Description: The Alfred Wegener Institute Climate Model (AWI‐CM) participates for the first time in the Coupled Model Intercomparison Project (CMIP), CMIP6. The sea ice‐ocean component, FESOM, runs on an unstructured mesh with horizontal resolutions ranging from 8 to 80 km. FESOM is coupled to the Max Planck Institute atmospheric model ECHAM 6.3 at a horizontal resolution of about 100 km. Using objective performance indices, it is shown that AWI‐CM performs better than the average of CMIP5 models. AWI‐CM shows an equilibrium climate sensitivity of 3.2°C, which is similar to the CMIP5 average, and a transient climate response of 2.1°C which is slightly higher than the CMIP5 average. The negative trend of Arctic sea‐ice extent in September over the past 30 years is 20–30% weaker in our simulations compared to observations. With the strongest emission scenario, the AMOC decreases by 25% until the end of the century which is less than the CMIP5 average of 40%. Patterns and even magnitude of simulated temperature and precipitation changes at the end of this century compared to present‐day climate under the strong emission scenario SSP585 are similar to the multi‐model CMIP5 mean. The simulations show a 11°C warming north of the Barents Sea and around 2°C to 3°C over most parts of the ocean as well as a wetting of the Arctic, subpolar, tropical, and Southern Ocean. Furthermore, in the northern middle latitudes in boreal summer and autumn as well as in the southern middle latitudes, a more zonal atmospheric flow is projected throughout the year.

Description: An abundance of evidence indicates that the tropics are expanding. Despite many attempts to decipher the cause, the underlying dynamical mechanism driving tropical expansion is still not entirely clear. Here, based on observations, multimodel simulations from the Coupled Model Intercomparison Project phase 5 (CMIP5) and purposefully designed numerical experiments, the variations and trends of the tropical width are explored from a regional perspective. We find that the width of the tropics closely follows the displacement of oceanic midlatitude meridional temperature gradients (MMTG). Under global warming, as a first‐order response, the subtropical ocean experiences more surface warming because of the mean Ekman convergence of anomalously warm water. The enhanced subtropical warming, which is partially independent of natural climate oscillations, such as the Pacific Decadal Oscillation, leads to poleward advance of the MMTG and drives the tropical expansion. Our results, supported by both observations and model simulations, imply that global warming may have already significantly contributed to the ongoing tropical expansion, especially over the ocean‐dominant Southern Hemisphere.

Description: Multimodel Arctic Ocean “climate response function” experiments are analyzed in order to explore the effects of anomalous wind forcing over the Greenland Sea (GS) on poleward ocean heat transport, Atlantic Water (AW) pathways, and the extent of Arctic sea ice. Particular emphasis is placed on the sensitivity of the AW circulation to anomalously strong or weak GS winds in relation to natural variability, the latter manifested as part of the North Atlantic Oscillation. We find that anomalously strong (weak) GS wind forcing, comparable in strength to a strong positive (negative) North Atlantic Oscillation index, results in an intensification (weakening) of the poleward AW flow, extending from south of the North Atlantic Subpolar Gyre, through the Nordic Seas, and all the way into the Canadian Basin. Reconstructions made utilizing the calculated climate response functions explain ∼50% of the simulated AW flow variance; this is the proportion of variability that can be explained by GS wind forcing. In the Barents and Kara Seas, there is a clear relationship between the wind‐driven anomalous AW inflow and the sea ice extent. Most of the anomalous AW heat is lost to the atmosphere, and loss of sea ice in the Barents Sea results in even more heat loss to the atmosphere, and thus effective ocean cooling. Release of passive tracers in a subset of the suite of models reveals differences in circulation patterns and shows that the flow of AW in the Arctic Ocean is highly dependent on the wind stress in the Nordic Seas.

Description: PM2.5 (Particulate Matter 2.5) samples were collected at Mount Heng and analyzed for polycyclic aromatic hydrocarbons (PAHs). During sampling, a sandstorm from northern China struck Mount Heng and resulted in a mean PM2.5 concentration of 150.61 μg/m3, which greatly exceeded the concentration measured under normal conditions (no sandstorm: 58.50 μg/m3). The average mass of PAHs in PM2.5 was 30.70 μg/g, which was much lower than in the non-sandstorm samples (80.80 μg/g). Therefore, the sandstorm increased particle levels but decreased PAH concentrations due to dilution and turbulence. During the sandstorm, the concentrations of 4- and 5-ring PAHs were below their detection limits, and 6-ring PAHs were the most abundant. Under normal conditions, the concentrations of 2-, 3- and 6-ring PAHs were higher, and 4- and 5-ring PAHs were lower relative to the other sampling sites. In general, the PAH contamination was low to medium at Mount Heng. Higher LMW (low molecular weight) concentrations were primarily linked to meteorological conditions, and higher HMW (high molecular weight) concentrations primarily resulted from long-range transport. Analysis of diagnostic ratios indicated that PM2.5 PAHs had been emitted during the combustion of coal, wood or petroleum. The transport characteristics and origins of the PAHs were investigated using backwards Lagrangian particle dispersion modeling. Under normal conditions, the “footprint” retroplumes and potential source contributions of PAHs for the highest and lowest concentrations indicated that local sources had little effect. In contrast, long-range transport played a vital role in the levels of PM2.5 and PAHs in the high-altitude atmosphere.

