{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ENV/ATM 415: Climate Laboratory\n",
    "\n",
    "[Brian E. J. Rose](http://www.atmos.albany.edu/facstaff/brose/index.html), University at Albany\n",
    "\n",
    "# Lecture 10: Climate sensitivity and feedback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________\n",
    "<a id='section1'></a>\n",
    "\n",
    "## 1. Radiative forcing\n",
    "____________\n",
    "\n",
    "We've seen the concept of Radiative Forcing before. It is the short-term change in the TOA energy budget when we add a forcing agent to the climate system, **before the surface has a chance to warm up**.\n",
    "\n",
    "The standard reference forcing is a **doubling of atmospheric CO$_2$**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **radiative forcing** is a number in W m$^{-2}$, defined so that it is **positive if the system is gaining energy**:\n",
    "\n",
    "$$ \\Delta R = \\left(\\text{ASR}_{2xCO2} - \\text{OLR}_{2xCO2}\\right) - \\left(\\text{ASR}_{ref} - \\text{OLR}_{ref}\\right)$$\n",
    "\n",
    "$\\Delta R$ is a measure of the rate at which energy begins to accumulate in the climate system after an abrupt increase in greenhouse gases, but *before any change in climate* (i.e. temperature)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Radiative forcing in a single-column model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's set up a single-column Radiative-Convective model and look carefully at what happens when we add extra CO$_2$ to the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "from xarray.ufuncs import cos, deg2rad, log\n",
    "import climlab\n",
    "from metpy.plots import SkewT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Get the observed air temperature\n",
    "temperature_filename = 'air.mon.1981-2010.ltm.nc'  # temperature\n",
    "ncep_url = \"http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/pressure/\"\n",
    "ncep_air = xr.open_dataset(ncep_url + temperature_filename, decode_times=False)\n",
    "#  Take global, annual average \n",
    "weight = cos(deg2rad(ncep_air.lat)) / cos(deg2rad(ncep_air.lat)).mean(dim='lat')\n",
    "Tglobal = (ncep_air.air * weight).mean(dim=('lat','lon','time'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Get the water vapor data\n",
    "datapath = \"http://ramadda.atmos.albany.edu:8080/repository/opendap/latest/Top/Users/BrianRose/CESM_runs/\"\n",
    "endstr = \"/entry.das\"\n",
    "atm_control = xr.open_dataset( datapath + 'som_1850_f19/som_1850_f19.cam.h0.clim.nc' + endstr, decode_times=False)\n",
    "Qglobal = ((atm_control.Q * atm_control.gw)/atm_control.gw.mean(dim='lat')).mean(dim=('lat','lon','time'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting ozone data from /Users/br546577/anaconda3/lib/python3.6/site-packages/climlab/radiation/data/ozone/apeozone_cam3_5_54.nc\n"
     ]
    }
   ],
   "source": [
    "#  Make a model on same vertical domain as the GCM\n",
    "state = climlab.column_state(lev=Qglobal.lev, water_depth=2.5)\n",
    "rad = climlab.radiation.RRTMG(name='Radiation',\n",
    "                              state=state, \n",
    "                              specific_humidity=Qglobal.values,\n",
    "                              timestep = climlab.constants.seconds_per_day,\n",
    "                              albedo = 0.25,  # tuned to give reasonable ASR for reference cloud-free model\n",
    "                             )\n",
    "conv = climlab.convection.ConvectiveAdjustment(name='Convection',\n",
    "                                               state=state,\n",
    "                                               adj_lapse_rate=6.5,\n",
    "                                               timestep=rad.timestep,)\n",
    "rcm = rad + conv\n",
    "rcm.name = 'Radiative-Convective Model'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.process.time_dependent_process.TimeDependentProcess'>. \n",
      "State variables and domain shapes: \n",
      "  Ts: (1,) \n",
      "  Tatm: (26,) \n",
      "The subprocess tree: \n",
      "Radiative-Convective Model: <class 'climlab.process.time_dependent_process.TimeDependentProcess'>\n",
      "   Radiation: <class 'climlab.radiation.rrtm.rrtmg.RRTMG'>\n",
      "      SW: <class 'climlab.radiation.rrtm.rrtmg_sw.RRTMG_SW'>\n",
      "      LW: <class 'climlab.radiation.rrtm.rrtmg_lw.RRTMG_LW'>\n",
