Station Relocations Research Articles

Long-term trend of surface wind speed in the Guangdong-Hong Kong-Macau Greater Bay Area during 1980–2020: Spatiotemporal variation and urbanization effect

Understanding the surface wind speed variations is important for the climate and air quality assessment in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), known as the world class urban agglomeration in China. This study investigates the spatial and temporal variations of surface mean wind speed (MWS) in the GBA from 1980 to 2020, drawing upon ground observations and reanalysis data. After accounting for station relocations, the observed wind speed demonstrated a fluctuating declining trend of −0.036 m∙s−1 per decade over the examined period in line with reanalysis data. Wind speeds were found stronger during winter and daytime than summer and nighttime. The southern coastal regions exhibited higher MWS compared with northern mountainous areas. The Empirical Orthogonal Function (EOF) revealed the dominant mode of MWS changes was characterized by a cyclical pattern lasting over 20 years, probably influenced by atmospheric circulation. The urbanization reduced the observed wind speeds by 4.71% - 10.11% over the past decade (2011−2020) in urban areas of the GBA based on urban-minus-rural (UMR), observation-minus-reanalysis (OMR), and urban impact factor (UIF) methods. The long-term spatio-temporal analyses of MWS could benefit the climate and air quality studies and provide data support for regional and micro-scale modeling in the GBA.

Impacts of anemometer changes, site relocations and processing methods on wind speed trends in China

Abstract. In situ surface wind observation is a critical meteorological data source for various research fields. However, data quality is affected by factors such as surface friction changes, station relocations and anemometer updates. Previous methods to address discontinuities have been insufficient, and processing methods have not always adhered to World Meteorological Organization (WMO) World Climate Programme guidelines. We analyzed data discontinuity caused by anemometer changes and station relocations in China's daily in situ near-surface (∼ 10 m) wind speed observations and the impact of the processing methods on wind speed trends. By comparing the wind speed discontinuities with the recorded location changes, we identified 90 stations that showed abnormally increasing wind speeds due to relocation. After removing those stations, we followed a standard quality control method recommended by the World Meteorological Organization to improve the data reliability and applied Thiessen polygons to calculate the area-weighted average wind speed. The result shows that China's recent reversal of wind speed was reduced by 41 % after removing the problematic stations, with an increasing trend of 0.017 m s−1 yr−1 (R2 = 0.64, P < 0.05), emphasizing the importance of robust quality control and homogenization protocols in wind trend assessments.

Homogenization of Observed Surface Wind Speed Based on Geostrophic Wind Theory over China from 1970 to 2017

Abstract The observed surface wind speed (SWS) over China has declined in the past four decades, and recently, the trend has reversed, which is known as SWS stilling and recovery. The observed SWS is vulnerable to changes in nonclimatic factors, i.e., inhomogeneity. Unfortunately, most of the existing studies on the long-term trend of SWS were based on raw datasets without homogenization. In this study, by means of geostrophic wind speed and penalized maximal t test, we conduct a systematic homogeneity test and exploration of the homogenization impact for SWS at over 2000 stations in China from 1970 to 2017. The results show that the inhomogeneity in the observed SWS over China is detectable at 59% of national weather stations. The breakpoint years are mainly concentrated in the late 1970s, mid-1990s, and early 2000s. Overall, 18% of breakpoints are caused by station relocations, and the remaining breakpoints are likely related to anemometer replacement and measurement environment changes that occurred during the mid-1990s and early 2000s. After homogenization, the decreasing trend in SWS during 1970–2017 decreased from −0.15 to −0.05 m s−1 decade−1. The homogenized SWS recovery period advanced from the early twenty-first century to the early 1990s, which is consistent with the SWS variations, excluding the impact of urbanization around weather stations. The phase change in the Western Hemisphere warm pool (WHWP) might be one of the causes of homogenized SWS recovery.

