Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4331-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4331-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
| 
31 Jul 2023
Model description paper | Highlight paper |
| 31 Jul 2023
| 31 Jul 2023
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
Energy & Resources Group, University of California, Berkeley, California, USA
Global Policy Lab, Goldman School of Public Policy, University of California, Berkeley, California, USA
The Rhodium Group, Oakland, California, USA
Global Policy Lab, Goldman School of Public Policy, University of California, Berkeley, California, USA
BlackRock, San Francisco, California, USA
Daniel Allen
Global Policy Lab, Goldman School of Public Policy, University of California, Berkeley, California, USA
Jun Ho Choi
Kenneth C. Griffin Department of Economics, University of Chicago, Chicago, Illinois, USA
Michael Delgado
The Rhodium Group, Oakland, California, USA
Michael Greenstone
National Bureau of Economic Research, Cambridge, Massachusetts, USA
Kenneth C. Griffin Department of Economics, University of Chicago, Chicago, Illinois, USA
Ali Hamidi
The Rhodium Group, Oakland, California, USA
BlackRock, San Francisco, California, USA
Trevor Houser
The Rhodium Group, Oakland, California, USA
Robert E. Kopp
Department of Earth & Planetary Sciences and Rutgers Institute of Earth, Ocean and Atmospheric Sciences, Rutgers University, New Brunswick, New Jersey, USA
Solomon Hsiang
Global Policy Lab, Goldman School of Public Policy, University of California, Berkeley, California, USA
National Bureau of Economic Research, Cambridge, Massachusetts, USA
Related authors
No articles found.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2183, https://doi.org/10.5194/egusphere-2024-2183, 2024
Short summary
Short summary
PaleoSTeHM v1.0-rc is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
Short summary
Short summary
The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Short summary
Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Erica L. Ashe, Nicole S. Khan, Lauren T. Toth, Andrea Dutton, and Robert E. Kopp
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 1–29, https://doi.org/10.5194/ascmo-8-1-2022, https://doi.org/10.5194/ascmo-8-1-2022, 2022
Short summary
Short summary
We develop a new technique to integrate realistic uncertainties in probabilistic models of past sea-level change. The new framework performs better than past methods (in precision, accuracy, bias, and model fit) because it enables the incorporation of previously unused data and exploits correlations in the data. This method has the potential to assess the validity of past estimates of extreme sea-level rise and highstands providing better context in which to place current sea-level change.
Eric Larour, Lambert Caron, Mathieu Morlighem, Surendra Adhikari, Thomas Frederikse, Nicole-Jeanne Schlegel, Erik Ivins, Benjamin Hamlington, Robert Kopp, and Sophie Nowicki
Geosci. Model Dev., 13, 4925–4941, https://doi.org/10.5194/gmd-13-4925-2020, https://doi.org/10.5194/gmd-13-4925-2020, 2020
Short summary
Short summary
ISSM-SLPS is a new projection system for future sea level that increases the resolution and accuracy of current projection systems and improves the way uncertainty is treated in such projections. This will pave the way for better inclusion of state-of-the-art results from existing intercomparison efforts carried out by the scientific community, such as GlacierMIP2 or ISMIP6, into sea-level projections.
André Düsterhus, Alessio Rovere, Anders E. Carlson, Benjamin P. Horton, Volker Klemann, Lev Tarasov, Natasha L. M. Barlow, Tom Bradwell, Jorie Clark, Andrea Dutton, W. Roland Gehrels, Fiona D. Hibbert, Marc P. Hijma, Nicole Khan, Robert E. Kopp, Dorit Sivan, and Torbjörn E. Törnqvist
Clim. Past, 12, 911–921, https://doi.org/10.5194/cp-12-911-2016, https://doi.org/10.5194/cp-12-911-2016, 2016
Short summary
Short summary
This review/position paper addresses problems in creating new interdisciplinary databases for palaeo-climatological sea-level and ice-sheet data and gives an overview on new advances to tackle them. The focus therein is to define and explain strategies and highlight their importance to allow further progress in these fields. It also offers important insights into the general problem of designing competitive databases which are also applicable to other communities within the palaeo-environment.
Related subject area
Climate and Earth system modeling
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
CropSuite – A comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – The ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
A non-intrusive, multi-scale, and flexible coupling interface in WRF
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4
Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification
TorchClim v1.0: a deep-learning plugin for climate model physics
The very-high resolution configuration of the EC-Earth global model for HighResMIP
ZEMBA v1.0: An energy and moisture balance climate model to investigate Quaternary climate
Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0
Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
Short summary
Short summary
CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used for climate impact assessments, adaptation and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++ and Python) and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged between WRF and any other model (e.g. an ocean model) using the coupling library Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
Short summary
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary
Short summary
Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
Short summary
Short summary
We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
Short summary
Short summary
Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
Short summary
Short summary
Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
Short summary
Short summary
The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
Short summary
Short summary
We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
Short summary
Short summary
Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-119, https://doi.org/10.5194/gmd-2024-119, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10-15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100-km and a 25-km grid. The three models are compared with observations to study the improvements thanks to the increased in the resolution.
