Articles | Volume 17, issue 4
https://doi.org/10.5194/essd-17-1367-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-1367-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
EEAR-Clim: a high-density observational dataset of daily precipitation and air temperature for the Extended European Alpine Region
Giulio Bongiovanni
CORRESPONDING AUTHOR
Department of Science, Technology and Society, University School for Advanced Studies Pavia (IUSS), Pavia, Italy
Department of Civil, Environmental and Mechanical Engineering (DICAM), University of Trento, Trento, Italy
Michael Matiu
Department of Civil, Environmental and Mechanical Engineering (DICAM), University of Trento, Trento, Italy
Alice Crespi
Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy
Anna Napoli
Department of Civil, Environmental and Mechanical Engineering (DICAM), University of Trento, Trento, Italy
Center for Agriculture Food Environment (C3A), University of Trento, San Michele all'Adige, Italy
Bruno Majone
Department of Civil, Environmental and Mechanical Engineering (DICAM), University of Trento, Trento, Italy
Dino Zardi
Department of Civil, Environmental and Mechanical Engineering (DICAM), University of Trento, Trento, Italy
Related authors
No articles found.
Andrea Galletti, Soroush Zarghami Dastjerdi, and Bruno Majone
Earth Syst. Sci. Data, 17, 3353–3373, https://doi.org/10.5194/essd-17-3353-2025, https://doi.org/10.5194/essd-17-3353-2025, 2025
Short summary
Short summary
IAR-HP (Italian Alpine Region HydroPower) is a detailed inventory of large hydropower systems in Italy's Alpine Region, aimed at improving their inclusion in hydrological modeling by providing relevant information with a consistent level of detail. It includes structural, geographic, and operational data for over 300 hydropower plants and their related reservoirs and water intakes. Validated through modeling, IAR-HP accurately reproduces observed hydropower, capturing 96.2 % of actual production.
Marc Lemus-Canovas, Alice Crespi, Elena Maines, Stefano Terzi, and Massimiliano Pittore
EGUsphere, https://doi.org/10.5194/egusphere-2025-1347, https://doi.org/10.5194/egusphere-2025-1347, 2025
Short summary
Short summary
We studied a severe compound drought and heatwave event in the Adige River basin in May 2022 and found that similar events are now hotter and drier due to current warming. These changes worsen water stress and river drying. We show that timing matters: events in June are now more critical than in April, as the snowmelt contribution to streamflow in June has become much lower than in the past. However, many climate models still fail to capture these changes.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
Short summary
Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Giorgio Doglioni, Valentina Aquila, Sampa Das, Peter R. Colarco, and Dino Zardi
Atmos. Chem. Phys., 22, 11049–11064, https://doi.org/10.5194/acp-22-11049-2022, https://doi.org/10.5194/acp-22-11049-2022, 2022
Short summary
Short summary
We use a global chemistry climate model to analyze the perturbations to the stratospheric dynamics caused by an injection of carbonaceous aerosol comparable to the one caused by a series of pyrocumulonimbi that formed over British Columbia, Canada on 13 August 2017. The injection of light-absorbing aerosol in an otherwise clean lower stratosphere causes the formation of long-lasting stratospheric anticyclones at the synoptic scale.
Bruno Majone, Diego Avesani, Patrick Zulian, Aldo Fiori, and Alberto Bellin
Hydrol. Earth Syst. Sci., 26, 3863–3883, https://doi.org/10.5194/hess-26-3863-2022, https://doi.org/10.5194/hess-26-3863-2022, 2022
Short summary
Short summary
In this work, we introduce a methodology for devising reliable future high streamflow scenarios from climate change simulations. The calibration of a hydrological model is carried out to maximize the probability that the modeled and observed high flow extremes belong to the same statistical population. Application to the Adige River catchment (southeastern Alps, Italy) showed that this procedure produces reliable quantiles of the annual maximum streamflow for use in assessment studies.
Michael Matiu and Florian Hanzer
Hydrol. Earth Syst. Sci., 26, 3037–3054, https://doi.org/10.5194/hess-26-3037-2022, https://doi.org/10.5194/hess-26-3037-2022, 2022
Short summary
Short summary
Regional climate models not only provide projections on temperature and precipitation, but also on snow. Here, we employed statistical post-processing using satellite observations to reduce bias and uncertainty from model projections of future snow-covered area and duration under different greenhouse gas concentration scenarios for the European Alps. Snow cover area/duration decreased overall in the future, three times more strongly with 4–5° global warming as compared to 1.5–2°.
