Research article
29 Jul 2021
Research article
| 29 Jul 2021
Improving the potential accuracy and usability of EURO-CORDEX estimates of future rainfall climate using frequentist model averaging
Stephen Jewson et al.
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Stephen Jewson, Giuliana Barbato, Paola Mercogliano, and Maximiliano Sassi
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2022-7, https://doi.org/10.5194/npg-2022-7, 2022
Publication in NPG not foreseen
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It may be possible to make climate model projections more precise using spatial smoothing. We introduce a new spatial smoothing method that differs from previously used methods in that it varies the amount of smoothing by location. For the European rainfall projections we apply the method to, we show that the new method is three times more effective than standard smoothing methods. This improved precision may benefit applications of climate model projections.
Giuseppe Giugliano, Veronica Villani, Giuliana Barbato, Pasquale Schiano, Antonio D'Ambrosio, Piero Cau, Giuseppe Onorati, and Paola Mercogliano
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Nat. Hazards Earth Syst. Sci., 22, 1487–1497, https://doi.org/10.5194/nhess-22-1487-2022, https://doi.org/10.5194/nhess-22-1487-2022, 2022
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The majority of natural-hazard risk research focuses on single hazards (a flood, a drought, a volcanic eruption, an earthquake, etc.). In the international research and policy community it is recognised that risk management could benefit from a more systemic approach. In this perspective paper, we argue for an approach that addresses multi-hazard, multi-risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards.
Stephen Jewson, Giuliana Barbato, Paola Mercogliano, and Maximiliano Sassi
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2022-7, https://doi.org/10.5194/npg-2022-7, 2022
Publication in NPG not foreseen
Short summary
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It may be possible to make climate model projections more precise using spatial smoothing. We introduce a new spatial smoothing method that differs from previously used methods in that it varies the amount of smoothing by location. For the European rainfall projections we apply the method to, we show that the new method is three times more effective than standard smoothing methods. This improved precision may benefit applications of climate model projections.
Mattia Amadio, Arthur H. Essenfelder, Stefano Bagli, Sepehr Marzi, Paolo Mazzoli, Jaroslav Mysiak, and Stephen Roberts
Nat. Hazards Earth Syst. Sci., 22, 265–286, https://doi.org/10.5194/nhess-22-265-2022, https://doi.org/10.5194/nhess-22-265-2022, 2022
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We estimate the risk associated with storm surge events at two case study locations along the North Adriatic Italian coast, considering sea level rise up to the year 2100, and perform a cost–benefit analysis of planned or proposed coastal renovation projects. The study uses nearshore hydrodynamic modelling. Our findings represent a useful indication for disaster risk management, helping to understand the importance of investing in adaptation and estimating the economic return on investments.
Mattia Amadio, Anna Rita Scorzini, Francesca Carisi, Arthur H. Essenfelder, Alessio Domeneghetti, Jaroslav Mysiak, and Attilio Castellarin
Nat. Hazards Earth Syst. Sci., 19, 661–678, https://doi.org/10.5194/nhess-19-661-2019, https://doi.org/10.5194/nhess-19-661-2019, 2019
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Jaroslav Mysiak, Sergio Castellari, Blaz Kurnik, Rob Swart, Patrick Pringle, Reimund Schwarze, Henk Wolters, Ad Jeuken, and Paul van der Linden
Nat. Hazards Earth Syst. Sci., 18, 3137–3143, https://doi.org/10.5194/nhess-18-3137-2018, https://doi.org/10.5194/nhess-18-3137-2018, 2018
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Reducing disaster risks and adapting to climate change are ever more important policy goals. However, policies, methods, and practices across both policy areas often lack coherence, and opportunities are not fully exploited to build up resilience. The report "Climate change adaptation and disaster risk reduction in Europe" of the European Environment Agency identified several ways for how coherence and resilience can be built through knowledge sharing, collaboration, and investments.
Roozbeh Hasanzadeh Nafari, Mattia Amadio, Tuan Ngo, and Jaroslav Mysiak
Nat. Hazards Earth Syst. Sci., 17, 1047–1059, https://doi.org/10.5194/nhess-17-1047-2017, https://doi.org/10.5194/nhess-17-1047-2017, 2017
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Floods are frequent natural hazards in Italy, triggering significant adverse consequences on the economy every year. Their impact is expected to worsen in the near future due to socio-economic development and climate variability. To be able to reduce the probability and magnitude of expected economic losses, flood risk managers need to be correctly informed about the potential damage from flood hazards. In this study, we have developed a new and accurate model for Italian residential buildings.
