- Effects from climate change, adaptations and
greenhouse gas reduction at 2050

Abstract:

Continuously rising atmospheric CO2 concentrations may lead to an increased transfer of organic C from plants to the soil through rhizodeposition and may affect the interaction between the C- and N-cycle. For instance, fumigation of soils with elevated CO2 (eCO2) concentrations (20% higher compared to current atmospheric concentrations) at the Giessen Free-Air Carbon Dioxide Enrichment (GiFACE) sites resulted in a more than two fold increase of long-term N2O emissions and an increase in dissimilatory reduction of nitrate compared to ambient CO2 (aCO2). We hypothesized that the observed differences in soil functioning were based on differences in the abundance and composition of microbial communities in general and especially of those which are responsible for N-transformations in soil. We also expected eCO2 effects on soil parameters, such as on nitrate as previously reported. To explore the impact of long-term eCO2 on soil microbial communities, we applied a molecular approach (qPCR, T-RFLP, and 454 pyrosequencing). Microbial groups were analyzed in soil of three sets of two FACE plots (three replicate samples from each plot), which were fumigated with eCO2 and aCO2, respectively. N-fixers, denitrifiers, archaeal and bacterial ammonia oxidizers, and dissimilatory nitrate reducers to ammonia were targeted by analysis of functional marker genes and the overall archaeal community by 16S rRNA genes. Remarkably, soil parameters as well as the abundance and composition of microbial communities in the top soil under eCO2 differed only slightly from soil under aCO2. Wherever differences in microbial community abundance and composition were detected, they were not linked to CO2 level but rather determined by differences in soil parameters (e.g. soil moisture content) due to the localization of the GiFACE sets in the experimental field. We concluded that +20% eCO2 had little to no effect on the overall microbial community involved in N-cycling in the soil but that spatial heterogeneity over extended periods had shaped microbial communities at particular sites in the field. Hence, microbial community composition and abundance alone cannot explain the functional differences leading to higher N2O emissions under eCO2 and future studies should aim at exploring the active members of the soil microbial community.

Abstract:

Future increase in atmospheric CO2 concentrations will potentially enhance grassland biomass production and shift the functional group composition with consequences for ecosystem functioning. In the “GiFACE” experiment (Giessen Free Air Carbon dioxide Enrichment), fertilized grassland plots were fumigated with elevated CO2(eCO2) year-round during daylight hours since 1998, at a level of +20% relative to ambient concentrations (in 1998, aCO2 was 364 ppm and eCO2 399 ppm; in 2014, aCO2 was 397 ppm and eCO2 518 ppm). Harvests were conducted twice annually through 23 years including 17 years with eCO2 (1998 to 2014). Biomass consisted of C3 grasses and forbs, with a small proportion of legumes. The total aboveground biomass (TAB) was significantly increased under eCO2 (p = .045 and .025, at first and second harvest). The dominant plant functional group grasses responded positively at the start, but for forbs, the effect of eCO2 started out as a negative response. The increase in TAB in response to eCO2 was approximately 15% during the period from 2006 to 2014, suggesting that there was no attenuation of eCO2 effects over time, tentatively a consequence of the fertilization management. Biomass and soil moisture responses were closely linked. The soil moisture surplus (c. 3%) in eCO2 manifested in the latter years was associated with a positive biomass response of both functional groups. The direction of the biomass response of the functional group forbs changed over the experimental duration, intensified by extreme weather conditions, pointing to the need of long-term field studies for obtaining reliable responses of perennial ecosystems to eCO2 and as a basis for model development.

Abstract:

The presentation of the Working package B2 at the general assembly of the FACE2FACE consortium at the 4th of July, 2017.