      "   Convection: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(rcm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's take a look at the default CO$_2$ amount in our reference model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000348"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcm.subprocess['Radiation'].absorber_vmr['CO2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's 348 parts per million (ppm). Our atmosphere was at this level around the late 1980s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can look at the effects of a CO2 perturbation we need to integrate our reference model out to equilibrium:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1826 steps, 1826.2110000000002 days, or 5 years.\n",
      "Total elapsed time is 4.99942230115 years.\n"
     ]
    }
   ],
   "source": [
    "rcm.integrate_years(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are we close to energy balance?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ -3.12861590e-06])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcm.ASR - rcm.OLR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clone the model and perturb CO2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make an exact clone with same temperatures\n",
    "rcm_2xCO2 = climlab.process_like(rcm)\n",
    "rcm_2xCO2.name = 'Radiative-Convective Model (2xCO2 initial)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000348"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Check to see that we indeed have the same CO2 amount\n",
    "rcm_2xCO2.subprocess['Radiation'].absorber_vmr['CO2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Now double it!\n",
    "rcm_2xCO2.subprocess['Radiation'].absorber_vmr['CO2'] *= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000696"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  and verify\n",
    "rcm_2xCO2.subprocess['Radiation'].absorber_vmr['CO2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantaneous radiative forcing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest measure of radiative forcing is **the instantaneous change** in the energy budget **before the temperature have a chance to adjust**.\n",
    "\n",
    "To get this we need to call the `compute_diagnostics` method, but not any forward timestep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcm_2xCO2.compute_diagnostics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now take a look at the changes in the SW and LW budgets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 0.06281552])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcm_2xCO2.ASR - rcm.ASR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([-2.11372722])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcm_2xCO2.OLR - rcm.OLR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what is instantaneous radiative forcing for the doubling of CO2?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 2.17654274])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DeltaR_instant = (rcm_2xCO2.ASR - rcm_2xCO2.OLR) - (rcm.ASR - rcm.OLR)\n",
    "DeltaR_instant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The radiative forcing for a doubling of CO2 in this model is 2.18 W m$^{-2}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, almost all of the radiative forcing appears in the longwave. We have made the atmosphere **more optically thick** by adding CO$_2$.\n",
    "\n",
    "Think about this the same way we increased the absorptivity / emissivity parameter $\\epsilon$ in the simple grey-gas model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statosphere-adjusted radiative forcing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The point of measuring radiative forcing is that it should give us some information about how much global warming we should expect from a particular forcing agent.\n",
    "\n",
    "We will need to use our model to quantify the **eventual** temperature change associated with this forcing.\n",
    "\n",
    "It turns out, for reasons we won't get into here, that a more useful measure of the global warming impact of a forcing agent comes from thinking about **changes in radiative flux at the tropopause** rather than the Top of Atmosphere.\n",
    "\n",
    "The idea here is that we will **let the stratosphere adjust to the extra CO$_2$** while **holding the troposphere and surface temperatures fixed**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this model levels 0 through 12 are in the statosphere; levels 13 and larger are in the troposphere:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 226.513265,  266.481155,  313.501265,  368.81798 ,  433.895225,\n",
       "        510.455255,  600.5242  ,  696.79629 ,  787.70206 ,  867.16076 ,\n",
