Showing papers by "Nanjing University of Information Science and Technology published in 2020"

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Abstract: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Tropical Cyclones and Climate Change Assessment: Part II: Projected Response to Anthropogenic Warming

Abstract: Model projections of tropical cyclone (TC) activity response to anthropogenic warming in climate models are assessed. Observations, theory, and models, with increasing robustness, indicate ...

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

Abstract: The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

Muscle-Inspired Self-Healing Hydrogels for Strain and Temperature Sensor.

Abstract: Recently, self-healing hydrogel bioelectronic devices have raised enormous interest for their tissue-like mechanical compliance, desirable biocompatibility, and tunable adhesiveness on bioartificial organs. However, the practical applications of these hydrogel-based sensors are generally limited by their poor fulfillment of stretchability and sensitivity, brittleness under subzero temperature, and single sensory function. Inspired by the fiber-reinforced microstructures and mechano-transduction systems of human muscles, a self-healing (90.8%), long-lasting thermal tolerant and dual-sensory hydrogel-based sensor is proposed, with high gauge factor (18.28) within broad strain range (268.9%), low limit of detection (5% strain), satisfactory thermosensation (-0.016 °C-1), and highly discernible temperature resolution (2.7 °C). Especially by introducing a glycerol/water binary solvent system, desirable subzero-temperature self-healing performance, high water-retaining, and durable adhesion feature can be achieved, resulting from the ice crystallization inhibition and highly dynamic bonding. On account of the advantageous mechanoreception and thermosensitive capacities, a flexible touch keyboard for signature identification and a "fever indicator" for human forehead's temperature detection can be realized by this hydrogel bioelectronic device.

Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox

Abstract: Hyperspectral images (HSIs) provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands), which can be used to accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to conventional techniques (the so-called curse of dimensionality) for accurate analysis of HSIs.

Lead toxicity: Health hazards, influence on food Chain, and sustainable remediation approaches

Abstract: Lead (Pb) toxicity has been a subject of interest for environmental scientists due to its toxic effect on plants, animals, and humans. An increase in several Pb related industrial activities and use of Pb containing products such as agrochemicals, oil and paint, mining, etc. can lead to Pb contamination in the environment and thereby, can enter the food chain. Being one of the most toxic heavy metals, Pb ingestion via the food chain has proven to be a potential health hazard for plants and humans. The current review aims to summarize the research updates on Pb toxicity and its effects on plants, soil, and human health. Relevant literature from the past 20 years encompassing comprehensive details on Pb toxicity has been considered with key issues such as i) Pb bioavailability in soil, ii) Pb biomagnification, and iii) Pb- remediation, which has been addressed in detail through physical, chemical, and biological lenses. In the review, among different Pb-remediation approaches, we have highlighted certain advanced approaches such as microbial assisted phytoremediation which could possibly minimize the Pb load from the resources in a sustainable manner and would be a viable option to ensure a safe food production system.

Deep Learning Models for Real-time Human Activity Recognition with Smartphones

Abstract: With the widespread application of mobile edge computing (MEC), MEC is serving as a bridge to narrow the gaps between medical staff and patients. Relatedly, MEC is also moving toward supervising in ...

Freeze Casting: From Low-Dimensional Building Blocks to Aligned Porous Structures-A Review of Novel Materials, Methods, and Applications.

Abstract: Freeze casting, also known as ice templating, is a particularly versatile technique that has been applied extensively for the fabrication of well-controlled biomimetic porous materials based on ceramics, metals, polymers, biomacromolecules, and carbon nanomaterials, endowing them with novel properties and broadening their applicability. The principles of different directional freeze-casting processes are described and the relationships between processing and structure are examined. Recent progress in freeze-casting assisted assembly of low dimensional building blocks, including graphene and carbon nanotubes, into tailored micro- and macrostructures is then summarized. Emerging trends relating to novel materials as building blocks and novel freeze-cast geometries-beads, fibers, films, complex macrostructures, and nacre-mimetic composites-are presented. Thereafter, the means by which aligned porous structures and nacre mimetic materials obtainable through recently developed freeze-casting techniques and low-dimensional building blocks can facilitate material functionality across multiple fields of application, including energy storage and conversion, environmental remediation, thermal management, and smart materials, are discussed.

MXene hydrogels: fundamentals and applications.