Updated analysis of surface warming trends in North China based on in-depth homogenized data (1951-2020)

The reliability of climate change detection and research is significantly impacted by the inhomogeneity of surface climate observation data. However, there is an ongoing debate regarding whether comprehensive homogenization has been performed in large-scale homogenized data sets. In this study, we examined the homogeneity of the original maximum and minimum temperature (Tmax and Tmin) data for 662 meteorological stations in North China by using multiple methods and combining with metadata. The quantile matching method was employed to adjust the daily Tmax and Tmin series. In order to avoid the potential systematic bias resulting from homogenization, no reference series were introduced during the adjustment process. The adjustment results indicate that Tmin in North China is significantly affected by non-climatic factors, particularly station relocations and environmental changes around the stations. The application of homogenization in this study led to a notable increase in the overall temperature trends of the stations, with Tmin exhibiting a larger increase and the diurnal temperature range demonstrating a more significant downward trend. Based on the homogenized data, the annual and seasonal mean temperature trends in North China from 1951 to 2020 were re-evaluated. These temperature trends generally surpass those reported in previous research for the same period from 1961 to 2000. The higher estimate of temperature trends may be attributed to the recovered urbanization effect in the newly homogenized data. Thus, the obtained homogenization data still exhibit a significant urbanization bias that requires further assessment and adjustment.

The EUSTACE global land station daily air temperature dataset

AbstractWe describe a global dataset of quality‐controlled in situ daily air temperature observations covering the period 1850–2015, developed in the framework of the EUSTACE (EU Surface Temperature for All Corners of Earth) project (www.eustaceproject.org). The dataset includes a total of 35,364 daily series of maximum and minimum temperature obtained from seven different collections. About 97% of the series are publicly available in a common format, while the remaining 3% can be obtained from the original data providers. Unlike other similar products, duplicates have been removed without blending of series, which simplifies data traceability and improves the temporal homogeneity of the individual series at the cost of a smaller average length. Residual artificial signals (breakpoints) in the series caused by station relocations, changes in instrumentation, etc., have been detected by means of the combination of four breakpoint detection tests, four variables and three temporal aggregations. The combined results give not only the most probable position of the breakpoints, but also a measure of their likelihood. The reliability of the detection was estimated for each year of each target series, based on the number of reference series and on their correlation with the target series. Moreover, its general performance was evaluated through a benchmark of synthetic series. This product will be combined with datasets of marine and ice in situ air temperature observations and with measurements from satellite to produce the first complete global statistical reconstruction of daily near‐surface air temperature.

High‐quality sea surface temperature measurements along coast of the Bohai and Yellow Seas in China and their long‐term trends during 1960–2012

AbstractAs semi‐enclosed and shallow seas, the Bohai and Yellow Seas (hereafter, BYS) marine ecosystems are more easily affected by natural and anthropogenic climate changes than those in the open oceans. An accurate assessment of sea surface temperature (SST) change is crucial for the near‐shore water environment. In this work, new high‐quality monthly mean SST time series from 11 in situ coastal hydrological stations at BYS for the period of 1960–2012 have been generated. Monthly mean SST time series were initially subjected to penalized maximal t (PMT) test, using homogeneous monthly mean surface air temperature (SAT) series as references. Homogenized monthly mean SST series were obtained by adjusting all significant change points which have been supported by historic metadata. Our study shows that there are 29 significant change points in these 11 SST series. The majority of them are caused by instrument changes and station relocations—both account for about 51.7 and 31%, respectively. Then, homogeneous SST series are used to assess the long‐term trends. As the regional average, the annual mean homogeneous SST series shows a rapid warming trend, with the rate of 0.21°C/decade. Compared to the homogeneous SST series, the original SST series underestimate the long‐term trend (only 0.13°C/decade), indicating the artificial change points usually result in a warm‐bias in the early period measurements. Seasonally, the most significant warming occurs in boreal winter during the period analysed. The relationships of the significant warming in boreal winter and atmospheric circulation modes have also been investigated. Results reflect the remarkable and direct influence of the East Asian Trough (EAT) on SST along the coastal BYS.

