LocalizedProjections.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![actual brick](./brick_logo.png)\n",
    "\n",
    "# BRICK: Localized projections\n",
    "\n",
    "* Make localized projections using results from [Wong and Keller (2017)](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017EF000607)\n",
    "* Uses local sea level fingerprints of [Slangen et al. (2014)](https://link.springer.com/article/10.1007/s10584-014-1080-9)\n",
    "* User-defined latitude/longitude for local sea level\n",
    "* Result is a netCDF file of localized annual mean sea levels\n",
    "* Include or not include sudden, rapid sea-level contribution from the Antarctic ice sheet (AIS fast dynamics)\n",
    "* Include any of RCP2.6, 4.5, 6.0 and 8.5\n",
    "\n",
    "Question?  I'm always happy to chat!  Tony Wong (<anthony.e.wong@colorado.edu>)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Location\n",
    "\n",
    "Where would you like localized projections?  Enter latitude and longitude as degrees north and degrees east. Here, we demonstrate using the latitude and longitude coordinates of Norfolk, Virginia (USA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lat <- 37    # degrees North\n",
    "lon <- -76   # degrees East"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BRICK output file\n",
    "\n",
    "Which BRICK output file would you like? Defaults to the results from Wong and Keller (2017) using `gamma` prior distributions for the Antarctic ice sheet fast dynamics parameters (as in [Wong et al. (2017)](https://link.springer.com/article/10.1007/s10584-017-2039-4)), but you could change this to other BRICK output files if you would like.  This path is assumed relative to the BRICK home directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename.brick <- paste('output_model/BRICK_physical_fd-gamma_08May2017.nc', sep='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Antarctic fast dynamics\n",
    "\n",
    "If you want projections that do not include the Antarctic ice sheet fast dynamics contributions, the set this to FALSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "use_fastdynamics <- TRUE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do a check to see if the requested file is local.  If not, go grab it from download server. Check out the `GrabOutputFiles.R` routine, and the model output in the `output_model` directory for some ideas.\n",
    "\n",
    "Note that the `list.files` function does not prepend the file name and directory path with `./` or anything like that. Doing so will confuse it. So, just start with the directory/file name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Found file! Proceeding with projections.\"\n"
     ]
    }
   ],
   "source": [
    "files <- list.files(recursive=TRUE)\n",
    "ifile <- which(files==filename.brick)\n",
    "found <- length(ifile) > 0\n",
    "\n",
    "# Check if the file is found\n",
    "if(found) {\n",
    "    print('Found file! Proceeding with projections.')\n",
    "} else {\n",
    "    print(paste('File ',filename.brick,' not found. Check the spelling, or go grab it from the download server...', sep=''))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the results file\n",
    "\n",
    "Edit here the `rcps` variable if you only want a particular subset of the RCP scenarios. And we will initialize lists for the global mean sea level, local sea level, Antarctic ice sheet contribution to sea level, Greenland ice sheet contribution to sea level, glaciers and ice caps contribution to sea level, thermal expansion contribution to sea level, land water storage contribution to sea level, global mean temperature, and ocean heat uptake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure ncdf4 package is installed; install it, if not\n",
    "if('ncdf4' %in% rownames(installed.packages()) == FALSE) {install.packages('ncdf4')}\n",
    "library(ncdf4)\n",
    "\n",
    "# make projections for all 4 RCP scenarios\n",
