The Reliability of Global and Hemispheric Surface Temperature Records

The purpose of this review article is to discuss the development and associated estimation of uncertainties in the global and hemispheric surface temperature records. The review begins by detailing the groups that produce surface temperature datasets. After discussing the reasons for similarities and differences between the various products, the main issues that must be addressed when deriving accurate estimates, particularly for hemispheric and global averages, are then considered. These issues are discussed in the order of their importance for temperature records at these spatial scales： biases in SST data, particularly before the 1940s; the exposure of land-based thermometers before the development of louvred screens in the late 19th century; and urbanization effects in some regions in recent decades. The homogeneity of land-based records is also discussed; however, at these large scales it is relatively unimportant. The article concludes by illustrating hemispheric and global temperature records from the four groups that produce series in near-real time.

Projections of the Advance in the Start of the Growing Season during the 21st Century Based on CMIP5 Simulations

It is well-known that global warming due to anthropogenic atmospheric greenhouse effects advanced the start of the vegetation growing season （SOS） across the globe during the 20th century. Projections of further changes in the SOS for the 21st century under certain emissions scenarios （Representative Concentration Pathways, RCPs） are useful for improving understanding of the consequences of global warming. In this study, we first evaluate a linear relationship between the SOS （defined using the normalized difference vegetation index） and the April temperature for most land areas of the Northern Hemisphere for 1982-2008. Based on this relationship and the ensemble projection of April temperature under RCPs from the latest state-of-the-art global coupled climate models, we show the possible changes in the SOS for most of the land areas of the Northern Hemisphere during the 21st century. By around 2040-59, the SOS will have advanced by -4.7 days under RCP2.6, -8.4 days under RCP4.5, and -10.1 days under RCPS.5, relative to 1985-2004. By 2080-99, it will have advanced by -4.3 days under RCP2.6, -11.3 days under RCP4.5, and -21.6 days under RCP8.5. The geographic pattern of SOS advance is considerably dependent on that of the temperature sensitivity of the SOS. The larger the temperature sensitivity, the larger the date-shift-rate of the SOS.

Debris flow simulation 2D(DFS 2D):Numerical modelling of debris flows and calibration of friction parameters

Debris flows are rapid mass movements with a mixture of rock,soil and water.High-intensity rainfall events have triggered multiple debris flows around the globe,making it an important concern from the disaster management perspective.This study presents a numerical model called debris flow simulation 2D(DFS 2D)and applicability of the proposed model is investigated through the values of the model parameters used for the reproduction of an occurred debris flow at Yindongzi gully in China on 13 August 2010.The model can be used to simulate debris flows using three different rheologies and has a userfriendly interface for providing the inputs.Using DFS 2D,flow parameters can be estimated with respect to space and time.The values of the flow resistance parameters of model,dry-Coulomb and turbulent friction,were calibrated through the back analysis and the values obtained are 0.1 and 1000 m/s^(2),respectively.Two new methods of calibration are proposed in this study,considering the crosssectional area of flow and topographical changes induced by the debris flow.The proposed methods of calibration provide an effective solution to the cumulative errors induced by coarse-resolution digital elevation models(DEMs)in numerical modelling of debris flows.The statistical indices such as Willmott's index of agreement,mean-absolute-error,and normalized-root-mean-square-error of the calibrated model are 0.5,1.02 and 1.44,respectively.The comparison between simulated and observed values of topographic changes indicates that DFS 2D provides satisfactory results and can be used for dynamic modelling of debris flows.

Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak

The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 countries.The World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,2020.Normally,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely manner.COVID19 is a major threat worldwide due to its ability to rapidly spread.No vaccines are yet available for COVID-19.The objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of COVID-19.We applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the pandemic.We also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the virus.This study considered the cases from 22 January 2020 to 25 April 2020.

Recent Study of Anomaly of Global Annual Geopotential Height and Global Warming

This paper studies the anomaly of global annual 500 hpa geopotential anomaly and global warming through the period (1950-2011). Anomaly method, linear trend and linear correlation coefficient techniques are referred to identify and describe the correlation between anomaly of global geopotential height field and global surface air temperature, North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), El-Nino3.4. The results revealed that, the anomaly of global annual geopotential height is completely controlled by global warming and NAO, SOI, El-Nino3.4 during the study period. However, the trend of the global surface air temperature anomaly completely coincides with the trend of 500 hpa geopotential height anomaly. This result uncovers the exist of abnormal weather phenomena through the last decades.

Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China

Rapid industrialization and urbanization along with a growing population are contributing significantly to air pollution in China.Evaluation of long-term aerosol optical depth(AOD)data from models and reanalysis,can greatly promote understanding of spatiotemporal variations in air pollution in China.To do this,AOD(550 nm)values from 2000 to 2014 were obtained from the Coupled Model Intercomparison Project(CIMP6),the second version of Modern-Era Retrospective analysis for Research,and Applications(MERRA-2),and the Moderate Resolution Imaging Spectroradiometer(MODIS;flying on the Terra satellite)combined Dark Target and Deep Blue(DTB)aerosol product.We used the TerraMODIS DTB AOD(hereafter MODIS DTB AOD)as a standard to evaluate CMIP6 Ensemble AOD(hereafter CMIP6 AOD)and MERRA-2 reanalysis AOD(hereafter MERRA-2 AOD).Results show better correlations and smaller errors between MERRA-2 and MODIS DTB AOD,than between CMIP6 and MODIS DTB AOD,in most regions of China,at both annual and seasonal scales.However,significant under-and over-estimations in the MERRA-2 and CMIP6 AOD were also observed relative to MODIS DTB AOD.The long-term(2000-2014)MODIS DTB AOD distributions show the highest AOD over the North China Plain(0.71)followed by Central China(0.69),Yangtse River Delta(0.67),Sichuan Basin(0.64),and Pearl River Delta(0.54)regions.The lowest AOD values were recorded over the Tibetan Plateau(0.13±0.01)followed by Qinghai(0.19±0.03)and the Gobi Desert(0.21±0.03).Large amounts of sand and dust particles emitted from natural sources(the Taklamakan and Gobi Deserts)may result in higher AOD in spring compared to summer,autumn,and winter.Trends were also calculated for 2000-2005,for2006-2010(when China introduced strict air pollution control policies during the 11 th Five Year Plan or FYP),and for 2011-2014(during the 12 th FYP).An increasing trend in MODIS DTB AOD was observed throughout the country during 2000-2014.The uncontrolled industrialization,urbanization,and rapid economic development that mostly occurred from 2000 to 2005 probably contributed to the overall increase in AOD.Finally,China’s air pollution control policies helped to reduce AOD in most regions of the country;this was more evident during the 12 th FYP period(2011-2014)than during the 11 th FYP period(2006-2010).Therefore this study strongly advises the authority to retain or extend these policies in the future for improving air quality.

A Recent Study on the Relationship between Global Radiative Forcing and Global Annual Climatic Variability

The present paper investigates the relationship between the global radiative forcing (GRF) and global annual climatic variability. The relation between the GRF and global annual changes in the operational weather and climatic parameters is uncovered. There are several datasets which have been used to challenge this goal. The NCEP/NCAR Reanalysis dataset of several meteorological elements, such as air temperature, wind, surface pressure, outgoing long wave radiation, precipitation rate and geopotential height at level 500 hPa, etc. for the globe for the period (1948-2012), has been used. Furthermore, the GRF data for greenhouse gases through the period (1979-2010) has been used. Also, datasets of climatic indices NAO, SOI, El Nino 3.4 and SST during the period (1948-2012) have been used through this study. Time series analysis, anomaly and correlation coefficient technique methods have been used to analyze the datasets. The results reveal that there is an outstanding positive correlation coefficient (more than +0.80) between GRF and the global annual weather elements of surface air temperature, temperature and geopotential height at level 500 hPa, precipitation rate and sea surface temperature. CO2 has a significant correlation coefficient (+0.89) with the outcomes longwave radiation and sea surface temperature. There is a significant relationship between the global annual variability of weather and climatic elements and GHGs, global warming and climatic indices, NAO, SOI, El Nino 3.4 and SST.

