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Now showing 1 - 10 of 24
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    CHASE-PL—Future Hydrology Data Set: Projections of Water Balance and Streamflow for the Vistula and Odra Basins, Poland
    (Basel : MDPI, 2017) Piniewski, Mikołaj; Szcześniak, Mateusz; Kardel, Ignacy
    There is considerable concern that the water resources of Central and Eastern Europe region can be adversely affected by climate change. Projections of future water balance and streamflow conditions can be obtained by forcing hydrological models with the output from climate models. In this study, we employed the SWAT hydrological model driven with an ensemble of nine bias-corrected EURO-CORDEX climate simulations to generate future hydrological projections for the Vistula and Odra basins in two future horizons (2024–2050 and 2074–2100) under two Representative Concentration Pathways (RCPs). The data set consists of three parts: (1) model inputs; (2) raw model outputs; (3) aggregated model outputs. The first one allows the users to reproduce the outputs or to create the new ones. The second one contains the simulated time series of 10 variables simulated by SWAT: precipitation, snow melt, potential evapotranspiration, actual evapotranspiration, soil water content, percolation, surface runoff, baseflow, water yield and streamflow. The third one consists of the multi-model ensemble statistics of the relative changes in mean seasonal and annual variables developed in a GIS format. The data set should be of interest of climate impact scientists, water managers and water-sector policy makers. In any case, it should be noted that projections included in this data set are associated with high uncertainties explained in this data descriptor paper.
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    Photodynamic opening of the blood-brain barrier using different photosensitizers in mice
    (Basel : MDPI, 2019) Semyachkina-Glushkovskaya, Oxana; Borisova, Ekaterina; Mantareva, Vanya; Angelov, Ivan; Eneva, Ivelina; Terskov, Andrey; Mamedova, Aysel; Shirokov, Alexander; Khorovodov, Alexander; Klimova, Maria; Agranovich, Ilana; Blokhina, Inna; Lezhnev, Nikita; Kurths, Jurgen
    In a series of previous studies, we demonstrated that the photodynamic therapy (PDT), as a widely used tool for treatment of glioblastoma multiforme (GBM), also site-specifically opens the blood-brain barrier (BBB) in PDT-dose and age-related manner via reversible disorganization of the tight junction machinery. To develop the effective protocol of PDT-opening of the BBB, here we answer the question of what kind of photosensitizer (PS) is the most effective for the BBB opening. We studied the PDT-opening of the BBB in healthy mice using commercial photosensitizers (PSs) such as 5-aminolevulenic acid (5-ALA), aluminum phthalocyanine disulfonate (AlPcS), zinc phthalocyanine (ZnPc) and new synthetized PSs such as galactose functionalized ZnPc (GalZnPc). The spectrofluorimetric assay of Evans Blue albumin complex (EBAC) leakage and 3-D confocal imaging of FITC-dextran 70 kDa (FITCD) extravasation clearly shows a revisable and dose depended PDT-opening of the BBB toEBACand FITCD associated with a decrease in presence of tight junction (TJ) in the vascular endothelium. The PDT effects on the BBB permeability, TJ expression and the fluorescent signal from the brain tissues are more pronounced in PDT-GalZnPc vs. PDT-5-ALA/AlPcS/ZnPc. These pre-clinical data are the first important informative platform for an optimization of the PDT protocol in the light of new knowledge about PDT-opening of the BBB for drug brain delivery and for the therapy of brain diseases. © 2019 by the authors.
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    Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models
    (Amsterdam [u.a.] : Elsevier Science, 2018) Krey, Volker; Guo, Fei; Kolp, Peter; Zhou, Wenji; Schaeffer, Roberto; Awasthy, Aayushi; Bertram, Christoph; de Boer, Harmen-Sytze; Fragkos, Panagiotis; Fujimori, Shinichiro; He, Chenmin; Iyer, Gokul; Keramidas, Kimon; Köberle, Alexandre C.; Oshiro, Ken; Reis, Lara Aleluia; Shoai-Tehrani, Bianka; Vishwanathan, Saritha; Capros, Pantelis; Drouet, Laurent; Edmonds, James E.; Garg, Amit; Gernaat, David E.H.J.; Jiang, Kejun; Kannavou, Maria; Kitous, Alban; Kriegler, Elmar; Luderer, Gunnar; Mathur, Ritu; Muratori, Matteo; Sano, Fuminori; van Vuuren, Detlef P.