    "rcps <- c('RCP26','RCP45','RCP60','RCP85'); n.rcp <- length(rcps)\n",
    "\n",
    "# initialize lists for model output\n",
    "gmsl <- lsl <- ais <- gis <- gsic <- \n",
    "            te <- lws <- temp <- ocheat <- vector('list', n.rcp)\n",
    "\n",
    "# the first level of each list will be for each RCP scenario's projections\n",
    "# of that quantity\n",
    "names(gmsl) <- names(lsl) <- names(ais) <- names(gis) <- names(gsic) <- \n",
    "            names(te) <- names(lws) <- names(temp) <- names(ocheat) <- rcps\n",
    "\n",
    "ncdata <- nc_open(filename.brick)\n",
    "\n",
    "for (rcp in rcps) {\n",
    "    gmsl[[rcp]]   <- ncvar_get(ncdata, paste('GlobalSeaLevel_',rcp, sep=''))\n",
    "    ais[[rcp]]    <- ncvar_get(ncdata, paste('AIS_',rcp, sep=''))\n",
    "    gis[[rcp]]    <- ncvar_get(ncdata, paste('GIS_',rcp, sep=''))\n",
    "    gsic[[rcp]]   <- ncvar_get(ncdata, paste('GSIC_',rcp, sep=''))\n",
    "    te[[rcp]]     <- ncvar_get(ncdata, paste('TE_',rcp, sep=''))\n",
    "    lws[[rcp]]    <- ncvar_get(ncdata, paste('LWS_',rcp, sep=''))\n",
    "    temp[[rcp]]   <- ncvar_get(ncdata, paste('temp_',rcp, sep=''))\n",
    "    ocheat[[rcp]] <- ncvar_get(ncdata, paste('ocheat_',rcp, sep=''))\n",
    "    if(!use_fastdynamics) {\n",
    "        gmsl_nofd   <- ncvar_get(ncdata, paste('GlobalSeaLevel_nofd_',rcp, sep=''))\n",
    "        ais[[rcp]]  <- ais[[rcp]] - (gmsl[[rcp]] - gmsl_nofd)\n",
    "        gmsl[[rcp]] <- gmsl_nofd\n",
    "    }\n",
    "}\n",
    "\n",
    "t.proj     <- ncvar_get(ncdata, 'time_proj')\n",
    "n.ensemble <- length(ncvar_get(ncdata, 'ens'))\n",
    "\n",
    "nc_close(ncdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local fingerprinting\n",
    "\n",
    "Load the global fingerprinting data set (Slangen et al 2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename.fingerprints = './fingerprints/FINGERPRINTS_SLANGEN_Bakker.nc'\n",
    "\n",
    "ncdata  <- nc_open(filename.fingerprints)\n",
    "lat.fp  <- ncvar_get(ncdata, 'lat')\n",
    "lon.fp  <- ncvar_get(ncdata, 'lon')\n",
    "fp.gsic <- ncvar_get(ncdata, 'GLAC')\n",
    "fp.gis  <- ncvar_get(ncdata, 'GIS')\n",
    "fp.ais  <- ncvar_get(ncdata, 'AIS')\n",
    "nc_close(ncdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get local fingerprints\n",
    "\n",
    "Convert the given latitude and longitude to degrees East/North, and find the fingerprinting data set location closest to the local sea level lat/lon given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if(lon < 0) {lon <- lon + 360}  # convert longitude to degrees East\n",
    "ilat <- which( abs(lat.fp-lat)==min(abs(lat.fp-lat)) )\n",
    "ilon <- which( abs(lon.fp-lon)==min(abs(lon.fp-lon)) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible there were multiple lats/lons 'closest' to your given point. In this case, take the average of the ones that are actually numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fp.ais.loc <- mean(fp.ais[ilon,ilat],na.rm=TRUE)\n",
    "fp.gsic.loc <- mean(fp.gsic[ilon,ilat],na.rm=TRUE)\n",
    "fp.gis.loc <- mean(fp.gis[ilon,ilat],na.rm=TRUE)\n",
    "fp.te.loc <- 1.0   # TE response is to global mean temperature, so global mean sea level response is same everywhere\n",
    "fp.lws.loc <- 1.0  # assume LWS changes uniformly (likely not quite true,\n",
    "                   # but a small contribution anyway)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if the nearest spot ended up on land. If it did, take the average everywhere around the location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if(is.na(fp.ais.loc) | is.na(fp.gsic.loc) | is.na(fp.gis.loc) | is.na(fp.te.loc)) {\n",
    "    fp.ais.loc <- mean(fp.ais[(ilon-1):(ilon+1),(ilat-1):(ilat+1)], na.rm=TRUE)\n",
    "    fp.gsic.loc <- mean(fp.gsic[(ilon-1):(ilon+1),(ilat-1):(ilat+1)], na.rm=TRUE)\n",
    "    fp.gis.loc <- mean(fp.gis[(ilon-1):(ilon+1),(ilat-1):(ilat+1)], na.rm=TRUE)\n",
    "    fp.te.loc <- 1.0\n",
