• Energy Research
• National Science Foundation close
• GB close
• EU close
• IN close

The detected variety in chemistry and circumstellar shell morphology of the limited sample of Galactic post-AGB stars is so large that there is no consensus yet on how the different objects are linked by evolutionary channels. The evaluation is complicated by the fact that their distances and hence luminosities remain largely unknown. Via cross-correlation of the Spitzer SAGE catalogue with optical catalogues we selected a sample of LMC post-AGB candidates based on their [8]-[24] colour index and estimated luminosity. We determined the fundamental properties of the central stars of 105 of these objects using low-resolution, optical spectra that we obtained at Siding Spring Observatory and SAAO, and constructed a catalogue of 70 high probability and 1337 candidate post-AGB stars that is available at the CDS. The sample forms an ideal testbed for stellar evolution theory predictions of the final phase of low- and intermediate-mass stars, because the distance and hence luminosity and also the current and initial mass of these objects is well constrained. About half of the objects in our sample of post-AGB candidates show a spectral energy distribution (SED) that is indicative of a disc rather than an expanding and cooling AGB remnant. Like in the Galaxy, the disc sources are likely associated with binary evolution. Important side products of this research are catalogues of candidate young stellar objects, candidate supergiants with circumstellar dust, and discarded objects for which a spectrum was obtained. These too are available at the CDS.

Abstract. This paper aims to assess the spatial variability in the response of CO2 exchange to irradiance across the Arctic tundra during peak season using light response curve (LRC) parameters. This investigation allows us to better understand the future response of Arctic tundra under climatic change. Peak season data were collected during different years (between 1998 and 2010) using the micrometeorological eddy covariance technique from 12 circumpolar Arctic tundra sites, in the range of 64–74° N. The LRCs were generated for 14 days with peak net ecosystem exchange (NEE) using an NEE–irradiance model. Parameters from LRCs represent site-specific traits and characteristics describing the following: (a) NEE at light saturation (Fcsat), (b) dark respiration (Rd), (c) light use efficiency (α), (d) NEE when light is at 1000 μmol m−2 s−1 (Fc1000), (e) potential photosynthesis at light saturation (Psat) and (f) the light compensation point (LCP). Parameterization of LRCs was successful in predicting CO2 flux dynamics across the Arctic tundra. We did not find any trends in LRC parameters across the whole Arctic tundra but there were indications for temperature and latitudinal differences within sub-regions like Russia and Greenland. Together, leaf area index (LAI) and July temperature had a high explanatory power of the variance in assimilation parameters (Fcsat, Fc1000 and Psat, thus illustrating the potential for upscaling CO2 exchange for the whole Arctic tundra. Dark respiration was more variable and less correlated to environmental drivers than were assimilation parameters. This indicates the inherent need to include other parameters such as nutrient availability, substrate quantity and quality in flux monitoring activities.

For fast timescales or long prediction horizons, the AC optimal power flow (OPF) problem becomes a computational challenge for large-scale, realistic AC networks. To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network. The method takes as inputs the full AC network and a pre-computed library of AC load flow data and uses the graph Laplacian to constraint nodal reductions to only be feasible for neighbors of non-reduced nodes. This results in a highly effective MILP formulation which is embedded within an iterative scheme to successively improve the Kron-based network reduction until convergence. The resulting optimal network reduction is, thus, grounded in the physics of the full network. The accuracy of the network reduction methodology is then explored for a 100+ node medium-voltage radial distribution feeder example across a wide range of operating conditions. It is finally shown that a network reduction of 25-85% can be achieved within seconds and with worst-case voltage magnitude deviation errors within any super node cluster of less than 0.01pu. These results illustrate that the proposed optimization-based approach to Kron reduction of networks is viable for larger networks and suitable for use within various power system applications.

AbstractIndirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.

AbstractClimate change is a major threat to global biodiversity. Antarctic ecosystems are no exception. Investigating past species responses to climatic events can distinguish natural from anthropogenic impacts. Climate change produces ‘winners’, species that benefit from these events and ‘losers’, species that decline or become extinct. Using molecular techniques, we assess the demographic history and population structure of Pygoscelis penguins in the Scotia Arc related to climate warming after the Last Glacial Maximum (LGM). All three pygoscelid penguins responded positively to post-LGM warming by expanding from glacial refugia, with those breeding at higher latitudes expanding most. Northern (Pygoscelis papua papua) and Southern (Pygoscelis papua ellsworthii) gentoo sub-species likely diverged during the LGM. Comparing historical responses with the literature on current trends, we see Southern gentoo penguins are responding to current warming as they did during post-LGM warming, expanding their range southwards. Conversely, Adélie and chinstrap penguins are experiencing a ‘reversal of fortunes’ as they are now declining in the Antarctic Peninsula, the opposite of their response to post-LGM warming. This suggests current climate warming has decoupled historic population responses in the Antarctic Peninsula, favoring generalist gentoo penguins as climate change ‘winners’, while Adélie and chinstrap penguins have become climate change ‘losers’.

AbstractSpecies extinction risk is accelerating due to anthropogenic climate change, making it urgent to protect vulnerable species through legal frameworks in order to facilitate conservation actions that help mitigate risk. Here, we discuss fundamental concepts for assessing climate change risks to species using the example of the emperor penguin (Aptenodytes forsteri), currently being considered for protection under the US Endangered Species Act (ESA). This species forms colonies on Antarctic sea ice, which is projected to significantly decline due to ongoing greenhouse gas (GHG) emissions. We project the dynamics of all known emperor penguin colonies under different GHG emission scenarios using a climate‐dependent meta‐population model including the effects of extreme climate events based on the observational satellite record of colonies. Assessments for listing species under the ESA require information about how species resiliency, redundancy and representation (3Rs) will be affected by threats within the foreseeable future. Our results show that if sea ice declines at the rate projected by climate models under current energy system trends and policies, the 3Rs would be dramatically reduced and almost all colonies would become quasi‐extinct by 2100. We conclude that the species should be listed as threatened under the ESA.