Anna Napoli, Fabien Desbiolles, Antonio Parodi, and Claudia Pasquero
Atmos. Chem. Phys., 22, 3901–3909, https://doi.org/10.5194/acp-22-3901-2022, https://doi.org/10.5194/acp-22-3901-2022, 2022
Short summary
Short summary
Aerosols are liquid or solid particles suspended in the air that can interact with radiation and clouds, modifying the meteoclimatic conditions. Using an atmospheric model, we study the climatological impact of aerosols through their effects on clouds in the Alps, a region characterized by high pollution levels in the densely populated surrounding flatlands. Results show that cloud cover, temperature, and precipitation are affected by aerosols, and the response varies with elevation and season.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
Short summary
Short summary
The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Alice Crespi, Michael Matiu, Giacomo Bertoldi, Marcello Petitta, and Marc Zebisch
Earth Syst. Sci. Data, 13, 2801–2818, https://doi.org/10.5194/essd-13-2801-2021, https://doi.org/10.5194/essd-13-2801-2021, 2021
Short summary
Short summary
A 250 m gridded dataset of 1980–2018 daily mean temperature and precipitation records for Trentino–South Tyrol (north-eastern Italian Alps) was derived from a quality-controlled and homogenized archive of station observations. The errors associated with the final interpolated fields were assessed and thoroughly discussed. The product will be regularly updated and is meant to support regional climate studies and local monitoring and applications in integration with other fine-resolution data.
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
Short summary
Short summary
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.
Cited articles
Aggarwal, C. C.: Outlier Analysis, in: An Introduction to Outlier Analysis, Springer International Publishing, Cham, 34 pp., ISBN 978-3-319-47578-3, https://doi.org/10.1007/978-3-319-47578-3_1, 2017. a
Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A. M. G., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F., Tagipour, A., Rupa Kumar, K., Revadekar, J., Griffiths, G., Vincent, L., Stephenson, D. B., Burn, J., Aguilar, E., Brunet, M., Taylor, M., New, M., Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. L.: Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res.-Atmos., 111, D05109, https://doi.org/10.1029/2005JD006290, 2006. a
Alexandersson, H.: A homogeneity test applied to precipitation data, J. Climatol., 6, 661–675, https://doi.org/10.1002/joc.3370060607, 1986. a
Alexandersson, H. and Moberg, A.: Homogenization of Swedish Temperature Data. Part I: Homogeneity Test For Linear Trends, Int. J. Climatol., 17, 25–34, https://doi.org/10.1002/(SICI)1097-0088(199701)17:1<25::AID-JOC103>3.0.CO;2-J, 1997. a
Andrighetti, M., Zardi, D., and de Franceschi, M.: History and analysis of the temperature series of Verona (1769–2006), Meteorol. Atmos. Phys., 103, 267–277, https://doi.org/10.1007/s00703-008-0331-6, 2009. a
Auer, I., Böhm, R., Jurković, A., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Brunetti, M., Nanni, T., Maugeri, M., Briffa, K., Jones, P., Efthymiadis, D., Mestre, O., Moisselin, J.-M., Begert, M., Brazdil, R., Bochnicek, O., Cegnar, T., Gajić-Capka, M., Zaninović, K., Majstorović, Z., Szalai, S., Szentimrey, T., and Mercalli, L.: A new instrumental precipitation dataset for the greater alpine region for the period 1800–2002, Int. J. Climatol., 25, 139–166, https://doi.org/10.1002/joc.1135, 2005. a, b, c, d, e
Auer, I., Böhm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M., Müller-Westermeier, G., Kveton, V., Bochnicek, O., Stastny, P., Lapin, M., Szalai, S., Szentimrey, T., Cegnar, T., Dolinar, M., Gajic-Capka, M., Zaninovic, K., Majstorovic, Z., and Nieplova, E.: HISTALP – historical instrumental climatological surface time series of the Greater Alpine Region, Int. J. Climatol., 27, 17–46, https://doi.org/10.1002/joc.1377, 2007. a, b, c, d
Aybar, C., Fernández, C., Huerta, A., Lavado, W., Vega, F. V., and Felipe-Obando, O.: Construction of a high-resolution gridded rainfall dataset for Peru from 1981 to the present day, Hydrolog. Sci. J., 65, 770–785, https://doi.org/10.1080/02626667.2019.1649411, 2020. a
Azorin-Molina, C., Guijarro, J.-A., McVicar, T. R., Vicente-Serrano, S. M., Chen, D., Jerez, S., and Espírito-Santo, F.: Trends of daily peak wind gusts in Spain and Portugal, 1961–2014, J. Geophys. Res.-Atmos., 121, 1059–1078, https://doi.org/10.1002/2015JD024485, 2016. a
