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Predicting the variations in climate for the coming 1–10 years is of great interest for decision makers, as this time horizon coincides with the strategic planning of stakeholders from climate-vulnerable sectors such as agriculture. This study attempts to illustrate the potential value of decadal predictions in the development of climate services by establishing interactions and collaboration with stakeholders concerned with food production and security. Building on our experience from interacting with users and the increased understanding of their needs gathered over the years through our participation in various European activities and initiatives, we developed a decadal forecast product that provides tailored and user-friendly information about multi-year dry conditions for the coming five years over global wheat harvesting regions. This study revealed that the coproduction approach, where the interaction between the user and climate service provider is established at an early stage of forecast product development, is a fundamental step to successfully provide useful and ultimately actionable information to the interested stakeholders. The study also provides insights that shed light on the reasons for the delayed entry of decadal predictions in the climate services discourse and practice, obtained from surveying climate scientists and discussing with decadal prediction experts. Finally, it shows the key challenges that this new source of climate information still faces. We would like to acknowledge financial support from the European Union’s Horizon 2020 Research and Innovation programme (MED-GOLD; Grant No. 776467, EUCP; Grant No. 776613 and FOCUS-Africa; Grant No. 869575). This study has also received support from C3S_34c (contract number: ECMWF/COPERNICUS/2019/ C3S_34c_DWD) of the Copernicus Climate Change Service (C3S) operated by ECMWF. We thank Angel G. Muñoz and an anonymous reviewer for their invaluable comments on the manuscript. BSM acknowledges additional financial support from the Marie Sklodowska-Curie fellowship (Grant No. 713673) and from a fellowship of ’la Caixa’ Foundation (ID 100010434). The fellowship code is LCF/BQ/IN17/11620038.

CSIndicators is an R package that gathers generalised methods for the flexible computation of climate-related indicators. Each method represents a specific mathematical approach which is combined with the possibility of selecting a flexible time period to define the indicator. This enables a wide range of possibilities for tailoring indicators to sectorial climate service applications. This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time scales. Additionally, this package is compatible with the CSTools R package for climate forecast post-processing. This package was developed in the context of H2020 MED-GOLD (776467), S2S4E (776787), VITIGEOSS (869565) projects and Horizon Europe ASPECT project (101081460).

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development. The results discussed in this article were supported by the project APPLICATE (727862), funded by the European Union's Horizon 2020 research and innovation programme. PO was additionally supported by the Spanish fellowship RYC-2017-22772. Peer Reviewed Article signat per 29 autors/es: Pablo Ortega (1), Edward W. Blockley (2), Morten Køltzow (3), François Massonnet (4), Irina Sandu (5), Gunilla Svensson (6), Juan C. Acosta Navarro (1), Gabriele Arduini (5), Lauriane Batté (7), Eric Bazile (7), Matthieu Chevallier (8), Rubén Cruz-García (1), Jonathan J. Day (5), Thierry Fichefet (4), Daniela Flocco (9), Mukesh Gupta (4), Kerstin Hartung (6,10), Ed Hawkins (9), Claudia Hinrichs (11), Linus Magnusson (5), Eduardo Moreno-Chamarro (1), Sergio Pérez-Montero (1), Leandro Ponsoni (4), Tido Semmler (11), Doug Smith (2), Jean Sterlin (4), Michael Tjernström (6), Ilona Välisuo (7,12), and Thomas Jung (11,13) // (1) Barcelona Supercomputing Center, Barcelona, Spain | (2) Met Office, Exeter, UK | (3) Norwegian Meteorological Institute, Oslo, Norway | (4) Université catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Louvain-la-Neuve, Belgium | (5) European Centre for Medium-Range Weather Forecasts, Reading, UK | (6) Department of Meteorology, Stockholm University, Stockholm, Sweden | (7) CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France | (8) Météo-France, Toulouse, France | (9) National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK. | (10) Now at: Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany | (11) Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany | (12) Now at: Meteorology Unit, Finnish Meteorological Institute, Helsinki, Finland | (13) Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). Decadal climate predictions are a relatively new source of climate information for inter-annual 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 multi-decadal variability index (AMV) and global near-surface air temperature anomalies (GSAT) generated from all the available retrospective decadal predictions contributing to the Coupled Model Intercomparison Project Phase 6 (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 sub-ensemble 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. This study has been performed in the framework of the C3S_34c contract (ECMWF/ COPERNICUS/2019/C3S_34c_DWD) of the Copernicus Climate Change Service (C3S) operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the European Commission H2020 EUCP project (Grant 776613). CDT thanks the funding by the Spanish Ministry for Science and Innovation (FPI PRE2019-088646). MGD is grateful for funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964. BSM acknowledges financial support from the Marie Sklodowska-Curie fellowship (Grant 713673) and from a fellowship of La Caixa Foundation (ID 100010434).

* This essay stems from international conferences on subseasonal to seasonal and seasonal to decadal prediction jointly convened by WWRP and WCRP in September 2018 in Boulder, Colorado: (www.wcrp-climate.org/s2s-s2d-2018-home). Adapted from “Current and Emerging Developments in Subseasonal to Decadal Prediction,”. Published Online in BAMS, June 2020. For the full, citable article, see DOI:10.1175 /BAMS-D-19-0037.1. © Copyright 2020 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). Tremendous recent progress in climate prediction on subseasonal to decadal time scales has been enabled by better observations, data assimilation, and models originating from the weather prediction and climate simulation communities together with ever-increasing computational power. World Climate Research Program (WCRP) efforts led initially to predictions one to two seasons ahead becoming part of the WMO operational infrastructure. More recently, a joint World Weather Research Program (WWRP) and WCRP Subseasonal to Seasonal Prediction Project has started tackling the weather–climate gap (from two weeks to a season). The NOAA-led Subseasonal Experiment project has similar aims. New frontiers have been enabled by Earth system models that represent the carbon and other biogeochemical cycles in addition to the physical climate system. As a result, skillful multiyear prediction is likely achievable for biogeochemical and ecological Earth system components. The ultimate collective subseasonal to seasonal (S2S; 2 weeks to season) and seasonal to decadal (S2D) endeavor is to improve the prediction of the spatial–temporal continuum connecting weather to climate through a coordinated, seamless, and integrated Earth system approach. The S2S and S2D communities share common scientific and technical challenges. This essay* synthesizes those commonalities across time scales and Earth system components, and from basic research to operational delivery. Peer Reviewed Article signat per 65 autors/es: 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.