absorbed_solar_radiation(S=self.S, alpha=self.alpha)\n", " - self.outgoing_thermal_radiation(current_T, A=self.A, B=self.B)\n", " + self.greenhouse_effect(self.co2(current_t), a=self.a, CO2_PI=self.co2_pi)\n", " )\n", "\n", " @property\n", " def timestep(self):\n", " new_T = self.T[-1] + self.deltat * self.tendency() if self.T.size > 1 else self.T + self.deltat * self.tendency()\n", " new_t = self.t[-1] + self.deltat if self.T.size > 1 else self.t + self.deltat\n", " \n", " self.T = np.append(self.T, new_T)\n", " self.t = np.append(self.t, new_t)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4) Running simulations of the energy balance model\n", "\n", "Let's define a function that runs an EBM simulation by timestepping forward until a given end_year.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def run_model(model, end_year):\n", " for year in range(end_year):\n", " model.timestep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, let us consider the case where CO₂ emissions increase by 1% year-over-year from the preindustrial value [CO$_2$] = $280.0$ ppm, starting at T=T₀=14°C in year t=0 and with a timestep Δt = 1 year." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def CO2_test(t, CO2_PI=280):\n", " return CO2_PI ** (1 + 1/100)**t" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_parameters = {\n", " \"T\":T0,\n", " \"t\":0,\n", " \"deltat\":1,\n", " \"CO2\":CO2_test,\n", " \"C\":C,\n", " \"a\":a,\n", " \"A\":A,\n", " \"B\":B,\n", " \"CO2_PI\":280,\n", " \"alpha\":alpha,\n", " \"S\":S\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{note}\n", "\n", "In Python, the ** syntax in a function call is used for unpacking key-value pairs from dictionaries directly into function arguments. This is particularly useful when working with functions that accept a variable number of keyword arguments.\n", "\n", "In summary, ** in a function call is used to unpack dictionary items as keyword arguments.\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = EBM(**model_parameters)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.timestep" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([14., 14.])" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3) Energy balance model applications\n", "### 3.1) Why was Earth's preindustrial climate so stable?\n", "Let us consider the simple case where CO₂ concentrations remain at their pre-industrial temperatures." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def CO2_test(t):\n", " return 280\n", "\n", "model_parameters[\"CO2\"] = CO2_test" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = EBM(**model_parameters)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "run_model(model, 200) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'temperature [°C]')" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/QZZk/rf6f73eg8Z7/PEU/dxa8t38t8do/de/+2yLQCAQCAT7MkLM9CeaEy75LxSMgvBm+Pe5EG7qc3MnDDiBuyfdDVh70LxY9WKv6mdffDH5N/8QgM0PPUTgzTf7bItAIBAIBPsqQsz0N85suPx/4B8ArevgP+dDPNjn5i4cfiHXj7segAfmPMDb697uVf3c668n+zvfAaDurp8Snjmzz7YIBAKBQLAvIsTM7sBXDJe/Cq48qF8M/70EktE+N3fjuBu5ePjFmJjc/endTK+e3uO6kiRReNed+M46C9Jpan54M9EFvduUTyAQCASCfRkhZnYXeUPg8pfB7oONn8ILl0E60aemJEnirkl3cXbl2eimzu0zbmd23eye15dlSh56EPcxR2PG42y69jpiS/vuoCwQCAQCwb6EEDO7k5IJcNlLoLlg7XR46SrQ+7bviyzJ3Df5vm67BC9qXNTj+pKmUfbww527BFd///vEV67sky0CgUAgEOxLCDGzuxlwOFzyPCh2qJoGr1wDht6nplRZ5TfH/IbJJZOJpWPc+OGNfNX8VY/ry04nZY8/jnPcOIxAgOqrv0tizZo+2SIQCAQCwb6CEDN7gsHHwkXPgqzBV6/C6zeBYfSpKZti48/H/ZmDCw4mlApxzQfX8FVLzwWN4nFT/sTfcYwahd7Wxsarryaxfn2fbBEIBAKBYF9AiJk9xbCTrX1oJAUWPwdv3NTnERqn6uRvJ/6NCQUTCCVDXPP+NSxvWd7j+orPR/mTT2AfPhy9qZnqq64muXFjn2wRCAQCgWBvI8TMnuSgs+H8JyxBs+g/8NqNfRY0bs3NYyc+xvj88X0SNGp2NgOe+ge2ykrSmzez8TtXkFgnRmgEAoFAsP8hxMyeZvT5cME/LEGz5Hl49XrQe372Ulc6BM24/HEEk0Guef8aVrSs6HF9NTeXimf+iW1IJenGRjZeeYXwoREIBALBfocQM3uDUefCt/8JsgpLX4RXr+2zoPHYPDx+4uOdguZ773+PxU2Le1xfzcuj4l//wj5sGHpTMxuvuJL4qlV9skUgEAgEgr2BEDN7i4POhm8/YzkFL/sfvHx1n/eh6RA0Bxcc3DnlNK9hXo/rqzk5DHjmn9hHjkRvbaX6iiuJr+j5CI9AIBAIBHsTIWb2JiPPhAv/BYoNVrwB/70YkpE+NeWxeXjsxMc4vPhwYukYN3x4A7NqZvW4vpqdTcXTT1mrnNrb2XjlVcQWLeqTLQKBQCAQ7EmEmNnbjDgdLn1xy8Z6/z4PYu19asqluXj0hEeZUjaFhJ7ghx//kA83ftjj+orfz4Cnn8I5YQJGMEjdtdfiqhJTTgKBQCDYtxFiZl+g8ji44nVwZMGmOfDMmX0+bduu2PnjcX/k1IGnkjbS/GjGj3hl9Ss9rq/4fAz4x5O4jz4aMxan9JlnCL//fp9sEQgEAoFgT7BXxcyvfvUrDj30ULxeLwUFBXzrW9+iqqqqWxnTNLn33nspKSnB6XQyZcoUvvqq55vE7TeUHwZXvQ3ufGhYCk+fCm192/tFkzV+ffSvOXfIuRimwS8+/wVPLn0S0zR7VF92uSj/66N4TjkFSddpuOPHtL/8cp9sEQgEAoFgd7NXxcyMGTOYOnUqc+bM4YMPPiCdTnPyyScTiWzxG/ntb3/LH//4Rx599FHmzZtHUVERJ510EqFQaC9abtFTcdBjisbA1e9CVjm0rIF/nAT1S/rUlCIr3Df5Pr43+nsAPLzgYX4z7zcYZs92HpZsNgp/82vaDzsMDIP6n91D8+OP93+fBQKBQCD4mqh78+Hvvvtut+unn36agoIC5s+fzzHHHINpmvz5z3/m7rvv5rzzzgPgmWeeobCwkOeee47rrrtumzYTiQSJxJZVQcFgEIBUKkUq1bdDHrdHYyjBD/67iJOy6dd2yaqAK95GfeFipMblmE+fhn7BM5iDju1Tc1PHTsVv8/OHBX/gPyv+Q1OkifuPuB+bYttl3bRh0HjeuQwYdRDBp/9J058fJlFTS/7dP0VS9+pHp1/o+Ln1689vH+NA76Po3/7Pgd5H0b+v33ZPkMx96E/tNWvWMHToUJYuXcro0aNZt24dlZWVLFiwgAkTJnSWO+ecc/D7/TzzzDPbtHHvvfdy3333bZP/3HPP4XK5+s3WZ1bJLGiRcSgmVw8zGOHv39eo6lEOW/cw+eEVGCgsrLiGmpzJfW5vcXIxr0RfQUdnkDqIS12X4pSdPa6f9flsCt54A8k0CY8cQf2ll2Ladi2IBAKBQCDoC9FolEsvvZRAIIDP59tp2X1GzJimyTnnnENbWxuzZllLij///HOOPPJIamtrKSkp6Sx77bXXsnHjRt57771t2tneyEx5eTnNzc27fBm9oT2a4sbnFjJvYzuqLHH/2SP59sSyfmsfgHQC5c2pyMtfA0Cf8jOMyTeDJPWpuTn1c7h91u1E01EG+gbyyJRHKPPs2OZUKsUHH3zASSedhKZphD+azuaf/AQzkcA+ejTFj/4FNTe3T7bsC2zdvwORA72Pon/7Pwd6H0X/+k4wGCQvL69HYmafmSu46aabWLJkCZ9++uk296StvrxN09wmrwO73Y7dbt8mX9O0fn3R+VkaT191CFf99X2+bJb56WvLqQsk+dHJw3ZoW6/RNLjgafigDGY/ivLJAyjtG+DMP4Ha+1GRowcczb9O+xdTP5rKhuAGrnr/Kh45/hHG5Y/bhRnWu8s+9RTshQXU3HAjiWXLqL3scsr+9jccw4f1rX/7CP392dgXOdD7KPq3/3Og91H0r29t9pR9Ymn2D37wA9544w0+/vhjysq2jBQUFRUB0NDQ0K18Y2MjhYWFe9TG7WFXZS4fYjB1ymAAHv14DTc/v4h4qm+HR24XWYZTHoTTfw+SDIuehX9/C6KtfWpueM5wnjvjOUbmjKQ13sr33vse72/o+dJr14QJVPz3ObQBA0jV1rLxkksITf+4T7YIBAKBQNAf7FUxY5omN910E6+88grTp09n0KBB3e4PGjSIoqIiPvjgg868ZDLJjBkzmDy57/4j/YkkwS0nDOF3F4xFlSXeWFzHhf83m/pArH8fdNg1cOlLYPPCxs/gyROgeXWfmipwFfDPU//JsWXHktAT/GjGj3hs0WM9XulkHzSIgS88j+vwwzGiUWqmTqXlyZ4v/RYIBAKBoD/Zq2Jm6tSpPPvsszz33HN4vV4aGhpoaGggFrOEgCRJ3HLLLTz00EO8+uqrLFu2jKuuugqXy8Wll166N03fhm8fUs6/vzeJbJfGkpoAZ/3lM77c0LfRkx0y9ET43vuQNQBa11mCZnXPd/jtiktz8fBxD3P5yMsB+Nviv3Hrx7cSSfXsOAU1O5sBT/wd/8UXgWnS+Ps/UH/nnRiJvp0vJRAIBAJBX9mrYuaxxx4jEAgwZcoUiouLO8MLL7zQWebHP/4xt9xyCzfeeCOHHHIItbW1vP/++3i93r1o+fY5ojKXN246ihFFXprDCS55Yg7//aK6fx9SeBBcMx3KDoN4AP5zAcz8PfRhVESRFX5y2E+4f/L9aLLG9E3Tuezty6gO9sxmSdMovvdeCn9+DygKgdffoPqKK0k39W33YoFAIBAI+sJen2baXrjqqqs6y0iSxL333kt9fT3xeJwZM2YwevTovWf0LijPcfHKjZM5Y0wxKd3krleWcs9ry0jpPZvC6RGefLjqLZh4FWDC9F/CC5dDom8bCZ479Fz+eeo/KXAWsDawlovfvpjPaj/rcf2cSy9lwBN/R/b5iC1ezPoLLyK27ADcpVkgEAgE+yT7hAPwgYbLpvLopRO445ThSBL8e85GLntyLs3hfpyCUe1w1sNWUGyw8i144vg++9GMzR/L82c+z7j8cYSSIW786EaeWf5Mj/1g3JMnM/CF57ENGkS6vp6Nl1xC2/MvCD8agUAgEOx2hJjZTUiSxNTjhvDEdw7BY1f5Yn0rZ/3l0/73o5l4FVz9DnhLoHkV/P04WPl2n5rKd+Xz1ClPcf7Q8zFMg4cXPcyL0Rd77EfT4RjsOf54zFSKhnvvtfxootE+2SMQCAQCQU8QYmY3c+JBhbw2dTKD89zUB+Jc9Pc5PD5jLYbRjyMWZYfAdTNgwGRIhuD5S+GjX4Ke7nVTNsXGL474BXdPuhtVUlmaWspl717GytaVPaqv+HyU/fVRCm7/UacfzYaLLiKxbn2vbREIBAKBoCcIMbMHGFLg5Y0fHMXZ40rQDZNfv7OS7//rS9oiyf57iKcArnwDJl1vXc/6PTxzFgRqet2UJElcPOJinjjxCbKkLKpD1Vz29mW8sLJn00aSJJH7/e8z4OmnUPLzSKxew4YLLiD4zju9tkUgEAgEgl0hxMwewmNXefji8Tx07hhsqsz0lY2c8cgs5m9s67+HKBqc9hs4/x/WfjTVn8PjR8HKaX1qblz+OKZ6p3JM6TEkjSQPzH2AO2beQSjZM0dj92GHMfiVV3AdeihGNErtrbfR8OBDGMl+FHECgUAg+MYjxMweRJIkLp00gNduPJJBeW7qAnEu+r/Z/H1mP087jbkArp8JJRMg1gbPXwLv/ATSvXdAdsku/nTMn7j9kNtRJZX3NrzHRW9dxPKW5T2qr+bnM+Dpp8i95hoA2v79bzZcdDGJtWt7bYtAIBAIBNtDiJm9wEElPt78wVGcNa6EtGHy0LSVXPXPeWwOxvvvITmD4bvvwxE3WddzH4cnT4TmNb1uSpIkrhx1Jf887Z+UuEvYFNrE5dMu59nlz/Zo12BJVSn40W2UPfY3lOxsEitWsP6882l97jmx2kkgEAgEXxshZvYSHrvKIxeP58FzR2NXZWauauKUP8/k7SX1/fcQ1Wad63TpS+DKhYYl8H/HwMJn+7TJ3rj8cbx41oscV34cKSPFb+b9hms/uJaGSMOuKwPe445j0Ouv4T7ySMxEgs33/5Ka628g3dLSa1sEAoFAIOhAiJm9iCRJXDapgrd+cBSjS320R1NMfW4Bt76wiEAs1X8PGnYyXP8ZDDwaUhF4fSr89xIIbe51U1n2LB4+7mF+NulnOFUnc+vnct7r5/Hm2jd7NMqiFRRQ/sTfKfzpXUg2G+EZM1h39jmEZ87sS88EAoFAIBBiZl9gaKGXV244kpuOG4IswasLazntzzOZvbYfRyx8xXDF63DivdYme6vegb9NgmX/63VTkiRx0YiLeOmslxibN5ZQKsRPP/0pP5rxI9riu3ZolmSZnCuuYOBLL2EfOhS9pYVN115Hw/2/FHvSCAQCgaDXCDGzj2BTZW4/ZTgvXX8EA3Jc1AXiXPrkHB54aznxlN4/D5EVOOpWuHYGFI21nINf/i68dBVEei+cKnwVPHPaM9w0/iZUSeWDjR9w3hvnMbOmZ6MsjuHDGPjSi2R/5zsAtD33HOvOPofInDm9tkUgEAgE31yEmNnHmFiRwzs3H80lh5VjmvDkp+s57eFZzFnXj6M0HYdVHnsnSAp89Sr87fA+LeFWZZXrxl3Hs2c8y+CswTTHmpn60VR+9unPCCQCu6wvOxwU3f1Typ98ErWkmFRNDdVXXU39z3+BHg73pXcCgUAg+IYhxMw+iNuu8qvzxvLkFYdQ4LWzvjnCxX+fw12vLCUY7ydfGkWD4+6C738I+SMg0mgt4X75exBu7HVzo3JH8cKZL3D5yMuRkHh97euc/drZvLvh3R750niOOpLBb7yJ/5KLAWh/8UXWnXmW8KURCAQCwS4RYmYf5sSDCvngtmO55LABAPz3i2pO+uMM3v+qZ6uHekTpwda00+QfgCTDspfh0UNh/jPQg2XXXXGoDn5y2E/412n/YnDWYFrjrdwx4w5+OP2HPVrxpHjcFP/iFwx45hm08nLSDQ1suvY66u68C729vY8dFAgEAsGBjhAz+zhZTo1fnTeG5689nEF5bjYHE1z77/nc+J/5NIb6aV8azQEnPwDf/8jypYm3w5s/RHn2HDzxul43N75gPC+d9RI3jrsRVVb5pOYTznntHP678r892pfGPekwBr/+GjlXXgmSROC111h7+hm0v/aa2JdGIBAIBNsgxMx+wuGDc3nn5qO5cUoliiwxbWkDJ/5hBv+avYG03rsRlB1SejBc8zGc/CBoLuTq2UxZ+TPkmb/p9e7BNsXGDeNv4OWzXmZc/jii6SgPzX2IK965okeHVsouF4V33UnFc//BVlmJ3tpK/Z13sfE73yG+alVfeygQCASCAxAhZr4G6T08SuDQFH586gjeuOlIxpRmEYyn+fnrX3HWo5/x5YbW/nmIosLkm+DGORiVJ6KYaZRZv4PHjoQ1H/a6uUp/Jf867V/cPelu3JqbxU2Lueiti3hgzgM9chB2TZjA4FdfoeD2HyE5ncS+nM/6c89j829/hxGJ9KWHAoFAIDjAEGKmj3wVjnHkl6uZo7kx9rCoGVWSxWtTj+SX3xpNllNjRX2QCx6fzW0vLKKxv45EyK5Av+i/zBs4FdNdAC2r4dnz4bmLoaV35yrJkszFIy7mtXNe47SBp2GYBi9UvcCZr57JS6teQjd2vvRcstnI/f73qXz7LbwnnQi6TutTT7H2jDMJvve+mHoSCASCbzhCzPSRx6obqUukeNqZx9mL1zMvsGdHCRRZ4juHV/Dx7VO45LByJAleWVjL8