       "        929.648875,  970.55483 ,  992.5561  ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcm.lev[13:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So to compute stratosphere-adjusted forcing, we'll timestep the model, but continually reset the temperatures to their reference values below 226 hPa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcm_2xCO2_strat = climlab.process_like(rcm_2xCO2)\n",
    "rcm_2xCO2_strat.name = 'Radiative-Convective Model (2xCO2 stratosphere-adjusted)'\n",
    "for n in range(1000):\n",
    "    rcm_2xCO2_strat.step_forward()\n",
    "    # hold tropospheric and surface temperatures fixed\n",
    "    rcm_2xCO2_strat.Tatm[13:] = rcm.Tatm[13:]\n",
    "    rcm_2xCO2_strat.Ts[:] = rcm.Ts[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute the stratosphere-adjusted radiative forcing for the doubling of CO2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 4.28716552])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DeltaR = (rcm_2xCO2_strat.ASR - rcm_2xCO2_strat.OLR) - (rcm.ASR - rcm.OLR)\n",
    "DeltaR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is about 4.3 W m$^{-2}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________\n",
    "\n",
    "<a id='section2'></a>\n",
    "\n",
    "## 2. Equilibrium climate sensitivity (without feedback)\n",
    "____________\n",
    "\n",
    "\n",
    "We now ask the question: How much warming will we get (eventually) in response to this positive radiative forcing?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the **Equilibrium Climate Sensitivity** (denoted **ECS** or $\\Delta T_{2xCO2}$):\n",
    "\n",
    "*The global mean surface warming necessary to balance the planetary energy budget after a doubling of atmospheric CO$_2$.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can go ahead and calculate ECS in our single-column model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1826 steps, 1826.2110000000002 days, or 5 years.\n",
      "Total elapsed time is 12.7367538581 years.\n"
     ]
    }
   ],
   "source": [
    "rcm_2xCO2_eq = climlab.process_like(rcm_2xCO2_strat)\n",
    "rcm_2xCO2_eq.name = 'Radiative-Convective Model (2xCO2 equilibrium)'\n",
    "rcm_2xCO2_eq.integrate_years(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([  1.23066002e-11])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# are we close to equilibrium?\n",
    "rcm_2xCO2_eq.ASR - rcm_2xCO2_eq.OLR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's follow what we have done before and plot the results on a nice Skew-T:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_skewT():\n",
    "    fig = plt.figure(figsize=(9, 9))\n",
    "    skew = SkewT(fig, rotation=30)\n",
    "    skew.plot(Tglobal.level, Tglobal, color='black', linestyle='-', linewidth=2, label='Observations')\n",
    "    skew.ax.set_ylim(1050, 10)\n",
    "    skew.ax.set_xlim(-90, 45)\n",
    "    # Add the relevant special lines\n",
    "    skew.plot_dry_adiabats(linewidth=0.5)\n",
    "    skew.plot_moist_adiabats(linewidth=0.5)\n",
    "    #skew.plot_mixing_lines()\n",
    "    skew.ax.legend()\n",
    "    skew.ax.set_xlabel('Temperature (degC)', fontsize=14)\n",
    "    skew.ax.set_ylabel('Pressure (hPa)', fontsize=14)\n",
    "    return skew\n",
    "\n",
    "def add_profile(skew, model, linestyle='-', color=None):\n",
    "    line = skew.plot(model.lev, model.Tatm - climlab.constants.tempCtoK,\n",
    "             label=model.name, linewidth=2)[0]\n",
    "    skew.plot(1000, model.Ts - climlab.constants.tempCtoK, 'o', \n",
    "              markersize=8, color=line.get_color())\n",
    "    skew.ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lMTExtG/fXupv4Wq6Wq0WBweHUh+Ko0ePsn37dgB69epFRsYDMjOzKSiAQYMG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\nmE3AMNB3dcGp1cIaEzPtV1YrQVeXHsnJY1CpaBCoRqORbfJua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4Obkg7sdrKFJifVGxpK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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1229822b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skew = make_skewT()\n",
    "add_profile(skew, rcm)\n",
    "add_profile(skew, rcm_2xCO2_strat)\n",
    "add_profile(skew, rcm_2xCO2_eq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see here? What has changed?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the ECS\n",
    "\n",
    "It is just the difference in surface temperature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 1.3008774])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ECS_nofeedback = rcm_2xCO2_eq.Ts - rcm.Ts\n",
