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.

Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

Long-term ocean temperature trend and marine heatwaves

Marine heatwaves(MHWs)can cause irreversible damage to marine ecosystems and livelihoods.Appropriate MHW characterization remains difficult,because the choice of a sea surface temperature(SST)temporal baseline strongly influences MHW identification.Following a recent work suggesting that there should be a communicating baseline for long-term ocean temperature trends(LTT)and MHWs,we provided an effective and quantitative solution to calculate LTT and MHWs simultaneously by using the ensemble empirical mode decomposition(EEMD)method.The long-term nonlinear trend of SST obtained by EEMD shows superiority over the traditional linear trend in that the data extension does not alter prior results.The MHWs identified from the detrended SST data exhibited low sensitivity to the baseline choice,demonstrating the robustness of our method.We also derived the total heat exposure(THE)by combining LTT and MHWs.The THE was sensitive to the fixed-period baseline choice,with a response to increasing SST that depended on the onset time of a perpetual MHW state(identified MHW days equal to the year length).Subtropical areas,the Indian Ocean,and part of the Southern Ocean were most sensitive to the long-term global warming trend.

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper,we describe and analyze two datasets entitled“Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021”and“Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”.These datasets provide the homogenised monthly and daily mean(TG),minimum(TN),and maximum(TX)temperature and precipitation(RR)records since 1960 at 366 stations in China and 56stations in Greece.The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720.For China,the regional mean annual TG,TX,TN,and RR series during 1960–2021 showed significant warming or increasing trends of 0.27℃(10 yr)^(-1),0.22℃(10 yr)^(-1),0.35℃(10 yr)^(-1),and 6.81 mm(10 yr)-1,respectively.Most of the seasonal series revealed trends significant at the 0.05level,except for the spring,summer,and autumn RR series.For Greece,there were increasing trends of 0.09℃(10 yr)-1,0.08℃(10 yr)^(-1),and 0.11℃(10 yr)^(-1)for the annual TG,TX,and TN series,respectively,while a decreasing trend of–23.35 mm(10 yr)^(-1)was present for RR.The seasonal trends showed a significant warming rate for summer,but no significant changes were noted for spring(except for TN),autumn,and winter.For RR,only the winter time series displayed a statistically significant and robust trend[–15.82 mm(10 yr)^(-1)].The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Analysis of Temperature Trends and Variations in the Arabian Peninsula’s Upper Atmosphere

In this study, the trends of upper-air temperatures are analysed by utilising radiosonde observations for the barometric levels at 700, 500, 300, 200, 150, 100 and 50 hPa from five meteorological stations within the Arabian Peninsula from January 1986 to August 2015. The mean monthly variations of the temperatures at these levels are characterised and established. The magnitudes of the annual trends of the mean temperatures for each site for the selected barometric levels are studied and statistically tested using Mann-Kendall rank statistics at different significance levels. The temperature trends at different pressure levels show that the upper troposphere and lower stratosphere are warming, while the middle troposphere is cooling which is consistent with the findings of other studies. The variations in upper air temperature observed in this study can be attributed to a range of factors, including increasing greenhouse gas concentrations, changes in atmospheric circulation patterns, variations in solar activity, aerosols and volcanic eruptions, and land use and land cover change.

Application of Multiple Analysis of Series for Homogenization to Beijing Daily Temperature Series (1960-2006)