Daniel Francis James Gunning, Kerim Hestnes Nisancioglu, Emilie Capron, and Roderik van de Wal
EGUsphere, https://doi.org/10.5194/egusphere-2024-1384, https://doi.org/10.5194/egusphere-2024-1384, 2024
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth’s orbit. We demonstrate ZEMBA reproduces many features of the Earth’s climate for both the pre-industrial period and the Earth’s most recent cold extreme- the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
Short summary
Short summary
Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
Short summary
Short summary
Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
EGUsphere, https://doi.org/10.5194/egusphere-2024-1518, https://doi.org/10.5194/egusphere-2024-1518, 2024
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. For this, it is crucial to understand possible shortcomings in the models. New features of the software package ESMValTool allow for comparing and visualizing a model's performance in reproducing observations within the context of other climate models in an easy and user-friendly way. The aim is to help model developers to assess and monitor climate simulations more efficiently.
K. Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
EGUsphere, https://doi.org/10.5194/egusphere-2024-1431, https://doi.org/10.5194/egusphere-2024-1431, 2024
Short summary
Short summary
The study aimed to improve the representation of spring wheat and rice in the CLM5. The modified CLM5 model performed significantly better than the default model in simulating crop phenology, yield, carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific parameters for accurately simulating vegetation processes and land surface processes.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
Short summary
Short summary
Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Cited articles
Arino, O., Ramos Perez, J. J., Kalogirou, V., Bontemps, S., Defourny, P., and Van Bogaert, E.: Global Land Cover Map for 2009 (GlobCover 2009), © European Space Agency (ESA) & Université catholique de Louvain (UCL), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.787668, 2012. a, b
Armstrong, S. B., Lazarus, E. D., Limber, P. W., Goldstein, E. B., Thorpe, C.,
and Ballinger, R. C.: Indications of a positive feedback between coastal
development and beach nourishment, Earth's Future, 4, 626–635,
https://doi.org/10.1002/2016EF000425, 2016. a, b, c
Bakkensen, L. A. and Mendelsohn, R. O.: Risk and Adaptation: Evidence from
Global Hurricane Damages and Fatalities, Journal of the Association
of Environmental and Resource Economists, 3, 555–587, https://doi.org/10.1086/685908,
2016. a
Bakkensen, L. A., Park, D.-S. R., and Sarkar, R. S. R.: Climate costs of
tropical cyclone losses also depend on rain, Environ. Res. Lett.,
13, 074034, https://doi.org/10.1088/1748-9326/aad056, 2018. a
Berlemann, M. and Wesselhöft, J.-E.: Aggregate Capital Stock Estimations
for 122 Countries: An Update, Rev. Econ., 68, 75–92,
https://doi.org/10.1515/roe-2017-0004, 2017. a, b, c
Berrang-Ford, L., Siders, A. R., Lesnikowski, A., Fischer, A. P., Callaghan,
M. W., Haddaway, N. R., Mach, K. J., Araos, M., Shah, M. A. R., Wannewitz,
M., Doshi, D., Leiter, T., Matavel, C., Musah-Surugu, J. I., Wong-Parodi, G.,
Antwi-Agyei, P., Ajibade, I., Chauhan, N., Kakenmaster, W., Grady, C.,
Chalastani, V. I., Jagannathan, K., Galappaththi, E. K., Sitati, A., Scarpa,
G., Totin, E., Davis, K., Hamilton, N. C., Kirchhoff, C. J., Kumar, P.,
Pentz, B., Simpson, N. P., Theokritoff, E., Deryng, D., Reckien, D.,
Zavaleta-Cortijo, C., Ulibarri, N., Segnon, A. C., Khavhagali, V., Shang, Y.,
Zvobgo, L., Zommers, Z., Xu, J., Williams, P. A., Canosa, I. V., van Maanen,
N., van Bavel, B., van Aalst, M., Turek-Hankins, L. L., Trivedi, H., Trisos,
C. H., Thomas, A., Thakur, S., Templeman, S., Stringer, L. C., Sotnik, G.,
Sjostrom, K. D., Singh, C., Siña, M. Z., Shukla, R., Sardans, J., Salubi,
E. A., Safaee Chalkasra, L. S., Ruiz-Díaz, R., Richards, C., Pokharel, P.,
Petzold, J., Penuelas, J., Pelaez Avila, J., Murillo, J. B. P., Ouni, S.,
Niemann, J., Nielsen, M., New, M., Nayna Schwerdtle, P., Nagle Alverio, G.,
Mullin, C. A., Mullenite, J., Mosurska, A., Morecroft, M. D., Minx, J. C.,
Maskell, G., Nunbogu, A. M., Magnan, A. K., Lwasa, S., Lukas-Sithole, M.,
Lissner, T., Lilford, O., Koller, S. F., Jurjonas, M., Joe, E. T., Huynh, L.
