Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-6115-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6115-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
04 Aug 2022
Model description paper |
| 04 Aug 2022
| 04 Aug 2022
Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information
Barcelona Supercomputing Center (BSC), Barcelona, Spain
Louis-Philippe Caron
Barcelona Supercomputing Center (BSC), Barcelona, Spain
Ouranos, 550 Sherbrooke St W, Montreal, Quebec H3A9, Canada
Silvia Terzago
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Turin, Italy
Bert Van Schaeybroeck
Royal Meteorological Institute of Belgium, Brussels, Belgium
Llorenç Lledó
Barcelona Supercomputing Center (BSC), Barcelona, Spain
Nicolau Manubens
Barcelona Supercomputing Center (BSC), Barcelona, Spain
Emmanuel Roulin
Royal Meteorological Institute of Belgium, Brussels, Belgium
M. Carmen Alvarez-Castro
Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Climate Simulations and Predictions division, Bologna, Italy
Lauriane Batté
CNRM, Université de Toulouse, Météo-France, CNRS,
Toulouse, France
Pierre-Antoine Bretonnière
Barcelona Supercomputing Center (BSC), Barcelona, Spain
Susana Corti
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Turin, Italy
Carlos Delgado-Torres
Barcelona Supercomputing Center (BSC), Barcelona, Spain
Marta Domínguez
Delegación territorial (DT) Cantabria, Agencia Estatal de
Meteorología (AEMET), Santander, Spain
Federico Fabiano
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Turin, Italy
Ignazio Giuntoli
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Turin, Italy
School of Geography, Earth, and Environment Sciences, University of
Birmingham, Birmingham, UK
Jost von Hardenberg
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Turin, Italy
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
Eroteida Sánchez-García
Delegación territorial (DT) Cantabria, Agencia Estatal de
Meteorología (AEMET), Santander, Spain
Verónica Torralba
Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Climate Simulations and Predictions division, Bologna, Italy
Deborah Verfaillie
Earth and Life Institute, Université catholique de Louvain,
Louvain-la-Neuve, Belgium
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Federico Fabiano, Paolo Davini, Virna L. Meccia, Giuseppe Zappa, Alessio Bellucci, Valerio Lembo, Katinka Bellomo, and Susanna Corti
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Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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Matteo Pesce, Alberto Viglione, Jost von Hardenberg, Larisa Tarasova, Stefano Basso, Ralf Merz, Juraj Parajka, and Rui Tong
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Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
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A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Hervé Petetin, Marc Guevara, Steven Compernolle, Dene Bowdalo, Pierre-Antoine Bretonnière, Santiago Enciso, Oriol Jorba, Franco Lopez, Albert Soret, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 23, 3905–3935, https://doi.org/10.5194/acp-23-3905-2023, https://doi.org/10.5194/acp-23-3905-2023, 2023
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This study analyses the potential of the TROPOMI space sensor for monitoring the variability of NO2 pollution over the Iberian Peninsula. A reduction of NO2 levels is observed during the weekend and in summer, especially over most urbanized areas, in agreement with surface observations. An enhancement of NO2 is found during summer with TROPOMI over croplands, potentially related to natural soil NO emissions, which illustrates the outstanding value of TROPOMI for complementing surface networks.
Stefano Della Fera, Federico Fabiano, Piera Raspollini, Marco Ridolfi, Ugo Cortesi, Flavio Barbara, and Jost von Hardenberg
Geosci. Model Dev., 16, 1379–1394, https://doi.org/10.5194/gmd-16-1379-2023, https://doi.org/10.5194/gmd-16-1379-2023, 2023
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Silvia Terzago, Giulio Bongiovanni, and Jost von Hardenberg
Hydrol. Earth Syst. Sci., 27, 519–542, https://doi.org/10.5194/hess-27-519-2023, https://doi.org/10.5194/hess-27-519-2023, 2023
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Rashed Mahmood, Markus G. Donat, Pablo Ortega, Francisco J. Doblas-Reyes, Carlos Delgado-Torres, Margarida Samsó, and Pierre-Antoine Bretonnière
Earth Syst. Dynam., 13, 1437–1450, https://doi.org/10.5194/esd-13-1437-2022, https://doi.org/10.5194/esd-13-1437-2022, 2022
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Near-term climate change projections are strongly affected by the uncertainty from internal climate variability. Here we present a novel approach to reduce such uncertainty by constraining decadal-scale variability in the projections using observations. The constrained ensembles show significant added value over the unconstrained ensemble in predicting global climate 2 decades ahead. We also show the applicability of regional constraints for attributing predictability to certain ocean regions.
Hervé Petetin, Dene Bowdalo, Pierre-Antoine Bretonnière, Marc Guevara, Oriol Jorba, Jan Mateu Armengol, Margarida Samso Cabre, Kim Serradell, Albert Soret, and Carlos Pérez Garcia-Pando
Atmos. Chem. Phys., 22, 11603–11630, https://doi.org/10.5194/acp-22-11603-2022, https://doi.org/10.5194/acp-22-11603-2022, 2022
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This study investigates the extent to which ozone forecasts provided by the Copernicus Atmospheric Monitoring Service (CAMS) can be improved using surface observations and state-of-the-art statistical methods. Through a case study over the Iberian Peninsula in 2018–2019, it unambiguously demonstrates the value of these methods for improving the raw CAMS O3 forecasts while at the same time highlighting the complexity of improving the detection of the highest O3 concentrations.
Valerio Lembo, Federico Fabiano, Vera Melinda Galfi, Rune Grand Graversen, Valerio Lucarini, and Gabriele Messori
Weather Clim. Dynam., 3, 1037–1062, https://doi.org/10.5194/wcd-3-1037-2022, https://doi.org/10.5194/wcd-3-1037-2022, 2022
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Eddies in mid-latitudes characterize the exchange of heat between the tropics and the poles. This exchange is largely uneven, with a few extreme events bearing most of the heat transported across latitudes in a season. It is thus important to understand what the dynamical mechanisms are behind these events. Here, we identify recurrent weather regime patterns associated with extreme transports, and we identify scales of mid-latitudinal eddies that are mostly responsible for the transport.
