A methodology for investigating dust model performance using synergistic EARLINET/AERONET dust concentration retrievals

Systematic measurements of dust concentration profiles at a continental scale were recently made possible by the development of synergistic retrieval algorithms using combined lidar and sun photometer data and the establishment of robust remote-sensing networks in the framework of Aerosols, Clouds, and Trace gases Research Infra-Structure Network (ACTRIS)/European Aerosol Research Lidar Network (EARLINET). We present a methodology for using these capabilities as a tool for examining the performance of dust transport models. The methodology includes considerations for the selection of a suitable data set and appropriate metrics for the exploration of the results. The approach is demonstrated for four regional dust transport models (BSC-DREAM8b v2, NMMB/BSC-DUST, DREAM-ABOL, DREAM8-NMME-MACC) using dust observations performed at 10 ACTRIS/EARLINET stations. The observations, which include coincident multi-wavelength lidar and sun photometer measurements, were processed with the Lidar-Radiometer Inversion Code (LIRIC) to retrieve aerosol concentration profiles. The methodology proposed here shows advantages when compared to traditional evaluation techniques that utilize separately the available measurements such as separating the contribution of dust from other aerosol types on the lidar profiles and avoiding model assumptions related to the conversion of concentration fields to aerosol extinction values. When compared to LIRIC retrievals, the simulated dust vertical structures were found to be in good agreement for all models with correlation values between 0.5 and 0.7 in the 1-6 km range, where most dust is typically observed. The absolute dust concentration was typically underestimated with mean bias values of -40 to -20 mu g m(-3) at 2 km, the altitude of maximum mean concentration. The reported differences among the models found in this comparison indicate the benefit of the systematic use of the proposed approach in future dust model evaluation studies

Business Research, Self-fulfilling Prophecy, and the Inherent Responsibility of Scholars

Business research and teaching institutions play an important role in shaping the way businesses perceive their relations to the broader society and its moral expectations. Hence, as ethical scandals recently arose in the business world, questions related to the civic responsibilities of business scholars and to the role business schools play in society have gained wider interest. In this article, I argue that these ethical shortcomings are at least partly resulting from the mainstream business model with its taken-for granted basic assumptions such as specialization or the value-neutrality of business research. Redefining the roles and civic responsibilities of business scholars for business practice implies therefore a thorough analysis of these assumptions if not their redefinition. The taken-for-grantedness of the mainstream business model is questioned by the transformation of the societal context in which business activities are embedded. Its value-neutrality in turn is challenged by self-fulfilling prophecy effects, which highlight the normative influence of business schools. In order to critically discuss some basic assumptions of mainstream business theory, I propose to draw parallels with the corporate citizenship concept and the stakeholder theory. Their integrated approach of the relation between business practice and the broader society provides interesting insights for the social reembedding of business research and teachin

Data from: Tooth microwear and occlusal modes of euharamiyidans from the Jurassic Yanliao Biota reveal mosaic tooth evolution in Mesozoic allotherian mammals

‘Haramiyidans’ are extinct mammaliaforms often clustered with Multituberculata as Allotheria, and with a fossil record extending from the Upper Triassic to possibly the Upper Cretaceous. For many decades, ‘haramiyidans’ were known only from isolated teeth, and their relationships to other mammaliaforms remain unclear. With the discovery of several euharamiyidans represented by skeletal specimens from the Jurassic Yanliao Biota, north‐western China, our knowledge of this group has significantly advanced. Nonetheless, much morphology, including dentition and occlusal patterns, has only been briefly described for most taxa, and phylogenetic reconstruction and interpretation of the biology of the group still remain unclear or controversial. Here we provide systematic descriptions of the dental wear of shenshouids (Qishou and Shenshou) from the Yanliao Biota. We reconstruct the occlusal mode of shenshouids and compare it with those of other taxa based on our observation of specimens of most known ‘haramiyidan’ taxa. Several occlusal patterns are recognized. In particular, that of Thomasia, Haramiyavia and Maiopatagium is similar to the multituberculate M1/m1 occlusal relationship and probably represents the primitive occlusal condition in ‘haramiyidans’. The occlusal mode of shenshouids resembles the multituberculate M2/m2 occlusion. The double‐engaged mode in Arboroharamiya and Vilevolodon, as well as that of Eleutherodon, represents a more advanced occlusal pattern. The diverse dental occlusions suggest diverse diet adaptations and resource partitioning from co‐existing taxa in the Jurassic forests; they also reflect mosaic evolutionary transformations and complicated phylogenetic relationships within the group. Some aspects of the dental evolution remain challenging and require further vigorous testing

EARLINET VALIDATION OF CATS L2 PRODUCT

The Cloud-Aerosol Transport System (CATS) onboard the International Space Station (ISS), is a lidar system providing vertically resolved aerosol and cloud profiles since February 2015. In this study, the CATS aerosol product is validated against the aerosol profiles provided by the European Aerosol Research Lidar Network (EARLINET). This validation activity is based on collocated CATS-EARLINET measurements and the comparison of the particle backscatter coefficient at 1064nm