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  • Author or Editor: Francisco J. Doblas-Reyes x
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Omar Bellprat
,
Javier García-Serrano
,
Neven S. Fučkar
,
François Massonnet
,
Virginie Guemas
, and
Francisco J. Doblas-Reyes
Full access
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
Marco Turco
,
Sonia Jerez
,
Markus G. Donat
,
Andrea Toreti
,
Sergio M. Vicente-Serrano
, and
Francisco J. Doblas-Reyes

Abstract

Accurate and timely drought information is essential to move from postcrisis to preimpact drought-risk management. A number of drought datasets are already available. They cover the last three decades and provide data in near–real time (using different sources), but they are all “deterministic” (i.e., single realization), and input and output data partly differ between them. Here we first evaluate the quality of long-term and continuous climate data for timely meteorological drought monitoring considering the standardized precipitation index. Then, by applying an ensemble approach, mimicking weather/climate prediction studies, we develop Drought Probabilistic (DROP), a new global land gridded dataset, in which an ensemble of observation-based datasets is used to obtain the best near-real-time estimate together with its associated uncertainty. This approach makes the most of the available information and brings it to the end users. The high-quality and probabilistic information provided by DROP is useful for monitoring applications, and may help to develop global policy decisions on adaptation priorities in alleviating drought impacts, especially in countries where meteorological monitoring is still challenging.

Free access
Juan C. Acosta Navarro
,
Pablo Ortega
,
Javier García-Serrano
,
Virginie Guemas
,
Etienne Tourigny
,
Rubén Cruz-García
,
François Massonnet
, and
Francisco J. Doblas-Reyes
Full access
Marco Turco
,
Sonia Jerez
,
Markus G. Donat
,
Andrea Toreti
,
Sergio M. Vicente-Serrano
, and
Francisco J. Doblas-Reyes
Full access
Neven S. Fučkar
,
François Massonnet
,
Virginie Guemas
,
Javier García-Serrano
,
Omar Bellprat
,
Mario Acosta
, and
Francisco J. Doblas-Reyes
Full access
Adrian M. Tompkins
,
María Inés Ortiz De Zárate
,
Ramiro I. Saurral
,
Carolina Vera
,
Celeste Saulo
,
William J. Merryfield
,
Michael Sigmond
,
Woo-Sung Lee
,
Johanna Baehr
,
Alain Braun
,
Amy Butler
,
Michel Déqué
,
Francisco J. Doblas-Reyes
,
Margaret Gordon
,
Adam A. Scaife
,
Yukiko Imada
,
Masayoshi Ishii
,
Tomoaki Ose
,
Ben Kirtman
,
Arun Kumar
,
Wolfgang A. Müller
,
Anna Pirani
,
Tim Stockdale
,
Michel Rixen
, and
Tamaki Yasuda
Open access
Nick Dunstone
,
Julia Lockwood
,
Balakrishnan Solaraju-Murali
,
Katja Reinhardt
,
Eirini E. Tsartsali
,
Panos J. Athanasiadis
,
Alessio Bellucci
,
Anca Brookshaw
,
Louis-Philippe Caron
,
Francisco J. Doblas-Reyes
,
Barbara Früh
,
Nube González-Reviriego
,
Silvio Gualdi
,
Leon Hermanson
,
Stefano Materia
,
Andria Nicodemou
,
Dario Nicolì
,
Klaus Pankatz
,
Andreas Paxian
,
Adam Scaife
,
Doug Smith
, and
Hazel E. Thornton

