{ "cells": [ { "cell_type": "markdown", "source": [ "# py12box model usage\n", "\n", "This notebook shows how to set up and run the AGAGE 12-box model.\n", "\n", "## Model schematic\n", "\n", "The model uses advection and diffusion parameters to mix gases between boxes. Box indices start at the northern-most box and are as shown in the following schematic:\n", "\n", "\"Box\n", "\n", "## Model inputs\n", "\n", "We will be using some synthetic inputs for CFC-11. Input files are in:\n", "\n", "```data/example/CFC-11```\n", "\n", "The location of this folder will depend on where you've installed py12box and your system. Perhaps the easiest place to view the contents is [in the repository](https://github.com/mrghg/py12box/tree/develop/py12box/data/example/CFC-11).\n", "\n", "In this folder, you will see two files:\n", "\n", "```CFC-11_emissions.csv```\n", "\n", "```CFC-11_initial_conditions.csv```\n", "\n", "As the names suggest, these contain the emissions, initial conditions and lifetimes.\n", "\n", "### Emissions\n", "The emissions file has four columns: ```year, box_1, box_2, box_3, box_4```. \n", "\n", "The number of rows in this file determines the length of the box model simulation.\n", "\n", "The ```year``` column should contain a decimal date (e.g. 2000.5 for ~June 2000), and can be monthly or annual resolution.\n", "\n", "The other columns specify the emissions in Gg/yr in each surface box.\n", "\n", "### Initial conditions\n", "\n", "The initial conditions file can be used to specify the mole fraction in pmol/mol (~ppt) in each of the 12 boxes.\n" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## How to run\n", "\n", "Firstly import the ```Model``` class. This class contains all the input variables (emissions, initial conditions, etc., and run functions).\n", "\n", "We are also importing the get_data helper function, only needed for this tutorial, to point to input data files." ], "metadata": {} }, { "cell_type": "code", "execution_count": 1, "source": [ "# Import from this package\n", "from py12box.model import Model\n", "from py12box import get_data\n", "\n", "# Import matplotlib for some plots\n", "import matplotlib.pyplot as plt" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "The ```Model``` class takes two arguments, ```species``` and ```project_directory```. The latter is the location of the input files, here just redirecting to the \"examples\" folder.\n", "\n", "The initialisation step may take a few seconds, mainly to compile the model." ], "metadata": {} }, { "cell_type": "code", "execution_count": 2, "source": [ "# Initialise the model\n", "mod = Model(\"CFC-11\", get_data(\"example/CFC-11\"))\n" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Compiling model and tuning lifetime...\n", "... completed in 3 iterations\n", "... stratospheric lifetime: 52.1\n", "... OH lifetime: 1e12\n", "... ocean lifetime: 1e12\n", "... non-OH tropospheric lifetime: 1e12\n", "... overall lifetime: 52.0\n", "... done in 5.5602850914001465 s\n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Assuming this has compiled correctly, you can now check the model inputs by accessing elements of the model class. E.g. to see the emissions:" ], "metadata": {} }, { "cell_type": "code", "execution_count": 3, "source": [ "mod.emissions" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[100, 10, 0, 0],\n", " [100, 10, 0, 0],\n", " [100, 10, 0, 0],\n", " ...,\n", " [ 0, 0, 0, 0],\n", " [ 0, 0, 0, 0],\n", " [ 0, 0, 0, 0]])" ] }, "metadata": {}, "execution_count": 3 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "In this case, the emissions should be a 4 x 12*n_years numpy array. If annual emissions were specified in the inputs, the annual mean emissions are repeated each month." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "We can now run the model using:" ], "metadata": {} }, { "cell_type": "code", "execution_count": 4, "source": [ "# Run model\n", "mod.run()" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "... done in 0.024969100952148438 s\n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "The primary outputs that you'll be interested in are ```mf``` for the mole fraction (pmol/mol) in each of the 12 boxes at each timestep.