Articles | Volume 8, issue 1
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 117–134, 2022
https://doi.org/10.5194/ascmo-8-117-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 117–134, 2022
https://doi.org/10.5194/ascmo-8-117-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
02 Jun 2022
02 Jun 2022
Analysis of the evolution of parametric drivers of high-end sea-level hazards
Alana Hough and Tony E. Wong
Related authors
No articles found.
Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 53–63, https://doi.org/10.5194/ascmo-4-53-2018, https://doi.org/10.5194/ascmo-4-53-2018, 2018
Short summary
Short summary
Millions of people worldwide are at a risk of coastal flooding, and this number will increase as the climate continues to change. This study analyzes how climate change affects future flood hazards. A new model that uses multiple climate variables for flood hazard is developed. For the case study of Norfolk, Virginia, the model predicts 23 cm higher flood levels relative to previous work. This work shows the importance of accounting for climate change in effectively managing coastal risks.
Tony E. Wong, Alexander M. R. Bakker, Kelsey Ruckert, Patrick Applegate, Aimée B. A. Slangen, and Klaus Keller
Geosci. Model Dev., 10, 2741–2760, https://doi.org/10.5194/gmd-10-2741-2017, https://doi.org/10.5194/gmd-10-2741-2017, 2017
Short summary
Short summary
We present the Building blocks for Relevant Ice and Climate Knowledge (BRICK) model v0.2. BRICK is a model for hindcasting past and projecting future surface temperature and sea-level rise, resolving the sea-level contributions from glaciers and ice caps, the Greenland and Antarctic ice sheets, and thermal expansion. BRICK is specifically designed to support decision analyses through its transparency, and includes functionality to scale global sea-level estimates to regional projections.
Related subject area
Climate research
Comparing climate time series – Part 3: Discriminant analysis
Spatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zone
Spatial heterogeneity of 2015–2017 drought intensity in South Africa's winter rainfall zone
A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
A protocol for probabilistic extreme event attribution analyses
The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models
A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series
Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany
Possible impacts of climate change on fog in the Arctic and subpolar North Atlantic
Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models
Comparison and assessment of large-scale surface temperature in climate model simulations
Future climate emulations using quantile regressions on large ensembles
Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures
Estimates of climate system properties incorporating recent climate change
The joint influence of break and noise variance on the break detection capability in time series homogenization
A space–time statistical climate model for hurricane intensification in the North Atlantic basin
Building a traceable climate model hierarchy with multi-level emulators
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115, https://doi.org/10.5194/ascmo-8-97-2022, https://doi.org/10.5194/ascmo-8-97-2022, 2022
Short summary
Short summary
A common problem in climate studies is to decide whether a climate model is realistic. Such decisions are not straightforward because the time series are serially correlated and multivariate. Part II derived a test for deciding wether a simulation is statistically distinguishable from observations. However, the test itself provides no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between stochastic processes.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 31–62, https://doi.org/10.5194/ascmo-8-31-2022, https://doi.org/10.5194/ascmo-8-31-2022, 2022
Short summary
Short summary
Cape Town is situated in a small but ecologically and climatically highly diverse and vulnerable pocket of South Africa uniquely receiving its rain mostly in winter. We show complex structures in the spatial patterns of rainfall seasonality and year-to-year changes in rainfall within this domain, tied to spatial differences in the rain-bearing winds. This allows us to develop a new spatial subdivision of the region to help future studies distinguish spatially distinct climate change responses.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 63–81, https://doi.org/10.5194/ascmo-8-63-2022, https://doi.org/10.5194/ascmo-8-63-2022, 2022
Short summary
Short summary
The
Day Zerowater crisis affecting Cape Town after the severe 2015–2017 drought motivated renewed research interest into causes and projections of rainfall variability and change in this water-stressed region. Unusually few wet months and very wet days characterised the Day Zero Drought. Its extent expanded as it shifted gradually north-eastward, concurrent with changes in the weather systems driving drought. Our results emphasise the need to consider the interplay between drought drivers.