N. Hempelmann, C. Ehbrecht, E. Plesiat, G. Hobona, J. Simoes, D. Huard, T. J. Smith, U. S. McKnight, I. G. Pechlivanidis, and C. Alvarez-Castro
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 187–194, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-187-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-187-2022, 2022
Enza Di Tomaso, Jerónimo Escribano, Sara Basart, Paul Ginoux, Francesca Macchia, Francesca Barnaba, Francesco Benincasa, Pierre-Antoine Bretonnière, Arnau Buñuel, Miguel Castrillo, Emilio Cuevas, Paola Formenti, María Gonçalves, Oriol Jorba, Martina Klose, Lucia Mona, Gilbert Montané Pinto, Michail Mytilinaios, Vincenzo Obiso, Miriam Olid, Nick Schutgens, Athanasios Votsis, Ernest Werner, and Carlos Pérez García-Pando
Earth Syst. Sci. Data, 14, 2785–2816, https://doi.org/10.5194/essd-14-2785-2022, https://doi.org/10.5194/essd-14-2785-2022, 2022
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MONARCH reanalysis of desert dust aerosols extends the existing observation-based information for mineral dust monitoring by providing 3-hourly upper-air, surface and total column key geophysical variables of the dust cycle over Northern Africa, the Middle East and Europe, at a 0.1° horizontal resolution in a rotated grid, from 2007 to 2016. This work provides evidence of the high accuracy of this data set and its suitability for air quality and health and climate service applications.
Nicolau Pineda, Juan Carlos Peña, Xavier Soler, Montse Aran, and Núria Pérez-Zanón
Adv. Sci. Res., 19, 39–49, https://doi.org/10.5194/asr-19-39-2022, https://doi.org/10.5194/asr-19-39-2022, 2022
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Wildfire origins can be related to human activity or to natural phenomena, like lightning. Under favourable environmental conditions, lightning ignitions can develop into a fire. In the present study, we analyse the kind of weather that favours wildfires ignited by lightning in Catalonia. We have found that most fires occur under three types of weather. These results help to improve our understanding of lightning fires and are of great assistance to wildfire management agencies.
Paolo Davini, Federico Fabiano, and Irina Sandu
Weather Clim. Dynam., 3, 535–553, https://doi.org/10.5194/wcd-3-535-2022, https://doi.org/10.5194/wcd-3-535-2022, 2022
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In climate models, improvements obtained in the winter mid-latitude circulation following horizontal resolution increase are mainly caused by the more detailed representation of the mean orography. A high-resolution climate model with low-resolution orography might underperform compared to a low-resolution model with low-resolution orography. The absence of proper model tuning at high resolution is considered the potential reason behind such lack of improvements.
Joshua Dorrington, Kristian Strommen, and Federico Fabiano
Weather Clim. Dynam., 3, 505–533, https://doi.org/10.5194/wcd-3-505-2022, https://doi.org/10.5194/wcd-3-505-2022, 2022
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We investigate how well current state-of-the-art climate models reproduce the wintertime weather of the North Atlantic and western Europe by studying how well different "regimes" of weather are captured. Historically, models have struggled to capture these regimes, making it hard to predict future changes in wintertime extreme weather. We show models can capture regimes if the right method is used, but they show biases, partially as a result of biases in jet speed and eddy strength.
Matthieu Vernay, Matthieu Lafaysse, Diego Monteiro, Pascal Hagenmuller, Rafife Nheili, Raphaëlle Samacoïts, Deborah Verfaillie, and Samuel Morin
Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, https://doi.org/10.5194/essd-14-1707-2022, 2022
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This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Paolo Ghinassi, Federico Fabiano, and Susanna Corti
Weather Clim. Dynam., 3, 209–230, https://doi.org/10.5194/wcd-3-209-2022, https://doi.org/10.5194/wcd-3-209-2022, 2022
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In this work we examine the ability of global climate models in representing the atmospheric circulation in the upper troposphere, focusing on the eventual benefits of an increased horizontal resolution. Our results confirm that a higher horizontal resolution has a positive impact, especially in those models in which the resolution is increased in both the atmosphere and the ocean, whereas when the resolution is increased only in the atmosphere no substantial improvements are found.
Josep Cos, Francisco Doblas-Reyes, Martin Jury, Raül Marcos, Pierre-Antoine Bretonnière, and Margarida Samsó
Earth Syst. Dynam., 13, 321–340, https://doi.org/10.5194/esd-13-321-2022, https://doi.org/10.5194/esd-13-321-2022, 2022
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The Mediterranean has been identified as being more affected by climate change than other regions. We find that amplified warming during summer and annual precipitation declines are expected for the 21st century and that the magnitude of the changes will mainly depend on greenhouse gas emissions. By applying a method giving more importance to models with greater performance and independence, we find that the differences between the last two community modelling efforts are reduced in the region.
Elisa Brussolo, Elisa Palazzi, Jost von Hardenberg, Giulio Masetti, Gianna Vivaldo, Maurizio Previati, Davide Canone, Davide Gisolo, Ivan Bevilacqua, Antonello Provenzale, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 26, 407–427, https://doi.org/10.5194/hess-26-407-2022, https://doi.org/10.5194/hess-26-407-2022, 2022
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In this study, we evaluate the past, present and future quantity of groundwater potentially available for drinking purposes in the metropolitan area of Turin, north-western Italy. In order to effectively manage water resources, a knowledge of the water cycle components is necessary, including precipitation, evapotranspiration and subsurface reservoirs. All these components have been carefully evaluated in this paper, using observational datasets and modelling approaches.
