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Silicon nitride fibers-reinforced silicon nitride matrix (Si3N4f/BN/Si3N4) composites with a BN interphase were fabricated by chemical vapor infiltration (CVI) and precursor infiltration pyrolysis (PIP). It is shown that PIP Si3N4f/BN/Si3N4 composites possessed better mechanical properties and achieved effectively interface debonding and fiber pull-out, whose flexural strength and fracture toughness can achieve 189 ± 17 MPa and 5.6 ± 0.6 MPa·m1/2, about twice and 1.5 times as much as those of CVI Si3N4f/BN/Si3N4 composites, respectively. Combining the micro-nano mechanical and macro-mechanical properties investigations, it is revealed that weakening the interfacial bond and achieving proper modulus matching relationship between fibers and matrix by PIP process could remarkably increase the mechanical properties of the Si3N4f/BN/Si3N4 composites, compared with CVI process.

The existence of various quasiparticles of polarons because of electron-boson couplings plays important roles in determining electron transport in titanium dioxide (TiO2), which affects a wealth of physical properties from catalysis to interfacial superconductivity. In addition to the well-defined Frohlich polarons whose electrons are dressed by the phonon clouds, it has been theoretically predicted that electrons can also couple to their own plasmonic oscillations, namely, the plasmonic polarons. Here we experimentally demonstrate the formation of plasmonic polarons in highly doped anatase TiO2 using angle-resolved photoemission spectroscopy. Our results show that the energy separation of plasmon-loss satellites follows a dependence on √n, where n is the electron density, manifesting the characteristic of plasmonic polarons. The spectral functions enable to quantitatively evaluate the strengths of electron-plasmon and electron-phonon couplings, respectively, providing an effective approach for characterizing the interplays among different bosonic modes in the complicate many-body interactions.

In this study, three-dimensional silicon nitride fiber-reinforced silicon nitride matrix (3D Si3N4f/BN/Si3N4) composites with a boron nitride (BN) interphase were fabricated through chemical vapor infiltration. Through comparing the changes of microstructure, thermal residual stress, interface bonding state, and interface microstructure evolution of composites before and after heat treatment, the evolution of mechanical and dielectric properties of Si3N4f/BN/Si3N4 composites was analyzed. Flexural strength and fracture toughness of composites acquired the maximum values of 96 ± 5 MPa and 3.8 ± 0.1 MPa·m1/2, respectively, after heat treatment at 800 °C; however, these values were maintained at 83 ± 6 MPa and 3.1 ± 0.2 MPa·m1/2 after heat treatment at 1200 °C, respectively. The relatively low mechanical properties are mainly attributed to the strong interface bonding caused by interfacial diffusion of oxygen and subsequent interfacial reaction and generation of turbostratic BN interphase with relatively high fracture energy. Moreover, the Si3N4f/BN/Si3N4 composites also displayed moderate dielectric constant and dielectric loss fluctuating irregularly around 5.0 and 0.04 before and after heat treatment, respectively. They were mainly determined based on the intrinsic properties of materials system and complex microstructure of composites.

Silicon nitride (Si3N4) hollow microspheres with smaller particle size and narrower distribution can be used to prepare closed-cell ceramics as the pore-forming agent to improve heat insulation performance and wave-transparent performance of porous Si3N4 ceramics. In this work, Si3N4 hollow microspheres with a diameter of about 1 μm and a wall thickness of approximately 150 nm were prepared by using the template method combined with the carbothermal reduction nitridation method. The optimum preparation temperature of the Si3N4 hollow microspheres is 1450 °C. The morphology, microstructure, and phase composition of the prepared Si3N4 hollow microspheres were characterized. The formation mechanism of the Si3N4 hollow microspheres was discussed. The dielectric properties of Si3N4 hollow microspheres were measured using the waveguide method at 8.2-12.4 GHz. The results show that the wave-transparent performance of the Si3N4 hollow microspheres is similar to those of α-Si3N4 particles. It can be used as the pore-forming agent or matrix for preparing lightweight, heat-insulating, wave-transparent, and high-strength porous Si3N4 ceramics.