Baker, D. G.: Effect of Observation Time on Mean Temperature Estimation, J. Appl. Meteorol. Clim., 14, 471–476, https://doi.org/10.1175/1520-0450(1975)014<0471:EOOTOM>2.0.CO;2, 1975. a
Begert, M., Schlegel, T., and Kirchhofer, W.: Homogeneous Temperature and Precipitation Series of Switzerland from 1864 to 2000, Int. J. Climatol., 25, 65–80, https://doi.org/10.1002/joc.1118, 2005. a, b, c, d
Beniston, M.: Mountain Weather and Climate: A General Overview and a Focus on Climatic Change in the Alps, Hydrobiologia, 562, 3–16, https://doi.org/10.1007/s10750-005-1802-0, 2006. a, b
Beniston, M., Farinotti, D., Stoffel, M., Andreassen, L. M., Coppola, E., Eckert, N., Fantini, A., Giacona, F., Hauck, C., Huss, M., Huwald, H., Lehning, M., López-Moreno, J.-I., Magnusson, J., Marty, C., Morán-Tejéda, E., Morin, S., Naaim, M., Provenzale, A., Rabatel, A., Six, D., Stötter, J., Strasser, U., Terzago, S., and Vincent, C.: The European mountain cryosphere: a review of its current state, trends, and future challenges, The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, 2018. a
Bongiovanni, G., Matiu, M., Crespi, A., Napoli, A., Majone, B., and Zardi, D.: EEAR-Clim: A high density observational dataset of daily precipitation and air temperature for the Extended European Alpine Region (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.10951610, 2024. a, b
Bongiovanni, G., Matiu, M., Crespi, A., Napoli, A., Majone, B., and Zardi, D.: Air temperature and precipitation trends in the Extended European Alpine Region over 1961–2020 from a dense network of surface weather stations, Climatic Change, Springer, in review, 2025. a
Brunetti, M., Colacino, M., Maugeri, M., and Nanni, T.: Trends in the daily intensity of precipitation in Italy from 1951 to 1996, Int. J. Climatol., 21, 299–316, https://doi.org/10.1002/joc.613, 2001. a
Brunetti, M., Maugeri, M., Monti, F., and Nanni, T.: Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series, Int. J. Climatol., 26, 345–381, https://doi.org/10.1002/joc.1251, 2006. a, b
Brunetti, M., Lentini, G., Maugeri, M., Nanni, T., Auer, I., Böhm, R., and Schöner, W.: Climate variability and change in the Greater Alpine Region over the last two centuries based on multi-variable analysis, Int. J. Climatol., 29, 2197–2225, https://doi.org/10.1002/joc.1857, 2009. a, b, c
Buchmann, M., Coll, J., Aschauer, J., Begert, M., Brönnimann, S., Chimani, B., Resch, G., Schöner, W., and Marty, C.: Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods, The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, 2022. a, b
Böhm, R., Auer, I., Brunetti, M., Maugeri, M., Nanni, T., and Schöner, W.: Regional temperature variability in the European Alps: 1760–1998 from homogenized instrumental time series, Int. J. Climatol., 21, 1779–1801, https://doi.org/10.1002/joc.689, 2001. a
Caussinus, H. and Lyazrhi, F.: Choosing a Linear Model with a Random Number of Change-Points and Outliers, Ann. I. Stat. Math., 49, 761–775, https://doi.org/10.1023/A:1003230713770, 1997. a
Caussinus, H. and Mestre, O.: Detection and Correction of Artificial Shifts in Climate Series, J. R. Stat. Soc. C-Appl., 53, 405–425, 2004. a
Cerlini, P. B., Silvestri, L., and Saraceni, M.: Quality control and gap-filling methods applied to hourly temperature observations over central Italy, Meteorol. Appl., 27, e1913, https://doi.org/10.1002/met.1913, 2020. a, b
Chimani, B., Venema, V., Lexer, A., Andre, K., Auer, I., and Nemec, J.: Inter-comparison of methods to homogenize daily relative humidity, Int. J. Climatol., 38, 3106–3122, https://doi.org/10.1002/joc.5488, 2018. a
Chimani, B., Bochníček, O., Brunetti, M., Ganekind, M., Holec, J., Izsák, B., Lakatos, M., Tadić, M. P., Manara, V., Maugeri, M., Šťastný, P., Szentes, O., and Zardi, D.: Revisiting HISTALP precipitation dataset, Int. J. Climatol., 43, 7381–7411, https://doi.org/10.1002/joc.8270, 2023. a, b
Coll, J., Domonkos, P., Guijarro, J., Curley, M., Rustemeier, E., Aguilar, E., Walsh, S., and Sweeney, J.: Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results, Int. J. Climatol., 40, 6169–6188, https://doi.org/10.1002/joc.6575, 2020. a
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018. a, b
Cramer, W., Guiot, J., and Marini, K.: MedECC (2020) Climate and Environmental Change in the Mediterranean Basin – Current Situation and Risks for the Future. First Mediterranean Assessment Report, Tech. rep., Union for the Mediterranean, Plan Bleu, UNEP/MAP, Marseille, France, Zenodo, https://doi.org/10.5281/zenodo.4768833, 2020. a
