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Lucy M. Bricheno
,
Albert Soret
,
Judith Wolf
,
Oriol Jorba
, and
Jose Maria Baldasano

Abstract

Accurate representation of wind forcing and mean sea level pressure is important for modeling waves and surges. This is especially important for complex coastal zone areas. The Weather Research and Forecasting (WRF) model has been run at 12-, 4-, and 1.33-km resolution for a storm event over the Irish Sea. The outputs were used to force the coupled hydrodynamic and the Proudman Oceanographic Laboratory Coastal Ocean Modeling System (POLCOMS)–Wave Model (WAM) and the effect on storm surge and waves has been assessed. An improvement was observed in the WRF model pressure and wind speed when moving from 12- to 4-km resolution with errors in wind speed decreasing more than 10% on average. When moving from 4 to 1.33 km no further significant improvement was observed. The atmospheric model results at 12 and 4 km were then applied to the ocean model. Wave direction was seen to improve with increased ocean model resolution, and higher-resolution forcing was found to generally increase the wave height over the Irish Sea by up to 40 cm in places. Improved clustering of wave direction was observed when 4-km meteorological forcing was used. Large differences were seen in the coastal zone because of the improved representation of the coastline and, in turn, the atmospheric boundary layer. The combination of high-resolution atmospheric forcing and a coupled wave–surge model gave the best result.

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Marta Terrado
,
Llorenç Lledó
,
Dragana Bojovic
,
Asun Lera St. Clair
,
Albert Soret
,
Francisco J. Doblas-Reyes
,
Rodrigo Manzanas
,
Daniel San-Martín
, and
Isadora Christel

Abstract

Climate predictions, from three weeks to a decade into the future, can provide invaluable information for climate-sensitive socioeconomic sectors, such as renewable energy, agriculture, or insurance. However, communicating and interpreting these predictions is not straightforward. Barriers hindering user uptake include a terminology gap between climate scientists and users, the difficulties of dealing with probabilistic outcomes for decision-making, and the lower skill of climate predictions compared to the skill of weather forecasts. This paper presents a gaming approach to break communication and understanding barriers through the application of the Weather Roulette conceptual framework. In the game, the player can choose between two forecast options, one that uses ECMWF seasonal predictions against one using climatology-derived probabilities. For each forecast option, the bet is spread proportionally to the predicted probabilities, either in a single year game or a game for the whole period of 33 past years. This paper provides skill maps of forecast quality metrics commonly used by the climate prediction community (e.g., ignorance skill score and ranked probability skill score), which in the game are linked to metrics easily understood by the business sector (e.g., interest rate and return on investment). In a simplified context, we illustrate how in skillful regions the economic benefits of using ECMWF predictions arise in the long term and are higher than using climatology. This paper provides an example of how to convey the usefulness of climate predictions and transfer the knowledge from climate science to potential users. If applied, this approach could provide the basis for a better integration of knowledge about climate anomalies into operational and managerial processes.

Free access
Carlos Delgado-Torres
,
Markus G. Donat
,
Nube Gonzalez-Reviriego
,
Louis-Philippe Caron
,
Panos J. Athanasiadis
,
Pierre-Antoine Bretonnière
,
Nick J. Dunstone
,
An-Chi Ho
,
Dario Nicoli
,
Klaus Pankatz
,
Andreas Paxian
,
Núria Pérez-Zanón
,
Margarida Samsó Cabré
,
Balakrishnan Solaraju-Murali
,
Albert Soret
, and
Francisco J. Doblas-Reyes