Charles Pelletier, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, Samuel Helsen, Pierre-Vincent Huot, Christoph Kittel, François Klein, Sébastien Le clec'h, Nicole P. M. van Lipzig, Sylvain Marchi, François Massonnet, Pierre Mathiot, Ehsan Moravveji, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Niels Souverijns, Guillian Van Achter, Sam Vanden Broucke, Alexander Vanhulle, Deborah Verfaillie, and Lars Zipf
Geosci. Model Dev., 15, 553–594, https://doi.org/10.5194/gmd-15-553-2022, https://doi.org/10.5194/gmd-15-553-2022, 2022
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We present PARASO, a circumpolar model for simulating the Antarctic climate. PARASO features five distinct models, each covering different Earth system subcomponents (ice sheet, atmosphere, land, sea ice, ocean). In this technical article, we describe how this tool has been developed, with a focus on the
coupling interfacesrepresenting the feedbacks between the distinct models used for contribution. PARASO is stable and ready to use but is still characterized by significant biases.
Eduardo Moreno-Chamarro, Louis-Philippe Caron, Saskia Loosveldt Tomas, Javier Vegas-Regidor, Oliver Gutjahr, Marie-Pierre Moine, Dian Putrasahan, Christopher D. Roberts, Malcolm J. Roberts, Retish Senan, Laurent Terray, Etienne Tourigny, and Pier Luigi Vidale
Geosci. Model Dev., 15, 269–289, https://doi.org/10.5194/gmd-15-269-2022, https://doi.org/10.5194/gmd-15-269-2022, 2022
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Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.
Marco Gaetani, Benjamin Pohl, Maria del Carmen Alvarez Castro, Cyrille Flamant, and Paola Formenti
Atmos. Chem. Phys., 21, 16575–16591, https://doi.org/10.5194/acp-21-16575-2021, https://doi.org/10.5194/acp-21-16575-2021, 2021
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During the dry austral winter, biomass fires in tropical Africa emit large amounts of smoke in the atmosphere, with large impacts on climate and air quality. The study of the relationship between atmospheric circulation and smoke transport shows that midlatitude atmospheric disturbances may deflect the smoke from tropical Africa towards southern Africa. Understanding the distribution of the smoke in the region is crucial for climate modelling and air quality monitoring.
Constantin Ardilouze, Damien Specq, Lauriane Batté, and Christophe Cassou
Weather Clim. Dynam., 2, 1033–1049, https://doi.org/10.5194/wcd-2-1033-2021, https://doi.org/10.5194/wcd-2-1033-2021, 2021
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Forecasting temperature patterns beyond 2 weeks is very challenging, although occasionally, forecasts show more skill over Europe. Our study indicates that the level of skill varies concurrently for two distinct forecast systems. It also shows that higher skill occurs when forecasts are issued during specific patterns of atmospheric circulation that tend to be particularly persistent.
These results could help forecasters estimate a priori how trustworthy extended-range forecasts will be.
Gerard van der Schrier, Richard P. Allan, Albert Ossó, Pedro M. Sousa, Hans Van de Vyver, Bert Van Schaeybroeck, Roberto Coscarelli, Angela A. Pasqua, Olga Petrucci, Mary Curley, Mirosław Mietus, Janusz Filipiak, Petr Štěpánek, Pavel Zahradníček, Rudolf Brázdil, Ladislava Řezníčková, Else J. M. van den Besselaar, Ricardo Trigo, and Enric Aguilar
Clim. Past, 17, 2201–2221, https://doi.org/10.5194/cp-17-2201-2021, https://doi.org/10.5194/cp-17-2201-2021, 2021
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The 1921 drought was the most severe drought to hit Europe since the start of the 20th century. Here the climatological description of the drought is coupled to an overview of its impacts, sourced from newspapers, and an analysis of its drivers. The area from Ireland to the Ukraine was affected but hardest hit was the triangle between Brussels, Paris and Lyon. The drought impacts lingered on until well into autumn and winter, affecting water supply and agriculture and livestock farming.
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
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This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
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This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
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The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Federico Fabiano, Virna L. Meccia, Paolo Davini, Paolo Ghinassi, and Susanna Corti
Weather Clim. Dynam., 2, 163–180, https://doi.org/10.5194/wcd-2-163-2021, https://doi.org/10.5194/wcd-2-163-2021, 2021
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Global warming not only affects the mean state of the climate (i.e. a warmer world) but also its variability. Here we analyze a set of future climate scenarios and show how some configurations of the wintertime atmospheric flow will become more frequent and persistent under continued greenhouse forcing. For example, over Europe, models predict an increase in the NAO+ regime which drives intense precipitation in northern Europe and the British Isles and dry conditions over the Mediterranean.
Sara Top, Lola Kotova, Lesley De Cruz, Svetlana Aniskevich, Leonid Bobylev, Rozemien De Troch, Natalia Gnatiuk, Anne Gobin, Rafiq Hamdi, Arne Kriegsmann, Armelle Reca Remedio, Abdulla Sakalli, Hans Van De Vyver, Bert Van Schaeybroeck, Viesturs Zandersons, Philippe De Maeyer, Piet Termonia, and Steven Caluwaerts
Geosci. Model Dev., 14, 1267–1293, https://doi.org/10.5194/gmd-14-1267-2021, https://doi.org/10.5194/gmd-14-1267-2021, 2021
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Detailed climate data are needed to assess the impact of climate change on human and natural systems. The performance of two high-resolution regional climate models, ALARO-0 and REMO2015, was investigated over central Asia, a vulnerable region where detailed climate information is scarce. In certain subregions the produced climate data are suitable for impact studies, but bias adjustment is required for subregions where significant biases have been identified.
Roberto Bilbao, Simon Wild, Pablo Ortega, Juan Acosta-Navarro, Thomas Arsouze, Pierre-Antoine Bretonnière, Louis-Philippe Caron, Miguel Castrillo, Rubén Cruz-García, Ivana Cvijanovic, Francisco Javier Doblas-Reyes, Markus Donat, Emanuel Dutra, Pablo Echevarría, An-Chi Ho, Saskia Loosveldt-Tomas, Eduardo Moreno-Chamarro, Núria Pérez-Zanon, Arthur Ramos, Yohan Ruprich-Robert, Valentina Sicardi, Etienne Tourigny, and Javier Vegas-Regidor
Earth Syst. Dynam., 12, 173–196, https://doi.org/10.5194/esd-12-173-2021, https://doi.org/10.5194/esd-12-173-2021, 2021
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This paper presents and evaluates a set of retrospective decadal predictions with the EC-Earth3 climate model. These experiments successfully predict past changes in surface air temperature but show poor predictive capacity in the subpolar North Atlantic, a well-known source region of decadal climate variability. The poor predictive capacity is linked to an initial shock affecting the Atlantic Ocean circulation, ultimately due to a suboptimal representation of the Labrador Sea density.