The chemistry of acetaldehyde (CH3CHO) adsorbed on the anatase TiO2(001)-(1×4) surface has been investigated by temperature-programmed desorption (TPD) method. Our experimental results provide the direct evidence that the perfect lattice sites on the anatase TiO2(001)-(1×4) surface are quite inert for the reaction of CH3CHO, but the reduced defect sites on the surface are active for the thermally driven reductive carbon-carbon coupling reactions of CH3CHO to produce 2-butanone and butene. We propose that the coupling reactions of CH3CHO on the anatase TiO2(001)-(1×4) surface should undergo through the adsorption of paired CH3CHO molecules at the reduced defect sites, since the existing reduced Ti pairs provide the suitable adsorption sites.

When a metal single-atom (SA) catalyst is supported on a semiconducting photocatalyst, the charge transfer of the photoexcited carriers to metal SAs can provide a synergetic activity for the co-catalysts. Here, we report the interfacial electronic coupling of the Au SAs on the TiO2(110) surface using scanning tunneling microscopy/spectroscopy, in combination with first-principles calculations. Distinct energy and spatial distributions of the metal-induced gap states (MIGSs) are experimentally revealed for the Au SAs adsorbed at the terminal Ti sites and the oxygen vacancies. The localized MIGS below the Fermi level provides a dedicated channel for the transfer of a photoexcited hole from the TiO2 substrate to the adsorbed Au SAs. The hole can weaken the Ti-Au bonding and activate the diffusion of Au SAs. Our results shed light on combining the advantages of photocatalysis and metal SA catalysis using a co-catalyst, which is promising to promote chemical reactions at low temperatures.

International audience; Cryo-electron tomography (cryo-ET) is an imaging technique that allows us to three-dimensionally visualize both the structural details of macro-molecular assemblies under near-native conditions and its cellular context. Electrons strongly interact with biological samples, limiting electron dose. The latter limits the signal-to-noise ratio and hence resolution of an individual tomogram to about 50 (5 nm). Biological molecules can be obtained by averaging volumes, each depicting copies of the molecule, allowing for resolutions beyond 4 (0.4 nm). To this end, the ability to localize and classify components is crucial, but challenging due to the low signal-to-noise ratio. Computational innovation is key to mine biological information from cryo-electron tomography.To promote such innovation, we provide a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in cryo-electron tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure.In this paper, we have evaluated seven different methods of finding and classifying proteins. Six research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching, a traditional method widely used in cryo-ET research. We find that method performance correlates with particle size, especially noticeable for template matching which performance degrades rapidly as the size decreases. We learn that neural networks can achieve significantly better localization and classification performance, in particular convolutional networks with focus on high-resolution details such as those based on U-Net architecture.

Imaging the doping elements is critical for understanding the photocatalytic activity of doped TiO2 thin film. But it is still a challenge to characterize the interactions between the dopants and the TiO2 lattice at the atomic level. Here, we use high angle annular dark-field/annular bright-field scanning transmission electron microscope (HAADF/ABF-STEM) combined with electron energy loss spectroscopy (EELS) to directly image the individual Cr atoms doped in anatase TiO2(001) thin film from [100] direction. The Cr dopants, which are clearly imaged through the atomic-resolution EELS mappings while can not be seen by HADDF/ABF-STEM, occupy both the substitutional sites of Ti atoms and the interstitial sites of TiO2 matrix. Most of them preferentially locate at the substitutional sites of Ti atoms. These results provide the direct evidence for the doping structure of Cr-doped A-TiO2 thin film at the atomic level and also prove the EELS mapping is an excellent technique for characterizing the doped materials.

We report our investigation on the electronic properties of Cr-N codoped rutile TiO2(110) single crystal thin films, homoepitaxially grown by pulsed-laser-deposition method, and characterized using scanning tunneling microscopy and spectroscopy (STM/STS), X-ray/ultraviolet photoemission spectroscopy (XPS/UPS), in combination with first-principles calculations. Our results show that the bandgap reduction of the TiO2(110) surface is mainly contributed by the delocalized states whose position is at 2.0 eV below the Fermi level, introduced by the substitutional codoped Cr-2N pair, which is evidenced by the accordance of the results between the STS spectra and the calculated DOS. The codoped Cr-N pair contributes the gap state at about 0.8 eV below the Fermi level, in consistent with the theoretical calculations. While, the monodoped Cr contributes the states either close to the valence band maximum or the conduction band minimum, which should not contribute to the bandgap reduction too much. Our experimental results joint with theoretical calculations provide an atomic view of the bandgap reduction of the rutile TiO2(110) surface, which indicates that the excess substitutional N atoms should be important to efficiently narrow the bandgap by introducing the Cr-2N pairs.

