{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Radiocarbon"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "In this tutorial, we will simulate the radiocarbon age using the AIBECS by\n",
    "1. defining the transport `T(p)` and the sources and sinks `G(x,p)`,\n",
    "1. defining the parameters `p`,\n",
    "1. generating the state function `F(x,p)` and solving the associated steady-state problem,\n",
    "1. and finally making a plot of our simulated radiocarbon age."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "> *Note*\n",
    "> Although this tutorial is self-contained, it is slightly more complicated than the first tutorial for simulating the ideal age.\n",
    "> (So do not hesitate to start with the ideal-age tutorial if you wish.)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The tracer equation for radiocarbon is\n",
    "\n",
    "$$\\big(\\partial_t + \\mathbf{T} \\big) \\boldsymbol{R} = \\frac{\\lambda}{h} (\\overline{\\boldsymbol{R}}_\\mathsf{atm} - \\boldsymbol{R}) (\\boldsymbol{z} ≤ h) - \\boldsymbol{R} / \\tau.$$\n",
    "\n",
    "where the first term on the right of the equal sign represents the air–sea gas exchange with a piston velocity $λ$ over a depth $h$ and the second term represents the radioactive decay of radiocarbon with timescale $\\tau$."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "> Note:\n",
    "> We need not specify the value of the atmospheric radiocarbon concentration because it is not important for determining the age of a water parcel — only the relative concentration $\\boldsymbol{R}/\\overline{\\boldsymbol{R}}_\\mathsf{atm}$ matters."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We start by selecting the circulation for Radiocarbon.\n",
    ".)\n",
    "(And this time, we are using the OCCA matrix by *Forget* [1](https://doi.org/10.1175/2009JPO4043.1).)"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌ Warning: Over-writing registration of the datadep\n",
      "│   name = \"AIBECS-OCCA\"\n",
      "└ @ DataDeps ~/.julia/packages/DataDeps/ae6dT/src/registration.jl:15\n",
      "┌ Info: You are about to use the OCCA model.\n",
      "│ If you use it for research, please cite:\n",
      "│ \n",
      "│ - Forget, G., 2010: Mapping Ocean Observations in a Dynamical Framework: A 2004–06 Ocean Atlas. J. Phys. Oceanogr., 40, 1201–1221, https://doi.org/10.1175/2009JPO4043.1\n",
      "│ \n",
      "│ You can find the corresponding BibTeX entries in the CITATION.bib file\n",