Abstract: Hydrogels have recently garnered tremendous interest due to their potential application in soft electronics, human-machine interfaces, sensors, actuators, and flexible energy storage. Benefiting from their impressive combination of hydrophilicity, metallic conductivity, high aspect ratio morphology, and widely tuneable properties, when two-dimensional (2D) transition metal carbides/nitrides (MXenes) are incorporated into hydrogel systems, they offer exciting and versatile platforms for the design of MXene-based soft materials with tunable application-specific properties. The intriguing and, in some cases, unique properties of MXene hydrogels are governed by complex gel structures and gelation mechanisms, which require in-depth investigation and engineering at the nanoscale. On the other hand, the formulation of MXenes into hydrogels can significantly increase the stability of MXenes, which is often the limiting factor for many MXene-based applications. Moreover, through simple treatments, derivatives of MXene hydrogels, such as aerogels, can be obtained, further expanding their versatility. This tutorial review intends to show the enormous potential of MXene hydrogels in expanding the application range of both hydrogels and MXenes, as well as increasing the performance of MXene-based devices. We elucidate the existing structures of various MXene-containing hydrogel systems along with their gelation mechanisms and the interconnecting driving forces. We then discuss their distinctive properties stemming from the integration of MXenes into hydrogels, which have revealed an enhanced performance, compared to either MXenes or hydrogels alone, in many applications (energy storage/harvesting, biomedicine, catalysis, electromagnetic interference shielding, and sensing).

Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network

Abstract: Underwater image enhancement has received much attention in underwater vision research. However, raw underwater images easily suffer from color distortion, underexposure, and fuzz caused by the underwater scene. To address the above-mentioned problems, we propose a new multiscale dense generative adversarial network (GAN) for enhancing underwater images. The residual multiscale dense block is presented in the generator, where the multiscale, dense concatenation, and residual learning can boost the performance, render more details, and utilize previous features, respectively. And the discriminator employs computationally light spectral normalization to stabilize the training of the discriminator. Meanwhile, nonsaturating GAN loss function combining $L_1$ loss and gradient loss is presented to focus on image features of ground truth. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method, which outperforms nondeep and deep learning methods in both qualitative and quantitative evaluations. Furthermore, we perform an ablation study to show the contributions of each component and carry out application tests to further demonstrate the effectiveness of the proposed method.

Green route to synthesize Zinc Oxide Nanoparticles using leaf extracts of Cassia fistula and Melia azadarach and their antibacterial potential

Abstract: Development of plant based nanoparticles has many advantages over conventional physico-chemical methods and has various applications in medicine and biology. In present study, zinc oxide (ZnO) nanoparticles (NPs) were synthesized using leaf extracts of two medicinal plants Cassia fistula and Melia azadarach. 0.01 M zinc acetate dihydrate was used as a precursor in leaf extracts of respective plants for NPs synthesis. The structural and optical properties of NPs were investigated by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscope (SEM), ultraviolet-visible spectrophotometer (UV-Vis) and dynamic light scattering (DLS). The antibacterial potential of ZnO NPs was examined by paper disc diffusion method against two clinical strains of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) based on the zone of inhibition and minimal inhibitory indices (MIC). Change in color of the reaction mixture from brown to white indicated the formation of ZnO NPs. UV peaks at 320 nm and 324 nm, and XRD pattern matching that of JCPDS card for ZnO confirmed the presence of pure ZnO NPs. FTIR further confirmed the presence of bioactive functional groups involved in the reduction of bulk zinc acetate to ZnO NPs. SEM analysis displayed the shape of NPs to be spherical whereas DLS showed their size range from 3 to 68 nm. The C. fistula and M. azadarach mediated ZnO NPs showed strong antimicrobial activity against clinical pathogens compared to standard drugs, suggesting that plant based synthesis of NPs can be an excellent strategy to develop versatile and eco-friendly biomedical products.

BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing

Abstract: Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in the Internet of Things are offloaded to edge computing devices (ECDs) for implementation. However, ECDs are often overloaded or underloaded with disproportionate resource requests. In addition, during the process of task offloading, the transmitted information is vulnerable, which can result in data incompleteness. In view of this challenge, a blockchain-enabled computation offloading method, named BeCome, is proposed in this article. Blockchain technology is employed in edge computing to ensure data integrity. Then, the nondominated sorting genetic algorithm III is adopted to generate strategies for balanced resource allocation. Furthermore, simple additive weighting and multicriteria decision making are utilized to identify the optimal offloading strategy. Finally, performance evaluations of BeCome are given through simulation experiments.

Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19.

