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In the Central German Uplands, Fagus sylvatica and Picea abies have been particularly affected by climate change. With the establishment of beech forests about 3000 years ago and pure spruce stands 500 years ago, they might be regarded as ‘neophytes’ in the Hessian forests. Palaeoecological investigations at wetland sites in the low mountain ranges and intramontane basins point to an asynchronous vegetation evolution in a comparatively small but heterogenous region. On the other hand, palynological data prove that sustainably managed woodlands with high proportions of Tilia have been persisting for several millennia, before the spread of beech took place as a result of a cooler and wetter climate and changes in land management. In view of increasingly warmer and drier conditions, Tilia cordata appears especially qualified to be an important silvicultural constituent of the future, not only due to its tolerance towards drought, but also its resistance to browsing, and the ability to reproduce vegetatively. Forest managers should be encouraged to actively promote the return to more stress-tolerant lime-dominated woodlands, similar to those that existed in the Subboreal chronozone.
Global distributions of profiles of sulphur hexafluoride (SF6) have been retrieved from limb emission spectra recorded by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat covering the period September 2002 to March 2004. Individual SF6 profiles have a precision of 0.5 pptv below 25 km altitude and a vertical resolution of 4–6 km up to 35 km altitude. These data have been validated versus in situ observations obtained during balloon flights of a cryogenic whole-air sampler. For the tropical troposphere a trend of 0.230±0.008 pptv/yr has been derived from the MIPAS data, which is in excellent agreement with the trend from ground-based flask and in situ measurements from the National Oceanic and Atmospheric Administration Earth System Research Laboratory, Global Monitoring Division. For the data set currently available, based on at least three days of data per month, monthly 5° latitude mean values have a 1 o standard error of 1%. From the global SF6 distributions, global daily and monthly distributions of the apparent mean age of air are inferred by application of the tropical tropospheric trend derived from MIPAS data. The inferred mean ages are provided for the full globe up to 90° N/S, and have a 1 o standard error of 0.25 yr. They range between 0 (near the tropical tropopause) and 7 years (except for situations of mesospheric intrusions) and agree well with earlier observations. The seasonal variation of the mean age of stratospheric air indicates episodes of severe intrusion of mesospheric air during each Northern and Southern polar winter observed, long-lasting remnants of old, subsided polar winter air over the spring and summer poles, and a rather short period of mixing with midlatitude air and/or upward transport during fall in October/November (NH) and April/May (SH), respectively, with small latitudinal gradients, immediately before the new polar vortex starts to form. The mean age distributions further confirm that SF6 is destroyed in the mesosphere to a considerable degree. Model calculations with the Karlsruhe simulation model of the middle atmosphere (KASIMA) chemical transport model agree well with observed global distributions of the mean age only if the SF6 sink reactions in the mesosphere are included in the model.
Biogenic NO emissions from soils (SNOx) play important direct and indirect roles in tropospheric chemistry. The most widely applied algorithm to calculate SNOx in global models was published 15 years ago by Yienger and Levy (1995), and was based on very few measurements. Since then, numerous new measurements have been published, which we used to build up a compilation of world wide field measurements covering the period from 1978 to 2010. Recently, several satellite-based top-down approaches, which recalculated the different sources of NOx (fossil fuel, biomass burning, soil and lightning), have shown an underestimation of SNOx by the algorithm of Yienger and Levy (1995). Nevertheless, to our knowledge no general improvements of this algorithm, besides suggested scalings of the total source magnitude, have yet been published. Here we present major improvements to the algorithm, which should help to optimize the representation of SNOx in atmospheric-chemistry global climate models, without modifying the underlying principals or mathematical equations. The changes include: (1) using a new landcover map, with twice the number of landcover classes, and using annually varying fertilizer application rates; (2) adopting a fraction of 1.0 % for the applied fertilizer lost as NO, based on our compilation of measurements; (3) using the volumetric soil moisture to distinguish between the wet and dry states; and (4) adjusting the emission factors to reproduce the measured emissions in our compilation (based on either their geometric or arithmetic mean values). These steps lead to increased global annual SNOx, and our total above canopy SNOx source of 8.6 Tg yr−1 (using the geometric mean) ends up being close to one of the satellite-based top-down approaches (8.9 Tg yr−1). The above canopy SNOx source using the arithmetic mean is 27.6 Tg yr−1, which is higher than all previous estimates, but compares better with a regional top-down study in eastern China. This suggests that both top-down and bottom-up approaches will be needed in future attempts to provide a better calculation of SNOx.