Homogenization of climate observations remains a challenge to climate change researchers, especially in cases where metadata （e.g., probable dates of break points） are not always available. To examine the inffuence of metadata on homogenizing climate data, the authors applied the recently developed Multiple Analysis of Series for Homogenization （MASH） method to the Beijing （BJ） daily temperature series for 1960- 2006 in three cases with different references： （1） 13M-considering metadata at BJ and 12 nearby stations; （2） 13NOM-considering the same 13 stations without metadata; and （3） 21NOM-considering 20 further stations and BJ without metadata. The estimated mean annual, seasonal, and monthly inhomogeneities are similar between the 13M and 13NOM cases, while those in the 21NOM case are slightly different. The detected biases in the BJ series corresponding to the documented relocation dates are as low as -0.71~0C, -0.79~0C, and -0.5~0C for the annual mean in the 3 cases, respectively. Other biases, including those undocumented in metadata, are minor. The results suggest that any major inhomogeneity could be detected via MASH, albeit with minor differences in estimating inhomogeneities based on the different references. The adjusted annual series showed a warming trend of 0.337, 0.316, and 0.365~0C （10 yr）^（-1） for the three cases, respectively, smaller than the estimate of 0.453~0C （10 yr）^（-1） in the original series, mainly due to the relocation-induced biases. The impact of the MASH-type homogenization on estimates of climate extremes in the daily temperature series is also discussed.

Detection and Adjustment of Undocumented Discontinuities in Chinese Temperature Series Using a Composite Approach

Annually averaged daily maximum and minimum surface temperatures from southeastern China were evaluated for artificial discontinuities using three different tests for undocumented changepoints. Changepoints in the time series were identified by comparing each target series to a reference calculated from values observed at a number of nearby stations. Under the assumption that no trend was present in the sequence of target-reference temperature differences, a changepoint was assigned to the target series when at least two of the three tests rejected the null hypothesis of no changepoint at approximately the same position in the difference series. Each target series then was adjusted using a procedure that accounts for discontinuities in average temperature values from nearby stations that otherwise could bias estimates of the magnitude of the target series step change. A spatial comparison of linear temperature trends in the adjusted annual temperature series suggests that major relative discontinuities were removed in the homogenization process. A greater number of relative change points were detected in annual average minimum than in average maximum temperature series. Some evidence is presented which suggests that minimum surface temperature fields may be more sensitive to changes in measurement practice than maximum temperature fields. In addition, given previous evidence of urban heat island （i.e., local） trends in this region, the assumption of no slope in a target-reference difference series is likely to be violated more frequently in minimum than in maximum temperature series. Consequently, there may be greater potential to confound trend and step changes in minimum temperature series.

Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time-series and ARIMAX analyses

Objective:To study the number of leptospirosis cases in relations to the seasonal pattern,and its association with climate factors.Methods:Time series analysis was used to study the time variations in the number of leptospirosis cases.The Autoregressive Integrated Moving Average (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases. Results:We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest,namely the northern and northeastern region of Thailand,while the temperature played a role in the northeastern region only.The use of multivariate ARIMA(ARIMAX) model showed that factoring in rainfall(with an 8 months lag) yields the best model for the northern region while the model,which factors in rainfall(with a 10 months kg) and temperature(with an 8 months lag) was the best for the northeaslern region.Conclusions:The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions.The models can also be used to predict the next seasonal peak quite accurately.

Homogenized Daily Mean/Maximum/Minimum Temperature Series for China from 1960-2008

Inhomogeneities in the daily mean/maximum/ minimum temperature （Tm/Tmax/Tmin） series from 1960- 2008 at 549 National Standard Stations （NSSs） in China were analyzed by using the Multiple Analysis of Series for Homogenization （MASH） software package. Typical biases in the dataset were illustrated via the cases of Beijing （B J）, Wutaishan （WT）, Urumqi （UR） and Henan （HN） stations. The homogenized dataset shows a mean warming trend of 0.261/0.193/0.344℃/decade for the annual series of Tm/Tmax/Tmin, slightly smaller than that of the original dataset by 0.006/0.009/0.007℃/decade. However, considerable differences between the adjusted and original datasets were found at the local scale. The adjusted Tmin series shows a significant warming trend almost everywhere for all seasons, while there are a number of stations with an insignificant trend in the original dataset. The adjusted Tm data exhibit significant warming trends annually as well as for the autumn and winter seasons in northern China, and cooling trends only for the summer in the middle reaches of the Yangtze River and parts of central China and for the spring in southwestern China, while the original data show cooling trends at several stations for the annual and seasonal scales in the Qinghai, Shanxi, Hebei, and Xinjiang provinces. The adjusted Tmax data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Rivers and for springs and winters at a few stations in southwestern China, while the original data show cooling trends at three/four stations for the annual/autumn periods in the Qinghai and Yunnan provinces. In general, the number of stations with a cooling trend was much smaller in the adjusted Tm and Tmax dataset than in the original dataset. The cooling trend for summers is mainly due to cooling in August. The results of homogenization using MASH appear to be robust; in particular, different groups of stations with consideration of elevation led to minor effects in the results.