Crespi, A., Brunetti, M., Lentini, G., and Maugeri, M.: 1961–1990 high-resolution monthly precipitation climatologies for Italy, Int. J. Climatol., 38, 878–895, https://doi.org/10.1002/joc.5217, 2018. a, b
Curci, G., Guijarro, J. A., Antonio, L. D., Bacco, M. D., Lena, B. D., and Scorzini, A. R.: Building a local climate reference dataset: Application to the Abruzzo region (Central Italy), 1930–2019, Int. J. Climatol., 41, 4414–4436, https://doi.org/10.1002/joc.7081, 2021. a, b, c, d
Daly, C., Doggett, M. K., Smith, J. I., Olson, K. V., Halbleib, M. D., Dimcovic, Z., Keon, D., Loiselle, R. A., Steinberg, B., Ryan, A. D., Pancake, C. M., and Kaspar, E. M.: Challenges in Observation-Based Mapping of Daily Precipitation across the Conterminous United States, J. Atmos. Ocean. Tech., 38, 1979–1992, https://doi.org/10.1175/JTECH-D-21-0054.1, 2021. a
de Jong, C.: Challenges for mountain hydrology in the third millennium, Frontiers in Environmental Science, 3, 38, https://doi.org/10.3389/fenvs.2015.00038, 2015. a
Dijkstra, F., de Vos, R., Ruis, J., and Crok, M.: Reassessment of the homogenization of daily maximum temperatures in the Netherlands since 1901, Theor. Appl. Climatol., 147, 1185–1194, https://doi.org/10.1007/s00704-021-03887-4, 2022. a
Domonkos, P.: Homogenization of precipitation time series with ACMANT, Theor. Appl. Climatol., 122, 303–314, https://doi.org/10.1007/s00704-014-1298-5, 2015. a
Domonkos, P. and Coll, J.: Time series homogenisation of large observational datasets: impact of the number of partner series on efficiency, Clim. Res., 74, 31–42, https://doi.org/10.3354/cr01488, 2017a. a
Domonkos, P. and Coll, J.: Homogenisation of temperature and precipitation time series with ACMANT3: method description and efficiency tests, Int. J. Climatol., 37, 1910–1921, https://doi.org/10.1002/joc.4822, 2017b. a
Ducré-Robitaille, J.-F., Vincent, L. A., and Boulet, G.: Comparison of techniques for detection of discontinuities in temperature series, Int. J. Climatol., 23, 1087–1101, https://doi.org/10.1002/joc.924, 2003. a
Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G., and Vose, R. S.: Comprehensive Automated Quality Assurance of Daily Surface Observations, J. Appl. Meteorol. Clim., 49, 1615–1633, https://doi.org/10.1175/2010JAMC2375.1, 2010. a, b
Eccel, E., Cau, P., and Ranzi, R.: Data reconstruction and homogenization for reducing uncertainties in high-resolution climate analysis in Alpine regions, Theor. Appl. Climatol., 110, 345–358, https://doi.org/10.1007/s00704-012-0624-z, 2012. a, b
Faybishenko, B., Versteeg, R., Pastorello, G., Dwivedi, D., Varadharajan, C., and Agarwal, D.: Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data, Stoch. Env. Res. Risk A., 36, 1049–1062, https://doi.org/10.1007/s00477-021-02106-w, 2022. a, b
Fiebrich, C. A. and Crawford, K. C.: The Impact of Unique Meteorological Phenomena Detected by the Oklahoma Mesonet and ARS Micronet on Automated Quality Control, B. Am. Meteorol. Soc., 82, 2173–2188, https://doi.org/10.1175/1520-0477(2001)082<2173:TIOUMP>2.3.CO;2, 2001. a, b
Fioravanti, G., Fraschetti, P., Perconti, W., Piervitali, E., and Desiato, F.: Controlli di qualità delle serie di temperatura e precipitazione, Tech. Rep. 66, ISPRA, Stato dell'ambiente, https://www.isprambiente.gov.it/it/pubblicazioni/stato-dellambiente/controlli-di-qualita-delle-serie-di-temperatura-e-precipitazione (last access: 28 January 2025), 2016. a
Fioravanti, G., Piervitali, E., and Desiato, F.: A new homogenized daily data set for temperature variability assessment in Italy, Int. J. Climatol., 39, 5635–5654, https://doi.org/10.1002/joc.6177, 2019. a, b, c
Folland, C., Frich, R., Basnett, T., Rayner, N., Parker, D., and Horton, B.: Uncertainties in climate datasets – A challenge for WMO, Bulletin of the World Meteorological Organization, 49, 59–67, 2000. a
Gaffen, D. J. and Ross, R. J.: Climatology and Trends of U.S. Surface Humidity and Temperature, J. Climate, 12, 811–828, https://doi.org/10.1175/1520-0442(1999)012<0811:CATOUS>2.0.CO;2, 1999. a, b
Giovannini, L., Laiti, L., Serafin, S., and Zardi, D.: The thermally driven diurnal wind system of the Adige Valley in the Italian Alps, Q. J. Roy. Meteor. Soc., 143, 2389–2402, https://doi.org/10.1002/qj.3092, 2017. a
