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Effect of climate data on simulated carbon and nitrogen balances for Europe

Blanke, Jan Hendrik LU ; Lindeskog, Mats LU ; Lindström, Johan LU orcid and Lehsten, Veiko LU (2016) In Journal of Geophysical Research - Biogeosciences 121(5). p.1352-1371
Abstract

In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%–93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%–10.7% of the... (More)

In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%–93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%–10.7% of the total variability followed by GCMs (0.3%–7.6%), RCPs (0%–6.3%), and downscaling methods (0.1%–4.6%). The higher-order interactions effect that captures nonlinear relations between the factors and random effects is pronounced and accounts for 1.6%–45.8% to the total variability. The most distinct hot spots of variability include the mountain ranges in North Scandinavia and the Alps, and the Iberian Peninsula. Based on our findings, we advise to conduct the application of models such as LPJ-GUESS at a reasonably high spatial resolution which is supported by the model structure. There is no notable gain in simulations of ecosystem carbon and nitrogen stocks and fluxes from using regionally downscaled climate in preference to bias-corrected, bilinearly interpolated CMIP5 projections.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
climate data, DGVM, downscaling, LPJ-GUESS, sensitivity, spatial resolution
in
Journal of Geophysical Research - Biogeosciences
volume
121
issue
5
pages
20 pages
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:84971229248
  • wos:000378703200010
ISSN
2169-8953
DOI
10.1002/2015JG003216
language
English
LU publication?
yes
id
58bccab0-7ce4-47cb-b2e1-d0db4bcc2d79
date added to LUP
2016-07-08 12:42:49
date last changed
2025-04-04 14:09:27
@article{58bccab0-7ce4-47cb-b2e1-d0db4bcc2d79,
  abstract     = {{<p>In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%–93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%–10.7% of the total variability followed by GCMs (0.3%–7.6%), RCPs (0%–6.3%), and downscaling methods (0.1%–4.6%). The higher-order interactions effect that captures nonlinear relations between the factors and random effects is pronounced and accounts for 1.6%–45.8% to the total variability. The most distinct hot spots of variability include the mountain ranges in North Scandinavia and the Alps, and the Iberian Peninsula. Based on our findings, we advise to conduct the application of models such as LPJ-GUESS at a reasonably high spatial resolution which is supported by the model structure. There is no notable gain in simulations of ecosystem carbon and nitrogen stocks and fluxes from using regionally downscaled climate in preference to bias-corrected, bilinearly interpolated CMIP5 projections.</p>}},
  author       = {{Blanke, Jan Hendrik and Lindeskog, Mats and Lindström, Johan and Lehsten, Veiko}},
  issn         = {{2169-8953}},
  keywords     = {{climate data; DGVM; downscaling; LPJ-GUESS; sensitivity; spatial resolution}},
  language     = {{eng}},
  month        = {{05}},
  number       = {{5}},
  pages        = {{1352--1371}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Journal of Geophysical Research - Biogeosciences}},
  title        = {{Effect of climate data on simulated carbon and nitrogen balances for Europe}},
  url          = {{http://dx.doi.org/10.1002/2015JG003216}},
  doi          = {{10.1002/2015JG003216}},
  volume       = {{121}},
  year         = {{2016}},
}