Abstract:

Elevated CO2 (eCO2) reduces transpiration at the leaf level by inducing stomatal closure. However, this water saving effect might be offset at the canopy level by increased leaf area as a consequence of eCO2 fertilization. To investigate this bi-directional effect, we coupled a plant growth and a soil hydrological model. The model performance and the uncertainty in model parameters were checked using a 13 year data set of a Free Air Carbon dioxide Enrichment (FACE) experiment on grassland in Germany. We found a good agreement of simulated and observed data for soil moisture and total above-ground dry biomass (TAB) under ambient CO2 (?395 ppm) and eCO2 (?480 ppm). Optima for soil and plant growth model parameters were identified, which can be used in future studies. Our study presents a robust modelling approach for the investigation of effects of eCO2 on grassland biomass and water dynamics. We show an offset of the stomatal water saving effect at the canopy level because of a significant increase in TAB (6.5%, p < 0.001) leading to an increase in transpiration by +3.0 ± 6.0 mm, though insignificant (p = 0.1). However, the increased water loss through transpiration was counteracted by a significant decrease in soil evaporation (?2.1 ± 1.7 mm, p < 0.01) as a consequence of higher TAB. Hence, evapotranspiration was not affected by the increased eCO2 (+0.9 ± 4.9 mm, p = 0.5). This in turn led to a significantly better performance of the water use efficiency by 5.2% (p < 0.001). Our results indicate that mown, temperate grasslands can benefit from an increasing biomass production while maintaining water consumption at the +20% increase of eCO2 studied.

Abstract:

Progress Report of AP B3 for the FACE2FACE plenary meeting

Abstract:

A pink-pigmented, Gram-stain-negative, rod-shaped, non-spore-forming bacterial strain, PP-F2F-G21T, was isolated from the phyllosphere of Galium album. Phylogenetic analysis of the nearly full-length 16S rRNA gene sequence of strain PP-F2F-G21T showed the closest relationship to type strains of the species Mucilaginibacter lutimaris (97.7?%), Mucilaginibacter soli (97.3?%) and Mucilaginibacter rigui (97.1?%). Sequence similarities to all other type strains were below 97?%. The predominant cellular fatty acids of strain PP-F2F-G21T are C16?:?1 ?7c/iso-C15?:?0 2-OH (measured as summed feature 3 fatty acids) and iso-C15?:?0 followed by iso-C17?:?0 3-OH, C16?:?1 ?5c and C16?:?0. The major compound in the polyamine pattern was sym-homospermidine and the diamino acid of the peptidoglycan was meso-diaminopimelic acid. The quinone system was exclusively composed of menaquinone MK-7. The polar lipid profile contained the major lipid phosphatidylethanolamine and in addition 18 unidentified lipids. Based on phylogenetic, chemotaxonomic and phenotypic analyses, we propose a novel species of the genus Mucilaginibacter named Mucilaginibacter phyllosphaerae sp. nov. The type strain is PP-F2F-G21T (=CCM 8625T=CIP 110921T=LMG 29118T).

Abstract:

Four yellow-pigmented, Gram-stain-negative, rod-shaped bacteria, strains PP-WC-4G-234T, PP-CE-2G-454T, PP-WC-1G-202 and PP-CC-3G-650, were isolated from the phyllosphere of Galium album. The strains shared 99.7–100?% 16S rRNA gene sequence similarity but could be differentiated by genomic fingerprinting using rep- and random amplification of polymorphic DNA PCRs. Phylogenetic analysis based on the 16S rRNA gene placed the strains within the family Aurantimonadaceae with highest 16S rRNA gene sequence similarity of 97.2–97.3?% to the type strain of Aureimonas phyllosphaerae. Sequence similarities to all other Aurantimonadaceae were below 97?%. The main cellular fatty acids of the strains were C18?:?1 ?7c as the predominant fatty acid followed by C16?:?0 and summed feature 3 (C16?:?1 ?7c/C16?:?1 ?8c). The polyamine patterns of strains PP-WC-4G-234T and PP-CE-2G-454T contained sym-homospermidine as a major compound, and the major respiratory quinone was ubiquinone Q-10. Predominant polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylmonomethylethanolamine, phosphatidylglycerol, phosphatidylcholine, sulfoquinovosyldiacylglycerol, three unidentified phospholipids and one unidentified lipid only detectable after total lipid staining. The DNA G+C content was 66.4, 68.9, 67.4 and 70.5 mol% for strains PP-WC-4G-234T, PP-CE-2G-454T, PP-WC-1G-202 and PP-CC-3G-650, respectively. Based on phylogenetic, chemotaxonomic and phenotypic analyses we propose two novel species of the genus Aureimonas, Aureimonas galii sp. nov. with PP-WC-4G-234T (=LMG 28655T=CIP 110892T) as the type strain and Aureimonas pseudogalii sp. nov. with PP-CE-2G-454T (=LMG 29411T=CCM 8665T) as the type strain and two further strains representing the same species, PP-WC-1G-202 and PP-CC-3G-650.