Soil biogenic NO emissions (SNOx) play important direct and indirect roles in chemical processes of the troposphere. The most widely applied algorithm to calculate SNOx in global models was published 15 years ago by Yienger and Levy (1995), was based on very few measurements. Since then numerous new measurements have been published, which we used to build up a atabase of field measurements conducted world wide covering the period from 1978 to 2009, including 108 publications with 560 measurements.
Recently, several satellite based top-down approaches, which recalculated the different sources of NOx (fossil fuel, biomass burning, soil and lightning), have shown an underestimation of SNOx by the algorithm of Yienger and Levy (1995). Nevertheless, to our knowledge no general improvements of this algorithm have yet been published.
Here we present major improvements to the algorithm, which should help to optimize the representation of SNOx in atmospheric-chemistry global climate models, without modifying the underlying principal or mathematical equations. The changes include: 1) Using a new up to date land cover map, with twice the number of land cover classes, and using annually varying fertilizer application rates; 2) Adopting the fraction of SNOx induced by fertilizer application based on our database; 3) Switching from soil water column to volumetric soil moisture, to distinguish between the wet and dry state; 4) Tuning the emission factors to reproduce the measured emissions in our database and calculate the emissions based on their mean value. These steps lead us to increased global yearly SNOx, and our total SNOx source ends up being close to one of the top-down approaches. In some geographical regions the new results agree better with the top-down approach, but there are also distinct differences in other regions. This suggests that a ombination of both top-down and bottom-up approaches could be combined in a future attempt to provide an even better calculation of SNOx.
Laut jüdischem Kalender entstand die Welt vor genau 5778 Jahren, nach der Bibel vor 6021 Jahren. Doch als Forscher begannen, auf und in der Erde selbst nach Spuren ihres Alters zu suchen, mussten sie die Zahl immer weiter nach oben korrigieren. Nach heutigen Datierungsmethoden ist unser Planet zwischen 4,5 und 4,6 Milliarden Jahre alt.
Volatile organic compounds (VOCs) were analyzed in air and snow samples at the Jungfraujoch high alpine research station in Switzerland as part of CLACE 5 (CLoud and Aerosol Characterization Experiment) during February/March 2006. The fluxes of individual compounds in ambient air were calculated from gas phase concentrations and wind speed. The highest concentrations and flux values were observed for the aromatic hydrocarbons benzene (14.3 μg.m−2 s−1), 1,3,5-trimethylbenzene (5.27 μg.m−2 s−1), toluene (4.40 μg.m−2 −1), and the aliphatic hydrocarbons i-butane (7.87 μg.m−2 s−1), i-pentane (3.61 μg.m−2 s−1) and n-butane (3.23 μg.m−2 s−1). The measured concentrations and fluxes were used to calculate the efficiency of removal of VOCs by snow, which is defined as difference between the initial and final concentration/flux values of compounds before and after wet deposition. The removal efficiency was calculated at −24°C (−13.7°C) and ranged from 37% (35%) for o-xylene to 93% (63%) for i-pentane. The distribution coefficients of VOCs between the air and snow phases were derived from published poly-parameter linear free energy relationship (pp-LFER) data, and compared with distribution coefficients obtained from the simultaneous measurements of VOC concentrations in air and snow at Jungfraujoch. The coefficients calculated from pp-LFER exceeded those values measured in the present study, which indicates more efficient snow scavenging of the VOCs investigated than suggested by theoretical predictions.
