{
 "cells": [
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   "cell_type": "markdown",
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   "metadata": {
    "tags": []
   },
   "source": [
    "## Application of forcing\n",
    "\n",
    "Some of the forcing fields in the CAFE-f6 forecasts are applied in a way that is different than the CMIP DCPP recomendations. Principally, a number of the forcing fields switch from time-varying to fixed based on the *initialisation date* of the forecasts. **This means that the same calendar year can experience different forcing, depending on its lead time.** For example (see below), The lead 2 year forecast of 2002 (initialised 2000) will be forced with 2002 volcanic forcing, but the lead 1 forecast of 2002 (initialised 2001) will be forced with 2008 forcing. This could make the forecasts difficult to interpret and baseline.\n",
    "\n",
    "(Note that some of the files linked on this page are only accessible by members of the [CSIRO DCFP Github organisation](https://github.com/csiro-dcfp). These links are included for tracebility. Any relevant lines from linked files have also been copied to this page.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10be3819-b8a0-4de4-8df8-32b65c799fad",
   "metadata": {},
   "source": [
    "The relevant lines that specify the forcing in the CAFE-f6 model namelist (see https://github.com/csiro-dcfp/cm-forecasts/blob/fb22ae161bfce6803435c0d10fa74a9d49e4bffc/ref/MODEL/input.in) are:\n",
    "\n",
    "```\n",
    " &aerosol_nml\n",
    "    use_aerosol_timeseries = .false.\n",
    "    aerosol_dataset_entry =  INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "                             INPUT_AEROSOL_TIME,\n",
    "    family_names = \"small_dust\", \"large_dust\", \"sulfate\", \"aerosol\"\n",
    "    in_family1 = F,F,F,F,T,T,T,T,T,F,F,F,\n",
    "    in_family2 = F,F,F,F,F,F,F,F,F,T,T,T,\n",
    "    in_family3 = T,F,F,F,F,F,F,F,F,F,F,F,\n",
    "    in_family4 = T,T,T,T,T,T,T,T,T,T,T,T,\n",
    "    data_names = \"so4\", \"organic_carbon\", \"black_carbon\", \"sea_salt\",\n",
    "                 \"dust_0.1\", \"dust_0.2\", \"dust_0.4\", \"dust_0.8\",\n",
    "                 \"dust_1.0\", \"dust_2.0\", \"dust_4.0\", \"dust_8.0\",\n",
    "    filename = \"aerosol.climatology.nc\"\n",
    "/\n",
    " &aerosolrad_package_nml\n",
    "    volcanic_dataset_entry = 1, 1, 1, 0, 0, 0,\n",
    "    REPEAT_VOLCANO_YEAR\n",
    "    VOLCANO_YEAR_USED\n",
    "    using_volcanic_lw_files = .false.,\n",
    "    lw_ext_filename = \"             \"\n",
    "    lw_ext_root = \"     \"\n",
    "    lw_asy_filename = \"             \"\n",
    "    lw_asy_root = \"     \"\n",
    "    lw_ssa_filename = \"             \"\n",
    "    lw_ssa_root = \"     \"\n",
    "    using_volcanic_sw_files = .true.,\n",
    "    sw_ext_filename = \"extsw_data.nc\"\n",
    "    sw_ext_root = \"extsw\"\n",
    "    sw_ssa_filename = \"omgsw_data.nc\"\n",
    "    sw_ssa_root = \"omgsw\"\n",
    "    sw_asy_filename = \"asmsw_data.nc\"\n",
    "    sw_asy_root = \"asmsw\"\n",
    "    do_lwaerosol = .true.,\n",
    "    do_swaerosol = .true.,\n",
    "    aerosol_data_set = 'shettle_fenn',\n",
    "    optical_filename = \"aerosol.optical.dat\",\n",
    "    aerosol_optical_names = \"sulfate_30%\", \"sulfate_35%\", \"sulfate_40%\", \"sulfate_45%\",\n",
    "                            \"sulfate_50%\", \"sulfate_55%\", \"sulfate_60%\", \"sulfate_65%\",\n",
    "                            \"sulfate_70%\", \"sulfate_75%\", \"sulfate_80%\", \"sulfate_82%\",\n",
    "                            \"sulfate_84%\", \"sulfate_86%\", \"sulfate_88%\", \"sulfate_90%\",\n",
