Projected Changes in Terrestrial Carbon Storage in Europe under Climate and Land-use Change, 1990–2100

Zaehle, Sönke; Bondeau, Alberte; Carter, Timothy R.; Crämer, Wolfgang; Erhard, Markus; Prentice, I. Colin; Reginster, Isabelle; Rounsevell, Mark; Sitch, Stephen; Smith, Benjamin; Smith, P.; Sykes, Martin T.

doi:10.1007/s10021-007-9028-9

Cited by 140 publications

(121 citation statements)

References 61 publications

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“…LPJmL is a terrestrial ecosystem model describing the carbon and water cycles of natural, semi-natural and anthropogenic ecosystems (Sitch et al, 2003;Bondeau et al, 2007;Zaehle et al, 2007; Table 1). It includes representations of boreal and temperate evergreen needleleaf (enf), deciduous needleleaf (dnf), deciduous broadleaf (dbf), and evergreen broadleaf tree types (ebf), as well as two grass and 11 crop types.…”

Section: Lund-potsdam-jena Dynamic Global Vegetation Model For Managementioning

confidence: 99%

“…Vegetation dynamics and management are calculated separately for each landcover type. Dynamics of crops' and managed forest were simulated as described by and by Zaehle et al (2007) accordingly. To be Collatz et al (1992) + optimum canopy distribution of nitrogen (Haxeltine and Prentice, 1996) (leaf respiration is subtracted).…”

Section: Lund-potsdam-jena Dynamic Global Vegetation Model For Managementioning

confidence: 99%

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Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models

et al. 2008

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Abstract. Globally, the year 2003 is associated with one of the largest atmospheric CO 2 rises on record. In the same year, Europe experienced an anomalously strong flux of CO 2 from the land to the atmosphere associated with an exceptionally dry and hot summer in Western and Central Europe. In this study we analyze the magnitude of this carbon flux anomaly and key driving ecosystem processes using simulations of seven terrestrial ecosystem models of different complexity and types (process-oriented and diagnostic). We address the following questions: (1) how large were deviations in the net European carbon flux in 2003 relative to a shortterm baseline (1998)(1999)(2000)(2001)(2002) C for Western Europe and between 24 and -129 Tg C for Central Europe depending on the model used. All models responded to a dipole pattern of the climate anomaly in 2003. In Western and Central Europe NEP was reduced due to heat and drought. In contrast, lower than normal temperatures and higher air humidity decreased NEP over Northeastern Europe. While models agree on the sign of changes in simulated NEP and gross primary productivity in 2003 over Western and Central Europe, models diverge in the estimates of anomalies in ecosystem respiration. Except for two process models which simulate respiration increase, most models simulated a decrease in ecosystem respiration in 2003. The diagnostic models showed a weaker decrease in ecosystem respiration than the process-oriented models.Based on the multi-model simulations we estimated the total carbon flux anomaly over the 2003 growing season in Europe to range between -0.02 and -0.27 Pg C relative to the net carbon flux in 1998-2002.

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Section: Lund-potsdam-jena Dynamic Global Vegetation Model For Managementioning

confidence: 99%

Section: Lund-potsdam-jena Dynamic Global Vegetation Model For Managementioning

confidence: 99%

Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models

et al. 2008

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“…The direction of socio-economic drivers is referred to as deep uncertainties, which are addressed through the use of scenarios (van Vuuren et al, 2008). Cropland projections at the high end of the projected uncertainty range for global cropland would have profound consequences for, for example, global carbon and nitrogen fluxes, the global water balance, biodiversity, and other ecosystem services (Lindeskog et al, 2013;Pereira et al, 2012;Zaehle et al, 2007). Hence, quantifying and understanding the inherent uncertainties in the drivers of LULCC has important consequences for policy responses to support sustainable development.…”

Section: Introductionmentioning

confidence: 99%

Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

et al. 2016

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Abstract. We present a modelling framework to simulate probabilistic futures of global cropland areas that are conditional on the SSP (shared socio-economic pathway) scenarios. Simulations are based on the Parsimonious Land Use Model (PLUM) linked with the global dynamic vegetation model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) using socio-economic data from the SSPs and climate data from the RCPs (representative concentration pathways). The simulated range of global cropland is 893-2380 Mha in 2100 (± 1 standard deviation), with the main uncertainties arising from differences in the socio-economic conditions prescribed by the SSP scenarios and the assumptions that underpin the translation of qualitative SSP storylines into quantitative model input parameters. Uncertainties in the assumptions for population growth, technological change and cropland degradation were found to be the most important for global cropland, while uncertainty in food consumption had less influence on the results. The uncertainties arising from climate variability and the differences between climate change scenarios do not strongly affect the range of global cropland futures. Some overlap occurred across all of the conditional probabilistic futures, except for those based on SSP3. We conclude that completely different socio-economic and climate change futures, although sharing low to medium population development, can result in very similar cropland areas on the aggregated global scale.

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“…This makes prediction at the landscape scale difficult, as at that scale soil properties are driven by physical environmental gradients and boundaries, such as topography, parent material and hydrologically effective rainfall. One of the most important recent research themes of international interest is the anticipated change in terrestrial carbon stock under changing climate and land use (Yu et al, 2012;Zaehle et al, 2007). By modelling D b using these changing landscape attributes, it can be viewed as spatially variable rather than as a fixed soil property.…”

Section: K P Taalab Et Al: Modelling Soil Bulk Density At the Landmentioning

confidence: 99%

Modelling soil bulk density at the landscape scale and its contributions to C stock uncertainty

et al. 2013

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Soil bulk density (D_b) is a major contributor to uncertainties in landscape-scale carbon and nutrient stock estimation. However, it is time consuming to measure and is, therefore, frequently predicted using surrogate variables, such as soil texture. Using this approach is of limited value for estimating landscape-scale inventories, as its accuracy beyond the sampling point at which texture is measured becomes highly uncertain. In this paper, we explore the ability of soil landscape models to predict soil D_b using a suite of landscape attributes and derivatives for both topsoil and subsoil. The models were constructed using random forests and artificial neural networks.

Using these statistical methods, we have produced a spatially distributed prediction of D_b on a 100 m × 100 m grid, which was shown to significantly improve topsoil carbon stock estimation. In comparison to using mean values from point measurements, stratified by soil class, we found that the gridded method predicted D_b more accurately, especially for higher and lower values within the range. Within our study area of the Midlands, UK, we found that the gridded prediction of D_b produced a stock inventory of over 1 million tonnes of carbon greater than the stratified mean method. Furthermore, the 95% confidence interval associated with total C stock prediction was almost halved by using the gridded method. The gridded approach was particularly useful in improving organic carbon (OC) stock estimation for fine-scale landscape units at which many landscape–atmosphere interaction models operate

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Projected Changes in Terrestrial Carbon Storage in Europe under Climate and Land-use Change, 1990–2100

Cited by 140 publications

References 61 publications

Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models

Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models

Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

Modelling soil bulk density at the landscape scale and its contributions to C stock uncertainty

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