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Source code for the study as represented by the following abstract:Quantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.README file last updated by DJ Rasmussen (), dmr2-at-princeton-dot-edu, Wed Jul 13 14:00:55 PDT 2016 PLEASE click on README on the left bar to view the README file for the Climate Projection Code.This data set is intended to accompany these studies: (1) T. Houser, R.E. Kopp, S.M. Hsiang, M. Delgado, A.S. Jina, K. Larsen, M. Mastrandrea, S. Mohan, R. Muir-Wood, D.J. Rasmussen, J. Rising, and P. Wilson. (2015). American Climate Prospectus: Economic Risks in the United States. Columbia University Press. ISBN: 978-0231174565 (2) D. J. Rasmussen, M. Meinshausen, and R. E. Kopp. (2016). Probability- weighted ensembles of U.S. county-level climate projections for climate risk analysis. Journal of Applied Meteorology and Climatology. DOI: 10.1175/JAMC-D-15-0302.1Please cite these works when using any results generated with these projections. ---- Copyright (C) 2016 by ROBERT E. KOPP AND RHODIUM GROUP LLC This dataset is made available under a non-Commercial Creative Commons License. https://creativecommons.org/licenses/by-nc/3.0/us/ You are free to: 1. Share — copy and redistribute the material in any medium or format 2. Remix, transform, and build upon the material You must: 1. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. 2. You may not use the material for commercial purposes. No warranties are given.
Data Types:
  • Software/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 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 .
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The browser code was used to model all of the articles from the Signs journal (1975-2014), resulting in a 70-topic model. Each article is described as a mixture of verbal patterns or topics. Every topic is a family of words that tend to occur together in articles—as, for example, world, global, and states do. Every article is in turn divided into multiple topics. Words, too, may belong to more than one topic. For example, the word “women” is part of many topics. By following the shifting proportions of topics over time, the study provides ways into thinking about the history of Signs in its first four decades of publishing.These files contain the source code and all supporting files necessary to run "An Interactive Topic Model of Signs," a part of in Signs @ 40 (http://signsat40.signsjournal.org/topic-model).
Data Types:
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Data Types:
  • Software/Code