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Institution

Potsdam Institute for Climate Impact Research

FacilityPotsdam, Germany
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.


Papers
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Journal ArticleDOI
TL;DR: Both conceptual and empirical information is used in this paper to show that coastal adaptation embraces more than selecting one of the "technical" options to respond to sea-level rise (retreat, accommodate or protect).
Abstract: This paper evaluates the IPCC Technical Guidelines for Assessing Climate Change Impacts and Adaptations with respect to the guidance offered for coastal-adaptation assessment. It appears that the IPCC Technical Guidelines focus strongly on implementation. This paper uses both conceptual and empirical information is used in this paper to show that coastal adaptation embraces more than selecting one of the "technical" options to respond to sea-level rise (retreat, accommodate or protect). Coastal adaptation is a more complex and iterative process with a series of policy cycles. To be effective, an expanded adapta-tion framework involving four steps is suggested, including (i) information collection and awareness raising; (ii) planning and design; (iii) implementation; and (iv) monitoring and evaluation. The incom-plete coverage of these four steps in existing coastal-adaptation assessments constrains the development of adaptation strategies that are supported by the relevant actors and integrated into existing management. Researchers and policy-makers are recommended to work together to establish a framework for adaptation that is integrated within current coastal management processes and practices and takes a broader view on the subject.

182 citations

Journal ArticleDOI
TL;DR: In this paper, the authors developed a spatial allocation model that distributes yearly and subnational sown area statistics to the most agriculturally suitable plots to improve the estimation of tradeoffs involved in reclaiming abandoned croplands and thus in increasing agricultural production in this globally important agricultural region.
Abstract: [1] Widespread cropland abandonment occurred after the collapse of socialism across the former Soviet Union, but the rates and spatial patterns of abandoned lands are not well known. As a result, the potential of this region to contribute to global food production and estimates of the carbon sink developing on currently idle lands are highly uncertain. We developed a spatial allocation model that distributes yearly and subnational sown area statistics to the most agriculturally suitable plots. This approach resulted in new, high-resolution (1 km2) annual time series of cropland and abandoned lands in European Russia, Ukraine, and Belarus from 1990 to 2009. A quantitative validation of the cropland map confirms the reliability of this data set, especially for the most important agricultural areas of the study region. Overall, we found a total of 87 Mha of cropland and 31 Mha of abandoned cropland in European Russia, Ukraine, and Belarus combined, suggesting that abandonment has been severely underestimated in the past. The abandonment rates were highest in European Russia. Feeding our new map data set into the dynamic vegetation model LPJmL revealed that cropland abandonment resulted in a net carbon sink of 470 TgC for 1990 to 2009. Carbon sequestration was generally slow in the early years after abandonment, but carbon uptake increased significantly after approximately 10 years. Recultivation of older abandoned lands would be associated with high carbon emissions and lead to substantial amounts of carbon not being sequestered in vegetation formations currently developing on idle croplands. Our spatially and temporally explicit cropland abandonment data improve the estimation of trade-offs involved in reclaiming abandoned croplands and thus in increasing agricultural production in this globally important agricultural region.

182 citations

Journal ArticleDOI
TL;DR: In this paper, the authors couple a new permafrost module to a reduced complexity carbon-cycle climate model, which allows them to perform a large ensemble of simulations to span the uncertainties listed above and thereby the results provide an estimate of the potential strength of the feedback from newly thawed carbon.
Abstract: . Thawing of permafrost and the associated release of carbon constitutes a positive feedback in the climate system, elevating the effect of anthropogenic GHG emissions on global-mean temperatures. Multiple factors have hindered the quantification of this feedback, which was not included in climate carbon-cycle models which participated in recent model intercomparisons (such as the Coupled Carbon Cycle Climate Model Intercomparison Project – C4MIP) . There are considerable uncertainties in the rate and extent of permafrost thaw, the hydrological and vegetation response to permafrost thaw, the decomposition timescales of freshly thawed organic material, the proportion of soil carbon that might be emitted as carbon dioxide via aerobic decomposition or as methane via anaerobic decomposition, and in the magnitude of the high latitude amplification of global warming that will drive permafrost degradation. Additionally, there are extensive and poorly characterized regional heterogeneities in soil properties, carbon content, and hydrology. Here, we couple a new permafrost module to a reduced complexity carbon-cycle climate model, which allows us to perform a large ensemble of simulations. The ensemble is designed to span the uncertainties listed above and thereby the results provide an estimate of the potential strength of the feedback from newly thawed permafrost carbon. For the high CO2 concentration scenario (RCP8.5), 33–114 GtC (giga tons of Carbon) are released by 2100 (68 % uncertainty range). This leads to an additional warming of 0.04–0.23 °C. Though projected 21st century permafrost carbon emissions are relatively modest, ongoing permafrost thaw and slow but steady soil carbon decomposition means that, by 2300, about half of the potentially vulnerable permafrost carbon stock in the upper 3 m of soil layer (600–1000 GtC) could be released as CO2, with an extra 1–4 % being released as methane. Our results also suggest that mitigation action in line with the lower scenario RCP3-PD could contain Arctic temperature increase sufficiently that thawing of the permafrost area is limited to 9–23 % and the permafrost-carbon induced temperature increase does not exceed 0.04–0.16 °C by 2300.