We here report our investigation on the photocatalytic reactions of methanol on the anatase TiO2(001)-(1×4) surface using scanning tunneling microscopy and temperature-programmed desorption. Our results show that in the reduced surface the Ti-pair defect sites are photoinactive, but in the reoxidized surface the partially oxidized defect sites are photoactive to dissociate methanol and methoxy to formaldehyde. The perfect (1×4) lattice sites are photoactive, even though they are inactive in thermally driven reaction. The photocatalytic reaction of methanol (CD3OH) at the perfect lattice sites is evidenced by the conversion of CD3OH to various other deuterated methanol (CD3OD and CD2HOH) and a small amount of formaldehyde (CD2O) after ultraviolet light irradiation, in contrast with no such conversion reaction in the nonirradiated sample. The production of CD3OD and CD2HOH can be attributed to the reverse reaction between the produced CD2O and the H or D adatoms during or after ultraviolet light irradiation...

We report our investigation using temperature-programmed desorption to detect the activity and the photoactivity of the reduced anatase TiO2(001)-(1 × 4) surface by using CH2O as a probe. We obtain the adsorption energy of 0.55 eV for CH2O on the surface and find that the defect sites in the reduced surface are the only active sites for thermally driven reaction to produce C2H4. We also identify the pathways for photodecomposition of CH2O on the anatase TiO2(001)-(1 × 4) surface, where the C−H bond breaking to form the intermediates of HCOO− should be a key step in the reactions. After the ultraviolet light irradiation, the dissociation of HCOO– produces CO and H2O at elevated temperatures. Accompanied by desorption of H2O, we observe higher production of C2H4 than that in the non-irradiated sample. We interpret our experimental results by attributing the temporary change of the (1 × 4) ridge structure to water desorption, that is, the initially oxygen-rich five-fold coordinated Ti atoms change to four-fo...

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods. Different multivariate performance require different optimal multi-instance dictionaries.We propose the problem of learning performance-optimal multi-instance dictionary.We learn dictionary and classifier jointly to optimize a target performance measure.It outperforms both independent dictionary learning and performance optimization methods.

In this study, we investigate the spatiotemporal characteristics of drought in India and its impact on agriculture during the summer season (April–September). In the analysis, we use Standardized Precipitation Evapotranspiration Index (SPEI) datasets between 1982 and 2012 at the six-monthly timescale. Based on the criterion SPEI < −1, we obtain a map of the number of occurrences of drought and find that the humid subtropical Upper Middle Gangetic Plain (UMGP) region is highly drought-prone, with an occurrence frequency of 40%–45%. This UMGP region contributes at least 18%–20% of India’s annual cereal production. Not only the probability of drought, but the UMGP region has become increasingly drought-prone in recent decades. Moreover, cereal production in the UMGP region has experienced a gradual declining trend from 2000 onwards, which is consistent with the increase in drought-affected areas from 20%–25% to 50%–60%, before and after 2000, respectively. A higher correlation coefficient (−0.69) between the cereal production changes and drought-affected areas confirms that at least 50% of the agricultural (cereal) losses are associated with drought. While analyzing the individual impact of precipitation and surface temperature on SPEI at 6 month timescale [SPEI (6)] we find that, in the UMGP region, surface temperature plays the primary role in the lowering of the SPEI. The linkage is further confirmed by correlation analysis between SPEI (6) and surface temperature, which exhibits strong negative values in the UMGP region. Higher temperatures may have caused more evaporation and drying, which therefore increased the area affected by drought in recent decades.

