Articles | Volume 28, issue 3
https://doi.org/10.5194/npg-28-329-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-28-329-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jul 2021
Research article |
| 29 Jul 2021
| 29 Jul 2021
Improving the potential accuracy and usability of EURO-CORDEX estimates of future rainfall climate using frequentist model averaging
independent researcher: London, UK
Giuliana Barbato
Euro-Mediterranean Center on Climate Change (CMCC) Foundation, Via
Augusto Imperatore, 16, 73100, Lecce, Italy
Paola Mercogliano
Euro-Mediterranean Center on Climate Change (CMCC) Foundation, Via
Augusto Imperatore, 16, 73100, Lecce, Italy
Jaroslav Mysiak
Euro-Mediterranean Center on Climate Change (CMCC) Foundation, Via
Augusto Imperatore, 16, 73100, Lecce, Italy
Maximiliano Sassi
Risk Management Solutions Ltd, EC3R 7AG, London, UK
Related authors
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
Short summary
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
EGUsphere, https://doi.org/10.5194/egusphere-2022-287, https://doi.org/10.5194/egusphere-2022-287, 2022
Preprint archived
Short summary
Short summary
The present paper reports a detailed analysis of the observed and expected climate conditions over the Campania Region. Campania, as part of the Mediterranean area, is already testing relevant impacts related to climate change.
Philip J. Ward, James Daniell, Melanie Duncan, Anna Dunne, Cédric Hananel, Stefan Hochrainer-Stigler, Annegien Tijssen, Silvia Torresan, Roxana Ciurean, Joel C. Gill, Jana Sillmann, Anaïs Couasnon, Elco Koks, Noemi Padrón-Fumero, Sharon Tatman, Marianne Tronstad Lund, Adewole Adesiyun, Jeroen C. J. H. Aerts, Alexander Alabaster, Bernard Bulder, Carlos Campillo Torres, Andrea Critto, Raúl Hernández-Martín, Marta Machado, Jaroslav Mysiak, Rene Orth, Irene Palomino Antolín, Eva-Cristina Petrescu, Markus Reichstein, Timothy Tiggeloven, Anne F. Van Loon, Hung Vuong Pham, and Marleen C. de Ruiter
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
Short summary
Short summary
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Flood risk management relies on assessments performed using flood loss models of different complexities. We compared the performances of expert-based and empirical damage models on three major flood events in northern Italy. Our findings suggest that multivariate models have better potential to provide reliable damage estimates if extensive ancillary characterisation data are available. Expert-based approaches are better suited for transferability compared to empirically based approaches.
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Public–private partnerships (PPPs) have gained importance as a means of providing sustainable, equitable and affordable catastrophic natural hazard insurance. This paper reviews and summarizes the manifold legal background that influences the provision of insurance against natural catastrophes and examines how PPPs designed for sharing and transferring risk operate within the European regulatory constraints, illustrated on the example of the UK Flood Reinsurance Scheme.
Jaroslav Mysiak, Swenja Surminski, Annegret Thieken, Reinhard Mechler, and Jeroen Aerts
Nat. Hazards Earth Syst. Sci., 16, 2189–2193, https://doi.org/10.5194/nhess-16-2189-2016, https://doi.org/10.5194/nhess-16-2189-2016, 2016
Short summary
Short summary
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
Short summary
Short summary
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
Nat. Hazards Earth Syst. Sci., 14, 2157–2163, https://doi.org/10.5194/nhess-14-2157-2014, https://doi.org/10.5194/nhess-14-2157-2014, 2014
Related subject area
Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Prognostic assumed-probability-density-function (distribution density function) approach: further generalization and demonstrations
Bridging classical data assimilation and optimal transport: the 3D-Var case
Improving ensemble data assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC)
Evolution of small-scale turbulence at large Richardson numbers
How far can the statistical error estimation problem be closed by collocated data?
Using orthogonal vectors to improve the ensemble space of the ensemble Kalman filter and its effect on data assimilation and forecasting
Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning
Toward a multivariate formulation of the parametric Kalman filter assimilation: application to a simplified chemical transport model
Data-driven reconstruction of partially observed dynamical systems
Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset
Applying prior correlations for ensemble-based spatial localization
A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter
Ensemble Riemannian data assimilation: towards large-scale dynamical systems
Inferring the instability of a dynamical system from the skill of data assimilation exercises
Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz 96 system
Ensemble Riemannian data assimilation over the Wasserstein space
An early warning sign of critical transition in the Antarctic ice sheet – a data-driven tool for a spatiotemporal tipping point
Behavior of the iterative ensemble-based variational method in nonlinear problems
A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective
A method for predicting the uncompleted climate transition process
Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst
Correcting for model changes in statistical postprocessing – an approach based on response theory
Brief communication: Residence time of energy in the atmosphere
Seasonal statistical–dynamical prediction of the North Atlantic Oscillation by probabilistic post-processing and its evaluation
Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems
Order of operation for multi-stage post-processing of ensemble wind forecast trajectories
Jun-Ichi Yano
Nonlin. Processes Geophys., 31, 359–380, https://doi.org/10.5194/npg-31-359-2024, https://doi.org/10.5194/npg-31-359-2024, 2024
Short summary
Short summary
A methodology for directly predicting the time evolution of the assumed parameters for the distribution densities based on the Liouville equation, as proposed earlier, is extended to multidimensional cases and to cases in which the systems are constrained by integrals over a part of the variable range. The extended methodology is tested against a convective energy-cycle system as well as the Lorenz strange attractor.