Jaroslav Mysiak and C. Dionisio Pérez-Blanco
Nat. Hazards Earth Syst. Sci., 16, 2403–2419, https://doi.org/10.5194/nhess-16-2403-2016, https://doi.org/10.5194/nhess-16-2403-2016, 2016
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Jaroslav Mysiak, Swenja Surminski, Annegret Thieken, Reinhard Mechler, and Jeroen Aerts
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In March 2015, a new international blueprint for disaster risk reduction (DRR) has been adopted in Sendai, Japan, at the end of the Third UN World Conference on Disaster Risk Reduction (WCDRR, March 14–18, 2015). We review and discuss the agreed commitments and targets, as well as the negotiation leading the Sendai Framework for DRR (SFDRR), and discuss briefly its implication for the later UN-led negotiations on sustainable development goals and climate change.
Elco E. Koks, Lorenzo Carrera, Olaf Jonkeren, Jeroen C. J. H. Aerts, Trond G. Husby, Mark Thissen, Gabriele Standardi, and Jaroslav Mysiak
Nat. Hazards Earth Syst. Sci., 16, 1911–1924, https://doi.org/10.5194/nhess-16-1911-2016, https://doi.org/10.5194/nhess-16-1911-2016, 2016
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In this study we analyze the economic consequences for two flood scenarios in the Po River basin in Italy, using three regional disaster impact models: two hybrid IO models and a regionally CGE model. Modelling results indicate that the difference in estimated total (national) economic losses and the regional distribution of those losses may vary by up to a factor of 7 between the three models, depending on the type of recovery path. Total economic impact is negative in all models though.
M. P. Hare, C. van Bers, P. van der Keur, H. J. Henriksen, J. Luther, C. Kuhlicke, F. Jaspers, C. Terwisscha van Scheltinga, J. Mysiak, E. Calliari, K. Warner, H. Daniel, J. Coppola, and P. F. McGrath
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Related subject area
Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
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Cited articles
Barnes, E. A., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson, D.: Viewing Forced Climate Patterns Through an AI Lens, Geophys. Res. Lett., 46, 13389–13398, 2019.
Benestad, R., Haensler, A., Hennemuth, B., Illy, T., Jacob, D., Keup-Thiel,
E., Kotlarski, S., Nikulin, G., Otto, J., Rechid, D., Sieck, K., Sobolowski, S., Szabó, P., Szépszó, G., Teichmann, C., Vautard, R., Weber, T., and Zsebeházi, G.: Guidance for EURO-CORDEX, available at:
https://www.euro-cordex.net/imperia/md/content/csc/cordex/euro-cordex-guidelines-version1.0-2017.08.pdf (last access: 9 January 2021), 2017.
Bernardo, J. and Smith, A.: Bayesian Theory, Wiley, New York, ISBN 0 471 49464 X, 1993.
Brocker, J. and Smith, L.: Scoring Probabilistic Forecasts: The Importance
of Being Proper, Weather Forecast., 22, 382–388, 2007.
Burnham, K. and Anderson, D.: Model Selection and Multimodel Inference, Springer-Verlag, New York, ISBN 978-1-4419-2973-0, 2002.
Buser, C., Kunsch, H., and Schar, C.: Bayesian multi-model projections of
climate: generalisation and application to ENSEMBLES results, Clim. Dynam., 44, 227–241, 2010.
Charkhi, A., Claeskens, G., and Hansen, B.: Minimum mean squared error
model averaging in likelihood models, Stat. Sinica, 26, 809–840, 2016.
Chen, J., Brissette, F., Zhang, X., Chen, H., Guo, S., and Zhao, Y.: Bias
correcting climate model multi-member ensembles to assess climate change
impacts on hydrology, Clim. Change, 153, 361–377, 2019.
Christensen, J., Kjellstrom, E., Giorgi, F., Lenderink, G., and Rummukainen, M.: Weight assignment in regional climate models, Clim. Res., 44, 179–194, 2010.