Impacts of Reference Time Series on the Homogenization of Radiosonde Temperature

Using radiosonde temperatures of 92 selected stations in China,the uncertainties in homogenization processes caused by different reference series,including nighttime temperature,the NCEP （National Centers for Environmental Prediction） and ERA-40 （European Centre for Medium-Range Weather Forecasts） forecasting background,are examined via a two-phase regression approach.Although the results showed limited consistency in the temporal and spatial distribution of identified break points （BPs） in the context of metadata events of instrument model change and correction method,significant uncertainties still existed in BP identification,adjustment,and impact on the estimated trend.Reanalysis reference series generally led to more BP identification in homogenization.However,those differences were parts of global climatic shifts,which may have confused the BP calculations.Discontinuities also existed in the reanalysis series due to changes in the satellite input.The adjustment values deduced from the reanalysis series ranged widely and were larger than those from the nighttime series and,therefore,impacted the estimated temperature trend.

Analysis of Change Point in Surface Temperature Time Series Using Cumulative Sum Chart and Bootstrapping for Asansol Weather Observation Station, West Bengal, India

This paper aims to detect the short-term as well as long-term change point in the surface air temperature time series for Asansol weather observation station, West Bengal, India. Temperature data for the period from 1941 to 2010 of the said weather observatory have been collected from Indian Meteorological Department, Kolkata. Variations and trends of annual mean temperature, annual mean maximum temperature and annual minimum temperature time series were examined. The cumulative sum charts (CUSUM) and bootstrapping were used for the detection of abrupt changes in the time series data set. Statistically significant abrupt changes and trends have been detected. The major change point in the annual mean temperatures occurred around 1986 (0.57°C) at the period of 25 years in the long-term regional scale. On the other side, the annual mean maximum and annual mean minimum temperatures have distinct change points at level 1. There are abrupt changes in the year 1961 (Confidence interval 1961, 1963) for the annual mean maximum and 1994 (Confidence interval 1993, 1996) for the annual mean minimum temperatures at a confidence level of 100% and 98%, respectively. Before the change, the annual mean maximum and annual mean minimum temperatures were 30.90°C and 23.99°C, respectively, while after the change, the temperatures became 33.93°C and 24.84°C, respectively. Over the entire period of consideration (1941-2010), 11 forward and backward changes were found in total. Out of 11, there are 3 changes (1961, 1986 and 2001) in annual mean temperatures, 4 changes (1957, 1961, 1980 and 1994) in annual mean maximum temperatures, and rest 4 changes (1968, 1981, 1994 and 2001) are associated with annual mean minimum temperature data set.

Temperature and Daily Mortality in Shanghai:A Time-series Study

To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily total and cause-specific mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for long-term time trend, season and other variables. We also controlled for day of the week. Results A gently sloping V-like relationship between total mortality and temperature was found, with an optimum temperature (e.g. temperature with lowest mortality risk) value of 26.7癈 in Shanghai. For temperatures above the optimum value, total mortality increased by 0.73% for each degree Celsius increase; while for temperature below the optimum value, total mortality decreased by 1.21% for each degree Celsius increase. Conclusions Our findings indicate that temperature has an effect on daily mortality in Shanghai, and the time-series approach is a useful tool for studying the temperature-mortality association.