We report our investigation on the intrinsic chemical activity of the anatase TiO2(001)-(1 × 4) reconstructed surface, using epitaxially grown anatase TiO2(001) thin films and using methanol molecules as a probe, characterized by combining scanning tunneling microscopy and temperature-programmed desorption. Our results provide direct evidence that the perfect (1 × 4) lattice sites of the surface are intrinsically quite inert for the reaction of methanol. We obtain that the activation energy for desorption of molecular methanol is about 0.55–0.64 eV, which is in good agreement with our first-principle calculations based on the structural model with 5-fold coordinated Ti atoms at the ridges of (1 × 4) reconstruction. We find that two types of defect sites, that is, reduced Ti pairs and partially oxidized Ti pairs, are responsible for the chemical activity of the surface, evidenced by the desorption of water due to the dehydrogenation of methanol at the defect sites. The methoxy left at the reduced Ti-pair s...

We investigate the dynamic processes of formaldehyde (HCHO) molecules on 5-fold-coordinated titanium (Ti5c) sites of rutile TiO2(110) surface using scanning tunneling microscopy (STM) together with density functional theory simulations. Our results show that the adsorbed HCHO molecules at Ti5c sites are present as two types of protrusions, either centered at Ti5c rows or centered at bridging oxygen (Ob) rows in the STM images, corresponding to the monodentate adsorption configuration through a O–Ti5c bond and to the bidentate adsorption configuration through both O–Ti5c and C–Ob bonds, respectively, which can be well supported by the simulated images. It is also observed that the monodentate adsorption tends to spontaneously switch to bidentate adsorption. Our results confirm the existence of the energetically more favored bidentate adsorption for HCHO at Ti5c sites. We obtain that the energy barriers are approximately 0.28 and 0.75 eV for the adsorbed HCHO molecules switching from monodentate adsorption ...

Understanding the physics of surface plasmons and related phenomena requires knowledge of the spatial, temporal, and spectral distributions of the total electromagnetic field excited within nanostructures and their interfaces, which reflects the electromagnetic mode excitation, confinement, propagation, and damping. We present a microscopic and spectroscopic study of the plasmonic response in single-crystalline Ag wires grown in situ on Si(001) substrates. Excitation of the plasmonic modes with broadly tunable (UV–IR) femtosecond laser pulses excites ultrafast multiphoton photoemission, whose spatial distribution is imaged with an aberration-corrected photoemission electron microscope, thereby providing a time-integrated map of the locally enhanced electromagnetic fields. We show by tuning the wavelength, polarization, and k-vector of the incident laser light that for a few micrometers long wires we can selectively excite either the propagating surface plasmon polariton modes or high-order multipolar reso...

We report our investigation of the reversible reaction of methanol and the migration of hydrogen adatom (Had) on TiO2(110)-(1 × 1) surface at various temperatures and methanol coverages using scanning tunneling microscopy joint with density functional theory (DFT) calculations. At a relatively low coverage measured at room temperature, the methanol species adsorbed at the oxygen vacancy (OV) sites are immobile and appear only as a dissociative form, and the observed Had migration events are very few. However, when the OV sites are fully filled by methanol in the methanol-overdosed sample, the methanol species at the OV sites keep immobile but frequently switch between molecular and dissociative forms, accompanied by dramatically enhanced Had migration. Meanwhile, an established equilibrium shows a concentration ratio of 1:3 between the molecular and dissociative methanol. At 235 K, we directly visualized and confirmed that the reversible reactions of methanol and the enhanced Had migration are mediated by...

We systematically investigated the photocatalytic reaction of methanol on the TiO2 (110)-(1 × 1) surface under irradiation with ultraviolet (UV) light performed at various conditions, using scanning tunneling microscopy (STM) jointed with temperature-programmed desorption (TPD) techniques. Our STM and TPD results show that the photocatalytic reaction is indeed initiated from the molecular methanol at the 5-fold coordinated Ti sites, as commonly ascribed to the methanol oxidation by the photogenerated holes, reflecting the highly photoactive nature of methanol. The formaldehyde yield from the TPD results is much smaller by a factor of 2/3 than the amount of dissociated methanol from the STM results at 80 K. This observation can be assigned to the reverse reaction during the TPD measurement, and may explain the lower yield of formaldehyde using molecular methanol than using methoxy. From the fractal-like reaction kinetics of methanol, we can associate the coverage-dependence of the spectral dimensions with ...