Gobiet, A., Kotlarski, S., Beniston, M., Heinrich, G., Rajczak, J., and Stoffel, M.: 21st century climate change in the European Alps – A review, Sci. Total Environ., 493, 1138–1151, https://doi.org/10.1016/j.scitotenv.2013.07.050, 2014. a
Gubler, S., Hunziker, S., Begert, M., Croci-Maspoli, M., Konzelmann, T., Brönnimann, S., Schwierz, C., Oria, C., and Rosas, G.: The influence of station density on climate data homogenization, Int. J. Climatol., 37, 4670–4683, https://doi.org/10.1002/joc.5114, 2017. a, b, c
Guijarro, J. A.: climatol: Climate Tools (Series Homogenization and Derived Products), R package version 4.0.0, https://cran.r-project.org/package=climatol (last access: 15 November 2025), 2023. a
Ha-Duong, M., Swart, R., Bernstein, L., and Petersen, A.: Uncertainty management in the IPCC: Agreeing to disagree, Global Environ. Chang., 17, 8–11, https://doi.org/10.1016/j.gloenvcha.2006.12.003, 2007. a
Han, J., Miao, C., Gou, J., Zheng, H., Zhang, Q., and Guo, X.: A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations, Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, 2023. a
Hartmann, D., Klein Tank, A., Rusticucci, M., Alexander, L., Brönnimann, S., Charabi, Y., Dentener, F., Dlugokencky, E., Easterling, D., Kaplan, A., Soden, B., Thorne, P., Wild, M., and Zhai, P.: Observations: Atmosphere and Surface, in: Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 159–254, ISBN 978-1-107-66182-0, https://doi.org/10.1017/CBO9781107415324.008, 2013. a, b, c
Hatono, M., Kiguchi, M., Yoshimura, K., Kanae, S., Kuraji, K., and Oki, T.: A 0.01-degree gridded precipitation dataset for Japan, 1926–2020, Scientific Data, 9, 422, https://doi.org/10.1038/s41597-022-01548-3, 2022. a
Hawkins, D. M.: Identification of outliers, Springer, ISBN 978-94-015-3994-4, https://doi.org/10.1007/978-94-015-3994-4, 1980. a
Herrera, S., Cardoso, R. M., Soares, P. M., Espírito-Santo, F., Viterbo, P., and Gutiérrez, J. M.: Iberia01: a new gridded dataset of daily precipitation and temperatures over Iberia, Earth Syst. Sci. Data, 11, 1947–1956, https://doi.org/10.5194/essd-11-1947-2019, 2019. a
Herzog, J. and Müller-Westermeier, G.: Homogenization of various climatological parameters in the German Weather Service, in: Proceedings of the first seminar for homogenization of surface climatological data, 101–111, https://www.researchgate.net/profile/John-Coll-3/publication/279187713_Ireland_with_HOMER/links/558d2aa608ae1f30aa80efcd/Ireland-with-HOMER.pdf (last access: 15 November 2024), 1996. a
Hijmans, R. J., Karney, C., Williams, E., and Vennes, C.: geosphere: Spherical Trigonometry, r package version 1.5-14, https://CRAN.R-project.org/package=geosphere (last access: 16 September 2024), 2021. a
Hock, R., Rasul, G., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y., Jackson, M., Kääb, A., Kang, S., Kutuzov, S., Al. Milner, U. M., Morin, S., Orlove, B., and Steltzer, H.: High Mountain Areas, Cambridge University Press, 131–202, https://doi.org/10.1017/9781009157964.004, 2022. a
Hofstra, N., Haylock, M., New, M., and Jones, P. D.: Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature, J. Geophys. Res.-Atmos., 114, D21101, https://doi.org/10.1029/2009JD011799, 2009. a
Hubbard, K. G., Goddard, S., Sorensen, W. D., Wells, N., and Osugi, T. T.: Performance of Quality Assurance Procedures for an Applied Climate Information System, J. Atmos. Ocean. Tech., 22, 105–112, https://doi.org/10.1175/JTECH-1657.1, 2005. a
Hunziker, S., Brönnimann, S., Calle, J., Moreno, I., Andrade, M., Ticona, L., Huerta, A., and Lavado-Casimiro, W.: Effects of undetected data quality issues on climatological analyses, Clim. Past, 14, 1–20, https://doi.org/10.5194/cp-14-1-2018, 2018. a, b
Huth, R. and Pokorná, L.: Simultaneous analysis of climatic trends in multiple variables: an example of application of multivariate statistical methods, Int. J. Climatol., 25, 469–484, https://doi.org/10.1002/joc.1146, 2005. a, b
Isotta, F. A., Frei, C., Weilguni, V., Tadić, M. P., Lassègues, P., Rudolf, B., Pavan, V., Cacciamani, C., Antolini, G., m. Ratto, S., Munari, M., Micheletti, S., Bonati, V., Lussana, C., Ronchi, C., Panettieri, E., Marigo, G., and Vertačnik, G.: The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data, Int. J. Climatol., 34, 1657–1675, https://doi.org/10.1002/joc.3794, 2014. a, b, c, d, e, f, g
Jones, P., Horton, E., Folland, C., Hulme, M., Parker, D., and Basnett, T.: The Use of Indices to Identify Changes in Climatic Extremes, Climatic Change, 42, 131–149, https://doi.org/10.1023/A:1005468316392, 1999. a