T. M., Hill, A., Hernandez, R. R., Hegde, G., Hawxwell, T., Harper, S.,
Harden, A., Haasnoot, M., Gilmore, E. A., Gichuki, L., Gatt, A., Garschagen,
M., Ford, J. D., Forbes, A., Farrell, A. D., Enquist, C. A. F., Elliott, S.,
Duncan, E., Coughlan de Perez, E., Coggins, S., Chen, T., Campbell, D.,
Browne, K. E., Bowen, K. J., Biesbroek, R., Bhatt, I. D., Bezner Kerr, R.,
Barr, S. L., Baker, E., Austin, S. E., Arotoma-Rojas, I., Anderson, C., Ajaz,
W., Agrawal, T., and Abu, T. Z.: A systematic global stocktake of evidence on
human adaptation to climate change, Nat. Clim. Change, 11, 989–1000,
https://doi.org/10.1038/s41558-021-01170-y, 2021. a
Bertram, G.: On the Convergence of Small Island Economies with Their
Metropolitan Patrons, World Development, 32, 343–364,
https://doi.org/10.1016/j.worlddev.2003.08.004, 2004. a
Bolliger, I., Depsky, N., Allen, D., Choi, J. H., Delgado, M., Greenstone, M., Hamidi, A., Houser, T., Hsiang, S., and Kopp, R. E.: SLIIDERS: Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (1.1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7693868, 2023a. a, b
Bolliger, I., Depsky, N., Allen, D., Choi, J. H., Delgado, M., Greenstone, M., Hamidi, A., Houser, T., Hsiang, S., and Kopp, R. E.: Estimates of Global Coastal Losses Under Multiple Sea Level Rise Scenarios (1.1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7693869, 2023b. a, b
Bono, A. D. and Chatenoux, B.: A Global Exposure Model for GAR 2015,
Input Paper prepared for the Global Assessment Report on Disaster
Risk Reduction, The United Nations Office for Disaster Risk Reduction,
Geneva, Switzerland,
https://www.preventionweb.net/english/hyogo/gar/2015/en/bgdocs/risk-section/De Bono, Andrea, Bruno Chatenoux. 2015. A Global Exposure Model for GAR 2015, UNEP-GRID.pdf (last access: 2 September 2021),
2014. a, b
Bootsma, J.: Evaluating methods to assess the coastal flood hazard on a global
scale : a comparative analysis between the Bathtub approach and the
LISFLOOD-AC model, http://essay.utwente.nl/90697/ (last access: 21 March 2023), 2022. a
Bower, E. and Weerasinghe, S.: Leaving Place, Restoring Home: Enhancing the Evidence Base on Planned Relocation Cases in the Context of Hazards, Disasters, and Climate Change, Platform on Disaster Displacement (PDD) and Andrew & Renata Kaldor Centre for International Refugee Law, 2021. a
Bunting, P., Rosenqvist, A., Lucas, R. M., Rebelo, L.-M., Hilarides, L.,
Thomas, N., Hardy, A., Itoh, T., Shimada, M., and Finlayson, C. M.: The
Global Mangrove Watch – A New 2010 Global Baseline of
Mangrove Extent, Remote Sens., 10, 1669, https://doi.org/10.3390/rs10101669,
2018. a, b
Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R., Thomas, N., Tadono, T., Worthington, T., Spalding, M., Murray, N., and Rebelo, L.-M.: Global Mangrove Watch (1996–2020) Version 3.0 Dataset (3.0), Zenodo [data set], https://doi.org/10.5281/zenodo.6894273, 2022. a, b
Carleton, T., Jina, A., Delgado, M., Greenstone, M., Houser, T., Hsiang, S.,
Hultgren, A., Kopp, R. E., McCusker, K. E., Nath, I., Rising, J., Rode, A.,
Seo, H. K., Viaene, A., Yuan, J., and Zhang, A. T.: Valuing the Global
Mortality Consequences of Climate Change Accounting for
Adaptation Costs and Benefits, Q. J. Econ.,
137, 2037–2105, https://doi.org/10.1093/qje/qjac020, 2022. a, b
Center for International Earth Science Information Network – CIESIN – Columbia University: Gridded Population of the World,
Version 4 (GPWv4): Population Density, NASA [data set],
https://doi.org/10.7927/H4NP22DQ,
2016. a
Church, J., Clark, P. U., Cazenave, A., Gregory, J. M., Jevrejeva, S.,
Levermann, A., Merrifield, M. A., Milne, G. A., Nerem, R. S., Dunn, P. D.,
Payne, A. J., Pfeffer, W. T., Stammer, D., and Unnikrishnan, A. S.: 2013: Sea
Level Change, in: Climate Change 2013: The Physical Science
Basis, Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by:
Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA,
https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter13_FINAL.pdf (last access: 31 January 2023),
2013. a
Craig, R. K.: Coastal adaptation, government-subsidized insurance, and perverse
incentives to stay, Clim. Change, 152, 215–226,
https://doi.org/10.1007/s10584-018-2203-5, 2019. a
daniellincke: daniellincke/DIVA_paper_migration: Coastal migration due to 21st century sea-level rise (1.0), Zenodo, https://doi.org/10.5281/zenodo.4459151, 2021. a
DeConto, R. M., Pollard, D., Alley, R. B., Velicogna, I., Gasson, E., Gomez,
N., Sadai, S., Condron, A., Gilford, D. M., Ashe, E. L., Kopp, R. E., Li, D.,
and Dutton, A.: The Paris Climate Agreement and future sea-level rise
from Antarctica, Nature, 593, 83–89, https://doi.org/10.1038/s41586-021-03427-0,
2021. a, b, c, d, e
Diaz, D. B.: Estimating global damages from sea level rise with the Coastal
Impact and Adaptation Model (CIAM), Clim. Change, 137, 143–156,
https://doi.org/10.1007/s10584-016-1675-4, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae, af, ag, ah, ai, aj, ak, al, am, an, ao, ap, aq, ar, as, at, au, av, aw, ax, ay, az, ba, bb, bc, bd, be, bf, bg, bh, bi, bj, bk, bl, bm, bn, bo, bp, bq, br, bs, bt, bu, bv, bw, bx, by, bz, ca, cb, cc, cd, ce, cf, cg, ch, ci, cj, ck, cl, cm, cn, co, cp, cq, cr, cs, ct, cu, cv, cw, cx, cy, cz, da, db, dc
Dronkers, J., Gilbert, J. T. E., Butler, L. W., Carey, J. J., Campbell, J.,
James, E., Mckenzie, C., Misdorp, R., Quin, N., Vallianos, L., and Dadelszen,
J. V.: Strategies for Adaptation to Sea Level Rise, Report of the IPCC Coastal Zone Management Subgroup: Intergovernmental Panel on Climate Change, http://papers.risingsea.net/federal_reports/IPCC-1990-adaption-to-sea-level-rise.pdf (last access: 21 March 2023), 1990. a
Eberenz, S.,
Stocker, D.,
Röösli, T., and
Bresch, D. N.: LitPop: Global Exposure Data for Disaster Risk Assessment, ETH Zürich [data set], https://doi.org/10.3929/ethz-b-000331316, 2020b. a
Fariss, C. J., Anders, T., Markowitz, J. N., and Barnum, M.: New Estimates of
Over 500 Years of Historic GDP and Population Data, J.