Differences in wind speeds according to measured and homogenized series in the Czech Republic, 1961–2015

Non‐meteorological factors may bias wind speed measurements in a number of ways, among them the types of instruments used, their calibration and standards of maintenance, station relocations, and changes in the physical surroundings of a given station. Moreover, homogenisation of series of such measurements is more complicated than that of other climatic variables. This contribution uses figures from the Czech Republic as an example to demonstrate that measured (raw) data may produce different results in mean daily wind speeds from those acquired in homogenous series. A basic set of measurements taken in 1961–2015 at 178 meteorological stations was quality‐checked and then homogenized using the Standard Normal Homogeneity test (SNHT) and the Maronna‐Yohai test. Subsequent analyses were based on homogenized series from 119 stations. Station relocations and automation of wind speed measurements were identified as the most important sources of break‐points in series of mean daily wind speeds. Generally lower mean wind speeds (as well as wind speed variability) were obtained from homogenous series in comparison with measured data, with reflections also found in their spatial distribution around the territory of the Czech Republic. Statistically significant decreasing linear trends calculated from measured and homogenized daily wind speed series from 119 stations confirm the existence of wind stilling as a typical feature of land mid‐latitudes in recent decades. However, trends from measured data are highly overestimated compared with those from homogenized series.

USE OF AMBIENT VIBRATIONS IN UNDERSTANDING LOCAL SITE EFFECTS AT BROADBAND SEISMIC STATIONS OF THE HELLENIC UNIFIED SEISMOLOGICAL NETWORK (HUSN)

The evaluation of ambient seismic noise at the Hellenic Unified Seismic Network (HUSN) stations is investigated in this study. Ambient vibration recordings combined with the horizontal to vertical (H/V) spectral ratio technique helps in characterizing local site effects. This technique was applied at 17 sites ambient noise measurements. We selected a number of 1-hr waveform segments during day and night for summer and winter. For each site the H/V spectral ratio was calculated and the results were combined with geological and geophysical information. The goal was to show the network performance as far as the station quality and noise level at each site concerns in order to provide possible structural improvements, seismic station relocations or to detecting operational problems.

Homogenization of Ukrainian air temperature data

ABSTRACTA database of monthly mean air temperature data collected in Ukraine has been created. The database contains records of 514 stations, which were working at one time or another during the period 1812–2014. Metadata have also been collected using the available descriptions of the stations' histories. Regular observations at a sufficiently large number of stations contained in the database began in the early 1880s. However, due to the disruptions that occurred during World Wars I and II and resulted in a lot of missing data, a reliable homogenized data set can be obtained only for the period 1946–2014. For this period, we have homogenized the data from 178 stations by means of the HOMER software. The total number of breaks that we have identified is 287. However, only approximately 31% of them can be explained by the station relocations or other reasons. The shift magnitudes in annual series do not exceed ±1 °C with the small negative mean value. Therefore, on average, the inhomogeneities of the time series have an effect of increasing the past temperature slightly over the territory of Ukraine and, consequently, decrease the rate of the temperature rise. The trend analysis of the raw and homogenized time series confirms this conclusion. The homogenized data show a more realistic pattern of the spatial distribution of the temperature tendencies, in contrast to many artificial spots of higher or lower trend values in the raw data. After the homogenization, the areas with insignificant trends were considerably reduced.

The Ås Temperature Series in Southern Norway–Homogeneity Testing And Climate Analysis

Abstract Homogeneity is important when analyzing climatic long-term time series. This is to ensure that the variability in the time series is not affected by changes such as station relocations, instrumentation changes and changes in the surroundings. The subject of this study is a long-term temperature series from the Norwegian University of Life Sciences at Ås in Southern Norway, located in a rural area about 30 km south of Oslo. Different methods for calculation of monthly mean temperature were studied and new monthly means were calculated before the homogeneity testing was performed. The statistical method used for the testing was the Standard Normal Homogeneity Test (SNHT) by Hans Alexandersson. Five breaks caused by relocations and changes in instrumentation were identified. The seasonal adjustments of the breaks lay between -0.4°C and +0.5°C. Comparison with two other homogenized temperature series in the Oslo fjord region showed similar linear trends, which suggests that the long-term linear temperature trends in the Oslo fjord region are not much affected by spatial climate variation.