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.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
Short summary
Short summary
Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
Short summary
Short summary
Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Mark D. Risser and Michael F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 115–139, https://doi.org/10.5194/ascmo-6-115-2020, https://doi.org/10.5194/ascmo-6-115-2020, 2020
Short summary
Short summary
Evaluation of modern high-resolution global climate models often does not account for the geographic location of the underlying weather station data. In this paper, we quantify the impact of geographic sampling on the relative performance of climate model representations of precipitation extremes over the United States. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance.
Donald P. Cummins, David B. Stephenson, and Peter A. Stott
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, https://doi.org/10.5194/ascmo-6-91-2020, https://doi.org/10.5194/ascmo-6-91-2020, 2020
Short summary
Short summary
We have developed a novel and fast statistical method for diagnosing effective radiative forcing (ERF), a measure of the net effect of greenhouse gas emissions on Earth's energy budget. Our method works by inverting a recursive digital filter energy balance representation of global climate models and has been successfully validated using simulated data from UK Met Office climate models. We have estimated time series of historical ERF by applying our method to the global temperature record.
Meagan Carney and Holger Kantz
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 61–77, https://doi.org/10.5194/ascmo-6-61-2020, https://doi.org/10.5194/ascmo-6-61-2020, 2020
Short summary
Short summary
Extremes in weather can have lasting effects on human health and resource consumption. Studying the recurrence of these events on a regional scale can improve response times and provide insight into a changing climate. We introduce a set of clustering tools that allow for regional clustering of weather recordings from stations across Germany. We use these clusters to form regional models of summer temperature extremes and find an increase in the mean from 1960 to 2018.
Richard E. Danielson, Minghong Zhang, and William A. Perrie
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 31–43, https://doi.org/10.5194/ascmo-6-31-2020, https://doi.org/10.5194/ascmo-6-31-2020, 2020
Short summary
Short summary
Visibility is estimated for the 21st century using global and regional climate model output. A baseline decrease in visibility in the Arctic (10 %) is more notable than in the North Atlantic (< 5 %). We develop an adjustment that yields greater consistency among models and explore the justification of our ad hoc adjustment toward ship observations during the historical period. Baseline estimates are found to be sensitive to the representation of temperature and humidity.
Sophie C. Lewis, Sarah E. Perkins-Kirkpatrick, and Andrew D. King
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 133–146, https://doi.org/10.5194/ascmo-5-133-2019, https://doi.org/10.5194/ascmo-5-133-2019, 2019
Short summary
Short summary
Extreme temperature and precipitation events in Australia have caused significant socio-economic and environmental impacts. Determining the factors contributing to these extremes is an active area of research. This paper describes a set of studies that have examined the causes of extreme climate events in recent years in Australia. Ideally, this review will be useful for the application of these extreme event attribution approaches to climate and weather extremes occurring elsewhere.
Raquel Barata, Raquel Prado, and Bruno Sansó
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 67–85, https://doi.org/10.5194/ascmo-5-67-2019, https://doi.org/10.5194/ascmo-5-67-2019, 2019
Matz A. Haugen, Michael L. Stein, Ryan L. Sriver, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 37–55, https://doi.org/10.5194/ascmo-5-37-2019, https://doi.org/10.5194/ascmo-5-37-2019, 2019
Short summary
Short summary
This work uses current temperature observations combined with climate models to project future temperature estimates, e.g., 100 years into the future. We accomplish this by modeling temperature as a smooth function of time both in the seasonal variation as well as in the annual trend. These smooth functions are estimated at multiple quantiles that are all projected into the future. We hope that this work can be used as a template for how other climate variables can be projected into the future.
Rasmus E. Benestad, Bob van Oort, Flavio Justino, Frode Stordal, Kajsa M. Parding, Abdelkader Mezghani, Helene B. Erlandsen, Jana Sillmann, and Milton E. Pereira-Flores
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 37–52, https://doi.org/10.5194/ascmo-4-37-2018, https://doi.org/10.5194/ascmo-4-37-2018, 2018
Short summary
Short summary
A new study indicates that heatwaves in India will become more frequent and last longer with global warming. Its results were derived from a large number of global climate models, and the calculations differed from previous studies in the way they included advanced statistical theory. The projected changes in the Indian heatwaves will have a negative consequence for wheat crops in India.
