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.

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.

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.

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.

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.

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.

Construction of the homogenized temperature series during 1910-2014 and its changes in Hunan Province

Based on the statistical method and the historical evolution of meteorological sta- tions, the temperature time series for each station in Hunan Province during 1910-2014 are tested for their homogeneity and then corrected. The missing data caused by war and other reasons at the 8 meteorological stations which had records before 1950 is filled by interpola- tion using adjacent observations, and complete temperature time series since the estab- lishment of stations are constructed. After that, according to the representative analysis of each station in different time periods, the temperature series of Hunan Province during 1910-2014 are built and their changes are analyzed. The results indicate that the annual mean temperature has a significant warming trend during 1910-2014 and the seasonal mean temperature has the largest rising amplitude in winter and spring, followed by autumn, but no significant change in summer. Temperature variation over Hunan Province has several sig- nificant warm-cold alternations and more frequent than that in whole China. Annual and seasonal mean temperatures except summer and autumn have abrupt warming changes in the recent 100 years. The wavelet analysis suggests that the annual and four seasonal mean temperatures in recent 100 years have experienced two climatic shifts from cold to warm.

Influence of Inhomogeneity on the Estimation of Mean and Extreme Temperature Trends in Beijing and Shanghai

Inhomogeneities in the temperature series from Beijing and Shanghai are analyzed, using the detailed histories of both sets of observations. The major corrections for different periods range from ?0.33 to 0.6°C for Beijing and ?0.33 to 0.3°C for Shanghai, Annual mean and extreme temperature series are deduced from the daily observations and trends in the adjusted and unadjusted series are compared. The adjusted yearly mean temperatures show a warming trend of 0.5°C/ century since the turn of this century and an enhanced one of 2.0°C/ century since the 1960s. In contrast, the unadjusted data show a twice this value trend for Shanghai but little trend for Beijing at the long-term scale and overestimate the recent warming by 50%–130%. Beijing experienced a decrease of frequency of the extremes together with a cooling during the 1940s–1970s and an increase of frequency of extremes together with a warming since then. The trends of frequency of extremes at Shanghai were more or less opposite. It is implied that the regional trends of strong weather variations may be different even when the regional mean temperatures coherently change. Key words Inhomogeneity - Daily temperature series - Climatic warming - Extreme temperature The study was supported by the China NKBRSF Project G 1999043400, IAP/ DF and CAS project (KZ951-A1-402).

On the Diversity of Long-Term Temperature Responses to Varying Levels of Solar Activity at Ten European Observatories

We analyze ten of the longest (127 to 230 year-long) time series of European daily temperatures available from five different Köppen-Geiger climate classes. We split these according to the level of solar cycle activity (H for “higher than median” and L for “lower than median”). This reveals coherent patterns in the temperature differences: when TH-TL?are stacked according to their calendar date, the daily averages from January 1 to December 31st disclose characteristic features in addition to the dominant annual seasonal wave, namely variations up to 2°C lasting for about 1.5 to 3 months. The five observatories at intermediate latitudes in a band from Oxford in the West to Prague in the East (same climate class) have very similar signatures. These similarities are most unlikely to be due to pure chance (confirmed by confidence levels in excess of 99% with the Kolmogorov-Smirnov and Kuiper nonparametric tests). The TH-TL patterns carry a regional signature, modulated by a more local response function. On the other hand, northern European observatories (St Petersburg and Arkhangelsk), those south of the Alps (Milan and Bologna), and the easternmost one in Astrakhan, corresponding to different climate classes, have different signatures. Similarly, preliminary study of long air pressure recordings confirms what emerges from the analysis of temperatures. These new observations lead us to conclude that the climate in different regions presents different responses to variations in solar activity. Moreover, the distributions of the lower, middle, and higher quartiles of the temperature and pressure indices in solar cycles with high versus low activity are significantly different, providing further robust statistical confirmation to this conclusion (confidence level higher to much higher than 99% using the Kuiper test).