Kaiser, D. P.: Decreasing cloudiness over China: An updated analysis examining additional variables, Geophys. Res. Lett., 27, 2193–2196, https://doi.org/10.1029/2000GL011358, 2000. a, b
Klein Tank, A. M. G., Wijngaard, J. B., Können, G. P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., Heino, R., Bessemoulin, P., Müller-Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., van Engelen, A. F. V., Forland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., Antonio López, J., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexander, L. V., and Petrovic, P.: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment, Int. J. Climatol., 22, 1441–1453, https://doi.org/10.1002/joc.773, 2002. a, b
Kuglitsch, F. G., Auchmann, R., Bleisch, R., Brönnimann, S., Martius, O., and Stewart, M.: Break detection of annual Swiss temperature series, J. Geophys. Res.-Atmos., 117, D13105, https://doi.org/10.1029/2012JD017729, 2012. a, b
Kuhn, M. and Johnson, K.: Applied predictive modeling, Springer, ISBN 978-1461468486, https://doi.org/10.1007/978-1-4614-6849-3, 2013. a
Kunert, L., Friedrich, K., Imbery, F., and Kaspar, F.: Homogenization of German daily and monthly mean temperature time series, Int. J. Climatol., 44, 775–791, https://doi.org/10.1002/joc.8355, 2024. a
Kunkel, K. E., Easterling, D. R., Hubbard, K., Redmond, K., Andsager, K., Kruk, M. C., and Spinar, M. L.: Quality Control of Pre-1948 Cooperative Observer Network Data, J. Atmos. Ocean. Tech., 22, 1691–1705, https://doi.org/10.1175/JTECH1816.1, 2005. a
Kyselý, J. and Plavcová, E.: A critical remark on the applicability of E-OBS European gridded temperature data set for validating control climate simulations, J. Geophys. Res.-Atmos., 115, D23118, https://doi.org/10.1029/2010JD014123, 2010. a
Laiti, L., Zardi, D., de Franceschi, M., Rampanelli, G., and Giovannini, L.: Analysis of the diurnal development of a lake-valley circulation in the Alps based on airborne and surface measurements, Atmos. Chem. Phys., 14, 9771–9786, https://doi.org/10.5194/acp-14-9771-2014, 2014. a
Laiti, L., Mallucci, S., Piccolroaz, S., Bellin, A., Zardi, D., Fiori, A., Nikulin, G., and Majone, B.: Testing the Hydrological Coherence of High-Resolution Gridded Precipitation and Temperature Data Sets, Water Resour. Res., 54, 1999–2016, https://doi.org/10.1002/2017WR021633, 2018. a, b
Leys, C., Ley, C., Klein, O., Bernard, P., and Licata, L.: Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median, J. Exp. Soc. Psychol., 49, 764–766, https://doi.org/10.1016/j.jesp.2013.03.013, 2013. a, b
Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R., Cayan, D. R., and Brekke, L.: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013, Scientific Data, 2, 150042, https://doi.org/10.1038/sdata.2015.42, 2015. a
Luna, M. Y., Guijarro, J. A., and López, J. A.: A monthly precipitation database for Spain (1851–2008): reconstruction, homogeneity and trends, Adv. Sci. Res., 8, 1–4, https://doi.org/10.5194/asr-8-1-2012, 2012. a
Lussana, C., Tveito, O. E., Dobler, A., and Tunheim, K.: seNorge_2018, daily precipitation, and temperature datasets over Norway, Earth Syst. Sci. Data, 11, 1531–1551, https://doi.org/10.5194/essd-11-1531-2019, 2019. a
Mamara, A., Argiriou, A. A., and Anadranistakis, M.: Homogenization of mean monthly temperature time series of Greece, Int. J. Climatol., 33, 2649–2666, https://doi.org/10.1002/joc.3614, 2013. a, b
Marchetti, M., Soldati, M., and Vandelli, V.: The Great Diversity of Italian Landscapes and Landforms: Their Origin and Human Imprint, Springer, Cham, 7–20, https://doi.org/10.1007/978-3-319-26194-2_2, 2017. a
Mateus, C. and Potito, A.: Development of a Quality-Controlled and Homogenised Long-Term Daily Maximum and Minimum Air Temperature Network Dataset for Ireland, Climate, 9, 158, https://doi.org/10.3390/cli9110158, 2021. a
Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., Schöner, W., Cat Berro, D., Chiogna, G., De Gregorio, L., Kotlarski, S., Majone, B., Resch, G., Terzago, S., Valt, M., Beozzo, W., Cianfarra, P., Gouttevin, I., Marcolini, G., Notarnicola, C., Petitta, M., Scherrer, S. C., Strasser, U., Winkler, M., Zebisch, M., Cicogna, A., Cremonini, R., Debernardi, A., Faletto, M., Gaddo, M., Giovannini, L., Mercalli, L., Soubeyroux, J.-M., Sušnik, A., Trenti, A., Urbani, S., and Weilguni, V.: Observed snow depth trends in the European Alps: 1971 to 2019, The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, 2021. a, b