    "ECS_nofeedback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doubling CO$_2$ in this model causes about 1.3 K of warming at equilibrium."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the energy budget? \n",
    "\n",
    "Remember that we have let the model warm up to its new equilibrium! \n",
    "\n",
    "If we look at the differences between the two equilibrium states (before and after doubling CO$_2$), we find only very small and offsetting changes in SW and LW:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 0.05158323])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcm_2xCO2_eq.OLR - rcm.OLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 0.05158635])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcm_2xCO2_eq.ASR - rcm.ASR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 1.3 K sensitivity we've just calculated is the warming that we would have *if there were no other changes (feedbacks) in response to the CO$_2$ induced warming!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The no-feedback response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have just calculated an equilibrium warming $\\Delta T_0$ (in K) resulting from a radiative forcing $\\Delta R$ (in W m$^{-2}$) for a model without feedback.\n",
    "\n",
    "Let's define the **no-feedback climate response parameter** $\\lambda_0$ as\n",
    "\n",
    "$$ \\lambda_0 = \\frac{\\Delta R}{\\Delta T_0 } $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the numbers we came up with above,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 3.29559537])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda0 = DeltaR / ECS_nofeedback\n",
    "lambda0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The no-feedback climate response parameter is $\\lambda_0 = 3.3$ W m$^{-2}$ K$^{-1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are some important processes that our model has neglected?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________\n",
    "<a id='section3'></a>\n",
    "\n",
    "## 3. The feedback concept\n",
    "____________\n",
    "\n",
    "A concept borrowed from electrical engineering. You have all heard or used the term before, but we’ll try take a more precise approach today.\n",
    "\n",
    "A feedback occurs when a portion of the output from the action of a system is added to the input and subsequently alters the output:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://www.atmos.albany.edu/facstaff/brose/classes/ENV415_Spring2018/images/feedback_sketch.png\" width=\"500\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of a loop system can either be **amplification** or **dampening** of the process, depending on the sign of the gain in the loop, which we will denote $f$.\n",
    "\n",
    "We will call amplifying feedbacks **positive** ($f>0$) and damping feedbacks **negative** ($f<0$).\n",
    "\n",
    "We can think of the “process” here as the entire climate system, which contains many examples of both positive and negative feedback."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: the water vapor feedback\n",
    "\n",
    "The capacity of the atmosphere to hold water vapor (saturation specific humidity) increases exponentially with temperature. Warming is thus accompanied by moistening (more water vapor), which leads to more warming due to the enhanced water vapor greenhouse effect.\n",
    "\n",
    "**Positive or negative feedback?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: the ice-albedo feedback\n",
    "\n",
    "Colder temperatures lead to expansion of the areas covered by ice and snow, which tend to be more reflective than water and vegetation. This causes a reduction in the absorbed solar radiation, which leads to more cooling. \n",
    "\n",
    "**Positive or negative feedback?**\n",
    "\n",
    "*Make sure it’s clear that the sign of the feedback is the same whether we are talking about warming or cooling.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____________\n",
    "<a id='section4'></a>\n",
    "## 4. Climate feedback: some definitions\n",
    "____________\n",
    "\n",
    "We start with an initial radiative forcing , and get a response\n",
    "$$ \\Delta T_0 = \\frac{\\Delta R}{\\lambda_0} $$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now consider what happens in the presence of a feedback process. For a concrete example, let’s take the **water vapor feedback**. For every degree of warming, there is an additional increase in the greenhouse effect, and thus additional energy added to the system.\n",
