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Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climate data difficult. In contrast to existing works that rely on simple relative drought indices as ground-truth data, we build upon soil moisture index (SMI) obtained from a hydrological model. This index is directly related to insufficiently available water to vegetation. Given ERA5-Land climate input data of six months with land use information from MODIS satellite observation, we compare different models with and without sequential inductive bias in classifying droughts based on SMI. We use PR-AUC as the evaluation measure to account for the class imbalance and obtain promising results despite a challenging time-based split. We further show in an ablation study that the models retain their predictive capabilities given input data of coarser resolutions, as frequently encountered in climate models.

In technischen Anwendungen ist das teilkristalline Polymer Polytetrafluorethylen (PTFE), wegen seines niedrigen Reibungskoeffizienten gegen viele Materialien sehr geschätzt. Die häufigsten tribologischen Untersuchungen mit PTFE wurden gegen Stahl, Glas und Silizium in Luft durchgeführt. In der Literatur hat sich mit der Zeit folgender Verlauf herauskristallisiert: Durch das Gleiten verschleißt PTFE anfangs sehr schnell und bildet dabei eine "Transferschicht" auf dem Gegenmaterial. Der weitere Reibungsverlauf hängt dann von der Wechselwirkung des PTFE gegenüber der Transferschicht ab. Der Einfluss von verschiedenen Oberflächen auf die tribologischen Eigenschaften von PTFE wurde bisher nur wenig erforscht. In dieser Studie wurden Reibung und Verschleiß von PTFE-Kugeln gegen unterschiedliche tribologische Beschichtungen auf 100Cr6 Stahl, mit einem Kugel-Scheibe-Tribometer untersucht. Bei den Beschichtungen handelt es sich um mehrere Modifikationen von Diamantartigen Kohlenstoffschichten (DLC) sowie Hartstoffschichten (Titannitrid (TiN) und Chromnitrid (CrN)). Es wurden Reibungskoeffizienten zwischen 0,08 bis 0,15 gemessen. Bemerkenswert ist die Tatsache, dass unter gleichen tribologischen Bedingungen Chromnitrid (CrN) gegen PTFE den gleichen Reibungskoeffizienten aufweist wie selbstgepaartes PTFE (0,08). Die entstandene PTFE-Transferschicht weist jedoch sehr unterschiedliche Eigenschaften auf. Auf den DLC-Proben ist die Schicht fast nicht erkennbar. In anderen Fällen, wie z. B. bei CrN, ist diese Schicht dagegen deutlich ausgeprägt. Das Verschleißvolumen der PTFE-Kugeln ist in beiden Fällen vergleichbar, aber geringer als gegen poliertem 100Cr6. Diese Ergebnisse deuten darauf hin, dass die Haftung zwischen dem PTFE-Transferfilm und verschiedenen Oberflächen sehr unterschiedlich ist.