Ruth Petrie, Sébastien Denvil, Sasha Ames, Guillaume Levavasseur, Sandro Fiore, Chris Allen, Fabrizio Antonio, Katharina Berger, Pierre-Antoine Bretonnière, Luca Cinquini, Eli Dart, Prashanth Dwarakanath, Kelsey Druken, Ben Evans, Laurent Franchistéguy, Sébastien Gardoll, Eric Gerbier, Mark Greenslade, David Hassell, Alan Iwi, Martin Juckes, Stephan Kindermann, Lukasz Lacinski, Maria Mirto, Atef Ben Nasser, Paola Nassisi, Eric Nienhouse, Sergey Nikonov, Alessandra Nuzzo, Clare Richards, Syazwan Ridzwan, Michel Rixen, Kim Serradell, Kate Snow, Ag Stephens, Martina Stockhause, Hans Vahlenkamp, and Rick Wagner
Geosci. Model Dev., 14, 629–644, https://doi.org/10.5194/gmd-14-629-2021, https://doi.org/10.5194/gmd-14-629-2021, 2021
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This paper describes the infrastructure that is used to distribute Coupled Model Intercomparison Project Phase 6 (CMIP6) data around the world for analysis by the climate research community. It is expected that there will be ~20 PB (petabytes) of data available for analysis. The operations team performed a series of preparation "data challenges" to ensure all components of the infrastructure were operational for when the data became available for timely data distribution and subsequent analysis.
Michela Angeloni, Elisa Palazzi, and Jost von Hardenberg
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-245, https://doi.org/10.5194/gmd-2020-245, 2020
Preprint withdrawn
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We compare the Planet Simulator, an Earth-system Model of Intermediate Complexity, using a 3D dynamical ocean, with two configurations using a simpler mixed-layer ocean. A tuning of oceanic parameters allows a reasonable mean climate in all cases. Model equilibrium climate sensitivity in abrupt CO2 concentration change experiments is found to be significantly affected by the sea-ice feedbacks and by the parameterization of meridional oceanic heat transport in the mixed-layer configurations.
María P. Amblar-Francés, Petra Ramos-Calzado, Jorge Sanchis-Lladó, Alfonso Hernanz-Lázaro, María C. Peral-García, Beatriz Navascués, Marta Dominguez-Alonso, María A. Pastor-Saavedra, and Ernesto Rodríguez-Camino
Adv. Sci. Res., 17, 191–208, https://doi.org/10.5194/asr-17-191-2020, https://doi.org/10.5194/asr-17-191-2020, 2020
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Climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this frame, the Pyrenees constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions. The impact of the scenarios becomes significant for the second half of the 21st century.
Axel Lauer, Veronika Eyring, Omar Bellprat, Lisa Bock, Bettina K. Gier, Alasdair Hunter, Ruth Lorenz, Núria Pérez-Zanón, Mattia Righi, Manuel Schlund, Daniel Senftleben, Katja Weigel, and Sabrina Zechlau
Geosci. Model Dev., 13, 4205–4228, https://doi.org/10.5194/gmd-13-4205-2020, https://doi.org/10.5194/gmd-13-4205-2020, 2020
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The Earth System Model Evaluation Tool is a community software tool designed for evaluation and analysis of climate models. New features of version 2.0 include analysis scripts for important large-scale features in climate models, diagnostics for extreme events, regional model and impact evaluation. In this paper, newly implemented climate metrics, emergent constraints for climate-relevant feedbacks and diagnostics for future model projections are described and illustrated with examples.
Cited articles
AEMET: Informe Mensual Climatológico Marzo de 2018,
http://www.aemet.es/documentos/es/serviciosclimaticos/vigilancia_clima/resumenes_climat/mensuales/2018/res_mens_clim_2018_03.pdf (last access:
25 October 2021), 2018.
Balling, R. C.: Classification in Climatology, in: Spatial Statistics and Models, edited by: Gaile, G. L. and Willmott, C. J., Theory and Decision Library, vol. 40, Springer, Dordrecht, https://doi.org/10.1007/978-94-017-3048-8_5, 1984.
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss
avalanche warning Part I: Numerical model, Cold Reg. Sci. Technol., 35,
123–145, https://doi.org/10.1016/S0165-232X(02)00074-5, 2002.
Boer, G. J., Smith, D. M., Cassou, C., Doblas-Reyes, F., Danabasoglu, G., Kirtman, B., Kushnir, Y., Kimoto, M., Meehl, G. A., Msadek, R., Mueller, W. A., Taylor, K. E., Zwiers, F., Rixen, M., Ruprich-Robert, Y., and Eade, R.: The Decadal Climate Prediction Project (DCPP) contribution to CMIP6, Geosci. Model Dev., 9, 3751–3777, https://doi.org/10.5194/gmd-9-3751-2016, 2016.
Brady, R. X. and Spring, A.: climpred: Verification of weather and climate
forecasts, J. Open Source Softw., 6, 2781, https://doi.org/10.21105/JOSS.02781,
2021.
Bröcker, J. and Smith, L. A.: From ensemble forecasts to predictive
distribution functions, Tellus A, 60, 663–678,
https://doi.org/10.1111/j.1600-0870.2008.00333.x, 2008.
BSC-CNS and Manubens, N.: startR: Automatically Retrieve Multidimensional
Distributed Data Sets, https://cran.r-project.org/package=startR (last access: 24 July 2022), 2020.
BSC-CNS, Manubens, N., and Hunter, A.: Apply Functions to Multiple
Multidimensional Arrays or Vectors, https://cran.r-project.org/package=multiApply (last access: 24 July 2022), 2019.
