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

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Effect of climate data on simulated carbon and nitrogen balances for Europe. / Blanke, Jan Hendrik; Lindeskog, Mats; Lindström, Johan; Lehsten, Veiko.

I: Journal of Geophysical Research - Biogeosciences, Vol. 121, Nr. 5, 01.05.2016, s. 1352-1371.

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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T1 - Effect of climate data on simulated carbon and nitrogen balances for Europe

AU - Blanke, Jan Hendrik

AU - Lindeskog, Mats

AU - Lindström, Johan

AU - Lehsten, Veiko

PY - 2016/5/1

Y1 - 2016/5/1

N2 - 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.

AB - 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.

KW - climate data

KW - DGVM

KW - downscaling

KW - LPJ-GUESS

KW - sensitivity

KW - spatial resolution

U2 - 10.1002/2015JG003216

DO - 10.1002/2015JG003216

M3 - Article

VL - 121

SP - 1352

EP - 1371

JO - Journal of Geophysical Research - Biogeosciences

JF - Journal of Geophysical Research - Biogeosciences

SN - 2169-8953

IS - 5

ER -