182 citations

Journal ArticleDOI
TL;DR: In this paper, the authors evaluate sources of uncertainty in projected hydrological changes under climate change in twelve large-scale river basins worldwide, considering the mean flow and the two runoff quantiles Q10 (high flow), and Q90 (low flow).
Abstract: This paper aims to evaluate sources of uncertainty in projected hydrological changes under climate change in twelve large-scale river basins worldwide, considering the mean flow and the two runoff quantiles Q10 (high flow), and Q90 (low flow). First, changes in annual low flow, annual high flow and mean annual runoff were evaluated using simulation results from a multi-hydrological-model (nine hydrological models, HMs) and a multi-scenario approach (four Representative Concentration Pathways, RCPs, five CMIP5 General Circulation Models, GCMs). Then, three major sources of uncertainty (from GCMs, RCPs and HMs) were analyzed using the ANOVA method, which allows for decomposing variances and indicating the main sources of uncertainty along the GCM-RCP-HM model chain. Robust changes in at least one runoff quantile or the mean flow, meaning a high or moderate agreement of GCMs and HMs, were found for five river basins: the Lena, Tagus, Rhine, Ganges, and Mackenzie. The analysis of uncertainties showed that in general the largest share of uncertainty is related to GCMs, followed by RCPs, and the smallest to HMs. The hydrological models are the lowest contributors of uncertainty for Q10 and mean flow, but their share is more significant for Q90.

182 citations

Journal ArticleDOI
TL;DR: In this paper, the importance of bioenergy to potential future energy transformation and climate change management is explored using a large inter-model comparison of 15 models, comprehensively characterize and analyze future dependence on, and the value of, bioenergy in achieving potential long-run climate objectives.
Abstract: This study explores the importance of bioenergy to potential future energy transformation and climate change management. Using a large inter-model comparison of 15 models, we comprehensively characterize and analyze future dependence on, and the value of, bioenergy in achieving potential long-run climate objectives. Model scenarios project, by 2050, bioenergy growth of 1 to 10 % per annum reaching 1 to 35 % of global primary energy, and by 2100, bioenergy becoming 10 to 50 % of global primary energy. Non-OECD regions are projected to be the dominant suppliers of biomass, as well as consumers, with up to 35 % of regional electricity from biopower by 2050, and up to 70 % of regional liquid fuels from biofuels by 2050. Bioenergy is found to be valuable to many models with significant implications for mitigation and macroeconomic costs of climate policies. The availability of bioenergy, in particular biomass with carbon dioxide capture and storage (BECCS), notably affects the cost-effective global emissions trajectory for climate management by accommodating prolonged near-term use of fossil fuels, but with potential implications for climate outcomes. Finally, we find that models cost-effectively trade-off land carbon and nitrous oxide emissions for the long-run climate change management benefits of bioenergy. The results suggest opportunities, but also imply challenges. Overall, further evaluation of the viability of large-scale global bioenergy is merited.

182 citations


Authors

Showing all 1589 results

NameH-indexPapersCitations
Carl Folke133360125990
Adam Drewnowski10648641107
Jürgen Kurths105103862179
Markus Reichstein10338653385
Stephen Polasky9935459148
Sandy P. Harrison9632934004
Owen B. Toon9442432237
Stephen Sitch9426252236
Yong Xu88139139268
Dieter Neher8542426225
Johan Rockström8523657842
Jonathan A. Foley8514470710
Robert J. Scholes8425337019
Christoph Müller8245727274
Robert J. Nicholls7951535729
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023101
2022107
2021479
2020486
2019332
2018355