adcroft/IPCC resolution v time.ipynb

## IPCC resolution v time.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Original data provided by Baylor Fox-Kemper.\n\n### References:\n\nB. Fox-Kemper, S. Bachman, B. Pearson, and S. Reckinger, 2014: Principles and advances in subgrid modeling for eddy-rich simulations. _CLIVAR Exchanges_, __19__(2) p42-46.  http://www.clivar.org/documents/exchanges-65"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy\nimport matplotlib.pyplot as plt\n%matplotlib inline",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ipcc_models = [\n    {'year': 1989, 'eqres':    4.5, 'merres':    4.5, 'zonres':   3.7, 'levels':   12, 'name': ''},\n    {'year': 1989, 'eqres':      5, 'merres':      5, 'zonres':     5, 'levels':    4, 'name': ''},\n    {'year': 1992, 'eqres':    2.5, 'merres':    2.5, 'zonres':   3.7, 'levels':   17, 'name': ''},\n    {'year': 1992, 'eqres':      4, 'merres':      4, 'zonres':     4, 'levels':   11, 'name': ''},\n    {'year': 1992, 'eqres':    4.5, 'merres':    4.5, 'zonres':   3.7, 'levels':   12, 'name': ''},\n    {'year': 1992, 'eqres':      5, 'merres':      5, 'zonres':     5, 'levels':    4, 'name': ''},\n    {'year': 1995, 'eqres':    0.5, 'merres':      2, 'zonres':   2.5, 'levels':   21, 'name': ''},\n    {'year': 1995, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   15, 'name': ''},\n    {'year': 1995, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   20, 'name': ''},\n    {'year': 1995, 'eqres':      1, 'merres':      1, 'zonres':     2, 'levels':   20, 'name': ''},\n    {'year': 1995, 'eqres':      1, 'merres':      1, 'zonres':     2, 'levels':   20, 'name': ''},\n    {'year': 1995, 'eqres':    1.8, 'merres':    1.8, 'zonres':   1.8, 'levels':   29, 'name': ''},\n    {'year': 1995, 'eqres':      2, 'merres':      2, 'zonres':     2, 'levels':   18, 'name': ''},\n    {'year': 1995, 'eqres':    2.5, 'merres':    2.5, 'zonres':   3.8, 'levels':   20, 'name': ''},\n    {'year': 1995, 'eqres':    2.8, 'merres':    2.8, 'zonres':   2.8, 'levels':    9, 'name': ''},\n    {'year': 1995, 'eqres':      3, 'merres':      3, 'zonres':     3, 'levels':   16, 'name': ''},\n    {'year': 1995, 'eqres':    3.2, 'merres':    3.2, 'zonres':   5.6, 'levels':   12, 'name': ''},\n    {'year': 1995, 'eqres':    3.2, 'merres':    3.2, 'zonres':   5.6, 'levels':   12, 'name': ''},\n    {'year': 1995, 'eqres':      4, 'merres':      4, 'zonres':     5, 'levels':   13, 'name': ''},\n    {'year': 1995, 'eqres':      4, 'merres':      4, 'zonres':     5, 'levels':   16, 'name': ''},\n    {'year': 1995, 'eqres':      4, 'merres':      4, 'zonres':     5, 'levels':   20, 'name': ''},\n    {'year': 1995, 'eqres':    5.6, 'merres':    5.6, 'zonres':   5.6, 'levels':   11, 'name': ''},\n    {'year': 2001, 'eqres':      2, 'merres':      2, 'zonres':     2, 'levels':   31, 'name': ''},\n    {'year': 2001, 'eqres':      2, 'merres':      2, 'zonres':     2, 'levels':   31, 'name': ''},\n    {'year': 2001, 'eqres':    3.2, 'merres':    3.2, 'zonres':   5.6, 'levels':   12, 'name': ''},\n    {'year': 2001, 'eqres':    3.2, 'merres':    3.2, 'zonres':   5.6, 'levels':   12, 'name': ''},\n    {'year': 2001, 'eqres':    2.8, 'merres':    2.8, 'zonres':   2.8, 'levels':   17, 'name': ''},\n    {'year': 2001, 'eqres':    1.8, 'merres':    1.8, 'zonres':   1.8, 'levels':   29, 'name': ''},\n    {'year': 2001, 'eqres':    1.8, 'merres':    1.8, 'zonres':   1.8, 'levels':   29, 'name': ''},\n    {'year': 2001, 'eqres':    1.5, 'merres':    1.5, 'zonres':   1.5, 'levels':   20, 'name': ''},\n    {'year': 2001, 'eqres':      3, 'merres':      3, 'zonres':     3, 'levels':   20, 'name': ''},\n    {'year': 2001, 'eqres':    3.2, 'merres':    3.2, 'zonres':   5.6, 'levels':   21, 'name': ''},\n    {'year': 2001, 'eqres':      2, 'merres':      2, 'zonres':   2.4, 'levels':   45, 'name': ''},\n    {'year': 2001, 'eqres':      2, 'merres':      2, 'zonres':   2.4, 'levels':   45, 'name': ''},\n    {'year': 2001, 'eqres':      4, 'merres':      4, 'zonres':     4, 'levels':   11, 'name': ''},\n    {'year': 2001, 'eqres':    2.8, 'merres':    2.8, 'zonres':   2.8, 'levels':   11, 'name': ''},\n    {'year': 2001, 'eqres':    4.5, 'merres':    4.5, 'zonres':   3.7, 'levels':   12, 'name': ''},\n    {'year': 2001, 'eqres':    4.5, 'merres':    4.5, 'zonres':   3.7, 'levels':   12, 'name': ''},\n    {'year': 2001, 'eqres':    1.9, 'merres':    1.9, 'zonres':   2.2, 'levels':   18, 'name': ''},\n    {'year': 2001, 'eqres':      4, 'merres':      4, 'zonres':     5, 'levels':   16, 'name': ''},\n    {'year': 2001, 'eqres':      4, 'merres':      4, 'zonres':     5, 'levels':   13, 'name': ''},\n    {'year': 2001, 'eqres':      4, 'merres':      4, 'zonres':     5, 'levels':   20, 'name': ''},\n    {'year': 2001, 'eqres':    2.5, 'merres':    2.5, 'zonres':   3.7, 'levels':   20, 'name': ''},\n    {'year': 2001, 'eqres':    1.3, 'merres':    1.3, 'zonres':   1.3, 'levels':   20, 'name': ''},\n    {'year': 2001, 'eqres':      2, 'merres':      2, 'zonres':     2, 'levels':   31, 'name': ''},\n    {'year': 2001, 'eqres':      2, 'merres':      2, 'zonres':     2, 'levels':   31, 'name': ''},\n    {'year': 2001, 'eqres':      2, 'merres':      2, 'zonres':   2.5, 'levels':   23, 'name': ''},\n    {'year': 2001, 'eqres':      2, 'merres':      2, 'zonres':   2.5, 'levels':   23, 'name': ''},\n    {'year': 2001, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   20, 'name': ''},\n    {'year': 2001, 'eqres':      1, 'merres':      1, 'zonres':     2, 'levels':   25, 'name': ''},\n    {'year': 2001, 'eqres':    0.7, 'merres':    0.7, 'zonres':   0.7, 'levels':   32, 'name': ''},\n    {'year': 2001, 'eqres':    2.8, 'merres':    2.8, 'zonres':   3.8, 'levels':   17, 'name': ''},\n    {'year': 2001, 'eqres':    1.8, 'merres':    1.8, 'zonres':   3.6, 'levels':   19, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    1.9, 'merres':    1.9, 'zonres':   1.9, 'levels':   30, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.5, 'merres':    1.5, 'zonres':   1.5, 'levels':   35, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.3, 'merres':      1, 'zonres':     1, 'levels':   40, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    1.9, 'merres':    1.9, 'zonres':   1.9, 'levels':   29, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.9, 'merres':    0.9, 'zonres':   1.4, 'levels':   29, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.5, 'merres':      2, 'zonres':     2, 'levels':   31, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.8, 