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

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:

Temperate grasslands play globally an important role, for example, for biodiversity conservation, livestock forage production, and carbon storage. The latter two are primarily controlled by biomass production, which is assumed to decrease with lower amounts and higher variability of precipitation, while increasing air temperature might either foster or suppress biomass production. Additionally, a higher atmospheric CO2 concentration ([CO2]) is supposed to increase biomass productivity either by directly stimulating photosynthesis or indirectly by inducing water savings (CO2 fertilization effect). Consequently, future biomass productivity is controlled by the partially contrasting effects of changing climatic conditions and [CO2], which to date are only marginally understood. This results in high uncertainties of future biomass production and carbon storage estimates. Consequently, this study aims at statistically estimating mid-21st century grassland aboveground biomass (AGB) based on 18 years of data (1998–2015) from a free air carbon enrichment experiment. We found that lower precipitation totals and a higher precipitation variability reduced AGB. Under drier conditions accompanied by increasing air temperature, AGB further decreased. Here AGB under elevated [CO2] was partly even lower compared to AGB under ambient [CO2], probably because elevated [CO2] reduced evaporative cooling of plants, increasing heat stress. This indicates a higher susceptibility of AGB to increased air temperature under future atmospheric [CO2]. Since climate models for Central Europe project increasing air temperature and decreasing total summer precipitation associated with an increasing variability, our results suggest that grassland summer AGB will be reduced in the future, contradicting the widely expected positive yield anomalies from increasing [CO2].

Abstract:

Compares simulations of plant biomass and CO2 emissions of the Giessen FACE grassland with and without groundwater-borne nitrate.

Abstract:

Shows how simulated biomass, soil moisture and greenhouse gas emissions are affected by the measured N deficit of the Giessen FACE grassland.

Abstract:

Displays how field site data from the Giessen FACE are processed into simulations of soil moisture, biomass and greenhouse gas emissions (CO2 and N2O). Simulations are displayed in GLUE-like uncertainty analysis. Gives an outlook how the N-deficit of the field site may be connected to groundwater N supply.

Abstract:

Overview of the model setup for the Giessen FACE grassland ecosystem, including preliminary simulation results for CO2 emissions and outlook.

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:

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:

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:

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:

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.

Abstract:

An increase in mean soil surface temperature has been observed over the last century and it is predicted to further increase in the future. The effect of increased temperature on ecosystem carbon fluxes in a permanent temperate grassland, was studied in a long term (6 years) field experiment, using multiple temperature increments induced by IR-lamps. Ecosystem respiration (R-eco) and net ecosystem exchange (NEE) were measured, and modelled by a modified Lloyd and Taylor model including a soil moisture component for R-eco (average R2 of 0.78) and inclusion of a photosynthetic component based on temperature and radiation for NEE (R2=0.65). Modelled NEE values ranged between 2.3 and 5.3 kg CO2 m-2 year-1, depending on treatment. An increase of 2 or 3°C led to increased carbon losses, lowering the carbon storage potential by around 4 tonnes of C ha-1 year-1. The majority of significant NEE differences were found during night-time compared to daytime. This suggests that during daytime the increased respiration could be offset by an increase in photosynthetic uptake. This was also supported by differences in ?13C and ?18O, indicating prolonged increased photosynthetic activity associated with the higher temperature treatments. However, this increase in photosynthesis was insufficient to counteract the 24hr increase in respiration, explaining the higher CO2 emissions due to elevated temperature.

Abstract:

Satellite map of the Umweltbeobachtungs und Klimafolgenforschungsstation Linden