Abstract

Decadal climate predictions are a relatively new source of climate information for interannual to decadal time scales, which is of increasing interest for users. Forecast quality assessment is essential to identify windows of opportunity (e.g., variables, regions, and forecast periods) with skill that can be used to develop climate services to inform users in several sectors and define benchmarks for improvements in forecast systems. This work evaluates the quality of multi-model forecasts of near-surface air temperature, precipitation, Atlantic multidecadal variability index (AMV), and global near-surface air temperature (GSAT) anomalies generated from all the available retrospective decadal predictions contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6). The predictions generally show high skill in predicting temperature, AMV, and GSAT, while the skill is more limited for precipitation. Different approaches for generating a multi-model forecast are compared, finding small differences between them. The multi-model ensemble is also compared to the individual forecast systems. The best system usually provides the highest skill. However, the multi-model ensemble is a reasonable choice for not having to select the best system for each particular variable, forecast period, and region. Furthermore, the decadal predictions are compared to the historical simulations to estimate the impact of initialization. An added value is found for several ocean and land regions for temperature, AMV, and GSAT, while it is more reduced for precipitation. Moreover, the full ensemble is compared to a subensemble to measure the impact of the ensemble size. Finally, the implications of these results in a climate services context, which requires predictions issued in near–real time, are discussed.

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Daniela I. V. Domeisen
,
Christopher J. White
,
Hilla Afargan-Gerstman
,
Ángel G. Muñoz
,
Matthew A. Janiga
,
Frédéric Vitart
,
C. Ole Wulff
,
Salomé Antoine
,
Constantin Ardilouze
,
Lauriane Batté
,
Hannah C. Bloomfield
,
David J. Brayshaw
,
Suzana J. Camargo
,
Andrew Charlton-Pérez
,
Dan Collins
,
Tim Cowan
,
Maria del Mar Chaves
,
Laura Ferranti
,
Rosario Gómez
,
Paula L. M. González
,
Carmen González Romero
,
Johnna M. Infanti
,
Stelios Karozis
,
Hera Kim
,
Erik W. Kolstad
,
Emerson LaJoie
,
Llorenç Lledó
,
Linus Magnusson
,
Piero Malguzzi
,
Andrea Manrique-Suñén
,
Daniele Mastrangelo
,
Stefano Materia
,
Hanoi Medina
,
Lluís Palma
,
Luis E. Pineda
,
Athanasios Sfetsos
,
Seok-Woo Son
,
Albert Soret
,
Sarah Strazzo
, and
Di Tian

Abstract

Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on time scales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on time scales of 3–4 weeks, while this time scale is 2–3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. ­Tropical cyclones, on the other hand, can exhibit probabilistic predictability on time scales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden–Julian oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event-dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

Full access
Christopher J. White
,
Daniela I. V. Domeisen
,
Nachiketa Acharya
,
Elijah A. Adefisan
,
Michael L. Anderson
,
Stella Aura
,
Ahmed A. Balogun
,
Douglas Bertram
,
Sonia Bluhm
,
David J. Brayshaw
,
Jethro Browell
,
Dominik Büeler
,
Andrew Charlton-Perez
,
Xandre Chourio
,
Isadora Christel
,
Caio A. S. Coelho
,
Michael J. DeFlorio
,
Luca Delle Monache
,
Francesca Di Giuseppe
,
Ana María García-Solórzano
,
Peter B. Gibson
,
Lisa Goddard
,
Carmen González Romero
,
Richard J. Graham
,
Robert M. Graham
,
Christian M. Grams
,
Alan Halford
,
W. T. Katty Huang
,
Kjeld Jensen
,
Mary Kilavi
,
Kamoru A. Lawal
,
Robert W. Lee
,
David MacLeod
,
Andrea Manrique-Suñén
,
Eduardo S. P. R. Martins
,
Carolyn J. Maxwell
,
William J. Merryfield
,
Ángel G. Muñoz
,
Eniola Olaniyan
,
George Otieno
,
John A. Oyedepo
,
Lluís Palma
,
Ilias G. Pechlivanidis
,
Diego Pons
,
F. Martin Ralph
,
Dirceu S. Reis Jr.
,
Tomas A. Remenyi
,
James S. Risbey
,
Donald J. C. Robertson
,
Andrew W. Robertson
,
Stefan Smith
,
Albert Soret
,
Ting Sun
,
Martin C. Todd
,
Carly R. Tozer
,
Francisco C. Vasconcelos Jr.
,
Ilaria Vigo
,
Duane E. Waliser
,
Fredrik Wetterhall
, and
Robert G. Wilson

Abstract

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

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