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Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi, This research was supported by the European Commission’s Horizon 2020 framework program (grant nos. 773051, 821205, H2020-MSCA-COFUND-2016-754433, H2020-MSCA-IF-2017-789630 and H2020-MSCA-IF-2016-747048), the National Aeronautics and Space Administration (grant nos. NNG14HH42I, 80NM0018D0004TS14, 80NSSC19K0056, 80NSSC19K0984, 80HQTR21CA005) as well as the EMIT project from the NASA Earth Venture Instrument program under the Earth Science Division of the Science Mission Directorate, the US DOE DE-SC0021302 project, the Ministerio de Economía y Competitividad of Spain (grant no. CGL2017-88911-R), the European Space Agency (grant no. ESA AO/1-10546/20/I-NB), the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550), the Helmholtz Association (grant no. VH-NG-1533), and the AXA Research Fund through the AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center (BSC)., Peer Reviewed, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Postprint (published version)

We introduce PARASO, a novel five-component fully coupled regional climate model over an Antarctic circumpolar domain covering the full Southern Ocean. The state-of-the-art models used are the fast Elementary Thermomechanical Ice Sheet model (f.ETISh) v1.7 (ice sheet), the Nucleus for European Modelling of the Ocean (NEMO) v3.6 (ocean), the Louvain-la-Neuve sea-ice model (LIM) v3.6 (sea ice), the COnsortium for Small-scale MOdeling (COSMO) model v5.0 (atmosphere) and its CLimate Mode (CLM) v4.5 (land), which are here run at a horizontal resolution close to 1/4°. One key feature of this tool resides in a novel two-way coupling interface for representing ocean–ice-sheet interactions, through explicitly resolved ice-shelf cavities. The impact of atmospheric processes on the Antarctic ice sheet is also conveyed through computed COSMO-CLM–f.ETISh surface mass exchange. In this technical paper, we briefly introduce each model's configuration and document the developments that were carried out in order to establish PARASO. The new offline-based NEMO–f.ETISh coupling interface is thoroughly described. Our developments also include a new surface tiling approach to combine open-ocean and sea-ice-covered cells within COSMO, which was required to make this model relevant in the context of coupled simulations in polar regions. We present results from a 2000–2001 coupled 2-year experiment. PARASO is numerically stable and fully operational. The 2-year simulation conducted without fine tuning of the model reproduced the main expected features, although remaining systematic biases provide perspectives for further adjustment and development., This research has been supported by the Fonds De La Recherche Scientifique – FNRS (grant no. O0100718F)., Peer Reviewed, Article signat per 23 autors/es: Charles Pelletier (1), Thierry Fichefet (1), Hugues Goosse (1), Konstanze Haubner (2), Samuel Helsen (3), Pierre-Vincent Huot (1), Christoph Kittel (4), François Klein (1), Sébastien Le clec'h (5), Nicole P. M. van Lipzig (3), Sylvain Marchi (3), François Massonnet (1), Pierre Mathiot (6,7), Ehsan Moravveji (3,8), Eduardo Moreno-Chamarro (9), Pablo Ortega (9), Frank Pattyn (2), Niels Souverijns (3,10), Guillian Van Achter (1), Sam Vanden Broucke (3), Alexander Vanhulle (5), Deborah Verfaillie (1), and Lars Zipf (2) // (1) Earth and Life Institute (ELI), UCLouvain, Louvain-la-Neuve, Belgium / (2) Laboratoire de Glaciologie, Université Libre de Bruxelles, Brussels, Belgium / (3) Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium / (4) Laboratory of Climatology, Department of Geography, SPHERES, University of Liège, Liège, Belgium / (5) Earth System Science and Departement Geografie, Vrije Universiteit Brussel, Brussels, Belgium, (6) Met Office, Exeter, United Kingdom / (7) Université Grenoble Alpes/CNRS/IRD/G-INP, IGE, Grenoble, France / (8) ICTS, KU Leuven, Leuven, Belgium / (9) Barcelona Supercomputing Center (BSC), Barcelona, Spain / (10) Environmental Modelling Unit, Flemish Institute for Technological Research (VITO), Mol, Belgium, Postprint (published version)

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., Peer Reviewed, Postprint (published version)

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, Postprint (published version)

© 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)., Peer Reviewed, Postprint (published version)