      "│ at the root of the AIBECS.jl package repository.\n",
      "└ (Look for the \"Forget_2010\" key.)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(, sparse([1, 2, 9551, 9606, 1, 2, 3, 57, 9552, 9607  …  79928, 84632, 84659, 84660, 84661, 79891, 79929, 84633, 84660, 84661], [1, 1, 1, 1, 2, 2, 2, 2, 2, 2  …  84660, 84660, 84660, 84660, 84660, 84661, 84661, 84661, 84661, 84661], [2.2827161967151829e-7, 1.8030621712982388e-10, -2.202759122121374e-7, -1.669794081839543e-8, -8.32821319857397e-8, 3.645587487556812e-7, -2.4345873581518986e-8, 2.346272528650749e-8, -3.294980961099172e-7, 6.798120022902578e-9  …  -2.337773104642457e-8, -1.0853897038905248e-8, -2.4801404742669764e-8, 7.658935248883193e-8, -2.49410804832536e-8, -2.2788300189327118e-9, -3.386139290468414e-8, -2.0539768803799346e-8, -1.8193948479846445e-8, 5.86467452504204e-8], 84661, 84661))"
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "cell_type": "code",
   "source": [
    "using AIBECS\n",
    "grd, T_OCCA = OCCA.load()"
   ],
   "metadata": {},
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": [
    "The local sources and sinks are simply given by"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "G (generic function with 1 method)"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "cell_type": "code",
   "source": [
    "function G(R,p)\n",
    "    @unpack λ, h, Ratm, τ = p\n",
    "    return @. λ / h * (Ratm - R) * (z ≤ h) - R / τ\n",
    "end"
   ],
   "metadata": {},
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can define `z` via"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "84661-element Vector{Float64}:\n   25.0\n   25.0\n   25.0\n   25.0\n   25.0\n   25.0\n   25.0\n   25.0\n   25.0\n   25.0\n    ⋮\n 5062.25\n 5062.25\n 5062.25\n 5062.25\n 5062.25\n 5062.25\n 5062.25\n 5062.25\n 5062.25"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "cell_type": "code",
   "source": [
    "z = depthvec(grd)"
   ],
   "metadata": {},
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "In this tutorial we will specify some units for the parameters.\n",
    "Such features **must** be imported to be used"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "import AIBECS: @units, units"
   ],
   "metadata": {},
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "We define the parameters using the dedicated API from the AIBECS, including keyword arguments and units this time"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "units (generic function with 20 methods)"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "cell_type": "code",
