As of 2022, the ProjectSL/LocalizeSL framework is no longer in development. The primary Rutgers Earth System Science & Policy Lab platform for sea-level rise projections is now the Framework for Assessing Changes To Sea-level (FACTS).
For most users, the IPCC AR6 sea level projections, developed using FACTS, are the most suitable global source of local sea-level projections. Datasets can be downloaded from Zenodo. The primary AR6 sea level projection data set is available at doi:10.5281/zenodo.5914710.
The NASA/IPCC Sea Level Projection Viewer provides a graphical interface to the AR6 sea-level projection dataset.
The archived version of LocalizeSL is available at doi:10.5281/zenodo.6029806.
README file last updated by Robert Kopp, robert-dot-kopp-at-rutgers-dot-edu, 26 July 2019
This code was originally developed to accompany the results of
R. E. Kopp, R. M. Horton, C. M. Little, J. X. Mitrovica, M. Oppenheimer,
D. J. Rasmussen, B. H. Strauss, and C. Tebaldi (2014). Probabilistic 21st
and 22nd century sea-level projections at a global network of tide gauge
sites. Earth's Future 2: 287–306, doi:10.1002/2014EF000239.
Please cite that paper when using any sea-level rise projections generated with this code.
Additional features have subsequently been added to this paper. Features related to sea-level rise allowances were developed for
M. K. Buchanan, R. E. Kopp, M. Oppenheimer, and C. Tebaldi (2016).
Allowances for evolving coastal flood risk under uncertain local sea-level
rise. Climatic Change 137, 347-362. doi:10.1007/s10584-016-1664-7.
Features related to developing discrete sea-level rise scenarios conditional on ranges of global mean sea level were developed for
W.V. Sweet, R. E. Kopp, C. P. Weaver, J. Obeysekera, R. Horton, E. R. Thieler,
and C. Zervas (2017). Global and Regional Sea Level Rise Scenarios for the
United States. Technical Report NOS CO-OPS 083. National Oceanic and
Atmospheric Administration.
Features related to the substitution of output from a physical Antarctic ice-sheet model into the projections framework were developed for
R. E. Kopp, R. M. DeConto, D. A. Bader, R. M. Horton, C. C. Hay, S. Kulp,
M. Oppenheimer, D. Pollard, and B. H. Strauss (2017). Implications of
Antarctic ice-cliff collapse and ice-shelf hydrofracturing mechanisms for
sea-level projections. Earth’s Future 5, 1217-1233. doi: 10.1002/2017EF000663.
Features related to the use of scenarios other than the RCPs (e.g., temperature-based scenarios) were developed for
D. J. Rasmussen, K. Bittermann, M. K. Buchanan, S. Kulp,
B. H. Strauss, R. E. Kopp, and M. Oppenheimer (2018). Extreme
sea level implications of 1.5 °C, 2.0 °C, and 2.5 °C temperature
stabilization targets in the 21st and 22nd centuries.
Environmental Research Letters 13, 034040. doi: 10.1088/1748-9326/aaac87.
Additional projections using structured expert judgement-based estimates of ice-sheet contributions at narrowly defined time points were developed for
J. L. Bamber, M. Oppenheimer, R. E. Kopp, W. Aspinall and
R. M. Cooke (2019). Ice sheet contributions to future sea level
rise from structured expert judgement. Proceedings of the
National Academy of Sciences. doi: 10.1073/pnas.1817205116.
Please cite the appropriate papers if making use of these features.
This code requires MATLAB with the Statistics Toolbox.
This 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:
- Local sea-level rise projections at decadal time points and arbitrary quantiles
- Localized Monte Carlo samples, disaggregatable by contributory process
- Localized variance decomposition plots
The IFILES directory contains the ~300 MB core files, which store 10,000+ Monte Carlo samples per emissions scenario 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. (Note that this files is stored in the Github archive using Git Large File Storage; if you do not have git-lfs set up, cloning the archive will only get you a pointer to this file, which you can download using the Github web interface.)
Functions are stored in the MFILES directory. Example scripts are stored in the scripts 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 core file. FOCUSSITES is the PSMSL ID or IDs of the site(s) of interest. (Please see psmsl.org or the supplementary tables of Kopp et al. (2017) 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: Alaska
2 - GIC: Western Canada/US
3 - GIC: Arctic Canada North
4 - GIC: Arctic Canada South
5 - GIC: Greenland peripheral glaciers
6 - GIC: Iceland
7 - GIC: Svalbard
8 - GIC: Scandinavia
9 - GIC: Russian Arctic
10 - GIC: North Asia
11 - GIC: Central Europe
12 - GIC: Caucasus
13 - GIC: Central Asia
14 - GIC: South Asia West
15 - GIC: South Asia East
16 - GIC: Low Latitude
17 - GIC: Southern Andes
18 - GIC: New Zealand
19 - Greenland Ice Sheet
20 - West Antarctic Ice Sheet
21 - East Antarctic Ice Sheet
22 - Land water storage
23 - Oceanographic processes (thermal expansion and ocean dynamics)
24 - GIA, tectonics, and other background processes
The 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. PlotSLRProjectionVariance generates a variance decomposition plot. WriteTableMC outputs Monte Carlo samples. WriteTableSLRProjection outputs desired quantiles of the projections.
Example scripts that serve the most common uses of LocalizeSL are provided in scripts/generic. Example scripts associated with various publications are also provided in the scripts directory.
Copyright (C) 2019 by Robert E. Kopp
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.