    "\n",
    "Let’s denote this extra energy as \n",
    "$$ f \\lambda_0 \\Delta T_0 $$\n",
    "\n",
    "where $f$ is the **feedback amount**, a number that represents what fraction of the output gets added back to the input. $f$ must be between $-\\infty$ and +1. \n",
    "\n",
    "For the example of the water vapor feedback, $f$ is positive (between 0 and +1) – the process adds extra energy to the original radiative forcing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The amount of energy in the full \"input\" is now\n",
    "\n",
    "$$ \\Delta R + f \\lambda_0 \\Delta T_0 $$\n",
    "\n",
    "or\n",
    "\n",
    "$$ (1+f) \\lambda_0 \\Delta T_0 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But now we need to consider the next loop. A fraction $f$ of the additional energy is also added to the input, giving us\n",
    "\n",
    "$$ (1+f+f^2) \\lambda_0 \\Delta T_0 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we can go round and round, leading to the infinite series\n",
    "\n",
    "$$ (1+f+f^2+f^3+ ...) \\lambda_0 \\Delta T_0 = \\lambda_0 \\Delta T_0 \\sum_{n=0}^{\\infty} f^n $$\n",
    "\n",
    "Question: what happens if $f=1$?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It so happens that this infinite series has an exact solution\n",
    "\n",
    "$$ \\sum_{n=0}^{\\infty} f^n = \\frac{1}{1-f} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the full response including all the effects of the feedback is actually\n",
    "\n",
    "$$ \\Delta T = \\frac{1}{1-f} \\Delta T_0 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is also sometimes written as \n",
    "$$ \\Delta T = g \\Delta T_0 $$\n",
    "\n",
    "where \n",
    "\n",
    "$$ g = \\frac{1}{1-f} = \\frac{\\Delta T}{\\Delta T_0} $$\n",
    "\n",
    "is called the **system gain** -- the ratio of the actual warming (including all feedbacks) to the warming we would have in the absence of feedbacks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So if the overall feedback is positive, then $f>0$ and $g>1$.\n",
    "\n",
    "And if the overall feedback is negative?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________\n",
    "<a id='section6'></a>\n",
    "## 6. Contribution of individual feedback processes to Equilibrium Climate Sensitivity\n",
    "____________\n",
    "\n",
    "\n",
    "Now what if we have several individual feedback processes occurring simultaneously?\n",
    "\n",
    "We can think of individual feedback amounts $f_1, f_2, f_3, ...$, with each  representing a physically distinct mechanism, e.g. water vapor, surface snow and ice, cloud changes, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each individual process takes a fraction $f_i$ of the output and adds to the input. So the feedback amounts are additive,\n",
    "\n",
    "$$ f = f_1 + f_2 + f_3 + ... = \\sum_{i=0}^N f_i $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us a way to compare the importance of individual feedback processes!\n",
    "\n",
    "The climate sensitivity is now\n",
    "\n",
    "$$ \\Delta T_{2xCO2} = \\frac{1}{1- \\sum_{i=0}^N f_i } \\Delta T_0 $$\n",
    "\n",
    "The climate sensitivity is thus **increased by positive feedback processes**, and **decreased by negative feedback processes**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Climate feedback parameters\n",
    "\n",
    "We can also write this in terms of the original radiative forcing as\t\n",
    "\n",
    "$$ \\Delta T_{2xCO2} = \\frac{\\Delta R}{\\lambda_0 - \\sum_{i=1}^{N} \\lambda_i} $$\n",
    "\n",
    "where\n",
    "\n",
    "$$ \\lambda_i = \\lambda_0 f_i $$\n",
    "\n",
    "known as **climate feedback parameters**, in units of W m$^{-2}$ K$^{-1}$. \n",
    "\n",
    "With this choice of sign conventions, $\\lambda_i > 0$ for a positive feedback process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Individual feedback parameters $\\lambda_i$ are then additive, and can be compared to the no-feedback parameter $\\lambda_0$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might decompose the net climate feedback into, for example\n",
    "\n",
    "- longwave and shortwave processes\n",
    "- cloud and non-cloud processes\n",
    "\n",
    "These individual feedback processes may be positive or negative. This is very powerful, because we can **measure the relative importance of different feedback processes** simply by comparing their $\\lambda_i$ values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Every climate model has a Planck feedback\n",
    "\n",
