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.

Persistent growth of CO2 emissions and implications for reaching climate targets

Abstract: Efforts to limit climate change below a given temperature level require that global emissions of CO2 cumulated over time remain below a limited quota. This quota varies depending on the temperature level, the desired probability of staying below this level and the contributions of other gases. In spite of this restriction, global emissions of CO2 from fossil fuel combustion and cement production have continued to grow by 2.5% per year on average over the past decade. Two thirds of the CO2 emission quota consistent with a 2 °C temperature limit has already been used, and the total quota will likely be exhausted in a further 30 years at the 2014 emissions rates. We show that CO2 emissions track the high end of the latest generation of emissions scenarios, due to lower than anticipated carbon intensity improvements of emerging economies and higher global gross domestic product growth. In the absence of more stringent mitigation, these trends are set to continue and further reduce the remaining quota until the onset of a potential new climate agreement in 2020. Breaking current emission trends in the short term is key to retaining credible climate targets within a rapidly diminishing emission quota.

Indication of a Universal Persistence Law Governing Atmospheric Variability

Abstract: We study the temporal correlations in the atmospheric variability by 14 meteorological stations around the globe, the variations of the daily maximum temperatures from their average values. We apply several methods that can systematically overcome possible nonstationarities in the data. We find that the persistence, characterized by the correlation C(s) of temperature variations separated by s days, approximately decays $C(s)\ensuremath{\sim}{s}^{\ensuremath{-}\ensuremath{\gamma}}$, with roughly the same exponent $\ensuremath{\gamma}\ensuremath{\cong}0.7$ for all stations considered. The range of this universal persistence law seems to exceed one decade, and is possibly even larger than the range of the temperature series considered.

Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

Abstract: . Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble (MTE) approach using an artificial data set derived from the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the MTE upscaling and associated problems of extrapolation capacity. We show that MTE is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of MTE over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while MTE is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with MTE is feasible and able to extract global patterns of carbon flux variability.

World Scientists’ Warning of a Climate Emergency

Abstract: 1 Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA 2 School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia 3 Conservation Biology Institute, 136 SW Washington Avenue, Suite 202, Corvallis, OR 97333, USA 4 African Climate and Development Initiative, University of Cape Town, Cape Town, 7700, South Africa. 5 The Fletcher School and Global Development and Environment Institute, Tufts University, Medford, MA, USA

Principles for knowledge co-production in sustainability research

Abstract: Research practice, funding agencies and global science organizations suggest that research aimed at addressing sustainability challenges is most effective when ‘co-produced’ by academics and non-academics. Co-production promises to address the complex nature of contemporary sustainability challenges better than more traditional scientific approaches. But definitions of knowledge co-production are diverse and often contradictory. We propose a set of four general principles that underlie high-quality knowledge co-production for sustainability research. Using these principles, we offer practical guidance on how to engage in meaningful co-productive practices, and how to evaluate their quality and success.