Probabilistic 21st and 22nd Century Sea-level Projections at a Global Network of Tide-gauge Sites: Source Code

Two software zip files (ProjectSLRCode.zip & LocalizeSLCode.zip) are included here:[ProjectSLRCode.zip]This code contains two directories, slr and lib. slr contains code for analyzing tide gauge data and generating sea-level rise projections. lib contains supporting files.This code requires MATL...

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Bibliographic Details
Main Author: Kopp, Robert E.
Format: Article in Journal/Newspaper
Language:unknown
Published: No Publisher Supplied 2014
Subjects:
Online Access:https://dx.doi.org/10.7282/t3rx9f58
https://rucore.libraries.rutgers.edu/rutgers-lib/49573/
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Summary:Two software zip files (ProjectSLRCode.zip & LocalizeSLCode.zip) are included here:[ProjectSLRCode.zip]This code contains two directories, slr and lib. slr contains code for analyzing tide gauge data and generating sea-level rise projections. lib contains supporting files.This code requires MATLAB to run. It uses the Optimization and Mapping toolkits, though some of the functionality should be available without those toolkits.The code directory does not include some needed input files, which go in the IFILES directory (specified in configureSLRProjections). These include:* the CSIRO GSL reconstruction,* the ICE5G-VM290 GIA model (NetCDF),* land ice static-equilibrium fingerprints,* Marzeion et al. 2012 glacier and ice cap projections,* PSMSL tide gauge data.(The LocalizeSLCode ZIP file available here contains this content.)## Sea level rise projectionsrunTrainGPSLModel.m will generate a set of parameter files with optimized hyperparameters for each of the regions described in the coastlines.txt parameter files. (It is recommended that you use the default specifications, which are stored in slr/PARAMS; the training process is slow).runSLRProjections.m will generate the sea-level rise projections, using the configuration specified in configureSLRProjections.m and generating the output files specified in outputSLRProjections.m. You will need the slr/, slr/MFILES, and slr/MFILES/scripts directories in your path.You will need to modify the paths in configureSLRProjections.m to match your system.----Copyright (C) 2014 by Robert E. KoppThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.[LocalizeSLCode.zip]# LocalizeSL: Offline sea-level localization code for Kopp et al. (2014)README file last updated by Robert Kopp, robert-dot-kopp-at-rutgers-dot-edu, Tue May 05 17:37:56 EDT 2015## CitationThis code is intended to accompany the results ofR. E. Kopp, R. M. Horton, C. M. Little, J. X. Mitrovica, M. Oppenheimer,D. J. Rasmussen, B. H. Strauss, and C. Tebaldi (2014). Probabilistic 21stand 22nd century sea-level projections at a global network of tidegaugesites. Earth's Future 2: 287–306, doi:10.1002/2014EF000239. Please cite that paper when using any results generated with this code.## OverviewThis MATLAB code is intended to help end-users who wish to work with the sea-level rise projections of Kopp et al. (2014) in greater detail than provided by the supplementary tables accompanying that table but without re-running the full global analysis using the supplementary code accompanying the paper. Key functionality these routines provide include:1. Local sea-level rise projections at decadal time points and arbitrary quantiles2. Localized Monte Carlo samples, disaggregatable by contributory process3. Localized variance decomposition plots These routines do not provide the extreme flood level analysis in Kopp et al. (2014), but the Monte Carlo time series samples they produce can be combined with other analyses to look at probabilistic changes in flood frequency over time.The IFILES directory contains the ~200 MB file SLRProjections140523core.mat, which stores 10,000 Monte Carlo samples for each of the processes contributing to global sea-level change, along with metadata. The code loads these samples without regenerating them and then localizes them.Functions are stored in the MFILES directory.The most important function is **LocalizeStoredProjections**: [sampslocrise,sampsloccomponents,siteids,sitenames,targyears,scens,cols] = LocalizeStoredProjections(focussites,storefile)LocalizeStoredProjections takes as input