Celliers, L., Costa, M., Williams, D., and Rosendo, S.: The `last mile' for
climate data supporting local adaptation, Global Sustainability, 4, E14,
https://doi.org/10.1017/sus.2021.12, 2021
Chacon, S. and Straub, B.: Pro git, Apress, Apress Berkeley, CA, https://doi.org/10.1007/978-1-4842-0076-6, 2014.
Cortesi, N., Torralba, V., González-Reviriego, N., Soret, A., and
Doblas-Reyes, F. J.: Characterization of European wind speed variability
using weather regimes, Clim. Dynam., 53, 4961–4976,
https://doi.org/10.1007/s00382-019-04839-5, 2019.
DelSole, T., Yang, X., and Tippett, M. K.: Is unequal weighting significantly
better than equal weighting for multi-model forecasting?, Q. J. Roy. Meteor.
Soc., 139, 176–183, https://doi.org/10.1002/QJ.1961, 2013.
Doblas-Reyes, F. J., Hagedorn, R., and Palmer, T. N.: The rationale behind
the success of multi-model ensembles in seasonal forecasting – II.
Calibration and combination, Tellus A, 57, 234–252,
https://doi.org/10.1111/j.1600-0870.2005.00104.x, 2005.
D'Onofrio, D., Palazzi, E., Von Hardenberg, J., Provenzale, A., and
Calmanti, S.: Stochastic rainfall downscaling of climate models, J.
Hydrometeorol., 15, 830–843, https://doi.org/10.1175/JHM-D-13-096.1, 2014.
Drechsel, S., Mayr, G. J., Messner, J. W., and Stauffer, R.: Wind speeds at
heights crucial for wind energy: Measurements and verification of forecasts,
J. Appl. Meteorol. Climatol., 51, 1602–1617,
https://doi.org/10.1175/JAMC-D-11-0247.1, 2012.
Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., Hermanson, L.,
and Robinson, N.: Do seasonal-to-decadal climate predictions underestimate
the predictability of the real world?, Geophys. Res. Lett., 41,
5620–5628, https://doi.org/10.1002/2014GL061146, 2014.
ECMWF: SEAS5 user guide, 43 pp., https://www.ecmwf.int/sites/default/files/medialibrary/2017-10/System5_guide.pdf (last access: 22 October 2020), 2017.
Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Brötz, B., Caron, L.-P., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., Davin, E. L., Davini, P., Debeire, K., de Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., Gier, B. K., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Massonnet, F., Müller, B., Pandde, A., Pérez-Zanón, N., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Torralba, V., Vegas-Regidor, J., von Hardenberg, J., Weigel, K., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP, Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, 2020.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016a.
Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav, A., Andrews, O., Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C., Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes, M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A., Mäkelä, J., Martin, G., Mason, E., Phillips, A. S., Read, S., Rio, C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J., Wang, S., and Williams, K. D.: ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP, Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, 2016b.
Faranda, D., Messori, G., and Yiou, P.: Dynamical proxies of North Atlantic
predictability and extremes, Sci. Rep.-UK, 7, 1–10, https://doi.org/10.1038/srep41278,
2017.
Faranda, D., Alvarez-Castro, M. C., Messori, G., Rodrigues, D., and Yiou,
P.: The hammam effect or how a warm ocean enhances large scale atmospheric
predictability, Nat. Commun., 10, 1–7, https://doi.org/10.1038/s41467-019-09305-8,
2019.
Ferranti, L. and Corti, S.: New clustering products, ECMWF Newsl.,
127, 6–11, https://doi.org/10.21957/lr3bcise, 2011.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37,
4302–4315, https://doi.org/10.1002/joc.5086, 2017.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The
climate hazards infrared precipitation with stations – A new environmental
record for monitoring extremes, Sci. Data, 2, 1–21,
https://doi.org/10.1038/sdata.2015.66, 2015.
Gudmundsson, L.: qmap: Statistical Transformations for Post-Processing
Climate Model Output, CRAN, https://cran.r-project.org/package=qmap (last access: 22 July 2022), 2016.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
Hagedorn, R., Doblas-Reyes, F. J., and Palmer, T. N.: The rationale behind
the success of multi-model ensembles in seasonal forecasting-I. Basic
concept, Tellus A, 57, 219–233,
https://doi.org/10.3402/tellusa.v57i3.14657, 2005.
Hamill, T. M.: Addressing model uncertainty through statistical
post-processing using reforecasts, ECMWF Work. Model Uncertain., 20–24, https://www.ecmwf.int/sites/default/files/elibrary/2011/9753-addressing-model-uncertainty-through-statistical-post-processing-using-reforecasts.pdf (last access: 24 July 2022), 2011.
Hemri, S., Bhend, J., Liniger, M. A., Manzanas, R., Siegert, S., Stephenson,
D. B., Gutiérrez, J. M., Brookshaw, A., and Doblas-Reyes, F. J.: How to
create an operational multi-model of seasonal forecasts?, Clim. Dynam.,
55, 1141–1157, https://doi.org/10.1007/s00382-020-05314-2, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Hijmans, R. J.: raster: Geographic Data Analysis and Modeling, CRAN, https://cran.r-project.org/package=raster (last access: 24 July 2022), 2020.
Information Resources Management Association: Software Design and
Development: Concepts, Methodologies, Tools, and Applications, IGI
Global, USA, edited by: Khosrow-Pour, M., DBA
Contemporary Research in Information Science and Technology, Book Series,
https://doi.org/10.4018/978-1-4666-4301-7, 2013.
Iturbide, M., Bedia, J., Herrera, S., Baño-Medina, J., Fernández,
J., Frías, M. D., Manzanas, R., San-Martín, D., Cimadevilla, E.,
Cofiño, A. S., and Gutiérrez, J. M.: The R-based climate4R open
framework for reproducible climate data access and post-processing, Environ.
Model. Softw., 111, 42–54, https://doi.org/10.1016/J.ENVSOFT.2018.09.009, 2019.
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019.
Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A.,
Zhang, Q., Van Den Dool, H., Saha, S., Mendez, M. P., Becker, E., Peng, P.,
Tripp, P., Huang, J., Dewitt, D. G., Tippett, M. K., Barnston, A. G., Li,
S., Rosati, A., Schubert, S. D., Rienecker, M., Suarez, M., Li, Z. E.,
Marshak, J., Lim, Y. K., Tribbia, J., Pegion, K., Merryfield, W. J., Denis,
B., and Wood, E. F.: The North American multimodel ensemble: Phase-1
seasonal-to-interannual prediction; phase-2 toward developing intraseasonal
prediction, B. Am. Meteorol. Soc., 95, 585–601,
https://doi.org/10.1175/BAMS-D-12-00050.1, 2014.
Lehning, M., Bartelt, P., Brown, B., and Fierz, C.: A physical SNOWPACK
model for the Swiss avalanche warning Part III: Meteorological forcing, thin
layer formation and evaluation, Cold Reg. Sci. Technol., 35, 169–184,
https://doi.org/10.1016/S0165-232X(02)00072-1, 2002a.
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A
physical SNOWPACK model for the Swiss avalanche warning Part II. Snow
microstructure, Cold Reg. Sci. Technol., 35, 147–167,
https://doi.org/10.1016/S0165-232X(02)00073-3, 2002b.
Lledó, L. and Doblas-Reyes, F. J.: Predicting daily mean wind speed in
Europe weeks ahead from MJO status, Mon. Weather Rev., 148,
3413–3426,
https://doi.org/10.1175/mwr-d-19-0328.1, 2020.
Lledó, L., Cionni, I., Torralba, V., Bretonnière, P. A., and
Samsó, M.: Seasonal prediction of Euro-Atlantic teleconnections from
multiple systems, Environ. Res. Lett., 15, 074009,
https://doi.org/10.1088/1748-9326/ab87d2, 2020.
Manubens, N., Caron, L.-P., Hunter, A., Bellprat, O., Exarchou, E., Fuckar,
N. S., Garcia-Serrano, J., Massonnet, F., En Egoz, M. M., Sicardi, V., Batt
E C , Chlo E Prodhomme, L., Onica Torralba, V., Cortesi, N., Mula-Valls, O.,
Serradell, K., Guemas, V., and Doblas-Reyes, F. J.: An R package for climate
forecast verification, Environ. Model. Softw., 103, 29–42,
https://doi.org/10.1016/j.envsoft.2018.01.018, 2018.
Manzanas, R., Gutiérrez, J. M., Bhend, J., Hemri, S., Doblas-Reyes, F.
J., Torralba, V., Penabad, E., and Brookshaw, A.: Bias adjustment and
ensemble recalibration methods for seasonal forecasting: a comprehensive
intercomparison using the C3S dataset, Clim. Dynam., 53, 1287–1305,
https://doi.org/10.1007/s00382-019-04640-4, 2019.
Maraun, D. and Widmann, M.: Statistical Downscaling and Bias Correction for
Climate Research, Cambridge University Press, https://doi.org/10.1017/9781107588783, 2018.
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J.,
Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themel, M., Venema, V. K.
C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and
Thiele-Eich, I.: Precipitation downscaling under climate change: Recent
developments to bridge the gap between dynamical models and the end user,
Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314, 2010.
Marcos, R.: Improvement of seasonal forecasting techniques applied to water
resources and forest fires, Universitat de Barcelona, http://www.tdx.cat (last access: 1 October 2020), 2016.
Merryfield, W. J., Baehr, J., Batté, L., Becker, E. J., Butler, A. H.,
Coelho, C. A. S., Danabasoglu, G., Dirmeyer, P. A., Doblas-Reyes, F. J.,
Domeisen, D. I. V., Ferranti, L., Ilynia, T., Kumar, A., Müller, W. A.,
Rixen, M., Robertson, A. W., Smith, D. M., Takaya, Y., Tuma, M., Vitart, F.,
White, C. J., Alvarez, M. S., Ardilouze, C., Attard, H., Baggett, C.,
Balmaseda, M. A., Beraki, A. F., Bhattacharjee, P. S., Bilbao, R., De
Andrade, F. M., DeFlorio, M. J., Díaz, L. B., Ehsan, M. A.,
Fragkoulidis, G., Grainger, S., Green, B. W., Hell, M. C., Infanti, J. M.,
Isensee, K., Kataoka, T., Kirtman, B. P., Klingaman, N. P., Lee, J. Y.,
Mayer, K., McKay, R., Mecking, J. V., Miller, D. E., Neddermann, N., Ng, C.
H. J., Ossó, A., Pankatz, K., Peatman, S., Pegion, K., Perlwitz, J.,
Recalde-Coronel, G. C., Reintges, A., Renkl, C., Solaraju-Murali, B.,
Spring, A., Stan, C., Sun, Y. Q., Tozer, C. R., Vigaud, N., Woolnough, S.,
and Yeager, S.: Current and emerging developments in subseasonal to decadal
prediction, B. Am. Meteorol. Soc., 101, E869–E896,
https://doi.org/10.1175/BAMS-D-19-0037.1, 2020.
MeteoSwiss: easyVerification: Ensemble Forecast Verification for Large Data
Sets, CRAN, https://cran.r-project.org/package=easyVerification (last access: 24 July 2022), 2017.
Min, Y. M., Kryjov, V. N., and Oh, S. M.: Assessment of APCC multimodel
ensemble prediction in seasonal climate forecasting: Retrospective
(1983–2003) and real-time forecasts (2008–2013), J. Geophys. Res.,
119, 12132–12150, https://doi.org/10.1002/2014JD022230, 2014.
Mishra, N., Prodhomme, C., and Guemas, V.: Multi-Model Skill Assessment of
Seasonal Temperature and Precipitation Forecasts over Europe, Clim. Dynam.,
52, 4207–4225, https://doi.org/10.1007/s00382-018-4404-z, 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
National Academies of Sciences, Engineering and Medicine: Next Generation
Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts,
Natl. Acad. Press, https://doi.org/10.17226/21873, 2016.
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil,
F., and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall-runoff model? Part 2 – Towards a simple and efficient potential
evapotranspiration model for rainfall-runoff modelling, J. Hydrol.,
303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005.
