The ocean carbon sink – impacts, vulnerabilities and challenges

Carbon dioxide (CO2) is, next to water vapour, considered to be the most important natural greenhouse gas on Earth. Rapidly rising atmospheric CO2 concentrations caused by human actions such as fossil fuel burning, land-use change or cement production over the past 250 years have given cause for concern that changes in Earth’s climate system may progress at a much faster pace and larger extent than during the past 20 000 years. Investigating global carbon cycle pathways and finding suitable adaptation and mitigation strategies has, therefore, become of major concern in many research fields. The oceans have a key role in regulating atmospheric CO2 concentrations and currently take up about 25% of annual anthropogenic carbon emissions to the atmosphere. Questions that yet need to be answered are what the carbon uptake kinetics of the oceans will be in the future and how the increase in oceanic carbon inventory will affect its ecosystems and their services. This requires comprehensive investigations, including high-quality ocean carbon measurements on different spatial and temporal scales, the management of data in sophisticated databases, the application of Earth system models to provide future projections for given emission scenarios as well as a global synthesis and outreach to policy makers. In this paper, the current understanding of the ocean as an important carbon sink is reviewed with respect to these topics. Emphasis is placed on the complex interplay of different physical, chemical and biological processes that yield both positive and negative air–sea flux values for natural and anthropogenic CO2 as well as on increased CO2 (uptake) as the regulating force of the radiative warming of the atmosphere and the gradual acidification of the oceans. Major future ocean carbon challenges in the fields of ocean observations, modelling and process research as well as the relevance of other biogeochemical cycles and greenhouse gases are discussed

Most Rotational Variables Dominated by a Single Bright Feature are α2\alpha^2 CVn Stars

We previously reported a rare class of variable star light curves isolated from a sample of 4.7 million candidate variables from the ATLAS survey. Dubbed `UCBH' light curves, they have broad minima and narrow, symmetrical maxima, with typical periods of 1-10 days and amplitudes of 0.05--0.20 mag. They maintain constant amplitude, shape, and phase coherence over multiple years, but do not match any known class of pulsating variables. A localized bright spot near the equator of a rotating star will produce a UCBH-type light curve for most viewing geometries. Most stars that exhibit rotational variability caused primarily by a single bright feature should therefore appear as UCBH stars, although a rotating bright spot is not the only thing that could produce a UCBH-type lightcurve. We have spectroscopically investigated fourteen UCBH stars and found ten of them to be Ap/Bp stars: A-type or B-type stars with greatly enhanced photospheric abundances of specific heavy elements. Rotationally variable Ap/Bp stars are referred to as $\alpha^2$ CVn variables. Most ATLAS UCBH stars are therefore $\alpha^2$ CVn stars, although only a minority of $\alpha^2$ CVn stars in the literature have UCBH light curves. The fact that $\alpha^2$ CVn stars dominate the UCBH class suggests that lone bright spots with sufficient size and contrast develop more readily on Ap/Bp stars than on any other type. The $\alpha^2$ CVn UCBH stars may be characterized by a specific magnetic field topology, making them intriguing targets for future Zeeman-Doppler imaging.Comment: 18 pages, 8 figures, accepted to A

The dopaminergic midbrain participates in human episodic memory formation: Evidence from genetic imaging

Recent data from animal studies raise the possibility that dopaminergic neuromodulation promotes the encoding of novel stimuli. We investigated a possible role for the dopaminergic midbrain in human episodic memory by measuring how polymorphisms in dopamine clearance pathways affect encoding-related brain activity (functional magnetic resonance imaging) in an episodic memory task. In 51 young, healthy adults, successful episodic encoding was associated with activation of the substantia nigra. This midbrain activation was modulated by a functional variable number of tandem repeat (VNTR) polymorphism in the dopamine transporter (DAT1) gene. Despite no differences in memory performance between genotype groups, carriers of the (low expressing) 9-repeat allele of the DAT1 VNTR showed relatively higher midbrain activation when compared with subjects homozygous for the 10-repeat allele, who express DAT1 at higher levels. The catechol-O-methyl transferase (COMT) Val108/158Met polymorphism, which is known to modulate enzyme activity, affected encoding-related activity in the right prefrontal cortex (PFC) and in occipital brain regions but not in the midbrain. Moreover, subjects homozygous for the (low activity) Met allele showed stronger functional coupling between the PFC and the hippocampus during encoding. Our finding that genetic variations in the dopamine clearance pathways affect encoding-related activation patterns in midbrain and PFC provides strong support for a role of dopaminergic neuromodulation in human episodic memory formation. It also supports the hypothesis of anatomically and functionally distinct roles for DAT1 and COMT in dopamine metabolism, with DAT1 modulating rapid, phasic midbrain activity and COMT being particularly involved in prefrontal dopamine clearance

Evaluation of large-eddy simulations forced with mesoscale model output for a multi-week period during a measurement campaign

Large-eddy simulations (LESs) of a multi-week period during the HD(CP)2 (High-Definition Clouds and Precipitation for advancing Climate Prediction) Observational Prototype Experiment (HOPE) conducted in Germany are evaluated with respect to mean boundary layer quantities and turbulence statistics. Two LES models are used in a semi-idealized setup through forcing with mesoscale model output to account for the synoptic-scale conditions. Evaluation is performed based on the HOPE observations. The mean boundary layer characteristics like the boundary layer depth are in a principal agreement with observations. Simulating shallow-cumulus layers in agreement with the measurements poses a challenge for both LES models. Variance profiles agree satisfactorily with lidar measurements. The results depend on how the forcing data stemming from mesoscale model output are constructed. The mean boundary layer characteristics become less sensitive if the averaging domain for the forcing is large enough to filter out mesoscale fluctuations. © Author(s) 2017.BMBF/01LK1203BBMBF/01LK1203