A probabilistic risk assessment for the vulnerability of the European carbon cycle to weather extremes: The ecosystem perspective

Extreme weather events are likely to occur more often under climate change and the resulting effects on ecosystems could lead to a further acceleration of climate change. But not all extreme weather events lead to extreme ecosystem response. Here, we focus on hazardous ecosystem behaviour and identify coinciding weather conditions. We use a simple probabilistic risk assessment based on time series of ecosystem behaviour and climate conditions. Given the risk assessment terminology, vulnerability and risk for the previously defined hazard are estimated on the basis of observed hazardous ecosystem behaviour. We apply this approach to extreme responses of terrestrial ecosystems to drought, defining the hazard as a negative net biome productivity over a 12-month period. We show an application for two selected sites using data for 1981–2010 and then apply the method to the pan-European scale for the same period, based on numerical modelling results (LPJmL for ecosystem behaviour; ERA-Interim data for climate). Our site-specific results demonstrate the applicability of the proposed method, using the SPEI to describe the climate condition. The site in Spain provides an example of vulnerability to drought because the expected value of the SPEI is 0.4 lower for hazardous than for non-hazardous ecosystem behaviour. In northern Germany, on the contrary, the site is not vulnerable to drought because the SPEI expectation values imply wetter conditions in the hazard case than in the non-hazard case. At the pan-European scale, ecosystem vulnerability to drought is calculated in the Mediterranean and temperate region, whereas Scandinavian ecosystems are vulnerable under conditions without water shortages. These first model-based applications indicate the conceptual advantages of the proposed method by focusing on the identification of critical weather conditions for which we observe hazardous ecosystem behaviour in the analysed data set. Application of the method to empirical time series and to future climate would be important next steps to test the approach

Impact of droughts on the carbon cycle in European vegetation : a probabilistic risk analysis using six vegetation models

Peer reviewedPublisher PD

The expression of B7-H1 and B7-H4 molecules on immature myeloid and lymphoid dendritic cells in cord blood of healthy neonates.

The aim of our study was to estimate both B7-H1 and B7-H4 molecules on immature myeloid and lymphoid dendritic cells in umbilical cord blood of healthy neonates in comparison with peripheral blood of healthy adults. Thirty nine healthy full-term neonates from physiological single pregnancies and 27 healthy adults were included in the study. The expression of B7-H1 and B7-H4 was revealed using the immunofluorescence method. Statistical analysis was performed using a non-parametric test (Mann-Whitney U-Test). The percentages of BDCA-1+ dendritic cells with B7-H1 and B7-H4 expressions were significantly higher in peripheral blood of healthy adults (

Parkinson's disease subtypes in the Oxford Parkinson disease centre (OPDC) discovery cohort

Background: Within Parkinson’s there is a spectrum of clinical features at presentation which may represent sub-types of the disease. However there is no widely accepted consensus of how best to group patients. Objective: Use a data-driven approach to unravel any heterogeneity in the Parkinson’s phenotype in a well-characterised, population-based incidence cohort. Methods: 769 consecutive patients, with mean disease duration of 1.3 years, were assessed using a broad range of motor, cognitive and non-motor metrics. Multiple imputation was carried out using the chained equations approach to deal with missing data. We used an exploratory and then a confirmatory factor analysis to determine suitable domains to include within our cluster analysis. K-means cluster analysis of the factor scores and all the variables not loading into a factor was used to determine phenotypic subgroups. Results: Our factor analysis found three important factors that were characterised by: psychological well-being features; non-tremor motor features, such as posture and rigidity; and cognitive features. Our subsequent five cluster model identified groups characterised by (1) mild motor and non-motor disease (25.4%), (2) poor posture and cognition (23.3%), (3) severe tremor (20.8%), (4) poor psychological well-being, RBD and sleep (18.9%), and (5) severe motor and non-motor disease with poor psychological well-being (11.7%). Conclusion: Our approach identified several Parkinson’s phenotypic sub-groups driven by largely dopaminergic-resistant features (RBD, impaired cognition and posture, poor psychological well-being) that, in addition to dopaminergic-responsive motor features may be important for studying the aetiology, progression, and medication response of early Parkinson’s

Projecting grassland sensitivity to climate change from an ensemble of models

The grassland biome covers about one-quarter of the earth’s land area and contributes to the livelihoods of ca. 800 million people. Increased aridity and persistent droughts are projected in the twenty-first century for most of Africa, southern Europe and the Middle East, most of the Americas, Australia and South East Asia. A number of these regions have a large fraction of their land use covered by grasslands and rangelands. Grasslands are the ecosystems that respond most rapidly to precipitation variability. However, global projections of climate change impacts on grasslands are still lacking in the scientific literature. Within AgMIP, based on the C3MP protocol initially developed for crops, we have explored the sensitivity of temperate grasslands to climate change drivers with an ensemble of models. Site calibrated models are used to provide projections under probabilistic climate change scenarios, which are defined by a combination of air temperature, precipitation and atmospheric CO2 changes resulting in 99 runs for each model times site combination. This design provides a test of grassland production, GHG (N2O and CH4) emissions and soil carbon sensitivity to climate change drivers. This integrated approach has been tested for 12 grassland simulation models applied to 19 sites over three continents. We show here that a single polynomial emulator can be fitted with high significance to the results of all models and sites, when these are expressed as relative changes from the optimal combination of climate drivers. This polynomial emulator shows that elevated atmospheric CO2 expands the thermal and hydric range which allows for the development of temperate grasslands. Moreover, we calculate the climatic response surface of GHG emissions per unit grassland production and we show that this surface varies with elevated CO2. From these results we provide first estimates of the impacts of climate change on temperate grasslands based on a range of climate scenarios

