Prevalence of vegetation browning in China’s drylands under climate change

Vegetation greening has long been acknowledged,but recent studies have pointed out that vegetation greening is possibly stalled or even reversed.However,detailed analyses about greening reversal or increased browning of vegetation remain scarce.In this study,we utilized the normalized difference vegetation index(NDVI)as an indicator of vegetation to investigate the trends of vegetation greening and browning(monotonic,interruption,and reversal)through the breaks for the additive season and trend(BFAST)method across China’s drylands from 1982 to 2022.It also reveals the impacts of ecological restoration programs(ERPs)and climate change on these vegetation trends.We find that the vegetation displays an obvious pattern of east-greening and west-browning in China’s drylands.Greening trends mainly exhibits monotonic greening(29.8%)and greening with setback(36.8%),whereas browning shows a greening to browning reversal(19.2%).The increase rate of greening to browning reversal is 0.0342/yr,which is apparently greater than that of greening with setback,0.0078/yr.This research highlights that,under the background of widespread vegetation greening,vegetation browning is pro-gressively increasing due to the effects of climate change.Furthermore,the ERPs have significantly increased vegetation coverage,with the increase rate in 2000-2022 being twice as much as that of 1982-1999 in reveg-etation regions.Vegetation browning in southwestern Qingzang Plateau is primarily driven by adverse climatic factors and anthropogenic disturbances,which offset the efforts of ERPs.

Preface to the Special Issue on Causes, Impacts, and Predictability of Droughts for the Past, Present, and Future

Drought is a recurring dry condition with below-normal precipitation and is often associated with warm temperatures or heatwaves. A drought event can develop slowly over several weeks or suddenly within days, commonly under abnormal atmospheric conditions(e.g., quasi-stationary high-pressure systems), and can persist for weeks, months, or even years.

Progress in Semi-arid Climate Change Studies in China

This article reviews recent progress in semi-arid climate change research in China.Results indicate that the areas of semiarid regions have increased rapidly during recent years in China,with an increase of 33%during 1994-2008 compared to 1948-62.Studies have found that the expansion rate of semi-arid areas over China is nearly 10 times higher than that of arid and sub-humid areas,and is mainly transformed from sub-humid/humid regions.Meanwhile,the greatest warming during the past 100 years has been observed over semi-arid regions in China,and mainly induced by radiatively forced processes.The intensity of the regional temperature response over semi-arid regions has been amplified by land-atmosphere interactions and human activities.The decadal climate variation in semi-arid regions is modulated by oceanic oscillations,which induce land-sea and north-south thermal contrasts and affect the intensities of westerlies,planetary waves and blocking frequencies.In addition,the drier climates in semi-arid regions across China are also associated with the weakened East Asian summer monsoon in recent years.Moreover,dust aerosols in semi-arid regions may have altered precipitation by affecting the local energy and hydrological cycles.Finally,semi-arid regions in China are projected to continuously expand in the 21st century,which will increase the risk of desertification in the near future.

Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness.We present a first step towards building an artificial intelligence(AI)framework,with predictive analytics(PA)capabilities applied to real patient data,to provide rapid clinical decision-making support.COVID-19 has presented a pressing need as a)clinicians are still developing clinical acumen given the disease’s novelty,and b)resource limitations in a rapidly expanding pandemic require difficult decisions relating to resource allocation.The objectives of this research are:(1)to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes,and(2)to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation.The predictive models learn from historical data to help predict specifically who will develop acute respiratory distress syndrome(ARDS),a severe outcome in COVID-19.Our experimental results based on two hospitals in Wenzhou,Zhejang,China identify features most predictive of ARDS in COVID-19 initial presentation which would not have stood out to clinicians.A mild increase in elevated alanine aminotransferase(ALT)(a liver enzyme)),a presence of myalgias(body aches),and an increase in hemoglobin,in this order,are the clinical features,on presentation,that are the most predictive.Those two centers’COVID-19 case series symptoms on initial presentation can help predict severe outcomes.Predictive models that learned from historical data of patients from two Chinese hospitals achieved 70%to 80%accuracy in predicting severe cases.

