Effect of climate data on simulated carbon and nitrogen balances for Europe
Blanke, Jan Hendrik LU ; Lindeskog, Mats LU ; Lindström, Johan LU
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.
(Less)
- author
- Blanke, Jan Hendrik
LU
; Lindeskog, Mats
LU
; Lindström, Johan
LU
and Lehsten, Veiko
LU
- organization
- publishing date
- 2016-05-01
- 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
- Wiley
- 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
- 2024-04-05 03:40:34
@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 = {{Wiley}},
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}},
}