Alex G. Libardoni, Chris E. Forest, Andrei P. Sokolov, and Erwan Monier
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 19–36, https://doi.org/10.5194/ascmo-4-19-2018, https://doi.org/10.5194/ascmo-4-19-2018, 2018
Short summary
Short summary
We present new probabilistic estimates of model parameters in the MIT Earth System Model using more recent data and an updated method. Model output is compared to observed climate change to determine which sets of model parameters best simulate the past. In response to increasing surface temperatures and accelerated heat storage in the ocean, our estimates of climate sensitivity and ocean diffusivity are higher. Using a new interpolation algorithm results in smoother probability distributions.
Ralf Lindau and Victor Karel Christiaan Venema
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 1–18, https://doi.org/10.5194/ascmo-4-1-2018, https://doi.org/10.5194/ascmo-4-1-2018, 2018
Short summary
Short summary
Climate data contain spurious breaks, e.g., by relocation of stations, which makes it difficult to infer the secular temperature trend. Homogenization algorithms use the difference time series of neighboring stations to detect and eliminate this spurious break signal. For low signal-to-noise ratios, i.e., large distances between stations, the correct break positions may not only remain undetected, but segmentations explaining mainly the noise can be erroneously assessed as significant and true.
Erik Fraza, James B. Elsner, and Thomas H. Jagger
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 105–114, https://doi.org/10.5194/ascmo-2-105-2016, https://doi.org/10.5194/ascmo-2-105-2016, 2016
Short summary
Short summary
Climate influences on hurricane intensification are investigated by averaging hourly intensification rates over the period 1975–2014 in 8° by 8° latitude–longitude grid cells. The statistical effects of hurricane intensity, sea-surface temperature (SST), El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Madden–Julian Oscillation (MJO) are quantified. Intensity, SST, and NAO had a positive effect on intensification rates. The NAO effect should be further studied.
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17–37, https://doi.org/10.5194/ascmo-2-17-2016, https://doi.org/10.5194/ascmo-2-17-2016, 2016
Short summary
Short summary
In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
Cited articles
Bakker, A. M., Applegate, P. J., and Keller, K.: A simple, physically motivated model of sea-level contributions from the Greenland ice sheet in response to temperature changes, Environ. Modell. Softw., 83, 27–35, https://doi.org/10.1016/j.envsoft.2016.05.003, 2016. a
Bakker, A. M. R., Wong, T. E., Ruckert, K. L., and Keller, K.: Sea-level projections representing the deeply uncertain contribution of the West Antarctic ice sheet, Sci. Rep.-UK, 7, 3880, https://doi.org/10.1038/s41598-017-04134-5, 2017. a, b
Bakker, P., Schmittner, A., Lenaerts, J. T. M., Abe-Ouchi, A., Bi, D., van den Broeke, M. R., Chan, W.-L., Hu, A., Beadling, R. L., Marsland, S. J., Mernild, S. H., Saenko, O. A., Swingedouw, D., Sullivan, A., and Yin, J.: Fate of the Atlantic Meridional Overturning Circulation: Strong decline under continued warming and Greenland melting, Geophys. Res. Lett., 43, 12252–12260, https://doi.org/10.1002/2016GL070457, 2016. a
Church, J., Clark, P., Cazenave, A., Gregory, J., Jevrejeva, S., Levermann, A., Merrifield, M., Milne, G., Nerem, R., Nunn, P., Payne, A., Pfeffer, W., Stammer, D., and Unnikrishnan, A.: Sea Level Change, Sect. 13, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1137–1216, https://doi.org/10.1017/CBO9781107415324.026, 2013. a