'merres':    0.8, 'zonres':   1.9, 'levels':   31, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    1.5, 'merres':    1.5, 'zonres':   1.5, 'levels':   40, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.5, 'merres':    2.8, 'zonres':   2.8, 'levels':   20, 'name': 'SRES'},\n    {'year': 2007, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   16, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.3, 'merres':      1, 'zonres':     1, 'levels':   50, 'name': 'SRES'},\n    {'year': 2007, 'eqres':      3, 'merres':      3, 'zonres':     4, 'levels':   16, 'name': 'SRES'},\n    {'year': 2007, 'eqres':      2, 'merres':      2, 'zonres':     2, 'levels':   16, 'name': 'SRES'},\n    {'year': 2007, 'eqres':      4, 'merres':      4, 'zonres':     5, 'levels':   13, 'name': 'SRES'},\n    {'year': 2007, 'eqres':      2, 'merres':      2, 'zonres':   2.5, 'levels':   33, 'name': 'SRES'},\n    {'year': 2007, 'eqres':      2, 'merres':      2, 'zonres':     2, 'levels':   31, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.2, 'merres':    0.2, 'zonres':   0.3, 'levels':   47, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.5, 'merres':    1.4, 'zonres':   1.4, 'levels':   43, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.5, 'merres':      2, 'zonres':   2.5, 'levels':   23, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.5, 'merres':    0.7, 'zonres':   1.1, 'levels':   40, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    1.3, 'merres':    1.3, 'zonres':   1.3, 'levels':   20, 'name': 'SRES'},\n    {'year': 2007, 'eqres':    0.3, 'merres':      1, 'zonres':     1, 'levels':   40, 'name': 'SRES'},\n    {'year': 2014, 'eqres':    0.3, 'merres':      1, 'zonres':     1, 'levels':   60, 'name': 'NCC'},\n    {'year': 2014, 'eqres':    0.3, 'merres':  0.187, 'zonres':  0.28, 'levels':   49, 'name': 'MIROC4h'},\n    {'year': 2014, 'eqres':    0.5, 'merres':    1.4, 'zonres':   1.4, 'levels':   49, 'name': 'MIROC5'},\n    {'year': 2014, 'eqres':    0.3, 'merres':      1, 'zonres':     1, 'levels':   60, 'name': 'CCSM4'},\n    {'year': 2014, 'eqres':   0.94, 'merres':    1.4, 'zonres':  0.94, 'levels':   40, 'name': 'CanESM4'},\n    {'year': 2014, 'eqres':   0.94, 'merres':    1.4, 'zonres':  0.94, 'levels':   40, 'name': 'CanCM4'},\n    {'year': 2014, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   42, 'name': 'CNRM-CM5'},\n    {'year': 2014, 'eqres':   1.25, 'merres':   1.25, 'zonres':  1.25, 'levels':   20, 'name': 'HadCM3'},\n    {'year': 2014, 'eqres':    0.5, 'merres':    0.5, 'zonres':     1, 'levels':   50, 'name': 'MRI-CGCM3'},\n    {'year': 2014, 'eqres':    0.5, 'merres':    0.5, 'zonres':     1, 'levels':   50, 'name': 'NorESM1-M'},\n    {'year': 2014, 'eqres':    0.5, 'merres':      2, 'zonres':     2, 'levels':   31, 'name': 'IPSL-CM5A (ORCA2)'},\n    {'year': 2014, 'eqres':    0.5, 'merres':      1, 'zonres':     1, 'levels':   40, 'name': 'bcc-csm1-1'},\n    {'year': 2014, 'eqres':    0.5, 'merres':      1, 'zonres':     1, 'levels':   50, 'name': 'BNU-ESM'},\n    {'year': 2014, 'eqres':  0.937, 'merres':  0.937, 'zonres': 1.875, 'levels':   31, 'name': 'CSIRO-Mk3.6'},\n    {'year': 2014, 'eqres':    0.3, 'merres':      1, 'zonres':     1, 'levels':   30, 'name': 