Mongolia Contributed More than 42%of the Dust Concentrations in Northern China in March and April 2023

Dust storms are one of the most frequent meteorological disasters in China,endangering agricultural production,transportation,air quality,and the safety of people’s lives and property.Against the backdrop of climate change,Mongolia’s contribution to China’s dust cannot be ignored in recent years.In this study,we used the Weather Research and Forecasting model coupled with Chemistry(WRF-Chem),along with dynamic dust sources and the HYSPLIT model,to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023.The results show that the frequency of dust storms in 2023 was the highest observed in the past decade.Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China.Specifically,Mongolia contributed more than 42%of dust,while the Taklimakan Desert accounted for 26%.A cold high-pressure center,a cold front,and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China,where they affected most parts of the region.Moreover,two machine learning methods[the XGBoost algorithm and the Synthetic Minority Oversampling Technique(SMOTE)]were used to forecast the dust storms in March 2023,based on ground observations and WRF-Chem simulations over East Asia.XGBoost-SMOTE performed well in predicting hourly PM10 concentrations in China in March 2023,with a mean absolute error of 33.8μg m−3 and RMSE of 54.2μg m−3.

Atmospheric Warming Slowdown during 1998-2013 Associated with Increasing Ocean Heat Content

Although atmospheric greenhouse gas concentrations continuously increased, there was relatively little change in global-averaged surface temperatures from 1998 to 2013, which is known as atmospheric warming slowdown. For further understanding the mechanism involved, we explored the energy redistribution between the atmosphere and ocean in different latitudes and depths by using data analysis as well as simulations of a coupled atmosphere–ocean box model. The results revealed that, compared with observational changes of ocean heat content (OHC) associated with rapid warming, the OHC changes related to warming slowdown are relatively larger in multiple ocean basins, particularly in the deeper layer of the Atlantic. The coupled box model also showed that there is a larger increasing trend of OHC under the warming slowdown scenario than the rapid warming scenario. Particularly, during the warming slowdown period, the heat storage in the deeper ocean increases faster than the ocean heat uptake in the surface ocean. The simulations indicated that the warming patterns under the two scenarios are accompanied by distinct outgoing longwave radiation and atmospheric meridional heat transport, as well as other related processes, thus leading to different characteristics of ocean heat uptake. Due to the global energy balance, we suggest this slowdown has a tight relationship with the accelerated heat transport into the global ocean.

Estimation of ground heat flux and its impact on the surface energy budget for a semi-arid grassland

Three approaches, i.e., the harmonic analysis （HA） technique, the thermal diffusion equation and correction （TDEC） method, and the calorimetric method used to estimate ground heat flux, are evaluated by using observations from the Semi-Arid Climate and Environment Observatory of Lanzhou University （SACOL） in July, 2008. The calorimetric method, which involves soil heat flux measurement with an HFP01SC self-calibrating heat flux plate buried at a depth of 5 cm and heat storage in the soil between the plate and the surface, is here called the ITHP approach. The results show good linear relationships between the soil heat fluxes measured with the HFP01SC heat flux plate and those calculated with the HA technique and the TDEC method, respectively, at a depth of 5 cm. The soil heat fluxes calculated with the latter two methods well follow the phase measured with the HFP01SC heat flux plate. The magnitudes of the soil heat flux calculated with the HA technique and the TDEC method are close to each other, and they are about 2 percent and 6 percent larger than the measured soil heat flux, respectively, which mainly occur during the nighttime. Moreover, the ground heat fluxes calculated with the TDEC method and the HA technique are highly correlated with each other （R2= 0.97）, and their difference is only about 1 percent. The TDEC-calculated ground heat flux also has a good linear relationship with the ITttP-calculated ground heat flux （R2 = 0.99）, but their difference is larger （about 9 percent）. Furthermore, compared to the HFP01SC direct measurements at a depth of 5 cm, the ground heat flux calculated with the HA technique, the TDEC method, and the ITHP approach can improve the surface energy budget closure by about 6 percent, 7 percent, and 6 percent at SACOL site, respectively. Therefore, the contribution of ground heat flux to the surface energy budget is very important for the semi-arid grassland over the Loess Plateau in China. Using turbulent heat fluxes with common corrections, soil heat storage between the surface and the heat flux plate can improve the surface energy budget closure by about 6 to 7 percent, resulting in a closure of 82 to 83 percent at the SACOL site.