Mean-field and high temperature series expansion calculations of some magnetic properties of Ising and XY antiferromagnetic thin-films

In this work, the magnetic properties of Ising and XY antiferromagnetic thin-films are investigated each as a function of Neel temperature and thickness for layers （n = 2, 3, 4, 5, 6, and bulk （∞） by means of a mean-field and high temperature series expansion （HTSE） combined with Pade approximant calculations. The scaling law of magnetic susceptibility and magnetization is used to determine the critical exponent γ, veff （mean）, ratio of the critical exponents γ/v, and magnetic properties of Ising and XY antiferromagnetic thin-films for different thickness layers n = 2, 3, 4, 5, 6, and bulk （∞）.

Significance of Transients in Soil Temperature Series

The primary objective of this study was to investigate the impact of observation scale on the estimation of soil thermal properties.Transients are usually filtered out and ignored when classical Fourier approaches are used to deconstruct and model temperature time series.It was hypothesized that examination of such transients may be more important in identifying and quantifying short-term perturbations in internal soil heat transfer induced by agronomic disturbances. Data-logged temperatures were collected at 10-minute intervals from thermistor probes installed at 10 and 25 cm depths in isolated areas of two grassed plots.One plot(6T)had been treated twice with 6 Mg ha^(-1)composted turkey litter as received.The other plot(NPK)was fertilized at the same time with NPK fertilizer.Various methods were used to analyze the series to obtain apparent soil thermal diffusivity(D-value)at various time scales.Results supported the hypothesis that short-term differences in internal soil heat transfer between the 6T and NPK plots were more manifest and effectively captured by estimated D-values calculated from the monthly and daily partial series.The 6T plot had higher soil organic matter content than the NPK plot and had lower apparent soil thermal diffusivity.Diurnal soil temperature amplitudes, required to calculate the mean D-values from partial series,were more effectively obtained using a temperature change rate method.The more commonly used Fourier analysis tended to be effective for this purpose when the partial series reasonably presented well-defined diurnal patterns of increasing and decreasing temperatures.

Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT)

Any change in technical or environmental conditions of observations may result in bias from the precise values of observed climatic variables. The common name of these biases is inhomogeneity (IH). IHs usually appear in a form of sudden shift or gradual trends in the time series of any variable, and the timing of the shift indicates the date of change in the conditions of observation. The seasonal cycle of radiation intensity often causes marked seasonal cycle in the IHs of observed temperature time series, since a substantial portion of them has direct or indirect connection to radiation changes in the micro-environment of the thermometer. Therefore the magnitudes of temperature IHs tend to be larger in summer than in winter. A new homogenisation method (ACMANT) has recently been developed which treats in a special way the seasonal changes of IH-sizes in temperature time series. The ACMANT is a further development of the Caussinus-Mestre method, that is one of the most effective tool among the known homogenising methods. The ACMANT applies a bivariate test for searching the timings of IHs, the two variables are the annual mean temperature and the amplitude of seasonal temperature-cycle. The ACMANT contains several further innovations whose efficiencies are tested with the benchmark of the COST ES0601 project. The paper describes the properties and the operation of ACMANT and presents some verification results. The results show that the ACMANT has outstandingly high performance. The ACMANT is a recommended method for homogenising networks of monthly temperature time series that observed in mid- or high geographical latitudes, because the harmonic seasonal cycle of IH-size is valid for these time series only.

Comparative Analysis of China Surface Air Temperature Series for the Past 100 Years

Temperature change plays a crucial role in global change sciences. In the past several decades, comprehensive find- ings have been achieved on temperature change in China for the past 100 years. Several time series have been created to illustrate the averaged surface air temperature for the country. The correlations of these series range from 0.73 to 0.97. It is also achieved in better data quality, wider spatial data coverage, improved homogeneity of time series, and enhanced reliability of findings. The results show an annual mean temperature increase by 0.78±0.27℃ per 100 years in China for the period 1906-2005. After prolonging the period till 2007, it is found that 2007 is rated as the warmest year in the past 100 years. Although all the series, except one, reflect temperature changes in the eastern part of China before the 1930s, they represent the general temperature change in most parts of the country after the 1930s.