'FIO-ESM'},\n    {'year': 2014, 'eqres':      2, 'merres':    2.5, 'zonres':     2, 'levels':   33, 'name': 'inmcm4'},\n    {'year': 2014, 'eqres':    1.5, 'merres':    1.5, 'zonres':   1.5, 'levels':   40, 'name': 'MPI-ESM-LR'},\n    {'year': 2014, 'eqres':    0.4, 'merres':    0.4, 'zonres':   0.4, 'levels':   40, 'name': 'MPI-ESM-MR'},\n    {'year': 2014, 'eqres':      1, 'merres':   1.25, 'zonres':     1, 'levels':   32, 'name': 'GISS-E2-R'},\n    {'year': 2014, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   40, 'name': 'HadGem2-AO'},\n    {'year': 2014, 'eqres':    0.3, 'merres':      1, 'zonres':     1, 'levels':   63, 'name': 'ESM2G'},\n    {'year': 2014, 'eqres':    0.3, 'merres':      1, 'zonres':     1, 'levels':   50, 'name': 'ESM2M'},\n    {'year': 2014, 'eqres':    0.3, 'merres':      1, 'zonres':     1, 'levels':   50, 'name': 'CM3   '},\n    {'year': 2021, 'eqres':    0.1, 'merres':    0.1, 'zonres':   0.3, 'levels':   42, 'name': 'McClean et al. 2011'},\n    {'year': 2021, 'eqres':    0.4, 'merres':   0.25, 'zonres':  0.16, 'levels':   50, 'name': 'GFDL cm2p4'},\n    {'year': 2021, 'eqres':   0.15, 'merres':   0.07, 'zonres':  0.07, 'levels':   50, 'name': 'GFDL cm2p5'},\n    {'year': 2021, 'eqres':   0.15, 'merres':   0.07, 'zonres':  0.07, 'levels':   50, 'name': 'GFDL cm2p6'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   50, 'name': 'ACCESS'},\n    {'year': 2021, 'eqres':   0.25, 'merres':   0.25, 'zonres':  0.25, 'levels':   95, 'name': 'AWI'},\n    {'year': 2021, 'eqres':    0.5, 'merres':    0.5, 'zonres':   0.5, 'levels':   56, 'name': 'BCC'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   28, 'name': 'BESM'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   50, 'name': 'BNU'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   50, 'name': 'CAMS'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   41, 'name': 'CanESM'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   30, 'name': 'CAS'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   60, 'name': 'CESM'},\n    {'year': 2021, 'eqres':    0.5, 'merres':    0.5, 'zonres':   0.5, 'levels':   46, 'name': 'CIESM'},\n    {'year': 2021, 'eqres':   0.25, 'merres':   0.25, 'zonres':  0.25, 'levels':   50, 'name': 'CMCC'},\n    {'year': 2021, 'eqres':   0.25, 'merres':   0.25, 'zonres':  0.25, 'levels':   75, 'name': 'CNRM'},\n    {'year': 2021, 'eqres':    2.5, 'merres':    2.5, 'zonres':   2.5, 'levels':   31, 'name': 'CSIRO'},\n    {'year': 2021, 'eqres':    0.5, 'merres':    0.5, 'zonres':   0.5, 'levels':   60, 'name': 'ES3M'},\n    {'year': 2021, 'eqres':    0.1, 'merres':    0.1, 'zonres':   0.1, 'levels':   75, 'name': 'EC-Earth'},\n    {'year': 2021, 'eqres':   0.25, 'merres':   0.25, 'zonres':  0.25, 'levels':   75, 'name': 'ECMWF'},\n    {'year': 2021, 'eqres':    2.5, 'merres':    2.5, 'zonres':   2.5, 'levels':   40, 'name': 'EMAC'},\n    {'year': 2021, 'eqres':    0.1, 'merres':    0.1, 'zonres':   0.1, 'levels':   55, 'name': 'FGOALS'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   60, 'name': 'FIO'},\n    {'year': 2021, 'eqres':   0.25, 'merres':   0.25, 'zonres':  0.25, 'levels':   75, 'name': 'GFDL'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   32, 'name': 'GISS'},\n    {'year': 2021, 'eqres':    0.1, 