Marc Bocquet, Pierre J. Vanderbecken, Alban Farchi, Joffrey Dumont Le Brazidec, and Yelva Roustan
Nonlin. Processes Geophys., 31, 335–357, https://doi.org/10.5194/npg-31-335-2024, https://doi.org/10.5194/npg-31-335-2024, 2024
Short summary
Short summary
A novel approach, optimal transport data assimilation (OTDA), is introduced to merge DA and OT concepts. By leveraging OT's displacement interpolation in space, it minimises mislocation errors within DA applied to physical fields, such as water vapour, hydrometeors, and chemical species. Its richness and flexibility are showcased through one- and two-dimensional illustrations.
Man-Yau Chan
Nonlin. Processes Geophys., 31, 287–302, https://doi.org/10.5194/npg-31-287-2024, https://doi.org/10.5194/npg-31-287-2024, 2024
Short summary
Short summary
Forecasts have uncertainties. It is thus essential to reduce these uncertainties. Such reduction requires uncertainty quantification, which often means running costly models multiple times. The cost limits the number of model runs and thus the quantification’s accuracy. This study proposes a technique that utilizes users’ knowledge of forecast uncertainties to improve uncertainty quantification. Tests show that this technique improves uncertainty reduction.
Lev Ostrovsky, Irina Soustova, Yuliya Troitskaya, and Daria Gladskikh
Nonlin. Processes Geophys., 31, 219–227, https://doi.org/10.5194/npg-31-219-2024, https://doi.org/10.5194/npg-31-219-2024, 2024
Short summary
Short summary
The nonstationary kinetic model of turbulence is used to describe the evolution and structure of the upper turbulent layer with the parameters taken from in situ observations. As an example, we use a set of data for three cruises made in different areas of the world ocean. With the given profiles of current shear and buoyancy frequency, the theory yields results that satisfactorily agree with the measurements of the turbulent dissipation rate.
Annika Vogel and Richard Ménard
Nonlin. Processes Geophys., 30, 375–398, https://doi.org/10.5194/npg-30-375-2023, https://doi.org/10.5194/npg-30-375-2023, 2023
Short summary
Short summary
Accurate estimation of the error statistics required for data assimilation remains an ongoing challenge, as statistical assumptions are required to solve the estimation problem. This work provides a conceptual view of the statistical error estimation problem in light of the increasing number of available datasets. We found that the total number of required assumptions increases with the number of overlapping datasets, but the relative number of error statistics that can be estimated increases.
Yung-Yun Cheng, Shu-Chih Yang, Zhe-Hui Lin, and Yung-An Lee
Nonlin. Processes Geophys., 30, 289–297, https://doi.org/10.5194/npg-30-289-2023, https://doi.org/10.5194/npg-30-289-2023, 2023
Short summary
Short summary
In the ensemble Kalman filter, the ensemble space may not fully capture the forecast errors due to the limited ensemble size and systematic model errors, which affect the accuracy of analysis and prediction. This study proposes a new algorithm to use cost-free pseudomembers to expand the ensemble space effectively and improve analysis accuracy during the analysis step, without increasing the ensemble size during forecasting.
Eugenia Kalnay, Travis Sluka, Takuma Yoshida, Cheng Da, and Safa Mote
Nonlin. Processes Geophys., 30, 217–236, https://doi.org/10.5194/npg-30-217-2023, https://doi.org/10.5194/npg-30-217-2023, 2023
Short summary
Short summary
Strongly coupled data assimilation (SCDA) generates coherent integrated Earth system analyses by assimilating the full Earth observation set into all Earth components. We describe SCDA based on the ensemble Kalman filter with a hierarchy of coupled models, from a coupled Lorenz to the Climate Forecast System v2. SCDA with a sufficiently large ensemble can provide more accurate coupled analyses compared to weakly coupled DA. The correlation-cutoff method can compensate for a small ensemble size.