X+YwZOz1pHqj6knSaIuexLp6+dYZzzJqrXZ3l8nwQc/h0TPTs/uoMhdxG+P/S1PnfIUQ/xDaE+0c//s+7l02qUsaly0y/paSQllf/kLZY8/hlZWRrqhgdqbb6b66u8SX7Gij50UCAQCwf6OEDN95PfDy7lrYAEO02BxOMZZC1Zz3Vcb2BRP7lE7ctw2fnXeWF678UjGlfsJJ9I88PYKTnt4Fh8u39w/oxYOn3XG0w2fQ+UJYKTgs4fhLxNh0XNg9E44HVp0KC+d9RJ3HnYnXs3L8pblfOed73D3p3f3aNWTd8oUBr/1Jnk33oBksxGdM4f1551P3c9+Rqqx98vKBQKBQLB/I8RMH3EoMjeW5fPLcC2XFmYjAa83tnPU3BX8el09kXQ/7drbQ8aV+3n1hsn85vwx5LhtrGkM8/1/fcnFf5/Dkpr2/nlI/nC4/H9wyfOQPQjCm+G1G+DJE2D9rF41pcoql428jDfPfZNzh5wLwBtr3+DMV8/kz/P/TCi581Ef2eEg/4c/ZPC0afhOPw1Mk8DL/2PtqafR/NhjGLFYn7spEAgEgv0LIWa+Jj7T4DdDS/jgkGFM9ntIGCZ/3riZI+au4JnaZlLGnvPnkGWJiw4dwMe3T+H6YyuxqTJz17dy9qOf8cP/LmRTaz+ceyRJMPw0mDrXOufJ5oG6BfDMmfDsBdCwrFfN5Tpzuf/I+/nvGf9lYuFEEnqCfyz7B6e/cjrPLn+WlL7zTQJtZaWU/vGPVPz3ORzjxmJGozQ9/AhrTzudwBtvYPZy1EggEAgE+x9CzPQTo70u/je+kqdHD2Sg00ZjMs1PVtVw9BcreGVz2x51Es5yatx52gg+vn0K500oRZLgjcV1nPCHGTz49nLao/0wFabarXOefrAADv2+5U+z5gN4/Ch45Tpo29ir5kbnjebpU57mL8f/hcFZg2lPtPObeb/p8S7CrgkTGPj885T84feoJcWkGxqo+/FPWH/ueYSmTxdOwgKBQHAAI8RMPyJJEqfl+5lx2AgeGFpKnqayIZbkxuUbOXFeFe83B/bol2qp38kfLxrPmzcdxZFDcknqBk/MWs/Rv/mYP32wqn+ORvAWwhl/gKlfwKhzAROWPA+PHgLv/rRXB1hKksSU8in87+z/8YsjfkGeM4+acA13zLiDi966iJk1M3f6/iRJIuuMM6icNo38W29F9npJVFVRc+NUNlx8MZHZs79+fwUCgUCwzyHEzG7ALst8vyyfuYeP5M5BRXgVmeWROFcsXc/ZC9bweduePUBxdGkWz35vEk9ffSgjiryEEmke/mg1R/16Oo9OX004kf76D8mthG//0zrAcuDRoCdhzl/hz2Pgw3t7JWpUWeWCYRfw9rlvc+P4G3GqTla0rmDqR1O5bNplfFr76U5FjexwkHfdtQz54H1yr7kGyekkvngJ1Vd/l41XXkV04cKv31+BQCAQ7DMIMbMbcasKtwws4osjDmLqgAIcssS8YITzFq3hWwtWM7M1tMdGaiRJ4rjhBUz74dH89dKDGVrgIRhP8/v3V3H0b6bz+Iy1RJP9IGpKJ8KVb1qOwkVjIRWBT/9kiZoPfgGR5h435dJc3DDuBt49/12uHn01TtXJ0ual3PDhDXznne8wu272Tt+f4vdT8KPbGPL+e2RffjmSphGdO5eNl1xK9XXXEV+8+Ov3VyAQCAR7HbUnhR555JFeN3z11Vfj9Xp7Xe9AJFtTuaeyhGvK8vnThgaeq29lTiDChYvXMtHn4taBRZyQ40WSpN1uiyxLnDG2mFNHF/HWkjoe/nA165oj/PqdlTw5ax3fO2owlx8+AK9D6/tDJAmGnGgt4656Bz75FTQsgc/+DF88AYddYx1s6c7rUXM5jhxum3gbVxx0BU8ve5oXql5gcdNirv3gWg4uOJjrxl7HESVH7PD9qfn5FP3sbnK/ezVNf/sbgVdfIzJjJpEZMykdMoRoXh6+yZP3yPsXCAQCQf/TIzFzyy23UFZWhqIoPWp006ZNnHnmmULMbEWRXeM3w8u5uaKQv1Y38p/6FuYHo1y+ZB1jvU5urSjklLws5D3wparIEueML+WMMcW8vqiOhz9aTXVrlN+8u5K/fbKGK46o4DuHlX29h0gSjDjdWv206l1L1NQv3iJqJl4Jh98I/vIeNZfnzOOOQ+/gqlFX8Y9l/+ClqpdY0LiA6z68jpE5I/nemO9x4oATUeTtf061khJKHniAvO9/n+a/P0HgjTdwr1lD3fe+T9v48eRefx2eY48VokYgEAj2M3o8zfTll1+yfv36HgWn07k7bd7vKXHYeHBYGV8cfhA3lOfjlGWWhGJcvWwDU76o4rn6FhJ7aEmxqsicP7GMj350LH/49jiGFHgIxdP89eO1HPuHWby8Tqam7Wvu2dKxnPvaGXDJC1A83pp+mvM3eGS8tfpp81c9bi7flc+dh93JtPOmcdnIyzp9am6fcTtnv3Y2L696maS+4xVbtoEDKXnoQSrefov2Iw5HstmILVpEzfU3sP788wm+8w5muh+m3AQCgUCwR+iRmPnFL36Bx+PpcaM//elPycnJ6bNR3xQK7Bq/GFLKl0ccxM0VhXgUmVXROLet3MShs5fz8IbNtKX2zJeqlhE1799yDP/3nYmMK/eTSBvM2ixz4p8/5dYXFrGsdtcHQ+4USYLhp8K1n8Bl/4NBx4CRtlY/PTbZ2qdmw6fQQz+iQnchdx52J++d/x43jLsBn81Hdaia+2bfx6n/O5Wnlz1NMBnccZ9LSmj81reoePcdcr77XSSXi8TyFdTeehtrTz6Fln/+Ez28Z521BQKBQNB7eixmXC5Xjxu966678Pv9fbXpG0euTeWuwcXMP+Ig7qksodiu0ZhM86v19Rz8+XJ+uqqGDbHEHrFFliVOGVXEazdO5l9XT2RYloFumLy6sJYz//IpF/7fbN5dVo/+dTYDlCQYeqLlKHzNx9aSbkm29qn55xnwxPGw5EVI92w/nGxHNjeOv5EPLviAOw65gwJXAU2xJv44/4+c+NKJPDDnAdYH1u+wvpqfT+GP72DIRx+SN3UqSnY2qbo6Gn/9G9YcO4XNv/4Nqbq6vvdXIBAIBLuVHk8zxWIx3njjDUKhbbeZDwaDvPHGGyQSe+YL90AlS1OZOqCAuYeP5NGRAxjtcRIzDJ6qbWbynBVctXQdM/bQCihJkjhicC5TDzJ45fpJnDO+BFWW+GJ9K9c/u4Bjf/cxT85a9/X3qik92FrS/YP51uZ7qsPaUfiVa+BPo+DjX0Fo1+c1gbX66YpRV/Duee9y/+T7GeIfQiwd44WqFzj7tbO5/sPr+az2sx2+PzU7m/wf3MSQj6dTdP992AYPxohEaP3nP1lz0snU3nYb0QULxQZ8AoFAsI/RYzHz97//nYcffni7Tr0+n49HHnmEJ598sl+N+6Zik2UuKMrhg0OG8dK4So7L8WIA7zYHuWjxWo6au5InNjUR2ENTUGNKs3j44gl8+pPjmXpcJdkujZq2GA+8vYLDH/qIe15bxsqGHU/n9Iicwdbme7csg+N+Bt5iiDTCjF9boubl78GmL3o0BaUpGucOPZdXzn6FJ05+gillU5CQ+Kz2M67/8HrOef0cnl/5POHU9qeQZIeD7AsvZPBbb1L+f4/jOuJw0HWC095h46WXsv7c82h7/gWMyJ49KV0gEAgE26fHYuY///kPt9xyyw7v33LLLTzzzDP9YZMggyRJHJ3j5b/jKpl52Ai+W5qHR5FZG0twz5paxn++nDuqNrE8vGcOVSzKcnDHKSOYfdcJ/Pq8MQwr9BBN6vx7zkZO/fMszn/sc/43v4Z46mscsunJh2PvgFuWwgVPQfnhll/NspfhHyfB34+FL5+C+K7FkyRJHF58OH854S+8de5bXDbyMtyam/WB9Tw490FOfuVkXo2+yvKW5duvL8t4jj2WiqefZtBrr5J13nlIdjuJlStpuPdeVh9zLA2/fIDEmjV9769AIBAIvjY9FjOrV69m3LhxO7w/duxYVq9e3S9GCbZlmNvBQ8PKWDR5FL8eVsZwt4OYYfDvuhaOn1fFaV+u4t91zYT2wGndDk3h4sMG8N4tx/Ds9yZx2ugiFFli/sY2fvTSYiY99BH3v7mcNY1fw3lW0WD0+fC996xVUOMvA8VuLe1+61b4wwh4fSrUfNmj0ZoBvgHcedidfHjBh/zk0J8wKGsQcT3O/OR8Ln/vci5880JeWvUSkdT2R1scI0ZQ8tCDDJ3xCQV3/gRbRQVGJELbf/7DujPPYuPl3yHwxhvitG6BQCDYC/RYzKTTaZqamnZ4v6mpibRYzrrb8agKV5Xm8cmhw3l1whDOLvCjSrAwFOWOqhrGfvYVP1yxkTnt4d3u2yFJEkcNzeOxyycy+87juf3kYZT6nQRiKZ76bD0n/nEG3378c16YV03o6/jWlIyHb/0NblsOJz8AuUOtpd0Ln4UnT4DHjoS5/wfR1l025bF5uPygy3n9nNd54sQnGKuNRZM1VrSu4P7Z93P8i8dz7+f3smDzgu2+P8XvJ/eqqxj8zjTK//Ek3pNOBFkm+uWX1P34J6w++hjqf3EvsSVLhG+NQCAQ7CF6tGkewKhRo/jwww+ZOHHidu9/8MEHjBo1qt8ME+wcSZI4wu/hCL+HpmSKlxvaeK6+hdXRBC82tPFiQxuVTjsXF+dwXmE2pQ7bbrWnwOfgpuOHcsOUIcxc1cR/5lYzfeVm5m1oY96GNn7xxlecNrqYCyaWccTgXGS5DxvTufOsnYOPuAmqZ8P8Z2D5a9D4FbzzY3j/ZzDsFBh3CQw5CdQd91mSJCYWTORC94UccfwRvFP9Di+vepkNwQ38b/X/+N/q/1HuLefsyrM5q/IsSj2l3evLMp4jj8Rz5JGk6utpf/VVAq+8SqqmhvYXXqD9hRewDanEf975ZJ19Fmpez3Y7FggEAkHv6bGY+e53v8ttt93GqFGjOPPMM7vde/PNN3nggQf44x//2O8GCnZNvk3jhgEFXF+ez5fBKM/Vt/B6YztrYwkeXFfPQ+vqOcLv4fzCbM7MzyJL6/GPvdcossRxIwo4bkQBDYE4ry6s5eX5m1jbFOHVhbW8urCWkiwH5x1cxrcmlDCkoA+7REsSVEy2wmm/tpZxL/gXbF4GK960gjPHmqYad4m1Ymonu/pmO7K5ctSVXHHQFXy5+UveWPsG7294n02hTfx10V/566K/cmjRoZxdeTYnDjgRj637nktacTH5N95I3vXXE/1iHu2v/I/Qe++TXLOWxt/+lsY//AH35MlknXUm3hNOQHa7e99ngUAgEOyQHn+rXXvttcycOZOzzz6bESNGMHz4cCRJYsWKFaxatYoLL7yQa6+9dnfaKtgFkiRxaJabQ7Pc/HJIKW80tvNig3UO1OftYT5vD3PXqhpOyvNxfmE2J+T6sMu776zRoiwHN0yp5PpjB7O4JsDL8zfxxqI66gJxHv14DY9+vIaRxT7OGlfMWWNLKM/p+V5GnTizYdJ1VmhYCoufh6UvQXgzzHvCCrlDYcwF1n42+cN32JQkSRxadCiHFh3KXYfdxUfVH/H62tf5ov4L5jXMY17DPH45+5ccU3YMpw06jaPLjsapbtntWpJl3IdPwn34JPR77iE47R3aX/kf8cVLiMyaRWTWLCSHA+/xx+M760w8Rx6JZNu9I2YCgUDwTaBXf6I/++yznH322Tz33HOsWrUK0zQZPnw49913HxdeeOHuslHQBzyqwqUluVxaksumeJLXNrfx8uY2qiJx3m4K8HZTAK8ic0peFmfm+zk2x4tT2T3CRpIkxpf7GV/u52dnHMSHKzbz6oJaZq5uYkV9kBX1QX77bhUHD/Bz1rgSzhhbTIHX0fsHFY2xwon3wfpPLGGz4i1oWW2dC/XJr6BgFIw+F0adB74BO2zKpbk4q/Iszqo8i/pwPW+te4s31r7BhuAGPqz+kA+rP8SlujhuwHGcNvA0JpdMRlO2HM6peL1kX3Qh2RddSGL9eoJvTyP45pskN24kOG0awWnTULKy8J58Mt6TT8Y96TAhbAQCgaCP9Hq+4cILLxTCZT+j3GHjBxWF3DSggOWROC83tPJaYzv1iRQvZ0SOS5E5KdfHmfl+js/14u7hoaK9xaEpnDm2hDPHltAeTfLusgbeWFzH7HUtLKhuZ0F1O/e/tZyJA7I5ZVQRp4wqYkBuL0dsFNU6tXvIiZAIWYLmq1dg7XTLv2b6VzD9AdTCMQyVR0DLUCg6aIfNFXuKuWbsNXx/zPdZ1baKaeun8e76d6mL1PH2urd5e93beG1eppRN4fgBxzO5ZDIubYvN9kGDyL9pKnlTbyS+7CuCb71JYNo09KZm2l96ifaXXkL2+fAeN8USNkceiezog5gTCASCbyi7z3lCsM8hSRKjPE5GDSnlnsoSvgxEeLspwFtN7dQmUrze2M7rje04ZYnjc32cmuNFZ/edIO132bj4sAFcfNgAGoNx3l5azxuL61hY3c6XG9v4cmMbD05bwYgib6ewGVns7d2p1nYvjL/ECtFWWPm2JWzWzUDavJSDWAqPv2RNRY04HYafAWWHwnam3yRJYnjOcIbnDOeWg29hSfMS3l3/Lu9ueJfmWDNvrnuTN9e9iV2xc0TJEZww4ASOLTuWbEd2Z33nmNE4x4ym4Mc/JvrFFwTfe4/Qhx+hNzcTeP0NAq+/geRy4Tn2GHwnn4znmGOEj41AIBDsAsns4frROXPm8L3vfY94PM6DDz7IxRdfvLtt6xeCwSBZWVkEAgF8Pl+/tWsYKdLpNO+88z6nn346mqbtutI+immaLAxFLWHT2M7G+JYzkRTT5Ai/h5PyszgpN4vBLvtut6euPcYHyzfz3lcNzF3f2u0cqAE5Lk4+qJBTRhcxodyP2tepsUgL6WWv0vrp0+RHqpCMLkvH3QXWgZjDz4DBx4K281PgdUNnYeNCpm+azvTq6dSGazvvyZLMxMKJHF9+PMcPOJ4ST8k29U1dJ7ZwIcH33yf0wYek6+s770k2G+4jjsAz5Vg8xx6LVrJt/Z2RSqWYNm3afv8Z3RGif/s/B3ofRf/6Tm++v3ssZg4++GB+9rOfMWHCBMaMGUNLSwt2++7/Yvu67C4xs7nxHVasuItEfBAjD/o2BfnH4XAU91v7ewvTNFkWjvF2U4A3G9tYG+t+2GOl086JuT5OyvNxWJYb2250IAZoiyT5aGUj733VwMxVTSTSRuc9n0Pl6GH5TBmWz7HD8inw9W5qpvMf4fFHoW38xBq1Wf0BJLrsLqy5oPJ4GHoSVJ4A/vKdtmmaJqvaVjG9ejofVX9EVVtVt/tD/EM4qvQojiw9koMLDsam2LapH1+2jND77xN8/31SG6u73bcPHYLnWEvYOMePR9rFLw/xi3T/5kDvHxz4fRT96zu9+f7u8TRTa2srZWVlFBYWkkgkiEaj+4WY2V20t81F10Oo2hJWr17C6tXgdg8jN/cYcnOOwe8/BFne/96PJEmM8boY43Xxo/I8nn73ffQxE5neHmZOe4S1sQRra5r4v5omPIrMlBwvx+f6OCbbS9lu2Msm223jgollXDCxjGgyzcxVzbz/VQPTqxppj6Z4e0k9by+xRjIOKvYxZXg+U4YXcPCAXozaOHzWMu7R51sndW/8FFZOg6ppEKyFlW9ZASBvmOWLU3kCDDxym1GbrlNRN4y/gZpQDdOrpzN903QWNi5kTfsa1rSv4Z9f/ROn6mRS0SSOKj2Ko8qOotRTmpmKGoNzzBjyb7uNxKrVhD/5hPDMmcQWLiSxeg2J1WtoefIfyF4v7qOOtMTNUUeJvWwEAsE3lh6LmbvvvpvLL7+c7OxsrrrqKrKzs3enXfs8w4bdQ17emcz94iny8hoIhZYSiawiEllFdfWTyLKTnOwjyMk9mpzsI3G5BvfO12MfodBIc3ppLjcMLCKU1pnRGuLDliAftgRpTqV5qynAW00BwBq1OTrHyzHZHib7Pfj7eT8bl03l1NFFnDq6CN0wWVzTzidVTXxS1ciSmgDL64Msrw/yt0/W4nWoHD00j8mVeUyuzGVQnrtn71+1WSMxlcfD6b+D+kWw6n1Y+xHUzIPmVVaY8zfrhO+KyZawGXIC5I/YZj+bMm8ZV4y6gitGXUEgEWB23Wxm1c7is9rPaIm38EnNJ3xS8wnMhYG+gRxVehSTiicxsXAiXpsXx/BhOIYPI++6a9Hb2wl/+hnhmTOIzJyF3t5O6J13Cb3zLgD2oUNxHXE47iOOwHXooSgez7b9EwgEggOQHn/bXHPNNZx66qkEg0Gx0y8gSQo+3zhSyVOYMP50IExr62e0tMykpXUmyWQTzS3TaW6ZDoDNlk929uFk+w8nO/twnM6K/U7ceFWFMwv8nFngxzBNFodifNASYGZriIWhqDVqU5vgn7XNyMA4r4tjcrwcne3hEJ8bRz8u/VZkiYMHZHPwgGxuO2kYzeEEM1c18UlVEzNXN9EeTTFtaQPTljYAUOizM7kyjyMqc5lcmUtZdg9WSEkSlEywwpSfQKwN1s2ANR9aK6OCtVa8djq8f7flazPwKCsMOgZyh3QTN1n2LE4ddCqnDjoVwzSoaq3is7rPmFUzi8VNi9kQ3MCG4AaeXfEssiRzUM5BHFZ8GIcVHcaEggm4/H6yzjyDrDPPsPxsliwhPHMm4RkzSCxfQWL1ahKrV9P2r3+DouAcMwbHpEk4MTGTSTgAh7gFAoEAeuEzs7+yu3xmYMdzhaZpEo5U0doyg5bWWQQCCzCMRLe6dntRp7DJzj4ch6NsnxM3vZkLDaZ1ZreHmdkaYmZbiNXRrforS0zwujjc72FSZmM/j7p7ln93jNrMWtXM7HXNLNjYTlI3upUZkOPi8EHZ2APVfP+cKVTk9/KzYZrQVJURNh/Bxs8hHe9exlOYETdHWyG3coc7EQeTQebWz+Xzus+Z1zCPjcGN3e6rssqYvDEcVmSJmzH5Y7pt2JdubSU6dy6R2XOIzJlDqrq7r43kcOA6eALOiRNxTTwE57ixyM6dOzbvDwh/hP2fA72Pon99p98dgIPBYK+EQCgUwuvtwzb1u4G9IWa2RtcTBIOLaGubQ1v7HAKBRZhmd8dau72IrKyDyco6GH/WRDyekcjy3v3gf50PaX0iyay2LeKmMdn9EFIZGO11cniWh8P9bg7L8pBn2z07BcRTOgs2tvH52hY+X9vM4ppAtxVSAMVZDg4ZmMMhFdkcMjCbEUU+lN6cH5VOWCd4b/gUNsyCTV+A3l3Q4S2G8klbQtGYHZ4f1RBpYF7DPObWz+WLhi+oj9R3u69KKiNyRjC+YDwTCiYwoWAC+a78zvvJmlqic2YT+uxz2mfNQg1vdYK5quIYdRCuiYfgOmQizgkTUPfDqWPxRbH/c6D3UfSv7/S7mFEUhfr6egoKCnpkgM/nY9GiRQwePLhnFu9G9gUxszW6HiMQWNApboLBJZjmVl/2shOfbyz+rImdIkfTsvrV/l3RXx9S0zRZF0swtz3C7ECYue0RquPJbcoNcdmZ4HMx0efmYJ+LkW4nWl8OpNwF4USaeetb+XR1I+8v2kBdTN5G3HjsKhMG+DmkIodDBmYzpiwLn6MX7yAVh9ovYf0sS+DUfAH6Vn1WHVByMJQflhE4h1mHaW6FaZrUhGs6xc2Xm7+kMdq4TblST2mnsBlfMJ7KrEoM3WDa229zwtChJBcvJvblfKLz55PevHmb+rYhlbgmTMAxdizOseOwD6lE2k2bJ/YX4oti/+dA76PoX9/p99VMpmny5JNP4umhQ2Eqldp1oW8wiuIkJ+dIcnKOBCxxEwwuIRCYT3tgPoHAAtLpIO3tc2lvn9tZz+UajM87Bq9vDD7fWLyeg1CUfX+qQJIkKl0OKl0OLi3JBaAunmRuIMKc9jBzAhGqInHWRBOsiSZ4qaENAKcsMdbr4mCfi4N9bib6XJT0w4opj13luBEFHFWZzVhjLVNOPIGvGiJ8ucHaqG/hxjZCiTSzVjcza3Vzpg8wOM/NuDI/48r9jC3LYmSxD4e2gy97zbHFfwYgFYPa+daIzaa5Voi1QfXnVuggZzCUTsz46hwMxWORbG7KveWUe8s5b+h5mKZJfaSehY0LWdi4kEWNi1jVtoracC214VreWmetvHKqTkbmjMQZd6LYVcaddTRll1ximVNbS2z+fKIZcZNct47kmrUk16yFl14GQHa5cIwejXPcWBxjxuAcNw6tsPBrv3+BQCDob3okZgYMGMATTzzR40aLiooOSAW6u1AUJ9nZk8jOngSAaRpEomsJtFvCpj0wn1hsA9HoOqLRdTRsfh2wnJDd7mHdBI7HPQxZ3vfP+Clx2DjXYePcQmtqozWVZkEwyoJghAWBKAtDUQJpnbmBCHMDEaAJgAKbyhiPi7FeJ2O8TsZ4XZTZta/lb+SyqZlVT9aoiG6YVDWEmL+xlXkb2lhQ3UZNW4y1TRHWNkV4ZaG1KZ6mSIwo8jGuPIuxZX4OKvYxtNCDfXu+QJqzu7gxTWhZs0XYVM+F5ipoXWeFpS9Z5STZWiXV4YhccjBS0WhKPCWUeEo4Y/AZAISTYZY0LWFhkyVwljQtIZaOsaBxAQCfffYZAH67n1F5oxidO5rRB49mxIk3MthViN7WRnT+fOJLlhBbvITYsmUY0SjRL74g+sUXnd1QCwtxjBmNY8RIHAeNxDFyJGpR0T7n7yUQCL5Z9EjMbNiwYTebIeiKJMl43EPxuIdSWmrttJxMthAMLSUUXEowtJRgcAnJZBPh8ArC4RVQ/2KmrorbVYnHMxKPdwQez0i8nhHYbPv2HiQ5msqJuT5OzLWGEg3TZG00sUXgBKMsj8RoTKb5qDXIR61bNrbLVpVOYTPG42S018lAhx21j1NUiixxUImPg0p8fOeIgQC0hBMsqQmwuKadxZvaWVwToDWSZGltgKW1AcByuFVliSEFHg4qtuofVOxjZLGPbPdWAlOSIG+oFSZcbuVFW6F2AdQthLpMHKqHxuVWWPQfq5ysQt5wKBwFRaOhcBSewjFMLp3M5NLJgLUr8frAehY3LuadBe8Q8oRY1b6K9kQ7n9V+xme1n3WakmXPYkT2CIblDGPEt0Yw/OozGeypwFhfTXxpRtwsWUJi9WrSmzcT3ryZ8IcfbXlffj/2kSNwjDwIx8iROEaOwDZo0D4/RSUQCA4c9urZTDNnzuR3v/sd8+fPp76+nldffZVvfetbnfevuuoqnnnmmW51Jk2axJw5c/awpXsfmy2XvNwp5OVOAaypv0TC2t8mGFxCMCNy0ukA4UgV4UgVbO5aPx+PZwRez0hL6HhG4HINRpb3zeO5ZEliqNvBULeDi4pzAIjqBsvDMZaEoiwNx1gairEyEqMtrTOzLczMti1OrnZZYpjLwQiPgxFuJyPdDka4HRT3cRQn12PnuBEFHDfC8hszTZOatlinuFlSE2BFfZBgPM3KhhArG0KdIzhgORh3CJyRxT6GFXqpyHWhdV2u7sqBoSdaoYNgvbXXTVeRE22xDsxs/AqWvrilrDvfEjiFo1EKRzOkaDQVFaeirlQ5/dTTMWVrd+JlzctY1ryMr1q+Yn1gPYFEgLkNc5nbsGVKU5VVKrMqGV40nOEjhzP8xjMYbCvBtaaexMoVxJevIL5iBYm1a9Hb24nOnkN09pZ/l5LDgX34MBwjRmIfMgT70CHYKytR8vLEKI5AIOh39uo3WSQSYdy4cVx99dWcf/752y1z6qmn8vTTT3de22z7/hTKnkCSJByOYhyOYvLzTwY6BE49ofAKwqEVhMMrCYVXEIttJJlsorW1idbWWV3a0HC5BuJ2DcHlrsTtqsTtHorLNQhF2fdObXYpModkuTkka8vBiwnDYGUkztJQRuRkBE7MMC3BE44BbZ3ls1SFERlhM9Rpo02xc1gyTZGq9upLVpIkynNclOe4OHOsdV6SaZrUBeIsrwuyvC7IiswmftWtUeoDceoDcT5aucVxV1MkBuW5GVrgZUiBh6GFHoYWeBmY59oyVeUrtsLw08g8xNrfpmEZbM6EhmXQuhYiTbDuEytkUGWV420FKNEXkQtGMDp/OKPzRsDgs8HmIqEnWNu+lqrWKqraqjrjUDJkXW91HIPf7mdwwWAGDxtM5ZXfZrCrnAHN4F7XaImcFSuJV1VhRqPEFy8hvnhJt/pKVha2oUMsgTNkaCauRMnNFSJHIBD0mb0qZk477TROO+20nZax2+0UFRXtIYv2byyBU4LDUUJ+3gmd+boeJRyuskROeGVmaqoKXY8QiawmElnd4ZLS0RJORzlO1yBsdpmGhhg+3zCczoFoWvY+9aVjl2XGeV2M87oAy7lYN02qY0lWRmKsiMRZGYmzMhxnbSy+lR8O4C7iD19UkaUqVLrsVLrsDHE6OtODnPYeb/YnSRKlfielficnHbTFUTYUT7GyIdRN4KxpDBNN6qzaHGbV5u7LphVZoiLXxdACS9wMznczMM/NoFy3NV2VVWaF4aduqZSMQtOKjMj5qlPkSIkA3ngdVNVB1VvdDfYPwJ43nIPyh3NQ3jAomgKjrsF0ZlMfbaCqtYqVbStZ1bqKqrYqakI1tCfaWdC4oNMXpwO35qZyYiWDjh9MpfcEhkTcFNXG8VQ3k163gcSa1aSqN6EHAsS+nE/sy/nd++z3Yx8yBNugQdgGDsQ2sALbwIFo5eXI4g8YgUCwC/bNOYYufPLJJxQUFOD3+zn22GN58MEHd7pEPJFIkEhs2d8jGLR8K1KpVL+vsupob99fvaXhco3G5RpNYebVmaZBItFANLqWaGytFWdCOh0gFq8mFq/GZoNVqz/ubElVfTgcA3A6B+B0DMTpHIAjk96XhE6ZJlPmd3Oiv/sozrpYkpWROFXRhDVl1RqgVVYJpPWMf060WzsSUG7XGOyyU+m0MdBhY4DDRoXDRplDw96DgzYdCowv9TK+1AuUAmAYJvXBOGubIqxpDLMmE69ujBBOpFnXFGFdU4T3vuq+hDrLqVKR46Ii18XAXBcVuW4qcpwMzHXjLxgLBWO3FDZN0q0bWPjBCxxS4UVpW4vUvAqpZTVStAXaq62w5oPuBtt9FGUPoihnEMdkD8bMngAV5xHzlbBRj7IuuJ51gXWsz8SbQpuIpCIsaV7CkubuIzFyvkxRRRHlZ5cz0D6JoQEXZc0mufURHDVNpNeuJ1VTY01Vffkl0S+/7G6LLKMWF2MbWIFWMRBtwAC0gRXYKipQi4pIZ3aW2Pf/DfaN/ed3TN850Pso+vf12+4J+8wOwJIkbeMz88ILL+DxeKioqGD9+vXcc889pNNp5s+fv8NDLu+9917uu+++bfKfe+45XK4ebGH/jcdEksLI8mYkeTNyZ2hCltt3XtN0YBh5GEYeppGDYWZ3ibOBffPgzSQSTbJKg6yxWVbZLGs0KBqbZY2YtGOxIpkmflMn30iTlwn5Rop800p7TIPeSjvThGAKGqISDTErboxDc1yiPbnz1lyqSb4DcuwmOfZtY1sXf1xbOoQnXoc3EzrSrlTLTp+Rlu1E7IVWsBUQteUS1HLZpClsVHQaaKNJb6LZaKZFbyHJtvsJdSAhkSVnUaRnM7jVzoBmmcJWndzWBN7mMI6WNpREYof1TVkm5feTyskhnZ1NKieHVE42qWwr1j2eHe64LBAI9n2i0SiXXnpp/22atzWzZs3i//7v/1i7di0vv/wypaWl/Pvf/2bQoEEcddRRfTJ6e2Jma+rr66moqOD555/nvPPO226Z7Y3MlJeX09zcvFs2zfvggw846aSTDsil6Fv3T9fjxOObiMU2EotvJB6zRm9isY0kEvXAzj9KmpqN3VGKw16Cw1GKPRNb6VJUdc8ejLirn59pmjSndNbEEqyLJVgXS7IxlqQ6nmRjPEXUMLbT6hacskSpXaPErnXGXdPFdg1HD0Z2Oogldapbo2xoscLG1igbW6ywObTjL/0Oct02SrMdlGWmwkqznZT5HZT6nRRlOfDYVWs/nPaNSK3rkNrWQ9t6pLb1SK3rIViDZO68z6YzB7LKMLPKMXxltHry2Gi3s1Ey2WTE2RTbTHV4E5tCm4imozttS5UUhhr5jIxkMTDooKQVsptiuBoCKHWNkNz5X22S04FWWopaWopWUopaWIhaVIhaVGSlCwuR9vF/twf67xg48Pso+td3gsEgeXl5/bdpXlf+97//8Z3vfIfLLruMhQsXdgqHUCjEQw89xLRp0/pmdQ8oLi6moqKC1atX77CM3W7f7qiNpmm77YO0O9veF+jon6ZpOBwH4fcftE0ZXU8Qj28iGt2QETu1xDtDDel0iFS6jVS4jXB42Xafo6pe7PYi7LYC7PbCTCjqlrbZcpGk/l3yu7OfX4kNStxOjtkq3xI6aapjSTbEEmyMW0JnYyZdn0gRM0zWxJKsie14dCLfplJi1yhz2Ci12yixaxTZNQrtGoU2jUK7ijuzxFnTNEa7HYwuz9mmnWgyzYbmKNWtEWraYltCa4T1zSESukRLJElLJMmSmuA29QG8dpXCLAfFWQ4KfQMp8o2gqMBB0RAHRVkOitwSOckG5Pb11l44bRugfZM1VRWohngAKdYKsVakhiXIQEEmHNrxEJsXfCWY3iJaPPlUO91Uqwr1skmtkaAuFaIu3kxDpIG0qbNCamCFpwE8QMkWWyXTJDesMSTqo7AZhqWzKAiCvyWJqymE2hKEWHzLRoDbQ5JQ8nLRiorRigpRi4rRiorQiotQi4rQiopQ8/KQ9gGfnQP9dwwc+H0U/etbmz2l12LmgQce4PHHH+eKK67g+eef78yfPHky999/f2+b6xUtLS1s2rSJ4uLi3focQe9RFDtu9xDc7iHbvZ9KBbuJm1iXdDxeRyrVRjodIp0OWQ7JO0CSFGy2/G4ix2bL6x40K1aU3TetJUkS+TaNfJvGxC6rqzqI6Qb1iRS18SS1iSS18dQ2ccwwaEqmaUqmWRyK7fBZHkWmyK5RYMsIHZtKYSbdkZdvUxlZ7OWgku5/vaRSKd5+expHHX8SDaFURuREuwieKLVtMUKJtBUaw6xpDO/AEmsFVqHPQZFvHIVZk8j32MkvspPnsVFkT1JsNpGrN5KVqEcNboLAJkvwBDZZq62SIWiuQmquIg/IAw7e5uUqpD2FNHnzqfVkU2d3Uauq1Es6dXqM2lSAhngrzV6dZm8ACgEC3ZpQ0zJ5QShoNxkQdlAedVAYVskJmvjaEzhaI8gpHb2pGb2pmfjSpTvss+L3o+bno+bnoebno+RZsZqXn8m37skezz7jMyYQfNPotZipqqrimGO2/jvVOo+pvb29V22Fw2HWrFnTeb1+/XoWLVpETk4OOTk53HvvvZx//vkUFxezYcMGfvrTn5KXl8e5557bW7MFexlN86FpPrzekdu9n06HSSQaSCQ2d8bxLulEYjPJZDOmqWfyGnb5TEXxZARO7raCx5aLLPuRpCZSqQCqmoO0E/+Y3uJUZAa77Ax2bV9QmaZJW1q3xE4XgVOXSLI5mWJzIk1DMkVUNwjrRudRDzvDLkvkaip5mkquTSVXU8lRZZrtPsLBMIUOO4UDfBxUmU2eTcPVZZVWOJGmIRBnc9BaQm7FMRoCic68lkiClG52CqEdowLlZDkHk++1hE5+mYMil8EgtZViuY08o5VsvRlPsglXvBEt2oAUaoBwA5g6aqiO4lAdO/qzRQdaFZnNDi/Vqov2rByabA4aVZXNGGy2JWm0RWjISbKEJGztu2OCN6aQF4TcoEluCPJDEsURG/lhmeyAjqc9gaKb6O3t6O3tJHYyIgzW3jpqXh5qbi5KTg5KTjZqTg5KdiadnW3lZ+eg5mQjCx8+gaDf6LWYKS4uZs2aNQwcOLBb/qefftrrgyW//PJLjjvuuM7r2267DYArr7ySxx57jKVLl/Kvf/2L9vZ2iouLOe6443jhhRf2mRO5Bf2HqnpQ1R2P7AAYRppkqrmb4OkQOVZo6YxNM4muh4nFwsRiG3bYptsDs+f8CklSUNUsNM2fCdloapd0Z+ia5+/z0RGSJJGjqeRoKmN28nEOp3Uakik2J1JsTqbZnEjRkEzR2BlboieiGyQMk7pEirrEVr4kjmxeWV23TdtOWSbPZomfbE0hW1Pxqwr+HIXswizGqzn4NZVsVcGvKbiRScTSNIfjNAQSNATjNIcTNIcSNIUTmXSS5nCCtGESiKUIxFKs2eZMTHcmlHd5H+B3auS6FQY5olTYApSr7RTJbeSbrWTrrXj1NpzJVmyJFtRYM/l6kvxIgNEEIFC/9UOs9ydJNKoKDYpKo6bSZPfQYnPSomk0u2VaPCaritMEzY53tuXdSaaMOwbZEfCHTfzhLensMGRHJXIiMv6wgSNuYMbjpGpqSNXU7PgH2gXJ4UDJ7iJycrJRs3NQsv3IPh+KLwvT48axaRPJjRuRcnNRfD6xs7JAsB16LWauu+46br75Zp566ikkSaKuro7Zs2dz++238/Of/7xXbU2ZMoWd+R+/9957vTVPcAAjyyoOexEOexEwboflTNMknQ5tETmprkKnq/BpIhZrRJKSmKZOKtVKKtXaK5sUxYWq+lBVbyb4usRb8rVu11vSsmzf6dSER1UYoioMce18E8OobtCSStOcTNOcTHWmGxNJlqzfiKOwiNaUbuWn0iQMk5hhsCmeZNN2TjDfGT5Vxq+q+B0K2R6NrHIHeYrCIFXGpyp4FBk1bWLGddKJNImYTjyWIhxNEYymCERTtEaStEWTtEaShOJpTBPaoinaoinWIAPZmTBoB1aY+IhSpIYokdoY6E5QrIYpVILkESCbAH6jHU+6lfJkK4NTUYgDoe1PoSWBVkWhRVFoUWSaO9MKLW4bbVkajYpClSzRLpkkuzm7y9hSEv4w+COQFTHxRckEK+2NbUlnRUHTwYzHSdfXk67fvhDrYABQ/ehftzzN60Xx+ZCzfChZWSi+LBSfD8WfZYkgrxfZ40X2uFE8HmSvF9ntQfG4rakwdZ/fkUMg6DW9/lT/+Mc/JhAIcNxxxxGPxznmmGOw2+3cfvvt3HTTTbvDRoGgV0iS1Dmt5XbveLSw4+j6U089EQiTSrWTSrVtP053zbMCGOh6FF2P9mjaa/u22jICx4OqeFBUN4riQlHcqIo7c+1GVVwomftq5r6Sua8qbjTFTanNTbmj+9RFKpVi2ooFnD5qUqcznWmahLuJnzRt6TTtKZ32tE5bKk17Wqc9ZeW1ZdIh3VrJFEwbBNNJquO96SidAzJO2YZXdeJTFcoUBY8sY9dN7GkTJWVA0sBIGqSTOqlEmmRcJx5PE42nCcdSVpxIEzTdBNNuVlEE7Tt/vJ0kWUTIlkJkS2H8hPFLYfLkCAVqhFw5TI4UwW+GGJIKMSEZwqWHUNC3acsEYpJEuyLTJsu0KwptskxAkWlzK7T7rPw6ReGrzP2QIhPrEK2miSPZIXC2iJyOtCcOnji4Y+CJm7gz186M5jRCIYxQCGprt7GtRzgcltDxelG6ih6PF9njyVx7kd0uZKcTyeVCdrqQXS5kl9OKnVYsOZ1IvViRJxDsLnolZnRd59NPP+VHP/oRd999N8uXL8cwDA466CA8nj27rFYg6C9k2YamWc7EPcU0DWuFVqqdtB4inQp2OjCn08FMsNKp9PbvgYlpJkmlWkjtYn+XnvfFnhE6LhTFiSw5cDqjLF32KqrqQpGdyIoDRXGiyA5yFScFHXmaE9nhQJGt+3KmjKJ4kGUnhmQnbGgEDambyGlP6wQzIZTWCaYNK9Y7rq28WGYpe8wwiSXTNCbT2++EAjgzASUTtprOM01Im5A2sKV0XKaM3QCbbqKmQU4bSGkTM2VgpHT0pEEq6aM2WcC6pE4qlVlirtN1ZmkrTLzE8EshfETxSVF8RDJxFG8m9klRvEQZTJQsOdp5300UhS1L2ZNASJYJyTJBxYpDqkzQLxPMkQnJEiFZZkOmTNeyQVnGNMCVAE8sI3biJp4YnWKnQ/i4EuBMgDNh4kqAK2ld2ztedzyOEY9jNLfsuOu9wHTYMB12S9g4HcguN4rLheJ2o7jcqC4Pistl3XM4kOwOZIe9MzZUFdeq1cSKFpB2e6x7DgeyfUuM1rcz1QTfHHolZhRF4ZRTTmHFihXk5ORwyCGH7C67BIJ9GkmS0bQsNC2rT/VN00DXI5kl65a40fUIejqCrkdJ62Fr1CcdIa1HrHt6BD0dzVxb99NpK9/M+HwYRgLDSHSbLlNUaGvbwfLkPiBJCrJsR5NsFMl2imUbsmxH7hbbkDU7sn1LniE5iOMiiouo6SQqOYmYdqKmnYhpI2JoxE2NmKkQNRVihkzUkIkaElEDooZETIeoYRLRwdAk0GSSTnUnW/PtAMME3RJDUkectgSSpBuWUNJN2tJe2tL5yLqBooOsm1Y53cBMG5gpEyO1o713TNzEuwifCB4pjps4LimOhxguEnikGG7iDOi4RxyPtOWeK5NnyDpRWSIiyURkiYhLJuqWiMjWdVSSicgyjZkyUVki3OVe3JTQ0xJmUkJJyRnRYwkeZ9ISQa6EiTNhpR0psCfBnjJxpMCRzORl8jvGY6R4EimehPaQ9WozoTdCqQyo/cc/dnjflCQMm4ph1zBtGtg1sNsywW7tF2SzIdtsSDYbss2OnIkVux3F7kCxWbFqc6DYLZEk22ygaZ31ugVNy8Q2JFumjKYJYbWP0utppjFjxrBu3ToGDdrRXLZAINgVkiR3+tM4um6g0kcMI5kRR9GM0Img63GSyTDz589m7LiRSKTQjRiGHkM34uj6lvSWOI5uxND1OIYRs8pkynZsimiaOroeBaLsYGxll6iALxP6ggmksBHHkQlOEpKHpOQmIblJSC4SuEjiJCXZSeIgKdlIYs8EjSQ2EqaNJCpJNOKmStJUSZgqCRQSZg9/PZoZYaSbSMaWNLpJUjdp63bPsNJdyki62SmuuudbZTFMJAM00riwBI+7iyhykMRJEqeUwGH1FC8JCqQkTjJ5UqYMCRxSEhtxkFOgJS11IqcxpBRJGeKyTFySiEkSMVmiVZKISzIxWerMjyORNmT0tISRCaQlSElIuoSUAltK6iKCTGwpsKXZEqfBlja3ut5y397lwyWZJkoihbK1c/tO6BBVff2M7rRtGQxZwlAkTEW2YtlKm4qMqcqgyJiKgqlIZKVSfPnPP1v+SqoCHbGiImlqJl9DUlVLRGkakqohq5aAkjUbsqYhqxqSomyJFauurGwJkqqiKCqSoiKrKoqqbbmvaiiZusgKkqqALCMpipWnbHstyTKoqhV3XO+D9FrMPPjgg9x+++388pe/ZOLEibjd3ffY6O9ddvdVTNMkmUyi6zrJZHKnjsz7K6lUSvRvvyLjR6Pkd+akUikSiTA52ad8rQ2tTNPMrBCLYRhJDDOBaSQzI0FJDCOJaXa9TmCYmfyOPDOBYaQwjC51O8tsdW0mMYwUppnGNFMYhhWbZhrDSCGhYyOJjSQ+MpsAmuxqE+re9RlLMCU7g71L2kYajRQqKWykFI20opJC68y34p5cZ9roTG9VRrKBaRI3IdQhfIwOcUT3646g0/26R+UNNCOFU0/iNOLYzRR2I4ndTGI3U9jMFHYzic1M4TAti51SGnvGejspbFIKu5zCZk+j2RPIcgpFSiFLSSQphSSnQUohSTrIaXTJJCYZRCQdXTLQJRNdNklJpjUDaICuS6QNCUOXMXSpM5CWkHQs8aQDhpWWM9eyDpJBZkRNQtUtx2s1E7R0JtbNLffSVrx1OWWrz5VsgGxkpjrZ0cjcFqzx21C/fS639eTas+gyGJIl6kwJymR47b3H+fbf3tlrNvX6OAO5iyrrOtRmmiaSJKHre/s1dycYDJKVldWj7ZB7QzKZ5KGHHuq39gQCQW8wkSQDSTKQZSuWZAM5k7fD/J2krXpm9xhzqzwTuqS3qUP3PHZUbjt12O5968vLQEZHJY2CjoKO2hlvnZfexf2t89K7aMtAyTzfig1kdFNBN2V0Y0tsGJl8Q8Y0ZXRDxsjEpiGjmzKmIWGYEqYhY3SkuwTrWgbTRDYMNDONZqQycRrVTGMzUqimjs1IoZk6mpm5b2bud4m35OtoJJFJIpFGQkeS0sikMTGQSIOURsLAlAyQ0piS3nltmDqKaYChIxsGkmkgGyaSaSCZljCUTLNL2rDElWGNKmGYZD46YJjIBmBiiTGzIy11y5MMyapvdIgnyWrTtK4lk8425S6x3BFn0srW+cZ27m3nurfMPcTNVc9+ueuCvaA339+9Hpn5+OOPd11IIBAIdisSpqlgmgq7OCJrP8f6prP+bsyIqU6BtSWNZCJB97wu5VRMNMn6htpS3kSSkkCiWx4SnSJuS565VR7b5IFVj857GfulHeV3pOnWJnJGwEmS5SuDjImEIcvW25BkjI6FYVhlIONXI0mdVpiSlLnfNS1hImOiYEo29I77SBiSnKlr5VnHxEqZ6SoJs0PUmVKnqDM70qaUEX1gmBl7kTNpuqS7CDcythiAafXfNEAxM+/TkJBNA5MOsWK9Q9m0fraW4DAyo0aWkJLNzM/LNCzLTcsSybSssM5Ws6Sp9dKMLT3srIMl+AwT2Ux3ijbZ3CLkJNNE1nWrB4aBZBjgKOrzp7w/6LWYOfbYY3eHHfsdmqZxxx138N5773HKKV9vCH9fJZVKif7t5/RXH03TcpI1UxnH185gZoKBqRtWmc58w/IR6ainm1vidCa/My9TxjCh4zrjO2LqBhimVbbDJ+VAmBXsJ0zTxPrPyEyXZq4z+daqOWNL/jb3epJvdKa73cvkk/m/dc+K2ZKTMbRbqS13Op9Jt/Y7+0eXPnXmde8/mREsunw0trRp7QzQmW92b2OLDOvIkawyEt3ytjxU6nIpdW+MLmW2N+nR5d1YNqW3CJmMcZ0Cz+xqYZf30ZnsquLNLv3sKCBn8uTOEt0synTS7Na+iSl1tGR0s9dEAmnLZ6DzWSYgmaR8kW37uwfptZiZOXPmTu9v76iDAxFJkrDZbCiKgs1mOyC/DCVJEv3bDzAzy5SNRBozaWAkdMykFYxYkrw2J/qydkxDwkwamCkdI2VYZVJGl6B33t8mL21s5xf27kOCLb/Ud5HbiWLN3yODmfmla0pG5mvesP5v6hhmR6yjG2l0M42upzEMK+6aNjAwTSMTdwgGozO/4wt9y7VVzthlue7XRqcw2Pp6W3FiZr5kMuMiIMlW55EzR3JkrjvTMkgyUqYMW5WRupShs4yyVX7mxXY+z7qWkOmYC7OmWUDOCCfZ7DJa0BEbujXOYmT+ujeNzjpWfSNTvut9Kw/TmnMxM1M/JjpG5tqQdGs6SNqS1uWOayutSzqG3HFtOdmYkuVYY2bmbaSOuSFZ7zLVZ837SB15smF94UtG5iNpYipSJi1nRoJkaxRIAlNSMmkZU5JBtmJTzvyMZBUJBVNRARVJVjBlFUlSQdaQFQ1J1pAkFVnWUCQFSdZQZAVZyjj2SiqKYuUpig1ZtpyAFcWGqmjIsoKaaUeRNWRVRZaVTNsKspppU1GRFAWli0OxLKsoGSdjq00ZVZaQZQlVljD0NB9/9BEXnH7K1/1n/7XotZiZMmXKNnldfWf2NZ8ZgWBfxDRMzISOEU9jxNJWOqFjZoRIR9pIZvI67ne51zUPY8dKYzAegqvX9Z/xsoSkykiqFaPK3a53lGcqWALCTKPrKXQzTTqdJKUnSaUTpNMJUukEqVSCdCpBKhUnmUyQSsZIJGOkEjES8SjJRIxEIoph6BjoXb70dx+SpCDJKpKsgaQioWKiYJoypqlYQkHK7IkjdVzbO69BQUK10lLXMkr3MpK6nesO0ZARGqaEYqaRjRSysf1YycTSVteykUIyO/KSnXWscmlkM41kGphmGl3W0aVMLOvospVOyzppxYpTSpqUopNWTVIKJFVIKZBSIaVImRh01czoJzPTPcsxQ5ZNFCkTy6BIJqpsomZiRTKxSab1BiQZyZSRTAXJUMFUMVAxDBXTVDFRMUwF09QwDAXd1JAkDUmyI8l2ZMmGpNiRFSc2xY6k2kG2g2oDxYak2pBUO5LqQNJsyKodRbMja3YUzYGs2VE1O5pmw64p2FQZm6KgKZKVVmXsHXmqhE2x8iTT4IP33uXMM07fr/9o2hGpVAq3Bg5t7x6z0Wsx09bW1u06lUqxcOFC7rnnHh588MF+M0wg2JcxDdMSIbF0pyAx4jpmZzqNGdc700Y8jRnTt6QT+m4Z6ZA0GcmuINkUZJslKlqDbeQW5aPYVeu+JiNpCpKtS1qTrTqd97uWse4bskkyGSUeDRMPh4mFQ8TDIeLhMIlohGQsQiIaJRG24mQ0QiIWs+JolHRy5wdl9rnPiobD6bK+eBQ7smLtc4OkYaJavjW6gq4rGLqMnpYxdAUkLSMWVEugdE1jiRYyeT06hNQ0UcwUipFC1pMo6bgVG0kUPYmciRU92pnuzDOSVtmu6S5lugqVlGIQt2EFDRIaJDWJhApJzRIUiY58dcu9Ldcd9yUM1USSTSTVRFYtUaEqJjbZxGkYOEwTp2niMMxMOpNnmLhNE7thIpmqJS4MG5g2dMNG2rCTNh2kTDtJw0EMJ7HMUvg4NlKSHUN2gOrAVB1Ekzoebx6S3YVpd2HanGB3ITlcGDYXms2G06bgUBXsmoxDU6ygdkl35GfK2FV5n9gPJpVKIe99Mw54ei1msrKytsk76aSTsNvt3HrrrcyfP79fDBMI9hRmyiAdTWBEUhjRNEa0B3Es3T9iRJWRnQqyXUWyK8gZIbLd9NZ5W92TbArSVr81U6kUc6ZN4/TTj+52nEEyFiUaDBANBIgFA8TCQeKN4Yw4sQRKZxyx8pKxnZ2U3XM0uwO7y4XN5bZipwtFc6CoTiTZBmhA5q9rXcVIKaRTCqmkTCqRCUkZa1dgtfMLSwd0nZ2vW5VA1qwAoEgGqqSjYo1UqOkYSiKAnAgjx8PWtZ5ATcdRdCuoegIlnYn1eOc92UhtdxIspUDUAREbxOwQs0mWEHFBrEOU2CBuk6xrzSoX16y8uG1LuaQGTmRcpoHbMHEbBi7Tit2Gmcm30nmmgStTxt2lDIaGadjRUy7iKSch00kYJyHTRZiOtJMIDqKmnXYcJCQH2NxIdjeyzYPi9KA5PNicbrxODY9dw+tQcdsUXHYVt03Fa1Nw2RRcNhWXvUvapqB1ObG941iR008/MEcuBHuGfjtxLD8/n6qqqv5qTiDoM6ZpYsbS6OEUeiiJEU5hhJPo4RRGOIWeSeuhBOOD2TTO/qLPz5JsCrJTRXJYsexQkR0KUmdaRXIqVjqT17WspH79DahM0ySViBNrbiYaCBANtncKlUh7G5tXLOe1pV8SDwWJBtuJBQPo6T5uJSZJOFxuHB4vDo8Hh8eL3e3B7nJhd7mxu9zYnC4k2Q7YMHQNXddIJRVScYVkXCYRNYiFU8TDSSLhFG3NvZ+a7viDW1ElMJN4nAqanEYzEqipKEoyjBoLooTbkIPNKNEAajqKlo6hpqMZkRLP+HfsnJRdIeZUiDokwg6DoE0nYjeJ2DMixS4RtUPUIVt5domIg0wepNQtEkcyTTymiU838BoGPsOKvYZBtmEwwNiSb5Ux8cYNfFEr325ACBcB000AN+2mhyBu2jPXAdNNI65OYRIynYRwkVTcKE4fmtOLz+XE59TIcmr4XVbsc6h4HRplDhWvQ8Vr1/A4VDx263pfGeUQCHZEr8XMkiVLul2bpkl9fT2//vWvGTduXL8ZJhBsjakb6KEUejCBHkiiBxMYwSR6KCNUIimMUBI9krJWvPQApeNvaRlkp4bsVpFdWiaonbHS9dq9JV9Svr4Y2WF/MyMo4dZWIu2thNtaibRtFWfy04mdT+Fsb7suzeHE5fPh9GXh9Pq2CBS3F4fHizMjVjryNYebdEolGkwTaU8QaU8QCyWJBpJEQ0laG5NEg0lioZS16oiOYZJdn0gpSeDwaDjcGnY72OU0mhlHS0fR4kHUaBtquBmlvQm5vRGpuQ41FkI2ey7KTFkm5bGTyLYRcNkJOW2023VabEla7CnCTgg5IeSUOtNhB6TVzMqOTqyfud2U8Jsm2ek0fj2FXzeoMAyydQO/ruOPGvhDBn5DJysjUjyG2XkMQMS004aXFtNHq+mlFSuuNb0szwiTdjxdhIubiOTC73KQ47aR47aR67HibJcNv8tGoVPD79TIyogUv1PD59T2uj+DQLC76bWYGT9+PJIkbbNj6uGHH85TTz3Vb4YJvlkYiTR6ewI9mOwUKlY6EwcTGOFUr6Z2JIeC4rEhezQUbyZ2a8heG4pHw3DIzPzyM44/7URsXsce/cvTNE1ioSCh5iaCLU1W3NxEqKWZSBex0hs/E1Wz4czKwuXz4/L5cGX5sXu8bKyt4+DDJuHJycncy8Lp86HZHZ11k7E04TZLoITb40TaE2zelCTSFifcHiDS3kgs1LtjCR1uDVeWDafXhstnw+mSsZsxbGlr1EQNt6AENiM31yM11qB/1Ui6uZnebBxjup1EHCpmfhZRr0a7G1ocaRrscWq0EE2OFAE3BF3W1I0ppdj+qUEyKhK5kkquAYNSKXKTEXLDaXJ1gxxdx69bwiRbN/AbBs6tfgfGsdOIn0Yji1bTEinr8dJq+mgxfRnhYl234iWOHUmCXLedfK+dvIwwyXHbKHbbGOW247PLrFo6n9NPOJbCLBdZTg1ZOGAIBNvQazGzfv36bteyLJOfn4/D4dhBDcE3nY5pn3R7Ar0tTrotE2eu9fYERrSHf2HLEorPZoUsuyVSMuKkM/bYUNwakrbzUZNUKkVymWFNE/WzkEklE4SamzNipdFKt2QESyakUz07HtHucuPOzsGTnY07OxdPdg5ufw6enBzc/mzc2Vas2bcVZKlUisDb0xg08WjiQZ1gS4yG9XGCLe2EWuKEWmIEW+Kk4j2b6pFVCY/fjjsTXD5bZ3CoOlq8DS3YhNJch95QR6qujtSSOlK1tehbLR4w2cG5OYqCmp+HmZtNIsdNKMtGmwcaHUnqbBE2KO2sl9todaVJqR3iZEf+PBJe2U6JbKfQlMhPp8mLR8iNB8lNJcnTdXJ1nVzdmtrZ3qcgJGfRLOXSoPtYoWfRaPppMv00ZkITVhyxjvkGwKHJFPgc5HvtFHgtsTIkExd4t+TnuG2oOxndS6VS6BuhMt8t/EkEgp3QazEzY8YMLrroIux2e7f8ZDLJ888/zxVXXNFvxgn2H8yUTro1Tro5RrolTjojUjrEi5nY9Zel5FAsgeKzofjsKFmZuEO4+GzIbm0bJ9e9gWmaRAPtBBobaN/cQGBzQyZdT2BzA+G21l03Arizc/Dm5uHLzcebl483N98SKdk5ePw5uLOzu42g7IhkPE1zTZhgc4xQS5xgS9yKm6O0bfbwr3fn7LINu0vF7bd3Eyue7C7CxSWhtDWQqt5EsnoVqU01pOZbQiVVV2c5C+/iGbLbjVpchFpQiJ6bRdhvo9ULja40mxxR1msB1srN1Mc2o5stO28LhQKbD0/CZKDTQ5GuU5CIUhBppTDcQmFaJ1/Xce3gxBYTiZCaQ7OczxI5m/VpP9V6DvVmLvVmDg1mDk34SW31a9JtUyj2OynOcjAwy8ERWVa62O+kJMtBYZYDr73/BbJAINgxvRYzV199NaeeeioFBQXd8kOhEFdffbUQMwcwRlJH7xQsGdGSSeuBXY8yyG4NJduOmu2wYr+j27Vs7zd/9H6hQ7C01m6ita6WtvpaS7g0WuIlldj5V7dmd+DLL8Cbl2+Jldw8K52XjzevAE9OLmov/tpOJXUCjTECjVHaG6NWuilGe2OU6E7fv/Wl6vLZ8OY68OU68OY6u6QdeLIdaHYFIxYjWb2JZPVGUtXVJL+sJlldTXv1RprrG7a/q2kXlOxstJIStNJS1JJikvlZtPoV6rxp1jlCrDM2Ux2spjq0lFh6q9GUrVYiabJGsTOfEs1HqaRRkkpRHAtTGmqkpK2GvGR8p7/A0oqLgKuY9XIhG9O5rI5nsTbhp87MoYFcNpvZ2wgVSYIin4PybBeH5zgp9TspznJS7HdYgiXLic8hhIpAsK/R62+PjgMlt6ampma7y7YF+xemaWKEU6Q2R0k0hChf56L1qeXoLXGM4M4Fi2RXUPOcqLkO1BwHit+Bmm1HyXag+O3Itn3TCTGdTNBeV2MJlroaWuszcV0tyVh0xxUlCW9uHv6CIrIKi/AXFpNVUIi/sBhfQSFOr6/XX3qmaRJpT9BaH6GtPkprQ4T2hiiBphiR9p37zzg8Gln5zm5ixeXXWLB0Dmd862ScbkfnM/SWFhJr15FcspbE2nWE1q0jsW4d6YaGnT5DdrnQBlZgG1CBrbwMrbQUigpoyoL1zhBr47WsC6xjQ2AD1aHZlmAJY4Wt25JkStwllHtKKFVclBpQnIhTGmmjpL2OvJYNyKm1O35XskbUVUx10kfAM4h16Xy+ivpZGvGzySygFS9Etn3/OW4b5dlOxuW4KM92UZ7jzMQuSvwO7Oq++TkVCAQ7psdiZsKECUiShCRJnHDCCajqlqq6rrN+/XpOPfXU3WKkoP8xDRO9PUGqMUq6MdoljmHGt3gyFOAgRbDzWnKoqHkO1FznFuGSScuuffsv1nQqRWvtJpo3baR500aaNqynZk0Vf/vvkzsccZAkGV9BATnFpWSXlOEvKs6Il2J8+QW9GlnpimmYhFrj3URLW32E1vrITv1X7G4Vf4GLrAInWfku/AVOsgqs2O7qbotpmsQ2biSrZiWh/zbStmEDybWWaDECgR0+Q/Z6sVVUYBswAK1iQCZdgVFaQLXSzqrAOtYF1rGufR3rAp+wqXoTurl9m2VJpthdTIWvgnJ3MRWSnYpUmvJIK2WttWibVkL7bHbk2W1KCrqvnICrgjqlhNXpQhZF8/i8PYt18WyMaMbfJNi9XpZTY3yem8F5bgbluRnYJfbsYyOAAoHg69Pjf9Xf+ta3AFi0aBGnnHIKHo+n857NZmPgwIGcf/75/W6g4OtjJNKkGqKk6sOk6iNWaIhgJnewakQCJceBkuegOlTPsEmjsBd7UXOdKO593wnRMHQCmxss0VK9sVO8tNXXYu5gpYzD7SG7pJSckjKyizNxSSn+opI+C5YOErE0LTUhmjaFaakJ01wTpq0hQnoH71+SJfwFTrKL3eQUu/EXWuLFX+DCsYP3b0SjxBYvJ76yikTVSuJVq0hUVWGEw5QB23ifSBJaaSm2ysHYB1dirxyMbXAltkEDkbOyqIvUsbKtiqrWKqpaP6ZqfRV1S+t2eGyAR/Mw2D+YwVmDqfQNYpBkZ0AsRGlbDbbmNbB8DrRtZIfL0Vx5pHOH0eyoYINZzPJkPl+Gcvm8xU375u0LZE2RGJLrxpUOcsSYSoYU+BiYETDZbtv2nyMQCA5IeixmfvGLXwAwcOBALrroIrF6aR/ENE30tkRGsFjCJdkQQW/ZgW+HIqHmOdEKXKgFLrQCJ2qBGy3PiaTJpFIpZk9bx7iDC/bZlRTpZJLmTRtpXL+WzevX0Lh+Lc2bqne4pNnudpNXPpC8AQPJLiljdU0tp513Ab6c3K89qmSaJqGWOM0ZwdK8KURzTZjQDt6/rEpkF7o6RUt2kZvsYhf+AhfKDjbTM02TdF0d8aoqElVVlnhZuZJkdfX2R5dUlURuLjljx+IcOgRbh3AZOBDZ6SSejrO2fS1VbVWsbH2HqjlVrGpbRTi1nXkhwG/3U+mvtESLv5LBnjIGJ1MUtFYjbV4GK2bD5ichtYPpOVcuZv4IIllD2aQMYGmqhM+D+cxrlKldvf0VSZoiMSjPzdBCL8MKvAwr9DC00ENFrhsM3do99sSh++xnVCAQ7H56Pd565ZVX7g47BL3ENE30QJJUTYhkTYhkTZhkTbjbFFFXZK8NrdiNrdiNlglqngtJ2XenhbYmlYjTuGE9jevXsHn9WhrXr6WlphpjO4ebqpqN3PIB5JVXbAkDBuLOzukULalUippp03D5svrk2xJsjtO4McjmDUGaNoZoqQ2T2MESc0+OnbwyL3nlHvLKPOSWePDlOZB3seleavNm4suWEVu6lPiyr4gvW4be3r7dskpeHo7hw7EPH45jxHDsw0cgl5fxzgcfMOr00zFkg6rWKpY1L+Sr+c+yvGU56wPrtztFpMoqQ/xDGJ49nOE5wxmePZwhrmJy2jZC3SKoXwoL34GmKtjeFJPmgsLRmIWjaXFXslIvZV4kn3lNKss3BWmv6rrXyxbhOSDHxagSH8OLvAwrtIRLRa672/b33d6PIQ62FQgEfRAzuq7zpz/9iRdffJHq6mqSye5Ooa2tPVuSKugdejhJsiacES9hkjUhaxO5rVEktHxXp2DpCIpn/xp2Nw2D1rpa6tdUUb96JfVrVtFcvWG700QOr4/CQZUUDqqkYNAQ8isG4S8qQpb7z5EzGkzSuCHI5o1BGjcEadwQIh7Z9v3LikROiZu8Mo8lXso85JZ5djg91BW9vZ3YkiXEli0jvnQZ8WXLSDc1bVtQVbFXVmIfPgzH8BHYRwzHMXw4al7elrYMnXWBdSyunsY70Xf4z7v/YXX7atLGtmIr257NsJxhjMgewfCc4QzLHsZgbwVayxqonQ9rvoCax6BpBWxv+39XLhSNheKxhPwjWWZUMKfdz4KaEIvntxPsFNhb/HRUWWJooZeDin2MKrHCyBIfPocYXREIBL2n12Lmvvvu48knn+S2227jnnvu4e6772bDhg289tpr/PznP98dNn7jMA2TdFOUxIYgyY1WSG9vqkIGrdCNrcyLVuax4kJXv5z3s6eJBgPUr66iYU0Vdaur2Lx2NYloZJty7uycTtFixZV4c/P61fHYMExaasLUrw1Qv7adzeuChFq3ff+yIpFX5qFwoI/8Ch/5AzxkF7l3OEXUFdMwSG7YQGzBAqILFxJbuIjkunXbFpRl7EOG4BgzGufo0ThGj8Y+fDiyrbs4DSVDLKn9jIWNC1nUuIglzUu6L33O/I2Rbc9mdN5oRueNZlTuKEbmjiTfmY8UaYZNc2DdPKh5HOoWQmrb94+vFEomQPE40gWjWCUNZnaTnUU1ARYtbGNTawxoywQLuyozqsTHmNIsDirxMaoki6GFHrFqSCAQ9Bu9FjP/+c9/eOKJJzjjjDO47777uOSSS6isrGTs2LHMmTOHH/7wh7vDzgMaI6mT3BQiuSFIsjpIYmNou9NFar6zm3CxlbiR9tMzV4LNTdSu/IqaFcuoXbmclprqbcqoNjuFg4dQPHS4FYYMx5ubt53Wvh6phE7D2hD1awI0rG2nYX1w2xVFEmQXuigc6KMgE/JKPSi72GW4AyMeJ7Z4CbGFC62waBH6dlYU2QYOxDF2TEa4jMExcgSy09mtjGma1IZrO4XLwsaFrG5bvY1zrkt1MTJnJM6Ak7MOPYtxheMocZdYu860bYDqObDwZdg4G1pWb2u0zWMJl7JDoPQQYgXjWNjm5IsNrcxb3crCj9qJJrcVYEMKPIwr8zN+gJ8J5X6GF3l3OE0kEAgE/UGvxUxDQwNjxowBwOPxEMj8Qj7zzDO55557+te6AxQjqZPcGCSxNkBiXTvJmlD3c+wASZOxlXuxDfRhq/BhH+BDdu6fS0pN06StvpaaFV9Ru2IZNSuXE2zavE25nJIyioeOoHjoMIqHjiCvvAJZ6X+xloylqVvTTvXyFjbPd/HP9z7fZvbE5lAoGpxFUWUWxZVZFFT4sPXi/RuJBLGFi4h+8QWRL+YSX7wEM9V9WkpyOHCOGYNzwgScE8bjHD8eNTt7m7ZM06QmVMMXDV8wt2Eu8zfPpzHauE25Mk8ZEwomML5gPOMLxlOZVYmhG0x7+21OcpSiLZ8GGz+H6tkQqt/W6IKDoPwwKD0Eyg4h4BrE3I3tzNvQyhfT2/iqdilpo7tgynJqHFKRzYQBfsaXZzO2PEtMFQkEgj1Or78dy8rKqK+vZ8CAAQwZMoT333+fgw8+mHnz5m1zxIHAwkwZJKqDJNa2k1gXILkptM2pzkqWDVtFRrhU+NCKPfuVc+7WhFqa2bh0EdVLF1G9bDGR9u7n8kiyTMHASspGjqJ05ChKhx+Ey7d7Nl1MJXUa1gaoqWqjtqqNxo2hzKnOAJZY8uTYKa70U1yZRfGQLHJKPL060M9IJoktWkT0i3lE584ltngx5lb+ZGpBAc6JB+OaMAHnhAk4RoxA2sEKnIZIgyVe6ucyr2Ee9ZHu4kOVVEbmjmR8wXgmFExgQsEE8pxdRq3aNsLCZ5HWfswpqz5CW7TVRiyyZo26VBwBA46A8knEtSzmbWjlszUtfP55M8tq17GVdqEky8Ghg3I4dKAVhhb07j0JBALB7qDXYubcc8/lo48+YtKkSdx8881ccskl/OMf/6C6uppbb711d9i432GaJqn6CPFVbSRWtZGoDkJ6W/FiH+zHXpmFfbAfNWf/Xuoej4TZtHwp1UsXsXHpYtrqarrdVzSN4iHDLfEyYhQlw0Zgc7p2iy2GYdK4IcimFa3UrGyjYX0AY6v3n5XvpHhoFo2R9Zx6/rFkF3h20Nr2MU2T5Jr/b+/Ow6Ks9gCOfwcYhkUWQRCQRcBdEcV939I0t9LM0tJKK1u0Mm/ZYmqZlZm3fblZadvV7KqZO+a+7wuoCIqgKDuyL7O8949RlFSYQbbB3+d5eJ6Zd+acOec9M74/z3uWGHJ27CR3507yDh1CKSw5HdzGwwOHTp1w6NgBx06dUPv733ZsT1ZRFnsv7WX3pd0cSDxAfHbJ2242Vja0rteajt4d6VC/AyEeIdjb3HD7KTcVIv4H57ZB7DbjbSTACrADFLUDKv/O4N/VGMD4hKGztuN4Qia7Y1LZte0Mh+IzKNKV7KIK9nCkU5A7HRu60b5hXXzrVk6bCSHEnTA7mPnggw+KHz/44IP4+fmxa9cuGjVqxLBhwyq0cJZEn6ulMCaDgqgMCqIzMGSXvKVg5WSLJtgFu6sBjLXbzTscWxLFYCDxbDTnjhwg7tgREs9Go9xwr0alssIruDH+IW0ICAnFu3EzbGwrb0ZVXlYR8SfTiI9II/5UOoW5JcccObpq8G1WF9+mdWnQtC5ObnZotVrWro2mTl3TehT1mZnk7tlDzo4d5O7chS6p5K0ya496OHboWBzA2DZseNs2NigGTqefZlfCLnYm7ORYyrESU6StVFa0dG9JR6+OdPTqSBvPNjiobwgkDHqI3wfRG41/icdLfoCVDTRojz6gO7uTNHQe+TxqO0eSswrYGpXClp2n2BmdSnZhyfPk7WJH1+B6dGvkTrdG9ajvbNlBthDi7mBWMKPVann66aeZMWMGQUFBAHTq1IlOnTpVSuFqMsWgUHQhG+8L9qR/G4E2IafE4qYqWys0wa7YNamLppErNvXsLTp4ASjMyyPu+GHOHT5A7NFD5GVeKfF6XR9fAkJC8Q9pg1+LEOwczevtMIfBoJAUm0V8ZBpxEWmkxGeXeN3W3ga/5nXxbeaGb9O6uHiaf/4VRaEgIpKcbdvI3bmT/OPH4Yap4SqNBoeOHanTvRuO3btjGxRU6mdkFmay+9JudibsZFfCLtIKSq7LG+gSSDefbnT27kxY/TCcbJ1KZpCXDjF/G4OXmE2Q/49lEDxbQlBvCOoFAV1B44S2sIjDy9axd2s826JTiUgoebvJxV5NlyD34uAlsJ6jxX9PhRB3H7OCGbVazYoVK2SgL3Dlr7Pk7rmMD/Zor+6ip/ZyQNPEzRjANHS2yCnS/1SUdYUj61YRd+wQF09FlligztbegYahYTRsE0ZASBuc63mWklMFlKVAx4WT6Zw7mkJcRNpNC9TV86tDQEt3/Fu54xXoXOaCdLeiFBWRu/8AOZv/Jnvzlps2XrRtFEyd7j1w7N4dh/btsCpjJeyL2Rf5O/5vNsdv5mjKUQw39F7Z29jTybsTPRr0oFuDbjSo0+AfhVEgKRLOrIfocLi4v+Q6L3YuENwPmtwLwX2hjvH8ZxVo2Xo6hS2nz7I1KpmMPBvg+qyjUF8Xejf1pE8zT0IauGAtY16EEBauXGNmVq5cydSpUyujPBZDE+RC3pFk0hzzaNijOY7N62HtYvkDoBVFITn2LNH7dxO1ZydXEi9x4+iNuj6+BLVtT1BYRxo0a4G1TeXOsCrI0RJ7PJVzR1O4cCodvfb6xVzjYINfCzcCWrrj18INx3Kef312Nnl79pDz99/kbN+BIef6Uv4qBwfqdOtGnV49cezWDbW3d6l5KYpCVEYUm+M383f835zJOFPi9UaujYqDlzDPMNTW6n9mYFyo7tQqOLkKMmJLvu7ZAhoPMAYwvh3B2nj+03IKCd8fz/rIRHbFpKK9YYC5vbVC72Ze9GvhRa8mHng4Wf73VAghbmT2lahRo0a8++677N69m3bt2uHo6Fji9btlnRn7Fu7YNHbh4IZ1NG/nibUF7wujKAqJMWc4s28X0ft2kZl8w1gQKyv8WoQQHNaRoLD21PVucPuMKkh2egGxx1I4dzSFS9GZN8w8Aud6dgS18SCwjUe5e18AdOnpZK5bT4MlS4h98y3QXe/lsfaoh1Ofvjj164tD585YlTFLT2/QcyT5CH/H/82WC1tIyEm4npfKmnb129HXvy99/PrgU8fn5gwMeuOaL6dWwam/IOt6emzsjLeOGg8w/rn6Fb+UmFnAhsiLrI9IZF9sWomZR40869CvuSc9gt1IjtzL0CGhsneREKLWMjuYWbhwIa6urhw6dIhDhw6VeE2lUt01wYzK2gqVwXK75xWDgYQzp4jet5vofbvJTru+bL6NrYbAtu0Ibt+ZM8lpDBl+f6VfCHOvFBJzKJnog0kkxZYc1+HuW4egNh4EtfHAvUH5x3Tor1whKzyc7HXryd23D/R6roXitsHBOPU1BjB2rVujsio9SFIUhWMpx1h/fj0bzm8gNT+1+DWNtYauPl3p59+PXr69cLVzvUVhdHB+O5z8E06vgdwbti2wrWMMXFoMg0b9QXN97FHClXzWHr/MuojLHI6/UiLLlj7ODGrlxcBWXjTyNI630Wq1rD1lzlkSQgjLY3YwExsbW/abRI2kKAopcbGc3LGFqF3byMm4PoBUbWdPUFgHmnTuRmBoO9R2xtk+MWvXVlp5CnK0nD1iDGASzly5PoBaBd7BLsYemFAPXDzsS8umVPqcHLI3hpO1fh25u/eU6IHRtGjBxYAA2j3/HI6NGpWZl6IonE4/zbrz69gQu4FLuZeKX3OydaKPXx/6+vWli0+XkjOPrmcACYfhxO8QsRxyb1j4zs4Vmt5nDGCC+oD6+licjNwi1kZc5s8jl9h/vuSg3zB/Vwa18ubell74u8u0aSHE3ancAx6KioqIjY0lODgYm0oeNyHuTFZKMqd2buXUzq0ltg2wtXegUftONO7cnYat21bq1OlrdEV6Yo+lErUvkQsn0zHccG/EK8iFxh08CQ7zLPf4FwBFpyN3924y/1xF9qZNJdZ/0TRrhvOgQTgPGojK25sTa9diGxBQan6Xci7x19m/WH1uNeezzhcfd7BxoK9/XwYFDqKLd5ebx79ckxoNJ5YZ/9JvWP7f3g1aDDcGMA17wA3p84v0bDqVxJ9HE9h2JqXEGJiOgW4MDjEGMF4uMnVaCCHMjkLy8vKYPHkyixcvBuDMmTMEBQUxZcoUfHx8mD59eoUXUpivKD+P07t3cHL7ZhJORxYft1arCQrrQPMefQhs0x6bKhhHoSjGadSn91wm+mAyRfnXe0fq+dWhcfv6NGrniXO98vfAABScPk3myj/JXL0afer12z62QUE4DxmM88BBaIICi49rtbfYdfyqPG0em+I38WfMn+xP3F98XGOtoadvTwYFDqJHgx7Y2dwmmMi/YlzE7sgvcOnw9eNqB2g2GEJGGWcg3RDAKIrCobgMlh28yOrjl8gtuj5zrIW3M8Pb+DA01Acf1zs7T0IIUduYHcy8/vrrHDt2jK1btzJw4MDi4/fccw8zZ86UYKYaKYrCpahTnNiykTN7dqItvLrTs0qFX4sQmnfvTeNOXSt1/Zcb5WQUELUvkdN7ErmSlFd8vI6bhmadvWncoT5u3o6l5FA2fWYmmX+u4sr//kdhVFTxceu6dXEePBiX4cOxa9XSpHE2iqJwMOkgK2NWEh4XXmLX6Y5eHRkWPIx7Au7BUX2bMhsMxnEwR34xDuTVXTv/1tConzGAaXpfiTEwAMlZBfzvcALLDl7gXOr1nar93OwZHtqA4W18aFz/H2vOCCGEKGZ2MLNy5UqWLl1K586dS1wgWrRowdmzZyu0cMI0uVcyOLl9Mye2hJfYRqCudwNa9elP8+69K2W36VsxGBTiI9KI3JFAXEQaytW7Iza2VgS39aRZFy8aNKmL6g7WNlEUhfyDB8lYtozsDRuLbyOp1Grq9O2Ly/Bh1OneHZWJt80yCjJYd2Ydf5z5o8RtJH8nf4YFD2No8NBbz0K6JvOiMYA58itk3jCR3aM5tH0UWo+GOh4lkuj0BjadSmbZwQtsPZOC/urtNgdbawaHeDOqvR8dGtaVBeyEEMIEZgczKSkpeHrevDhabm6u/MNbhRRF4eLJExzdsIaYg3uLF7Oz0Who2rkHrfr2p0HTFlXWJrlXCjm1+xKROy+Rk359jIp3Ixead/UmOMwTW7s7G1ulS08nc8VKrvzxB0U3DETXNG2K66hRuAwZjLWrq0l5KYrCoaRD/J77O7NXzkZrMN5yclQ7MrDhQO5vdD+hHqG3P38GA5zbAge+hzPrri9mp3GGkAeNQYxPGPwjfVJWAf/dH8+S/RdIzCooPt6hYV1GtfdjcIg3jhoZgyaEEOYw+1/NDh06sGbNGiZPngxQ/I/9d999R5cuXSq2dOImRfl5nNy+haMb15QYzOvdqCmt+vanaZeeaByqZlaLoihcPJ1BxPYEYo+lFq8Ho3G0oVkXb1p296Gu153dRgLIj4gk45dfyFqzBuXqOBeVgwMug+/DddQo7EJCTA7acopyWBmzkt/P/E5s5vWAqIV7C0Y1GcV9gffdeibSNXnpcPRXYxBz44J2DXtA2DhoNgRsS6ZXFIU9Z9P4eW8cG08mFffC1Ktjy4Pt/HiovS9BHlVz608IIWojs4OZ999/n4EDB3Ly5El0Oh2ffvopkZGR7Nmzh23btlVGGQWQdvECRzeu5uT2zRTlG8dyqDV2NO/RmzYDBuMREFhGDhVHW6TnzL5Ejm2+SMbl62M8vBu50LJHA4LDPLBRW9/RZyhaLVkbN5Lx8y/kHz1afNyuVStcHxqF832Dsa5jeqAUlxXHb6d+Y2XMSvJ0xvE79jb2tLRqyUt9XyK0fmjpGVw+Bvu+NQ7qvTYWRuMMbcZA+yfBo+lNSXILdSw7eIGf98ZxNuX6eerY0I1HuwQwsKUXtrVgywshhKhuZgczXbt2ZdeuXcyfP5/g4GA2btxIWFgYe/bsISQkpDLKeNdSFIX4E8c4uHo5549dnxFT18eXNgPuo2Wvfmgc7rznw1Q5GYWc2HaRyB0JxbtSqzXWxl6Ynj64+9x574IuLY2MJUu4smQpupSrC8mp1TgPHIjbo2OxDy0j6LiBoijsvbyXX0/9yvaL21GuLmQT5BLEmGZjGOA/gO3h22nh1uLWGRgMxk0d93wB53dcP+4VAh0mGgf02t58/i9n5rNo93n+uy+erALjeXK0tWZEmC9jO/vTzMvZ5DoIIYQoW7luzoeEhBRPzRYVT6/TErV7BwdXryAlzngrQ6WyIrh9R9oMGIJ/SCljOSpBSnw2EVsvc/ZQcvG6MM717Gjdx49mXb3R2N/5GI+iuDjSfvyRzOUrUIqKAOO2AnVHP0zd0Q9h4+FRRg7XafVaVp9bzU8nfyLmSkzx8Z6+PRnbfCxdvLugUqluPzW7KA+OL4E9X0FatPGYyhpa3g+dngXf9jeNhQE4cTGThTvPseb4ZXRXz1NgPUee6NaQEWG+1JGxMEIIUSnK9a+rXq9nxYoVnDp1CpVKRfPmzRk+fLgsnneHCnJzOL5pPUfW/0VOehpgvJXUqk9/wu4bjmt9ryori6IoJERlkLLfnhXrjhYfb9DEldZ9/WjYuh5WFbDbcv6JE6Qt/J7sjRu5NvXJLiQEt3HjcL53gMkzksC4NsyyM8v46eRPJOcZV9e1t7Hn/kb3M6bZGBq6NCw9g9w02PcNHFgI+VdX2tU4Q7vx0GkSuPjelERRFLZEJfPttnPsi72+Om/nIDcmdg+ibzPPCjlPQgghbs/s6CMiIoLhw4eTmJhI06bGcQJnzpzBw8ODVatWya2mcsjLyuTQmpUc3bC6eDyMY1032t47hNb9B2Ffp+rWGFEMCrHHUjm0/jzJcdmADSorFU061Cf0Hj88/O68LIqikLtzJ2nfLSRv//UF6Rx79aTexInYt29vVs9TRkEGv53+jd9O/UZWkXFfJ097Tx5r8Rgjm4zEybaMMmcnwoFv4OAPoL26Ho6rP3R+zjgrSXNzeoNBYX1kIl9sjuHkZeNn2lipGBrqw4TugbRq4GJy+YUQQtwZs4OZiRMn0rJlSw4ePEjdunUByMjI4PHHH+fpp59mz549FV7I2ionI52Dq1dwLHwtuqtrpbj7+tN+6AiadetVJavzXqPXGzizL4kjG+PISDRe0K3VVtj7FDD0yW64VcCibYqikLNlK6lffEHByZPGgzY2uAwejNuTT2LXtIlZ+SXmJrIochH/O/M/CvTGQbkNnRvyRKsnGBI0BFvrMnp1rsTT+sIibL58CvRXp5N7h0L3l6HZULC++eeh0xtYdewSX26JKR7U62BrzaOdA3iiW0O8XWR1XiGEqGpmBzPHjh0rEcgA1K1bl/fee48OHTpUaOFqq6zUFA6s+h8nNm9Af3XcRv2gRnQe8TDB7TqWuWNzRTLoDUTtS+Lg2liyUo0Bga29DSG9G9CihxdbdmzCye3O9v8xBjFbSP3iy+IgRuXgQN1Ro3B7fDxqb2+z8kvOS+b7E9+z7Myy4vVhWri3YGLIRPr69cXaqoyZVKkxsHMBNseXEmi4urWCX2foOQ0a3XPL8TBFOgN/HLrI19tiuJBu7D1ztrPh8W6BPNG1IXUdK39fKyGEELdmdjDTtGlTkpKSaNmyZYnjycnJNDJh5+G7WU56GnuXL+HE5nAMeuNF1LtJM7qMeJiGbdpV6aBeg0Eh+kASB9bEkplsvDjbO6lpc48/rXo2wNbeptS9i0yhKAo5mzeT8uWXFJ48BRiDGLexY3F78glsbgiITZGan1ocxBRe7UlpV78dz7R+hs7encs+f1fiYduHcPQ3UAyogGSnlrgNn4tNcK9bBjE6vYEVRxL49O9oLmYYz5O7oy0TegTyWOcAnOyqrvdMCCHErZkdzMydO5cpU6Ywa9YsOnfuDMDevXt55513+PDDD8nKyip+r7OzTEEFyM/OYv+ff3B0/Wp0WuNMHb8WIXQe+TB+LVtXaRCjGBRiDidzYHVs8e0kO0c1be/1J6SXL2rNna0PA9fHxCT/+9/FQYyVgwN1H30UtyceNzuISctP48eIH1katbT4dlJbz7Y83+Z5Onp1LPv8ZSfBjvlwaBHojeefxvei6zaVPceSuC+g202BjMGgsC4ikY/Dozh39XaSh5OGZ3sF80hHf+xt7/w8CSGEqBhmBzNDhgwB4KGHHiq+iChXZ6EMHTq0+LlKpUKv1986k7tEUUE+h9f8yYG/llOUbwwcfJq2oPvDj+HXomoHSiuKQlxEGntXniMtIQcAjYMNbfr707qP7x1vNXBN/okTJM//mLx9+4A7C2LytHksilzEoshFxZs+tq7XmufbPE8Xny5lBzF56bDrU+Nid9c2jQzsCX3fBr8OxtWEj60tkURRFLZGpfDRhqjigb2uDmqe7RXMuC4NJYgRQogayOwr2JYtWyqjHLWKTqvl+KZ17FvxO3mZVwDwCAik+8PjCGxr3kydipASn82u/8WQEJUBgK2dNaH3+BPaz69