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.

Intergovernmental Panel on Climate Change (IPCC)

Abstract: The Intergovernmental Panel on Climate Change (IPCC) is perceived as the leading international body for the assessment of climate change. In the 23 years since its founding, it has become a key framework for the exchange of scientific dialogue on climate change within the scientific community as well as across the science and policy arenas. This article provides an introduction to the IPCC (its establishment, structure, procedures, and publications) and briefly discusses the solutions proposed by the IPCC in the face of recent criticism and media scrutiny. The philosophical framework of the science/policy interface in which the IPCC functions is presented. Finally, this article concludes with a presentation of the challenges facing the IPCC in the ongoing preparation of its 5th assessment report including exploration of the entire solutions space, ensuring a comparable set of scenarios across IPCC working groups and a consistent treatment of uncertainty.

Early-warning signals for critical transitions

Abstract: Complex dynamical systems, ranging from ecosystems to financial markets and the climate, can have tipping points at which a sudden shift to a contrasting dynamical regime may occur. Although predicting such critical points before they are reached is extremely difficult, work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate for a wide class of systems if a critical threshold is approaching.

The RCP greenhouse gas concentrations and their extensions from 1765 to 2300

Abstract: We present the greenhouse gas concentrations for the Representative Concentration Pathways (RCPs) and their extensions beyond 2100, the Extended Concentration Pathways (ECPs). These projections include all major anthropogenic greenhouse gases and are a result of a multi-year effort to produce new scenarios for climate change research. We combine a suite of atmospheric concentration observations and emissions estimates for greenhouse gases (GHGs) through the historical period (1750-2005) with harmonized emissions projected by four different Integrated Assessment Models for 2005-2100. As concentrations are somewhat dependent on the future climate itself (due to climate feedbacks in the carbon and other gas cycles), we emulate median response characteristics of models assessed in the IPCC Fourth Assessment Report using the reduced-complexity carbon cycle climate model MAGICC6. Projected 'best-estimate' global-mean surface temperature increases (using inter alia a climate sensitivity of 3°C) range from 1.5°C by 2100 for the lowest of the four RCPs, called both RCP3-PD and RCP2.6, to 4.5°C for the highest one, RCP8.5, relative to pre-industrial levels. Beyond 2100, we present the ECPs that are simple extensions of the RCPs, based on the assumption of either smoothly stabilizing concentrations or constant emissions: For example,

Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series

Abstract: We develop a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA). We relate our multifractal DFA method to the standard partition function-based multifractal formalism, and prove that both approaches are equivalent for stationary signals with compact support. By analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series. By comparing the multifractal DFA results for original series with those for shuffled series we can distinguish multifractality due to long-range correlations from multifractality due to a broad probability density function. We also compare our results with the wavelet transform modulus maxima method, and show that the results are equivalent.