Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 329–346, 2021
https://doi.org/10.5194/npg-28-329-2021
Nonlin. Processes Geophys., 28, 329–346, 2021
https://doi.org/10.5194/npg-28-329-2021

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.

Related authors

Cost-benefit analysis of coastal flood defence measures in the North Adriatic Sea
Mattia Amadio, Arthur H. Essenfelder, Stefano Bagli, Sepehr Marzi, Paolo Mazzoli, Jaroslav Mysiak, and Stephen Roberts
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-414,https://doi.org/10.5194/nhess-2020-414, 2021
Preprint under review for NHESS
Short summary
Testing empirical and synthetic flood damage models: the case of Italy
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
Short summary
Brief communication: Strengthening coherence between climate change adaptation and disaster risk reduction
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
Short summary
Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures
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
Short summary
Partnerships for disaster risk insurance in the EU
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
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Ensemble Riemannian data assimilation over the Wasserstein space
Sagar K. Tamang, Ardeshir Ebtehaj, Peter J. van Leeuwen, Dongmian Zou, and Gilad Lerman
Nonlin. Processes Geophys., 28, 295–309, https://doi.org/10.5194/npg-28-295-2021,https://doi.org/10.5194/npg-28-295-2021, 2021
Short summary
An early warning sign of critical transition in the Antarctic ice sheet – a data-driven tool for a spatiotemporal tipping point
Abd AlRahman AlMomani and Erik Bollt
Nonlin. Processes Geophys., 28, 153–166, https://doi.org/10.5194/npg-28-153-2021,https://doi.org/10.5194/npg-28-153-2021, 2021
Short summary
Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz ’96 system
Zofia Stanley, Ian Grooms, and William Kleiber
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2021-8,https://doi.org/10.5194/npg-2021-8, 2021
Revised manuscript accepted for NPG
Short summary
Behavior of the iterative ensemble-based variational method in nonlinear problems
Shin'ya Nakano
Nonlin. Processes Geophys., 28, 93–109, https://doi.org/10.5194/npg-28-93-2021,https://doi.org/10.5194/npg-28-93-2021, 2021
Short summary
A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective
Olivier Pannekoucke, Richard Ménard, Mohammad El Aabaribaoune, and Matthieu Plu
Nonlin. Processes Geophys., 28, 1–22, https://doi.org/10.5194/npg-28-1-2021,https://doi.org/10.5194/npg-28-1-2021, 2021
Short summary

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. 
Download
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.