Articles | Volume 10, issue 2
https://doi.org/10.5194/ascmo-10-173-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-10-173-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales
Chalachew Muluken Liyew
Department of Computer Science, University of Turin, Turin, Italy
Faculty of Computing, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Ethiopia
Elvira Di Nardo
Department of Mathematics “Giuseppe Peano”, University of Turin, Turin, Italy
Rosa Meo
Department of Computer Science, University of Turin, Turin, Italy
Stefano Ferraris
CORRESPONDING AUTHOR
Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino and University of Turin, Turin, Italy
Related authors
No articles found.
Alessio Gentile, Jana von Freyberg, Davide Gisolo, Davide Canone, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 28, 1915–1934, https://doi.org/10.5194/hess-28-1915-2024, https://doi.org/10.5194/hess-28-1915-2024, 2024
Short summary
Short summary
Can we leverage high-resolution and low-cost EC measurements and biweekly δ18O data to estimate the young water fraction at higher temporal resolution? Here, we present the EXPECT method that combines two widespread techniques: EC-based hydrograph separation and sine-wave models of the seasonal isotope cycles. The method is not without its limitations, but its application in three small Swiss catchments is promising for future applications in catchments with different characteristics.
Giorgio Baiamonte, Carmelo Agnese, Carmelo Cammalleri, Elvira Di Nardo, Stefano Ferraris, and Tommaso Martini
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 51–67, https://doi.org/10.5194/ascmo-10-51-2024, https://doi.org/10.5194/ascmo-10-51-2024, 2024
Short summary
Short summary
In hydrology, the probability distributions are used to determine the probability of occurrence of rainfall events. In this study, two different methods for modeling rainfall time characteristics have been applied: a direct method and an indirect method that make it possible to relax the assumptions of the renewal process. The analysis was extended to two additional time variables that may be of great interest for practical hydrological applications: wet chains and dry chains.
Alessio Gentile, Davide Canone, Natalie Ceperley, Davide Gisolo, Maurizio Previati, Giulia Zuecco, Bettina Schaefli, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 27, 2301–2323, https://doi.org/10.5194/hess-27-2301-2023, https://doi.org/10.5194/hess-27-2301-2023, 2023
Short summary
Short summary
What drives young water fraction, F*yw (i.e., the fraction of water in streamflow younger than 2–3 months), variations with elevation? Why is F*yw counterintuitively low in high-elevation catchments, in spite of steeper topography? In this paper, we present a perceptual model explaining how the longer low-flow duration at high elevations, driven by the persistence of winter snowpacks, increases the proportion of stored (old) water contributing to the stream, thus reducing F*yw.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
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
Short summary
Short summary
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.
Cited articles
Acquaotta, F., Fratianni, S., and Garzena, D.: Temperature changes in the North-Western Italian Alps from 1961 to 2010, Theor. Appl. Climatol., 122, 619–634, https://doi.org/10.1007/s00704-014-1316-7, 2015. a
Alhaji, U., Yusuf, A. S., Edet, C. O., Oche, C., and Agbo, E. P.: Trend analysis of temperature in Gombe state using Mann Kendall trend test, J. Sci. Res. Rep., 20, 1–9, https://doi.org/10.9734/JSRR/2018/42029, 2018. a
Bhuyan, M. D. I., Islam, M. M., and Bhuiyan, M. E. K.: A trend analysis of temperature and rainfall to predict climate change for northwestern region of Bangladesh, Am. J. Clim. Change, 7, 115–134, https://doi.org/10.4236/ajcc.2018.72009, 2018. a, b, c
Blackport, R., Fyfe, J. C., and Screen, J. A.: Decreasing subseasonal temperature variability in the northern extratropics attributed to human influence, Nat. Geosci., 14, 719–723, 2021. a
