Emerging climate signals in the Lena River catchment: a non-parametric statistical approach
Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems, and the population's traditional livelihoods. In the Lena River catchment, eastern Siberia, changing climatic conditions and the associated impacts are already observed or expe...
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ftdoajarticles:oai:doaj.org/article:27c2760dc01b458ba5fec171b94a55ff 2023-05-15T17:07:37+02:00 Emerging climate signals in the Lena River catchment: a non-parametric statistical approach E. Pohl C. Grenier M. Vrac M. Kageyama 2020-05-01T00:00:00Z https://doi.org/10.5194/hess-24-2817-2020 https://doaj.org/article/27c2760dc01b458ba5fec171b94a55ff EN eng Copernicus Publications https://www.hydrol-earth-syst-sci.net/24/2817/2020/hess-24-2817-2020.pdf https://doaj.org/toc/1027-5606 https://doaj.org/toc/1607-7938 doi:10.5194/hess-24-2817-2020 1027-5606 1607-7938 https://doaj.org/article/27c2760dc01b458ba5fec171b94a55ff Hydrology and Earth System Sciences, Vol 24, Pp 2817-2839 (2020) Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 article 2020 ftdoajarticles https://doi.org/10.5194/hess-24-2817-2020 2022-12-31T15:53:53Z Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems, and the population's traditional livelihoods. In the Lena River catchment, eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as a PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scales. The method yields dissimilarities or emergence levels ranging from 0 % to 100 % and the direction of change as a continuous time series itself. First, we showcase the method's advantage over the Kolmogorov–Smirnov metric using a synthetic dataset that resembles signals observed in the utilized climate models. Then, we focus on the Lena River catchment, where significant environmental changes are already apparent. On average, the emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50 %–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by ... Article in Journal/Newspaper lena river permafrost Siberia Directory of Open Access Journals: DOAJ Articles Hydrology and Earth System Sciences 24 5 2817 2839 |
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Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 |
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Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 E. Pohl C. Grenier M. Vrac M. Kageyama Emerging climate signals in the Lena River catchment: a non-parametric statistical approach |
topic_facet |
Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 |
description |
Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems, and the population's traditional livelihoods. In the Lena River catchment, eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as a PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scales. The method yields dissimilarities or emergence levels ranging from 0 % to 100 % and the direction of change as a continuous time series itself. First, we showcase the method's advantage over the Kolmogorov–Smirnov metric using a synthetic dataset that resembles signals observed in the utilized climate models. Then, we focus on the Lena River catchment, where significant environmental changes are already apparent. On average, the emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50 %–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by ... |
format |
Article in Journal/Newspaper |
author |
E. Pohl C. Grenier M. Vrac M. Kageyama |
author_facet |
E. Pohl C. Grenier M. Vrac M. Kageyama |
author_sort |
E. Pohl |
title |
Emerging climate signals in the Lena River catchment: a non-parametric statistical approach |
title_short |
Emerging climate signals in the Lena River catchment: a non-parametric statistical approach |
title_full |
Emerging climate signals in the Lena River catchment: a non-parametric statistical approach |
title_fullStr |
Emerging climate signals in the Lena River catchment: a non-parametric statistical approach |
title_full_unstemmed |
Emerging climate signals in the Lena River catchment: a non-parametric statistical approach |
title_sort |
emerging climate signals in the lena river catchment: a non-parametric statistical approach |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/hess-24-2817-2020 https://doaj.org/article/27c2760dc01b458ba5fec171b94a55ff |
genre |
lena river permafrost Siberia |
genre_facet |
lena river permafrost Siberia |
op_source |
Hydrology and Earth System Sciences, Vol 24, Pp 2817-2839 (2020) |
op_relation |
https://www.hydrol-earth-syst-sci.net/24/2817/2020/hess-24-2817-2020.pdf https://doaj.org/toc/1027-5606 https://doaj.org/toc/1607-7938 doi:10.5194/hess-24-2817-2020 1027-5606 1607-7938 https://doaj.org/article/27c2760dc01b458ba5fec171b94a55ff |
op_doi |
https://doi.org/10.5194/hess-24-2817-2020 |
container_title |
Hydrology and Earth System Sciences |
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24 |
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5 |
container_start_page |
2817 |
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2839 |
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