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Deborah Verfaillie
,
Francisco J. Doblas-Reyes
,
Markus G. Donat
,
Núria Pérez-Zanón
,
Balakrishnan Solaraju-Murali
,
Verónica Torralba
, and
Simon Wild

Abstract

Decadal climate predictions are being increasingly used by stakeholders interested in the evolution of climate over the coming decade. However, investigating the added value of those initialized decadal predictions over other sources of information typically used by stakeholders generally relies on forecast accuracy, while probabilistic aspects, although crucial to users, are often overlooked. In this study, the quality of the near-surface air temperature from initialized predictions has been assessed in terms of reliability, an essential characteristic of climate simulation ensembles, and compared to the reliability of noninitialized simulations performed with the same model ensembles. Here, reliability is defined as the capability to obtain a true estimate of the forecast uncertainty from the ensemble spread. We show the limited added value of initialization in terms of reliability, the initialized predictions being significantly more reliable than their noninitialized counterparts only for specific regions and the first forecast year. By analyzing reliability for different forecast system ensembles, we further highlight the fact that the combination of models seems to play a more important role than the ensemble size of each individual forecast system. This is due to sampling different model errors related to model physics, numerics, and initialization approaches involved in the multimodel, allowing for a certain level of error compensation. Finally, this study demonstrates that all forecast system ensembles are affected by systematic biases and dispersion errors that affect the reliability. This set of errors makes bias correction and calibration necessary to obtain reliable estimates of forecast probabilities that can be useful to stakeholders.

Open access
Dragana Bojovic
,
Roberto Bilbao
,
Leandro B. Díaz
,
Markus Donat
,
Pablo Ortega
,
Yohan Ruprich-Robert
,
Balakrishnan Solaraju-Murali
,
Marta Terrado
,
Deborah Verfaillie
, and
Francisco Doblas-Reyes
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.

Restricted 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
William J. Merryfield
,
Johanna Baehr
,
Lauriane Batté
,
Emily J. Becker
,
Amy H. Butler
,
Caio A. S. Coelho
,
Gokhan Danabasoglu
,
Paul A. Dirmeyer
,
Francisco J. Doblas-Reyes
,
Daniela I. V. Domeisen
,
Laura Ferranti
,
Tatiana Ilynia
,
Arun Kumar
,
Wolfgang A. Müller
,
Michel Rixen
,
Andrew W. Robertson
,
Doug M. Smith
,
Yuhei Takaya
,
Matthias Tuma
,
Frederic Vitart
,
Christopher J. White
,
Mariano S. Alvarez
,
Constantin Ardilouze
,
Hannah Attard
,
Cory Baggett
,
Magdalena A. Balmaseda
,
Asmerom F. Beraki
,
Partha S. Bhattacharjee
,
Roberto Bilbao
,
Felipe M. de Andrade
,
Michael J. DeFlorio
,
Leandro B. Díaz
,
Muhammad Azhar Ehsan
,
Georgios Fragkoulidis
,
Sam Grainger
,
Benjamin W. Green
,
Momme C. Hell
,
Johnna M. Infanti
,
Katharina Isensee
,
Takahito Kataoka
,
Ben P. Kirtman
,
Nicholas P. Klingaman
,
June-Yi Lee
,
Kirsten Mayer
,
Roseanna McKay
,
Jennifer V. Mecking
,
Douglas E. Miller
,
Nele Neddermann
,
Ching Ho Justin Ng
,
Albert Ossó
,
Klaus Pankatz
,
Simon Peatman
,
Kathy Pegion
,
Judith Perlwitz
,
G. Cristina Recalde-Coronel
,
Annika Reintges
,
Christoph Renkl
,
Balakrishnan Solaraju-Murali
,
Aaron Spring
,
Cristiana Stan
,
Y. Qiang Sun
,
Carly R. Tozer
,
Nicolas Vigaud
,
Steven Woolnough
, and
Stephen Yeager
Full access
William J. Merryfield
,
Johanna Baehr
,
Lauriane Batté
,
Emily J. Becker
,
Amy H. Butler
,
Caio A. S. Coelho
,
Gokhan Danabasoglu
,
Paul A. Dirmeyer
,
Francisco J. Doblas-Reyes
,
Daniela I. V. Domeisen
,
Laura Ferranti
,
Tatiana Ilynia
,
Arun Kumar
,
Wolfgang A. Müller
,
Michel Rixen
,
Andrew W. Robertson
,
Doug M. Smith
,
Yuhei Takaya
,
Matthias Tuma
,
Frederic Vitart
,
Christopher J. White
,
Mariano S. Alvarez
,
Constantin Ardilouze
,
Hannah Attard
,
Cory Baggett
,
Magdalena A. Balmaseda
,
Asmerom F. Beraki
,
Partha S. Bhattacharjee
,
Roberto Bilbao
,
Felipe M. de Andrade
,
Michael J. DeFlorio
,
Leandro B. Díaz
,
Muhammad Azhar Ehsan
,
Georgios Fragkoulidis
,
Sam Grainger
,
Benjamin W. Green
,
Momme C. Hell
,
Johnna M. Infanti
,
Katharina Isensee
,
Takahito Kataoka
,
Ben P. Kirtman
,
Nicholas P. Klingaman
,
June-Yi Lee
,
Kirsten Mayer
,
Roseanna McKay
,
Jennifer V. Mecking
,
Douglas E. Miller
,
Nele Neddermann
,
Ching Ho Justin Ng
,
Albert Ossó
,
Klaus Pankatz
,
Simon Peatman
,
Kathy Pegion
,
Judith Perlwitz
,
G. Cristina Recalde-Coronel
,
Annika Reintges
,
Christoph Renkl
,
Balakrishnan Solaraju-Murali
,
Aaron Spring
,
Cristiana Stan
,
Y. Qiang Sun
,
Carly R. Tozer
,
Nicolas Vigaud
,
Steven Woolnough
, and
Stephen Yeager

Abstract

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

Free access