Earth system models have considerably increased their spatial resolution to solve more complex problems and achieve more realistic solutions. However, this generates an enormous amount of model data which requires proper management. Some Earth system models use inefficient sequential input/output (I/O) schemes that do not scale well when many parallel resources are used. In order to address this issue, the most commonly adopted approach is to use scalable parallel I/O solutions that offer both computational performance and efficiency. In this paper we analyse the I/O process of the European Centre for Medium-Range Weather Forecasts (ECMWF) operational Integrated Forecasting System (IFS) CY43R3. IFS can use two different output schemes: a parallel I/O server developed by Météo-France used operationally and an obsolete sequential I/O scheme. The latter is the only scheme that is being exposed by the OpenIFS variant of IFS. “Downstream” Earth system models that have adopted older versions of an IFS derivative as a component – such as the EC-Earth 3 climate model – also face a bottleneck due to the limited I/O capabilities and performance of the sequential output scheme. Moreover, it is often desirable to produce grid-point-space Network Common Data Format (NetCDF) files instead of the IFS native spectral and grid-point output fields in General Regularly-distributed Information in Binary form (GRIB), which requires the development of model-specific post-processing tools. We present the integration of the XML Input/Output Server (XIOS) 2.0 into IFS CY43R3. XIOS is an asynchronous Message Passing Interface (MPI) I/O server that offers features especially targeted at climate models: NetCDF output files, inline diagnostics, regridding, and, when properly configured, the capability to produce CMOR-compliant data. We therefore expect our work to reduce the computational cost of data-intensive (high-resolution) climate runs, thereby shortening the critical path of EC-Earth 4 e, This research has been supported by Horizon 2020 (ESiWACE2 (grant no. 823988) and PRIMAVERA (grant no. 641727))., Peer Reviewed, Postprint (published version)

The electron content distribution of the north and south polar ionosphere from 2001 to the beginning of 2019 is analyzed by using the UQRG global ionospheric map (GIM) of vertical total electron content (VTEC), computed every 15 min by UPC-IonSAT with a tomographic-kriging combined technique. We first show that the accuracy of UQRG GIM is slightly better than that of the GIMs of other analysis centers on the whole and also over both poles. Second, we show examples of polar VTEC features in UQRG GIM, previously reported by different authors and with higher-resolution techniques. Third, by means of an unsupervised clustering algorithm, learning vector quantization, we characterize the main features of the ionospheric electron content climatology, separately for the north and south polar regions., Postprint (published version)

Modes of climate variability affect global and regional climates on different spatio-temporal scales, and they have important impacts on human activities and ecosystems. As these modes are a useful tool for simplifying the understanding of the climate system, it is crucial that we gain improved knowledge of their long-term past evolution and interactions over time to contextualise their present and future behaviour. We review the literature focused on proxy-based reconstructions of modes of climate variability during the Holocene (i.e., the last 11.7 thousand years) with a special emphasis on i) proxy-based reconstruction methods; ii) available proxy-based reconstructions of the main modes of variability, i.e., El Niño Southern Oscillation, Pacific Decadal Variability, Atlantic Multidecadal Variability, the North Atlantic Oscillation, the Southern Annular Mode and the Indian Ocean Dipole; iii) major interactions between these modes; and iv) external forcing mechanisms related to the evolution of these modes. This review shows that modes of variability can be reconstructed using proxy-based records from a wide range of natural archives, but these reconstructions are scarce beyond the last millennium, partly due to the lack of robust chronologies with reduced dating uncertainties, technical issues related to proxy calibration, and difficulty elucidating their stationary impact (or not) on regional climates over time. While for each mode the available reconstructions tend to agree at mutidecadal timescales, they show notable disagreement on shorter timescales beyond the instrumental period. The reviewed evidence suggests that the intrinsic variability of modes can be modulated by external forcing, such as orbital, solar, volcanic, and anthropogenic forcing. The review also highlights some modes experience higher variability over the instrumental period, which is partly ascribed to anthropogenic forcing. These features stress the paramount importance of further studying, A.H. is supported by a Beatriu de Pinós –Marie Curie Cofund programme fellowship (2016 BP 00023) and HOLMODRIVE - North Atlantic Atmospheric Patterns influence on Western Iberia Climate: From the Lateglacial to the Present (PTDC/CTA-GEO/29029/2017) project funded by the Fundação para a Ciência e a Tecnologia, Portugal (FCT). A.H., S.G., and S.P-R. thank the Spanish research project PaleoModes (CGL2016-75281-C2-1-R) which provided some of their financial support. E.M.-C. contribution was funded by the project PARAMOUR (30454083) from the EOS program by the F.R.SFNRS. CM-P is supported by the Royal Society (ref: DH150185). P.O. contribution has been supported by the Ramon y Cajal senior tenure programme from the Spanish Ministry of Economy and Competitiveness. L.C-B. acknowledges support from the ERC-funded project GC2.0 (Global Change 2.0: Unlocking the past for a clearer future, grant number 694481). The Spanish PTI PolarCSIC ( https://www.polarcsic.es ) is also acknowledged for providing partial financial support to S.G. R.M. was partially supported by the Swedish Research Council (grant DNR2013-8421). A. P.'s work was supported by a Science Foundation Ireland Career Development Award (17/CDA/4695), a research centre award (12/RC/2289_P2), an investigator award (16/IA/4520), and a Marine Research Programme funded by the Irish Government, cofinanced by the European Regional Development Fund (Grant-Aid Agreement No. PBA/CC/18/01). A.A.S. was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. J.S. is supported by the strategic research program of Modelling the Regional and Global Earth system (MERGE) hosted by the Faculty of Science at Lund University. D.S. is supported by Blue-Action (European Union's Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union's Horizon 2020 research and innovation programme under grant agreement no 776613) projects as well as by the French national program LEFE/INSU, Peer Reviewed, Postprint (author's final draft)