Meropi, P., Bikos, C., and Zioutas, G.: Outlier dectection in skewed data, Simul. Model. Pract. Th., 87, 191–209, https://doi.org/10.1016/j.simpat.2018.05.010, 2018. a
Miller, J.: Short Report: Reaction Time Analysis with Outlier Exclusion: Bias Varies with Sample Size, Q. J. Exp. Psychol.-A, 43, 907–912, https://doi.org/10.1080/14640749108400962, 1991. a
Panziera, L., Giovannini, L., Laiti, L., and Zardi, D.: The relation between circulation types and regional Alpine climate. Part I: synoptic climatology of Trentino, Int. J. Climatol., 35, 4655–4672, https://doi.org/10.1002/joc.4314, 2015. a
Pavlidou, M. and Zioutas, G.: Kernel Density Outlier Detector, Topics in Nonparametric Statistics, ISBN 978-1-4939-0569-0, 2014. a
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 15 December 2024), 2022. a
Rayens, W. S. and Srinivasan, C.: Box–Cox transformations in the analysis of compositional data, J. Chemometr., 5, 227–239, https://doi.org/10.1002/cem.1180050310, 1991. a
Reek, T., Doty, S., and Owen, T.: A Deterministic Approach to the Validation of Historical Daily Temperature and Precipitation Data From the Cooperative Network, B. Am. Meteorol. Soc., 73, 753–765, https://doi.org/10.1175/1520-0477(1992)073<0753:ADATTV>2.0.CO;2, 1992. a
Reeves, J., Chen, J., Wang, X. L., Lund, R., and Lu, Q. Q.: A Review and Comparison of Changepoint Detection Techniques for Climate Data, J. Appl. Meteorol. Clim., 46, 900–915, https://doi.org/10.1175/JAM2493.1, 2007. a
Resch, G., Koch, R., Marty, C., Chimani, B., Begert, M., Buchmann, M., Aschauer, J., and Schöner, W.: A quantile-based approach to improve homogenization of snow depth time series, Int. J. Climatol., 43, 157–173, https://doi.org/10.1002/joc.7742, 2023. a
Ribeiro, S., Caineta, J., and Costa, A.: Review and discussion of homogenisation methods for climate data, Phys. Chem. Earth, 94, 167–179, https://doi.org/10.1016/j.pce.2015.08.007, 2016. a, b, c, d
Schär, C., Davies, T., Frei, C., Wanner, H., Widmann, M., Wild, M., and Davies, H.: Current alpine climate. Views from the Alps: Regional Perspectives on Climate Change, The MIT Press, ISBN 9780262519816, 1998. a
Schlegel, R. W. and Smit, A. J.: heatwaveR: Detect Heatwaves and Cold-Spells, r package version 0.4.6, https://CRAN.R-project.org/package=heatwaveR (last access: 3 June 2024), 2021. a
Schmidlin, T. W., Wilks, D. S., McKay, M., and Cember, R. P.: Automated Quality Control Procedure for the “Water Equivalent of Snow on the Ground” Measurement, J. Appl. Meteorol., 34, 143–151, http://www.jstor.org/stable/26187201 (last access: 11 June 2024), 1995. a
Serafin, S. and Zardi, D.: Daytime Development of the Boundary Layer over a Plain and in a Valley under Fair Weather Conditions: A Comparison by Means of Idealized Numerical Simulations, J. Atmos. Sci., 68, 2128–2141, https://doi.org/10.1175/2011JAS3610.1, 2011. a
Skrynyk, O., Sidenko, V., Aguilar, E., Guijarro, J., Skrynyk, O., Palamarchuk, L., Oshurok, D., Osypov, V., and Osadchyi, V.: Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine, Int. J. Climatol., 43, 1–17, https://doi.org/10.1002/joc.8080, 2023. a, b, c
Squintu, A. A., van der Schrier, G., Štěpánek, P., Zahradníček, P., and Tank, A. K.: Comparison of homogenization methods for daily temperature series against an observation-based benchmark dataset, Theor. Appl. Climatol., 140, 285–301, https://doi.org/10.1007/s00704-019-03018-0, 2020. a, b, c
Swart, R., Bernstein, L., Ha-Duong, M., and Petersen, A.: Agreeing to disagree: uncertainty management in assessing climate change, impacts and responses by the IPCC, Climatic Change, 92, 1–29, https://doi.org/10.1007/s10584-008-9444-7, 2009. a
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., and Whitfield, P. H.: SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018, Earth Syst. Sci. Data, 12, 2381–2409, https://doi.org/10.5194/essd-12-2381-2020, 2020. a
Thorne, P. W., Willett, K. M., Allan, R. J., Bojinski, S., Christy, J. R., Fox, N., Gilbert, S., Jolliffe, I., Kennedy, J. J., Kent, E., Tank, A. K., Lawrimore, J., Parker, D. E., Rayner, N., Simmons, A., Song, L., Stott, P. A., and Trewin, B.: Guiding the Creation of A Comprehensive Surface Temperature Resource for Twenty-First-Century Climate Science, B. Am. Meteorol. Soc., 92, ES40–ES47, https://doi.org/10.1175/2011BAMS3124.1, 2011. a
