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.

Evaluation of terrestrial carbon cycle models through simulations of the seasonal cycle of atmospheric CO2: First results of a model intercomparison study

Abstract: Results of an intercomparison among terrestrial biogeochemical models (TBMs) are reported, in which one diagnostic and five prognostic models have been run with the same long-term climate forcing. Monthly fields of net ecosystem production (NEP), which is the difference between net primary production (NPP) and heterotrophic respiration RH, at 0.5° resolution have been generated for the terrestrial biosphere. The monthly estimates of NEP in conjunction with seasonal CO2 flux fields generated by the seasonal Hamburg Model of the Oceanic Carbon Cycle (HAMOCC3) and fossil fuel source fields were subsequently coupled to the three-dimensional atmospheric tracer transport model TM2 forced by observed winds. The resulting simulated seasonal signal of the atmospheric CO2 concentration extracted at the grid cells corresponding to the locations of 27 background monitoring stations of the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory network is compared with measurements from these sites. The Simple Diagnostic Biosphere Model (SDBM1), which is tuned to the atmospheric CO2 concentration at five monitoring stations in the northern hemisphere, successfully reproduced the seasonal signal of CO2 at the other monitoring stations. The SDBM1 simulations confirm that the north-south gradient in the amplitude of the atmospheric CO2 signal results from the greater northern hemisphere land area and the more pronounced seasonality of radiation and temperature in higher latitudes. In southern latitudes, ocean-atmosphere gas exchange plays an important role in determining the seasonal signal of CO2. Most of the five prognostic models (i.e., models driven by climatic inputs) included in the intercomparison predict in the northern hemisphere a reasonably accurate seasonal cycle in terms of amplitude and, to some extent, also with respect to phase. In the tropics, however, the prognostic models generally tend to overpredict the net seasonal exchanges and stronger seasonal cycles than indicated by the diagnostic model and by observations. The differences from the observed seasonal signal of CO2 may be caused by shortcomings in the phenology algorithms of the prognostic models or by not properly considering the effects of land use and vegetation fires on CO2 fluxes between the atmosphere and terrestrial biosphere.

Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model.

Abstract: Functional diversity is critical for ecosystem dynamics, stability and productivity. However, dynamic global vegetation models (DGVMs) which are increasingly used to simulate ecosystem functions under global change, condense functional diversity to plant functional types (PFTs) with constant parameters. Here, we develop an individual- and trait-based version of the DGVM LPJmL (Lund-Potsdam-Jena managed Land) called LPJmL- flexible individual traits (LPJmL-FIT) with flexible individual traits) which we apply to generate plant trait maps for the Amazon basin. LPJmL-FIT incorporates empirical ranges of five traits of tropical trees extracted from the TRY global plant trait database, namely specific leaf area (SLA), leaf longevity (LL), leaf nitrogen content (Narea ), the maximum carboxylation rate of Rubisco per leaf area (vcmaxarea), and wood density (WD). To scale the individual growth performance of trees, the leaf traits are linked by trade-offs based on the leaf economics spectrum, whereas wood density is linked to tree mortality. No preselection of growth strategies is taking place, because individuals with unique trait combinations are uniformly distributed at tree establishment. We validate the modeled trait distributions by empirical trait data and the modeled biomass by a remote sensing product along a climatic gradient. Including trait variability and trade-offs successfully predicts natural trait distributions and achieves a more realistic representation of functional diversity at the local to regional scale. As sites of high climatic variability, the fringes of the Amazon promote trait divergence and the coexistence of multiple tree growth strategies, while lower plant trait diversity is found in the species-rich center of the region with relatively low climatic variability. LPJmL-FIT enables to test hypotheses on the effects of functional biodiversity on ecosystem functioning and to apply the DGVM to current challenges in ecosystem management from local to global scales, that is, deforestation and climate change effects.

Climate and land use change impacts on plant distributions in Germany.

Abstract: We present niche-based modelling to project the distribution of 845 European plant species for Germany using three different models and three scenarios of climate and land use changes up to 2080. Projected changes suggested large effects over the coming decades, with consequences for the German flora. Even under a moderate scenario (approx. +2.2°C), 15–19% (across models) of the species we studied could be lost locally—averaged from 2995 grid cells in Germany. Models projected strong spatially varying impacts on the species composition. In particular, the eastern and southwestern parts of Germany were affected by species loss. Scenarios were characterized by an increased number of species occupying small ranges, as evidenced by changes in range-size rarity scores. It is anticipated that species with small ranges will be especially vulnerable to future climate change and other ecological stresses.

Investigating afforestation and bioenergy CCS as climate change mitigation strategies

Abstract: The land-use sector can contribute to climate change mitigation not only by reducing greenhouse gas (GHG) emissions, but also by increasing carbon uptake from the atmosphere and thereby creating negative CO2 emissions. In this paper, we investigate two land-based climate change mitigation strategies for carbon removal: (1) afforestation and (2) bioenergy in combination with carbon capture and storage technology (bioenergy CCS). In our approach, a global tax on GHG emissions aimed at ambitious climate change mitigation incentivizes land-based mitigation by penalizing positive and rewarding negative CO2 emissions from the land-use system. We analyze afforestation and bioenergy CCS as standalone and combined mitigation strategies. We find that afforestation is a cost-efficient strategy for carbon removal at relatively low carbon prices, while bioenergy CCS becomes competitive only at higher prices. According to our results, cumulative carbon removal due to afforestation and bioenergy CCS is similar at the end of 21st century (600–700 GtCO2), while land-demand for afforestation is much higher compared to bioenergy CCS. In the combined setting, we identify competition for land, but the impact on the mitigation potential (1000 GtCO2) is partially alleviated by productivity increases in the agricultural sector. Moreover, our results indicate that early-century afforestation presumably will not negatively impact carbon removal due to bioenergy CCS in the second half of the 21st century. A sensitivity analysis shows that land-based mitigation is very sensitive to different levels of GHG taxes. Besides that, the mitigation potential of bioenergy CCS highly depends on the development of future bioenergy yields and the availability of geological carbon storage, while for afforestation projects the length of the crediting period is crucial.

A Global System for Monitoring Ecosystem Service Change

Abstract: Earth's life-support systems are in flux, yet no centralized system to monitor and report these changes exists. Recognizing this, 77 nations agreed to establish the Group on Earth Observations (GEO). The GEO Biodiversity Observation Network (GEO BON) integrates existing data streams into one platform in order to provide a more complete picture of Earth's biological and social systems. We present a conceptual framework envisioned by the GEO BON Ecosystem Services Working Group, designed to integrate national statistics, numerical models, remote sensing, and in situ measurements to regularly track changes in ecosystem services across the globe. This information will serve diverse applications, including stimulating new research and providing the basis for assessments. Although many ecosystem services are not currently measured, others are ripe for reporting. We propose a framework that will continue to grow and inspire more complete observation and assessments of our planet's life-support systems.