    "    fp.lws.loc <- 1.0\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there is still something wrong, probably the desired lat/lon location is accidentally not near the coast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if(is.na(fp.ais.loc) | is.na(fp.gsic.loc) | is.na(fp.gis.loc) | is.na(fp.te.loc)) {\n",
    "    print('WARNING -- local sea level fingerprints are NaN. Is the lat/lon point possibly over land?')\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate local sea level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for (rcp in rcps) {\n",
    "    lsl[[rcp]] <- mat.or.vec(nr=nrow(gmsl[[rcp]]), nc=ncol(gmsl[[rcp]]))\n",
    "    for (sow in 1:n.ensemble) {\n",
    "        lsl[[rcp]][,sow] <- fp.gis.loc  * gis[[rcp]][,sow]  +\n",
    "                            fp.ais.loc  * ais[[rcp]][,sow]  +\n",
    "                            fp.gsic.loc * gsic[[rcp]][,sow] +\n",
    "                            fp.te.loc   * te[[rcp]][,sow]   +\n",
    "                            fp.lws.loc  * lws[[rcp]][,sow]\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference period\n",
    "\n",
    "Check that all sea-level projections and temperatures are relative to 1986-2005 means. Or change this to whatever you want the reference period to be.  Set `reference_period` to the beginning and ending years of whatever reference period you would like!  Must be within the 1850-2100 simulation years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reference.period <- c(1986, 2005)\n",
    "\n",
    "ind.norm <- which(t.proj==reference.period[1]):which(t.proj==reference.period[2])\n",
    "\n",
    "for (rcp in rcps) {\n",
    "    lsl[[rcp]] <- lsl[[rcp]] - matrix(apply(X=lsl[[rcp]][ind.norm,], MARGIN=2, FUN=mean), \n",
    "                                      nrow=length(t.proj), ncol=n.ensemble, byrow=TRUE)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write output file\n",
    "\n",
    "Start by giving it a nice name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename.output <- './output_model/projections_norfolk.nc'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create the actual file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dim.tproj    <- ncdim_def('time_proj', 'years', as.double(t.proj))\n",
    "dim.lat      <- ncdim_def('lat', 'deg N', as.double(length(lat)))\n",
    "dim.lon      <- ncdim_def('lon', 'deg E', as.double(length(lon)))\n",
    "dim.ensemble <- ncdim_def('ens', 'ensemble member', as.double(1:ncol(lsl[[1]])), unlim=TRUE)\n",
    "\n",
    "def.lat <- ncvar_def('lat.lsl', 'deg N', list(dim.lat), -999, longname = 'latitude of local sea level point')\n",
    "def.lon <- ncvar_def('lon.lsl', 'deg E', list(dim.lon), -999, longname = 'longitude of local sea level point')\n",
    "\n",
    "def.lsl <- vector('list', n.rcp); names(def.lsl) <- rcps\n",
    "for (rcp in rcps) {\n",
    "    def.lsl[[rcp]] <- ncvar_def(paste('LocalSeaLevel_',rcp, sep=''), 'meters', \n",
    "                                list(dim.tproj, dim.ensemble), -999, longname = paste('Local sea level ',rcp, sep=''))\n",
    "}\n",
    "\n",
    "outlist = list()\n",
    "for (i in 1:n.rcp) {\n",
    "    outlist[[i]] <- def.lsl[[i]]\n",
    "}\n",
    "outlist[[i+1]] <- def.lat\n",
    "outlist[[i+2]] <- def.lon\n",
    "\n",
    "outnc <- nc_create(filename.output, outlist, force_v4=TRUE)\n",
    "\n",
    "ncvar_put(outnc, def.lat, lat)\n",
    "ncvar_put(outnc, def.lon, lon)\n",
    "for (rcp in rcps) {\n",
    "    ncvar_put(outnc, def.lsl[[rcp]], lsl[[rcp]])\n",
    "}\n",
    "\n",
    "nc_close(outnc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Sample plots\n",
    "\n",
    "Below are a couple of sample plots using the BRICK projections from Wong and Keller (2017) for analyses. Feel free to use the codes and model output as you would like, with the usual GPL license caveats, and please remember to cite any relevant works.\n",
    "\n",
    "### Probability distribution estimates\n",