'merres':    0.1, 'zonres':   0.1, 'levels':   75, 'name': 'HadGEM3'},\n    {'year': 2021, 'eqres':    0.5, 'merres':    0.5, 'zonres':   0.5, 'levels':   40, 'name': 'ICON'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   50, 'name': 'IITM'},\n    {'year': 2021, 'eqres':    0.1, 'merres':    0.1, 'zonres':   0.1, 'levels':   40, 'name': 'INM'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   75, 'name': 'IPSL'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   50, 'name': 'KACE'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   52, 'name': 'KIOST'},\n    {'year': 2021, 'eqres':    2.5, 'merres':    2.5, 'zonres':   2.5, 'levels':   18, 'name': 'MCM'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   63, 'name': 'MIROC'},\n    {'year': 2021, 'eqres':    0.5, 'merres':    0.5, 'zonres':   0.5, 'levels':   40, 'name': 'MPI'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   61, 'name': 'MRI'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   46, 'name': 'NESM'},\n    {'year': 2021, 'eqres':   0.25, 'merres':   0.25, 'zonres':  0.25, 'levels':   70, 'name': 'NORESM'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   60, 'name': 'SAMO'},\n    {'year': 2021, 'eqres':   0.25, 'merres':   0.25, 'zonres':  0.25, 'levels':   75, 'name': 'UKESM'},\n    {'year': 2021, 'eqres':      1, 'merres':      1, 'zonres':     1, 'levels':   60, 'name': 'UoT'},\n    {'year': 2021, 'eqres':   0.25, 'merres':   0.25, 'zonres':  0.25, 'levels':   35, 'name': 'VRESM'}\n]",
      "execution_count": 36,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# List comprehensions to convert list to plotable data\nyr = numpy.array( [ l['year'] for l in ipcc_models ] )\neres = numpy.array( [ l['eqres'] for l in ipcc_models ] )\nmres = numpy.array( [ l['merres'] for l in ipcc_models ] )\nzres = numpy.array( [ l['zonres'] for l in ipcc_models ] )\nnlev = numpy.array( [ l['levels'] for l in ipcc_models ] )\n\n# Equivalent horizontal resolution \n# (converts vertrical levels to zonal resolution and combines meridional/zonal/vertical into one length scale)\n# deg2km approximately converts zonal resolutino to km\n# mdeg2km average of cos(lat)\ndeg2km = 112.0\nmdeg2km = 71.0\neff_km = ( (eres*zres)**0.5 * deg2km * mres * mdeg2km * (40*deg2km/nlev) )**0.33",
      "execution_count": 37,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "syr, mdn, mn, mini = [],[],[], []\nfor y in numpy.unique(yr):\n    syr.append( numpy.int(numpy.median( yr[yr==y] )) )\n    mdn.append( numpy.median( eff_km[yr==y] ) )\n    mn.append( numpy.mean( eff_km[yr==y] ) )\n    mini.append( numpy.min( eff_km[yr==y] ) )\nars = ['', 'FAR','SAR', 'TAR', 'AR4', 'AR5', 'AR6']",
      "execution_count": 133,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# F-K's extrapolation\nfyr = numpy.linspace(syr[0],2100,2) # years to plot fit over\n\nk=slice(None,-1)\np = numpy.poly1d( numpy.polyfit(syr[k], numpy.log10(mini[k]), 1) )\nleadn = 10**p(fyr) # Bleeding edge fit\nprint('Bleeding edge =',p,'Doubling time =',-numpy.log10(2)/p[1],'years')\n\np = numpy.poly1d( numpy.polyfit(syr[k], numpy.log10(mdn[k]), 1) )\nmedn = 10**p(fyr) # Median fit\nprint('Median fit =',p,'Doubling time =',-numpy.log10(2)/p[1],'years')\n\n# Representation of Moore's law\nmdb = 12/18 # Moore's