two parameters. STOREFILE is the path of the SLRProjections140523core.mat file. FOCUSSITES is the PSMSL ID or IDs of the site(s) of interest. (Please see psmsl.org or the supplementary tables to Kopp et al. (2014) to identify the IDs corresponding to your site of interest. Specify 0 if you want GSL samples returned in the same format.)LocalizeStoredProjections outputs two M x N cell arrays of localized Monte Carlo samples, SAMPSLOCRISE and SAMPSLOCCOMPONENTS. In each cell array, the m rows correspond to the sites specified in FOCUSSITES and the N columns to different RCPs (specifically, RCP 8.5, RCP 6.0, RCP 4.5, and RCP 2.6). The individual cells of SAMPSLOCRISE are P x Q arrays, with the P rows being 10,000 Monte Carlo samples and the Q columns corresponding to decadal time points. The individual cells of SAMPSLOCRISE are P x Q arrays, with the P rows being 10,000 Monte Carlo samples and the Q columns corresponding to decadal time points. The individual cells of SAMPSLOCCOMPONENTS are P x R x Q arrays. The 1st and 3rd dimensions correspond to the rows and columns of SAMPSLOCRISE; the R columns represent 24 different factors contributing to sea-level rise. Specifically, these factors are:1 - GIC: Alaska2 - GIC: Western Canada/US3 - GIC: Arctic Canada North4 - GIC: Arctic Canada South5 - GIC: Greenland peripheral glaciers6 - GIC: Iceland7 - GIC: Svalbard8 - GIC: Scandinavia9 - GIC: Russian Arctic10 - GIC: North Asia11 - GIC: Central Europe12 - GIC: Caucasus13 - GIC: Central Asia14 - GIC: South Asia West15 - GIC: South Asia East16 - GIC: Low Latitude17 - GIC: Southern Andes18 - GIC: New Zealand19 - Greenland Ice Sheet20 - West Antarctic Ice Sheet21 - East Antarctic Ice Sheet22 - Land water storage23 - Oceanographic processes (thermal expansion and ocean dynamics)24 - GIA, tectonics, and other background processesThe other outputs of LocalizeStoredProjections are identifying information that can be passed out to the output commands. SITEIDS returns the PSMSL site IDs of selected sites; SITENAMES the names of those sites; TARGYEARS the years of the output; SCENS the RCPs; and COLS are column labels.Several other provided functions produce output, with detailed parameter specification described in the headers.**PlotSLRProjection** generates a time series plot analogous to Figure 3 of Kopp et al. (2014).**PlotSLRProjectionVariance** generates a variance decomposition plot analogous to Figure 4 of Kopp et al. (2014).**WriteTableMC** outputs Monte Carlo samples.**WriteTableSLRProjection** outputs desired quantiles of the projections.## Example usageselectedSite = 12; % use PSMSL ID here to select site% set up pathrootdir='~/Dropbox/Code/LocalizeSL';corefile=fullfile(rootdir,'IFILES/SLRProjections140523core.mat');addpath(fullfile(rootdir,'MFILES'));% generate local samples[sampslocrise,sampsloccomponents,siteids,sitenames,targyears,scens,cols] = LocalizeStoredProjections(selectedSite,corefile);% plot curvesfigure;hp1=PlotSLRProjection(sampslocrise,targyears,[],scens);xlim([2000 2100]); ylim([0 200]);title(sitenames{1});% plot variance decompositionfigure;hp2=PlotSLRProjectionVariance(sampsloccomponents,targyears,cols,[],1);subplot(2,2,1); title([ sitenames{1} ' - RCP 8.5']);figure;hp3=PlotSLRProjectionVariance(sampsloccomponents,targyears,cols,[],1,4);subplot(2,2,1); title([sitenames{1} ' - RCP 2.6']);% output quantiles of projectionsquantlevs=[.01 .05 .167 .5 .833 .95 .99 .995 .999];WriteTableSLRProjection(sampslocrise,quantlevs,siteids,sitenames,targyears,scens);% output Monte Carlo samplesWriteTableMC(sampslocrise,[],siteids,sitenames,targyears,scens);% output Monte Carlo samples without background trend,% to allow incorporation of alternative estimates of background trendWriteTableMC(sampsloccomponents,1:23,siteids,sitenames,targyears,scens,'LSLProj_nobkgd_');% output decompositionWriteTableDecomposition(sampsloccomponents,quantlevs,siteids,sitenames,targyears,cols,scens);% pull GSL samples[sampsGSLrise,sampsGSLcomponents,siteids,sitenames,targyears,scens,cols] = LocalizeStoredProjections(0,corefile);WriteTableDecomposition(sampsGSLcomponents,quantlevs,siteids,sitenames,targyears,cols,scens);----Copyright (C) 2015 by Robert E. KoppThis program is free software: you can redistribute it and/or modifyit under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program. If not, see .