    "Our no-feedback response parameter $\\lambda_0$ is often called the **Planck feedback**.\n",
    "\n",
    "It is not really a feedback at all.  It is the most basic and universal climate process, and is present in every climate model. It is simply an expression of the fact that a warm planet radiates more to space than a cold planet.\n",
    "\n",
    "As we will see, our estimate of $\\lambda_0 = 3.3 ~\\text{W} ~\\text{m}^{-2} ~\\text{K}^{-1} $ is essentially the same as the Planck feedback diagnosed from complex GCMs. Most climate models (and the real climate system) have other radiative feedback processes, such that \n",
    "\n",
    "$$\\lambda = \\lambda_0 - \\sum_{i=1}^{N} \\lambda_i \\ne \\lambda_0 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________\n",
    "<a id='section7'></a>\n",
    "## 7. Feedbacks diagnosed from complex climate models\n",
    "____________\n",
    "\n",
    "### Data from the IPCC AR5\n",
    "\n",
    "This figure is reproduced from the recent IPCC AR5 report. It shows the feedbacks diagnosed from the various models that contributed to the assessment.\n",
    "\n",
    "(Later in the term we will discuss how the feedback diagnosis is actually done)\n",
    "\n",
    "See below for complete citation information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![feedback AR5](http://www.climatechange2013.org/images/figures/WGI_AR5_Fig9-43.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 9.43** | (a) Strengths of individual feedbacks for CMIP3 and CMIP5 models (left and right columns of symbols) for Planck (P), water vapour (WV), clouds (C), albedo (A), lapse rate (LR), combination of water vapour and lapse rate (WV+LR) and sum of all feedbacks except Planck (ALL), from Soden and Held (2006) and Vial et al. (2013), following Soden et al. (2008). CMIP5 feedbacks are derived from CMIP5 simulations for abrupt fourfold increases in CO2 concentrations (4 × CO2). (b) ECS obtained using regression techniques by Andrews et al. (2012) against ECS estimated from the ratio of CO2 ERF to the sum of all feedbacks. The CO2 ERF is one-half the 4 × CO2 forcings from Andrews et al. (2012), and the total feedback (ALL + Planck) is from Vial et al. (2013).\n",
    "\n",
    "*Figure caption reproduced from the AR5 WG1 report*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Legend:\n",
    "\n",
    "- P: Planck feedback\n",
    "- WV: Water vapor feedback\n",
    "- LR: Lapse rate feedback\n",
    "- WV+LR: combined water vapor plus lapse rate feedback\n",
    "- C: cloud feedback\n",
    "- A: surface albedo feedback\n",
    "- ALL: sum of all feedback except Plank, i.e. ALL = WV+LR+C+A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to note:\n",
    "\n",
    "- The models all agree strongly on the Planck feedback. \n",
    "- The Planck feedback is about $-3.3 ~\\text{W} ~\\text{m}^{-2} ~\\text{K}^{-1} $ just like our above estimate of $\\lambda_0$ (but with opposite sign convention -- watch carefully for that in the literature)\n",
    "- The water vapor feedback is strongly positive in every model.\n",
    "- The lapse rate feedback is something we will study later. It is slightly negative.\n",
    "- For reasons we will discuss later, the best way to measure the water vapor feedback is to combine it with lapse rate feedback.\n",
    "- Models agree strongly on the combined water vapor plus lapse rate feedback."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The albedo feedback is slightly positive but rather small globally.\n",
    "- By far the largest spread across the models occurs in the cloud feedback.\n",
    "- Global cloud feedback ranges from slighly negative to strongly positive across the models.\n",
    "- Most of the spread in the total feedback is due to the spread in the cloud feedback.\n",
    "- Therefore, most of the spread in the ECS across the models is due to the spread in the cloud feedback."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________\n",
    "<a id='section8'></a>\n",
    "## 8. Water vapor feedback in the radiative-convective model\n",
    "____________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In nature, as in a complex GCM, water vapor tends to increase as the air temperature warms.\n",
    "\n",
    "The main reason for this is that **the saturation specific humidity** (i.e. how much water vapor the air can hold) **increases strongly with temperature**.\n",
    "\n",
    "We can **parameterize** this effect in the column model by insisting that the **relative humidity remain fixed** as the column warms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  actual specific humidity\n",