Comparison of PM_(2.5) and CO_(2) Concentrations in Large Cities of China during the COVID-19 Lockdown

Estimating the impacts on PM_(2.5)pollution and CO_(2)emissions by human activities in different urban regions is important for developing efficient policies.In early 2020,China implemented a lockdown policy to contain the spread of COVID-19,resulting in a significant reduction of human activities.This event presents a convenient opportunity to study the impact of human activities in the transportation and industrial sectors on air pollution.Here,we investigate the variations in air quality attributed to the COVID-19 lockdown policy in the megacities of China by combining in-situ environmental and meteorological datasets,the Suomi-NPP/VIIRS and the CO_(2)emissions from the Carbon Monitor project.Our study shows that PM_(2.5)concentrations in the spring of 2020 decreased by 41.87%in the Yangtze River Delta(YRD)and 43.30%in the Pearl River Delta(PRD),respectively,owing to the significant shutdown of traffic and manufacturing industries.However,PM_(2.5)concentrations in the Beijing-Tianjin-Hebei(BTH)region only decreased by 2.01%because the energy and steel industries were not fully paused.In addition,unfavorable weather conditions contributed to further increases in the PM_(2.5)concentration.Furthermore,CO_(2)concentrations were not significantly affected in China during the short-term emission reduction,despite a 19.52%reduction in CO_(2)emissions compared to the same period in 2019.Our results suggest that concerted efforts from different emission sectors and effective long-term emission reduction strategies are necessary to control air pollution and CO_(2)emissions.

The Electric Field Detector (EFD) onboard the ZH-1 satellite and firstobservational results

Previous studies have reported that, before or after occurrences of strong earthquakes, some low earth orbit satellites recorded ionospheric disturbances, including electromagnetic emissions and plasma fluctuations over the epicenter region or its conjugate point.Theoretically speaking, due to some electromagnetic coupling effect, electromagnetic emissions from the earthquake preparation zone could propagate from the lithosphere to the atmosphere, and could reach the ionosphere, even up to the inner magnetosphere. This paper introduces the electric field detector(EFD) onboard the ZhangHeng-1 satellite(ZH-1). The EFD is designed to measure electric field fluctuations within the broad frequency range of DC to 3.5 MHz, divided into 4 channels: ULF(DC–16 Hz), ELF(6 Hz–2.2 kHz), VLF(1.8 kHz–20 kHz) and HF(18 kHz–3.5 MHz). The sampling rates of the channels are 125 Hz, 5 kHz, 50 kHz and 10 MHz, respectively. The EFD includes4 spherical probes mounted on a over 4.5 m boom and an electronic box inside the satellite module. The resolution of the EFD is 1μV·m-1·Hz-1/2 at frequencies from DC to 16 Hz, and the sensitivity is 0.1 μV·m-1·Hz-1/2 at frequencies from 6 Hz to 2.2 kHz, 0.05 μV·m-1·Hz-1/2 in the band 1.8 kHz to 20 kHz, and 0.1μV·m-1·Hz-1/2 from 20 kHz to 3.5 MHz. The dynamic range from DC to 20 kHz is over 120 dB, and over96 dB from 20 kHz to 3.5 MHz. The EFD has two observation modes: survey mode and burst mode. The survey mode concentrates primarily on electric field power density values; the burst mode provides high sampling rate waveform data. The detailed configuration of the EFD onboard the ZH-1 is also introduced in this paper. During the six months' orbit test phase, the EFD recorded a number of natural electromagnetic emissions. Preliminary analysis of these data suggests that the EFD performs well onboard the ZH-1 and is meeting the requirements of the scientific objectives of ZH-1.

Improvement of the global prediction system of the COVID-19 pandemic based on the ensemble empirical mode decomposition(EEMD)and autoregressive moving average(ARMA)model in a hybrid approach

In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.