\n", "\n", "Let's plot this up:" ], "metadata": {} }, { "cell_type": "code", "execution_count": 5, "source": [ "plt.plot(mod.time, mod.mf[:, 0])\n", "plt.plot(mod.time, mod.mf[:, 3])\n", "plt.ylabel(\"%s (pmol mol$^{-1}$)\" % mod.species)\n", "plt.xlabel(\"Year\")\n", "plt.show()" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-05-19T11:56:17.659870\n image/svg+xml\n \n \n Matplotlib v3.3.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" ] }, "metadata": { "needs_background": "light" } } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "We can also view other outputs such as the burden and loss. Losses are contained in a dictionary, with keys:\n", "- ```OH``` (tropospheric OH losses)\n", "- ```Cl``` (losses via tropospheric chlorine)\n", "- ```other``` (all other first order losses)\n", "\n", "For CFC-11, the losses are primarily in the stratosphere, so are contained in ```other```:" ], "metadata": {} }, { "cell_type": "code", "execution_count": 6, "source": [ "plt.plot(mod.emissions.sum(axis = 1).cumsum())\n", "plt.plot(mod.burden.sum(axis = 1))\n", "plt.plot(mod.losses[\"other\"].sum(axis = 1).cumsum())" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": {}, "execution_count": 6 }, { "output_type": "display_data", "data": { "image/png": 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", "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-05-19T11:56:18.083719\n image/svg+xml\n \n \n Matplotlib v3.3.3, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" } } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Another useful output is the lifetime. This is broken down in a variety of ways. Here we'll plot the global lifetime:" ], "metadata": {} }, { "cell_type": "code", "execution_count": 7, "source": [ "plt.plot(mod.instantaneous_lifetimes[\"global_total\"])\n", "plt.ylabel(\"Global instantaneous lifetime (years)\")" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Text(0, 0.5, 'Global instantaneous lifetime (years)')" ] }, "metadata": {}, "execution_count": 7 }, { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" } } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Setting up your own model run\n", "\n", "To create your own project, create a project folder (can be anywhere on your filesystem). \n", "The folder must contain two files:\n", "\n", "```_emissions.csv```\n", "\n", "```_initial_conditions.csv```\n", "\n", "To point to the new project, py12box will expect a pathlib.Path object, so make sure you import this first:" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "from pathlib import Path\n", "new_model = Model(\"\", Path(\"path/to/project/folder\"))" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Once set up, you can run the model using:" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "new_model.run()" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Note that you can modify any of the model inputs in memory by modifying the model class. E.g. to see what happens when you double the emissions:" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "new_model.emissions *= 2.\n", "new_model.run()" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Changing lifetimes\n", "\n", "If no user-defined lifetimes are passed to the model, it will use the values in ```data/inputs/species_info.csv```\n", "\n", "However, you can start the model up with non-standard lifetimes using the following arguments to the ```Model``` class (all in years):\n", "\n", "```lifetime_strat```: stratospheric lifetime\n", "\n", "```lifetime_ocean```: lifetime with respect to ocean uptake\n", "\n", "```lifetime_trop```: non-OH losses in the troposphere\n", "\n", "e.g.:\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "new_model = Model(\"\", Path(\"path/to/project/folder\"), lifetime_strat=100.)" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "To change the tropospheric OH lifetime, you need to modify the ```oh_a``` or ```oh_er``` attributes of the ```Model``` class." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "To re-tune the lifetime of the model in-memory, you can use the ```tune_lifetime``` method of the ```Model``` class:" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "new_model.tune_lifetime(lifetime_strat=50., lifetime_ocean=1e12, lifetime_trop=1e12)" ], "outputs": [], "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "py12box", "language": "python", "name": "py12box" }, "toc-autonumbering": true, "toc-showcode": true, "toc-showmarkdowntxt": true }, "nbformat": 4, "nbformat_minor": 4 }