Abstract:

The increase in atmospheric greenhouse gas concentrations from anthropogenic activities is the major driver of recent global climate change1. The stimulation of plant photosynthesis due to rising atmospheric carbon dioxide concentrations ([CO2]) is widely assumed to increase the net primary productivity (NPP) of C3 plants—the CO2 fertilization effect (CFE). However, the magnitude and persistence of the CFE under future climates, including more frequent weather extremes, are controversial. Here we use data from 16 years of temperate grassland grown under ‘free-air carbon dioxide enrichment’ conditions to show that the CFE on above-ground biomass is strongest under local average environmental conditions. The observed CFE was reduced or disappeared under wetter, drier and/or hotter conditions when the forcing variable exceeded its intermediate regime. This is in contrast to predictions of an increased CO2 fertilization effect under drier and warmer conditions. Such extreme weather conditions are projected to occur more intensely and frequently under future climate scenarios. Consequently, current biogeochemical models might overestimate the future NPP sink capacity of temperate C3 grasslands and hence underestimate future atmospheric [CO2] increase.

Abstract:

The sampling design in the all six Gi-FACE rings is standardised. Subplots 1-3 are used for weekly measurements of greenhouse gas fluxes and biannual biomass harvest. Subplots 4-25 are used for biannual biomass harvests and cover more or less the two species inventory subplots for biannual vegetations surveys. Subplots 24 and 25 have been used in 2013 for a 15N-13C Tracing study and in Subplot 25 the topsoil (7.5 cm) was used in 2014 for an incubation study and the soil was replaced by soil from outside the rings. Small circles within the ring area indicate Licor soil colomne for soil respiration measurements. The yellow rectangles mark the area where soil profiles have been opened in 2015 to install new soil moisture and temperature sensors. "Needles" and asterix mark positions where soil samples have been taken.

Abstract:

The choice for specific parameter estimation methods is often more dependent on ist availability than ist Performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source Python package parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algoritms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-Independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for the parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.

Abstract:

The continuous increase in atmospheric carbon dioxide (CO2) contributes to changes in plant evapotranspiration and terrestrial water Budgets in two ways. Firstly, elevated CO2 can result in a water saving effect, since increasing CO2 reduces stomatal opening and therefore decreases transpiration. Secondly, CO2 fertilization increases biomass accumulation and leaf area at plant canopy Level, likely increasing plant transpiration. Vegetation and hydrological models can be used to investigate the CO2 Response and the bidirectional effects outlined above, including their relative contribution to the changes in the water cycle. However, the intrinsic plant-soil interaction and the uncertainty related to model parameterization have rarely been considered. Hence, we coupled a detailed plant growth and soil hydrological model by using the generic model frameworks Plant growth Modelling Framework (PMF) and Catchment Modelling Framework (CMF). Up to date response mechanisms have been implemented in PMF to simulate the various ways of how plant physiology is influenced by elevated CO2. Both models interact by using the Python computer language. Applying the coupled PMF-CMF model we investigate the effects of elevated CO2 in a number of plant physiological and environmental variables such as biomass, leaf area index and soil moisture using field data of a long-term Free Air Carbon dioxide Enrichment (FACE) Experiment in Giessen, Germany. In this Experiment, various grassland varieties (herbs, legumes, grasses) grow under elevated (+20%) and ambient CO2 since 1997. A Monte Carlo based uncertainty analysis (GLUE) is conducted to investigate the coupled PMF-CMF parameter space. The focus will be on the identification of parameters for plant and soil, which are the drivers for the CO2 Response of the terrestrial water balance. We will present first results of the simulation of biomass accumulation and transpiration under ambient and elevated CO2 concentrations.