A comparison of the pitting corrosion resistances of the 304 and new 200 series of stainless steels by potential dependent critical pitting corrosion temperatures

This study researched the relationship between the applied potential and the critical pitting temperature （CPT） of the 304 and new 200 series of stainless steels. The fluctuation about the potential dependent CPT for the stainless steels was investigated and the CPT range was obtained. The difference between the potential dependent CPTs of the 304 and 200 series of stainless steels with an applied potential of 100 mV （ vs SCE）, were presented, and by this means the pitting corrosion resistances of them were compared.

Measuring Global Warming: Global and Hemisphere Mean Temperature Anomalies Predictions Using Sliced Functional Time Series (SFTS) Model

In this study, the sliced functional time series (SFTS) model is applied to the Global, Northern and Southern temperature anomalies. We obtained the combined land-surface air and sea-surface water temperature from Goddard Institute for Space Studies (GISS), NASA. The data are available for Global mean, Northern Hemisphere mean and Southern Hemisphere means (monthly, quarterly and annual) since 1880 to present (updated through March 2019). We analyze the global surface temperature change, compare alternative analyses, and address the questions about the reality of global warming. We detected the outliers during the last century not only in global temperature series but also in northern and southern hemisphere series. The forecasts for the next twenty years are obtained using SFTS models. These forecasts are compared with ARIMA, Random Walk with drift and Exponential Smoothing State Space (ETS) models. The comparison is made on the basis of root mean square error (RMSE), mean absolute percentage error (MAPE) and the length of prediction intervals.

Analysis on Long-Term Change of Sea Surface Temperature in the China Seas

Long-term change of sea surface temperature （SST） in the China Seas from 1900 to 2006 is examined based on two different observation datasets （HadlSSTI and HadSST3）. Similar to the Atlantic, SST in the China Seas has been well observed during the past 107 years. A comparison between the reconstructed （HadISSTI） and un-interpolated （HadSST3） datasets shows that the SST wanning trends from both datasets are consistent with each other in most of the China Seas. The warming trends are stronger in winter than in summer, with a maximum rate of SST increase exceeding 2.7℃ （100year）-I in the East China Sea and the Taiwan Strait during winter based on HadISSTI. However, the SST from both datasets experienced a sudden decrease after 1999 in the China Seas. The estimated trend from HadlSSTI is stronger than that fi＇om HadSST3 in the East China Sea and the east of Taiwan Island, where the difference in the linear SST warming trends are as large as about 1℃ （100year）-I when using respectively HadISST1 and HadSST3 datasets. When compared to the linear winter warnling trend of the land surface air temperature （1.6℃ （100 year）-1）, HadSST3 shows a more reasonable trend of less than 2.1℃（ 100 year）-1 than HadISST 1 ＇s trend of larger than 2.7℃ （ 100 year）-1 at the mouth of the Yangtze River. The restllts also indicate large uncertainties in the estimate of SST warming patterns.

The long-term variability of sea surface temperature in the seas east of China in the past 40 a

The global surface temperature change since the mid-19th century has caused general concern and intensive study. However, long-term changes in the marginal seas, including the seas east of China, are not well understood because long-term observations are sparse and, even when they exist, they are over limited areas. Preliminary results on the long-term variability of sea surface temperature （SST） in summer and winter in the seas east of China during the period of 1957-2001 are reported using the Ocean Science Database of Institute of Oceanology, Chinese Academy of Sciences, the coastal hydrological station in situ and satellite data. The results show well-defined warming trends in the study area. However warming and cooling trends vary from decade to decade, with steady and rapid warming trends after the 1980s and complicated spatial patterns. The distribution of SST variation is intricate and more blurred in the areas far away from the Kuroshio system. Both historical and satellite data sets show significant warming trends after 1985. The warming trends are larger and spread to wider areas in winter than in summer, which means decrease in the seasonal cycle of SST probably linked with recently observed increase of the tropical zooplankton species in the region. Spatial structures of the SST trends are roughly consistent with the circulation pattern especially in winter when the meridional SST gradients are larger, suggesting that a horizontal advection may play an important role in the long-term SST variability in winter.