C1ogBKDp/nuRPPiV7/XrAuOlj3Ucfxf3pp8wOYnQGHcujl/PV0a9IKzBOUW/p3pLn2zxP9wbdyz5/2nzY+xXs/AQKr/YU+naAvjMgqNdtkx2Oz2DumlMcjDOepzoaGyZ0D2Rij0C5nSSEEDWY2VeyXr1ufzG42ymKQsyBPWz75QcykxIBcPXypttDj9K0S48qHdgLkJ1ewN4/z3JmXxIAVjYqWvfxo93AAOwcK+birEtJIeWrr7jy+zLQ60GlwmX4cDwmv4C6QQOz8lIUhS0XtvDJ4U+K903yc/LjxbAXGRAwoOwgRlGM2w1smgWZF4zH6reCvm9Bk4G3HBMDcDEjnwV/R/DXsUsAaGyseLxrQ57pFYybDOwVQogar1z/LS8oKOD48eMkJydjMBhKvDZs2DCT89m+fTsfffQRhw4d4vLly6xYsYL777+/+HVFUZg9ezb/+c9/yMjIoFOnTnz55Zc3DT6uCZLPn2Pr4u+4cPIEYFwnpuuDY2jZ+x6sq3gxwcI8LYfWx3F880X0OmP7NO5Qn87Dg3CuVzFThw1FRaQvWkzqN9+g5BlvodXp1QuPqVPNnmINcCLlBPMPzudwsnHbBleNK5NCJ/FQk4dQW5sQeMXvgw1vQMJB43PnBtBvpnHLgdsEkdkFOv6Ks+JfB3ZRpDOgUsGodr68MqAp9Z3vbAaXEEKIqmP2VXb9+vWMGzeO1NTUm14zd5xMbm4uoaGhPPHEE4wcOfKm1+fNm8eCBQtYtGgRTZo0Yc6cOfTv35+oqCicnKpuIbnS6PLz2PTdl5zc/jcoCjZqW9oPG0GHYSOxtavaNUcUg8Kp3ZfZs/IsBTnGmUgNmrjSdWQjPAMqZjD2tWnWSR98iDbeuGu3XevWeE57BceOHc3OLzU/lU8OfcKfZ/8EQGOt4bEWj/FkqyfLXuwOIOO8sScmcoXxudoRerwMnZ+/affqa3R6A0sPXuDjjVGk51oBBroGu/Pm4Oa09JHF7oQQwtKYHcy88MILjBo1irfffpv69evf0YcPGjSIQYMG3fI1RVH45JNPePPNNxkxYgQAixcvpn79+vz2228888wzd/TZd0pXVMTBv5YT99fvKDpj4NCsWy96jBmPcz3PKi9PUmwW25dEXV21F+p6OdB1RCMCQtwrbIxO4dmzJM19n9xduwCw8fDA81/TcB4yxOxbaFqDlqWnl/Ll0S/J0RoHJA8LHsbktpPxcjRh24aiPNi5wDjAV18EqCDsMejzFjjd/nu5Pzadt/+M4HSi8Tx52im8M7It97byqfKxTEIIISqG2cFMcnIyU6dOveNApiyxsbEkJiYyYMCA4mMajYZevXqxe/fu2wYzhYWFFF5dTRconiqu1WrveN2UG2389jNO79wKgGdQI3o9OgHvJs2KP6uq5GcXceCvOE7vTQQF1HbWtL8vgJY9vbGytkKn05U772v1KExPJ2Xh92T+97+g04Fajeu4cbg9NRErR0d0er1xvIyJDiQdYN7BeZzNPAtAc7fmvNb+NVrXa13ic29HFb0B6w2vo8o09gwZGvZAf8+7xvExxgxuSpOWU8iHG6NZccQ4LsbVXs1zvRpSL+MUPYPr3tF5qsmuncuq/E5WJamf5avtdZT63XneplAp1+ZVm+jJJ5+kW7duTJgwweyClVoQlarEmJndu3fTrVs3EhIS8PHxKX7f008/TVxcHBs2bLhlPrNmzWL27Nk3Hf/tt99wcLj1bYfyKMxI4/K2jbiHtqdOw0ZV/r96RYHceDWZZzQoOuNnOzTQ4tK0EGuNWU1a6ofUOX4cz1V/YZNj7D3JadGclMGD0darZ3Z2mYZM1uWvI0IbYSyvyoH+dv1pZ9sOK1XZPTv2RamEXPwF70zjuJp8tRsnfMdy2eXWu1gDGBTYnaRidbwV+XoVKhS6eCoM8TdQQWOghRBCVIK8vDzGjBlDZmZmmevWmd0z88UXXzBq1Ch27NhBSEgI6n/sHzRlyhRzsyzVP4OEa2vY3M7rr7/O1KlTi59nZWXh5+fHgAEDKnwRv8KRo/h782b69+9/03moTKkXctj+32iuXDAGGO6+jnQbFYxXUMWN99AmJJA8Zw75O423lNQNG+Lx+nQcunY1Oy+DYmBZ9DK+PPolebo8rFRWPNjoQZ5t/SwuGhPKrC/Cat/XWO2Yj0qXj2Jlg6HjJGx6TKOtbR3a3ibZiYRMZv51ihMJxt65Ft5OzB7anDZ+rsY6arWEh4dXeftVpdpeR6mf5avtdZT6ld+Ni/CWxexg5rfffmPDhg3Y29uzdevWEoGFSqWqsGDGy8s4biIxMRHvG/buSU5OLvUWl0ajQaPR3HRcrVZX2hepMvO+ka5Iz4E1sRwJv4BiUNA42NBpWBAtezbAyqpieoYUnY70n34m5fPPUfLzMVhbU++Zp/GYNAkrW/OnKcdkxDBrzyyOpRwDINQjlBmdZ9DUralpGcTvhVWTIfWM8XlAN1SDP8baszm3W74ut1DHRxuiWLznPIoCThobpt3blEc7B2B9i/NUVe1XnWp7HaV+lq+211HqV748TWV2MPPWW2/xzjvvMH36dKwqcd2UwMBAvLy8CA8Pp21b4/+9i4qK2LZtGx9++GGlfW5NlRCVwZZfTpOZYlzJNjjMkx6jG+PocnPgVl75JyK4PPPt4i0I7Nq351SvnjR5/HGszPySFumLWHhiId+d+A6dQYej2pGXwl7ioaYPmXRLiaJc+Ptd2PcNoICjBwyYA61H3/aWEsDO6FSmLz9evI/SA20b8Pp9zfB0kqnWQghRW5kdzBQVFTF69OgKCWRycnKIiYkpfh4bG8vRo0dxc3PD39+fl156iblz59K4cWMaN27M3LlzcXBwYMyYMXf82ZaiME/L7uVnObnTOHDV0cWWno80JaiNR4V9hqGwkJRPPyN90SIwGLBycaH+q//CYehQjq9bZ3Z+R5KPMGv3LM5lngOgt29v3uz8pmmzlABitxt7YzLOG5+3eRTunQP2t19JOKtAy/trT/Hf/cbF8hq42vPByBB6NK648ySEEKJmMjuYGT9+PEuXLuWNN9644w8/ePAgffr0KX5+bazL+PHjWbRoEa+++ir5+fk899xzxYvmbdy4scasMVPZYo+nsvXX0+RlGjdHbNmzAV0eCK6wLQjA2Btzafp0is4aZxY5Dx1K/emvYePubvbo9AJdAZ8e/pRfTv0CgLudO693et201XsBCrJg00w4+IPxubMvDPsUGt1TarLNp5N4Y3kEiVnGTSjHdQng1YHNqKOp2sUKhRBCVA+z/7XX6/XMmzePDRs20Lp165vuaS1YsMDkvHr37k1pk6lUKhWzZs1i1qxZ5hbTohUV6Ni1LJqTuy4D4FrfgT6PNsWnsXl7HJVGKSoi9ZtvSP32P6DXY+1RD+933sHphuDSHJGpkby+8/XibQgeaPQAr7R/xbQBvgAxf8OqKZB10fi8/QS4ZxbY3X7Qdmaeltl/RbL8SAIAAe4OzBvZmk5B7uWqgxBCCMtkdjBz4sSJ4jEsERERJV6TRcfu3OWzmWz6MZKs1AJQQZt+fnQaHoSNuuJ2ay6IOsOl6dMpPGUcG+N8333Un/GW2RtCgnFTyIUnFvLtsW/RKTrq2dfjna7v0MO3h2kZaPMhfCbs/9b4vG5DGPa5cXfrUuw5m8bU349yObMAlQomdAvklQFNZVdrIYS4C8mu2TWEXmfgwOpYDm+IQ1GgjpuGe8a3oEHTCuyN0etJ+/4HUj7/HLRarF1d8Zr5Ns63WYW5LOczz/Pmzjc5nnocgAEBA5jReQaudq6mZZAUCf+bCMknjc87Pm3sjbF1vG2SIp2BBeFn+Hb7WRQFGro78PFDbWgXUHHnSQghhGUp96CCmJgYzp49S8+ePbG3ty9z/Rdxe+mXcgn/MZLUq+vGNO3sRY/RTSp0bIw2KYlL0/5F3oEDANTp0wfvd2Zj42H+AFlFUVgatZSPD35Mgb4AJ7UTb3R+g8GBg037DhgMxllKm2YatyJw9IT7v4LG/UtNdjYlhxeXHCHi6roxD3fwY8aQFjjK2BghhLirmX0VSEtL46GHHmLLli2oVCqio6MJCgpi4sSJuLq68vHHH1dGOWslRVGI3HGJncui0WsN2Dmq6T22KcFhFbu3U/bWrVye/jr6K1dQOTjg9eabuIx4oFzBZ2ZhJjN2zWDLBWMPXSfvTszpNsf0mUrZibDyWTi72fi8yUAY9gXUuX1QpSgKv+2P593VJynQGnB1UPPBiBAGtvK+bRohhBB3D7ODmZdffhm1Wk18fDzNmzcvPj569GhefvllCWZMVJSvY8uvp4k5mAyAf0t3+o5rVqHrxihFRSR/vID0xYsB0LRoToOPP0YTGFiu/I6lHONf2/7F5dzLqK3UTG03lTHNx5i2bgxA1DpY+Rzkp4ONvXG6dfsJpa4bcyWviH/9cZzwk0kAdG9Uj/mjQvFykXVjhBBCGJkdzGzcuJENGzbg6+tb4njjxo2Ji4ursILVZikXstnwnwgyU/KxslLRZUQwof38KvQ2XVFcHAlTX6EgMhKAuuMew3PatHKt4qsoCosjF/PJoU/QKTr8nPyY32s+LdxbmJaBXgeb34Vdnxife4XAyO/Bo/RVgI9duMJzvx4m4Uo+ttZWvDqwKU92C6yw1Y6FEELUDmYHM7m5ubfcsDE1NfWW2wiI64pvK/0ejV5noE5dDfc+1apC91QCyFy9hsSZMzHk5mLt4oL3+3Nx6tu3XHnlGfJ4adtL7Li0A4B7G97LrC6zqGNbx7QMspPgjychbqfxeadJ0P8dsLn9d0VRFH7ZF8+7f52kSG8gwN2BL8eE0apBxZ4nIYQQtYPZwUzPnj356aefePfddwHjdGyDwcBHH31UYgE8UVJRvo6tv54m+uptpYAQd+4Z3wK7OhW3l4Wi1ZL04TwyfjEuWmffvh0N5s9H7WXieJZ/OJZyjC+zvyQzKxNbK1te6/gao5qMMr0H6fxOYyCTkwS2dWD4F9DygVKT5BbqeHPFCVYeNa54fG/L+nw0KhRnu9q7p4kQQog7Y3Yw89FHH9G7d28OHjxIUVERr776KpGRkaSnp7Nr167KKKPFS7+Uy9pvjpOZnI/KSkWX+4Npc48fqgq8XaJLTeXiSy+Rf/AQAO7PTsLj+edR2Zg/00dRFJZELWHe/nnoFB3+Tv583Ptjmrk1My0DgwF2fwp/vwOKATyaw+ifoV7jUpPFJGfz7C+HiU7OwdpKxfSBzZjYI1BmyQkhhCiV2Ve6Fi1acPz4cb7++musra3Jzc1lxIgRPP/88yV2txZGscdSCP/hJNpCPXXqahgwsRXewRV7uyT/6FEuTnkRXXIyVo6O+Hw0r9y3lYr0Rby37z2WRy8HIEQdwlcDv8LVwdXEwlwxzlaKWmt83vphGLKg1LVjAP46donX/necvCI9nk4avhwbRoeGbuWqgxBCiLuL2cFMfHw8fn5+zJ49+5av+fv7V0jBLJ2iKBxad559q4zL+/s0dmXg062wdzJ/AG5pMpb+TtKcOShaLbbBwfh+/jmaoPLNVkrJS+HlrS9zLOUYViorprSZgvs5dxzVpQcixVJj4L+jIS0GrG1h0Dxo93ips5UMBoX5G6P4aqtxb6iuwe58+nBbPJxk/JUQQgjTmB3MBAYGcvnyZTw9S66FkpaWRmBgIHq9vsIKZ6mKCnRsXnyKs0dSAAjp1YBuDzXG2vrOdxq/xlBURNK7c7iybBkATv374/3++1jXMTHw+IfjKcd5actLpOSn4GTrxEc9P6KjZ0fWxq41LYOzW2DZeCjING4Q+fAv4NO21CQ5hTpeXnq0eNr1pF7B/OvepljLbCUhhBBmMDuYud1Kvzk5OdjZydofWan5rP36OGkJuVhZq+j1SFNadPep0M/QpaZy8YXJ5B89CioVHi+9hPvTT5V7bMmK6BW8u/ddtAYtjVwb8WmfT/F39jdt12xFgQMLYd1roOjBtyM8/CvUKX3hvwvpeTz100FOJ2Zja2PFvJGtub9tg3KVXwghxN3N5GBm6tSpgHH20owZM0pMz9br9ezbt482bdpUeAEtycWoDDb8J4KCXC32zrYMeroV3o1cK/QzCs6c4eKkZ9FeuoSVszMNPp5PnR4mbur4D3qDnvkH5/PLKePsp37+/Xiv+3um31bSa2Hdq3DwB+Pz0EdgyCegLj2oPXA+nWd+PkR6bhEeThr+81g72vrL3kpCCCHKx+Rg5siRI4CxZ+bEiRPY3rD4mq2tLaGhoUybNq3iS2ghTu2+xNZfojAYFDwDnBg0KYQ6dSu2pypn+3YSXp6KITcXdYA/ft98U+7VfPO0efxr+7/YfnE7AM+3eZ6nWz9t+mq+eenw+zg4vwNQQf/Z0HVKqeNjAH4/cIE3V55Aq1do6ePMd+Pa4+NqX646CCGEEGBGMHNtt+wnnniCTz/9FGdn50orlCVRFDiw+jxHNlwAoHGH+vR9rBk2ttYV+jnpP/9C0vvvg8GAQ4cONPjsU2zqlq83Iyk3icmbJ3Mq/RQaaw3v93if/gGlb/JYQmoM/PogZMQa148ZuRCalr7ztsGg8MH60/xn+zkA7gvxYv6oUBxsZZNIIYQQd8bsK8mPP/5YGeWwSHqtgYzjdiRcMgYy7e9rSMehFbsuimIwkPzhvOL9lVxGjMB71kxU5diWACAqPYrn/n6O5Lxk3Ozc+Lzv57T2aG16BvH74L8PG/dXcvWHR5ZC/dK3NSjQ6nll2THWHL8MwIv9GvNiv8ayLYEQQogKIf8tLqeCXC1rvz5B3iU1KisVvcc2pUW3ih3oaygq4tJrr5G9bj0AHlOn4v7UxHIHS7sTdvPy1pfJ0+UR5BLEl/2+xNfJt+yE15z6C/43EXQF4BMGY34vdbdrgMw8LU/9fJD9semorVV89GCoDPQVQghRoSSYKaftS85wOSYLlY3CoGdaERhS+uwdc+mzsrj4wmTy9u8HtRqfuXNxGTqk3PmtObeGt3a+hU7R0dGrIwt6L8BFY8biffu/g7X/AhRoMhAe/KHMhfAuZuTx+I8HiEnOwUljw7ePtaNro3rlroMQQghxKxLMlFO3BxuRnZ6P4n0Z32YVOxNHm5jIhaeepjA6GitHR3y/+BzHLl3Knd/PJ39m3oF5AAxqOIj3ur+H2trEvY4UBast7xq3JwBo9wTcNx+sS//qnE7MYtz3+0nOLsTL2Y4fn+hAc28ZZyWEEKLiSTBTTo4uGoa+2Jp16xIqNN/C2FjiJ0xAd+kyNh4e+P3nW+yaNy9XXoqi8MnhT/ghwjh1emzzsbza4VXTZywZ9IRe+BHrtK3G531nQI9XypyxdCgugyd+3E9WgY6m9Z348YkOMmNJCCFEpZFg5g5U9AaIBadPEz9hIvq0NGwDA/Ff+B3qBuUbX6I36Jm9ZzYrYlYA8GLYi0xoNcH0MuuKsF75NA3TtqKorFAN/RTCxpWZbPuZFJ75+RD5Wj3tAuryw/gOuDjIjtdCCCEqjwQzNUTe4SNcmDQJQ1YWmhbN8V+4EBu38m20qNVreX3n62w4vwFrlTUzu8zkgcYPmJ5BUS4sfQyrs39jUFljeOA7bFqPLDPZ2hOXeXHJEbR6hZ5NPPjm0TCZei2EEKLSyZWmBsjZtYuLL0xGyc/HPiwMv2++xrqc6/gU6gt5ZesrbLu4DRsrG+b3nE+/gH6mZ5B/BX57CC7sQ1E7sNf/BTo0H1Zmst8PXGD68uMYFBgc4s2/R7fB1qbi9qISQgghbkeCmWqWvXkzCS++hKLV4ti9O76ff4aVffnGl+Rp85iyZQr7Lu9DY63hkz6f0L1BdzMySIefH4DLR8HOBf3oJaQcTykz2c9745ixMgKARzr6Mef+ENksUgghRJWR/zpXo+xNm7h4NZBxGjAAv6++LHcgk12UzaRNk9h3eR8ONg58fc/X5gUyuWnw0zBjIOPgDo+vQfHtUGayH3fFFgcyT3YLZO4DEsgIIYSoWtIzU02yNmwk4ZVXQKfDefBgfD78AJVN+ZojpyiHSZsmcTzlOE62TnxzzzfmreqbkwI/DYfkSHD0gHGrjKv6lrFr9nfbz/He2lMAPNMriOkDm1X4oGghhBCiLBLMVIOs9etJeGUa6PU4Dx2Kz/tz7yiQeWbTMxxPOY6LxoXv+n9Hc3czpnJnJxl7ZFJOQ536MP4v8GhaZrKvtsYwb30UAJP7NmJq/yYSyAghhKgWEsxUsay1a0n416ug1+MyfDjec99DZV2+TSlztbnFPTLOts7mBzI5KbB4KKRGgZOPMZCp16jMZN9sO1scyLx8TxNevKdxucovhBBCVAQJZqpQ1saN1wOZBx7Ae867dxbIhE/iWMoxYyAzwMxAJi/deGvpWiDz+GpwDy4z2Q87Y/lg3WkApg1owgt9JZARQghRvSSYqSI527YV31pyuf9+vN+bg8qqfOOv87R5PLfpOY6mHMXJ1onvBnxHC/fSd64uIf+KcdZScuT1W0smBDK/7I3jndUnAZjSr7EEMkIIIWoEmc1UBXL37OHi5Cmg1eJ836A7CmSK9EW8vPVlDicfxkldjkCmMBt+ffD6rKVxq0y6tfT7wQu8dXXW0jO9gnhZbi0JIYSoIaRnppLlHT7MheeeRykqok6/fvh8+GG5by3pDDpe3f4quy/txt7Gnq/u+YqW7i1Nz6AoD34bDRcPgJ0rjPsTPJuVmeyv45d57X8nAHi8a0OZtSSEEKJGkWCmEuVHRHLh6WdQ8vNx7N6dBv9egEpdvn2KDIqBt3e9zd/xf6O2UvNZ389o49nG9Az0Wvh9HMTtAo0zPLYCvELKTBaZoeKHfREoCozp5M/MoS0kkBFCCFGjyG2mSlIYG8uFp5/GkJODQ8eOxpV9bW3LlZeiKMzdN5e/zv2Ftcqaj3t9TGfvzqZnYDDAymchJhxs7GHsMmgQVmayQ3EZ/HjGCp1B4f42PswZ3koCGSGEEDWOBDOVQJuUzIUJE9Gnp2PXsiW+X31V7pV9Ab48+iVLo5aiQsV73d+jj38f0xMrCqyfDieWgZUNjP4Z/MsOhE5dzuLpX46gNajo1aQeH40KxUpW9hVCCFEDSTBTwfSZmVyYOBHtpUvYBgTg959vsa7jWO78fo/6nW+PfwvAW53fYnDQYPMy2P4R7Dem5/5voHH/MpPEp+Ux7of9ZBXoCHRS+Hx0KGpr+aoIIYSomWTMTAUyFBRw4bnnKYyOxsbDA7/vv8fG3b3c+f0d9zfv7XsPgGdDn+Whpg+Zl8GBhbDFmJ5B86D1qDKTpGQX8tgP+0jJLqRp/To87n8Fe9vyDVgWQgghqoL8d7uCKHo9CVNfIf/QIaycnfFbuBBb3wblzu9w0mFe3f4qBsXAyMYjeTb0WfMyOL0W1v7L+LjXa9DpmTKT5BXpmLD4AHFpefi52fPD+HY4SLgrhBCihpNLVQVQFIWk9+aSs3kzKo0Gv6+/wq5pk3LnF5MRwwubX6DIUERvv9681fkt8wbeJhyCP54ExQBh46H362Um0RsUpvz3CMcvZlLXQc1PT3bC06l8A5aFEEKIqiQ9MxUg8+dfyPjtN1Cp8Jk3D4d27cqdV0peCs/+/SzZRdm08WjDvJ7zsLEyI+ZMjzWuJaPLh0b3wOAFUEYgpCgKs1ZFsulUMhobKxaO70BgvfKP8xFCCCGqkvTM3KE6ERGk/vIrAJ6vvorzvQPKnVe+Lp/JmyeTmJtIQ+eGfNHvC+xtzJgFlZcOv46C3BTwag2jFoF12U383Y5z/Lw3DpUKPhndhnYBdctdByGEEKKqSc/MHSg4fhyv/y4BRaHumEdwe3x8ufMyKAbe2PEGkWmRuGpc+arfV7hoXEzPQFsAS8ZCWjQ4+8KY30HjVGay1ccvMXetcePIN+9rzqAQ7/JWQQghhKgWEsyUU9GFC1yePBkrnQ6Hnj2p/8Ybd7Sg3CeHP2FT/CbUVmo+7fMpfs5+pidWFPjrRYjfbVzdd+wycC47KDl64QpTfz8GGLcpmNA9sLzFF0IIIaqNBDPllPLZ5+jTMyjw8cHro3mobMp/x+5/Z/7HjxE/AvBOt3cIq1/26rwl7PoUji8BlTU89BPUL3vjycTMAp7+6SBFOgP9mnkyY4hsUyCEEMIyyZiZcvKePQvs7DjbpDGtHBzKnc/+y/uZs3cOAM+FPseQoCHmZXB6LWyaZXw86EMILnt14PwiPU/9dJDk7EKa1K/DJw+3wVpW9xVCCGGhpGemnKwcHPB8ewZ6Z+dy53Ex+yKvbHsFnaLjvsD7mBQ6ybwMkiJh+VOAAu0nQMenykyiKArT/jjGiYRM3Bxt+X58B5zsyrf5pRBCCFETSDBTTfK0eby45UWuFF6hpXtLZnedbd5tntxU+O/DUJQDDXsYe2VM8PnmGNYcv4zaWsXXY8Pwcyt/r5IQQghRE0gwUw0URWHGrhmcyTiDu507n/T5BDsbO9Mz0Gvh93FwJR7qBhrHyViX3buyITKRBeFnAJhzfys6BZV/qwUhhBCippBgphosPLGQjXEbsbGy4d99/o2Xo5d5GWycAXG7wNYJxiwFB7cyk8Qk5/DK1ZlLT3RryOgO/uUpuhBCCFHjSDBTxbZf3M7nRz4H4I1Ob9DWs615GRxfBvu+Nj4e8S14NC0zSU6hjmd+PkhOoY5OgW68eV9zc4sthBBC1FgSzFSh+Kx4pm+fjoLC6KajGdWk7F2sS0iMgFWTjY97TINmg8tMoigK034/xtmUXLyc7fhiTBg21tLsQgghag+5qlWRAl0BU7dOJVtr3HPptQ6vmZdBfgYsHWvccym4H/R5w6Rk32w7x/rIRGytrfj60TA8nDTlKL0QQghRc0kwU0Xm7ptLVEYUbnZuzO81H7UJA3aLGQyw/GnIOA+u/jByIVhZl5lsZ3QqH20wblUwc1gL2vrLnktCCCFqnxodzMyaNQuVSlXiz8vLzMGyNcCK6BWsiFmBlcqKD3t+SH3H+uZlsONjiN4INnYw+heTBvwmZhYwZckRDAo81N6XMR1lwK8QQojaqcavANyyZUs2bdpU/NzauuweiZrkdPpp3tv3HgDPt3mezt6dzcvg/E7YOtf4ePAC8A4tM4lOb2DKf4+QnltESx9n3hneSrYqEEIIUWvV+GDGxsbGrN6YwsJCCgsLi59nZWUBoNVq0Wq1FVq2a/ndLt/somxe3vIyhfpCuvt0Z3yz8eaVITcFmz+eRKUYMLR+BH2rh8CE9AvCo9l/Ph1HjTWfPtQaawxotQbTP/eqsupn6Wp7/aD211HqZ/lqex2lfneetylUiqIoFV6CCjJr1iw++ugjXFxc0Gg0dOrUiblz5xIUFFRqmtmzZ990/LfffsPhDvZQMpeiKCzNW0qENgJXK1eeq/McDlZmfL5ioMvZ+XhmR5Bl14DtTWahty578O6pKyq+OWXsvRrfWE9YvRrbvEIIIcRt5eXlMWbMGDIzM3EuY+ugGh3MrFu3jry8PJo0aUJSUhJz5szh9OnTREZG4u5+69Vrb9Uz4+fnR2pqapknw1xarZbw8HD69++PWl1yQO/ymOXM2T8HG5UNP/T/gVb1WpmVt9Wuf2O99T0UG3t0T4aDR7My0yRmFTDsyz1k5Gl5pIMv7wwre/fs0pRWv9qgttcPan8dpX6Wr7bXUepXfllZWdSrV8+kYKZG32YaNGhQ8eOQkBC6dOlCcHAwixcvZurUqbdMo9Fo0Ghu7sFQq9WV9kX6Z95nr5xl/qH5AEwJm0JbbzMXxovbDdveB0A1eD5qn5Ayk+j0Bl75I4KMPC3NvZ2ZOawVanXFjC+qzHNXE9T2+kHtr6PUz/LV9jpK/cqXp6lq9Gymf3J0dCQkJITo6OjqLsptFegKmLZtGgX6Arr5dGN8y/HmZZCXDn9MAMUArR+GNmNNSvbFlhj2x6bjaGvNV2PDsKugQEYIIYSo6SwqmCksLOTUqVN4e3tXd1Fua/7B+cRcicHdzp053edgpTLjFCsK/PUiZF8C90Yw+GMwYRbSobh0PvvbGOC990AIgfUcy1t8IYQQwuLU6GBm2rRpbNu2jdjYWPbt28eDDz5IVlYW48eb2dtRRTbFbWJp1FIA5nafSz37euZlcPRXOLUKrGyMC+Np6pSZJLtAy0tLj2JQ4P42PtzftkF5ii6EEEJYrBo9ZubixYs88sgjpKam4uHhQefOndm7dy8BAQHVXbSbJOUmMXP3TACeaPUEXRt0NS+DtLOw9lXj475vgY9p42xmrorkQno+vnXteed+8wYZCyGEELVBjQ5mlixZUt1FMImiKLy9+22yirJo6d6SyW0nm5eBXmvcrkCbCw17QNcpJiVbdewSyw8nYKWCT0a3wdmu9g4uE0IIIW6nRt9mshS/R//O7ku70VhrmNtjLmorM4OKbfMg4SDYucAD35i071LClXzeXHECgBf6NqZ9w7K3OBBCCCFqoxrdM2MJUvQpfHvkWwBebvcyQS63X9DvluL3wQ7jNG6GfAIuvmUmMRgUpi49SnaBjrb+rkzp28jMUgshhBC1h/TM3AGtQcsfeX9QoC+gi3cXHmn2iHkZFOXCyknGadihj0CrESYlW7T7PPti03GwteaT0W2wsZZmFEIIcfeSq+Ad+CHyBxL0CTipnXi327vmTcMG2DQb0s+BcwMY9KFJSc6l5DBvw2kA3hzcnAB3mYYthBDi7ibBTDmdSDnBwoiFALzR4Q3qO9Y3L4PY7bDfeHuKYZ8bx8uUQW9QmLbsGAVaAz0a12NMR39ziy2EEELUOhLMlNMf0X+gV/SEqEO4t+G95iUuzIY/nzc+bvcENOpnUrLvdpzjcPwVnDQ2fDiyNSoTFtQTQgghajsZAFxOM7vMpLFLY6zPlGPbgI0z4Eo8uPrDgHdNSnImKZsFG88AMGNoC3xc7c3/XCGEEKIWkp6ZcrJSWTG6yWgcrBzMSxjzNxz60fh4+JegcSoziVZv4JXfj1GkN9CnqQej2pU940kIIYS4W0gwU5UKs2HV1QXxOj4DgT1NSvaf7ec4kZCJi72aD+T2khBCCFGCBDNV6e93IOsiuAbAPTNNSnIuJYdPr24i+faQFtR3tqvMEgohhBAWR4KZqhK/D/Z/Z3w89FOwLXtKtcGg8PryExTpjLOXRoTJJpJCCCHEP0kwUxV0hbBqMqBAm7EQ3MekZEsPXmBfbDr2amvmPhAit5eEEEKIW5Bgpirs/DekRoGjBwyYY1KSpKwC5q49BcC0e5vi52bmQGMhhBDiLiHBTGVLPg3br+69NOhDcDBtQ8i3/4wgu0BHqK8Lj3dtWHnlE0IIISycBDOVyWAw3l4yaKHJQGhp2t5L6yMusyEyCRsrFR+MbI21ldxeEkIIIW5HgpnKdOgHuLgfbOvA4I/BhDEv2QVa3v4zEoBJvYJp7u1c2aUUQgghLJoEM5UlJxk2vWN83O9tcDFtobt/h0eTnF1IQ3cHXujbqBILKIQQQtQOEsxUlvC3oTATvFpDh4kmJYm8lMmi3bEAvDO8FXbqcmyVIIQQQtxlJJipDOd3wbH/AioY8m+wKjsoMRgUZqyMwKDA4BBvejbxqPxyCiGEELWABDMVTa+FNa8YH7d7HHzbm5Rs2aELHI6/gqOtNTOGtKi88gkhhBC1jAQzFW3v15ByChzcjWNlTJCRW8QH604D8HL/Jni5yJYFQgghhKkkmKlImRdh6wfGx/3fNXlNmXkbTpORp6WZlxPjZU0ZIYQQwiwSzFSk9a+DNhf8u0DoIyYlORyfwX/3XwDg3ftbobaWJhFCCCHMIVfOinJuG5xaBSpr45oyVmWfWoNBYfYq45oyD7bzpUND03pyhBBCCHGdBDMVwaAz9sqAcRp2/ZYmJVt+JIFjFzOpo7HhtYHNKrGAQgghRO0lwUwFsDryEyRHgn1d6D3dpDQ5hTo+XG8c9DulXyM8nDSVWUQhhBCi1pJg5g6pdTlYbbs66LfPmyYP+v1ySwwpV1f6fbxrYCWWUAghhKjdJJi5Q00TV6LKTweP5tDuCZPSxKXl8v0O40q/bw1uga2NNIMQQghRXnIVvRMpUQSmbDI+HvQBWNuYlOy9Naco0hvo0bge/Zp7VmIBhRBCiNpPgpnyUhSsw9/ECgOGJvdBUG+Tku2MTmXjySSsrVS8PaQFKhN20hZCCCHE7UkwU15n1mMVuxW9ygb9PbNNSqLTG3hntXEq9mOdA2hc36kySyiEEELcFSSYKa+43QCc9RwIdU0bwPvHoYucScrBxV7NS/c0rszSCSGEEHcN0wZ5iJsNeBddcH+ij13ClFAmr0jHgvAzAEzu2whXB9vKLZ8QQghxl5CemTug+HdBZ21v0nsX7oglObsQPzd7HusSUMklE0IIIe4eEsxUgZTsQr7ddhaAf93bDI2NdTWXSAghhKg9JJipAp/+fYbcIj2hvi4MCfGu7uIIIYQQtYoEM5UsJjmneFfs1+9rjpWVTMUWQgghKpIEM5Xsw/Wn0RsU7mnuSecg9+oujhBCCFHrSDBTiQ6cTyf8ZBJWKmRXbCGEEKKSSDBTSRRFYd7VXbFHd/CTBfKEEEKISiLBTCXZdiaFA+czsLWxYko/WSBPCCGEqCwSzFQCRVGYvzEKgHGdA/B2MW0tGiGEEEKYT4KZSrA+IpGIhCwcba15tndwdRdHCCGEqNUkmKlgeoPCx1e3LZjQPRD3OppqLpEQQghRu0kwU8FWHkkgJtm4meTEnkHVXRwhhBCi1pNgpgIV6Qz8e5OxV2ZSr2Cc7dTVXCIhhBCi9pNgpgItPXiBixn51KujYXxX2UxSCCGEqAoSzFSQAq2eLzZHAzC5byMcbG2quURCCCHE3UGCmQqy9MAFkrIK8XGx4+GOftVdHCGEEOKuIcFMBSjUGfh661kAnu3TCI2NdTWXSAghhLh7SDBTAf44dJHErAK8nO14qL1vdRdHCCGEuKtIMHOHdAb4ZnssAM/2DpZeGSGEEKKKSTBzh/alqEjMKqS+s4bRHWSsjBBCCFHVLCKY+eqrrwgMDMTOzo527dqxY8eO6i4SYFxXJvyi8RRO6hWMnVp6ZYQQQoiqVuODmaVLl/LSSy/x5ptvcuTIEXr06MGgQYOIj4+v7qKx4uglMopUeNSx5ZGO/tVdHCGEEOKuVOODmQULFjBhwgQmTpxI8+bN+eSTT/Dz8+Prr7+u1nJp9Qa+2XYOgKd6BEqvjBBCCFFNavTKbkVFRRw6dIjp06eXOD5gwAB27959yzSFhYUUFhYWP8/KygJAq9Wi1WorrGzLDiVw8UoBTmqFB9vUr9C8a4prdaqNdYPaXz+o/XWU+lm+2l5Hqd+d520KlaIoSoWXoIJcunSJBg0asGvXLrp27Vp8fO7cuSxevJioqKib0syaNYvZs2ffdPy3337DwcGhwsr2d4KKdRetGOxnoI9PjT2FQgghhEXKy8tjzJgxZGZm4uzsXOp7a3TPzDUqlarEc0VRbjp2zeuvv87UqVOLn2dlZeHn58eAAQPKPBnmuA+YlpHLvp3b6N+/P2p17dtUUqvVEh4eLvWzYLW9jlI/y1fb6yj1K79rd1ZMUaODmXr16mFtbU1iYmKJ48nJydSvX/+WaTQaDRqN5qbjarW6wk+0d11HbK0rJ++aROpn+Wp7HaV+lq+211HqV748TVWjBwDb2trSrl07wsPDSxwPDw8vcdtJCCGEEHevGt0zAzB16lQee+wx2rdvT5cuXfjPf/5DfHw8kyZNqu6iCSGEEKIGqPHBzOjRo0lLS+Odd97h8uXLtGrVirVr1xIQEFDdRRNCCCFEDVDjgxmA5557jueee666iyGEEEKIGqhGj5kRQgghhCiLBDNCCCGEsGgSzAghhBDCokkwI4QQQgiLJsGMEEIIISyaBDNCCCGEsGgSzAghhBDCokkwI4QQQgiLJsGMEEIIISyaRawAfCcURQHM20rcVFqtlry8PLKysmrlbqhSP8tX2+so9bN8tb2OUr/yu3bdvnYdL02tD2ays7MB8PPzq+aSCCGEEMJc2dnZuLi4lPoelWJKyGPBDAYDly5dwsnJCZVKVaF5Z2Vl4efnx4ULF3B2dq7QvGsCqZ/lq+11lPpZvtpeR6lf+SmKQnZ2Nj4+PlhZlT4qptb3zFhZWeHr61upn+Hs7Fwrv6TXSP0sX22vo9TP8tX2Okr9yqesHplrZACwEEIIISyaBDNCCCGEsGgSzNwBjUbDzJkz0Wg01V2USiH1s3y1vY5SP8tX2+so9asatX4AsBBCCCFqN+mZEUIIIYRFk2BGCCGEEBZNghkhhBBCWDQJZoQQQghh0SSYKaevvvqKwMBA7OzsaNeuHTt27KjuIpXL+++/T4cOHXBycsLT05P777+fqKioEu95/PHHUalUJf46d+5cTSU2z6xZs24qu5eXV/HriqIwa9YsfHx8sLe3p3fv3kRGRlZjic3XsGHDm+qoUql4/vnnActrv+3btzN06FB8fHxQqVSsXLmyxOumtFlhYSGTJ0+mXr16ODo6MmzYMC5evFiFtShdaXXUarW89tprhISE4OjoiI+PD+PGjePSpUsl8ujdu/dN7frwww9XcU1uraw2NOU7WZPbsKz63er3qFKp+Oijj4rfU5Pbz5TrQk37HUowUw5Lly7lpZde4s033+TIkSP06NGDQYMGER8fX91FM9u2bdt4/vnn2bt3L+Hh4eh0OgYMGEBubm6J9w0cOJDLly8X/61du7aaSmy+li1blij7iRMnil+bN28eCxYs4IsvvuDAgQN4eXnRv3//4j29LMGBAwdK1C88PByAUaNGFb/HktovNzeX0NBQvvjii1u+bkqbvfTSS6xYsYIlS5awc+dOcnJyGDJkCHq9vqqqUarS6piXl8fhw4eZMWMGhw8fZvny5Zw5c4Zhw4bd9N6nnnqqRLt+++23VVH8MpXVhlD2d7Imt2FZ9buxXpcvX+aHH35ApVIxcuTIEu+rqe1nynWhxv0OFWG2jh07KpMmTSpxrFmzZsr06dOrqUQVJzk5WQGUbdu2FR8bP368Mnz48Oor1B2YOXOmEhoaesvXDAaD4uXlpXzwwQfFxwoKChQXFxflm2++qaISVrwXX3xRCQ4OVgwGg6Iolt1+gLJixYri56a02ZUrVxS1Wq0sWbKk+D0JCQmKlZWVsn79+ioru6n+Wcdb2b9/vwIocXFxxcd69eqlvPjii5VbuApwq/qV9Z20pDY0pf2GDx+u9O3bt8QxS2k/Rbn5ulATf4fSM2OmoqIiDh06xIABA0ocHzBgALt3766mUlWczMxMANzc3Eoc37p1K56enjRp0oSnnnqK5OTk6iheuURHR+Pj40NgYCAPP/ww586dAyA2NpbExMQSbanRaOjVq5fFtmVRURG//PILTz75ZImNVS25/W5kSpsdOnQIrVZb4j0+Pj60atXKYts1MzMTlUqFq6trieO//vor9erVo2XLlkybNs2iehRL+07WpjZMSkpizZo1TJgw4abXLKX9/nldqIm/w1q/0WRFS01NRa/XU79+/RLH69evT2JiYjWVqmIoisLUqVPp3r07rVq1Kj4+aNAgRo0aRUBAALGxscyYMYO+ffty6NChal/1sSydOnXip59+okmTJiQlJTFnzhy6du1KZGRkcXvdqi3j4uKqo7h3bOXKlVy5coXHH3+8+Jglt98/mdJmiYmJ2NraUrdu3ZveY4m/0YKCAqZPn86YMWNKbOQ3duxYAgMD8fLyIiIigtdff51jx44V32asycr6TtamNly8eDFOTk6MGDGixHFLab9bXRdq4u9QgplyuvF/vWBs8H8eszQvvPACx48fZ+fOnSWOjx49uvhxq1ataN++PQEBAaxZs+amH2hNM2jQoOLHISEhdOnSheDgYBYvXlw84LA2teX333/PoEGD8PHxKT5mye13O+VpM0tsV61Wy8MPP4zBYOCrr74q8dpTTz1V/LhVq1Y0btyY9u3bc/jwYcLCwqq6qGYp73fSEtvwhx9+YOzYsdjZ2ZU4bintd7vrAtSs36HcZjJTvXr1sLa2vimyTE5OvilKtSSTJ09m1apVbNmyBV9f31Lf6+3tTUBAANHR0VVUuorj6OhISEgI0dHRxbOaaktbxsXFsWnTJiZOnFjq+yy5/UxpMy8vL4qKisjIyLjteyyBVqvloYceIjY2lvDw8BK9MrcSFhaGWq22yHb953eytrThjh07iIqKKvM3CTWz/W53XaiJv0MJZsxka2tLu3btbuoKDA8Pp2vXrtVUqvJTFIUXXniB5cuXs3nzZgIDA8tMk5aWxoULF/D29q6CElaswsJCTp06hbe3d3EX741tWVRUxLZt2yyyLX/88Uc8PT0ZPHhwqe+z5PYzpc3atWuHWq0u8Z7Lly8TERFhMe16LZCJjo5m06ZNuLu7l5kmMjISrVZrke36z+9kbWhDMPaUtmvXjtDQ0DLfW5Par6zrQo38HVb4kOK7wJIlSxS1Wq18//33ysmTJ5WXXnpJcXR0VM6fP1/dRTPbs88+q7i4uChbt25VLl++XPyXl5enKIqiZGdnK6+88oqye/duJTY2VtmyZYvSpUsXpUGDBkpWVlY1l75sr7zyirJ161bl3Llzyt69e5UhQ4YoTk5OxW31wQcfKC4uLsry5cuVEydOKI888oji7e1tEXW7kV6vV/z9/ZXXXnutxHFLbL/s7GzlyJEjypEjRxRAWbBggXLkyJHimTymtNmkSZMUX19fZdOmTcrhw4eVvn37KqGhoYpOp6uuapVQWh21Wq0ybNgwxdfXVzl69GiJ32VhYaGiKIoSExOjzJ49Wzlw4IASGxurrFmzRmnWrJnStm3bGlHH0upn6neyJrdhWd9RRVGUzMxMxcHBQfn6669vSl/T26+s64Ki1LzfoQQz5fTll18qAQEBiq2trRIWFlZiKrMlAW759+OPPyqKoih5eXnKgAEDFA8PD0WtViv+/v7K+PHjlfj4+OotuIlGjx6teHt7K2q1WvHx8VFGjBihREZGFr9uMBiUmTNnKl5eXopGo1F69uypnDhxohpLXD4bNmxQACUqKqrEcUtsvy1bttzyOzl+/HhFUUxrs/z8fOWFF15Q3NzcFHt7e2XIkCE1qs6l1TE2Nva2v8stW7YoiqIo8fHxSs+ePRU3NzfF1tZWCQ4OVqZMmaKkpaVVb8WuKq1+pn4na3IblvUdVRRF+fbbbxV7e3vlypUrN6Wv6e1X1nVBUWre71B1teBCCCGEEBZJxswIIYQQwqJJMCOEEEIIiybBjBBCCCEsmgQzQgghhLBoEswIIYQQwqJJMCOEEEIIiybBjBBCCCEsmgQzQgghhLBoEswIIYQQwqJJMCOEEEIIiybBjBDirqTX6zEYDNVdDCFEBZBgRghR7X766Sfc3d0pLCwscXzkyJGMGzcOgL/++ot27dphZ2dHUFAQs2fPRqfTFb93wYIFhISE4OjoiJ+fH8899xw5OTnFry9atAhXV1dWr15NixYt0Gg0xMXFVU0FhRCVSoIZIUS1GzVqFHq9nlWrVhUfS01NZfXq1TzxxBNs2LCBRx99lClTpnDy5Em+/fZbFi1axHvvvVf8fisrKz777DMiIiJYvHgxmzdv5tVXXy3xOXl5ebz//vssXLiQyMhIPD09q6yOQojKI7tmCyFqhOeee47z58+zdu1aAD799FM+++wzYmJi6NWrF4MGDeL1118vfv8vv/zCq6++yqVLl26Z37Jly3j22WdJTU0FjD0zTzzxBEePHiU0NLTyKySEqDISzAghaoQjR47QoUMH4uLiaNCgAW3atGHkyJHMmDEDR0dHDAYD1tbWxe/X6/UUFBSQm5uLg4MDW7ZsYe7cuZw8eZKsrCx0Oh0FBQXk5OTg6OjIokWLeOaZZygoKEClUlVjTYUQFc2mugsghBAAbdu2JTQ0lJ9++ol7772XEydO8NdffwFgMBiYPXs2I0aMuCmdnZ0dcXFx3HfffUyaNIl3330XNzc3du7cyYQJE9BqtcXvtbe3l0BGiFpIghkhRI0xceJE/v3vf5OQkMA999yDn58fAGFhYURFRdGoUaNbpjt48CA6nY6PP/4YKyvjUMDff/+9ysothKheEswIIWqMsWPHMm3aNL777jt++umn4uNvv/02Q4YMwc/Pj1GjRmFlZcXx48c5ceIEc+bMITg4GJ1Ox+eff87QoUPZtWsX33zzTTXWRAhRlWQ2kxCixnB2dmbkyJHUqVOH+++/v/j4vffey+rVqwkPD6dDhw507tyZBQsWEBAQAECbNm1YsGABH374Ia1ateLXX3/l/fffr6ZaCCGqmgwAFkLUKP3796d58+Z89tln1V0UIYSFkGBGCFEjpKens3HjRsaOHcvJkydp2rRpdRdJCGEhZMyMEKJGCAsLIyMjgw8//FACGSGEWaRnRgghhBAWTQYACyGEEMKiSTAjhBBCCIsmwYwQQgghLJoEM0IIIYSwaBLMCCGEEMKiSTAjhBBCCIsmwYwQQgghLJoEM0IIIYSwaP8HTJo4vBNHNAMAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t0s = np.arange(0,28,2)\n", "\n", "for i in t0s:\n", " model_parameters[\"T\"] = i\n", " model = EBM(**model_parameters)\n", " run_model(model, 200) \n", " plt.plot(model.T)\n", "\n", "plt.grid()\n", "plt.xlabel(\"year\")\n", "plt.ylabel(\"temperature [°C]\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This figure shows that, no matter where we start out, the overall negative feedbacks ($B<0$) restore the temperature to the preindustrial equilibrum value of $T_0$ = 14.0 °C, over an exponential timescale of about 100 years." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2) Historical global warming fueled by greenhouse gas emissions\n", "\n", "Human greenhouse gas emissions have fundamentally altered Earth's energy balance, moving us away from the stable preindustrial climate of the past few thousand years.\n", "\n", "Since human CO₂ emissions are the main driver of global warming, we expect that if we plug historical CO₂ increases into our model (\"forcing\" it), we should roughly reproduce the observed historical global warming.\n", "\n", "The observed increase of CO2 concentrations can be fairly accurately modelled by the simple cubic formula below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def co2_hist(t):\n", " return 280 * (1+ ((t-1850)/220)**3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_parameters[\"CO2\"] = co2_hist\n", "model_parameters[\"T\"] = T0\n", "model_parameters[\"t\"] = 1850\n", "model = EBM(**model_parameters)\n", "\n", "run_model(model, 170) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "temp_url = \"https://raw.githubusercontent.com/florianboergel/climateoftheocean/main/data/graph.txt\"\n", "try:\n", " temp = pd.read_csv(temp_url, header=None,\n", " skiprows=5, index_col=0,\n", " delim_whitespace=True,\n", " on_bad_lines='skip', # Skip problematic lines\n", " engine=\"python\")\n", " temp = temp + 14.15\n", "except Exception as e:\n", " print(f\"Error loading temperature data: {e}\")\n", "\n", "CO2_url = \"https://raw.githubusercontent.com/florianboergel/climateoftheocean/main/data/monthly_in_situ_co2_mlo.csv\"\n", "try:\n", " co2_data = pd.read_csv(CO2_url, header = 58,skiprows=8, index_col=0) \n", "except Exception as e:\n", " print(f\"Error loading CO2 data: {e}\")\n", "co2_data = co2_data.iloc[4:] \n", "co2_data = pd.to_numeric(co2_data.iloc[:,5]) \n", "co2_data[co2_data<= 0] = np.nan\n", "co2_data.index = pd.to_datetime(co2_data.index, format='%Y')\n", "co2_data = co2_data.groupby(co2_data.index.year).mean() " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, (ax, bx) = plt.subplots(1,2, figsize=(8,4))\n", "ax.plot(np.arange(1850, 2020), co2_hist(np.arange(1850, 2020)), label = \"EBM model\")\n", "ax.plot(co2_data.index, co2_data.values, label=\"Keeling Curve\")\n", "ax.set_ylabel(\"$CO_2$ concentration [ppm]\")\n", "ax.grid()\n", "ax.set_xlabel(\"Year\")\n", "ax.legend()\n", "\n", "\n", "bx.plot(np.arange(1850, 2021), model.T, label=\"EBM model\")\n", "temp.plot(ax = bx)\n", "bx.set_ylabel(\"Temperature [°C]\")\n", "bx.grid()\n", "bx.legend([\"EBM Model\", \"NASA Observations\", \"NASA Obs roll. mean\"])\n", "bx.set_xlabel(\"Year\")\n", "\n", "f.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{warning} CO2 Emissions: Beyond Trend Predictions to Climate Noise\n", "\n", "While CO2 emissions can be used to predict the overarching trend in climate, we must also consider the inherent climate noise present in observations. This noise doesn't arise from instrumental inaccuracies but represents genuine signals from the Earth's natural variability.\n", "\n", "Such natural fluctuations are mainly attributed to the turbulent and chaotic fluid dynamics of both the atmosphere and the ocean. We will delve deeper into this topic in **Lecture 4**. The dynamics are visually represented in the following illustrations:\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![Video Thumbnail](https://img.youtube.com/vi/llOh7N_VY7k/maxresdefault.jpg)](https://youtu.be/llOh7N_VY7k?si=f_7iRgEWvMT6pZxk)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've convinced ourselves that the model accurately reproduces historical warming, we can use it to project how much warming we might expect due to future CO₂ emissions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What We Learned Today\n", "\n", "- **Energy Balance Fundamentals**: Built a zero-dimensional climate model based on absorbed solar radiation, outgoing thermal radiation, and greenhouse effects\n", "- **Numerical Climate Modeling**: Implemented and solved differential equations using Python, creating a reusable EBM class that reproduces historical temperature trends\n", "- **Climate Change Projections**: Applied our model to different CO₂ scenarios, demonstrating how human emissions affect global temperature" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Additional content: Best- and worst-case projections of future global warming\n", "\n", "Consider two divergent hypothetical futures:\n", "\n", "1) a low-emissions world in which emissions decrease such that CO2 concentrations stay below 500 ppm by 2100 (known in climate circles as \"RCP2.6\") and\n", "\n", "2) a high-emissions world in which emissions continue increasing and CO2 concentrations soar upwards of 1200 ppm (\"RCP8.5\")." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def CO2_RCP26(t):\n", " return 280 * (1+ ((t-1850)/220)**3 * np.minimum(1., np.exp(-((t-1850)-170)/100)))\n", "def CO2_RCP85(t):\n", " return 280 * (1+ ((t-1850)/220)**3 * np.maximum(1., np.exp(((t-1850)-170)/100)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the low-emissions scenario, the temperature increase stays below $\\Delta T$ = 2 °C by 2100, while in the high-emissions scenario temperatures soar upwards of 3.5ºC above pre-industrial levels." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_parameters[\"CO2\"] = CO2_RCP26\n", "model1 = EBM(**model_parameters)\n", "\n", "model_parameters[\"CO2\"] = CO2_RCP85\n", "model2 = EBM(**model_parameters)\n", "\n", "run_model(model1, 249) \n", "run_model(model2, 249) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, (ax, bx) = plt.subplots(1,2, figsize = (8,4))\n", "\n", "ax.plot(np.arange(1850, 2100), CO2_RCP26(np.arange(1850,2100)), \n", " color = \"Blue\", label = \"RCP 2.6 low emissions\")\n", "ax.plot(np.arange(1850, 2100), CO2_RCP85(np.arange(1850,2100)),\n", " color = \"Red\", label = \"RCP 8.5 High emissions\")\n", "ax.plot(2023, CO2_RCP26(2023), marker=\".\",\n", " markersize = 20, label = \"we are here\", color = \"black\")\n", "ax.set_ylabel(\"$CO_2$ concentration [ppm]\")\n", "ax.legend()\n", "\n", "bx.plot(np.arange(1850, 2100), model1.T, color = \"Blue\")\n", "bx.plot(np.arange(1850, 2100), model2.T, color = \"Red\")\n", "bx.axhline(y = 16, label = \"Paris Agreement\\n threshold (2°C warming)\",\n", " ls=\"--\", color = \"black\")\n", "bx.set_ylabel(\"Temperature [°C]\")\n", "bx.plot(2023, model1.T[173], marker=\".\", \n", " markersize = 20, label = \"we are here\", color = \"black\")\n", "bx.legend()\n", "\n", "f.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{note}\n", "A big thank you to [Henri Drake](https://github.com/hdrake) for the insightful lecture!\n", "\n", "The content is sourced from the MIT course titled [Introduction to Computational Thinking](https://computationalthinking.mit.edu/Fall20/lecture20/). This course is structured around the [Julia programming language](http://www.julialang.org/). \n", "\n", "The original code corresponding to this lecture is available on GitHub: [simplEarth/1_energy_balance_model.jl](https://github.com/hdrake/simplEarth/blob/master/1_energy_balance_model.jl).\n", "\n", "Additionally, some content has been adapted from [Brian Rose's Climate Laboratory Book](https://brian-rose.github.io/ClimateLaboratoryBook/courseware/).\n", "```\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 4 }