Dayan, H., Le Cozannet, G., Speich, S., and Thiéblemont, R.: High-End Scenarios of Sea-Level Rise for Coastal Risk-Averse Stakeholders, Frontiers in Marine Science, 8, 514, https://doi.org/10.3389/fmars.2021.569992, 2021. a, b
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
Exec. Order No. 14008: Tackling the Climate Crisis at Home and Abroad, 86 F. R. 7619, 7619–7633, https://www.federalregister.gov/documents/2021/02/01/2021-02177/tackling-the-climate-crisis-at-home-and-abroad (last access: 19 May 2021), 2021. a
FAO: Global Blue Growth Initiative and Small Island Developing States (SIDS), Food and Agriculture Organization of the United Nations (FAO), http://www.fao.org/documents/card/en/c/c8aeb23f-f794-410e-804f-2aa82140d34a/ (last access: 19 May 2021), 2014. a
Fischbach, J. R., Johnson, D. R., Ortiz, D. S., Bryant, B. P., Hoover, M., and Ostwald, J.: Coastal Louisiana Risk Assessment Model: Technical Description and 2012 Coastal Master Plan Analysis Results, RAND Corporation, Santa Monica, CA, eISBN 978-0-8330-7985-5, 2012. a
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.: Evaluation of climate models, Sect. 9, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741–882, https://doi.org/10.1017/CBO9781107415324.020, 2013. a
Gaál, M., Moriondo, M., and Bindi, M.: Modelling the impact of climate change on the Hungarian wine regions using Random Forest, Appl. Ecol. Env. Res., 10, 121–140, https://doi.org/10.15666/aeer/1002_121140, 2012. a
Giorgi, F.: Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next?, J. Geophys. Res.-Atmos., 124, 5696–5723, https://doi.org/10.1029/2018JD030094, 2019. a
Grinsted, A., Moore, J., and Jevrejeva, S.: Reconstructing sea level from paleo and projected temperatures 200 to 2100 AD, Clim. Dynam., 34, 461–472, https://doi.org/10.1007/s00382-008-0507-2, 2010. a
Haasnoot, M., Kwakkel, J. H., Walker, W. E., and ter Maat, J.: Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world, Global Environ. Chang., 23, 485–498, https://doi.org/10.1016/j.gloenvcha.2012.12.006, 2013. a, b, c
Hackeling, G.: Mastering Machine Learning with Scikit-Learn – Second Edition: Apply Effective Learning Algorithms to Real-World Problems Using Scikit-Learn, 2nd edn., Packt Publishing, ISBN-13 978-1788299879,
ISBN-10 1788299876, 2017. a
Hartin, C. A., Patel, P., Schwarber, A., Link, R. P., and Bond-Lamberty, B. P.: A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0, Geosci. Model Dev., 8, 939–955, https://doi.org/10.5194/gmd-8-939-2015, 2015. a, b
Hegerl, G. C., Crowley, T. J., Hyde, W. T., and Frame, D. J.: Climate sensitivity constrained by temperature reconstructions over the past seven centuries, Nature, 440, 1029–1032, https://doi.org/10.1038/nature04679, 2006. a
Hinkel, J., Lincke, D., Vafeidis, A. T., Perrette, M., Nicholls, R. J., Tol, R. S. J., 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
Hough, A. and Wong, T.: Codes and model output supporting Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards (v1.0), Zenodo [data set], doi10.5281/zenodo.6514918, 2022. a
James, G., Witten, D., Hastie, T., and Tibshirani, R.: An Introduction to Statistical Learning: With Applications in R, Springer, https://doi.org/10.1007/978-1-4614-7138-7, 2013. a, b, c, d
Jelesnianski, C., Chen, J., and Shaffer, W.: SLOSH: Sea, Lake, and Overland Surges from Hurricanes, NOAA technical report NWS, U. S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, https://books.google.com/books?id=Wdg8mQfzkVcC (last access: 19 May 2021), 1992. a
Jevrejeva, S., Jackson, L. P., Grinsted, A., Lincke, D., and Marzeion, B.: Flood damage costs under the sea level rise with warming of 1.5 ∘C and 2 ∘C, Environ. Res. Lett., 13, 074014, https://doi.org/10.1088/1748-9326/aacc76, 2018. a
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models, J. Roy. Stat. Soc. B, 63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001. a
