Institution
Potsdam Institute for Climate Impact Research
Facility•Potsdam, 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 published on a yearly basis
Papers
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TL;DR: In this article, the authors highlight the importance of decision-making tools designed for situations where generally agreed-upon probability distributions are not available and stakeholders show different degrees of risk tolerance.
Abstract: Climate change studies rarely yield consensus on the probability distribution of exposure, vulnerability, or possible outcomes, and therefore the evaluation of alternative policy strategies is difficult. This Perspective highlights the importance of decision-making tools designed for situations where generally agreed-upon probability distributions are not available and stakeholders show different degrees of risk tolerance.
191 citations
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VU University Amsterdam1, University of Edinburgh2, Karlsruhe Institute of Technology3, Joint Global Change Research Institute4, Netherlands Environmental Assessment Agency5, Lund University6, National Institute for Environmental Studies7, International Institute for Applied Systems Analysis8, Potsdam Institute for Climate Impact Research9, University of Illinois at Urbana–Champaign10, United States Department of Agriculture11, University of Kassel12, Wageningen University and Research Centre13
TL;DR: It is concluded that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges.
Abstract: Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.
191 citations
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TL;DR: An approach that combines information about the equilibrium sea level response to global warming and last century's observed contribution from the individual components to constrain projections for this century is presented, which may lead to a better understanding of the gap between process-based and global semiempirical approaches.
Abstract: Sea level has been steadily rising over the past century, predominantly due to anthropogenic climate change. The rate of sea level rise will keep increasing with continued global warming, and, even if temperatures are stabilized through the phasing out of greenhouse gas emissions, sea level is still expected to rise for centuries. This will affect coastal areas worldwide, and robust projections are needed to assess mitigation options and guide adaptation measures. Here we combine the equilibrium response of the main sea level rise contributions with their last century's observed contribution to constrain projections of future sea level rise. Our model is calibrated to a set of observations for each contribution, and the observational and climate uncertainties are combined to produce uncertainty ranges for 21st century sea level rise. We project anthropogenic sea level rise of 28-56 cm, 37-77 cm, and 57-131 cm in 2100 for the greenhouse gas concentration scenarios RCP26, RCP45, and RCP85, respectively. Our uncertainty ranges for total sea level rise overlap with the process-based estimates of the Intergovernmental Panel on Climate Change. The "constrained extrapolation" approach generalizes earlier global semiempirical models and may therefore lead to a better understanding of the discrepancies with process-based projections.
191 citations
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TL;DR: In this paper, the authors study the inference of long-range correlations by means of Detrended Fluctuation Analysis (DFA) and argue that power-law scaling of the fluctuation function and thus long-memory may not be assumed a priori but have to be established.
Abstract: We study the inference of long-range correlations by means of Detrended Fluctuation Analysis (DFA) and argue that power-law scaling of the fluctuation function and thus long-memory may not be assumed a priori but have to be established. This requires the investigation of the local slopes. We account for the variability characteristic for stochastic processes by calculating empirical confidence regions. Comparing a long-memory with a short-memory model shows that the inference of long-range correlations from a finite amount of data by means of DFA is not specific. We remark that scaling cannot be concluded from a straight line fit to the fluctuation function in a log-log representation. Furthermore, we show that a local slope larger than α=0.5 for large scales does not necessarily imply long-memory. We also demonstrate, that it is not valid to conclude from a finite scaling region of the fluctuation function to an equivalent scaling region of the autocorrelation function. Finally, we review DFA results for the Prague temperature data set and show that long-range correlations cannot not be concluded unambiguously.
191 citations
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Joint Global Change Research Institute1, German Aerospace Center2, University of Bremen3, ETH Zurich4, École Polytechnique5, University of Exeter6, University of Leeds7, Met Office8, University of Denver9, Centre national de la recherche scientifique10, Netherlands Environmental Assessment Agency11, International Institute for Applied Systems Analysis12, University of Melbourne13, University of Maryland, College Park14, Potsdam Institute for Climate Impact Research15, National Center for Atmospheric Research16, Goddard Institute for Space Studies17, University of Paris18, University of Hamburg19, Max Planck Society20, Korea Meteorological Administration21, Commonwealth Scientific and Industrial Research Organisation22, Central Maine Community College23, Geophysical Fluid Dynamics Laboratory24, Pukyong National University25, Korean Ocean Research and Development Institute26, Nanjing University of Information Science and Technology27, Norwegian Meteorological Institute28, Indian Institute of Tropical Meteorology29, Ontario Ministry of Natural Resources30, University of Toulouse31, Alfred Wegener Institute for Polar and Marine Research32, Oak Ridge National Laboratory33, Deutscher Wetterdienst34, University of Arizona35, Japan Agency for Marine-Earth Science and Technology36, Lawrence Livermore National Laboratory37, Swedish Meteorological and Hydrological Institute38, China Meteorological Administration39, Danish Meteorological Institute40, Chinese Academy of Sciences41
TL;DR: In this paper, the authors present a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations, focusing mainly on the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation.
Abstract: . The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the primary future climate projections within the Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century encompassing the Tier 1 experiments (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by 1.15 °C) reached at the upper end of the 5–95 % envelope of the highest scenario, SSP5-8.5. This is due to both the wider range of radiative forcing that the new scenarios cover and to higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensembles' spread, according to a set of initial condition ensemble simulations available under SSP3-7.0. The same experiments suggest a tendency for internal variability to decrease along the course of the century, a new result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP, but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades in mid-century, as represented in SSP5-3.4OS, does not affect the end outcome in terms of temperature and precipitation changes by 2100, which return to the same level as those reached by the gradually increasing SSP4-3.4. Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level show all scenarios reaching 1.5 °C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering 20–28 years from present. 2 °C of warming is reached as early as the late '30s by the ensemble mean under SSP5-8.5, but as late as the late '50s under SSP1-2.6. The highest warming level considered, 5 °C, is reached only by the ensemble mean under SSP5-8.5, and not until the mid-90s.
190 citations
Authors
Showing all 1589 results
Name | H-index | Papers | Citations |
---|---|---|---|
Carl Folke | 133 | 360 | 125990 |
Adam Drewnowski | 106 | 486 | 41107 |
Jürgen Kurths | 105 | 1038 | 62179 |
Markus Reichstein | 103 | 386 | 53385 |
Stephen Polasky | 99 | 354 | 59148 |
Sandy P. Harrison | 96 | 329 | 34004 |
Owen B. Toon | 94 | 424 | 32237 |
Stephen Sitch | 94 | 262 | 52236 |
Yong Xu | 88 | 1391 | 39268 |
Dieter Neher | 85 | 424 | 26225 |
Johan Rockström | 85 | 236 | 57842 |
Jonathan A. Foley | 85 | 144 | 70710 |
Robert J. Scholes | 84 | 253 | 37019 |
Christoph Müller | 82 | 457 | 27274 |
Robert J. Nicholls | 79 | 515 | 35729 |