    "                            \"sulfate_91%\", \"sulfate_92%\", \"sulfate_93%\", \"sulfate_94%\",\n",
    "                            \"sulfate_95%\", \"sulfate_96%\", \"sulfate_97%\", \"sulfate_98%\",\n",
    "                            \"sulfate_99%\", \"sulfate_100%\",\"organic_carbon\",\"soot\",\n",
    "                            \"sea_salt\",    \"dust_0.1\",    \"dust_0.2\",    \"dust_0.4\",\n",
    "                            \"dust_0.8\",    \"dust_1.0\",    \"dust_2.0\",    \"dust_4.0\",\n",
    "                            \"dust_8.0\"\n",
    ".\n",
    ".\n",
    ".\n",
    "&ozone_nml\n",
    "    BASIC_OZONE_TYPE\n",
    "    OZONE_DATASET_ENTRY\n",
    "    data_name = \"ozone\",\n",
    "    FILENAME\n",
    ".\n",
    ".\n",
    ".\n",
    " &radiation_driver_nml\n",
    "       rad_time_step= 10800,\n",
    "       time_varying_solar_constant = .false.,\n",
    "       solar_dataset_entry = 1990,1,1,0,0,0,\n",
    "       rad_package = 'sea_esf',\n",
    "       do_clear_sky_pass=.true.,\n",
    "       calc_hemi_integrals = .false.,\n",
    "       renormalize_sw_fluxes=.true.,\n",
    "       all_step_diagnostics = .true.,\n",
    "       zenith_spec = 'diurnally_varying'\n",
    "       using_restart_file = .false.\n",
    "/\n",
    " &radiative_gases_nml\n",
    "       verbose = 3\n",
    "       gas_printout_freq = 240\n",
    "       time_varying_co2 = .true.,\n",
    "       co2_variation_type = 'linear',\n",
    "       co2_specification_type = 'time_series',\n",
    "       co2_floor = 100.0E-06,\n",
    "       co2_ceiling = 1600.0E-06,\n",
    "       co2_data_source = 'input'\n",
    "       time_varying_ch4 = .true.,\n",
    "       ch4_variation_type = 'linear'\n",
    "       ch4_specification_type = 'time_series'\n",
    "       ch4_data_source = 'input'\n",
    "       time_varying_n2o = .true.,\n",
    "       n2o_variation_type = 'linear'\n",
    "       n2o_specification_type = 'time_series'\n",
    "       n2o_data_source = 'input'\n",
    "       time_varying_f11 = .true.,\n",
    "       f11_variation_type = 'linear'\n",
    "       f11_specification_type = 'time_series'\n",
    "       f11_data_source = 'input'\n",
    "       time_varying_f12 = .true.,\n",
    "       f12_variation_type = 'linear'\n",
    "       f12_specification_type = 'time_series'\n",
    "       f12_data_source = 'input'\n",
    "       time_varying_f113 = .true.,\n",
    "       f113_variation_type = 'linear'\n",
    "       f113_specification_type = 'time_series'\n",
    "       f113_data_source = 'input'\n",
    "       time_varying_f22 = .true.,\n",
    "       f22_variation_type = 'linear'\n",
    "       f22_specification_type = 'time_series'\n",
    "       f22_data_source = 'input'\n",
    "```\n",
    "\n",
    "where the following replacements are made before submission (see https://github.com/csiro-dcfp/cm-forecasts/blob/fb22ae161bfce6803435c0d10fa74a9d49e4bffc/src/run_CAFE_forecasts.sh.in):\n",
    "\n",
    "**a.** Replace\n",
    " \n",
    "```\n",
    "INPUT_AEROSOL_TIME -> \"AYEAR, 1, 1, 0, 0, 0\"\n",
    "```\n",
    "   \n",
    "  where `AYEAR` is the middle year of the decade in which the forecast is initialised (e.g. `AYEAR`=2005 for any start date in the 2000s). If `AYEAR` is greater than 2015, then 2015 is used.\n",
    "   \n",
    "**b.** If the forecast is initialised after 2000 then replace,\n",
    " \n",
    "```\n",
    "REPEAT_VOLCANO_YEAR -> \"repeat_volcano_year=.true.\"\n",
    "VOLCANO_YEAR_USED -> \"volcano_year_used = 2008,\"\n",
    "```\n",
    "\n",
    "otherwise these lines are commented out.\n",
    "   \n",
    "**c.** If the forecast is initialised after 2004 then replace,\n",
    "\n",