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, Earths Future, 5, 1217–1233, https://doi.org/10.1002/2017EF000663, 2017. a
Kriegler, E.: Imprecise probability analysis for integrated assessment of climate change, Doctoral thesis, Universität Potsdam, http://opus.kobv.de/ubp/volltexte/2005/561/ (last access: 9 June 2021), 2005. a
Le Cozannet, G., Rohmer, J., Cazenave, A., Idier, D., van de Wal, R., de Winter, R., Pedreros, R., Balouin, Y., Vinchon, C., and Oliveros, C.: Evaluating uncertainties of future marine flooding occurrence as sea-level rises, Environ. Modell. Softw., 73, 44–56, https://doi.org/10.1016/j.envsoft.2015.07.021, 2015. a
Lenton, T. M., Rockström, J., Gaffney, O., Rahmstorf, S., Richardson, K., Steffen, W., and Schellnhuber, H. J.: Climate tipping points – too risky to bet against, Nature, 575, 592–595, https://doi.org/10.1038/d41586-019-03595-0, 2019. a, b
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review, Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005, 2005. a
Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration, Atmos. Chem. Phys., 11, 1417–1456, https://doi.org/10.5194/acp-11-1417-2011, 2011. a
Mengel, M., Levermann, A., Frieler, K., Robinson, A., Marzeion, B., and Winkelmann, R.: Future sea level rise constrained by observations and long-term commitment, P. Natl. Acad. Sci. USA, 113, 2597–2602, https://doi.org/10.1073/pnas.1500515113, 2016. a, b, c
Mengel, M., Nauels, A., Rogelj, J., and Schleussner, C.-F.: Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action, Nat. Commun., 9, 601, https://doi.org/10.1038/s41467-018-02985-8, 2018. a
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010. a
Nauels, A., Meinshausen, M., Mengel, M., Lorbacher, K., and Wigley, T. M. L.: Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0, Geosci. Model Dev., 10, 2495–2524, https://doi.org/10.5194/gmd-10-2495-2017, 2017a. a
Nauels, A., Rogelj, J., Schleussner, C.-F., Meinshausen, M., and Mengel, M.: Linking sea level rise and socioeconomic indicators under the Shared Socioeconomic Pathways, Environ. Res. Lett., 12, 114002, https://doi.org/10.1088/1748-9326/aa92b6, 2017b. a
New Orleans Health Department: Climate Change & Health Report, https://www.nola.gov/getattachment/Health/Climate-Change-(1)/Planning-Tools-and-Data/Climate-Change-and-Health-Report-2018-Final.pdf/ (last access: 19 May 2021), 2018. a
Nicodemus, K. K., Malley, J. D., Strobl, C., and Ziegler, A.: The behaviour of random forest permutation-based variable importance measures under predictor correlation, BMC Bioinformatics, 11, 110, https://doi.org/10.1186/1471-2105-11-110, 2010. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a, b, c, d, e
Probst, P., Wright, M. N., and Boulesteix, A.: Hyperparameters and tuning strategies for random forest, WIREs Data Min. Knowl., 9, e1301, https://doi.org/10.1002/widm.1301, 2019. a
Python 3.7.4: Python Language Reference, Python Software Foundation, https://www.python.org/ (last access: 18 March 2021), 2019. a
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 18 March 2021), 2019. a
Robinson, A., Calov, R., and Ganopolski, A.: Multistability and critical thresholds of the Greenland ice sheet, Nat. Clim. Change, 2, 429–432, https://doi.org/10.1038/nclimate1449, 2012. a, b
Rohmer, J., Lincke, D., Hinkel, J., Le Cozannet, G., Lambert, E., and Vafeidis, A. T.: Unravelling the Importance of Uncertainties in Global-Scale Coastal Flood Risk Assessments under Sea Level Rise, Water, 13, 774, https://doi.org/10.3390/w13060774, 2021. a, b
Ruckert, K. L., Srikrishnan, V., and Keller, K.: Characterizing the deep uncertainties surrounding coastal flood hazard projections: A case study for Norfolk, VA, Sci. Rep.-UK, 9, 11373, https://doi.org/10.1038/s41598-019-47587-6, 2019. a, b, c
