Emerging climate signals in the Lena River catchment: a non-parametric statistical approach
International audience 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 a...
Published in: | Hydrology and Earth System Sciences |
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Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2020
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Online Access: | https://hal.science/hal-02766161 https://hal.science/hal-02766161/document https://hal.science/hal-02766161/file/hess-24-2817-2020.pdf https://doi.org/10.5194/hess-24-2817-2020 |
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English |
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[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment |
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[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment Pohl, Eric Grenier, Christophe Vrac, Mathieu Kageyama, Masa Emerging climate signals in the Lena River catchment: a non-parametric statistical approach |
topic_facet |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment |
description |
International audience 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 show-case 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 ... |
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Université de Fribourg = University of Fribourg (UNIFR) Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA) Modélisation Hydrologique (HYDRO) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR) Modélisation du climat (CLIM) ANR-17-EURE-0006,IPSL-CGS,IPSL Climate graduate school(2017) |
format |
Article in Journal/Newspaper |
author |
Pohl, Eric Grenier, Christophe Vrac, Mathieu Kageyama, Masa |
author_facet |
Pohl, Eric Grenier, Christophe Vrac, Mathieu Kageyama, Masa |
author_sort |
Pohl, Eric |
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 |
HAL CCSD |
publishDate |
2020 |
url |
https://hal.science/hal-02766161 https://hal.science/hal-02766161/document https://hal.science/hal-02766161/file/hess-24-2817-2020.pdf https://doi.org/10.5194/hess-24-2817-2020 |
genre |
lena river permafrost Siberia |
genre_facet |
lena river permafrost Siberia |
op_source |
ISSN: 1027-5606 EISSN: 1607-7938 Hydrology and Earth System Sciences https://hal.science/hal-02766161 Hydrology and Earth System Sciences, 2020, 24 (5), pp.2817-2839. ⟨10.5194/hess-24-2817-2020⟩ |
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info:eu-repo/semantics/altIdentifier/doi/10.5194/hess-24-2817-2020 hal-02766161 https://hal.science/hal-02766161 https://hal.science/hal-02766161/document https://hal.science/hal-02766161/file/hess-24-2817-2020.pdf doi:10.5194/hess-24-2817-2020 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/hess-24-2817-2020 |
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Hydrology and Earth System Sciences |
container_volume |
24 |
container_issue |
5 |
container_start_page |
2817 |
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2839 |
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1801378734595375104 |
spelling |
ftanrparis:oai:HAL:hal-02766161v1 2024-06-09T07:47:33+00:00 Emerging climate signals in the Lena River catchment: a non-parametric statistical approach Pohl, Eric Grenier, Christophe Vrac, Mathieu Kageyama, Masa Université de Fribourg = University of Fribourg (UNIFR) Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA) Modélisation Hydrologique (HYDRO) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR) Modélisation du climat (CLIM) ANR-17-EURE-0006,IPSL-CGS,IPSL Climate graduate school(2017) 2020 https://hal.science/hal-02766161 https://hal.science/hal-02766161/document https://hal.science/hal-02766161/file/hess-24-2817-2020.pdf https://doi.org/10.5194/hess-24-2817-2020 en eng HAL CCSD European Geosciences Union info:eu-repo/semantics/altIdentifier/doi/10.5194/hess-24-2817-2020 hal-02766161 https://hal.science/hal-02766161 https://hal.science/hal-02766161/document https://hal.science/hal-02766161/file/hess-24-2817-2020.pdf doi:10.5194/hess-24-2817-2020 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 1027-5606 EISSN: 1607-7938 Hydrology and Earth System Sciences https://hal.science/hal-02766161 Hydrology and Earth System Sciences, 2020, 24 (5), pp.2817-2839. ⟨10.5194/hess-24-2817-2020⟩ [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment info:eu-repo/semantics/article Journal articles 2020 ftanrparis https://doi.org/10.5194/hess-24-2817-2020 2024-05-16T15:15:07Z International audience 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 show-case 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 ... Article in Journal/Newspaper lena river permafrost Siberia Portail HAL-ANR (Agence Nationale de la Recherche) Hydrology and Earth System Sciences 24 5 2817 2839 |