Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis
International audience A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, make use of land surface modelling (LSM) to predict future land surface conditions. Ther...
Published in: | Biogeosciences |
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Main Authors: | , , , , , , , , |
Other Authors: | , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2020
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Subjects: | |
Online Access: | https://hal.archives-ouvertes.fr/hal-02532743 https://hal.archives-ouvertes.fr/hal-02532743/document https://hal.archives-ouvertes.fr/hal-02532743/file/bg-17-1821-2020.pdf https://doi.org/10.5194/bg-17-1821-2020 |
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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 |
spellingShingle |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment Leibovici, Didier, Quegan, Shaun Comyn-Platt, Edward Hayman, Garry Val Martin, Maria Guimberteau, Mathieu Druel, Arsène Zhu, Dan Ciais, Philippe Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis |
topic_facet |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment |
description |
International audience A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, make use of land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and this may introduce predictive uncertainty when LSM outputs are used as inputs to inform a given application. For useful predictions for a specific application , one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fennoscandian and northwest Russian regions, and the approach is explored by comparing net primary production (NPP) estimates over the period 1998-2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high-latitude processes , as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability , is essentially a spatial ... |
author2 |
School of Mathematics and Statistics Sheffield (SoMaS) University of Sheffield Sheffield Centre for Ecology and Hydrology Wallingford (CEH) Natural Environment Research Council (NERC) Department of Animal and Plant Sciences Sheffield Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ) |
format |
Article in Journal/Newspaper |
author |
Leibovici, Didier, Quegan, Shaun Comyn-Platt, Edward Hayman, Garry Val Martin, Maria Guimberteau, Mathieu Druel, Arsène Zhu, Dan Ciais, Philippe |
author_facet |
Leibovici, Didier, Quegan, Shaun Comyn-Platt, Edward Hayman, Garry Val Martin, Maria Guimberteau, Mathieu Druel, Arsène Zhu, Dan Ciais, Philippe |
author_sort |
Leibovici, Didier, |
title |
Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis |
title_short |
Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis |
title_full |
Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis |
title_fullStr |
Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis |
title_full_unstemmed |
Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis |
title_sort |
spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://hal.archives-ouvertes.fr/hal-02532743 https://hal.archives-ouvertes.fr/hal-02532743/document https://hal.archives-ouvertes.fr/hal-02532743/file/bg-17-1821-2020.pdf https://doi.org/10.5194/bg-17-1821-2020 |
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ENVELOPE(140.917,140.917,-66.742,-66.742) |
geographic |
Jules |
geographic_facet |
Jules |
genre |
Fennoscandian |
genre_facet |
Fennoscandian |
op_source |
ISSN: 1726-4170 EISSN: 1726-4189 Biogeosciences https://hal.archives-ouvertes.fr/hal-02532743 Biogeosciences, European Geosciences Union, 2020, 17 (7), pp.1821-1844. ⟨10.5194/bg-17-1821-2020⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/bg-17-1821-2020 hal-02532743 https://hal.archives-ouvertes.fr/hal-02532743 https://hal.archives-ouvertes.fr/hal-02532743/document https://hal.archives-ouvertes.fr/hal-02532743/file/bg-17-1821-2020.pdf doi:10.5194/bg-17-1821-2020 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/bg-17-1821-2020 |
container_title |
Biogeosciences |
container_volume |
17 |
container_issue |
7 |
container_start_page |
1821 |
op_container_end_page |
1844 |
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1765998750101143552 |
spelling |
ftccsdartic:oai:HAL:hal-02532743v1 2023-05-15T16:13:08+02:00 Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis Leibovici, Didier, Quegan, Shaun Comyn-Platt, Edward Hayman, Garry Val Martin, Maria Guimberteau, Mathieu Druel, Arsène Zhu, Dan Ciais, Philippe School of Mathematics and Statistics Sheffield (SoMaS) University of Sheffield Sheffield Centre for Ecology and Hydrology Wallingford (CEH) Natural Environment Research Council (NERC) Department of Animal and Plant Sciences Sheffield Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ) 2020 https://hal.archives-ouvertes.fr/hal-02532743 https://hal.archives-ouvertes.fr/hal-02532743/document https://hal.archives-ouvertes.fr/hal-02532743/file/bg-17-1821-2020.pdf https://doi.org/10.5194/bg-17-1821-2020 en eng HAL CCSD European Geosciences Union info:eu-repo/semantics/altIdentifier/doi/10.5194/bg-17-1821-2020 hal-02532743 https://hal.archives-ouvertes.fr/hal-02532743 https://hal.archives-ouvertes.fr/hal-02532743/document https://hal.archives-ouvertes.fr/hal-02532743/file/bg-17-1821-2020.pdf doi:10.5194/bg-17-1821-2020 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 1726-4170 EISSN: 1726-4189 Biogeosciences https://hal.archives-ouvertes.fr/hal-02532743 Biogeosciences, European Geosciences Union, 2020, 17 (7), pp.1821-1844. ⟨10.5194/bg-17-1821-2020⟩ [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 ftccsdartic https://doi.org/10.5194/bg-17-1821-2020 2021-12-19T00:55:47Z International audience A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, make use of land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and this may introduce predictive uncertainty when LSM outputs are used as inputs to inform a given application. For useful predictions for a specific application , one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fennoscandian and northwest Russian regions, and the approach is explored by comparing net primary production (NPP) estimates over the period 1998-2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high-latitude processes , as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability , is essentially a spatial ... Article in Journal/Newspaper Fennoscandian Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Jules ENVELOPE(140.917,140.917,-66.742,-66.742) Biogeosciences 17 7 1821 1844 |