    "q = rcm.subprocess['Radiation'].specific_humidity\n",
    "#  saturation specific humidity (a function of temperature and pressure)\n",
    "qsat = climlab.utils.thermo.qsat(rcm.Tatm, rcm.lev)\n",
    "#  Relative humidity\n",
    "rh = q/qsat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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tarb33rBwYdhROZfmmjeHhx6yUSqHHgo33US3gQNtBMvGjWFHlzI8cYQoK8vK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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0b0860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot relative humidity in percent\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(q*1000, rcm.lev, 'b-')\n",
    "ax.invert_yaxis()\n",
    "ax.grid()\n",
    "ax.set_ylabel('Pressure (hPa)')\n",
    "ax.set_xlabel('Specific humidity (g/kg)', color='b')\n",
    "ax.tick_params('x', colors='b')\n",
    "ax2 = ax.twiny()\n",
    "ax2.plot(rh*100., rcm.lev, 'r-')\n",
    "ax2.set_xlabel('Relative humidity (%)', color='r')\n",
    "ax2.tick_params('x', colors='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  A radiative-convective model with fixed relative humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcm_2xCO2_h2o = climlab.process_like(rcm_2xCO2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in range(2000):\n",
    "    # At every timestep\n",
    "    # we calculate the new saturation specific humidity for the new temperature\n",
    "    #  and change the water vapor in the radiation model\n",
    "    #  so that relative humidity is always the same\n",
    "    qsat = climlab.utils.thermo.qsat(rcm_2xCO2_h2o.Tatm, rcm_2xCO2_h2o.lev)\n",
    "    rcm_2xCO2_h2o.subprocess['Radiation'].specific_humidity[:] = rh * qsat\n",
    "    rcm_2xCO2_h2o.step_forward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ -1.63225353e-05])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for energy balance\n",
    "rcm_2xCO2_h2o.ASR - rcm_2xCO2_h2o.OLR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the Equilibrium Climate Sensitivity of this new model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 2.98970885])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ECS = rcm_2xCO2_h2o.Ts - rcm.Ts\n",
    "ECS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the water vapor feedback, doubling CO$_2$ causes 3 K of warming at equilibrium, rather than the no-feedback sensitivity of 1.3 K.\n",
    "\n",
    "A pretty big difference!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The system gain is the ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 2.29822492])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = ECS / ECS_nofeedback\n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the moistening of the atmosphere more than doubles the global warming effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the feedback parameter?\n",
    "\n",
    "This is just the ratio of the radiative forcing $\\Delta R$ to the climate sensitivity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 1.43397425])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda_net = DeltaR / ECS\n",
    "lambda_net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The net feedback is $\\lambda = 1.43$ W m$^{-2}$ K$^{-1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the additive nature of the feedbacks, this means that the individual contribution of the water vapor feedback is\n",
    "\n",
    "$$ \\lambda_{h2o} = \\lambda_0 - \\lambda $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 1.86162112])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda_h2o = lambda0 - lambda_net\n",
    "lambda_h2o"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we measure a water vapor feedback of $\\lambda_{h2o} = 1.86$ W m$^{-2}$ K$^{-1}$.\n",
    "\n",
    "This is a **positive number** consistent with the **amplifying effect** of the water vapor feedback.\n",
    "\n",
    "The physical meaning of this number:\n",
    "\n",
    "**For every 1 degree of surface warming, the increased water vapor greenhouse effect provides an additional 1.86 W m$^{-2}$ of radiative forcing.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare back to the feedback figure from IPCC AR5. How does this analysis compare to the comprehensive GCMs? What is missing here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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  "language_info": {
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   "file_extension": ".py",
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