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org., Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were, Peer Reviewed, Postprint (published version)

* 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., Postprint (published version)

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven, DMS, AAS, NJD, LH and RE were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Econom´ıa y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Sk´lodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE.WAM, HP, KMand KP were supported by the German FederalMinistry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). NK, IB, FC and YW were supported by the Norwegian Research Council projects SFE (grant 270733) the Nordic Center of excellent ARCPATH (grant 76654) and the Trond Mohn Foundation, under the project number : BFS2018TMT01 and received grants for computer time from the Norwegian Program for supercomputing (NOTUR2, NN9039K) and storage grants (NORSTORE, NS9039K). JM, LFB and DS are supported by Blue-Action (European Union Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no 776613) projects. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 Grant OCE-1243015., Peer Reviewed, Postprint (author's final draft)

Uncovering meaningful regularities in complex oscillatory signals is a challenging problem with applications across a wide range of disciplines.Here, we present a novel approach, based on the Hilbert transform (HT).We show that temporal periodicity can be uncovered by averagingthe signal in a moving window of appropriated length,t, before applying the HT. As a case study, we investigate global gridded surface airtemperature (SAT) datasets. By analyzing the variation of the mean rotation period,T, of the Hilbert phase as a function oft, we discoverwell-de ned plateaus. In many geographical regions, the plateau corresponds to the expected 1-yr solar cycle; however, in regions where SATdynamics is highly irregular, the plateaus reveal non-trivial periodicities, which can be interpreted in terms of climatic phenomena such asEl Niño. In these regions, we also nd that Fourier analysis is unable to detect the periodicity that emerges whentincreases and graduallywashes out SAT variability. The values ofTobtained for di erentts are then given to a standard machine learning algorithm. The resultsdemonstrate that these features are informative and constitute a new approach for SAT time series classi cation. To support these results, weanalyze the synthetic time series generated with a simple model and con rm that our method extracts information that is fully consistent withour knowledge of the model that generates the data. Remarkably, the variation ofTwithtin the synthetic data is similar to that observed in thereal SAT data. This suggests that our model contains the basic mechanisms underlying the unveiled periodicities. Our results demonstrate thatHilbert analysis combined with temporal averaging is a powerful new tool for discovering hidden temporal regularity in complex oscillatorysignals., Peer Reviewed, Postprint (author's final draft)

The Med-CORDEX initiative is a unique framework in which the research community makes use of regional earth system models to increase the reliability of past and future regional climate information. The Mediterranean is expected to be one of the most prominent and vulnerable climate change “hot spots” of the 21st century, and the physical mechanisms underlying this finding are still not clear. Furthermore complex interactions and feedbacks involving ocean-atmosphere-land-biogeochemical processes play a prominent role in modulating the climate and environment of the Mediterranean region on a range of spatial and temporal scales. Therefore it is critical to provide robust climate change information for use in Vulnerability/Impact/Adaptation assessment studies considering the Mediterranean as a fully coupled environmental system. The Med-CORDEX initiative aims at coordinating the Mediterranean climate modeling community towards the development of fully coupled regional climate simulations, improving all relevant components of the system, from atmosphere and ocean dynamics to land surface, hydrology and biogeochemical processes. The primary goals of Med-CORDEX are to improve understanding of past climate variability and trends, and to provide more accurate and reliable future projections, assessing in a quantitative and robust way the added value of using high resolution and coupled regional climate models. The coordination activities and the scientific outcomes of Med-CORDEX can produce an important framework to foster the development of regional earth system models in several key regions worldwide., Peer Reviewed, Postprint (published version)