Claeskens, G. and Hjort, N.: Model Selection and Model Averaging, CUP, Cambridge, ISBN 978-0-521-85225-8, 2008.
Copas, J.: Regression, Prediction and Shrinkage, J. Roy. Stat. Soc. B Met., 45, 311–354, 1983.
DelSole, T., Yang, X., and Tippett, M.: Is Unequal Weighting Significantly
Better than Equal Weighting for Multi-Model Forecasting?, Q. J. Roy. Meteor.
Soc., 139, 176–183, 2013.
Deque, M. and Somot, S.: Weighted frequency distributions express
modelling uncertainties in the ENSEMBLES regional climate experiments,
Clim. Res., 44, 195–209, 2010.
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate change projections: the role of internal variability, Clim. Dynam., 38, 527–546, 2010.
European Environment Agency: Indicator Assessment: Mean Precipitation, available at:
https://www.eea.europa.eu/data-and-maps/indicators/european-precipitation-2/assessment
(last access: 15 April 2020), 2017.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Fletcher, D.: Model Averaging, Springer, Berlin, https://doi.org/10.1007/978-3-662-58541-2, 2019.
Frankcombe, L., England, M., Mann, M., and Steinman, B.: Separating
Internal Variability from the Externally Forced Climate Response, J. Climate, 28, 8184–8202, 2015.
Friedman, D.: Insurance and the Natural Hazards, ASTIN Bulletin, 7, 4–58, 1972.
Hansen, B.: Least Squares Model Averaging, Econometrica, 75, 1175–1189,
2007.
Hawkins, E. and Sutton, R.: The Potential to Narrow Uncertainty in
Regional Climate Predictions, B. Am. Meteorol. Soc., 90, 1095–1108, 2009.
Hawkins, E. and Sutton, R.: Time of emergence of climate signals, Geophys. Res. Lett., 39, L01702, https://doi.org/10.1029/2011GL050087, 2012.
Hingray, B. and Said, M.: Partitioning Internal Variability and Model
Uncertainty Components in a Multimember Multimodel Ensemble of Climate
Projections, J. Climate, 27, 6779–6798, 2014.
Hjort, N. and Claeskens, G.: Frequentist model average estimators, Journal
of the American Statistical Association, 98, 879–899, 2003.
Hoeting, J., Madigan, D., Raftery, A., and Volinsky, C.: Bayesian model
averaging: a tutorial, Stat. Sci., 14, 382–401, 1999.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C.,
Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014 (data available at: https://euro-cordex.net, last access: 19 July 2021).
Jacob, D., Teichmann, C., Sobolowski, S., et al.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020.
Jewson, S. and Hawkins, E.: Improving the expected accuracy of forecasts
of future climate using a simple bias-variance tradeoff,
arXiv [preprint], arXiv:0911.1904, 10 November 2009a.
Jewson, S. and Hawkins, E.: Improving Uncertain Climate Forecasts Using a
New Minimum Mean Square Error Estimator for the Mean of the Normal
Distribution, arXiv [preprint], arXiv:0912.4395, 22 December 2009b.
Jewson, S. and Penzer, J.: Estimating Trends in Weather Series:
Consequences for Pricing Derivatives, Stud. Nonlinear Dyn. E., 10, 1–10, 2006.
Jewson, S., Barnes, C., Cusack, S., and Bellone, E.: Adjusting catastrophe
model ensembles using importance sampling, with application to damage
estimation for varying levels of hurricane activity, Meteorol. Appl., 27, e1839, https://doi.org/10.1002/met.1839, 2019.
Jewson, S., Brix, A., and Ziehmann, C.: A new parametric model for the
assessment and calibration of medium-range ensemble temperature forecasts,
Atmos. Sci. Lett., 5, 96–102, 2004.
Jolliffe, I. and Stephenson, D.: Forecast verification, Wiley, Chichester, ISBN 0-471-49759-2, 2003.
Kaczmarska, J., Jewson, S., and Bellone, E.: Quantifying the sources of simulation uncertainty in natural catastrophe models, Stoch. Env. Res. Risk A., 32, 591–605, 2018.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G.: Challenges
in combining projections from multiple climate models, J. Climate, 23, 2739–2758, 2010.
