Past and future trends of diurnal temperature range and their correlation with vegetation assessed by MODIS and CMIP6.

Temperature anomalies and changes in the diurnal temperature range (DTR) are expected to pose physiological challenges to biota; hence, both spatial and temporal variations in DTR provide important insights into temperature-induced stress in humans, animals, and vegetation. Furthermore, vegetation could dampen temperature variability. Here, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data of Land Surface Temperature (LST) to evaluate the global variation in DTR and its rate of change in spatial and temporal scales for the two decades spanning from 2001 to 2020. We show that North America, Africa, and Antarctica, as well as the global mean, experienced statistically significant DTR rates of change over the last 20 years in either summer, winter, or the annual mean. The rates were all negative, indicating the day-night temperature differences are decreasing in those regions because night temperatures are increasing at a faster rate than day temperatures. MODIS data of the Normalized Difference Vegetation Index (NDVI) revealed a strongly negative correlation with DTR, with a spatial correlation coefficient of -0.61. This correlation demonstrates a prominent dampening effect of vegetation on diurnal temperature oscillations. For future DTR projections, we used 19 models in the Coupled Model Intercomparison Project 6 (CMIP6) to predict global DTR trends from 2021 to 2050 with low and high CO2 concentration scenarios. The high CO2 emission scenario projects significant decreases in DTR in circumpolar regions, central Africa, and India compared to the low CO2 scenario. This difference in the two scenarios underscores the substantial influence of increased global temperatures and elevated CO2 concentration on DTR and, consequently, on the ecosystems in certain regions.

Disentangling effects of natural and anthropogenic drivers on forest net ecosystem production.

Net Ecosystem Production (NEP) of forests is the net carbon dioxide (CO2) fluxes between land and the atmosphere due to forests' biogeochemical processes. NEP varies with natural drivers such as precipitation, air temperature, solar radiation, plant functional type (PFT), and soil texture, which affect the gross primary production and ecosystem respiration, and thus the net C sequestration. It is also known that deposition of sulphur and nitrogen influences NEP in forest ecosystems. These drivers' respective, unique effects on NEP, however, are often difficult to be individually identified by conventional bivariate analysis. Here we show that by analyzing 22 forest sites with 231 site-year data acquired from FLUXNET database across Europe for the years 2000-2014, the individual, unique effects of these drivers on annual forest CO2 fluxes can be disentangled using Generalized Additive Models (GAM) for nonlinear regression analysis. We show that S and N deposition have substantial impacts on NEP, where S deposition above 5 kg S ha-1 yr-1 can significantly reduce NEP, and N deposition around 22 kg N ha-1 yr-1 has the highest positive effect on NEP. Our results suggest that air quality management of S and N is crucial for maintaining healthy biogeochemical functions of forests to mitigate climate change. Furthermore, the empirical models we developed for estimating NEP of forests can serve as a forest management tool in the context of climate change mitigation. Potential applications include the assessment of forest carbon fluxes in the REDD+ framework of the UNFCCC.

Intelligent Detection of a Planetary Gearbox Composite Fault Based on Adaptive Separation and Deep Learning.

Due to the existence of multiple rotating parts in the planetary gearbox-such as the sun gear, planet gears, planet carriers, and its unique planetary motion, etc.-the vibration signals generated under multiple fault conditions are time-varying and nonstable, thus making fault diagnosis difficult. In order to solve the problem of planetary gearbox composite fault diagnosis, an improved particle swarm optimization variational mode decomposition (IPVMD) and improved convolutional neural network (I-CNN) are proposed. The method takes as input the spectrum of the original vibration signal that contains rich information. First, the automatic feature extraction of signal spectrum is performed by I-CNN, while a classifier is used to diagnose the fault modes. Second, the composite fault signal is decomposed into multiple single fault signals by adaptive variational mode, and the signal is decomposed as a model input to diagnose the single fault component. Finally, a complete intelligent diagnosis of planetary gearboxes is conducted. Through experimental verification, the composite fault diagnosis method combining IPVMD and I-CNN will diagnose the composite fault and effectively diagnose the sub-fault included in the composite fault.