   "source": [
    "@units struct RadiocarbonParameters{U} <: AbstractParameters{U}\n",
    "    λ::U    | u\"m/yr\"\n",
    "    h::U    | u\"m\"\n",
    "    τ::U    | u\"yr\"\n",
    "    Ratm::U | u\"M\"\n",
    "end"
   ],
   "metadata": {},
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "source": [
    "For the air–sea gas exchange, we use a constant piston velocity $\\lambda$ of 50m / 10years.\n",
    "And for the radioactive decay we use a timescale $\\tau$ of 5730/log(2) years."
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": " Row │ Symbol  Value      Unit\n     │ Symbol  Float64    FreeUnit…\n─────┼──────────────────────────────\n   1 │ λ          5.0     m yr⁻¹\n   2 │ h         50.0     m\n   3 │ τ       8266.64    yr\n   4 │ Ratm       4.2e-8  M"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "cell_type": "code",
   "source": [
    "p = RadiocarbonParameters(λ = 50u\"m\"/10u\"yr\",\n",
    "                          h = grd.δdepth[1],\n",
    "                          τ = 5730u\"yr\"/log(2),\n",
    "                          Ratm = 42.0u\"nM\")"
   ],
   "metadata": {},
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "> *Note*\n",
    "> The parameters are converted to SI units when unpacked.\n",
    ">  When you specify units for your parameters, you must supply their values in that unit."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We build the state function `F` and the corresponding steady-state problem (and solve it) via"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "84661-element Vector{Float64}:\n 3.7813682936931906e-5\n 3.775789970720447e-5\n 3.660570790298678e-5\n 3.707009029891997e-5\n 3.715744797475306e-5\n 3.71583992727843e-5\n 3.707936439247658e-5\n 3.7149283799005414e-5\n 3.7232859237789926e-5\n 3.735496095490033e-5\n ⋮\n 3.638493414383584e-5\n 3.6381532923815344e-5\n 3.637308898920196e-5\n 3.638148729939853e-5\n 3.641671381563023e-5\n 3.6455997988650354e-5\n 3.6506957827145925e-5\n 3.6522182554320104e-5\n 3.6535884095673e-5"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "cell_type": "code",
   "source": [
    "F = AIBECSFunction(T_OCCA, G)\n",
    "x = zeros(length(z)) # an initial guess\n",
    "prob = SteadyStateProblem(F, x, p)\n",
    "R = solve(prob, CTKAlg()).u"
   ],
   "metadata": {},
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "This should take a few seconds on a laptop.\n",