In the context of the ERAINT project, a number of human-in-the-loop simulations were conducted to study the implications of integrating a remote piloted aircraft system (RPAS) into the managed airspace. For the purpose of this study, the RPAS was assumed to be involved in surveillance missions, flying a large-endurance scan pattern. The area of surveillance was selected such that it crossed an active airway for approaches. Furthermore, the simulations also included situations in which the RPAS was involved in an emergency situation such as lost links and engine failures. From previous work, the results obtained from these simulations showed that the air traffic controllers (ATCs) could successfully manage the required separations for airspace safety assurance. Nevertheless, the number of total commands issued increased, in particular the number of requirements for altitude changes, and especially those destined to commercial aircraft. Given an aircraft's flying altitude impact on performance, one question rapidly arose: Is there an increase in flight costs for airlines as a result of the increased number of ATC commands issued to provide the necessary separation with the RPAS? For this purpose two metrics relating to time and fuel are defined such that they are targeted on quantifying the economic impact for commercial air traffic resulting from the presence of a RPAS. Both metrics are computed from the ADS-B traces of all aircraft in the sector and the results of each simulation are compared with those of a baseline simulation, in which the RPAS is not present. To improve on the comparison between each simulation's results we complement this study with a statistical analysis of the available data samples using paired t-test analyses to determine if the observed differences are statistically significant or simply due to random variability., Peer Reviewed, Postprint (published version)

The Hilbert transform is a well-known tool of time series analysis that has been widely used to investigate oscillatory signals that resemble a noisy periodic oscillation, because it allows instantaneous phase and frequency to be estimated, which in turn uncovers interesting properties of the underlying process that generates the signal. Here we use this tool to analyze atmospheric data: we consider daily-averaged Surface Air Temperature (SAT) time series recorded over a regular grid of locations covering the Earth’s surface. From each SAT time series, we calculate the instantaneous frequency time series by considering the Hilbert analytic signal. The properties of the obtained frequency data set are investigated by plotting the map of the average frequency and the map of the standard deviation of the frequency fluctuations. The average frequency map reveals well-defined large-scale structures: in the extra-tropics, the average frequency in general corresponds to the expected one-year period of solar forcing, while in the tropics, a different behaviour is found, with particular regions having a faster average frequency. In the standard deviation map, large-scale structures are also found, which tend to be located over regions of strong annual precipitation. Our results demonstrate that Hilbert analysis of SAT time-series uncovers meaningful information, and is therefore a promising tool for the study of other climatological variables., Peer Reviewed, Postprint (published version)

We analyze climatological data from a complex networks perspective, using techniques of nonlinear time series symbolic analysis. Specifically, we employ ordinal patterns and binary representations to analyze monthly averaged surface air temperature (SAT) anomalies. By computing the mutual information of the time series in regular grid points covering the Earth’s surface and then performing global thresholding, we construct climate networks that uncover short-term memory processes, as well as long ones (5–6 yr). Our results suggest that the time variability of the SAT anomalies is determined by patterns of oscillatory behavior that repeat from time to time with a periodicity related to intraseasonal variations and to El Niño on seasonal to interannual time scales. The present work is located at the triple intersection of three highly active interdisciplinary research fields in nonlinear science: symbolic methods for nonlinear time series analysis, network theory, and non linear processes in the earth climate. While a lot of effort is being done in order to improve our understanding of natural complex systems, with many different methods for mapping time series to network representations being investigated and employed in complex systems such as the human brain, our work is the first one aimed at characterizing the global climate network in terms of oscillatory patterns that tend to repeat from time to time, with various time scales. By mapping these processes into a global network, using ordinal patterns and binary representations, we find that the structure of the network changes drastically at different time scales., Peer Reviewed, Postprint (published version)

La création des modèles numériques utilisant les équations de la dinamique des fluides permet de modéliser des situations climatologiques futures. Une des problématiques actuelles est d'améliorer l'échelle du calcul, spatiale et temporelle, et profondire dans les interactions océan-atmosphère, dû a des modelisations coupolées. Nous avons realisé le couplage entre SYMPHONIE et Aladin, en utilisant le modèle coupleur Oasis 3. Pendant l'étude nous avons utilisé deux simulations differents. La primiere u les flux mer-atmophère sont calculès par l'atmosphère et la deuxieme où ces flux sont calculés pour le modèle océanique. Depuis du lourde treavaille d'ensamblage des logicielles, nous avons étudié differents transferts de mases d'eau dans la Mer Méditerranée qui sont importants sur les interactions mer.atmpsphère et, tout suit, sur le developpement de la climatologie., Outgoing