Toreti, A. and Desiato, F.: Changes in temperature extremes over Italy in the last 44 years, Int. J. Climatol., 28, 733–745, https://doi.org/10.1002/joc.1576, 2008. a
Toreti, A., Kuglitsch, F. G., Xoplaki, E., Della-Marta, P., Aguilar, E., Prohom, M., and Luterbacher, J.: A note on the use of the standard normal homogeneity test (SNHT) to detect inhomogeneities in climatic time series, Int. J. Climatol., 31, 630–632, https://doi.org/10.1002/joc.2088, 2011. a
Toreti, A., Kuglitsch, F. G., Xoplaki, E., and Luterbacher, J.: A Novel Approach for the Detection of Inhomogeneities Affecting Climate Time Series, J. Appl. Meteorol. Clim., 51, 317–326, https://doi.org/10.1175/JAMC-D-10-05033.1, 2012. a
Trewin, B.: Exposure, instrumentation, and observing practice effects on land temperature measurements, WIREs Climate Change, 1, 490–506, https://doi.org/10.1002/wcc.46, 2010. a
Trewin, B.: A daily homogenized temperature data set for Australia, Int. J. Climatol., 33, 1510–1529, https://doi.org/10.1002/joc.3530, 2013. a
Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti, M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., and Brandsma, T.: Benchmarking homogenization algorithms for monthly data, Clim. Past, 8, 89–115, https://doi.org/10.5194/cp-8-89-2012, 2012. a
Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratiannil, S., Cheval, S., Klancar, M., Brunetti, M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., Brandsma, T., and Willett, K.: Benchmarking homogenization algorithms for monthly data, AIP Conf. Proc., 1552, 1060–1065, https://doi.org/10.1063/1.4819690, 2013. a, b
Villarini, G., Khouakhi, A., and Cunningham, E.: On the impacts of computing daily temperatures as the average of the daily minimum and maximum temperatures, Atmos. Res., 198, 145–150, https://doi.org/10.1016/j.atmosres.2017.08.020, 2017. a
Vose, R. S., Schmoyer, R. L., Steurer, P. M., Peterson, T. C., Heim, R., Karl, T. R., and Eischeid, J. K.: The Global Historical Climatology Network: Long-term monthly temperature, precipitation, sea level pressure, and station pressure data, Tech. rep., Oak Ridge National Lab., TN (United States), Carbon Dioxide Information Analysis Center, https://doi.org/10.2172/10178730, 1992. a
Wang, J. X. L. and Gaffen, D. J.: Late-Twentieth-Century Climatology and Trends of Surface Humidity and Temperature in China, J. Climate, 14, 2833–2845, https://doi.org/10.1175/1520-0442(2001)014<2833:LTCCAT>2.0.CO;2, 2001. a, b
Wang, X. L.: Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test, J. Appl. Meteorol. Clim., 47, 2423–2444, https://doi.org/10.1175/2008JAMC1741.1, 2008. a
Weber, R.: Influence of different daily mean formulas on monthly and annual averages of temperature, Theor. Appl. Climatol., 47, 205–213, https://doi.org/10.1007/BF00866241, 1993. a
Weiss, A. and Hays, C. J.: Calculating daily mean air temperatures by different methods: implications from a non-linear algorithm, Agr. Forest Meteorol., 128, 57–65, https://doi.org/10.1016/j.agrformet.2004.08.008, 2005. a
Wijngaard, J. B., Klein Tank, A. M. G., and Können, G. P.: Homogeneity of 20th century European daily temperature and precipitation series, Int. J. Climatol., 23, 679–692, https://doi.org/10.1002/joc.906, 2003. a, b
WMO: Guide to Meteorological Instruments and Methods of Observation, WMO-No. 8, https://community.wmo.int/en/activity-areas/imop/wmo-no_8 (last access: 28 January 2025), 2008. a
WMO: Guide to climatological practices, WMO-No. 100, https://community.wmo.int/en/activity-areas/climate/draft-fourth-edition-guide-climatological-practices-wmo-no-100 (last access: 11 November 2021), 2018. a
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh, A.: APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1, 2012. a
Short summary
EEAR-Clim is a new and unprecedented observational dataset gathering in situ daily measurements of air temperature and precipitation from a network of about 9000 weather stations covering the European Alps. Data collected, including time series from recordings up to 2020 and time series significantly enhancing data coverage at high elevations, were tested for quality and homogeneity. The dataset aims to serve as a powerful tool for better understanding climate change over the European Alpine region.
EEAR-Clim is a new and unprecedented observational dataset gathering in situ daily measurements...
Altmetrics
Final-revised paper
Preprint