Application system and data description of the China Seismo-Electromagnetic Satellite

The China Seismo-Electromagnetic Satellite, launched into orbit from Jiuquan Satellite Launch Centre on February 2 nd, 2018, is China's first space satellite dedicated to geophysical exporation. The satellite carries eight scientific payloads including high-precision magnetometers to detect electromagnetic changes in space, in particular changes associated with global earthquake disasters. In order to encourage and facilitate use by geophysical scientists of data from the satellite's payloads, this paper introduces the application systems developed for the China Seismo-Electromagnetic Satellite by the Institute of Crustal Dynamics, China Earthquake Administration;these include platform construction, data classification, data storage, data format, and data access and acquisition.

Examples of unusual ionospheric observations by the CSES prior to earthquakes

The CSES(China seismic electromagnetic satellite) was launched on February 2, 2018 in a circular polar orbit at an altitude of～507 km. One of the main objectives of CSES is to search for and characterize ionospheric perturbations that can be associated with seismic activities, to better understand the generation mechanism of such perturbations. Its scientific payload can measure a broad frequency range of electromagnetic waves and some important plasma parameters. This paper is a first-hand study of unusual observations recorded by the CSES over seismic regions prior to four earthquakes with M >7.0 since the satellite's launch. CSES detectors measured irregularities near the epicenter of these four earthquakes. It is already clear that data from instruments onboard the CSES will be of significant help in studies of characteristics of ionospheric perturbations related to earthquakes and their generation mechanisms.

The Langmuir Probe onboard CSES: data inversion analysis method and first results

The Langmuir Probe(LAP), onboard the China Seismo-Electromagnetic Satellite(CSES), has been designed for in situ measurements of bulk parameters of the ionosphere plasma, the first Chinese application of in-situ measurement technology in the field of space exploration. The two main parameters measured by LAP are electron density and temperature. In this paper, a brief description of the LAP and its work mode are provided. Based on characteristics of the LAP, and assuming an ideal plasma environment, we introduce in detail a method used to invert the I-V curve; the data products that can be accessed by users are shown. Based on the LAP data available, this paper reports that events such as earthquakes and magnetic storms are preceded and followed by obvious abnormal changes. We suggest that LAP could provide a valuable data set for studies of space weather, seismic events, and the ionospheric environment.

China Seismo-Electromagnetic Satellite search coil magnetometerdata and initial results

Four levels of the data from the search coil magnetometer(SCM) onboard the China Seismo-Electromagnetic Satellite(CSES)are defined and described. The data in different levels all contain three components of the waveform and/or spectrum of the induced magnetic field around the orbit in the frequency range of 10 Hz to 20 kHz; these are divided into an ultra-low-frequency band(ULF,10–200 Hz), an extremely low frequency band(ELF, 200–2200 Hz), and a very low frequency band(VLF, 1.8–20 kHz). Examples of data products for Level-2, Level-3, and Level-4 are presented. The initial results obtained in the commission test phase demonstrated that the SCM was in a normal operational status and that the data are of high enough quality to reliably capture most space weather events related to low-frequency geomagnetic disturbances.

Preliminary results of the High Energetic Particle Package on-board the China Seismo-Electromagnetic Satellite

The high energetic particle package(HEPP) on-board the China Seismo-Electromagnetic Satellite(CSES) was launched on February 2, 2018. This package includes three independent detectors: HEPP-H, HEPP-L, and HEPP-X. HEPP-H and HEPP-L can detect energetic electrons from 100 keV to approximately 50 MeV and protons from 2 MeV to approximately 200 MeV. HEPP-X can measure solar X-rays in the energy range from 1 keV to approximately 20 keV. The objective of the HEPP payload was to provide a survey of energetic particles with high energy, pitch angle, and time resolutions in order to gain new insight into the space radiation environments of the near-Earth system. Particularly, the HEPP can provide new measurements of the magnetic storm related precipitation of electrons in the slot region, and the dynamics of radiation belts. In this paper, the HEPP scientific data sets are described and initial results are provided.The scientific data can show variations in the flux of energetic particles during magnetic storms.

Possible link between new coronavirus variants and atmospheric lightning and seawater intrusion

SARS-CoV-2(COVID-19) has been affecting the world for more than one year.The appearance of the new coronavirus variants makes the current situation full of uncertainty.In this respect,the authors discuss the connection between virus mutation and atmospheric factors.Based on the process of nitrogen fixation and transformation of nitrate inside the human body,the authors propose that the new coronavirus variants might be related to lightning and seawater intrusion.This study provides a new perspective in terms of the possible mechanism underlying the emergence of new coronavirus variants.