Palmer, T. N., Doblas-Reyes, F. J., Weisheimer, A., and Rodwell, M. J.:
Toward seamless prediction: Calibration of climate change projections using
seasonal forecasts, B. Am. Meteorol. Soc., 89, 459–470,
https://doi.org/10.1175/BAMS-89-4-459, 2008.
Peral García, C., Navascués Fernández-Victorio, B., and Ramos
Calzado, P.: Serie de precipitación diaria en rejilla con fines
climáticos, Nota Técnica 24 de AEMET, https://www.aemet.es/documentos/es/conocermas/recursos_en_linea/publicaciones_y_estudios/publicaciones/NT_24_AEMET/NT_24_AEMET.pdf (last access: 24 July 2022), 2017.
Pérez-Zanón, N., Caron, L.-P., Alvarez-Castro, C., Batte, L., Delgado, C., von Hardenberg, J., LLedó, L., Manubens, N., Palma, L., Sanchez-Garcia, E., van Schaeybroeck, B., Torralba, V., and Verfaillie, D.: CSTools (4.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.5549474, 2021a.
Pérez-Zanón, N., Chihchung, C., and Lledó, L.: CSIndicators:
Sectoral Indicators for Climate Services Based on Sub-Seasonal to Decadal
Climate Predictions, CRAN, https://cran.r-project.org/package=CSIndicators (last access: 24 July 2022), 2021b.
Pierce, D.: ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format
Data Files, CRAN, https://CRAN.R-project.org/package=ncdf4 (last access: 24 July 2022), 2019
Rajagopalan, B., Lall, U., and Zebiak, S. E.: Categorical climate forecasts
through regularization and optimal combination of multiple GCM ensembles,
Mon. Weather Rev., 130, 1792–1811,
https://doi.org/10.1175/1520-0493(2002)130<1792:CCFTRA>2.0.CO;2,
2002.
Ramon, J., Lledó, L., Bretonniere, P. A., Samsó, M., and
Doblas-Reyes, F. J.: A perfect prognosis downscaling methodology for
seasonal prediction of local-scale wind speeds, Environ. Res. Lett., 16,
54010, https://doi.org/10.1088/1748-9326/abe491, 2021.
R Core Team: R: A language and environment for statistical computing, R
Found. Stat. Comput., https://www.R-project.org/ (last access: 24 July 2022), 2017.
Rebora, N., Ferraris, L., von Hardenberg, J., and Provenzale, A.: Rainfall downscaling and flood forecasting: a case study in the Mediterranean area, Nat. Hazards Earth Syst. Sci., 6, 611–619, https://doi.org/10.5194/nhess-6-611-2006, 2006a.
Rebora, N., Ferraris, L., von Hardenberg, J., and Provenzale, A.: RainFARM:
Rainfall downscaling by a Filtered Autoregressive Model, J. Hydrometeorol.,
7, 724–738, https://doi.org/10.1175/JHM517.1, 2006b.
Red Eléctrica de España: Informe del Sistema Eléctrico
Español 2018, https://www.ree.es/sites/default/files/11_PUBLICACIONES/Documentos/InformesSistemaElectrico/2018/inf_sis_elec_ree_2018.pdf (last access:
7 June 2021), 2018.
Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020.
Robertson, A. W., Lall, U., Zebiak, S. E., and Goddard, L.: Improved
combination of multiple atmospheric GCM ensembles for seasonal prediction,
Mon. Weather Rev., 132, 2732–2744, https://doi.org/10.1175/MWR2818.1, 2004.
Rössler, O., Fischer, A. M., Huebener, H., Maraun, D., Benestad, R. E.,
Christodoulides, P., Soares, P. M. M., Cardoso, R. M., Pagé, C.,
Kanamaru, H., Kreienkamp, F., and Vlachogiannis, D.: Challenges to link
climate change data provision and user needs: Perspective from the
COST-action VALUE, Int. J. Climatol., 39, 3704–3716,
https://doi.org/10.1002/joc.5060, 2019.
Roulin, E. and Vannitsem, S.: Skill of medium-range hydrological ensemble
predictions, J. Hydrometeorol., 6, 729–744, https://doi.org/10.1175/JHM436.1, 2005.
Sánchez-García, E., Voces-Aboy, J., Navascués, B., and Rodríguez-Camino, E.: Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Adv. Sci. Res., 16, 165–174, https://doi.org/10.5194/asr-16-165-2019, 2019.
Siegert, S.: SpecsVerification: Forecast Verification Routines for Ensemble
Forecasts of Weather and Climate, CRAN, https://cran.r-project.org/package=SpecsVerification (last access: 24 July 2022), 2017.
Silverman, B. W.: Density Estimation for Statistics and Data Analysis 1st Edition, Chapman and Hall, London, 1986.
Sokal, R. R.: Numerical Taxonomy, Sci. Am., 215, 106–116,
https://doi.org/10.1038/scientificamerican1266-106, 1966.
Soret, A., Torralba, V., Cortesi, N., Christel, I., Palma, L.,
Manrique-Suñén, A., Lledó, L., González-Reviriego, N., and
Doblas-Reyes, F. J.: Sub-seasonal to seasonal climate predictions for wind
energy forecasting, J. Phys. Conf. Ser., 1222,
012009, https://doi.org/10.1088/1742-6596/1222/1/012009, 2019.
Schulzweida, U.: CDO User Guide (Version 1.9.8), Zenodo,
https://doi.org/10.5281/zenodo.3539275, 2019.
Straus, D. M., Corti, S., and Molteni, F.: Circulation Regimes: Chaotic
Variability versus SST-Forced Predictability, J. Climate, 20, 2251–2272,
https://doi.org/10.1175/JCLI4070.1, 2007.
Teetor, N.: Multiple, Unpacking, and Destructuring Assignment, CRAN, https://cran.r-project.org/package=zeallot (last access: 18 September 2020), 2018.
Terzago, S., Palazzi, E., and von Hardenberg, J.: Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology, Nat. Hazards Earth Syst. Sci., 18, 2825–2840, https://doi.org/10.5194/nhess-18-2825-2018, 2018.