Analysis of temperature time series based on Hilbert-Huang Transform

In this paper, with consideration of the nonlinear and non-stationary properties of the temperature time series, we employ the Hilbert-Huang Transform, based on the empirical mode decomposition（EMD）, to analyze the temperature time series from 1959 to 2012 in the Fengxian district of Shanghai, obtained from a certain monitoring station. The oscillating mode is drawn from the data, and its characteristics of the time series are investigated. The results show that the intrinsic modes of 1, 2 and 6 represent the periodic properties of 1 year, 2.5 years, and 27 years. The mean temperature shows periodic variations, but the main trend of this fluctuation is the rising of the temperature in the recent 50 years. The analysis of the reconstructed modes with the wave pattern shows that the variations are quite large from 1963 to 1964, from 1977 to 1982 and from 2003 to 2006, which indicates that the temperature rises and falls dramatically in these periods. The volatility from 1993 to 1994 is far more dramatic than in other periods. And the volatility is the most remarkable in recent 50 years. The log-linear plots of the mean time scales T and M show that each mode associated with a time scale almost twice as large as the time scale of the preceding mode. The Hilbert spectrum shows that the energy is concentrated in the range of low frequency from 0.05 to 0.1 Hz, and a very small amount of energy is distributed in the range of higher frequency over 0.1 Hz. In conclusion, the HHT is better than other traditional signal analysis methods in processing the nonlinear signals to obtain the periodic variation and volatility＇s properties of different time scales.

Detection of Chaoticity in Daily Mean Temperature Time Series

The chaoticity of daily mean temperature time series is investigated with complex systems theory. The data set that has been used in this analysis consists of daily mean temperature recorded at four stations in China. The power spectrums axe used to obtain some preliminaxy information regaxding the temperature time series. R/S analysis provides evidence for fractaiity in temperature time series. Cao method and correlation dimension, as well as the largest Lyapunov exponent, give consistent results, which does not exclude the possibility of deterministic chaos for the four daily mean temperature series. The research provides a principled basis for further study of temperature data with nonlinear dynamical methods.

Comparison of Two Homogenized Datasets of Daily Maximum/Mean/Minimum Temperature in China during 1960–2013

Two homogenized datasets of daily maximum temperature （Tmax）, mean temperature （Tm）, and min- imum temperature （Tmin） series in China have recently been developed. One is CHTM3.0, based on the Multiple Analysis of Series for Homogenization （MASH） method, and includes 753 stations for the period 1960-2013. The other is CHHTD1.0, based on the Relative Homogenization test （RHtest）, and includes 2419 stations over the period 1951-2011. The daily Tmax/Tm/Tmin series at 751 stations, which are in both datasets, are chosen and compared against the raw dataset, with regard to the number of breakpoints, long-term climate trends, and their geographical patterns. The results indicate that some robust break points associated with relocations can be detected, the inhomogeneities are removed by both the MASH and RHtest method, and the data quality is improved in both homogenized datasets. However, the differences between CHTM3.0 and CHHTD1.0 are notable. By and large, in CHHTD1.0, the break points detected are fewer, but the adjustments for inhomogeneities and the resultant changes of linear trend estimates are larger. In contrast, CHTM3.0 provides more reasonable geographical patterns of long-term climate trends over the region. The reasons for the differences between the datasets include： （1） different algorithms for creating reference series for adjusting the candidate series--more neighboring stations used in MASH and hence larger-scale regional signals retained; （2） different algorithms for cMculating the adjustments--larger adjustments in RHtest in general, partly due to the individual local reference information used; and （3） different rules for judging inhomogeneity--all detected break points are adjusted in CHTM3.0, based on MASH, while a number of break points detected via RHtest but without supporting metadata are overlooked in CHHTD1.0. The present results suggest that CHTM3.0 is more suitable for analyses of large-scale climate change in China, while CHHTD1.0 contains more original information regarding station temperature records.

Climatic warming in Shanghai during 1873-2019 based on homogenised temperature records

Xujahui(XJH)station in downtown Shanghai holds the longest continuous daily temperature series in China,which is unique for assessing modern climate change and impacts but must be homogenised.The present work established a set of homogenised monthly and daily surface air temperature(SAT)series during 1873-2019 at XJH.Two major inhomogeneous break points(around 1954 and 1993)were identified in the original SAT series,which had been overlooked in previous works.The inhomogeneous biases were adjusted via the inter-station deviation analysis.The adjusted SAT series shows a warming trend of 1.9℃ per century compared with 1.7℃ per century in the original series.The multi-decadal variations of quasi-32-year and quasi-64-year periods are weaker in the adjusted series than in the original series,suggesting overestimated multi-decadal variability in the original data due to inhomogeneous biases.Relative to the early period 1873-1900,the recent decade(the 2010s)is warmer by 2.2℃.Urbanisation has been responsible for approximately 19.3%of the rapid warming since the 1980s.The high and low SAT extremes exhibit significant warming,especially after the 1970s.The trend in the low SAT extremes in the adjusted series is substantially larger than that in the original series,implying that previous studies might underestimate the warming in cold extremes.