Bruley, E., Mouillot, F., Lauvaux, T., and Rambal, S.: Enhanced spring warming in a Mediterranean mountain by atmospheric circulation, Sci. Rep., 12, 7721, https://doi.org/10.1038/s41598-022-11837-x, 2022. 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, 2006. a
Byrne, M. P., Boos, W. R., and Hu, S.: Elevation-dependent warming: observations, models, and energetic mechanisms, Weather Clim. Dynam., 5, 763–777, https://doi.org/10.5194/wcd-5-763-2024, 2024. a
Cai, Q., Chen, W., Chen, S., Xie, S.-P., Piao, J., Ma, T., and Lan, X.: Recent pronounced warming on the Mongolian Plateau boosted by internal climate variability, Nat. Geosci., 17, 181–188, https://doi.org/10.1038/s41561-024-01377-6, 2024. a
Collins, M., Knutti, R., Arblaster, J., Dufresne, J. L., Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A. J., and Wehner, M.: Long-term Climate Change: Projections, Commitments and Irreversibility, 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, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013. a
Cox, D. R. and Stuart, A.: Some quick sign tests for trend in location and dispersion, Biometrika, 42, 80–95, https://doi.org/10.2307/2333424, 1955. a
Di Bernardino, A., Iannarelli, A. M., Diémoz, H., Casadio, S., Cacciani, M., and Siani, A. M.: Analysis of two-decade meteorological and air quality trends in Rome (Italy), Theor. Appl. Climatol., 149, 291–307, https://doi.org/10.1007/s00704-022-04047-y, 2022. a, b
Durand, Y., Laternser, M., Giraud, G., Etchevers, P., Lesaffre, B., and Mérindol, L.: Reanalysis of 44 yr of climate in the French Alps (1958–2002): methodology, model validation, climatology, and trends for air temperature and precipitation, J. Appl. Meteorol. Climatol., 48, 429–449, https://doi.org/10.1175/2008JAMC1808.1, 2009. a, b
El Kenawy, A., López-Moreno, J. I., and Vicente-Serrano, S. M.: Trend and variability of surface air temperature in northeastern Spain (1920–2006): linkage to atmospheric circulation, Atmos. Res., 106, 159–180, https://doi.org/10.1016/j.atmosres.2011.12.006, 2012. a, b, c
Farooq, I., Shah, A. R., Salik, K. M., and Ismail, M.: Annual, seasonal and monthly trend analysis of temperature in Kazakhstan during 1970–2017 using non-parametric statistical methods and GIS technologies, Earth Syst. Environ., 5, 575–595, https://doi.org/10.1007/s41748-021-00244-3, 2021. a, b
Fleig, A. K., Tallaksen, L. M., James, P., Hisdal, H., and Stahl, K.: Attribution of European precipitation and temperature trends to changes in synoptic circulation, Hydrol. Earth Syst. Sci., 19, 3093–3107, https://doi.org/10.5194/hess-19-3093-2015, 2015. a
Gil-Alaña, L. A., Gupta, R., Sauci, L., and Carmona-González, N.: Temperature and precipitation in the US states: long memory, persistence, and time trend, Theor. Appl. Climatol., 150, 1731–1744, https://doi.org/10.1007/s00704-022-04232-z, 2022. a
Giorgino, T.: Computing and visualizing dynamic time warping alignments in R: the dtw package, J. Stat. Softw., 31, 1–24, 2009. a
Hoffmann, P. and Spekat, A.: Identification of possible dynamical drivers for long-term changes in temperature and rainfall patterns over Europe, Theor. Appl. Climatol., 143, 177–191, https://doi.org/10.1007/s00704-020-03373-3, 2021. a
Huwald, H., Higgins, C. W., Boldi, M. O., Bou‐ Zeid, E., Lehning, M., and Parlange, M. B.: Albedo effect on radiative errors in air temperature measurements, Water Resour. Res., 45, https://doi.org/10.1029/2008WR007600, 2009. a
Hyndman, R. J. and Athanasopoulos, G.: Forecasting: principles and practice, OTEXTS, Melbourne, Australia, 2018. a
Isaac, V. and Van Wijngaarden, W.: Surface water vapor pressure and temperature trends in North America during 1948–2010, J. Climate, 25, 3599–3609, https://doi.org/10.1175/JCLI-D-11-00003.1, 2012. a, b
Johnson, G. C. and Lyman, J. M.: Warming trends increasingly dominate global ocean, Nat. Clim. Change, 10, 757–761, https://doi.org/10.1038/s41558-020-0822-0, 2020. a
Khavse, R., Deshmukh, R., Manikandan, N., Chaudhary, J., and Kaushik, D.: Statistical analysis of temperature and rainfall trend in Raipur district of Chhattisgarh, Current World Environment, 10, 305–312, 2015. a