Conflict Resolut., 66, 553–591, https://doi.org/10.1177/00220027211054432, 2022a. a, b
Fariss, C., Anders, T., Markowitz, J., and Barnum, M.: Latent Estimates of Historic Gross Domestic Product, GDP per capita, Surplus Domestic Product, and Population Data Version 1, Harvard Dataverse, V1 [data set], https://doi.org/10.7910/DVN/FALCGS, 2022b. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S. Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007. a
Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout,
S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Kopp, R. E., and Krinner,
G.: Ocean, cryosphere and sea level change, Climate Change 2021: The Physical
Science Basis, Contribution of Working Group I to the Sixth Assessment Report
of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V.,
Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y.,
Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R.,
Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B.,
1211–1362, https://doi.org/10.1017/9781009157896.011, 2021. a, b, c, d, e, f, g
GADM: https://gadm.org/index.html, last access: 21 March 2023. a
Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., Nowicki, S., Palmer, M. D., Smith, C., Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Golledge, N. R., Hemer, M., Krinner, G., Mix, A., Notz, D., Nurhati, I. S., Ruiz, L., Sallée, J.-B., Yu, Y., Hua, L., Palmer, T., Pearson, B.: IPCC AR6 Sea Level Projections (Version 20210809), Zenodo [data set], https://doi.org/10.5281/zenodo.6382554, 2021. a, b, c, d
GLOBE Task Team, Hastings, D. A., Dunbar, P. K., Elphingstone, G. M., Bootz, M., Murakami, H., Maruyama, H., Masaharu, H., Holland, P., Payne, J., Bryant, N. A., Logan, T. L., Muller, J.-P., Schreier, G., and MacDonald, J. S. (Eds.): The Global Land One-kilometer Base Elevation (GLOBE) Digital Elevation Model, Version 1.0. National Oceanic and Atmospheric Administration, National Geophysical Data Center [data set], http://www.ngdc.noaa.gov/mgg/topo/globe.html (last access: 21 March 2023), 1999. a
Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J. A.,
Fukimori, I., Gomez, N., Kopp, R. E., Landerer, F., Cozannet, G. L., Ponte,
R. M., Stammer, D., Tamisiea, M. E., and van de Wal, R. S. W.: Concepts and
Terminology for Sea Level: Mean, Variability and Change, Both
Local and Global, Surv. Geophys., 40, 1251–1289,
https://doi.org/10.1007/s10712-019-09525-z, 2019. a
Groningen Growth and Development Centre: Penn World Table version 10.0, DataverseNL [data set], https://doi.org/10.15141/S5Q94M, 2023. a, b, c, d
Haasnoot, M., Brown, S., Scussolini, P., Jimenez, J. A., Vafeidis, A. T., and
Nicholls, R. J.: Generic adaptation pathways for coastal archetypes under
uncertain sea-level rise, Environ. Res. Commun., 1, 1,
https://doi.org/10.1088/2515-7620/ab1871, 2019. a
Haer, T., Botzen, W. J., de Moel, H., and Aerts, J. C.: Integrating Household
Risk Mitigation Behavior in Flood Risk Analysis: An
Agent-Based Model Approach, Risk Analysis, 37, 1977–1992,
https://doi.org/10.1111/risa.12740, 2017. a, b
Hallegatte, S., Green, C., Nicholls, R. J., and Corfee-Morlot, J.: Future flood
losses in major coastal cities, Nat. Clim. Change, 3, 802–806,
https://doi.org/10.1038/nclimate1979, 2013. a
Heston, A., Summers, R., and Aten, B.: Penn World Table Version 7.0,
Tech. rep., Center for International Comparisons of Production, Income and
Prices at the University of Pennsylvania,
https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt-7.0 (last access: 21 March 2023),
2011. a
Hinkel, J. and Klein, R. J. T.: Integrating knowledge to assess coastal
vulnerability to sea-level rise: The development of the DIVA tool, Global
Environ. Change, 19, 384–395, https://doi.org/10.1016/j.gloenvcha.2009.03.002,
2009. a, b, c
Hinkel, J., van Vuuren, D. P., Nicholls, R. J., and Klein, R. J. T.: The
effects of adaptation and mitigation on coastal flood impacts during the 21st
century. An application of the DIVA and IMAGE models, Clim. Change,
117, 783–794, https://doi.org/10.1007/s10584-012-0564-8, 2013. a
Hinkel, J., Lincke, D., Vafeidis, A. T., Perrette, M., Nicholls, R. J., Tol,
R. S., Marzeion, B., Fettweis, X., Ionescu, C., and Levermann, A.: Coastal
flood damage and adaptation costs under 21st century sea-level rise,
P. Natl. Acad. Sci. USA, 111, 3292–3297, https://doi.org/10.1073/pnas.1222469111, 2014. a, b, c, d, e, f, g, h, i
Hinkel, J., Aerts, J. C. J. H., Brown, S., Jiménez, J. A., Lincke, D.,
Nicholls, R. J., Scussolini, P., Sanchez-Arcilla, A., Vafeidis, A., and Addo,
K. A.: The ability of societies to adapt to twenty-first-century sea-level
rise, Nat. Clim. Change, 8, 570–578, https://doi.org/10.1038/s41558-018-0176-z,
2018. a, b, c
Hoozemans, F. M., Marchand, M., and Pennekamp, H.: A global vulnerability