Shaffer, G.: Formulation, calibration and validation of the DAIS model (version 1), a simple Antarctic ice sheet model sensitive to variations of sea level and ocean subsurface temperature, Geosci. Model Dev., 7, 1803–1818, https://doi.org/10.5194/gmd-7-1803-2014, 2014. a
Sobol', I.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simulat., 55, 271–280, https://ideas.repec.org/a/eee/matcom/v55y2001i1p271-280.html (last access: 26 October 2021), 2001. a
Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T.: Bias in random forest variable importance measures: Illustrations, sources and a solution, BMC Bioinformatics, 8, 25, https://doi.org/10.1186/1471-2105-8-25, 2007. a
Thiéblemont, R., Le Cozannet, G., Toimil, A., Meyssignac, B., and Losada, I. J.: Likely and High-End Impacts of Regional Sea-Level Rise on the Shoreline Change of European Sandy Coasts Under a High Greenhouse Gas Emissions Scenario, Water, 11, 2607, https://doi.org/10.3390/w11122607, 2019. a
Urban, N. M. and Keller, K.: Probabilistic hindcasts and projections of the coupled climate, carbon cycle and Atlantic meridional overturning circulation system: a Bayesian fusion of century-scale observations with a simple model, Tellus A, 62, 737–750, https://doi.org/10.1111/j.1600-0870.2010.00471.x, 2010. a
Vega-Westhoff, B.: Updated MCMC chains and subsamples for Hector calibration paper, Zenodo, https://doi.org/10.5281/zenodo.3236413, 2019. a, b
Vega-Westhoff, B., Sriver, R. L., Hartin, C. A., Wong, T. E., and Keller, K.: Impacts of Observational Constraints Related to Sea Level on Estimates of Climate Sensitivity, Earths Future, 7, 677–690, https://doi.org/10.1029/2018EF001082, 2019. a, b
Wahl, T., Haigh, I. D., Nicholls, R. J., Arns, A., Dangendorf, S., Hinkel, J., and Slangen, A. B. A.: Understanding extreme sea levels for broad-scale coastal impact and adaptation analysis, Nat. Commun., 8, 16075, https://doi.org/10.1038/ncomms16075, 2017. a, b
Walker, W. E., Haasnoot, M., and Kwakkel, J. H.: Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty, Sustainability, 5, 955–979, https://doi.org/10.3390/su5030955, 2013. a, b, c
Wang, C., Soden, B. J., Yang, W., and Vecchi, G. A.: Compensation Between Cloud Feedback and Aerosol-Cloud Interaction in CMIP6 Models, Geophys. Res. Lett., 48, e2020GL091024, https://doi.org/10.1029/2020GL091024, 2021. a
Wang, Z., Lai, C., Chen, X., Yang, B., Zhao, S., and Bai, X.: Flood hazard risk assessment model based on random forest, J. Hydrol., 527, 1130–1141, https://doi.org/10.1016/j.jhydrol.2015.06.008, 2015. a, b
Wong, T. E. and Keller, K.: Deep Uncertainty Surrounding Coastal Flood Risk Projections: A Case Study for New Orleans, Earths Future, 5, 1015–1026, https://doi.org/10.1002/2017EF000607, 2017. a
Wong, T. E., Bakker, A. M. R., and Keller, K.: Impacts of Antarctic fast dynamics on sea-level projections and coastal flood defense, Climatic Change, 144, 347–364, https://doi.org/10.1007/s10584-017-2039-4, 2017a.
a, b
Wong, T. E., Bakker, A. M. R., Ruckert, K., Applegate, P., Slangen, A. B. A., and Keller, K.: BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections, Geosci. Model Dev., 10, 2741–2760, https://doi.org/10.5194/gmd-10-2741-2017, 2017b. a, b, c, d, e
Short summary
We use machine learning to assess how different geophysical uncertainties relate to the severity of future sea-level rise. We show how the contributions to coastal hazard from different sea-level processes evolve over time and find that near-term sea-level hazards are driven by thermal expansion and the melting of glaciers and ice caps, while long-term hazards are driven by ice loss from the major ice sheets.
We use machine learning to assess how different geophysical uncertainties relate to the severity...