Description: This paper reports the mineralogical and geochemical compositions of the Late Permian C2, C5a, C5b, C6a, and C6b semianthracite coals from the Mahe mine, northeastern Yunnan, China. Minerals in the coals are mainly made up of quartz, chamosite, kaolinite, mixed-layer illite/smectite (I/S), pyrite, and calcite; followed by anatase, dolomite, siderite, illite and marcasite. Similar to the Late Permian coals from eastern Yunnan, the authigenic quartz and chamosite were precipitated from the weathering solution of Emeishan basalt, while kaolinite and mixed-layer I/S occurring as lenses or thin beds were related to the weathering residual detrital of Emeishan basalt. However, the euhedral quartz and apatite particles in the Mahe coals were attributed to silicic-rock detrital input. It further indicates that there has been silicic igneous eruption in the northeastern Yunnan. Due to the silicic rock detrital input, the Eu/Eu* value of the Mahe coals is lower than that of the Late Permian coals from eastern Yunnan, where the detrital particles were mainly derived from the basalt. The high contents of Sc, V, Cr, Co, Ni, Cu, Ga, and Sn in the Mahe coals were mainly derived from the Kangdian Upland.

Description: An electronic nose (e-nose) was used to characterize sesame oils processed by three different methods (hot-pressed, cold-pressed, and refined), as well as blends of the sesame oils and soybean oil. Seven classification and prediction methods, namely PCA, LDA, PLS, KNN, SVM, LASSO and RF, were used to analyze the e-nose data. The classification accuracy and MAUC were employed to evaluate the performance of these methods. The results indicated that sesame oils processed with different methods resulted in different sensor responses, with cold-pressed sesame oil producing the strongest sensor signals, followed by the hot-pressed sesame oil. The blends of pressed sesame oils with refined sesame oil were more difficult to be distinguished than the blends of pressed sesame oils and refined soybean oil. LDA, KNN, and SVM outperformed the other classification methods in distinguishing sesame oil blends. KNN, LASSO, PLS, and SVM (with linear kernel), and RF models could adequately predict the adulteration level (% of added soybean oil) in the sesame oil blends. Among the prediction models, KNN with k = 1 and 2 yielded the best prediction results.

Description: Dynamic calibration is one of the important ways to acquire the dynamic performance parameters of a pressure sensor. This research focuses on the processing method for the output of calibrated pressure sensor, and mainly attempts to solve the problem of extracting the true information of step response under strong interference noise. A dynamic calibration system based on a shock tube is established to excite the time-domain response signal of a calibrated pressure sensor. A key processing on difference modeling is applied for the obtained signal, and several generating sequences are established. A fusion process for the generating sequences is then undertaken, and the true information of the step response of the calibrated pressure sensor can be obtained. Finally, by implementing the common QR decomposition method to deal with the true information, a dynamic model characterizing the dynamic performance of the calibrated pressure sensor is established. A typical pressure sensor was used to perform calibration tests and a frequency-domain experiment for the sensor was also conducted. Results show that the proposed method could effectively filter strong interference noise in the output of the sensor and the corresponding dynamic model could effectively characterize the dynamic performance of the pressure sensor.

Description: Land-surface reflectance, estimated from satellite observations through atmospheric corrections, is an essential parameter for further retrieval of various high level land-surface parameters, such as leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface albedo. Although great efforts have been made, land-surface reflectance products still contain considerable noise caused by, e.g., cloud or mixed-cloud pixels, which results in temporal and spatial inconsistencies in subsequent downstream products. In this study, a new method is developed to remove the residual clouds in the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface reflectance product and reconstruct time series of surface reflectance for the red, near infrared (NIR), and shortwave infrared (SWIR) bands. A smoothing method is introduced to calculate upper envelopes of vegetation indices (VIs) from the surface reflectance data and the cloud contaminated reflectance data are identified using the time series VIs and the upper envelopes of the time series VIs. Surface reflectance was then reconstructed according to cloud-free surface reflectance by incorporating the upper envelopes of the time series VIs as constraint conditions. The method was applied to reconstruct time series of surface reflectance from MODIS/TERRA surface reflectance product (MOD09A1). Temporal consistency analysis indicates that the new method can reconstruct temporally-continuous time series of land-surface reflectance. Comparisons with cloud-free MODIS/AQUA surface reflectance product (MYD09A1) over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites in 2003 demonstrate that the new method provides better performance for the red band (R2 = 0.8606 and RMSE = 0.0366) and NIR band (R2 = 0.6934 and RMSE = 0.0519), than the time series cloud detection (TSCD) algorithm (R2 = 0.5811 and RMSE = 0.0649; and R2 = 0.5005 and RMSE = 0.0675, respectively).