analysis: vulnerability assessment for population, coastal wetlands and rice
production on a global scale, Technical Report 2nd Edition, Delft
Hydraulics, the Netherlands,
https://www.researchgate.net/publication/282669649_A_global_vulnerability_analysis_vulnerability_assessment_for_population_coastal_wetlands_and_rice_production_on_a_global_scale (last access: 21 March 2023),
1993. a
Hsiang, S., Kopp, R., Jina, A., Rising, J., Delgado, M., Mohan, S., Rasmussen, D. J., Muir-Wood, R., Wilson, P., Oppenheimer, M., Larsen, K., and Houser, T.: Estimating economic damage from climate change in the United States, Science, 356, 1362–1369, https://doi.org/10.1126/science.aal4369, 2017. a
Hu, S., Niu, Z., and Chen, Y.: Global Wetland Datasets: a Review,
Wetlands, 37, 807–817, https://doi.org/10.1007/s13157-017-0927-z, 2017. a
International Monetary Fund: World Economic Outlook Database 2011, Tech. rep.,
https://www.imf.org/en/Publications/WEO/weo-database/2011/September (last access: 21 March 2023),
2011. a
Jones, B. and O'Neill, B. C.: Spatially explicit global population scenarios
consistent with the Shared Socioeconomic Pathways, Environ.
Res. Lett., 11, 084003, https://doi.org/10.1088/1748-9326/11/8/084003, 2016. a
Jonkman, S. and Vrijling, J.: Loss of life due to floods, J. Flood Risk
Manage., 1, 43–56, https://doi.org/10.1111/j.1753-318X.2008.00006.x, 2008. a
Kc, S. and Lutz, W.: The human core of the shared socioeconomic pathways:
Population scenarios by age, sex and level of education for all countries
to 2100, Global Environ. Change, 42, 181–192,
https://doi.org/10.1016/j.gloenvcha.2014.06.004, 2017. a, b
Kopp, R. E. and Rasmussen, D. J.: LocalizeSL (3.2), Zenodo [code], https://doi.org/10.5281/zenodo.6029807, 2021. a, b
Kopp, R. E., Horton, R. M., Little, C. M., Mitrovica, J. X., Oppenheimer, M.,
Rasmussen, D. J., Strauss, B. H., and Tebaldi, C.: Probabilistic 21st and
22nd century sea‐level projections at a global network of tide‐gauge
sites, Earth's Future, 2, 383–406, https://doi.org/10.1002/2014ef000239, 2014. a, b, c, d, e, f
Kopp, R. E., DeConto, R. M., Bader, D. A., Hay, C. C., Horton, R. M., Kulp, S.,
Oppenheimer, M., Pollard, D., and Strauss, B. H.: Evolving Understanding of
Antarctic Ice-Sheet Physics and Ambiguity in Probabilistic
Sea-Level Projections, Earth's Future, 5, 1217–1233,
https://doi.org/10.1002/2017EF000663, 2017. a, b
Kopp, R. E., Gilmore, E. A., Little, C. M., Lorenzo-Trueba, J., Ramenzoni,
V. C., and Sweet, W. V.: Usable Science for Managing the Risks of
Sea-Level Rise, Earth's Future, 7, 1235–1269,
https://doi.org/10.1029/2018EF001145, 2019. a, b
Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., and Smith, C.: The Framework for Assessing Changes To Sea-level (FACTS) v1.0-rc: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-14, 2023. a, b
Kulp, S. A. and Strauss, B. H.: CoastalDEM: A global coastal digital
elevation model improved from SRTM using a neural network, Remote Sens. Environ., 206, 231–239, https://doi.org/10.1016/j.rse.2017.12.026, 2018. a
Kulp, S. A. and Strauss, B. H.: New elevation data triple estimates of global
vulnerability to sea-level rise and coastal flooding, Nat. Commun.,
10, 4844, https://doi.org/10.1038/s41467-019-12808-z, 2019. a, b, c, d
Lickley, M. J., Lin, N., and Jacoby, H. D.: Analysis of coastal protection
under rising flood risk, Climate Risk Manage., 6, 18–26,
https://doi.org/10.1016/j.crm.2015.01.001, 2014. a
Lorie, M., Neumann, J. E., Sarofim, M. C., Jones, R., Horton, R. M., Kopp,
R. E., Fant, C., Wobus, C., Martinich, J., O'Grady, M., and Gentile, L. E.:
Modeling coastal flood risk and adaptation response under future climate
conditions, Climate Risk Management, 29, 100233,
https://doi.org/10.1016/j.crm.2020.100233, 2020. a
McNamara, D. E. and Keeler, A.: A coupled physical and economic model of the
response of coastal real estate to climate risk, Nat. Clim. Change, 3,
559–562, https://doi.org/10.1038/nclimate1826, 2013. a
McNamara, D. E., Gopalakrishnan, S., Smith, M. D., and Murray, A. B.: Climate
adaptation and policy-induced inflation of coastal property value, PLoS ONE,
10, 1–12, https://doi.org/10.1371/journal.pone.0121278, 2015. a, b
Mendelsohn, R. O., Schiavo, J. G., and Felson, A.: Are American Coasts
Under-Protected?, Coast. Manage., 48, 23–37,
https://doi.org/10.1080/08920753.2020.1691482, 2020. a
Merkens, J.-L., Reimann, L., Hinkel, J., and Vafeidis, A. T.: Gridded
population projections for the coastal zone under the Shared
Socioeconomic Pathways, Global Planet. Change, 145, 57–66,
https://doi.org/10.1016/j.gloplacha.2016.08.009, 2016. a
Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. J. H., and Ward, P. J.: A
global reanalysis of storm surges and extreme sea levels, Nat.
