- 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:

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:

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:

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) 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:

Hyperspectral remote sensing is a promising tool for a variety of applications including ecology and geology but also analytical chemistry and medical research. Here, the new hsdar-package for R statistical software is presented, which allows to perform a large variety of analysis steps during a typical hyperspectral remote sensing approach. Therefore, the package introduces a new class to e?ciently store even large hyperspectral datasets such as hyperspectral cubes within R. The package includes several important hyperspectral analysis tools such as continuum removal, normalized ratio indices and integrates two widely used radiation transfer models. Besides this, the package provides methods to directly use the functionality of the caret-package for machine learning tasks. To demonstrate the range of functions of the hsdar-package, two case studies are included. The ?rst one shows the estimation of plant leave chlorophyll content and the second one the ability to detect cancer in the human larynx from hyperspectral data.

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

Abstract:

Soil respiration of terrestrial ecosystems, a major component in the global carbon cycle is affected by elevated atmospheric CO2 concentrations. However, seasonal differences of feedback effects of elevated CO2 have rarely been studied. At the Gießen Free-Air CO2 Enrichment (GiFACE) site, the effects of +20% above ambient CO2 concentration have been investigated since 1998 in a temperate grassland ecosystem. We defined five distinct annual seasons, with respect to management practices and phenological cycles. For a period of 3 years (2008–2010), weekly measurements of soil respiration were carried out with a survey chamber on vegetation-free subplots. The results revealed a pronounced and repeated increase of soil respiration under elevated CO2 during late autumn and winter dormancy. Increased CO2 losses during the autumn season (September–October) were 15.7% higher and during the winter season (November–March) were 17.4% higher compared to respiration from ambient CO2 plots. However, during spring time and summer, which are characterized by strong above- and below-ground plant growth, no significant change in soil respiration was observed at the GiFACE site under elevated CO2. This suggests (1) that soil respiration measurements, carried out only during the growing season under elevated CO2 may underestimate the true soil-respiratory CO2 loss (i.e. overestimate the C sequestered), and (2) that additional C assimilated by plants during the growing season and transferred below-ground will quickly be lost via enhanced heterotrophic respiration outside the main growing season.

Abstract:

The rising concentration of carbon dioxide in the atmosphere ([CO2]) has a direct effect on terrestrial vegetation through shifts in the rates of photosynthetic carbon uptake and transpirational water-loss. Free Air CO2 Enrichment (FACE) experiments aim to predict the likely responses of plants to increased [CO2] under normal climatic conditions. The Giessen FACE system operates a lower [CO2] enrichment regime (480 mmol mol–1) than standard FACE (550–600 mmol mol–1), permitting the analysis of a mixed species temperate meadow under a [CO2] level equivalent to that predicted in 25–30 years. We analysed the physiological and morphological responses of six species to investigate the effect of moderate [CO2] on spring biomass production. Carbon dioxide enrichment stimulated leaf photosynthetic rates and supressed respiration, contributing to enhanced net assimilation and a 23% increase in biomass. The capacity for photosynthetic assimilation was unaffected by [CO2] enrichment, with no downregulation of rates of carboxylation of Rubisco or regeneration of ribulose- 1,5-bisphosphate. Foliar N content was also not influenced by increased [CO2]. Enhanced [CO2] reduced stomatal size, but stomatal density and leaf area index remained constant, suggesting that the effect on gas exchange was minimal.