Abstract: China's policy interventions to reduce the spread of the coronavirus disease 2019 have environmental and economic impacts. Tropospheric nitrogen dioxide indicates economic activities, as nitrogen dioxide is primarily emitted from fossil fuel consumption. Satellite measurements show a 48% drop in tropospheric nitrogen dioxide vertical column densities from the 20 days averaged before the 2020 Lunar New Year to the 20 days averaged after. This decline is 21 ± 5% larger than that from 2015 to 2019. We relate this reduction to two of the government's actions: the announcement of the first report in each province and the date of a province's lockdown. Both actions are associated with nearly the same magnitude of reductions. Our analysis offers insights into the unintended environmental and economic consequences through reduced economic activities.

Increases in surface ozone pollution in China from 2013 to 2019: anthropogenic and meteorological influences

Abstract: . Surface ozone data from the Chinese Ministry of Ecology and Environment (MEE) network show sustained increases across the country over the 2013–2019 period. Despite Phase 2 of the Clean Air Action Plan targeting ozone pollution, ozone was higher in 2018–2019 than in previous years. The mean summer 2013–2019 trend in maximum 8 h average (MDA8) ozone was 1.9 ppb a −1 ( p ) across China and 3.3 ppb a −1 ( p ) over the North China Plain (NCP). Fitting ozone to meteorological variables with a multiple linear regression model shows that meteorology played a significant but not dominant role in the 2013–2019 ozone trend, contributing 0.70 ppb a −1 ( p ) across China and 1.4 ppb a −1 ( p=0.02 ) over the NCP. Rising June–July temperatures over the NCP were the main meteorological driver, particularly in recent years (2017–2019), and were associated with increased foehn winds. NCP data for 2017–2019 show a 15 % decrease in fine particulate matter (PM 2.5 ) that may be driving the continued anthropogenic increase in ozone, as well as unmitigated emissions of volatile organic compounds (VOCs). VOC emission reductions, as targeted by Phase 2 of the Chinese Clean Air Action Plan, are needed to reverse the increase in ozone.

Rapid Increases in Warm-Season Surface Ozone and Resulting Health Impact in China since 2013

Abstract: China’s nationwide ozone monitoring network initiated in 2013 has observed severe surface ozone pollution. This network, combined with the recent Tropospheric Ozone Assessment Report (TOAR) data set, offers a more comprehensive view on global surface ozone distribution and trends. Here, we report quantitative estimates of the warm-season (April–September) surface ozone trends and resulting health impacts at Chinese cities in 2013–2019. Both the parametric and nonparametric linear trends for 12 ozone metrics relevant to human health and vegetation exposure are derived. We find that all ozone metrics averaged from Chinese urban sites have increased significantly since 2013. The warm-season daily maximum 8-h average (MDA8) ozone levels increased by 2.4 ppb (5.0%) year–¹, with over 90% of the sites showing positive trends and 30% with trends larger than 3.0 ppb year–¹. These rates are among the fastest trends, even faster in some Chinese cities, compared with the urban ozone trends in any other region worldwide reported in TOAR. Ozone metrics reflecting the cumulative exposure effect on human health and vegetation such as SOMO35 and AOT40 have increased at even faster rates (>10% year–¹). We estimate that the total premature respiratory mortalities attributable to ambient MDA8 ozone exposure in 69 Chinese cities are 64,370 in 2019, which has increased by 60% compared to 2013 levels and requires urgent attention.

Building and identifying highly active oxygenated groups in carbon materials for oxygen reduction to H2O2.

Abstract: The one-step electrochemical synthesis of H2O2 is an on-site method that reduces dependence on the energy-intensive anthraquinone process. Oxidized carbon materials have proven to be promising catalysts due to their low cost and facile synthetic procedures. However, the nature of the active sites is still controversial, and direct experimental evidence is presently lacking. Here, we activate a carbon material with dangling edge sites and then decorate them with targeted functional groups. We show that quinone-enriched samples exhibit high selectivity and activity with a H2O2 yield ratio of up to 97.8 % at 0.75 V vs. RHE. Using density functional theory calculations, we identify the activity trends of different possible quinone functional groups in the edge and basal plane of the carbon nanostructure and determine the most active motif. Our findings provide guidelines for designing carbon-based catalysts, which have simultaneous high selectivity and activity for H2O2 synthesis. The identity of catalytic sites for H2O2 generation in carbon-based materials remains controversial with limited experimental evidence to date. Here, the authors decorate various target functional groups on carbon materials and quinone-enriched samples exhibit the highest activity and selectivity.