doubling time in years\nmoore = mini[0]/( 2**((1/4)*(fyr-syr[0])*mdb) )\n\n# Reproduce F-K's plot\nplt.semilogy( yr, eff_km, 'b.') # individual models\nplt.semilogy( syr[k], mdn[k], 'k-', label='Median') # median resolution\nplt.semilogy( fyr, medn, 'k--', label='Median fit')\nplt.semilogy( syr[k], mini[k], 'r-', label='Finest') # finest resolution\nplt.semilogy( fyr, leadn, 'r--', label='Finest fit') # Bleeding edge\nplt.semilogy( fyr, moore, 'b:', label=\"Moore's Law\")\n# Plot AR labels\nfor i in range(len(syr)):\n    plt.text(syr[i], 800*(2-i%2), ars[i], horizontalalignment='center')\n# Annotations\ny = 230; plt.gca().axhline(y, color='k', linestyle=':')\nplt.text(2025,y*1.2,'1st Rossby radius')\ny = 10; plt.gca().axhline(y, color='k', linestyle=':')\nplt.text(1985,y*1.2,'Mesoscale')\ny = 0.1; plt.gca().axhline(y, color='k', linestyle=':')\nplt.text(1985,y*1.2,'Sub-mesoscale')\ny = 5e-3; plt.gca().axhline(y, color='k', linestyle=':')\nplt.text(1985,y*1.2,'Langmuir turbulence')\nplt.xlim(1980,2100); plt.xlabel('Year')\nplt.ylim(1e-3,3e3); plt.ylabel('Equivalent Ocean Model Resolution (km)')\nplt.legend()\nplt.title('Resolution of Ocean Component of Coupled IPCC models')\nplt.text(1981,1.2e-3,'(based on Fox-Kemper et al., 2014)', horizontalalignment='left', fontsize=8, color='grey');",
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Bleeding edge =  \n-0.0434 x + 88.77 Doubling time = 6.936510203297592 years\nMedian fit =  \n-0.02898 x + 60.26 Doubling time = 10.387519318352194 years\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "for l in ipcc_models:\n    if l['merres']<l['eqres'] or 'GFDL' in l['name']: print(l)\nplt.plot( numpy.vstack((yr,yr)), numpy.vstack((eres, mres)), '.-');",
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "text": "{'year': 2014, 'eqres': 0.3, 'merres': 0.187, 'zonres': 0.28, 'levels': 49, 'name': 'MIROC4h'}\n{'year': 2021, 'eqres': 0.4, 'merres': 0.25, 'zonres': 0.16, 'levels': 50, 'name': 'GFDL cm2p4'}\n{'year': 2021, 'eqres': 0.15, 'merres': 0.07, 'zonres': 0.07, 'levels': 50, 'name': 'GFDL cm2p5'}\n{'year': 2021, 'eqres': 0.15, 'merres': 0.07, 'zonres': 0.07, 'levels': 50, 'name': 'GFDL cm2p6'}\n{'year': 2021, 'eqres': 0.25, 'merres': 0.25, 'zonres': 0.25, 'levels': 75, 'name': 'GFDL'}\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "idof = 360/zres # DOF in zonal direction\njdof = 0.9 * 180/mres + .1* 180/eres # DOF in meridional direction\nhdof = idof * jdof # Horizontal DOF\ndof = ijdof * nlev\ndx  = numpy.minimum( numpy.minimum( eres, zres) * deg2km, mres * mdeg2km )*1e3 # Smallest dx (in m)\ndt = numpy.minimum( dx/3/4, 3600)\nnt = numpy.round( 1 * 365 * 86400 / dt )\ncost = dof*nt\n\ncost_mdn, cost_max = [],[]\nfor y in numpy.unique(yr):\n    cost_mdn.append( numpy.median( cost[yr==y] ) )\n    cost_max.append( numpy.max( cost[yr==y] ) )",
      "execution_count": 176,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(yr, dt/60, '.')\nplt.xlabel('Year')\nplt.ylabel('$\\Delta t$ (minutes)')\nplt.title('Likely model time step');",