    "```\n",
    "BASIC_OZONE_TYPE -> \"fixed_year\"\n",
    "OZONE_DATASET_ENTRY -> \"ozone_dataset_entry=2014, 1, 1, 0, 0, 0,\"\n",
    "FILENAME -> \"cm3_2014_o3.padded.nc\"\n",
    "```\n",
    "\n",
    "otherwise replace,\n",
    "\n",
    "```\n",
    "BASIC_OZONE_TYPE -> \"time_varying\"\n",
    "FILENAME -> \"CM3_CMIP6_1950-2014_O3.nc\"\n",
    "```\n",
    "\n",
    "and `OZONE_DATASET_ENTRY` is commented out.\n",
    "\n",
    "In addition, the following is added to the field table (see https://github.com/csiro-dcfp/cm-forecasts/blob/fb22ae161bfce6803435c0d10fa74a9d49e4bffc/ref/MODEL/field_table_bgc):\n",
    "\n",
    "**d.** If the forecast is initialised after 2007 then,\n",
    "\n",
    "```\n",
    "aco2_file = \"INPUT/co2_obs.padded.nc\"\n",
    "```\n",
    "\n",
    "otherwise,\n",
    "\n",
    "```\n",
    "aco2_file = \"INPUT/co2_obs.nc\"\n",
    "```\n",
    "\n",
    "These files are used by the bgc component of the model. Notably, `co2_obs.padded.nc` includes projected values into the future, but both files are identical (see below) in their overlapping period so the switch from `co2_obs.nc` to `co2_obs.padded.nc` seems to be redundant.\n",
    "\n",
    "Forcing files are copied from `/g/data/v14/vxk563/CAFE/CM21_c5/INIT/STATIC/` when a forecast is run (see https://github.com/csiro-dcfp/cm-forecasts/blob/fb22ae161bfce6803435c0d10fa74a9d49e4bffc/settings.sh#L29 and https://github.com/csiro-dcfp/cm-forecasts/blob/fb22ae161bfce6803435c0d10fa74a9d49e4bffc/src/run_CAFE_forecasts.sh.in#L90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13927e43-6265-47da-920b-9b7551755f73",
   "metadata": {},
   "source": [
    "### So what does this mean?\n",
    "\n",
    "#### Aerosols climatologies\n",
    "\n",
    "Aerosol forcing climatologies are provided per decade. Forecasts initialised prior to 2020 are forced using the climatologies corresponding to the decade of their initialialisation (e.g. 2000s forcing climatologies for every forecast initialised in 2000-2009). Forecasts initialised in 2020 or later use the 2010s aerosol forcing climatologies.\n",
    "\n",
    "**However**, the aerosol climatologies provided are actually identical to the 2000s climatology for all decades after the 2000s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f4a2702c-d5bb-402e-998f-93ebc3a31824",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "plt.rcParams[\"font.size\"] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09f1feb2-c1a7-4a55-9294-9af453c12dce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "INIT_dir = \"/g/data/v14/vxk563/CAFE/CM21_c5/INIT/STATIC/\"\n",
    "\n",
    "ds = xr.open_dataset(f\"{INIT_dir}/aerosol.climatology.nc\")\n",
    "\n",
    "periods = range(1975, 2030, 10)\n",
    "for p in periods:\n",
    "    so4 = ds[\"so4\"].isel(sigma_full=0).sel(time=str(p)).mean([\"lat\", \"lon\"])\n",
    "    plt.plot(range(1, 13), so4)\n",
    "\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"so4\")\n",
    "plt.grid()\n",
    "_ = plt.legend(periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01f65ae-c1f9-4da5-a954-85c374d9b0e7",
   "metadata": {},
   "source": [
    "Therefore\n",
    "\n",
    "**If initialised during or before 1999, CAFE-f6 uses the aerosol forcing climatologies corresponding to the decade of initialisation. If initialised after 1999, CAFE-f6 uses the aerosol forcing climatologies for the 2000s**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58987a72-391d-4fbb-a18e-ea4a01e32339",
   "metadata": {},
   "source": [
    "#### Volcanic forcing\n",
    "Time-varying (monthly) volcanic forcing is used for forecasts initialised prior to or during 2000, and 2008 volcanic forcing is used for those initialised after 2000.\n",
