Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis
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...
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ftnerc:oai:nora.nerc.ac.uk:527822 2023-05-15T16:13:08+02:00 Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis Leibovici, Didier G. Quegan, Shaun Comyn-Platt, Edward Hayman, Garry Val Martin, Maria Guimberteau, Mathieu Druel, Arsène Zhu, Dan Ciais, Philippe 2020-04-03 text http://nora.nerc.ac.uk/id/eprint/527822/ https://nora.nerc.ac.uk/id/eprint/527822/1/N527822JA.pdf https://doi.org/10.5194/bg-17-1821-2020 en eng EGU https://nora.nerc.ac.uk/id/eprint/527822/1/N527822JA.pdf Leibovici, Didier G.; Quegan, Shaun; Comyn-Platt, Edward; Hayman, Garry; Val Martin, Maria; Guimberteau, Mathieu; Druel, Arsène; Zhu, Dan; Ciais, Philippe. 2020 Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis. Biogeosciences, 17 (7). 1821-1844. https://doi.org/10.5194/bg-17-1821-2020 <https://doi.org/10.5194/bg-17-1821-2020> cc_by_4 CC-BY Ecology and Environment Meteorology and Climatology Publication - Article PeerReviewed 2020 ftnerc https://doi.org/10.5194/bg-17-1821-2020 2023-02-04T19:50:43Z 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 north-west 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 correction applied to ORCHIDEE-HLveg ... Article in Journal/Newspaper Fennoscandian Natural Environment Research Council: NERC Open Research Archive Jules ENVELOPE(140.917,140.917,-66.742,-66.742) Biogeosciences 17 7 1821 1844 |
institution |
Open Polar |
collection |
Natural Environment Research Council: NERC Open Research Archive |
op_collection_id |
ftnerc |
language |
English |
topic |
Ecology and Environment Meteorology and Climatology |
spellingShingle |
Ecology and Environment Meteorology and Climatology Leibovici, Didier G. 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 |
Ecology and Environment Meteorology and Climatology |
description |
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 north-west 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 correction applied to ORCHIDEE-HLveg ... |
format |
Article in Journal/Newspaper |
author |
Leibovici, Didier G. Quegan, Shaun Comyn-Platt, Edward Hayman, Garry Val Martin, Maria Guimberteau, Mathieu Druel, Arsène Zhu, Dan Ciais, Philippe |
author_facet |
Leibovici, Didier G. Quegan, Shaun Comyn-Platt, Edward Hayman, Garry Val Martin, Maria Guimberteau, Mathieu Druel, Arsène Zhu, Dan Ciais, Philippe |
author_sort |
Leibovici, Didier G. |
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 |
EGU |
publishDate |
2020 |
url |
http://nora.nerc.ac.uk/id/eprint/527822/ https://nora.nerc.ac.uk/id/eprint/527822/1/N527822JA.pdf https://doi.org/10.5194/bg-17-1821-2020 |
long_lat |
ENVELOPE(140.917,140.917,-66.742,-66.742) |
geographic |
Jules |
geographic_facet |
Jules |
genre |
Fennoscandian |
genre_facet |
Fennoscandian |
op_relation |
https://nora.nerc.ac.uk/id/eprint/527822/1/N527822JA.pdf Leibovici, Didier G.; Quegan, Shaun; Comyn-Platt, Edward; Hayman, Garry; Val Martin, Maria; Guimberteau, Mathieu; Druel, Arsène; Zhu, Dan; Ciais, Philippe. 2020 Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis. Biogeosciences, 17 (7). 1821-1844. https://doi.org/10.5194/bg-17-1821-2020 <https://doi.org/10.5194/bg-17-1821-2020> |
op_rights |
cc_by_4 |
op_rightsnorm |
CC-BY |
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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1765998736411983872 |