A probabilistic risk assessment for the vulnerability of the European carbon cycle to extreme events: the ecosystem perspective

Extreme weather events are likely to occur more often under climate change and the resulting effects on ecosystems could lead to a further acceleration of climate change. But not all extreme weather events lead to extreme ecosystem response. Here, we focus on hazardous ecosystem behaviour and identify coinciding weather conditions. We use a simple probabilistic risk assessment based on time series of ecosystem behaviour and climate conditions. Given the risk assessment terminology, vulnerability and risk for the previously defined hazard are estimated on the basis of observed hazardous ecosystem behaviour. We apply this approach to extreme responses of terrestrial ecosystems to drought, defining the hazard as a negative net biome productivity over a 12-month period. We show an application for two selected sites using data for 1981–2010 and then apply the method to the pan-European scale for the same period, based on numerical modelling results (LPJmL for ecosystem behaviour; ERA-Interim data for climate). Our site-specific results demonstrate the applicability of the proposed method, using the SPEI to describe the climate condition. The site in Spain provides an example of vulnerability to drought because the expected value of the SPEI is 0.4 lower for hazardous than for non-hazardous ecosystem behaviour. In northern Germany, on the contrary, the site is not vulnerable to drought because the SPEI expectation values imply wetter conditions in the hazard case than in the non-hazard case. At the pan-European scale, ecosystem vulnerability to drought is calculated in the Mediterranean and temperate region, whereas Scandinavian ecosystems are vulnerable under conditions without water shortages. These first model-based applications indicate the conceptual advantages of the proposed method by focusing on the identification of critical weather conditions for which we observe hazardous ecosystem behaviour in the analysed data set. Application of the method to empirical time series and to future climate would be important next steps to test the approach

LPJmL4 - A dynamic global vegetation model with managed land - Part 1: Model description

This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates - internally consistently - the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process

Livestock in a changing climate: production system transitions as an adaptation strategy for agriculture

Livestock farming is the world's largest land use sector and utilizes around 60% of the global biomass harvest. Over the coming decades, climate change will affect the natural resource base of livestock production, especially the productivity of rangeland and feed crops. Based on a comprehensive impact modeling chain, we assess implications of different climate projections for agricultural production costs and land use change and explore the effectiveness of livestock system transitions as an adaptation strategy. Simulated climate impacts on crop yields and rangeland productivity generate adaptation costs amounting to 3% of total agricultural production costs in 2045 (i.e. 145 billion US$). Shifts in livestock production towards mixed crop-livestock systems represent a resource- and cost-efficient adaptation option, reducing agricultural adaptation costs to 0.3% of total production costs and simultaneously abating deforestation by about 76 million ha globally. The relatively positive climate impacts on grass yields compared with crop yields favor grazing systems inter alia in South Asia and North America. Incomplete transitions in production systems already have a strong adaptive and cost reducing effect: a 50% shift to mixed systems lowers agricultural adaptation costs to 0.8%. General responses of production costs to system transitions are robust across different global climate and crop models as well as regarding assumptions on CO2 fertilization, but simulated values show a large variation. In the face of these uncertainties, public policy support for transforming livestock production systems provides an important lever to improve agricultural resource management and lower adaptation costs, possibly even contributing to emission reduction

Multimodel Evaluation of Nitrous Oxide Emissions From an Intensively Managed Grassland

Process‐based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands. They can be used to construct national GHG inventories using a Tier 3 approach. However, accurate simulations of nitrous oxide (N$_{2}$O) fluxes remain challenging. Models are limited by our understanding of soil‐plant‐microbe interactions and the impact of uncertainty in measured input parameters on simulated outputs. To improve model performance, thorough evaluations against in situ measurements are needed. Experimental data of N$_{2}$O emissions under two management practices (control with typical fertilization versus increased clover and no fertilization) were acquired in a Swiss field experiment. We conducted a multimodel evaluation with three commonly used biogeochemical models (DayCent in two variants, PaSim, APSIM in two variants) comparing four years of data. DayCent was the most accurate model for simulating N$_{2}$O fluxes on annual timescales, while APSIM was most accurate for daily N$_{2}$O fluxes. The multimodel ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the Intergovernmental Panel on Climate Change (IPCC)‐derived method for the Swiss agricultural GHG inventory (IPCC‐Swiss), but individual models were not systematically more accurate than IPCC‐Swiss. The model ensemble overestimated the N$_{2}$O mitigation effect of the clover‐based treatment (measured: 39–45%; ensemble: 52–57%) but was more accurate than IPCC‐Swiss (IPCC‐Swiss: 72–81%). These results suggest that multimodel ensembles are valuable for estimating the impact of climate and management on N$_{2}$O emissions

Alpha-synuclein RT-QuIC in the CSF of patients with alpha-synucleinopathies

We have developed a novel real-time quaking-induced conversion RT-QuICbased assay to detect alpha-synuclein aggregation in brain and cerebrospinal fluid from dementia with Lewy bodies and Parkinson’s disease patients. This assay can detect alpha-synuclein aggregation in Dementia with Lewy bodies and Parkinson’s disease cerebrospinal fluid with sensitivities of 92% and 95%, respectively, and with an overall specificity of 100% when compared to Alzheimer and control cerebrospinal fluid. Patients with neuropathologically confirmed tauopathies (progressive supranuclear palsy; corticobasal degeneration) gave negative results. These results suggest that RT-QuiC analysis of cerebrospinal fluid is potentially useful for the early clinical assessment of patients with alpha-synucleinopathies