[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124342-en":3,"doc-seo-124342-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124342,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Structure of Rise in Monthly Temperature in Europe as Estimated by Machine Learning","Rising air temperature is a central research issue due to its wide-ranging, harmful effects on people and activities, requiring continuous monitoring with increasingly objective tools such as artificial intelligence. This study uses unsupervised machine learning to analyze the structure of mean monthly air-temperature increase across Europe. Using station data from 210 sites for 1951–2020, the authors apply hierarchical clustering and k-means in two phases and identify 1999 as the onset of the sustained linear rise.","Pure Appl. Geophys. 182 (2025), 2631–2653 􀀃 2025 The Author(s) [https://doi.org/10.1007/s00024-025-03742-x](https://doi.org/10.1007/s00024-025-03742-x)  \nStructure of Rise in Monthly Temperature in Europe as Estimated by Machine Learning  \nANNA FRANCZYK, 1  ROBERT TWARDOSZ,2  and ADAM WALANUS 1   \nAbstract—The rise in air temperature is a leading research topic. This is not only from the cognitive point of view, but also for practical reasons because it involves many effects that are dangerous to humans and their activities. Although this is not a new issue, it requires continuous monitoring as well as the application of multiple methods, including the latest, apparently most objective methods offered by, inter alia, artificial intelligence. In the present paper, the authors have undertaken to investigate the structure of the rise in mean monthly air temperatures in Europe using unsupervised machine learning methods. The last 70 years can be divided into two periods, one of which is relatively stable and the second of which shows an evident rise in temperature. The correct determination of the year in which that change occurred is crucial. Mean monthly temperatures in Europe and its direct surroundings were used for this purpose. The data originated from 210 meteorological stations and covered the period 1951–2020 . The analysis was performed using the hierarchical clustering and k-means clustering methods. The research was conducted in two phases. The first phase involved the analysis of area-average values, followed by the analysis of each station separately. Clear results were obtained, which confirms the usefulness of machine learning as a tool for monitoring temperature change. The quantitative change in the behavior of monthly temperature recorded from 1950 all over Europe is positioned at 1999, when the linear rise started.  \nKeywords: Machine learning, k-means clustering method, air temperature rise, Europe.  \n1. Introduction  \nClimate change and climate variability are a leading topic of contemporary scientific research, which is an obvious consequence of the global warming occurring in front of our eyes and broadly  \n1 Faculty of Geology, Geophysics and Environmental Protection, AGH, University of Science and Technology, al. Mickiewicza 30, 30-059 Krak´ow, Poland. E-mail: franczyk@a[gh.edu.pl](gh.edu.pl); [a@adamwalanus.pl](a@adamwalanus.pl)  \n2 Faculty of Geography and Geology, Jagiellonian University, ul. Gronostajowa 7, 30-387 Krak´ow, Poland. E-mail: [r.twardosz@uj.edu.pl](r.twardosz@uj.edu.pl)  \npresented in IPCC publications (IPCC, 2023) . The rise in temperature has been recorded from the end of the Little Ice Age, and its pace rapidly accelerated atthe end of the twentieth century. In the period 1880–2019, the rise totalled approximately 07 􀀂 C/  \n100 years, whereas it reached as much as approximately 0.2 􀀂 C/100 years in the last 30 years (1990–2019) (Kundzewicz et al., 2020) . Trenberth et al., (2007) provided evidence that the warming shows clear spatial variation: it is greater over land than over the sea. The European continent stands out from the other continents with the greatest warming (Luterbacher et al., 2016; van der Schrier et al., 2013), manifested with an increased frequency and intensity of heat waves, as well as the occurrence of entire monthly periods, or even seasons, with temperatures significantly exceeding the long-term average (e.g. Liu et al., 2020; Twardosz & Kossowska-Cezak, 2021) . According to accepted scenarios, the rapid rate of warming will continue (Vautard et al., 2014) .  \nOn the basis of the data from various sources and years, it was evident that the rise in air temperature in Europe and its direct surroundings is not, however, uniform in time and place (Chen et al., 2015; Krauskopf & Huth, 2020; Twardosz et al., 2021) . This means that contemporary warming is a complex process that is spatially diversified (Hegerl et al., 2018; Ji et al., 2014; Krauskopf & Huth, 2020; Twardosz e","cbCaiurPy39lpKW5","https://ap.wps.com/l/cbCaiurPy39lpKW5","pdf",3910629,1,23,"English","en",105,"# Abstract\n# Introduction\n## Background on climate change and monitoring\n## Motivation for machine-learning-based analysis\n## Study objective and approach","[{\"question\":\"What modeling approach is used to investigate temperature changes in Europe?\",\"answer\":\"The study applies unsupervised machine learning, specifically hierarchical clustering and k-means clustering, to identify the structure of mean monthly temperature changes.\"},{\"question\":\"Which time period and data source are used in the analysis?\",\"answer\":\"Mean monthly temperatures from Europe and nearby areas are taken from 210 meteorological stations covering 1951–2020.\"},{\"question\":\"How do the authors determine the year when the temperature-change pattern shifts?\",\"answer\":\"The analysis aims to identify the year marking the beginning of rapid warming acceleration, and the results place the quantitative change around 1999.\"}]","Structure of Rise in Monthly Temperature in Europe as Estimated by Machine Learning | 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modeling approach is used to investigate temperature changes in Europe?","Question",{"text":75,"@type":76},"The study applies unsupervised machine learning, specifically hierarchical clustering and k-means clustering, to identify the structure of mean monthly temperature changes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which time period and data source are used in the analysis?",{"text":80,"@type":76},"Mean monthly temperatures from Europe and nearby areas are taken from 210 meteorological stations covering 1951–2020.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the authors determine the year when the temperature-change pattern shifts?",{"text":84,"@type":76},"The analysis aims to identify the year marking the beginning of rapid warming acceleration, and the results place the quantitative change around 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