Despite globalization and the scale of international food trade, access to sufficient food remains a major challenge in Africa. The most effective way to mitigate food insecurity is to increase crop production. To answer the question that whether African countries have the capacity to mitigate food shortages by best cultivating practices observed on current cropland, in this study, we use the local net primary productivity scaling (LNS) method to evaluate the currently attainable potential yield-gap (CAYgap). The CAYgap is initially used to suggest steps towards best regional agricultural practices and provide an indicator of regional divergence of cropland productivity in each homogeneous agro-climatic zone. Results indicate that under current climatic conditions, improving each countries’ productivity to the zonal optimal level, around ~ 90% of all African countries have the capacity to mitigate their current energy shortages independently. Thus, to achieve ending hunger, possible efforts are needed which include (1) clarifying what and how socio-economic and institutional factors cause yield divergence across agro-climatic zones and establishing relevant practical policies; (2) strengthening the resilience of food access to make national food availability favors households and individuals; and (3) establishing systematically monitoring platforms on dynamics of crop yields from pixel to regional and from growth phrase to decadal scales. Furthermore, our study demonstrates the feasibility of applying satellite-derived indicators for the maximum yield achieved method to quantify and map the current cropland yield divergence by LNS method, and this method could be applied on different spatial level from regional to global scale with reasonable homogeneous zone scheme.

Finding homologous proteins is the indispensable first step in many protein biology studies. Thus, building highly efficient “search engines” for protein databases is a highly desired function in protein bioinformatics. As of August 2018, there are more than 140,000 protein structures in PDB, and this number is still increasing rapidly. Such a big number introduces a big challenge for scanning the whole structure database with high speeds and high sensitivities at the same time. Unfortunately, classic sequence alignment tools and pairwise structure alignment tools are either not sensitive enough to remote homologous proteins (with low sequence identities) or not fast enough for the task. Therefore, specifically designed computational methods are required for quickly scanning structure databases for homologous proteins.Here, we propose a novel ContactLib-DNN method to quickly scan structure databases for homologous proteins. The core idea is to build structure fingerprints for proteins, and to perform alignment-free comparisons with the fingerprints. Specifically, the fingerprints are low-dimensional vectors representing the contact groups within the proteins. Notably, the Cartesian distance between two fingerprint vectors well matches the RMSD between the two corresponding contact groups. This is done by using RMSD as the domain knowledge to supervise the deep neural network learning. When comparing to existing methods, ContactLib-DNN achieves the highest average AUROC of 0.959. Moreover, the best candidate found by ContactLib-DNN has a probability of 70.0% to be a true positive. This is a significant improvement over 56.2%, the best result produced by existing methods.GitHub: https://github.com/Chenyao2333/contactlib/Index Termshomologous proteins, protein structures, remote protein homolog detection, alignment-free comparisons

The mechanical and dielectric properties of two types of amorphous silicon nitride (Si3N4) fibers prior to and following annealing at 800 &deg, C were studied. The tensile strengths of the Si3N4 fiber bundles were measured using unidirectional tensile experimentation at room temperature, whereas the permittivity values were measured at 8.2&ndash, 12.4 GHz using the waveguide method. The results demonstrated that the tensile strength and dielectric properties of Si3N4 fibers were correlated to the corresponding composition, microstructure, and intrinsic performance of electrical resistance. The Si3N4 fibers with a lower content of amorphous SiNxOy presented an improved thermal stability, a higher tensile strength, a higher conductivity, and a significantly stable wave-transparent property. These were mainly attributed to the highly pure composition and decomposition of less amorphous SiNxOy.