    Integrated assessment models are extensively used in the analysis of climate change mitigation and are informing national decision makers as well as contribute to international scientific assessments. This paper conducts a comprehensive review of techno-economic assumptions in the electricity sector among fifteen different global and national integrated assessment models. Particular focus is given to six major economies in the world: Brazil, China, the EU, India, Japan and the US. The comparison reveals that techno-economic characteristics are quite different across integrated assessment models, both for the base year and future years. It is, however, important to recognize that techno-economic assessments from the literature exhibit an equally large range of parameters as the integrated assessment models reviewed. Beyond numerical differences, the representation of technologies also differs among models, which needs to be taken into account when comparing numerical parameters. While desirable, it seems difficult to fully harmonize techno-economic parameters across a broader range of models due to structural differences in the representation of technology. Therefore, making techno-economic parameters available in the future, together with of the technology representation as well as the exact definitions of the parameters should become the standard approach as it allows an open discussion of appropriate assumptions. © 2019 The Authors
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    Yield trends, variability and stagnation analysis of major crops in France over more than a century
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2018) Schauberger, Bernhard; Ben-Ari, Tamara; Makowski, David; Kato, Tomomichi; Kato, Hiromi; Ciais, Philippe
    France is a major crop producer, with a production share of approx. 20% within the European Union. Yet, a discussion has recently started whether French yields are stagnating. While for wheat previous results are unanimously pointing to recent stagnation, there is contradictory evidence for maize and few to no results for other crops. Here we analyse a data set with more than 120,000 yield observations from 1900 to 2016 for ten crops (barley, durum and soft wheat, maize, oats, potatoes, rapeseed, sugar beet, sunflower and wine) in the 96 mainland French départements (NUTS3 administrative division). We dissect the evolution of yield trends over time and space, analyse yield variation and evaluate whether growth of yields has stalled in recent years. Yields have, on average across crops, multiplied four-fold over the course of the 20th century. While absolute yield variability has increased, the variation relative to the mean has halved – mean yields have increased faster than their variability. But growth of yields has stagnated since the 1990’s for winter wheat, barley, oats, durum wheat, sunflower and wine on at least 25% of their areas. Reaching yield potentials is unlikely as a cause for stagnation. Maize, in contrast, shows no evidence for stagnation.
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    The implications of initiating immediate climate change mitigation - A potential for co-benefits?
    (Amsterdam [u.a.] : Elsevier Science, 2014) Schwanitz, Valeria Jana; Longden, Thomas; Knopf, Brigitte; Capros, Pantelis
    Fragmented climate policies across parties of the United Nations Framework on Climate Change have led to the question of whether initiating significant and immediate climate change mitigation can support the achievement of other non-climate objectives. We analyze such potential co-benefits in connection with a range of mitigation efforts using results from eleven integrated assessment models. These model results suggest that an immediate mitigation of climate change coincide for Europe with an increase in energy security and a higher utilization of non-biomass renewable energy technologies. In addition, the importance of phasing out coal is highlighted with external cost estimates showing substantial health benefits consistent with the range of mitigation efforts.