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "k=slice(None,-1)\np = numpy.poly1d( numpy.polyfit(syr[k], numpy.log10(cost_max[k]), 1) )\ncost_lead = 10**p(fyr) # Bleeding edge fit\nprint('Bleeding edge =',p,'Doubling time =',numpy.log10(2)/p[1],'years')\n\np = numpy.poly1d( numpy.polyfit(syr[k], numpy.log10(cost_mdn[k]), 1) )\ncost_exp = 10**p(fyr) # Bleeding edge fit\nprint('Expected mdn =',p,'Doubling time =',numpy.log10(2)/p[1],'years')\n\nplt.semilogy( yr, cost, '.')\nplt.semilogy( syr[k], cost_mdn[k], 'k-', label='Median')\nplt.semilogy( fyr, cost_exp, 'k--', label='Median fit')\nplt.semilogy( syr[k], cost_max[k], 'r-', label='Biggest')\nplt.semilogy( fyr, cost_lead, 'r--', label='Biggest fit')\nplt.semilogy( fyr, 4e8 * 2**((fyr-1989)*(12/18)), 'b:', label=\"Moore's Law\" )\nplt.legend()\nplt.xlabel('Year')\nplt.ylabel('~DOF x steps/SMY')\nplt.title('\"Work\" per simulated year')",
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Bleeding edge =  \n0.1544 x - 298.3 Doubling time = 1.949846959474601 years\nExpected mdn =  \n0.07996 x - 150.6 Doubling time = 3.764549979331175 years\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 184,
          "data": {
            "text/plain": "Text(0.5, 1.0, '\"Work\" per simulated year')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "edx = eres*deg2km\ne_mdn, e_min = [],[]\nfor y in numpy.unique(yr):\n    e_mdn.append( numpy.median( edx[yr==y] ) )\n    e_min.append( numpy.min( edx[yr==y] ) )\n\nk=slice(None,-1)\np = numpy.poly1d( numpy.polyfit(syr[k], numpy.log10(e_min[k]), 1) )\ne_lead = 10**p(fyr) # Bleeding edge fit\nprint('Bleeding edge =',p,'Doubling time =',-numpy.log10(2)/p[1],'years')\n\np = numpy.poly1d( numpy.polyfit(syr[k], numpy.log10(e_mdn[k]), 1) )\ne_exp = 10**p(fyr) # Bleeding edge fit\nprint('Expected mdn =',p,'Doubling time =',-numpy.log10(2)/p[1],'years')\n\nmoore = e_lead[0]/( 2**((1/4)*(fyr-syr[0])*mdb) )\n\nplt.semilogy( yr, edx, 'b.') # individual models\nplt.semilogy( syr[k], e_mdn[k], 'k-', label='Median')\nplt.semilogy( fyr, e_exp, 'k--', label='Median fit') # median resolution\nplt.semilogy( syr[k], e_min[k], 'r-', label='Finest')\nplt.semilogy( fyr, e_lead, 'r--', label='Finest fit') # median resolution\nplt.semilogy( fyr, moore, 'b:', label=\"Moore's Law\")\nplt.legend()\nplt.xlim(1980,2060); plt.xlabel('Year')\nplt.ylim(1e-3,3e3); plt.ylabel('Along-Equator Ocean Model Resolution (km)')\nplt.title('Equatorial Zonal Resolution of Ocean (IPCC)')\n# Annotations\ny = 230; plt.gca().axhline(y, color='k', linestyle=':')\nplt.text(2025,y*1.2,'1st Rossby radius')\ny = 10; plt.gca().axhline(y, color='k', linestyle=':')\nplt.text(1985,y*1.2,'Mesoscale')\ny = 0.1; plt.gca().axhline(y, color='k', linestyle=':')\nplt.text(1985,y*1.2,'Sub-mesoscale')\n",
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Bleeding edge =  \n-0.04709 x + 96.1 Doubling time = 6.392970837948591 years\nExpected mdn =  \n-0.0405 x + 83.32 Doubling time = 7.432498613688902 years\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 194,
          "data": {
            "text/plain": "Text(1985, 0.12, 'Sub-mesoscale')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.7",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "21143a746dedd4ef53602c8d6b7ea041",
      "data": {
        "description": "GitHub/space-time-scale-schematics/IPCC resolution v time.ipynb",
        "public": false
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/21143a746dedd4ef53602c8d6b7ea041"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}