A review on hydrothermal carbonization of biomass and plastic wastes to energy products

Abstract: Hydrothermal carbonization (HTC) as a promising thermochemical process can convert organic solid wastes (e.g., biomass, plastics) into valuable products (i.e., hydrochar) at relatively low temperatures (180–250 °C) and saturated pressures (2–10 MPa). Hydrothermal conversion generally occurs via dehydration, polymerization and finally carbonization reactions. The carbon materials derived from hydrochar have high potential in various applications such as solid fuel, supercapacitor, fuel cell, and sorbent. Although the energy densification of hydrochar was increased at higher temperatures, most of the benefit was achieved at modest temperatures. Chemical structures of hydrochars include crosslinks of aromatic polymer, surface porosity, organic functional groups and ultimate components. All of these characteristics can be changed significantly by HTC, influencing the reactivity and fuel properties of hydrochars. The reaction pathways including negative and positive effects during (co)-HTC of biomass and plastic wastes are thoroughly concluded. In particular, the co-HTC of chlorinated plastic (e.g., PVC) and biomass can enhance the dechlorination and inorganics removal from hydrochar.

2D Metal–Organic Frameworks (MOFs) for High‐Performance BatCap Hybrid Devices

Abstract: Two identical layered metal-organic frameworks (MOFs) (CoFRS and NiFRS) are constructed by using flexible 1,10-bis(1,2,4-triazol-1-yl)decane as pillars and 1,4-benzenedicarboxylic acid as rigid linkers The single-crystal structure analysis indicates that the as-synthesized MOFs possess fluctuant 2D networks with large interlayer lattices Serving as active electrode elements in supercapacitors, both MOFs deliver excellent rate capabilities, high capacities, and longstanding endurances Moreover, the new intermediates in two electrodes before and after long-lifespan cycling are also examined, which cannot be identified as metal hydroxides in the peer reports After assembled into battery-supercapacitor (BatCap) hybrid devices, the NiFRS//activated carbon (AC) device displays better electrochemical results in terms of gravimetric capacitance and cycling performance than CoFRS//AC devices, and a higher energy-density value of 287 Wh kg-1 compared to other peer references with MOFs-based electrodes Furthermore, the possible factors to support the distinct performances are discussed and analyzed

Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark

Abstract: This study had two main aims: (1) to provide a comprehensive review of terrestrial laser scanner (TLS) point cloud registration methods and a better understanding of their strengths and weaknesses; and (2) to provide a large-scale benchmark data set (Wuhan University TLS: Whu-TLS) to support the development of cutting-edge TLS point cloud registration methods, especially deep learning-based methods. In particular, we first conducted a thorough review of TLS point cloud registration methods in terms of pairwise coarse registration, pairwise fine registration, and multiview registration, as well as analyzing their strengths, weaknesses, and future research trends. We then reviewed the existing benchmark data sets (e.g., ETH Dataset and Robotic 3D Scanning Repository) for TLS point cloud registration and summarized their limitations. Finally, a new benchmark data set was assembled from 11 different environments (i.e., subway station, high-speed railway platform, mountain, forest, park, campus, residence, riverbank, heritage building, underground excavation, and tunnel environments) with variations in the point density, clutter, and occlusion. In addition, we summarized future research trends in this area, including auxiliary data-guided registration, deep learning-based registration, and multi-temporal point cloud registration.

Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic.

Abstract: Author(s): Liu, Zhu; Ciais, Philippe; Deng, Zhu; Lei, Ruixue; Davis, Steven J; Feng, Sha; Zheng, Bo; Cui, Duo; Dou, Xinyu; Zhu, Biqing; Guo, Rui; Ke, Piyu; Sun, Taochun; Lu, Chenxi; He, Pan; Wang, Yuan; Yue, Xu; Wang, Yilong; Lei, Yadong; Zhou, Hao; Cai, Zhaonan; Wu, Yuhui; Guo, Runtao; Han, Tingxuan; Xue, Jinjun; Boucher, Olivier; Boucher, Eulalie; Chevallier, Frederic; Tanaka, Katsumasa; Wei, Yiming; Zhong, Haiwang; Kang, Chongqing; Zhang, Ning; Chen, Bin; Xi, Fengming; Liu, Miaomiao; Breon, Francois-Marie; Lu, Yonglong; Zhang, Qiang; Guan, Dabo; Gong, Peng; Kammen, Daniel M; He, Kebin; Schellnhuber, Hans Joachim | Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20254-5.