Terzago, S., Bongiovanni, G., and von Hardenberg, J.: High quality climate
prediction, Medcope Proj., 29 pp., https://drive.google.com/file/d/1qp2gbtKdBl4XmsyOeaEhFENwpeUuJwkf/view
(last access: 23 November 2020), 2020.
Terzago, S., Bongiovanni, G., and von Hardenberg, J.: Seasonal forecasting of snow resources at Alpine sites, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2022-32, in review, 2022.
Tödter, J. and Ahrens, B.: Generalization of the Ignorance Score:
Continuous Ranked Version and Its Decomposition, Mon. Weather Rev., 140,
2005–2017, https://doi.org/10.1175/MWR-D-11-00266.1, 2012.
Torralba, V.: Seasonal climate prediction for the wind energy sector:
methods and tools for the development of a climate service, Universidad
Complutense de Madrid, https://eprints.ucm.es/56841 (last access: 24 July 2022), 2019.
Torralba, V., Doblas-Reyes, F. J., MacLeod, D., Christel, I., and Davis, M.:
Seasonal climate prediction: A new source of information for the management
of wind energy resources, J. Appl. Meteorol. Climatol., 56, 1231–1247,
https://doi.org/10.1175/JAMC-D-16-0204.1, 2017.
Torralba, V., Gonzalez-Reviriego, N., Cortesi, N., Manrique-Suñén,
A., Lledó, L., Marcos, R., Soret, A., and Doblas-Reyes, F. J.:
Challenges in the selection of atmospheric circulation patterns for the wind
energy sector, Int. J. Climatol., 41, 1525–1541, https://doi.org/10.1002/joc.6881,
2021.
Troccoli, A., Harrison, M., Coughlan, M., and Williams, J. B.: Seasonal
Forecasts in Decision Making, Seas. Clim. Forecast. Manag. Risk, 82, 13–41,
https://doi.org/10.1007/978-1-4020-6992-5_2, 2008.
Van Schaeybroeck, B. and Vannitsem, S.: Post-processing through linear regression, Nonlin. Processes Geophys., 18, 147–160, https://doi.org/10.5194/npg-18-147-2011, 2011.
Van Schaeybroeck, B. and Vannitsem, S.: Ensemble post-processing using
member-by-member approaches: theoretical aspects, Q. J. Roy. Meteor. Soc.,
141, 807–818, https://doi.org/10.1002/qj.2397, 2015.
Van Schaeybroeck, B. and Vannitsem, S.: Postprocessing of Long-Range
Forecasts, in: Statistical Postprocessing of Ensemble Forecasts,
Statistical Postprocessing of Ensemble Forecasts, edited by: Vannitsem, S., Wilks, D. S., and Messner, J. W., 267–290,
https://doi.org/10.1016/B978-0-12-812372-0.00010-8, 2019.
Verfaillie, D., Déqué, M., Morin, S., and Lafaysse, M.: The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models, Geosci. Model Dev., 10, 4257–4283, https://doi.org/10.5194/gmd-10-4257-2017, 2017.
Verfaillie, D., Doblas-Reyes, F. J., Donat, M. G., Pérez-Zanón, N.,
Solaraju-Murali, B., Torralba, V., and Wild, S.: How reliable are decadal
climate predictions of near-surface air temperature?, J. Climate, 34,
697–713, https://doi.org/10.1175/JCLI-D-20-0138.1, 2021.
Vitart, F., Huddleston, M. R., Déqué, M., Peake, D., Palmer, T. N.,
Stockdale, T. N., Davey, M. K., Ineson, S., and Weisheimer, A.:
Dynamically-based seasonal forecasts of Atlantic tropical storm activity
issued in June by EUROSIP, Geophys. Res. Lett., 34, L16815,
https://doi.org/10.1029/2007GL030740, 2007.
Wang, B., Lee, J. Y., Kang, I. S., Shukla, J., Park, C. K., Kumar, A.,
Schemm, J., Cocke, S., Kug, J. S., Luo, J. J., Zhou, T., Wang, B., Fu, X.,
Yun, W. T., Alves, O., Jin, E. K., Kinter, J., Kirtman, B., Krishnamurti,
T., Lau, N. C., Lau, W., Liu, P., Pegion, P., Rosati, T., Schubert, S.,
Stern, W., Suarez, M., and Yamagata, T.: Advance and prospectus of seasonal
prediction: Assessment of the APCC/ CliPAS 14-model ensemble retrospective
seasonal prediction (1980-2004), Clim. Dynam., 33, 93–117,
https://doi.org/10.1007/s00382-008-0460-0, 2009.
White, C. J., Carlsen, H., Robertson, A. W., Klein, R. J. T., Lazo, J. K.,
Kumar, A., Vitart, F., Coughlan de Perez, E., Ray, A. J., Murray, V.,
Bharwani, S., MacLeod, D., James, R., Fleming, L., Morse, A. P., Eggen, B.,
Graham, R., Kjellström, E., Becker, E., Pegion, K. V., Holbrook, N. J.,
McEvoy, D., Depledge, M., Perkins-Kirkpatrick, S., Brown, T. J., Street, R.,
Jones, L., Remenyi, T. A., Hodgson-Johnston, I., Buontempo, C., Lamb, R.,
Meinke, H., Arheimer, B., and Zebiak, S. E.: Potential applications of
subseasonal-to-seasonal (S2S) predictions, Meteorol. Appl., 24, 315–325,
https://doi.org/10.1002/met.1654, 2017.
Yiou, P., Salameh, T., Drobinski, P., Menut, L., Vautard, R., and Vrac, M.:
Ensemble reconstruction of the atmospheric column from surface pressure
using analogues, Clim. Dynam., 41, 1333–1344,
https://doi.org/10.1007/s00382-012-1626-3, 2013.
Short summary
CSTools (short for Climate Service Tools) is an R package that contains process-based methods for climate forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. In addition to describing the structure and methods in the package, we also present three use cases to illustrate the seasonal climate forecast post-processing for specific purposes.
CSTools (short for Climate Service Tools) is an R package that contains process-based methods...