    "Once the radiocarbon concentration is computed, we can convert it into the corresponding age in years, via"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "84661-element Vector{Quantity{Float64, 𝐓, Unitful.FreeUnits{(yr,), 𝐓, nothing}}}:\n  867.9858872976015 yr\n  880.1899484002798 yr\n 1136.3776304295714 yr\n 1032.1660922537071 yr\n  1012.708218981491 yr\n 1012.4965806538096 yr\n  1030.098224932373 yr\n 1014.5247521962259 yr\n   995.948020099204 yr\n  968.8826854822956 yr\n                     ⋮\n  1186.385775950517 yr\n 1187.1585679085028 yr\n 1189.0774285370644 yr\n 1187.1689347334582 yr\n 1179.1685985315798 yr\n 1170.2558452939318 yr\n 1158.7084282578335 yr\n 1155.2616567592718 yr\n 1152.1609520256272 yr"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "cell_type": "code",
   "source": [
    "@unpack τ, Ratm = p\n",
    "C14age = @. log(Ratm / R) * τ * u\"s\" |> u\"yr\""
   ],
   "metadata": {},
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "and plot it at 700 m using the `horizontalslice` Plots recipe"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Plot{Plots.GRBackend() n=1}",
      "image/png": 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      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "cell_type": "code",
   "source": [
    "using Plots\n",
    "plothorizontalslice(C14age, grd, depth=700u\"m\", color=:viridis)"
   ],
   "metadata": {},
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": [
    "look at a zonal average using the `zonalaverage` plot recipe"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Plot{Plots.GRBackend() n=1}",
      "image/png": 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      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "cell_type": "code",
   "source": [
    "plotzonalaverage(C14age, grd; color=:viridis)"
   ],
   "metadata": {},
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "or look at a meridional slice through the Atlantic at 30°W using the `meridionalslice` plot recipe"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Plot{Plots.GRBackend() n=1}",
      "image/png": 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tW7asz9oCAIA+0FQiTElJOXToUHp6empq6qFDh+h5LqNGjXr69On69esfPHgwe/bsSZMm0Z2fn3zyyaJFi5KTk/fv33/ixImYmBhCSHR0tEgk+uqrr1JSUmbMmDFixAh7e3sN1RYAAJSSEo5E/R9t11o9mhojTElJOXbsGN2ZeejQITMzM29vbwsLi/Pnzy9dunT//v09evRYunQpXXjGjBmVlZUzZ860tLRMSEjw8PAghBgaGp49e3bRokUnT57s3Lnz119/raGqAgCAKhRRf2FeXbt8QlOJcNy4cePGjWPHW7dunZCQwAhyudz58+fPnz+fEffx8Tl48KCGaggAAEBwr1EAAKhCjW66jRYhAAA0FhThSNUc81O3vNYhEQIAgEoUUbtFWPW9RjMyMhITE+/du+fr6xsbG0sHKyoqZsyYISsTERExbNgw+vHp06d37NjB5XInT57cu3dvOlhSUrJy5cqUlJSWLVsuXLjQyspKrRoy6FgDFgAAdNqePXsOHjx469atc+fOyYJisXjr1q3BwcEhISEhISGyW69cvXp11KhRgwcP7t+//7Bhw2QX4I0fP/7PP/+cMWNGRkbG8OHDa1kl/W0R2huUVF2gkmK+OBVSQ0ZEaFDNC2jBFcpvuhgWMgow5hnzOcwFp2owEVmiuMQVl3UEQ8VAiZTHKCCQGjEiIsIsw3pShS9VItaTCqTGik9qwiiQK2rCiKQLbeU3c4TMAgKRQj2FYubbUVqhUKCk3JhRQFjKPFNukcJBDIuYXxYNFT84PBFhUlxhjGK9cqzXm1lGyfdv9buaOIq3HTRk3YWQrxjhsApwFE+E9dkkXDErUqmwyRMxV1tj7MKRMgswnkVJrVi7MF5wJcdknKmYXaC6FdVZy8ZxpNW8JaxXj3kEiqsb/YeU+l2dVZdfuHAhIWTFihXsNYViYmKMjBT+JNevX//ZZ5+NGTOGEPL48eONGzfu3r07LS3t1KlTOTk5VlZWXbp0cXBwSElJCQoKUquS8tAiBAAAlejJMur+1Oy55s6dO2PGjPj4eNmCtcnJyWFhYfTjLl260C3CO3fu+Pv7092hJiYmbdu2lbUUawaJEAAAtIzH4/3nP/8JCQlp1qzZZ599JlukLzc319b2fz1Dtra22dnZhJCcnBwbGxvZvnZ2djk5ObV5dv3tGgUAgGpRRO2FdqUUp6SkxMvLSz7o7e19+vRpVbsYGxv/9NNP9ONevXq1a9fu66+/trCwMDU1FQr/N8BUXl5ubm5OCDEzM6uoqJDtW1ZWVsvbTSMRAgCASvTCvGrtIiFcMzMz+bkwhJD3z1W+vr5SqTQvL8/CwsLNzS09Pb1z586EkPT0dDc3N0IIHaQoisPh0PFmzZqpVUMGJEIAAFCJomrSIuRyuZ6enu+/S25urpWVFT1T5qeffnJycmrevDkhZMSIEf/9739HjRpFUdSuXbtGjBhBCOnWrVtlZeWZM2eioqKSkpKys7MjIyPVqiEDEiEAANSfuLi4mTNnCoVCiURiY2MTExOzZs2aM2fOzJw509fXNz8/XyQS7d+/n8fjEUKmT59+/PjxwMBAiqLs7e2nTp1KCOHz+Zs2bRo7dmxAQMCjR482bNiArlEAANAUqroL5NmqLj9ixIjo6GjZJt0KHD9+fP/+/V++fGlpaenh4WFo+L9r1SwtLZOSkh49esTlcv39/WV7DRs2rHfv3s+fP/fw8JDNpqkxJEIAAFCpBmOEVa8+YWRkxLhYkGZra6s0pXE4nICAAHbcysqqXbt2alVMFVw+AQAAeg0tQgAAUKlmk2U0VBkNQSIEAACVKEr9hXmxDBMAADQa+rAMk47lbQAAgLqFFiEAAKgkJUSi5pifuuW1DokQAABUqtEYoY4lQnSNAgCAXkOLEAAAVKrZ6hMaqoyGIBECAIBKdb5CfQOERAgAACpJKY66k190rkWIMUIAANBraBECAIBKFMGdZQAAQI/pw71GdSxvAwAA1C20CAEAQCXMGgUAAL2GrlEAAIBGDi1CAABQSap+C0/nWoRIhAAAoFJNukYxRggAAI2GPlxHqGPVBQAAqFtoEQIAgEoU4ajb1UmhaxQAABoNimr8k2XQNQoAAHoNLUIAAFAJC/MCAIBeq1HXqIbqoin6mwhdDAurLiBRfwYwjyNlRMw4lfKbbgYljAJNuDyFI7AGmQ05PEaEq9ihLaIkjALPxQqbmWIbRoG0Sgf5zdR3zowCr94xdykSmshvVkqYtZJIFWollTJPpEKk8GETVTA/e9JK5jEpseJBWH+MHAOFPzgOj/n6c7gKBaRi1ntaxnp5yxXKcJivLpEaKW7yWbVi1IJZKWUU/3GwPkfVlFe6C6PmXHYBxc8JV8QswBVTipusAqwXhyNRfEfe59yrxBWx3lP2P1mqugKK/5iVFKi1S6fmMyI9e62sZh+ObjSbKMKh1EyE6pbXOowRAgCAXtPfFiEAAFQLq08AAIBew+oTAAAAjRxahAAAoJI+XFCPRAgAACrpw6xRJEIAAFCpBmOEOpcIMUYIAAB6DS1CAABQicIK9TVDUdS1a9cuXLiQl5fn5+c3ceJEMzMz+ldv37796aefcnJyevbsOXToUNkuFy5cSEhIsLS0jI2NbdasGR0sKSnZvHnzy5cvO3fuPG7cOI6O3IgBAKDRoJTcyKj6XXSLRrpG8/LyPvroI5FI1KpVqyNHjoSHh1dWVhJCxGJxeHj4s2fP2rRpM3/+/PXr19PlExISRo4c6e/vX15e3rFjx4KCAjoeFRV1/fr19u3br1mz5ssvv9REVQEAQM9ppEVoa2v79OlTHo9HCJk4caK9vf3du3dDQ0OPHj0qkUh27tzJ5XLd3d0nTpz4ySefGBgYrFy5cuXKlZMnTyaEPH78eNeuXbNmzbp8+fLz588vXbpkaGgYEhLSrVu3BQsWyFqWAABQD3BBfQ3xeDw6CxJCKioqKioqrKysCCHXrl3r0aMHl8slhHTv3j07Ozs9Pb2ysvLWrVu9evWiy/fs2fPKlSuEkKtXr4aHhxsaGhJCgoODDQ0NHz58qInaAgBAFSiKo+6PtqusHo1PlpkxY8aAAQP8/PwIIdnZ2d7e3v97YgMDa2vrrKwsIyMjiqLs7OzouIODQ3Z2NiEkJyfH3t5edhx7e/usrCylT1FWViYUCjV7GgAAekkfWoSaTYSLFy9+8ODBxYsX6U0+ny8W/7uUS2VlpZGREZ/PJ4TI4nSQLiyf3mRxNiMjIwMDTH8FAICa0OB1hCtWrIiPjz979qy1tTUdcXV1/fvvv+nHAoGgpKTE1dXV3t6ez+e/fv2ajr9+/drV1ZVRWCwW5+bm0nE2Ho+HRAgAoAmUHnSNaioRrl27dvfu3efOnXNw+HcN2EGDBp05c6aoqIgQ8uuvv4aEhDRt2pTL5Q4YMGD//v2EkMrKyvj4+EGDBhFCBg4ceOXKFbqb9MSJEw4ODkFBQRqqLQAAKEVRRKr+j27RSEMqPT199uzZHh4eAwcOpCPffvttREREx44dIyMjQ0NDg4OD//jjjwMHDtC/Xbp0aa9evZ48efLq1SsnJ6chQ4YQQry8vKZOndq5c+fQ0NDz58///PPP9CwbAACAOqSRROjs7Hz79m35iKenJ/0gLi7u1q1bWVlZ69evd3JyooMBAQF//fXXlStXmjRpEhYWJptx+sMPP4wfP/7ly5erV692c3PTRFUBAKAKNejq1LmuUY0kQiMjo5CQEFW/7dChAztoZWU1YMAAdrx169atW7euy8oBAIAa1E+EWKEeAAAaDX1YjxCjbgAAoNfQIgQAAJX04abbSIQAAFAF9a8LRNcoAACADkGLEAAAVKIotS+HwOUTAADQeFCEUGoO+qlbXuuQCAEAQLWaXFCvoapoCsYIAQBAr6FFCAAAKmGMEAAA9F2jHyNE1ygAAOg15S1CqVR6//79lJSU/Px8AwMDBweHDh06tGjRop4rBwAA2qWPq0/89ddfmzdv3rNnz9u3bxm/8vT0jImJmTx5svxauwAA0LjpUddofn5+bGxsq1atfv/995iYmISEhNTU1Nzc3MzMzJSUlLi4uIiIiHXr1rVo0eKbb76prKzUYqUBAKDxqaioEAqF71mYoqjCwkKpVFr75/03EZ46derVq1eXLl16+vTpqlWrBg0a5Ofn5+Dg4OLiEhgYOG7cuC1btmRmZu7YsePAgQPp6em1f24AAGjoKEIojto/qh05ciQiIsLJyemjjz6SBa9du9a2bVsrKysHB4fOnTs/efKEjq9atcpGTn5+Ph1PSkpyd3dv1aqVm5vbH3/8UctT/DcRjhw58vTp0126dKmitKGh4bBhw+7fv+/h4VHLJwYAgIaPIhx6mFCtnyoOaGZmNnXq1BEjRrx7904W5HA4a9asKS0tLSgoaN269aRJk+h4eXn58OHDX/zD1taWECKVSj/88MOlS5dmZWVt2LBh3LhxIpGoNuf4byLk8/nvuw+Xa2hoWJtnBQAAXUGp/1OFPn36DB8+3NHRUT7YuXPnXr168Xg8AwODkSNHylqEhBBjY2Prf3A4HELItWvXSkpKxo8fTwgZOnQon88/d+5cbU6wqssnKisrMzIy0hTV5skAAACqdvjw4R49esg2d+zYwefzvby81q9fT0fS0tK8vb15PB696evr++LFi9o8o/LLJ9LT06dPn/77779TrNk/7AgAADRa1Y35Kd1FIpHcuXNHPmZpaent7V3trgcPHjx06NCtW7fozZEjR8bGxtrb21+7dm3o0KGOjo6jRo0qLi42NTWV7WJhYVFUVKReDRUpT4TDhg3LyMhYtmxZixYtZFkXAAD0Tc1WnygvL4+NjZUPuru7Hz58uOodjx8/PmPGjNOnTzdr1oyOtGzZkn7QrVu3KVOmHD9+fNSoUfb29sXFxbK9CgsLa3lRn5JEWFhYeOfOnYSEhEGDBtXm0AAAoJ/Mzc1v376t1i5nz56dMmXKsWPHWrdurbRAWVmZkZERIaRVq1aPHj0qLy83MTERi8X379//+uuva1NbJYnQ2NjYwMDAycmpNscFAIDGoNrZL0p3US0zMzM1NfXFixdv3rw5f/5806ZN/fz8rl27Nnjw4IULFwoEgvPnzxNCevbsyeVy169f36FDBxsbm6tXr27bti0hIYEQEhQU1KZNm/nz58+ZM2fLli1ubm6dO3eu8fkRpYnQxMRk9OjRcXFxHTt2rM2hAQBA19X56hMpKSk//PADIcTIyGjVqlUDBgzw8/PLysoKCwtLTExMTEyki3Xt2tXIyCg3N3fWrFlFRUVeXl6HDh2KiIigf/vrr7/OmTOnb9++/v7+dHasDeVjhJs3bx43blxUVFRERISFhYX8r6ZOnVrLpwQAAF1Spy3Cvn379u3blxEcPnz48OHD2YW/+eYbpQdxdXU9cOCAmtVSSXkivHLlyuXLlwsKCs6cOcP4FRIhAAA0JkoSoUQimThxoqen5+HDh318fORnqQIAgH6pweUTRMdXnyCE5OXl5eTkHDp0qOrbrQEAgD7Qo9UnZGxsbMzNzcVicf3XBgAAoJ4pSYR8Pn/BggXLly+XvyMqAADoo7pefaIBUj5Zpqio6NGjR15eXmFhYXZ2dvK/+vnnn+ulYgAA0DDU6azRBkh5Irx8+TI9R+b+/fv1Wx8AAGhI6vqC+gZIeSK8efNmPdcDAABAK5QnQgAAAEJquPqEZqqiKf9OlsnJyamoqHifffLz80tLSzVWJQAAaEjqdmXehuffRHjhwgVvb++NGzcWFBSoKp2Wlvb55597enrm5OTUS/UAAAA069+u0VGjRpWWli5ZsmTu3Lndu3cPDQ318fGxtbUVi8X5+fkpKSlJSUm3bt3y9/c/ePBgixYttFjpOuHEE1RdQKLs2pKq8Yi06gKOPD4jYsIxkt+Uso5QQYkYkatCY/nNvyqcGQUyKmzlN/MqzRkF8oUKkdx3FowCxWXGjEjFO4WaUyLmi8OpUIhwxMy+EUaEW8kswGO9eJzqvlcyO0Z9mWgAACAASURBVGCq64/hsQ7IZV0uy5EobrLfUqrKTUIoxmvDWtBTYsSMSBXHKJhHIIRSrDrFXiSUfe6MCPvFVIwoebWrO1NlOFVssSPsMyVchadJmzGv2qcMG/694kHZFa3mk1HtJ439MbhyZG7Vu/xxYUE1B9UVejVZhsfjxcbGjh8//tdff929e/fq1auFQqHst1ZWVj179jx58mRkZCSXq3aGAAAAXdXYxwiZk2VMTEwmTJgwYcIEoVD49OnT/Px8AwMDBwcHHx8f5D8AAGh8VM4aNTY2DgoKqs+qAAAA1D9cPgEAAKrp1RghAACAEo09EWLYDwAA9BpahAAAoBrF4ag5C1Td8lqHRAgAAFVC1ygAAOivGtxfrR4T4datW5OTk2t5EJUtwpMnTyYkJGRmZopECnc2OXfuXC2fEgAAoE6sXr161apV7du3r81BlLcI58yZEx0d/fvvv5eVldXm6AAAoNsadoswNDT0wYMHtTyIkhahRCLZsmXLtGnTNmzYwOOx72kIAAD6o0Evw7R48eIhQ4ZYWlr27dvX2dmZw/nfU5uamhoZse7qq4KSFmF+fn5ZWdmkSZNqmQXFYnFRURE7LhKJiouL2fGSkhL2OlCqDgIAADBv3rzU1NR58+YFBATY2tra/GPnzp3vfxAlidDOzs7R0TEzM7M2lRs0aFCTJk28vb2tra2/+eYbWXzr1q2Ojo4+Pj7t2rV79eoVHSwsLOzdu7e7u7ujo+NXX30lK3zw4EFnZ2c/Pz9/f//Hjx/Xpj4AAFADHKomP/Vm2bJl55SJjo5+/4Mo6Rrl8Xg//PDD4sWLg4ODmzdvXrPKLV++/LfffjMwMPjzzz9DQ0N79OjRqVOnV69ezZ49OykpKSgoaNasWTNnzkxISCCELF261NLSMi8vLzs7u23btr179w4LCyssLIyJifn999+7du26YsWKqVOnXr16tWaVAQCAmmvAl0O0bt269gf5NxGeOXPmu+++k23m5OT4+Pj4+/vb2dnJ7/Ces0YDAwPpBwEBAc7Oznl5eYSQffv29ezZk76X9+zZsz08PAoKCmxsbOLi4o4ePcrlcl1dXUeNGhUXFxcWFvbbb78FBAR07dqVEPLpp58uW7bsxYsXXl5etT5lAAB4bw37XqOffvqpk5PTpEmTnJ2ZK7O+P5XXEQYFBYWHhzOyoFoSExN/+umnMWPG+Pn5RUVFEULS0tL8/Pzo37q5uRkZGb169aqwsLCwsFAW9/X1TUtLowu3bNmSDlpaWjo7O9NxpaTSalbEBQCAxqdVq1Y//vhj8+bNhw4deubMmZrlgn9bhJGRkZGRkXVXPZKXl/f48eOnT5/6+flJJBJCSHFxsaurq6yAhYVFUVERPXHG1NSUDpqbm9OzY4qLi2VBWWGlT1RaWlpeXl6HNQcAABqHqD3mV59jhB9//PGUKVMuXry4devW/v37Ozs7jx079pNPPnFzc3v/gyhvEcbFxeXm5jKCubm5W7duff9DDxs27Mcff7x169bTp09/+eUXQoi9vb18MisqKnJwcLC3tyeEyOaR0kG6sPzk0sLCQjrOZm5ubmZm9v4VAwCA99WwryMkhPB4vN69ex88ePDZs2fjx4/fvXu3l5fXiBEjLl++/J5HUJ4I582b9+LFC0YwLS0tNjZW3SpyuVxPT8/8/HxCSEBAwJ07d+j4w4cPDQ0NPTw8zMzMPDw8ZPHbt28HBATQhW/fvk0Hs7Ky8vLyZN2nAAAADGVlZaWlpUKh0MLCIjs7u3v37v3793+f28Koca/R0tJSc3Pz9yn59u3bdevW3bt3LzU1dcuWLSdOnBg4cCAhZPTo0X/++ef27dtfvHjxf//3f+PHj6c7Pz/++OMlS5Y8efLk+PHjR48ejYmJIYQMHDhQIBB8//336enpc+bMGTx4sKOj4/vXFgAA6gbFUfunHpWXl+/evbtLly4BAQE3b95cu3ZtZmbmlStXrl27dv/+/YMHD1Z7BIXLJx4+fHj9+nX6uEePHv3zzz9lv6qoqNi3b997tsn4fP6jR4/i4uKEQqGPj8+JEyfatWtHCLGysjp16tTixYu/++67iIiIb7/9li4/Z86c0tLS4cOHN2nSZN++fd7e3vRBTp8+vWDBgm3btnXu3Fl+RisAANQT9a8LrM8xwrVr1y5btkwkEo0ePXrDhg1t27aV/apTp049evR4n2viFRLh+fPnZ8+eTT9evXo1o6i3tzc91FctCwsLVSVDQ0PZF2DweLxly5YtW7aMEQ8MDDx58uT7PCMAAOihrKysZcuWjR8/vkmTJuzfLl++/H1utKaQCP/zn/9MmDCBEOLr6xsXF9ehQwfZr0xMTIyNjWtbZQAA0C0N+zrCNWvWVPFbd3f39zmIQiI0Njams11ycrKjoyMyHwAANOREWCeUr0dI31ktJSXl3r17mZmZjo6OAQEBHTt2rN+6AQCAlnEIUXfqS71OlakLyhNhUVHRuHHjGONzYWFhBw8edHFxqZeKAQAA1Afll09MmjTp4sWLa9asefnyZVlZWUZGxpYtW548eTJ06NB6rh8AAGhTg7+gvvaUtAgFAsHRo0d/+eWXSZMm0RE3N7fY2NjmzZv37dv32bNn9OUNAACgFxr7GKGSFqFQKJRKpWFhYYx4ly5dCCHv3r2rj3oBAADUCyWJ0N7e3sfHh73435UrV6ytrXGfMwAAPdKwF+atE0q6Rjkczs6dO0eNGlVQUDB06FBHR8f8/PxTp06tWrUqLi4O11QAAOiXxt41qnzW6JAhQ3Jzc+fPnz9//nz5eHR0tOzxL7/8MnnyZM3WDgAAtKthX1BfJ5QnwkWLFlU7Fih/3xkAAAAdpTwRTp8+vZ7rAQAADVANxvx0boywmmWYcnNzHz58WD9VAQCAhkf9NZjqdxmm2lOeCCmKWrVqlZ2dnZOTU9++feng9OnTp02bVo91AwAA0DjliXDFihWLFy8eO3bs0qVLZcHevXvv3bu3srKynqoGAABapweXTyhJhGKx+Lvvvlu+fPn69eu7d+8ui7dt21YgELx+/br+agcAAFrX2G+xpiQRvnnzpri4uH///oy4jY0NIeTt27f1US8AAGgIGnsWJEoTobm5OYfDycvLY8QfP35MCHF0dKyPegEAANQLJYnQ0tKyU6dOy5cvLy8v53D+N/nn3bt3CxcubNWqVbNmzeq3hgAAoDUc0vjHCJVfR7hu3bru3bu3atUqICCgpKRk2rRpJ06cyMnJOXPmTD3XDwAAtEkP7iyjfNZo+/btb9261bp164sXLwoEgu3bt/v6+l6+fLlHjx71XD8AAACNUt4iJIS0atUqPj6eECIQCCwsLGR9pAAAoF8ae4tQZSIk/yxMaGlpWW+1AQCAhkU/b7F28+bNYcOG2djYmJiYmJmZWVhY9O/f/9y5c/VfOQAA0LIaXD6ha4mQ2SLcu3fvhAkTKIoKCQlxd3fncrl///33+fPn6fUI582bp5VaAgAAaIhCIszKyoqNjW3duvX+/ftbtGghH580adLnn38eGRkZFBRU75UEAADQFIWu0T179lAUdfz4cfksSAhxcXE5fPiwo6Pjtm3b6rd6AACgTfpwHaFCIrx69Wp0dLSTkxO7nLm5+ejRo69evVpfFQMAAKgPConw1atXrVq1UlU0ICAgPT1d4zUCAICGQwOTZbKysn7//ffr168rPA9FnTp1av369VeuXJGP5+bmbt26ddu2bYwbf168eHH9+vVnz56t/SkqJMKSkhJzc3NVRS0sLAQCQe2fEgAAdEWdd41+/vnnPj4+MTEx33//vXx8+vTp8+bNy87OHj9+/MqVK+lgenp6YGDgjRs3rl69GhwcnJmZSceXLl06efLk7OzsmTNnzp07t5bnqJAIpVJpFRfOczgciURSy+cDAABdUtctwvnz5xcXF0+fPl0+mJGRsWPHjvPnz69cuTIhIeHbb78tLS0lhKxbty46OnrHjh07d+7s2bPnxo0bCSFFRUV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     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "cell_type": "code",
   "source": [
    "plotmeridionalslice(C14age, grd, lon=-30, color=:viridis)"
   ],
   "metadata": {},
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "source": [
    "---\n",
    "\n",
    "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*"
   ],
   "metadata": {}
  }
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