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.

Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design

Abstract: . The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006–2015), the second two being estimates of a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.

On the importance of cascading moisture recycling in South America

Abstract: Continental moisture recycling is a crucial process of the South American climate system. In particular, evapotranspiration from the Amazon basin contributes substantially to precipitation regionally as well as over other remote regions such as the La Plata basin. Here we present an in-depth analysis of South American moisture recycling mechanisms. In particular, we quantify the importance of cascading moisture recycling (CMR), which describes moisture transport between two locations on the continent that involves re-evaporation cycles along the way. Using an Eulerian atmospheric moisture tracking model forced by a combination of several historical climate data sets, we were able to construct a complex network of moisture recycling for South America. Our results show that CMR contributes about 9–10% to the total precipitation over South America and 17–18% over the La Plata basin. CMR increases the fraction of total precipitation over the La Plata basin that originates from the Amazon basin from 18–23 to 24–29% during the wet season. We also show that the south-western part of the Amazon basin is not only a direct source of rainfall over the La Plata basin, but also a key intermediary region that distributes moisture originating from the entire Amazon basin towards the La Plata basin during the wet season. Our results suggest that land use change in this region might have a stronger impact on downwind rainfall than previously thought. Using complex network analysis techniques, we find the eastern side of the sub-tropical Andes to be a key region where CMR pathways are channeled. This study offers a better understanding of the interactions between the vegetation and the atmosphere on the water cycle, which is needed in a context of land use and climate change in South America.

From Planetary Boundaries to national fair shares of the global safe operating space — How can the scales be bridged?

Abstract: The planetary boundaries framework proposes quantitative global limits to the anthropogenic perturbation of crucial Earth system processes, and thus marks out a planetary safe operating space for human activities. Yet, decisions regarding resource use and emissions are mostly made at less aggregated scales, by national and sub-national governments, businesses, and other local actors. To operationalize the planetary boundaries concept, the boundaries need to be translated into and aligned with targets that are relevant at these decision-making scales. In this paper, we develop a framework that addresses the biophysical, socio-economic, and ethical dimensions of bridging across scales, to provide a consistently applicable approach for translating the planetary boundaries into national-level fair shares of Earth’s safe operating space. We discuss our findings in the context of previous studies and their implications for future analyses and policymaking. In this way, we link the planetary boundaries framework to widely-applied operational and policy concepts for more robust strong sustainability decision-making.

Web technologies for environmental Big Data

Abstract: Recent evolutions in computing science and web technology provide the environmental community with continuously expanding resources for data collection and analysis that pose unprecedented challenges to the design of analysis methods, workflows, and interaction with data sets. In the light of the recent UK Research Council funded Environmental Virtual Observatory pilot project, this paper gives an overview of currently available implementations related to web-based technologies for processing large and heterogeneous datasets and discuss their relevance within the context of environmental data processing, simulation and prediction. We found that, the processing of the simple datasets used in the pilot proved to be relatively straightforward using a combination of R, RPy2, PyWPS and PostgreSQL. However, the use of NoSQL databases and more versatile frameworks such as OGC standard based implementations may provide a wider and more flexible set of features that particularly facilitate working with larger volumes and more heterogeneous data sources. We review web service related technologies to manage, transfer and process Big Data.We examine international standards and related implementations.Many existing algorithms can be easily exposed as services and cloud-enabled.The adoption of standards facilitate the implementation of workflows.Use of web technologies to tackle environmental issues is acknowledged worldwide.

The effect of soil water content, soil temperature, soil pH-value and the root mass on soil CO2 efflux - A modified model

Abstract: To quantify the effects of soil temperature (Tsoil), and relative soil water content (RSWC) on soil respiration we measured CO2 soil efflux with a closed dynamic chamber in situ in the field and from soil cores in a controlled climate chamber experiment Additionally we analysed the effect of soil acidity and fine root mass in the field The analysis was performed on three meadow, two bare fallow and one forest sites The influence of soil temperature on CO2 emissions was highly significant with all land-use types, except for one field campaign with continuous rain Where soil temperature had a significant influence, the percentage of variance explained by soil temperature varied from site to site from 13–46% in the field and 35–66% in the climate chamber Changes of soil moisture influenced only the CO2 efflux on meadow soils in field and climate chamber (14–34% explained variance), whereas on the bare soil and the forest soil there was no visible effect The spatial variation of soil CO2 emission in the field correlated significantly with the soil pH and fine root mass, explaining up to 24% and 31% of the variability A non-linear regression model was developed to describe soil CO2 efflux as a function of soil temperature, soil moisture, pH-value and root mass With the model we could explain 60% of the variability in soil CO2 emission of all individual field chamber measurements Through the model analysis we highlight the temporal influence of rain events The model overestimated the observed fluxes during and within four hours of the last rain event Conversely, after more than 72h without rain the model underestimated the fluxes Between four and 72 h after rainfall, the regression model of soil CO2 emission explained up to 91% of the variance