Li, Q., Sheng, B., Huang, J., Li, C., Song, Z., Chao, L., Sun, W., Yang, Y., Jiao, B., Guo, Z., Liao, L., Li, X., Sun, C., Li, W., Huang, B., Dong, W., and Jones, P.: Different climate response persistence causes warming trend unevenness at continental scales, Nat. Clim. Change, 12, 343–349, https://doi.org/10.1038/s41558-022-01313-9, 2022. a
Liyew, C. M.: cliyew/temperature_trends: Temperature Trends (temperature_trends), Zenodo [code], https://doi.org/10.5281/zenodo.14070482, 2024. a
Maechler, M., Rousseeuw, P., Croux, C., Todorov, V., Ruckstuhl, A., Salibian-Barrera, M., Verbeke, T., Koller, M., Conceicao, E. L. T., and Anna di Palma, M.: robustbase: Basic Robust Statistics, r package version 0.99-0, http://robustbase.r-forge.r-project.org/ (last access: 16 May 2023), 2023a. a
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., and Hornik, K.: cluster: Cluster Analysis Basics and Extensions, r package version 2.1.5, https://CRAN.R-project.org/package=cluster (last access: 23 September 2023), 2023b. a
Manara, V., Brunetti, M., Wild, M., and Maugeri, M.: Variability and trends of the total cloud cover over Italy (1951–2018), Atmos. Res., 285, 106625, https://doi.org/10.1016/j.atmosres.2023.106625, 2023. a
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945. a
Meshram, S. G., Kahya, E., Meshram, C., Ghorbani, M. A., Ambade, B., and Mirabbasi, R.: Long-term temperature trend analysis associated with agriculture crops, Theor. Appl. Climatol., 140, 1139–1159, https://doi.org/10.1007/s00704-020-03137-z, 2020. a
Meyer, D. and Buchta, C.: proxy: Distance and Similarity Measures, r package version 0.4-27, https://cran.r-project.org/web/packages/proxy/index.html (last access: 23 September 2023), 2022. a
Mohsin, T. and Gough, W. A.: Trend analysis of long-term temperature time series in the Greater Toronto Area (GTA), Theor. Appl. Climatol., 101, 311–327, https://doi.org/10.1007/s00704-009-0214-x, 2010. a
Moritz, S. and Bartz-Beielstein, T.: imputeTS: Time Series Missing Value Imputation in R, The R Journal, 9, 207–218, https://doi.org/10.32614/RJ-2017-009, 2017. a
MRI: Elevation-dependent warming in mountain regions of the world, Nat. Clim. Change, 5, 424–430, https://doi.org/10.1038/nclimate2563, 2015. a
Mudelsee, M.: Trend analysis of climate time series: A review of methods, Earth-science reviews, 190, 310–322, https://doi.org/10.1016/j.earscirev.2018.12.005, 2019. a
NOAA: Climate at a Glance: Global Time Series, published November 2023, J. Comput. Appl. Math., 20, 53–65, 1987. a
Patterson, M.: North-West Europe hottest days are warming twice as fast as mean summer days, Geophys. Res. Lett., 50, 1–10, https://doi.org/10.1029/2023GL102757, 2023. a, b
Radhakrishnan, K., Sivaraman, I., Jena, S. K., Sarkar, S., and Adhikari, S.: A climate trend analysis of temperature and rainfall in India, Clim. Change Environ. Sustain., 5, 146–153, https://doi.org/10.5958/2320-642X.2017.00014.X, 2017. a, b, c
Rebetez, M. and Reinhard, M.: Monthly air temperature trends in Switzerland 1901–2000 and 1975–2004, Theor. Appl. Climatol., 91, 27–34, https://doi.org/10.1007/s00704-007-0296-2, 2008. a, b
Rizzo, M. and Szekely, G.: energy: E-Statistics: Multivariate Inference via the Energy of Data, r package version 1.7-11, https://cran.r-project.org/web/packages/energy/index.html (last access: 1 October 2023), 2022. a
Robson, J., Ortega, P., and Sutton, R.: A reversal of climatic trends in the North Atlantic since 2005, Nat. Geosci., 9, 513–517, https://doi.org/10.1038/NGEO2727, 2016. a
Rogora, M., Arisci, S., and Mosello, R.: Recent trends of temperature and precipitation in alpine and subalpine areas in North Western Italy, Geogr. Fis. Dinam. Quat., 27, 151–158, 2004. a
Rousseeuw, P. and Yohai, V.: Robust regression by means of S-estimators, in: Robust and Nonlinear Time Series Analysis: Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 “Stochastische Mathematische Modelle”, Heidelberg 1983, 256–272, Springer, https://doi.org/10.1007/978-1-4615-7821-5_15, 1984. a