Volatile organic compounds (VOCs) were analyzed in air and snow samples at the Jungfraujoch high alpine research station in Switzerland as part of CLACE 5 (CLoud and Aerosol Characterization Experiment) during February/March 2006. The fluxes of individual compounds in ambient air were calculated from gas phase concentrations and wind speed. The highest flux values were observed for the aromatic hydrocarbons benzene (14.3 µg m−2s−1), 1,3,5-trimethylbenzene (5.27 µg m−2s−1), toluene (4.40 µg m−2s−1), and the aliphatic hydrocarbons i-butane (7.87 µg m−2s−1), i-pentane (3.61 µg m−2s−1) and n-butane (3.23 µg m−2s−1). The fluxes were used to calculate the efficiency of removal of VOCs by snow, and the effect of temperature on removal efficiency. The removal efficiency was calculated at – 24◦C (−13.7◦C) and ranged from 37% (35%) for o-xylene to 93% (63%) for i-pentane. The distribution coefficients of VOCs between the air and snow phases were derived from published poly-parameter linear free energy relationship (pp-LFER) data, and compared with distribution coef- ficients obtained from the simultaneous measurements of VOC concentrations in air and snow at Jungfraujoch. The coefficients calculated from pp-LFER exceeded those values measured in the present study, which indicates more efficient snow scavenging of the VOCs investigated than suggested by theoretical predictions.
Under the Kigali Amendment to the Montreal Protocol, new controls are being implemented to reduce emissions of HFC-23 (CHF3), a by-product during the manufacture of HCFC-22 (CHClF2). Starting in 2015, China and India, who dominate global HCFC-22 production (75% in 2017), set out ambitious programs to reduce HFC-23 emissions. Here, we estimate that these measures should have seen global emissions drop by 87% between 2014 and 2017. Instead, atmospheric observations show that emissions have increased and in 2018 were higher than at any point in history (15.9 ± 0.9 Gg yr−1). Given the magnitude of the discrepancy between expected and observation-inferred emissions, it is likely that the reported reductions have not fully materialized or there may be substantial unreported production of HCFC-22, resulting in unaccounted-for HFC-23 by-product emissions. The difference between reported and observation-inferred estimates suggests that an additional ~309 Tg CO2-equivalent emissions were added to the atmosphere between 2015 and 2017.
Within the world’s oceans, regionally distinct ecological niches develop due to differences in water temperature, nutrients, food availability, predation and light intensity. This results in differences in the vertical dispersion of planktonic foraminifera on the global scale. Understanding the controls on these modern-day distributions is important when using these organisms for paleoceanographic reconstructions. As such, this study constrains modern depth habitats for the northern equatorial Indian Ocean, for 14 planktonic foraminiferal species (G. ruber, G. elongatus, G. pyramidalis, G. rubescens, T. sacculifer, G. siphonifera, G. glutinata, N. dutertrei, G. bulloides, G. ungulata, P. obliquiloculata, G. menardii, G. hexagonus, G. scitula) using stable isotopic signatures (δ18O and δ13C) and Mg/Ca ratios. We evaluate two aspects of inferred depth habitats: (1) the significance of the apparent calcification depth (ACD) calculation method/equations and (2) regional species-specific ACD controls. Through a comparison with five global, (sub)tropical studies we found the choice of applied equation and δ18Osw significant and an important consideration when comparing with the published literature. The ACDs of the surface mixed layer and thermocline species show a tight clustering between 73–109 m water depth coinciding with the deep chlorophyll maximum (DCM). Furthermore, the ACDs for the sub-thermocline species are positioned relative to secondary peaks in the local primary production. We surmise that food source plays a key role in the relative living depths for the majority of the investigated planktonic foraminifera within this oligotrophic environment of the Maldives and elsewhere in the tropical oceans.
Observed global and European spatiotemporal related fields of surface air temperature, mean-sea-level pressure and precipitation are analyzed statistically with respect to their response to external forcing factors such as anthropogenic greenhouse gases, anthropogenic sulfate aerosol, solar variations and explosive volcanism, and known internal climate mechanisms such as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). As a first step, a principal component analysis (PCA) is applied to the observed spatiotemporal related fields to obtain spatial patterns with linear independent temporal structure. In a second step, the time series of each of the spatial patterns is subject to a stepwise regression analysis in order to separate it into signals of the external forcing factors and internal climate mechanisms as listed above as well as the residuals. Finally a back-transformation leads to the spatiotemporally related patterns of all these signals being intercompared. Two kinds of significance tests are applied to the anthropogenic signals. First, it is tested whether the anthropogenic signal is significant compared with the complete residual variance including natural variability. This test answers the question whether a significant anthropogenic climate change is visible in the observed data. As a second test the anthropogenic signal is tested with respect to the climate noise component only. This test answers the question whether the anthropogenic signal is significant among others in the observed data. Using both tests, regions can be specified where the anthropogenic influence is visible (second test) and regions where the anthropogenic influence has already significantly changed climate (first test).