Antoine Perrot, Olivier Pannekoucke, and Vincent Guidard
Nonlin. Processes Geophys., 30, 139–166, https://doi.org/10.5194/npg-30-139-2023, https://doi.org/10.5194/npg-30-139-2023, 2023
Short summary
Short summary
This work is a theoretical contribution that provides equations for understanding uncertainty prediction applied in air quality where multiple chemical species can interact. A simplified minimal test bed is introduced that shows the ability of our equations to reproduce the statistics estimated from an ensemble of forecasts. While the latter estimation is the state of the art, solving equations is numerically less costly, depending on the number of chemical species, and motivates this research.
Pierre Tandeo, Pierre Ailliot, and Florian Sévellec
Nonlin. Processes Geophys., 30, 129–137, https://doi.org/10.5194/npg-30-129-2023, https://doi.org/10.5194/npg-30-129-2023, 2023
Short summary
Short summary
The goal of this paper is to obtain probabilistic predictions of a partially observed dynamical system without knowing the model equations. It is illustrated using the three-dimensional Lorenz system, where only two components are observed. The proposed data-driven procedure is low-cost, is easy to implement, uses linear and Gaussian assumptions and requires only a small amount of data. It is based on an iterative linear Kalman smoother with a state augmentation.
Elia Gorokhovsky and Jeffrey L. Anderson
Nonlin. Processes Geophys., 30, 37–47, https://doi.org/10.5194/npg-30-37-2023, https://doi.org/10.5194/npg-30-37-2023, 2023
Short summary
Short summary
Older observations of the Earth system sometimes lack information about the time they were taken, posing problems for analyses of past climate. To begin to ameliorate this problem, we propose new methods of varying complexity, including methods to estimate the distribution of the offsets between true and reported observation times. The most successful method accounts for the nonlinearity in the system, but even the less expensive ones can improve data assimilation in the presence of time error.
Chu-Chun Chang and Eugenia Kalnay
Nonlin. Processes Geophys., 29, 317–327, https://doi.org/10.5194/npg-29-317-2022, https://doi.org/10.5194/npg-29-317-2022, 2022
Short summary
Short summary
This study introduces a new approach for enhancing the ensemble data assimilation (DA), a technique that combines observations and forecasts to improve numerical weather predictions. Our method uses the prescribed correlations to suppress spurious errors, improving the accuracy of DA. The experiments on the simplified atmosphere model show that our method has comparable performance to the traditional method but is superior in the early stage and is more computationally efficient.
Andrey A. Popov, Amit N. Subrahmanya, and Adrian Sandu
Nonlin. Processes Geophys., 29, 241–253, https://doi.org/10.5194/npg-29-241-2022, https://doi.org/10.5194/npg-29-241-2022, 2022
Short summary
Short summary
Numerical weather prediction requires the melding of both computational model and data obtained from sensors such as satellites. We focus on one algorithm to accomplish this. We aim to aid its use by additionally supplying it with data obtained from separate models that describe the average behavior of the computational model at any given time. We show that our approach outperforms the standard approaches to this problem.
Sagar K. Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys., 29, 77–92, https://doi.org/10.5194/npg-29-77-2022, https://doi.org/10.5194/npg-29-77-2022, 2022
Short summary
Short summary
The outputs from Earth system models are optimally combined with satellite observations to produce accurate forecasts through a process called data assimilation. Many existing data assimilation methodologies have some assumptions regarding the shape of the probability distributions of model output and observations, which results in forecast inaccuracies. In this paper, we test the effectiveness of a newly proposed methodology that relaxes such assumptions about high-dimensional models.
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649, https://doi.org/10.5194/npg-28-633-2021, https://doi.org/10.5194/npg-28-633-2021, 2021
Short summary
Short summary
Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate forecast. These properties are often used to inform the design of data assimilation (DA), a method used to estimate the exact initial conditions. However, obtaining the instability properties is burdensome for complex problems, both numerically and analytically. Here, we suggest a different viewpoint. We show that the skill of DA can be used to infer the instability properties of a dynamical system.
Zofia Stanley, Ian Grooms, and William Kleiber
Nonlin. Processes Geophys., 28, 565–583, https://doi.org/10.5194/npg-28-565-2021, https://doi.org/10.5194/npg-28-565-2021, 2021
Short summary
Short summary
In weather forecasting, observations are incorporated into a model of the atmosphere through a process called data assimilation. Sometimes observations in one location may impact the weather forecast in another faraway location in undesirable ways. The impact of distant observations on the forecast is mitigated through a process called localization. We propose a new method for localization when a model has multiple length scales, as in a model spanning both the ocean and the atmosphere.
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
Short summary
Data assimilation aims to improve hydrologic and weather forecasts by combining available information from Earth system models and observations. The classical approaches to data assimilation usually proceed with some preconceived assumptions about the shape of their probability distributions. As a result, when such assumptions are invalid, the forecast accuracy suffers. In the proposed methodology, we relax such assumptions and demonstrate improved performance.