Rousseeuw, P. J.: Least median of squares regression, J. Am. Stat. A., 79, 871–880, https://doi.org/10.2307/2288718, 1984. a, b
Rousseeuw, P. J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53–65, 1987. a
Royston, J. P.: An extension of Shapiro and Wilk's W test for normality to large samples, J. Roy. Stat. Soc. C, 31, 115–124, 1982. a
Salerno, F., Guyennon, N., Yang, K., Shaw, T. E., Lin, C., Colombo, N., Romano, E., Gruber, S., Bolch, T., Alessandri, A., Cristofanelli, P., Putero, D., Diolaiuti, G., Tartari, G., Verza, G., Thakuri, S., Balsamo, G., Miled, E. S., and Pellicciotti, F.: Local cooling and drying induced by Himalayan glaciers under global warming, Nat. Geosci., 16, 1120–1127, https://doi.org/10.1038/s41561-023-01331-y, 2023. a
Sardá-Espinosa, A.: Comparing time-series clustering algorithms in r using the dtwclust package, R package vignette, 12, 41, https://cran.radicaldevelop.com/web/packages/dtwclust/vignettes/dtwclust.pdf (last access: 18 September 2023), 2017. a
Sayemuzzaman, M., Mekonnen, A., and Jha, M. K.: Diurnal temperature range trend over North Carolina and the associated mechanisms, Atmos. Res., 160, 99–108, https://doi.org/10.1016/j.atmosres.2015.03.009, 2015. a, b
Shen, S. and Chi, M.: Clustering Student Sequential Trajectories Using Dynamic Time Warping., International Educational Data Mining Society, https://api.semanticscholar.org/CorpusID:19096679 (last access: 24 September 2023), 2017. a
Shen, X., Liu, B., Li, G., Wu, Z., Jin, Y., Yu, P., and Zhou, D.: Spatiotemporal change of diurnal temperature range and its relationship with sunshine duration and precipitation in China, J. Geophys. Res.-Atmos., 119, 13–163, https://doi.org/10.1002/2014JD022326, 2014. a
Shen, X., Liu, B., and Lu, X.: Weak cooling of cold extremes versus continued warming of hot extremes in China during the recent global surface warming hiatus, J. Geophys. Res.-Atmos., 123, 4073–4087, https://doi.org/10.1002/2017JD027819, 2018. a
Simmons, A., Hersbach, H., Munoz-Sabater, J., Nicolas, J., Vamborg, F., Berrisford, P., de Rosnay, P., Willett, K., and Woollen, J.: Low frequency variability and trends in surface air temperature and humidity from ERA5 and other datasets, ECMWF Technical Memoranda, 881, https://doi.org/10.21957/ly5vbtbfd, 2021. a
Székely, G. J. and Rizzo, M. L.: Brownian distance covariance, Ann. Appl. Stat., 3, 1236–1265, 2009. a
Tang, R., He, B., Chen, H. W., Chen, D., Chen, Y., Fu, Y. H., Yuan, W., Li, B., Li, Z., Guo, L., Hao, X., Sun, L., Liu, H., Sun, C., and Yang, Y.: Increasing terrestrial ecosystem carbon release in response to autumn cooling and warming, Nat. Clim. Change, 12, 380–385, https://doi.org/10.1038/s41558-022-01304-w, 2022. a
Twardosz, R., Walanus, A., and Guzik, I.: Warming in Europe: Recent trends in annual and seasonal temperatures, Pure Appl. Geophys., 178, 4021–4032, https://doi.org/10.1007/s00024-021-02860-6, 2021. a
Vinnikov, K. Y., Robock, A., and Basist, A.: Diurnal and seasonal cycles of trends of surface air temperature, J. Geophys. Res.-Atmos., 107, ACL 13-1–ACL 13-9, https://doi.org/10.1029/2001JD002007, 2002. a, b
Wooldridge, J. M.: Introductory econometrics: A modern approach, Cengage learning, 2015. a
Zhang, Y., Piao, S., Sun, Y., Rogers, B. M., Li, X., Lian, X., Liu, Z., Chen, A., and Peñuelas, J.: Future reversal of warming-enhanced vegetation productivity in the Northern Hemisphere, Nat. Clim. Change, 12, 581–586, https://doi.org/10.1038/s41558-022-01374-w, 2022. a
Short summary
Global warming is a big issue: it is necessary to know more details to make a forecast model and plan adaptation measures. Warming varies in space and time and models often average it over large areas. However, it shows great variations between months of the year. It also varies between regions of the world and between lowland and highland regions. This paper uses statistical and machine learning techniques to quantify such differences between Italy and the UK at different altitudes.
Global warming is a big issue: it is necessary to know more details to make a forecast model and...