Potsdam Institute for Climate Impact Research

About: Potsdam Institute for Climate Impact Research is a facility organization based out in Potsdam, Germany. It is known for research contribution in the topics: Climate change & Global warming. The organization has 1519 authors who have published 5098 publications receiving 367023 citations.

Comparing global models of terrestrial net primary productivity (NPP): importance of vegetation structure on seasonal NPP estimates

Abstract: This paper was published as Global Change Biology, 1999, 5 (S1), pp. 35-45. It is available from http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2486.1999.00005.x/abstract. Doi: 10.1046/j.1365-2486.1999.00005.x

Sources of uncertainty in hydrological climate impact assessment: a cross-scale study

Abstract: Climate change impacts on water availability and hydrological extremes are major concerns as regards the Sustainable Development Goals. Impacts on hydrology are normally investigated as part of a modelling chain, in which climate projections from multiple climate models are used as inputs to multiple impact models, under different greenhouse gas emissions scenarios, which are resulting in different amounts of global temperature rise. While the goal is generally to investigate the relevance of changes in climate for the water cycle, water resources or hydrological extremes, it is often the case that variations in other components of the model chain obscure the effect of climate scenario variation. This is particularly important when assessing the impacts of relatively lower magnitudes of global warming, such as those associated with the aspirational goals of the Paris Agreement. In our study, we use ANOVA (ANalyses Of VAriance) to allocate and quantify the main sources of uncertainty in the hydrological impact modelling chain. In turn we determine the statistical significance of different sources of uncertainty. We achieve this by using a set of 5 climate models and up to 13 hydrological models, for 9 large scale river basins across the globe, under 4 emissions scenarios. The impact variable we consider in our analysis is daily river discharge. We analyze overall water availability and flow regime, including seasonality, high flows and low flows. Scaling effects are investigated by separately looking at discharge generated by global and regional hydrological models respectively. Finally, we compare our results with other recently published studies. We find that small differences in global temperature rise associated with some emissions scenarios have mostly significant impacts on river discharge – however, climate model related uncertainty is so large that it obscures the sensitivity of the hydrological system.

Carbon balance of bioenergy from logging residues

Abstract: Bioenergy as a substitute for fossil energy is regarded a possibility to reduce the energy related carbon dioxide emissions to the atmosphere, because ‘the carbon, which is set free from biomass combustion, is taken up again by regrowing plants and thus the carbon cycle of bioenergy is closed’, as it is often argued. In a more detailed analysis of bioenergy strategies, two main effects have to be investigated: on the one hand, carbon in fossil fuels is substituted and thus not emitted to the atmosphere, while on the other hand, the use of biofuels might result in a reduction of carbon stored in the biosphere (plants, litter and soil). One of the possibilities to use biomass for energy is to burn logging residues from conventional forestry for heat and/or power production. For this type of bioenergy strategy, a model has been developed which allows one to calculate the change in carbon storage in three soil carbon pools and the carbon fluxes to and from these pools. The model results indicate that the carbon stored in the forest soil is reduced when logging residues are removed for bioenergy to displace fossil fuels. However, this effect is limited, as eventually a new equilibrium of carbon storage in the forest soil is reached, while fossil fuel substitution is continued further on. The time-dependent characteristic value ‘carbon neutrality’ (CN), which is the ratio of net emission reduction (fossil fuel substitution minus carbon losses of the soil) to the ‘saved’ carbon emissions from the substituted reference energy system, reflects this effect. CN equal to one means that bioenergy is completely ‘CO2-neutral’. For bioenergy from logging residues, CN is very low at the beginning when bioenergy is introduced, increases continuously and approaches one at infinity. According to the results of parameter studies, CN of bioenergy from logging residues in temperate and boreal forests lies between 0.49 and 0.82 after 20 years and between 0.75 and 0.88 after 100 years.

Diagnostic assessment of European gross primary production

Abstract: We present an approach to estimate gross primary production (GPP) using a remotely sensed biophysical vegetation product (fraction of absorbed photosynthetically active radiation, FAPAR) from the European Commission Joint Research Centre (JRC) in conjunction with GPP estimates from eddy covariance measurement towers in Europe. By analysing the relationship between the cumulative growing season FAPAR and annual GPP by vegetation type, we find that the former can be used to accurately predict the latter. The root mean square error of prediction is of the order of 250 gC m -2 yr -1 . The cumulative growing season FAPAR integrates over a number of effects relevant for GPP such as the length of the growing season, the vegetation's response to environmental conditions and the amount of light harvested that is available for photosynthesis. We corroborate the proposed GPP estimate (noted FAPAR-based productivity assessment + land cover, FPA+LC) on the continental scale with results from the MOD17+ radiation-use efficiency model, an artificial neural network up-scaling approach (ANN) and the Lund-Potsdam-Jena managed Land biosphere model (LPJmL). The closest agreement of the mean spatial GPP pattern among the four models is between FPA + LC and ANN (R 2 = 0.74). At least some of the discrepancy between FPA-LC and the other models result from biases of meteorological forcing fields for MOD17 +, ANN and LPJmL. Our analysis further implies that meteorological information is to a large degree redundant for GPP estimation when using the JRC-FAPAR. A major advantage of the FPA + LC approach presented in this paper lies in its simplicity and that it requires no additional meteorological input driver data that commonly introduce substantial uncertainty. We find that results from different data-oriented models may be robust enough to evaluate process-oriented models regarding the mean spatial pattern of GPP, while there is too little consensus among the diagnostic models for such purpose regarding inter-annual variability.