Abstract:

Biochar may become a key instrument at the nexus of managed carbon fluxes, including value added potential in soil Amelioration, climate protection, energy supply and organic waste management. This article reflects the potential use of biochar in agriculture from the perspective of the farming economy. Biochar soil amendment in crop production is regarded as a win-win Situation, both for assumed increases in cropping yields and carbon Sequestration in soil organic matter. However, an extensive review on biochar effecton crop yield has not yet been able to provide compelling arguments to foster more widespread biochar use in cropping systems. Furthermore, the half-lives of biochar are frequently shorter than commonly suggested, and other financial incentives, such as including biochar in carbon credit Systems, are not in place to compensate for the extra cost of applying biochar. As a result, we conclude with a somewhat skeptical view for a widespread use of biochar in agriculture in the near future.

Abstract:

In this paper, two new methods, Temporal evolution of Detrended Cross-Correlation Analysis (TDCCA) and Temporal evolution of Detrended Partial-Cross-Correlation Analysis (TDPCCA), are proposed by generalizing DCCA and DPCCA. Applying TDCCA/TDPCCA, it is possible to study correlations on multi-time scales and over different periods. To illustrate their properties, we used two climatological examples: i) Global Sea Level (GSL) versus North Atlantic Oscillation (NAO); and ii) Summer Rainfall over Yangtze River (SRYR) versus previous winter Pacific Decadal Oscillation (PDO). We find significant correlations between GSL and NAO on time scales of 60 to 140 years, but the correlations are nonsignificant between 1865–1875. As for SRYR and PDO, significant correlations are found on time scales of 30 to 35 years, but the correlations are more pronounced during the recent 30 years. By combining TDCCA/TDPCCA and DCCA/DPCCA, we proposed a new correlation-detection system, which compared to traditional methods, can objectively show how two time series are related (on which time scale, during which time period). These are important not only for diagnosis of complex system, but also for better designs of prediction models. Therefore, the new methods offer new opportunities for applications in natural sciences, such as ecology, economy, sociology and other research fields.

Abstract:

In this paper, a new method, detrended partial-cross-correlation analysis (DPCCA), is proposed. Based on detrended cross-correlation analysis (DCCA), this method is improved by including partial-correlation technique, which can be applied to quantify the relations of two non-stationary signals (with influences of other signals removed) on different time scales. We illustrate the advantages of this method by performing two numerical tests. Test I shows the advantages of DPCCA in handling non-stationary signals, while Test II reveals the ‘‘intrinsic’’ relations between two considered time series with potential influences of other unconsidered signals removed. To further show the utility of DPCCA in natural complex systems, we provide new evidence on the winter-time Pacific Decadal Oscillation (PDO) and the winter-time Nino3 Sea Surface Temperature Anomaly (Nino3-SSTA) affecting the Summer Rainfall over the middle-lower reaches of the Yangtze River (SRYR). By applying DPCCA, better significant correlations between SRYR and Nino3-SSTA on time scales of 6 , 8 years are found over the period 1951 , 2012, while significant correlations between SRYR and PDO on time scales of 35 years arise. With these physically explainable results, we have confidence that DPCCA is an useful method in addressing complex systems.

Abstract:

Long term memory (LTM) in climate variability is studied by means of fractional integral techniques. By using a recently developed model, Fractional Integral Statistical Model (FISM), we in this report proposed a new method, with which one can estimate the long-lasting influences of historical climate states on the present time quantitatively, and further extract the influence as climate memory signals. To show the usability of this method, two examples, the Northern Hemisphere monthly Temperature Anomalies (NHTA) and the Pacific Decadal Oscillation index (PDO), are analyzed in this study. We find the climate memory signals indeed can be extracted and the whole variations can be further decomposed into two parts: the cumulative climate memory (CCM) and the weather-scale excitation (WSE). The stronger LTM is, the larger proportion the climate memory signals will account for in the whole variations. With the climate memory signals extracted, one can at least determine on what basis the considered time series will continue to change. Therefore, this report provides a new perspective on climate prediction.