A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems

Abstract: Nowadays, with the development of cyber-physical systems (CPS), there are an increasing amount of applications deployed in the CPS to connect cyber space with physical world better and closer than ever. Furthermore, the cloud-based CPS bring massive computing and storage resource for CPS, which enables a wide range of applications. Meanwhile, due to the explosive expansion of applications deployed on the CPS, the energy consumption of the cloud-based CPS has received wide concern. To improve the energy efficiency in the cloud environment, the virtualized technology is employed to manage the resources, and the applications are generally hosted by virtual machines (VMs). However, it remains challenging to meet the Quality-of-Service (QoS) requirements. In view of this challenge, a QoS-aware VM scheduling method for energy conservation, named QVMS, in cloud-based CPS is designed. Technically, our scheduling problem is formalized as a standard multi-objective problem first. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is adopted to search the optimal VM migration solutions. Besides, SAW (Simple Additive Weighting) and MCDM (Multiple Criteria Decision Making) are employed to select the most optimal scheduling strategy. Finally, simulations and experiments are conducted to verify the effectiveness of our proposed method.

Bifunctional electrocatalysts for Zn–air batteries: recent developments and future perspectives

Abstract: As one of the most promising alternatives for future energy systems, the rechargeable Zn–air battery (ZAB) has attracted extensive attention due to its extraordinarily high theoretical specific energy density. However, several obstacles restrict its practical application. One challenge is the sluggish kinetics of oxygen-reduction reaction (ORR) and oxygen-evolution reaction (OER) in the discharging and charging processes of ZABs. In addition, when using unifunctional ORR or OER electrocatalysts as air electrodes, like noble metal catalysts (Pt/C or Ru/IrO2), there are the disadvantages of high cost and poor stability. Therefore, rational design of non-noble metal bifunctional ORR/OER electrocatalysts with high activity and stability is essential for the development of ZABs. In this review, we discuss the latest developments of non-noble metal bifunctional ORR/OER electrocatalysts for ZABs. Firstly, the related reaction mechanisms of ORR and OER are introduced. Then, the latest developments of bifunctional ORR/OER materials for ZABs are discussed in detail from three aspects: (i) MOF-based catalysts, including pristine MOFs and their derivatives; (ii) metal-free-based carbon catalysts, including heteroatom-doped carbon and defective carbon; (iii) metal-based catalysts, including metal–nitrogen–carbon materials (such as metals/alloys, single-atom) and metal compound materials. Finally, some challenges and outlooks for the optimal design of bifunctional air electrodes for rechargeable ZABs with high activity and ultra-long lifetime are put forward.

The Eighth Visual Object Tracking VOT2020 Challenge Results

Abstract: The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).

Does CMIP6 Inspire More Confidence in Simulating Climate Extremes over China

Abstract: Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during 1961–2005, the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), and 30 models from phase 5 of CMIP (CMIP5), are assessed in terms of spatial distribution and interannual variability. The CMIP6 multi-model ensemble mean (CMIP6-MME) can simulate well the spatial pattern of annual mean temperature, maximum daily maximum temperature, and minimum daily minimum temperature. However, CMIP6-MME has difficulties in reproducing cold nights and warm days, and has large cold biases over the Tibetan Plateau. Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices. Compared to CMIP5, CMIP6 models show improvements in the simulation of climate indices over China. This is particularly true for precipitation indices for both the climatological pattern and the interannual variation, except for the consecutive dry days. The areal-mean bias for total precipitation has been reduced from 127% (CMIP5-MME) to 79% (CMIP6-MME). The most striking feature is that the dry biases in southern China, very persistent and general in CMIP5-MME, are largely reduced in CMIP6-MME. Stronger ascent together with more abundant moisture can explain this reduction in dry biases. Wet biases for total precipitation, heavy precipitation, and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME, but smaller, compared to CMIP5-MME.

Puzzling haze events in China during the coronavirus (COVID-19) shutdown.

Abstract: It is a puzzle as to why more severe haze formed during the New Year Holiday in 2020 (NYH-20), when China was in an unprecedented state of shutdown to contain the coronavirus (COVID-19) outbreak, than in 2019 (NYH-19). We performed a comprehensive measurement and modeling analysis of the aerosol chemistry and physics at multiple sites in China (mainly in Shanghai) before, during, and after NYH-19 and NYH-20. Much higher secondary aerosol fraction in PM2.5 were observed during NYH-20 (73%) than during NYH-19 (59%). During NYH-20, PM2.5 levels correlated significantly with the oxidation ratio of nitrogen (r 2 = 0.77, p < 0.01), and aged particles from northern China were found to impede atmospheric new particle formation and growth in Shanghai. A markedly enhanced efficiency of nitrate aerosol formation was observed along the transport pathways during NYH-20, despite the overall low atmospheric NO2 levels.