    "\n",
    "**However**, the time-varying volcanic forcing provided is actually identical to 1999-12 for all values after 1999-12:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b9b6c49-ed10-42b4-ab9b-4c02cd5d2c86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All extsw_data.nc are constant after 1999-12: True\n",
      "All omgsw_data.nc are constant after 1999-12: True\n",
      "All asmsw_data.nc are constant after 1999-12: True\n"
     ]
    }
   ],
   "source": [
    "ds = xr.open_dataset(f\"{INIT_dir}/extsw_data.nc\")\n",
    "matched = all(\n",
    "    (ds.sel(time=\"1999-12\").squeeze() == ds.sel(time=slice(\"2000\", None))).all()\n",
    ")\n",
    "print(f\"All extsw_data.nc are constant after 1999-12: {matched}\")\n",
    "\n",
    "ds = xr.open_dataset(f\"{INIT_dir}/omgsw_data.nc\")\n",
    "matched = all(\n",
    "    (ds.sel(time=\"1999-12\").squeeze() == ds.sel(time=slice(\"2000\", None))).all()\n",
    ")\n",
    "print(f\"All omgsw_data.nc are constant after 1999-12: {matched}\")\n",
    "\n",
    "ds = xr.open_dataset(f\"{INIT_dir}/asmsw_data.nc\")\n",
    "matched = all(\n",
    "    (ds.sel(time=\"1999-12\").squeeze() == ds.sel(time=slice(\"2000\", None))).all()\n",
    ")\n",
    "print(f\"All asmsw_data.nc are constant after 1999-12: {matched}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9943462c-8aa0-4c38-8940-f4afcc183af6",
   "metadata": {},
   "source": [
    "Therefore\n",
    "\n",
    "**CAFE-f6 uses time varying volcanic forcing up until 1999-12, and then 1999-12 levels from 2000-01 onwards. (Because the forcing files don't actually change after 1999-12, the switch to 2008 forcing in our forecast scripts for initialisation dates after 2000 actually does nothing, other than to allow forecasts to be run beyond the maximum time of the forcing files, 2010-12).**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d378d290-00e1-4e35-a0a1-bf64f16a401c",
   "metadata": {},
   "source": [
    "#### Ozone forcing\n",
    "Time varying (monthly) ozone forcing is used for forecasts initialised prior to or during 2004, and 2014 ozone forcing is used for those initialised after 2004."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d5ae7d-0563-44c2-b121-a585db2038ab",
   "metadata": {},
   "source": [
    "Therefore\n",
    "\n",
    "**If initialised during or before 2004, CAFE-f6 uses time varying monthly (CMIP6) ozone forcing.\n",
    "If initialised after 2004, CAFE-f6 uses 2014 ozone forcing**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad5aeff-207d-4ec7-8b86-bc02b81b0fb2",
   "metadata": {},
   "source": [
    "#### Solar forcing\n",
    "The namelist (see above) suggests that fixed 1990 solar forcing is applied for all forecasts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e826614a-9b4b-447d-b586-f885024e066a",
   "metadata": {},
   "source": [
    "#### Radiative gas forcing\n",
    "These are taken from the `*_gblannualdata` files and are time varying regardless of forecast initialisation date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "509cb145-91c9-4491-b5ae-5d3207b64ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def read_gblannualdata(file_name):\n",
    "    with open(file_name, \"r\") as data:\n",
    "        next(data)\n",
    "        x = []\n",
    "        y = []\n",
    "        for line in data:\n",
    "            p = line.split()\n",
    "            x.append(float(p[0]))\n",
    "            y.append(float(p[1]))\n",
    "\n",
    "    return xr.DataArray(y, dims=(\"year\"), coords={\"year\": x})\n",
    "\n",
    "\n",
    "files = [\n",
    "    \"ch4_gblannualdata\",\n",
    "    \"co2_gblannualdata\",\n",