The objective of this study was to identify genomic regions and candidate genes associated with feed efficiency in lactating Holstein cows. In total, 4,916 cows with actual or imputed genotypes for 60,671 single nucleotide polymorphisms having individual feed intake, milk yield, milk composition, and body weight records were used in this study. Cows were from research herds located in the United States, Canada, the Netherlands, and the United Kingdom. Feed efficiency, defined as residual feed intake (RFI), was calculated within location as the residual of the regression of dry matter intake (DMI) on milk energy (MilkE), metabolic body weight (MBW), change in body weight, and systematic effects. For RFI, DMI, MilkE, and MBW, bivariate analyses were performed considering each trait as a separate trait within parity group to estimate variance components and genetic correlations between them. Animal relationships were established using a genomic relationship matrix. Genome-wide association studies were performed separately by parity group for RFI, DMI, MilkE, and MBW using the Bayes B method with a prior assumption that 1% of single nucleotide polymorphisms have a nonzero effect. One-megabase windows with greatest percentage of the total genetic variation explained by the markers (TGVM) were identified, and adjacent windows with large proportion of the TGVM were combined and reanalyzed. Heritability estimates for RFI were 0.14 (+/- 0.03; +/- SE) in primiparous cows and 0.13 (+/- 0.03) in multiparous cows. Genetic correlations between primiparous and multiparous cows were 0.76 for RFI, 0.78 for DMI, 0.92 for MBW, and 0.61 for MilkE. No single 1-Mb window explained a significant proportion of the TGVM for RFI; however, after combining windows, significance was met on Bos taurus autosome 27 in primiparous cows, and nearly reached on Bos taurus autosome 4 in multiparous cows. Among other genes, these regions contain 6-3 adrenergic receptor and the physiological candidate gene, leptin, respectively. Between the 2 parity groups, 3 of the 10 windows with the largest effects on DMI neighbored windows affecting RFI, but were not in the top 10 regions for MilkE or MBW. This result suggests a genetic basis for feed intake that is unrelated to energy consumption required for milk production or expected maintenance as determined by MBW. In conclusion, feed efficiency measured as RFI is a polygenic trait exhibiting a dynamic genetic basis and genetic variation distinct from that underlying expected maintenance requirements and milk energy output. USDA National Needs Graduate Fellowship Competitive Grant (Washington, DC) [2013-38420-20496]; Agriculture and Food Research Initiative Competitive Grant (Washington, DC) [2011-68004-30340] This project received financial support from the USDA National Needs Graduate Fellowship Competitive Grant no. 2013-38420-20496 (Washington, DC) and the Agriculture and Food Research Initiative Competitive Grant no. 2011-68004-30340 (Washington, DC). Public domain – authored by a U.S. government employee

Lossow, S. et. al., In the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), the amplitudes and phases of the annual, semi-annual and quasi-biennial variation in stratospheric and lower mesospheric water were compared using 30 data sets from 13 different satellite instruments. These comparisons aimed to provide a comprehensive overview of the typical uncertainties in the observational database which can be considered in subsequent observational and modelling studies. For the amplitudes, a good agreement of their latitude and altitude distribution was found. Quantitatively there were differences in particular at high latitudes, close to the tropopause and in the lower mesosphere. In these regions, the standard deviation over all data sets typically exceeded 0.2 ppmv for the annual variation and 0.1 ppmv for the semi-annual and quasi-biennial variation. For the phase, larger differences between the data sets were found in the lower mesosphere. Generally the smallest phase uncertainties can be observed in regions where the amplitude of the variability is large. The standard deviations of the phases for all data sets were typically smaller than a month for the annual and semi-annual variation and smaller than 5 months for the quasi-biennial variation. The amplitude and phase differences among the data sets are caused by a combination of factors. In general, differences in the temporal variation of systematic errors and in the observational sampling play a dominant role. In addition, differences in the vertical resolution of the data, the considered time periods and influences of clouds, aerosols as well as non-local thermodynamic equilibrium (NLTE) effects cause differences between the individual data sets. © 2017 Author(s)., The Atmospheric Chemistry Experiment (ACE), also known as SCISAT, is a Canadian-led mission mainly supported by the Canadian Space Agency and the Natural Sciences and Engineering Research Council of Canada. We would like to thank the European Space Agency (ESA) for making the MIPAS level-1b data set available. SCIAMACHY spectral data have been provided by ESA. The work on the SCIAMACHY water vapour data products has been funded by DLR (German Aerospace Center) and the University of Bremen. The SCIAMACHY limb water vapour data set v3.01 is a result of the DFG (German Research Council) Research Unit >Stratospheric Change and its Role for Climate Prediction> (SHARP) and the ESA SPIN (ESA SPARC Initiative) project and were partly calculated using resources of the German HLRN (High-Performance Computer Center North). Stefan Lossow was also funded by the SHARP project under contract STI 210/9-2. He would like to thank D. Lindelof and C. Cuse for their support: this is the beginning of the end. We want to express our gratitude to SPARC and WCRP (World Climate Research Programme) for their guidance, sponsorship and support of the WAVAS-II programme. We thank E. Remsberg, H. Pumphrey and an anonymous referee for valuable comments on the manuscript.