Superconductivity is mutually exclusive with ferromagnetism, because the ferromagnetic exchange field is often destructive to superconducting pairing correlation. Well-designed chemical and physical methods have been devoted to realize their coexistence only by structural integrity of inherent superconducting and ferromagnetic ingredients. However, such coexistence in freestanding structure with nonsuperconducting and nonferromagnetic components still remains a great challenge up to now. Here, we demonstrate a molecule-confined engineering in two-dimensional organic–inorganic superlattice using a chemical building-block approach, successfully realizing first freestanding coexistence of superconductivity and ferromagnetism originated from electronic interactions of nonsuperconducting and nonferromagnetic building blocks. We unravel totally different electronic behavior of molecules depending on spatial confinement: flatly lying Co(Cp)2 molecules in strongly confined SnSe2 interlayers weaken the coordinatio...

Electronic heating of cold crystal lattices in nonlinear multiphoton excitation can transiently alter their physical and chemical properties. In metals where free electron densities are high and the relative fraction of photoexcited hot electrons is low, the effects are small, but in semimetals, where the free electron densities are low and the photoexcited densities can overwhelm them, the intense femtosecond laser excitation can induce profound changes. In semimetal graphite and its derivatives, strong optical absorption, weak screening of the Coulomb potential, and high cohesive energy enable extreme hot electron generation and thermalization to be realized under femtosecond laser excitation. We investigate the nonlinear interactions within a hot electron gas in graphite through multiphoton-induced thermionic emission. Unlike the conventional photoelectric effect, within about 25 fs, the memory of the excitation process, where resonant dipole transitions absorb up to eight quanta of light, is erased to produce statistical Boltzmann electron distributions with temperatures exceeding 5000 K; this ultrafast electronic heating causes thermionic emission to occur from the interlayer band of graphite. The nearly instantaneous thermalization of the photoexcited carriers through Coulomb scattering to extreme electronic temperatures characterized by separate electron and hole chemical potentials can enhance hot electron surface femtochemistry, photovoltaic energy conversion, and incandescence, and drive graphite-to-diamond electronic phase transition.

This paper discussed the analysis of the survey on sustainability of bioenergy conducted in the Philippines, India and China. It acquired general perceptions of the people by asking them (a) specific questions about their level of familiarity with bioenergy; (b) relationship of their work to bioenergy; and (c) their opinion on contribution of various feedstock on the economy and impact of bioenergy production on food security. In addition to these questions, we estimated preference weights of various feedstock based on the conjoint choices on bioenergy’s contribution to social stability, social welfare and ecological balance. The estimates revealed significant trade-offs not only among these three dimensions of sustainability but also the relative importance of energy security, food security and ecosystem capacity to other economic, social and environmental objectives. The types of first generation feedstock that are currently used for biofuel production in the respective countries and those that offer alternative household use are perceived as important to the economy and preferred bioenergy feedstock. Based on the results of the study, the preferred role of bioenergy for sustainable development reflects the social and economic concerns in the respective Asian countries, e.g., energy security in China, food security in India, and ecosystem degradation in the Philippines.

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU; International audience; A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C−1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

An evaluation of precipitation and evapotranspiration simulated by mesoscale models is carried out within the African Monsoon Multidisciplinary Analysis (AMMA) program. Six models performed simulations of a mesoscale convective system (MCS) observed to cross part of West Africa in August 2005. Initial and boundary conditions are found to significantly control the locations of rainfall at synoptic scales as simulated with either mesoscale or global models. When initialized and forced at their boundaries by the same analysis, all models forecast a westward-moving rainfall structure, as observed by satellite products. However, rainfall is also forecast at other locations where none was observed, and the nighttime northward propagation of rainfall is not well reproduced. There is a wide spread in the rainfall rates across simulations, but also among satellite products. The range of simulated meridional fluctuations of evapotranspiration (E) appears reasonable, but E displays an overly strong zonal symmetry. Offline land surface modeling and surface energy budget considerations show that errors in the simulated E are not simply related to errors in the surface evaporative fraction, and involve the significant impact of cloud cover on the incoming surface shortwave flux. The use of higher horizontal resolution (a few km) enhances the variability of precipitation, evapotranspiration, and precipitable water (PW) at the mesoscale. It also leads to a weakening of the daytime precipitation, less evapotranspiration, and smaller PW amounts. The simulated MCS propagates farther northward and somewhat faster within an overall drier atmosphere. These changes are associated with a strengthening of the links between PW and precipitation.