    "\n",
    "Here is an example plot of the estimated probability distributions of global mean sea level in 2100, relative to 1986-2005 global mean sea level, for each of the four RCP scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first, fit kernel density estimates to each of the projections\n",
    "kde26 <- density(lsl$RCP26[251,])\n",
    "kde45 <- density(lsl$RCP45[251,])\n",
    "kde60 <- density(lsl$RCP60[251,])\n",
    "kde85 <- density(lsl$RCP85[251,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAFoCAYAAAAYZo/6AAAEGWlDQ1BrQ0dDb2xvclNwYWNl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",
      "text/plain": [
       "Plot with title “Norfolk, Virginia”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, a figure!\n",
    "options(repr.plot.width=3.5, repr.plot.height=3)\n",
    "par(mfrow=c(1,1), mai=c(1,1,.2,.2))\n",
    "plot(kde85$x, kde85$y, type='l', lwd=2, lty=2, xlim=c(0,2.5), ylim=c(0,5.1),\n",
    "     xaxt='n', yaxt='n', ylab='', yaxs='i', main='Norfolk, Virginia',\n",
    "     xlab='2100 sea level, relative\\nto 1986-2005 mean [m]')\n",
    "lines(kde60$x, kde60$y, lty=3, lwd=2)\n",
    "lines(kde45$x, kde45$y, lty=1, lwd=2)\n",
    "lines(kde26$x, kde26$y, lty=4, lwd=2)\n",
    "axis(1,seq(0,2.5,.5),lab=c(\"0\",\"0.5\",\"1\",\"1.5\",\"2\",\"2.5\"))\n",
    "mtext('Density', side=2, line=1, cex=1)\n",
    "legend(1.5,5,c(\"RCP2.6\",\"RCP4.5\",\"RCP6.0\",\"RCP8.5\"),\n",
    "       lty=c(4,1,3,2), lwd=1, cex=0.9, bty='n', y.intersp=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probabilistic projections\n",
    "\n",
    "Here is an example plot of the sea level and temperature trajectories under the RCP8.5 scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# first, get the ensemble median and 5-95% range for each year for the projections\n",
    "\n",
    "# indices for the projections from 2000 to 2100\n",
    "idx <- which(t.proj >= 2000)\n",
    "\n",
    "# initialize\n",
    "lsl.q <- mat.or.vec(length(idx), 3)\n",
    "temp.q <- mat.or.vec(length(idx), 3)\n",
    "\n",
    "# calculate quantiles over the projections period\n",
    "for (iy in 1:length(idx)) {\n",
    "    lsl.q[iy,1] <- quantile(lsl$RCP85[idx[iy],], 0.05)\n",
    "    lsl.q[iy,2] <- quantile(lsl$RCP85[idx[iy],], 0.50)\n",
    "    lsl.q[iy,3] <- quantile(lsl$RCP85[idx[iy],], 0.95)\n",
    "    temp.q[iy,1] <- quantile(temp$RCP85[idx[iy],], 0.05)\n",
    "    temp.q[iy,2] <- quantile(temp$RCP85[idx[iy],], 0.50)\n",
    "    temp.q[iy,3] <- quantile(temp$RCP85[idx[iy],], 0.95)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8AAAAFoCAYAAACYBpIxAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now... a figure!\n",
    "options(repr.plot.width=8, repr.plot.height=3)\n",
    "par(mfrow=c(1,2), mai=c(1,.75,.2,.2))\n",
    "plot(t.proj[idx], lsl.q[,2], type='l', lwd=2, xlab='', ylab='', main='',\n",
    "     ylim=c(0,1.6), xaxt='n', yaxt='n', xaxs='i')\n",
    "lines(t.proj[idx], lsl.q[,1], lwd=2, lty=2)\n",
    "lines(t.proj[idx], lsl.q[,3], lwd=2, lty=2)\n",
    "axis(1,seq(2000,2100,20))\n",
    "axis(2,seq(0,1.5,0.25),c('0','','0.5','','1','','1.5'))\n",
    "mtext('Year', side=1, line=2.2, cex=1)\n",
    "mtext('Sea level [m]', side=2, line=2.2, cex=1)\n",
    "legend(2000,1.6,c(\"Median\",\"5-95% range\"),\n",
    "       lty=c(1,2), lwd=1, cex=0.9, bty='n', y.intersp=3)\n",
    "\n",
    "plot(t.proj[idx], temp.q[,2], type='l', lwd=2, xlab='', ylab='', main='',\n",
    "     ylim=c(0, 5), xaxt='n', yaxt='n', xaxs='i')\n",
    "lines(t.proj[idx], temp.q[,1], lwd=2, lty=2)\n",
    "lines(t.proj[idx], temp.q[,3], lwd=2, lty=2)\n",
    "axis(1,seq(2000,2100,20))\n",
    "axis(2,seq(0,5,1))\n",
    "mtext('Year', side=1, line=2.2, cex=1)\n",
    "mtext('Temperature [deg C]', side=2, line=2.2, cex=1)\n",
    "legend(2000,5,c(\"Median\",\"5-95% range\"),\n",
    "       lty=c(1,2), lwd=1, cex=0.9, bty='n', y.intersp=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}