Commun., 7, 11969, https://doi.org/10.1038/ncomms11969, 2016. a
Muis, S., Apecechea, M. I., Dullaart, J., de Lima Rego, J., Madsen, K. S., Su,
J., Yan, K., and Verlaan, M.: A High-Resolution Global Dataset of
Extreme Sea Levels, Tides, and Storm Surges, Including Future
Projections, Front. Marine Sci., 7, 263,
https://doi.org/10.3389/fmars.2020.00263, 2020a. a, b, c, d, e, f
Muis, S., Apecechea, M. I., Dullaart, J., de Lima Rego, J., Madsen, K. S., Su,
J., Yan, K., and Verlaan, M.: CoDEC Dataset – Data underlying the paper “A high-resolution global dataset of extreme sea levels, tides and storm surges including future projections”, in: Frontiers of Marine Sciences, Zenodo [data set], https://doi.org/10.5281/zenodo.3660927, 2020b. a, b, c
Mulet, S., Rio, M.-H., Etienne, H., Artana, C., Cancet, M., Dibarboure, G., Feng, H., Husson, R., Picot, N., Provost, C., and Strub, P. T.: The new CNES-CLS18 global mean dynamic topography, Ocean Sci., 17, 789–808, https://doi.org/10.5194/os-17-789-2021, 2021. a
Multihazard Mitigation Council: Natural Hazard Mitigation Saves 2017 Interim Report: An Independent Study – Summary of Findings, Principal Investigator: Porter, K., co-Principal Investigators: Scawthorn, C., Dash, N., Santos, J., Schneider, P., Director, MMC. National Institude of Building Sciences, Washington, 2017. a
Neumann, B., Vafeidis, A. T., Zimmermann, J., and Nicholls, R. J.: Future
coastal population growth and exposure to sea-level rise and coastal flooding
– A global assessment, PLoS ONE, 10, 3, https://doi.org/10.1371/journal.pone.0118571,
2015. a
Nicholls, R. J., Klein, R. J. T., and Tol, R. S. J.: Managing coastal vulnerability and
climate change: a national to global perspective, in: Managing Coastal Vulnerability, edited by: Mcfadden, L., Nicholls, R. J., and Penning-Rowsell, E., 223–241, Elsevier, ISBN 9780080447032, 2006. a
Nicholls, R. J.: Analysis of global impacts of sea-level rise: a case study of
flooding, Phys. Chem. Earth, Parts A/B/C, 27, 1455–1466,
https://doi.org/10.1016/S1474-7065(02)00090-6, 2002. a
Nicholls, R. J.: Coastal flooding and wetland loss in the 21st century: changes
under the SRES climate and socio-economic scenarios, Global Environ.