Knutti, R., Sedlacek, J., Sanderson, B., Lorenz, R., Fischer, E., and
Eyring, V.: A climate model projection weighting scheme accounting for
performance and interdependence, Geophys. Res. Lett., 44, 1909–1918, 2017.
Lee, P.: Bayesian Statistics, 2nd edn., Arnold, London, ISBN 0 340 67785 6, 1997.
Lehner, F., Deser, C., and Terray, L.: Toward a New Estimate of “Time of
Emergence” of Anthropogenic Warming: Insights from Dynamical Adjustment and
a Large Initial-Condition Model Ensemble, J. Climate., 30, 7739–7756, 2017.
Liu, C.: Distribution theory of the least squares averaging estimator,
J. Econometrics, 186, 142–159, 2014.
Mearns, L., Bukovsky, M., and Schweizer, V.: Potential Value of Expert
Elicitation for Determining Differential Credibility of Regional Climate
Change Simulations: An Exercise with the NARCCAP co-PIs for the Southwest
Monsoon Region of North America, B. Am. Meteorol. Soc., 98, 29–35, 2017.
Meinshausen, M., Smith, S., Calvin, K., Daniel, J., Kainuma, M., Lamarque,
J., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A.,
Velders, G. J. M., and van Vuuren, D. P. P.: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213, https://doi.org/10.1007/s10584-011-0156-z, 2011.
Mezghani, A., Dobler, A., Benestad, R., Haugen, J., Parding, K., Piniewski,
M., and Kundzewicz, Z.: Subsampling Impact on the Climate Change Signal
over Poland Based on Simulations from Statistical and Dynamical Downscaling,
J. Appl. Meteorol. Clim., 58, 1061–1078, 2019.
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., Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, 2010.
Pachauri, K. and Meyer, L.: IPCC 2014: Climate Change 2014: Synthesis
Report, Contribution of Working Groups I, II and III to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, 2014.
Raisanen, J. and Ylhaisi, J.: How Much Should Climate Model Output Be
Smoothed in Space?, J. Climate, 24, 867–880, 2010.
Sanderson, B., Knutti, R., and Caldwell, P.: Addressing interdependency in
a multimodel ensemble by interpolation of model properties, J. Climate, 28,
5150–5170, 2015.
Sassi, M., Nicotina, L., Pall, P., Stone, D., Hilberts, A., Wehner, M., and
Jewson, S.: Impact of climate change on European winter and summer flood
losses, Adv. Water Resour., 129, 165–177, 2019.
Sippel, S., Meinshausen, N., Merrifield, A., Lehner, F., Pendergrass, A.,
Fischer, E., and Knutti, R.: Uncovering the Forced Climate Response from a
Single Ensemble Member Using Statistical Learning, J. Climate, 32, 5677–5699, 2019.
Taylor, K., Stouffer, R., and Meehl, G.: An Overview of CMIP5 and the
Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, 2012.
Thompson, D., Barnes, E., Deser, C., Foust, W., and Phillips, A.:
Quantifying the Role of Internal Climate Variability in Future Climate
Trends, J. Climate, 28, 6443–6456, 2015.
Wilks, D.: Statistical Methods in the Atmospheric Sciences, 3rd edn., AP, Oxford, ISBN 978-0-12-385022-5, 2011.
Wills, R., Battisti, D., Armour, K., Schneider, T., and Deser, C.: Pattern
Recognition Methods to Separate Forced Responses from Internal Variability
in Climate Model Ensembles and Observations, J. Climate, 33, 8693–8719,
2020.
Winkler, R.: Scoring rules and the evaluation of probability assessors,
J. Am. Stat. Assoc., 64, 1073–1078, 1969.
Yip, S., Ferro, C., Stephenson, D., and Hawkins, E.: A Simple, Coherent
Framework for Partitioning Uncertainty in Climate Predictions, J. Climate.,
24, 4634–4643, 2011.
Short summary
Climate model simulations are uncertain. In some cases this makes it difficult to know how to use them. Significance testing is often used to deal with this issue but has various shortcomings. We describe two alternative ways to manage uncertainty in climate model simulations that avoid these shortcomings. We test them on simulations of future rainfall over Europe and show they produce more accurate projections than either using unadjusted climate model output or statistical testing.
Climate model simulations are uncertain. In some cases this makes it difficult to know how to...