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
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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.
Deifilia Aurora To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
EGUsphere, https://doi.org/10.5194/egusphere-2024-1714, https://doi.org/10.5194/egusphere-2024-1714, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers three-dimensional 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 accessibility of training and working with the model.
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
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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
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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
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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.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
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We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
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. Discuss., https://doi.org/10.5194/gmd-2024-98, https://doi.org/10.5194/gmd-2024-98, 2024
Revised manuscript accepted for GMD
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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 range of data is likely encountered outside the calibration period. The end result is highly dependent on the method used, and we show that one needs to exclude data in the calibration range to activate the extrapolation functionality also in that time period, else there will be discontinuities in the timeseries.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
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The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
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This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
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We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1456, https://doi.org/10.5194/egusphere-2024-1456, 2024
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We evaluate downscaled products by examining locally relevant covariances during convective and frontal precipitation events. Common statistical downscaling techniques preserve expected covariances during convective precipitation. However, they dampen future intensification of frontal precipitation captured in global climate models and dynamical downscaling. This suggests statistical downscaling may not fully resolve non-stationary hydrologic processes as compared to dynamical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-97, https://doi.org/10.5194/gmd-2024-97, 2024
Revised manuscript accepted for GMD
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Research software is crucial for scientific progress but is often developed by scientists with limited training, time, and funding, leading to software that is hard to understand, (re)use, modify, and maintain. Our study across 10 research sectors highlights strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. Recommendations include workshops, code quality metrics, funding, and adherence to FAIR standards.
Giovanni G. Seijo-Ellis, Donata Giglio, Gustavo M. Marques, and Frank O. Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1378, https://doi.org/10.5194/egusphere-2024-1378, 2024
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A CESM/MOM6 regional configuration of the Caribbean Sea was developed as a response to the rising need of high-resolution models for climate impact studies. The configuration is validated for the period of 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 across the multiple passages in the Caribbean Sea agree with observations.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
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. Discuss., https://doi.org/10.5194/gmd-2024-85, https://doi.org/10.5194/gmd-2024-85, 2024
Revised manuscript accepted for GMD
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This study provides the first comprehensive assessment of historical fire simulations from 19 CMIP6 ESMs. Most models reproduce global total, spatial pattern, seasonality, and regional historical changes well, but fail to simulate the recent decline in global burned area and underestimate the fire sensitivity to wet-dry conditions. They addressed three critical issues in CMIP5. We present targeted guidance for fire scheme development and methodologies to generate reliable fire projections.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Giovanni Di Virgilio, Jason Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew Riley, and Jyothi Lingala
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-87, https://doi.org/10.5194/gmd-2024-87, 2024
Revised manuscript accepted for GMD
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models, and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleußner
EGUsphere, https://doi.org/10.5194/egusphere-2024-278, https://doi.org/10.5194/egusphere-2024-278, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Their joint distribution largely determines the division into climate regimes. Yet, projecting 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 to generate monthly means of local precipitation and temperature at low computational costs.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
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We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
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In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Emily Black, John Ellis, and Ross Maidment
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-75, https://doi.org/10.5194/gmd-2024-75, 2024
Revised manuscript accepted for GMD
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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 forecasting data into probabilistic hazard assessments. As such, it complements existing systems and enhances their utility for actionable hazard assessment.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
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...