Change, 14, 69–86, https://doi.org/10.1016/j.gloenvcha.2003.10.007, 2004. a
OECD: Regional economy (Edition 2022), OECD Regional Statistics [data set], https://doi.org/10.1787/4856f683-en, 2022a. a, b, c
OECD: Regional demography (Edition 2022), OECD Regional Statistics [data set], https://doi.org/10.1787/ab091b70-en, 2022b. a, b
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K.,
Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K.,
Levy, M., and Solecki, W.: The roads ahead: Narratives for shared
socioeconomic pathways describing world futures in the 21st century, Global
Environ. Change, 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004,
2017. a
Oppenheimer, M., Glavovic, B., Hinkel, J., van de Wal, R., Magnan, A. K.,
Abd-Elgawad, A., Cai, R., Cifuentes-Jara, M., Deconto, R. M., Ghosh, T., Hay,
J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Chapter 4:
Sea Level Rise and Implications for Low Lying Islands, Coasts
and Communities, in: IPCC Special Report on the Ocean and
Cryosphere in a Changing Climate, edited by: Pörtner,
H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M.,
Poloczanska, E., Mintenbeck, K., Alegréia, A., Nicolai,
M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., https://doi.org/10.1017/9781009157964.006, 2019. a, b, c
Pardaens, A. K., Lowe, J. A., Brown, S., Nicholls, R. J., and de Gusmão, D.:
Sea-level rise and impacts projections under a future scenario with large
greenhouse gas emission reductions, Geophys. Res. Lett., 38, 12,
https://doi.org/10.1029/2011GL047678, 2011. a
Pyo, H. K. and Kim, M.: Estimating Capital Stock in North Korea and
Its Implications, working paper, https://doi.org/10.2139/ssrn.3727104, 2020. a, b, c, d
Rasmussen, D. J., Bittermann, K., Buchanan, M. K., Kulp, S., Strauss, B. H.,
Kopp, R. E., and Oppenheimer, M.: Extreme sea level implications of 1.5 ∘C, 2.0 ∘C, and 2.5 ∘C temperature stabilization targets in the 21st
and 22nd centuries, Environ. Res. Lett., 13, 034040,
https://doi.org/10.1088/1748-9326/aaac87, 2018. a
Rasmussen, D. J., Buchanan, M. K., Kopp, R. E., and Oppenheimer, M.: A Flood
Damage Allowance Framework for Coastal Protection With Deep
Uncertainty in Sea Level Rise, Earth's Future, 8, e2019EF001340,
https://doi.org/10.1029/2019EF001340, 2020. a
Rennert, K., Errickson, F., Prest, B. C., Rennels, L., Newell, R. G., Pizer,
W., Kingdon, C., Wingenroth, J., Cooke, R., Parthum, B., Smith, D., Cromar,
K., Diaz, D., Moore, F. C., Müller, U. K., Plevin, R. J., Raftery, A. E.,
Ševčíková, H., Sheets, H., Stock, J. H., Tan, T., Watson, M., Wong,
T. E., and Anthoff, D.: Comprehensive evidence implies a higher social cost
of CO2, Nature, 610, 687–692, https://doi.org/10.1038/s41586-022-05224-9, 2022. a
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp,
A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S.,
Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva,
L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T.,
Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M.,
Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z.,
Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.:
The Shared Socioeconomic Pathways and their energy, land use, and
greenhouse gas emissions implications: An overview, Global Environ.
Change, 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017. a, b, c, d
Rode, A., Carleton, T., Delgado, M., Greenstone, M., Houser, T., Hsiang, S.,
Hultgren, A., Jina, A., Kopp, R. E., McCusker, K. E., Nath, I., Rising, J.,
and Yuan, J.: Estimating a social cost of carbon for global energy
consumption, Nature, 598, 308–314, https://doi.org/10.1038/s41586-021-03883-8, 2021. a
Román, M. O., Wang, Z., Sun, Q., Kalb, V., Miller, S. D., Molthan, A.,
Schultz, L., Bell, J., Stokes, E. C., Pandey, B., Seto, K. C., Hall, D., Oda,
T., Wolfe, R. E., Lin, G., Golpayegani, N., Devadiga, S., Davidson, C.,
Sarkar, S., Praderas, C., Schmaltz, J., Boller, R., Stevens, J.,
Ramos González, O. M., Padilla, E., Alonso, J., Detrés, Y., Armstrong, R.,
Miranda, I., Conte, Y., Marrero, N., MacManus, K., Esch, T., and Masuoka,
E. J.: NASA's Black Marble nighttime lights product suite, Remote
Sens. Environ., 210, 113–143, https://doi.org/10.1016/j.rse.2018.03.017, 2018. a
Sadoff, C. W., Hall, J. W., Grey, D., Aerts, J. C. J. H., Ait-Kadi, M., Brown,
C., Cox, A., Dadson, S., Garrick, D., Kelman, J., McCornick, P., Ringler, C.,
Rosegrant, M., Whittington, D., and Wiberg, D.: Securing water, sustaining
growth. Report of the GWP/OECD Task Force on Water Security and
Sustainable Growth, Tech. rep., University of Oxford,
http://www.water.ox.ac.uk/wp-content/uploads/2015/04/SCHOOL-OF-GEOGRAPHY-SECURING-WATER-SUSTAINING-GROWTH-DOWNLOADABLE.pdf (last access: 21 March 2023),
2015. a
Scussolini, P., Aerts, J. C. J. H., Jongman, B., Bouwer, L. M., Winsemius, H. C., de Moel, H., and Ward, P. J.: FLOPROS: an evolving global database of flood protection standards, Nat. Hazards Earth Syst. Sci., 16, 1049–1061, https://doi.org/10.5194/nhess-16-1049-2016, 2016. a
Sims, K., Reith, A., Bright, E., McKee, J., and Rose, A.: LandScan Global
2021, edition: 2021 Place: Oak Ridge, TN Section:
1 July 2022, https://doi.org/10.48690/1527702, 2022. a, b, c
StatBank Norway: StatBank Norway data set, https://www.ssb.no/en/statbank, last access: 21 March 2023. a
Statistics | Ålands: Statistics, Statistics | Ålands [data set],
https://www.asub.ax/en/statistics, last access: 21 March 2023. a
Suckall, N., Tompkins, E. L., Nicholls, R. J., Kebede, A. S., Lázár, A. N.,
Hutton, C., Vincent, K., Allan, A., Chapman, A., Rahman, R., Ghosh, T., and
Mensah, A.: A framework for identifying and selecting long term adaptation
policy directions for deltas, Sci. Total Environ., 633,
946–957, https://doi.org/10.1016/j.scitotenv.2018.03.234, 2018. a, b
Sweet, W., Horton, R., Kopp, R., LeGrande, A., and Romanou, A.: Ch. 12: Sea