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
Short summary
This paper introduces a tool for data-driven discovery of early warning signs of critical transitions in ice shelves from remote sensing data. Our directed spectral clustering method considers an asymmetric affinity matrix along with the associated directed graph Laplacian. We applied our approach to reprocessing the ice velocity data and remote sensing satellite images of the Larsen C ice shelf.
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
Short summary
The ensemble-based variational method is a method for solving nonlinear data assimilation problems by using an ensemble of multiple simulation results. Although this method is derived based on a linear approximation, highly uncertain problems, in which system nonlinearity is significant, can also be solved by applying this method iteratively. This paper reformulated this iterative algorithm to analyze its behavior in high-dimensional nonlinear problems and discuss the convergence.
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
Short summary
Numerical weather prediction involves numerically solving the mathematical equations, which describe the geophysical flow, by transforming them so that they can be computed. Through this transformation, it appears that the equations actually solved by the machine are then a modified version of the original equations, introducing an error that contributes to the model error. This work helps to characterize the covariance of the model error that is due to this modification of the equations.
Pengcheng Yan, Guolin Feng, Wei Hou, and Ping Yang
Nonlin. Processes Geophys., 27, 489–500, https://doi.org/10.5194/npg-27-489-2020, https://doi.org/10.5194/npg-27-489-2020, 2020
Short summary
Short summary
A system transiting from one stable state to another has to experience a period. Can we predict the end moment (state) if the process has not been completed? This paper presents a method to solve this problem. This method is based on the quantitative relationship among the parameters, which is used to describe the transition process of the abrupt change. By using the historical data, we extract some parameters for predicting the uncompleted climate transition process.
Reinhold Hess
Nonlin. Processes Geophys., 27, 473–487, https://doi.org/10.5194/npg-27-473-2020, https://doi.org/10.5194/npg-27-473-2020, 2020
Short summary
Short summary
Forecasts of ensemble systems are statistically aligned to synoptic observations at DWD in order to provide support for warning decision management. Motivation and design consequences for extreme and rare meteorological events are presented. Especially for probabilities of severe wind gusts global logistic parameterisations are developed that generate robust statistical forecasts for extreme events, while local characteristics are preserved.
Jonathan Demaeyer and Stéphane Vannitsem
Nonlin. Processes Geophys., 27, 307–327, https://doi.org/10.5194/npg-27-307-2020, https://doi.org/10.5194/npg-27-307-2020, 2020
Short summary
Short summary
Postprocessing schemes used to correct weather forecasts are no longer efficient when the model generating the forecasts changes. An approach based on response theory to take the change into account without having to recompute the parameters based on past forecasts is presented. It is tested on an analytical model and a simple model of atmospheric variability. We show that this approach is effective and discuss its potential application for an operational environment.
Carlos Osácar, Manuel Membrado, and Amalio Fernández-Pacheco
Nonlin. Processes Geophys., 27, 235–237, https://doi.org/10.5194/npg-27-235-2020, https://doi.org/10.5194/npg-27-235-2020, 2020
Short summary
Short summary
We deduce that after a global thermal perturbation, the Earth's
atmosphere would need about a couple of months to come back to equilibrium.
André Düsterhus
Nonlin. Processes Geophys., 27, 121–131, https://doi.org/10.5194/npg-27-121-2020, https://doi.org/10.5194/npg-27-121-2020, 2020
Short summary
Short summary
Seasonal prediction of the of the North Atlantic Oscillation (NAO) has been improved in recent years by improving dynamical models and ensemble predictions. One step therein was the so-called sub-sampling, which combines statistical and dynamical predictions. This study generalises this approach and makes it much more accessible. Furthermore, it presents a new verification approach for such predictions.
Courtney Quinn, Terence J. O'Kane, and Vassili Kitsios
Nonlin. Processes Geophys., 27, 51–74, https://doi.org/10.5194/npg-27-51-2020, https://doi.org/10.5194/npg-27-51-2020, 2020
Short summary
Short summary
This study presents a novel method for reduced-rank data assimilation of multiscale highly nonlinear systems. Time-varying dynamical properties are used to determine the rank and projection of the system onto a reduced subspace. The variable reduced-rank method is shown to succeed over other fixed-rank methods. This work provides implications for performing strongly coupled data assimilation with a limited number of ensemble members on high-dimensional coupled climate models.
Nina Schuhen
Nonlin. Processes Geophys., 27, 35–49, https://doi.org/10.5194/npg-27-35-2020, https://doi.org/10.5194/npg-27-35-2020, 2020
Short summary
Short summary
We present a new way to adaptively improve weather forecasts by incorporating last-minute observation information. The recently measured error, together with a statistical model, gives us an indication of the expected future error of wind speed forecasts, which are then adjusted accordingly. This new technique can be especially beneficial for customers in the wind energy industry, where it is important to have reliable short-term forecasts, as well as providers of extreme weather warnings.
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...