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    CO2 emission mitigation and fossil fuel markets: Dynamic and international aspects of climate policies
    (Amsterdam [u.a.] : Elsevier Science, 2013) Bauer, Nico; Bosetti, Valentina; Hamdi-Cherif, Meriem; Kitous, Alban; McCollum, David; Méjean, Aurélie; Rao, Shilpa; Turton, Hal; Paroussos, Leonidas; Ashina, Shuichi; Calvin, Katherine; Wada, Kenichi; van Vuuren, Detlef
    This paper explores a multi-model scenario ensemble to assess the impacts of idealized and non-idealized climate change stabilization policies on fossil fuel markets. Under idealized conditions climate policies significantly reduce coal use in the short- and long-term. Reductions in oil and gas use are much smaller, particularly until 2030, but revenues decrease much more because oil and gas prices are higher than coal prices. A first deviation from optimal transition pathways is delayed action that relaxes global emission targets until 2030 in accordance with the Copenhagen pledges. Fossil fuel markets revert back to the no-policy case: though coal use increases strongest, revenue gains are higher for oil and gas. To balance the carbon budget over the 21st century, the long-term reallocation of fossil fuels is significantly larger—twice and more—than the short-term distortion. This amplifying effect results from coal lock-in and inter-fuel substitution effects to balance the full-century carbon budget. The second deviation from the optimal transition pathway relaxes the global participation assumption. The result here is less clear-cut across models, as we find carbon leakage effects ranging from positive to negative because trade and substitution patterns of coal, oil, and gas differ across models. In summary, distortions of fossil fuel markets resulting from relaxed short-term global emission targets are more important and less uncertain than the issue of carbon leakage from early mover action.
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    Carbon lock-in through capital stock inertia associated with weak near-term climate policies
    (Amsterdam [u.a.] : Elsevier Science, 2013) Bertram, Christoph; Johnson, Nils; Luderer, Gunnar; Riahi, Keywan; Isaac, Morna; Eom, Jiyong
    Stringent long-term climate targets necessitate a limit on cumulative emissions in this century for which sufficient policy signals are lacking. Using nine energy-economy models, we explore how policies pursued during the next two decades impact long-term transformation pathways towards stringent long-term climate targets. Less stringent near-term policies (i.e., those with larger emissions) consume more of the long-term cumulative emissions budget in the 2010–2030 period, which increases the likelihood of overshooting the budget and the urgency of reducing GHG emissions after 2030. Furthermore, the larger near-term GHG emissions associated with less stringent policies are generated primarily by additional coal-based electricity generation. Therefore, to be successful in meeting the long-term target despite near-term emissions reductions that are weaker than those implied by cost-optimal mitigation pathways, models must prematurely retire significant coal capacity while rapidly ramping up low-carbon technologies between 2030 and 2050 and remove large quantities of CO2 from the atmosphere in the latter half of the century. While increased energy efficiency lowers mitigation costs considerably, even with weak near-term policies, it does not substantially reduce the short-term reliance on coal electricity. However, increased energy efficiency does allow the energy system more flexibility in mitigating emissions and, thus, facilitates the post-2030 transition.
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    Network-based identification and characterization of teleconnections on different scales
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Agarwal, Ankit; Caesar, Levke; Marwan, Norbert; Maheswaran, Rathinasamy; Merz, Bruno; Kurths, Jürgen
    Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.
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    Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Kurths, Juergen; Feigin, Alexander
    Currently, causes of the middle Pleistocene transition (MPT) – the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before – are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δ18O records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation.
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    Statistical Properties and Predictability of Extreme Epileptic Events
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Frolov, Nikita S.; Grubov, Vadim V.; Maksimenko, Vladimir A.; Lüttjohann, Annika; Makarov, Vladimir V.; Pavlov, Alexey N.; Sitnikova, Evgenia; Pisarchik, Alexander N.; Kurths, Jürgen; Hramov, Alexander E.
    The use of extreme events theory for the analysis of spontaneous epileptic brain activity is a relevant multidisciplinary problem. It allows deeper understanding of pathological brain functioning and unraveling mechanisms underlying the epileptic seizure emergence along with its predictability. The latter is a desired goal in epileptology which might open the way for new therapies to control and prevent epileptic attacks. With this goal in mind, we applied the extreme event theory for studying statistical properties of electroencephalographic (EEG) recordings of WAG/Rij rats with genetic predisposition to absence epilepsy. Our approach allowed us to reveal extreme events inherent in this pathological spiking activity, highly pronounced in a particular frequency range. The return interval analysis showed that the epileptic seizures exhibit a highly-structural behavior during the active phase of the spiking activity. Obtained results evidenced a possibility for early (up to 7 s) prediction of epileptic seizures based on consideration of EEG statistical properties.