Level Rise. Climate Science Special Report: Fourth National
Climate Assessment, Volume I, Tech. rep., U.S. Global Change Research
Program, https://doi.org/10.7930/J0VM49F2, 2017. a
Sweet, W. V., Hamlington, B. D., Kopp, R. E., Weaver, C. P., Barnard, P. L., Bekaert, D., Brooks, W., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A. S., Krasting, J. P., Larour, E., Marcy, D., Marra, J. J., Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W., White, K. D., and Zuzak, C.: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines. NOAA Technical Report NOS 01, National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, 111 pp., https://oceanservice.noaa.gov/hazards/sealevelrise/noaa-nos-techrpt01-global-regional-SLR-scenarios-US.pdf (last access: 21 March 2023), 2022a. a, b, c, d, e, f, g, h
Sweet, W. V., Hamlington, B. D., Kopp, R. E., Weaver, C. P., Barnard, P. L., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A. S., Krasting, J. P., Larour, E., Marcy, D., Marra, J. J., Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W. C., White, K. D., Zuzak, C.: Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines (Version 1.1), Zenodo [data set], https://doi.org/10.5281/zenodo.6067895, 2022b. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc.,
93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
Tebaldi, C., Ranasinghe, R., Vousdoukas, M., Rasmussen, D. J., Vega-Westhoff,
B., Kirezci, E., Kopp, R. E., Sriver, R., and Mentaschi, L.: Extreme sea
levels at different global warming levels, Nat. Clim. Change, 11,
746–751, https://doi.org/10.1038/s41558-021-01127-1, 2021. a
The World Bank: World Development Indicators, The World Bank [data set], https://doi.org/10.57966/6rwy-0b07, 2022. a, b
Tiggeloven, T., de Moel, H., Winsemius, H. C., Eilander, D., Erkens, G., Gebremedhin, E., Diaz Loaiza, A., Kuzma, S., Luo, T., Iceland, C., Bouwman, A., van Huijstee, J., Ligtvoet, W., and Ward, P. J.: Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures, Nat. Hazards Earth Syst. Sci., 20, 1025–1044, https://doi.org/10.5194/nhess-20-1025-2020, 2020. a, b, c
Tol, R. S. J.: The damage costs of climate change towards a dynamic
representation, Ecol. Econ., 19, 67–90,
https://doi.org/10.1016/0921-8009(96)00041-9, 1996. a, b
Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., and
Wessel, P.: Global Bathymetry and Topography at 15 Arc Sec:
SRTM15+, Earth Space Sci., 6, 1–18, https://doi.org/10.1029/2019EA000658,
2019. a, b
UN DESA, Department of Economic and Social Affairs, P. U. N.: World
Population Prospects 2012: Volume I: Comprehensive Tables, Tech.
rep.,
https://population.un.org/wpp/Publications/Files/WPP2019_Volume-I_Comprehensive-Tables.pdf (last access: 21 March 2023),
2012. a
UN DESA, Department of Economic and Social Affairs, P. U. N.: World
Population Prospects 2019: Volume I: Comprehensive Tables, Tech.
rep.,
https://population.un.org/wpp/Publications/Files/WPP2019_Volume-I_Comprehensive-Tables.pdf (last access: 21 March 2023),
2019.
a
United Nations, Department of Economic and Social Affairs, Population
Division: World Population Prospects 2022, Online Edition, United Nations [data set],
https://population.un.org/wpp/Download/Standard/MostUsed/ (last access: 21 March 2023),
2022. a
von Krogh, G. and von Hippel, E.: The Promise of Research on Open
Source Software, Manage. Sci., 52, 975–983,
https://doi.org/10.1287/mnsc.1060.0560, 2006. a
Vousdoukas, M. I., Voukouvalas, E., Mentaschi, L., Dottori, F., Giardino, A., Bouziotas, D., Bianchi, A., Salamon, P., and Feyen, L.: Developments in large-scale coastal flood hazard mapping, Nat. Hazards Earth Syst. Sci., 16, 1841–1853, https://doi.org/10.5194/nhess-16-1841-2016, 2016. a
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M.,
Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E.,
Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O.,
Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G.,
Groth, P., Goble, C., Grethe, J. S., Heringa, J., ’t Hoen, P. A. C., Hooft,
R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A.,
Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R.,
Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz,
M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J.,
Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.:
The FAIR Guiding Principles for scientific data management and
stewardship, Sci. Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016. a, b
Yohe, G. and Tol, R. S.: Indicators for social and economic coping capacity -
Moving toward a working definition of adaptive capacity, Global
Environ. Change, 12, 25–40, https://doi.org/10.1016/S0959-3780(01)00026-7, 2002. a
Yohe, G., Neumann, J., and Ameden, H.: Assessing the economic cost of
greenhouse-induced sea level rise: methods and application in support of a
national survey, J. Environ. Econ. Manag., 29,
S78–S97, 1995. a
Executive editor
Sea level rise represents one of the most compelling aspects of anthropogenic climate change. The potential social and economic impacts are enormous, with little that can be done to mitigate them. It is therefore of critical importance that we are able to correctly anticipate these impacts in advance. This study presents a new, open-source platform that integrates numerical modelling with socioeconomic and physical datasets, whilst also allowing for the uncertainty in climate change projections. This tool therefore allows for new and improved estimates of the global costs of future sea level rise and is likely to be of widespread interest.
Sea level rise represents one of the most compelling aspects of anthropogenic climate change....
Short summary
This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
This work presents a novel open-source modeling platform for evaluating future sea level rise...