    "    \"f113_gblannualdata\",\n",
    "    \"f11_gblannualdata\",\n",
    "    \"f12_gblannualdata\",\n",
    "    \"f22_gblannualdata\",\n",
    "    \"n2o_gblannualdata\",\n",
    "]\n",
    "for file in files:\n",
    "    ds = read_gblannualdata(f\"{INIT_dir}/{file}\")\n",
    "    ds.plot()\n",
    "plt.grid()\n",
    "_ = plt.legend(files)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e208bf-ef72-44cc-95f0-844f3887ad1d",
   "metadata": {},
   "source": [
    "Additional, `co2_obs.nc` and `co2_obs.padded.nc` are provided to the bgc component of the model (see above). Their values are identical in the overlap period, but the future scenario appears to be different than that used for `co2_gblannualdata`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cf91587-22df-4c29-a643-376f7d85473d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds1 = xr.open_dataset(f\"{INIT_dir}/co2_obs.nc\", decode_times=False)\n",
    "ds2 = xr.open_dataset(f\"{INIT_dir}/co2_obs.padded.nc\", decode_times=False)\n",
    "\n",
    "ds1_gbl = ds1[\"co2\"].mean([\"grid_x_T\", \"grid_y_T\"])\n",
    "ds2_gbl = ds2[\"co2\"].mean([\"grid_x_T\", \"grid_y_T\"])\n",
    "\n",
    "plt.plot(ds2_gbl.time + 1700, ds2_gbl, label=\"co2_obs.padded.nc\")\n",
    "plt.plot(ds1_gbl.time + 1700, ds1_gbl, label=\"co2_obs.nc\")\n",
    "ds = read_gblannualdata(f\"{INIT_dir}/co2_gblannualdata\")\n",
    "ds.plot(label=\"co2_gblannualdata\", linestyle=\"--\")\n",
    "\n",
    "plt.grid()\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "040f8f89-c4f9-4152-b28c-816536dcb5b6",
   "metadata": {},
   "source": [
    "Therefore\n",
    "\n",
    "**CAFE-f6 uses time varying observed and projected radiative gas forcing out to 2110. However, it's not immediately clear what scenario is used, or when the switch is made from observations to projections. The scenario used is different for `co2_gblannualdata` and `co2_obs.padded.nc`. \n",
    "[O'Kane et al 2021](https://doi.org/10.1175/JCLI-D-20-0974.1) note that radiative gas forcings (including `co2_gblannualdata`) were extended beyond 2006 using RCP4.5, and it looks as though `co2_obs.padded.nc` uses RCP 8.5.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3db9161-56fb-45c2-9cfe-5a0293207de3",
   "metadata": {},
   "source": [
    "### How is this inconsistent with CMIP6 DCPP?\n",
    "The CMIP6 docs (https://doi.org/10.5194/gmd-9-1937-2016) request historical forcings are based as far as possible on observations and cover the period 1850–2014:\n",
    "\n",
    ">The historical forcings are based as far as possible on observations and cover the period 1850–2014. These include:\\\n",
    "– emissions of short-lived species and long-lived greenhouse gases (GHGs),\\\n",
    "– GHG concentrations,\\\n",
    "– global gridded land-use forcing data sets,\\\n",
    "– solar forcing,\n",
    "– stratospheric aerosol data set (volcanoes),\\\n",
    "– AMIP sea surface temperatures (SSTs) and sea ice concentrations (SICs),\\\n",
    "– for simulations with prescribed aerosols, a new approach to prescribe aerosols in terms of optical properties and fractional change in cloud droplet effective radius to provide a more consistent representation of aerosol forcing, and\\\n",
    "– for models without ozone chemistry, time-varying gridded ozone concentrations and nitrogen deposition.\n",
    "\n",
    "In CAFE-f6\n",
    "\n",
    " - **Aerosol climatology** data is just replicated from the 2000s onwards,\n",
    " - **Volcanic forcing** data is just replicated from 1999-12 onwards,\n",
    " - **Solar forcing** doesn't change\n",
    " \n",
    "so these aren't consistent with CMIP6 specifications...\n",
    "\n",