Abstract:

Long term memory (LTM) scaling behavior in worldwide tree-ring proxies and subsequent climate reconstructions is analyzed for and compared with the memory structure inherent to instrumental temperature and precipitation data. Detrended fluctuation analysis is employed to detect LTM, and its scaling exponent ? is used to evaluate LTM. The results show that temperature and precipitation reconstructions based on ringwidthmeasurements (mean ? = 0.8) containmorememory than records based onmaximumlatewood density (mean ? = 0.7). Both exceed thememory inherent to regional instrumental data (? = 0.6 for temperature, ? = 0.5 for precipitation) in the time scales ranging from1 year up to 50 years.We comparememory-free (? = 0.5) pseudo-instrumental precipitation datawith pseudo-reconstructed precipitation datawith LTM (? > 0.5), and demonstrate the biasing influences ofLTMon climate reconstructions. Wecall for attention to statistical analysis with regard to the variability of proxy-based chronologies or reconstructions, particularly with respect to the contained (i) trends, (ii) past warm/cold period and wet/dry periods; and (iii) extreme events.

Abstract:

We review direct and indirect effects of climate change on both the grapevine plant as a host for phytophagous insects, as well as on grape insect pests, their natural enemies and corresponding future grape plant protection strategies. Phenology, voltinism and distribution ranges are well known traits of many arthropods influenced by temperature as the key abiotic factor and thus by current and future climate change scenarios. Case studies of grapevine pests based on data from three decades point to clear changes in phenology of grape berry moths, shifts in distribution ranges of leafhoppers as vectors of grapevine diseases and range expansion of grapevine mealybugs. These case studies also illustrate the need to include data on putatively changed tri-trophic interactions in vineyards when predicting impacts of climate change on grapevine pest insects. Hence, future pest management strategies should be based on a sound set of field data obtained for both pests and antagonists under changed abiotic conditions, which can also build the basis for refining and extending currently existing models for forecasting population levels of respective insect pests.

Abstract:

tThe tracking of land use since 1990 presents a major challenge in greenhouse gas (GHG) reporting underthe United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol becausethere is often limited availability of data, especially for the base year of 1990. There is even less landmanagement and soil moisture data, which are needed to track climate change mitigation activities sincesoil moisture is one of the main drivers of GHG emissions of organic soils. Information is also needed for thereporting of land-based activities such as grazing land management or wetland drainage and rewetting oforganic soils. Different spatial and thematic resolutions of land-use data produce inconsistent time serieswith a strong overestimation of land-use change (LUC) if not adequately accounted for. Our aim was tocreate a consistent time series of land use since 1990 that is in line with GHG reporting under the UNFCCCand the Kyoto Protocol by combining official cadastral data with colour-infrared aerial photography usedfor biodiversity monitoring in six federal states in northern and eastern Germany. We developed a generichierarchical classification by land use, management and drainage status, and a translation key for dataharmonisation into a consistent time series. This time series enabled the quantification of LUC on organicsoils between 1992 and 2013 in a spatially explicit manner. Furthermore we used this time series todevelop indicators for changes in land management and drainage to evaluate the success of protectionstatuses on peatland restoration.The study area encompassed one million hectares, half of which had some type of legal nature pro-tection status. Areas with no protection status tended to become more intensively farmed and drier,while highly protected areas (e.g. Natura 2000) showed the opposite trend. Land-use trends also dif-fered greatly between federal states. In Schleswig-Holstein organic soils tended to become drier duringthe study period, while in Mecklenburg-Western Pomerania they tended to become wetter overall. Thetrends and differences in LUC between federal states were linked to German reunification, changes in theEuropean Common Agricultural Policy (CAP) and Germany’s Renewable Energy Act (EEG). A large-scalepeatland protection programme also had major impact.In conclusion, our study demonstrates how data derived for biodiversity monitoring and other highlydetailed land-use data can be used to track changes in land use, management and drainage status inaccordance with the reporting requirements under the UNFCCC and the Kyoto Protocol.