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature （SAT） series at 32 stations in China back to the 19th century had previously been developed based on the RHtest method by Cao et al., but some inhomogeneities remained in the dataset. The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization （MASH） method. The MASH procedure detects 33 monthly temperature records as erroneous outliers and 152 meaningful break points in the monthly SAT series since 1924 at 28 stations. The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series. The new data show significant warming trends during 1924-2016 at all the stations, ranging from 0.48 to 3.57℃ （100 yr）^-1, with a regional mean trend of 1.65℃ （100 yr）^-1 ; whereas, the previous results ranged from a slight cooling at two stations to considerable warming, up to 4.5℃ （100 yr）^-1. It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century. The new data axe available online at http：//www.dx.doi.org/10.11922/sciencedb.516.

Dominant SST Mode in the Southern Hemisphere Extratropics and Its Influence on Atmospheric Circulation

The variability in the Southern Ocean（SO） sea surface temperature（SST） has drawn increased attention due to its unique physical features; therefore, the temporal characteristics of the SO SST anomalies（SSTA） and their influence on extratropical atmospheric circulation are addressed in this study. Results from empirical orthogonal function analysis show that the principal mode of the SO SSTA exhibits a dipole-like structure, suggesting a negative correlation between the SSTA in the middle and high latitudes, which is referred to as the SO Dipole（SOD） in this study. The SOD features strong zonal symmetry, and could reflect more than 50% of total zonal-mean SSTA variability. We find that stronger（weaker） Subantarctic and Antarctic polar fronts are related to the positive（negative） phases of the SOD index, as well as the primary variability of the large-scale SO SSTA meridional gradient. During December–January–February, the Ferrel cell and the polar jet shift toward the Antarctic due to changes in the SSTA that could be associated with a positive phase of the SOD, and are also accompanied by a poleward shift of the subtropical jet. During June–July–August, in association with a positive SOD, the Ferrel cell and the polar jet are strengthened, accompanied by a strengthened subtropical jet. These seasonal differences are linked to the differences in the configuration of the polar jet and the subtropical jet in the Southern Hemisphere.

Energy: Preface

Energy is a vital commodity that sustains human lives,as well as economic processes.The challenges towards energy generation,demand and supply are plenty owing to the use of fossil fuels leading to climate change and environmental problems like water and air pollution.With the increasing awareness over climate change,post Paris Agreement,the role of energy plays a key role towards achieving the proposed target.The contributions in this Special Issue of Geoscience Frontiers on Energy includes 8 papers from esteemed research groups worldwide which explores,highlights and provide new insights towards the various aspects of energy.

Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques

Australia is a relatively stable continental region but not tectonically inert,having geological conditions that are susceptible to liquefaction when subjected to earthquake ground motion.Liquefaction hazard assessment for Australia was conducted because no Australian liquefaction maps that are based on modern Al techniques are currently available.In this study,several conditioning factors including Shear wave velocity(Vs30),clay content,soil water content,soil bulk density,soil thickness,soil pH,distance from river,slope and elevation were considered to estimate the liquefaction potential index(LPI).By considering the Probabilistic Seismic Hazard Assessment(PSHA)technique,peak ground acceleration(PGA)was derived for 50 yrs period(500 and 2500 yrs return period)in Australia.Firstly,liquefaction hazard index(LHI)(effects based on the size and depth of the liquefiable areas)was estimated by considering the LPI along with the 2%and 10%exceedance probability of earthquake hazard.Secondly,ground acceleration data from the Geoscience Australia projecting 2%and 10%exceedance rate of PGA for 50 yrs were used in this study to produce earthquake induced soil liquefaction hazard maps.Thirdly,deep neural net-works(DNNs)were also exerted to estimate liquefaction hazard that can be reported as liquefaction hazard base maps for Australia with an accuracy of 94%and 93%,respectively.As per the results,very-high liquefaction hazard can be observed in Western and Southern Australia including some parts of Victoria.This research is the first ever country-scale study to be considered for soil liquefaction hazard in Australia using geospatial information in association with PSHA and deep learning techniques.This study used an earthquake design magnitude threshold of Mw 6 using the source model characterization.The resulting maps present the earthquake-triggered liquefaction hazard and are intending to establish a conceptual structure to guide more detailed investigations as may be required in the future.The limitations of deep learning models are complex and require huge data,knowledge on topology,parameters,and training method whereas PSHA follows few assumptions.The advantages deal with the reusability of model codes and its transferability to other similar study areas.This research aims to support stakeholders'on decision making for infrastructure investment,emergency planning and prioritisation of post-earthquake reconstruction projects.