    "CAFE-f6 uses the CMIP6 **ozone forcing** data. But, not clearly in a way consistent with the CMIP6 DCPP protocols (https://doi.org/10.5194/gmd-9-3751-2016) which states (Appendix A):\n",
    "\n",
    "> The A1 hindcast experiment parallels the corresponding CMIP5 decadal prediction experiment in using the same specified forcing as is used for the CMIP6 historical climate simulations. This forcing is also used for the historical simulations of experiment A2. For forecasts which extend beyond the period for which historical forcing is specified, the “medium” SSP2-4.5 forcing of ScenarioMIP (O’Neill et al., 2016) is used.\n",
    "\n",
    "and, from Table A1:\n",
    "\n",
    "> Prescribed CMIP6 historical values of atmospheric composition and/or emissions (and other conditions including volcanic aerosols). Future forcing as the SSP2-4.5 scenario.\n",
    "\n",
    "Does SSP2-4.5 provide ozone forcing? If so, the switch to 2014 forcing for CAFE-f6 forecasts initialised after 2004 goes against DCPP requests. If not, it's not clear what the DCPP expects one to do for future ozone.\n",
    "\n",
    "CAFE-f6 **raditive gas forcings** are based on CMIP 3 and 5 and transition from observations to scenarios in 2006."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a8f6c2c-cc76-46c5-a2a3-6d88331d1db7",
   "metadata": {},
   "source": [
    "### How is this inconsistent with CMIP5?\n",
    "The CMIP5 docs (https://pcmdi.llnl.gov/mips/cmip5/docs/Taylor_CMIP5_design.pdf) request that historical forcings are based as far as possible on observations and cover the period 1850–2005:\n",
    "> - All forcings should be included as observed values for past dates, with prescribed concentrations of well-mixed GHGs. The details should be the same as used in the CMIP5 historical (20th century) runs (see Table 3), with the same flexibility on the treatment of ozone and aerosol and the same specified observational datasets. Note that with the exception of experiment 1.3, aerosols from observed volcanic eruptions should be included in all of the simulations.\n",
    "\n",
    "where Table 3 gives:\n",
    "\n",
    "> for CMIP5 historical (1850 - at least 2005), impose changing conditions (consistent with observations), which may include atmospheric composition (including CO2), due to both anthropogenic and volcanic influences; solar forcing; emissions or concentrations of short-lived species and natural and anthropogenic aerosols or their precursors; land use)\n",
    "\n",
    "Also:\n",
    "\n",
    "> - For future dates, the RCP4.5 scenario should be used if possible. Specification of reactive species and aerosols will follow those used in the long-term projection runs (see Table 4). With the exception of experiment 1.4, assume that there are no volcanic eruptions in the future (i.e., after year 2009).\n",
    "> - Note the treatment of volcanic aerosol: observed values should be used for past dates, as per CMIP5, but values to be used after 2005 should be specified based on the assumption of no further volcanic eruptions. The model runs are thus configured to predict what will happen to climate, relative to the observed past, if no major eruptions take place, which is a possible outcome for a thirty year period.\n",
    "\n",
    "But as above, in CAFE-f6\n",
    "\n",
    " - **Aerosol climatology** data is replicated from the 2000s onwards,\n",
    " - **Volcanic forcing** data is replicated from 1999-12 onwards,\n",
    " - **Solar forcing** doesn't change\n",
    " \n",
    "which don't appear to be consistent with CMIP5 DCCP specifications either (although the specifications are less clearn for CMIP5 than CMIP6)."
   ]
  }
 ],
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