Optimal parameterization of COVID-19 epidemic models

At the time of writing,coronavirus disease 2019(COVID-19)is seriously threatening human lives and health throughout the world.Many epidemic models have been developed to provide references for decision-making by governments and the World Health Organization.To capture and understand the characteristics of the epidemic trend,parameter optimization algorithms are needed to obtain model parameters.In this study,the authors propose using the Levenberg–Marquardt algorithm(LMA)to identify epidemic models.This algorithm combines the advantage of the Gauss–Newton method and gradient descent method and has improved the stability of parameters.The authors selected four countries with relatively high numbers of confirmed cases to verify the advantages of the Levenberg–Marquardt algorithm over the traditional epidemiological model method.The results show that the Statistical-SIR(Statistical-Susceptible–Infected–Recovered)model using LMA can fit the actual curve of the epidemic well,while the epidemic simulation of the traditional model evolves too fast and the peak value is too high to reflect the real situation.

The Variability of Air-sea O_(2)Flux in CMIP6:Implications for Estimating Terrestrial and Oceanic Carbon Sinks

The measurement of atmospheric O_(2)concentrations and related oxygen budget have been used to estimate terrestrial and oceanic carbon uptake.However,a discrepancy remains in assessments of O_(2)exchange between ocean and atmosphere(i.e.air-sea O_(2)flux),which is one of the major contributors to uncertainties in the O_(2)-based estimations of the carbon uptake.Here,we explore the variability of air-sea O_(2)flux with the use of outputs from Coupled Model Intercomparison Project phase 6(CMIP6).The simulated air-sea O_(2)flux exhibits an obvious warming-induced upward trend(~1.49 Tmol yr−2)since the mid-1980s,accompanied by a strong decadal variability dominated by oceanic climate modes.We subsequently revise the O_(2)-based carbon uptakes in response to this changing air-sea O_(2)flux.Our results show that,for the 1990−2000 period,the averaged net ocean and land sinks are 2.10±0.43 and 1.14±0.52 GtC yr−1 respectively,overall consistent with estimates derived by the Global Carbon Project(GCP).An enhanced carbon uptake is found in both land and ocean after year 2000,reflecting the modification of carbon cycle under human activities.Results derived from CMIP5 simulations also investigated in the study allow for comparisons from which we can see the vital importance of oxygen dataset on carbon uptake estimations.

Observation of CO2^++ dication in the dayside Martian upper atmosphere

Doubly charged positive ions(dications)are an important component of planetary ionospheres because of the large energy required for their formation.Observations of these ions are exceptionally difficult due to their low abundances;until now,only atomic dications have been detected.The Neutral Gas and Ion Mass Spectrometer(NGIMS)measurements made on board the recent Mars Atmosphere and Volatile Evolution mission provide the first opportunity for decisive detection of molecular dications,CO2^++in this case,in a planetary upper atmosphere.The NGIMS data reveal a dayside averaged CO2^++distribution declining steadily from 5.6 cm^−3 at 160 km to below 1 cm^−3 above 200 km.The dominant CO2^++production mechanisms are double photoionization of CO2 below 190 km and single photoionization of CO2^+at higher altitudes;CO2++destruction is dominated by natural dissociation,but reactions with atmospheric CO2 and O become important below 160 km.Simplified photochemical model calculations are carried out and reasonably reproduce the data at low altitudes within a factor of 2 but underestimate the data at high altitudes by a factor of 4.Finally,we report a much stronger solar control of the CO2^++density than of the CO2+density.

分子机器中链状分子热传导性质的理论研究

运用晶格动力学理论推导了分子机器中链状分子振动的能量通量公式,在此基础上再应用格林–久保公式推导了链状分子的热传导系数公式。由于计算链状分子的热传导系数时需要声子的谱线宽度,因此还推导了声子谱线宽度公式。数值计算结果表明,链状分子的热传导系数随着其长度的增加而增加,并且在其长度趋于无限时而趋于无限;等长的链状分子热传导系数与环状分子热传导系数有很大的差异。数值计算结果还表明,长链状分子的热传导系数主要来自于短波矢声子的贡献。