Abstract

The decadal time scale (∼1–10 years) bridges the gap between seasonal predictions and longer-term climate projections. It is a key planning time scale for users in many sectors as they seek to adapt to our rapidly changing climate. While significant advances in using initialized climate models to make skillful decadal predictions have been made in the last decades, including coordinated international experiments and multimodel forecast exchanges, few user-focused decadal climate services have been developed. Here we highlight the potential of decadal climate services using four case studies from a project led by four institutions that produce real-time decadal climate predictions. Working in co-development with users in agriculture, energy, infrastructure, and insurance sectors, four prototype climate service products were developed. This study describes the challenge of trying to match user needs with the current scientific capability. For example, the use of large ensembles (achieved via a multisystem approach) and skillfully predicted large-scale environmental conditions, are found to improve regional predictions, particularly in midlatitudes. For each climate service, a two-page “product sheet” template was developed that provides users with both a concise probabilistic forecast and information on retrospective performance. We describe the development cycle, where valuable feedback was obtained from a “showcase event” where a wider group of sector users were engaged. We conclude that for society to take full and rapid advantage of useful decadal climate services, easier and more timely access to decadal climate prediction data are required, along with building wider community expertise in their use.

Full access
Thomas Jung
,
Neil D. Gordon
,
Peter Bauer
,
David H. Bromwich
,
Matthieu Chevallier
,
Jonathan J. Day
,
Jackie Dawson
,
Francisco Doblas-Reyes
,
Christopher Fairall
,
Helge F. Goessling
,
Marika Holland
,
Jun Inoue
,
Trond Iversen
,
Stefanie Klebe
,
Peter Lemke
,
Martin Losch
,
Alexander Makshtas
,
Brian Mills
,
Pertti Nurmi
,
Donald Perovich
,
Philip Reid
,
Ian A. Renfrew
,
Gregory Smith
,
Gunilla Svensson
,
Mikhail Tolstykh
, and
Qinghua Yang

Abstract

The polar regions have been attracting more and more attention in recent years, fueled by the perceptible impacts of anthropogenic climate change. Polar climate change provides new opportunities, such as shorter shipping routes between Europe and East Asia, but also new risks such as the potential for industrial accidents or emergencies in ice-covered seas. Here, it is argued that environmental prediction systems for the polar regions are less developed than elsewhere. There are many reasons for this situation, including the polar regions being (historically) lower priority, with fewer in situ observations, and with numerous local physical processes that are less well represented by models. By contrasting the relative importance of different physical processes in polar and lower latitudes, the need for a dedicated polar prediction effort is illustrated. Research priorities are identified that will help to advance environmental polar prediction capabilities. Examples include an improvement of the polar observing system; the use of coupled atmosphere–sea ice–ocean models, even for short-term prediction; and insight into polar–lower-latitude linkages and their role for forecasting. Given the enormity of some of the challenges ahead, in a harsh and remote environment such as the polar regions, it is argued that rapid progress will only be possible with a coordinated international effort. More specifically, it is proposed to hold a Year of Polar Prediction (YOPP) from mid-2017 to mid-2019 in which the international research and operational forecasting communites will work together with stakeholders in a period of intensive observing, modeling, prediction, verification, user engagement, and educational activities.

Full access
Leon Hermanson
,
Doug Smith
,
Melissa Seabrook
,
Roberto Bilbao
,
Francisco Doblas-Reyes
,
Etienne Tourigny
,
Vladimir Lapin
,
Viatcheslav V. Kharin
,
William J. Merryfield
,
Reinel Sospedra-Alfonso
,
Panos Athanasiadis
,
Dario Nicoli
,
Silvio Gualdi
,
Nick Dunstone
,
Rosie Eade
,
Adam Scaife
,
Mark Collier
,
Terence O’Kane
,
Vassili Kitsios
,
Paul Sandery
,
Klaus Pankatz
,
Barbara Früh
,
Holger Pohlmann
,
Wolfgang Müller
,
Takahito Kataoka
,
Hiroaki Tatebe
,
Masayoshi Ishii
,
Yukiko Imada
,
Tim Kruschke
,
Torben Koenigk
,
Mehdi Pasha Karami
,
Shuting Yang
,
Tian Tian
,
Liping Zhang
,
Tom Delworth
,
Xiaosong Yang
,
Fanrong Zeng
,
Yiguo Wang
,
François Counillon
,
Noel Keenlyside
,
Ingo Bethke
,
Judith Lean
,
Jürg Luterbacher
,
Rupa Kumar Kolli
, and
Arun Kumar

Abstract

As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.

Full access