Detection and correction of inhomogeneities in Greek climate temperature series

Long term time series of climate observations measured at meteorological stations provide one of the most accurate records of climate in the past. Nevertheless, there are a number of factors that affect measurements of climatic parameters which may cause abrupt or more gradual shifts and trends in the corresponding time series. Thus in order to draw sound conclusions about the climate, homogenized data series are required. Because of the large number of different monthly homogenization methods a coordinated European initiative was launched namely the COST Action HOME ES0601: Advances in Homogenization Methods of Climate Series: an integrated approach (HOME). Its main objective was to review and improve common homogenization methods and to assess their impact on climate time series. In this paper the homogenization method HOMER resulted by the COST Action ES0601, was applied to 52 monthly temperature series of synoptic stations for the period 1960–2004, belonging to the operational weather network of the Hellenic National Meteorological Service (HNMS). Eight stations passed the homogeneity test successfully without any break point or outlier, for two stations only outliers were reported, but not any break points and the remaining 42 stations presented one or more breaks. Approximately 32% of the breaks could be explained by the stations' history. Station relocations as well as changes in observation practices caused most of the temperature anomalies. The adjustments in yearly temperature series were in the range from −1.2 to 2.0 °C. Seasonal and annual anomalies of raw and homogenized series were calculated using as reference the 30-year period 1961–1990. Also, seasonal and annual mean temperature trends before and after homogenization were analysed and their statistical significance calculated. A reduction of trend magnitude was found to be noticeable in all seasons. The most pronounced trend differences were found in summer followed by winter.

Changepoint Detection in Climate Time Series with Long-Term Trends

Abstract Climate time series often have artificial shifts induced by instrumentation changes, station relocations, observer changes, etc. Climate time series also often exhibit long-term trends. Much of the recent literature has focused on identifying the structural breakpoint time(s) of climate time series—the so-called changepoint problem. Unfortunately, application of rudimentary mean-shift changepoint tests to scenarios with trends often leads to the erroneous conclusion that a mean shift occurred near the series' center. This paper examines this problem in detail, constructing some simple homogeneity tests for series with trends. The asymptotic distribution of the proposed statistic is derived; en route, an attempt is made to unify the asymptotic properties of the changepoint methods used in today's climate literature. The tests presented here are linked to the ubiquitous t test. Application is made to two temperature records: 1) the continental United States record and 2) a local record from Jacksonville, Illinois.

Ambient Noise Levels in Greece as Recorded at the Hellenic Unified Seismic Network

Abstract We investigate the characteristics of ambient noise across Greece as recorded at the Hellenic Unified Seismic Network (HUSN). Power spectral densities (PSDs) and their corresponding probability density functions (PDFs) have been estimated for 110 broadband seismic stations using the continuous waveform data for a four‐year period from 2007 to 2010. Using PDFs we monitor and show network performance in terms of overall station quality and the level of noise at each site. At high frequencies (>7 Hz), the main source of noise is cultural with strong diurnal variations. Stations with constantly increased noise levels across this band indicate poor vault construction or poor site selection and provide an indication to the network operators for possible structural improvements or even station relocations. The microseismic noise levels show a clear seasonal variation at all stations. Specifically, for the double‐frequency (DF; period range 4–8 s) band, the average noise level differs between stations on the mainland and those located on the islands, reaching a value as high as 10 dB. Furthermore, the DF noise peak is observed at all of the HUSN stations, and it is correlated well with local sea wave height measurements at buoys deployed in the Aegean and the Ionian seas. This indicates that the HUSN seismic network also monitors local sea–weather conditions within a range of a few hundred kilometers. The longer period single‐frequency (SF) band is affected by sea–weather conditions at much longer distances in the North Atlantic. Finally, we calculate the HUSN mode noise model (HMLNM) that represents the highest probability ambient noise level in Greece. This model is a realistic noise threshold for future seismic station installations. Online Material: Table of seismic sensor description, and tables, figures, and movie describing microseismic noise measurements.

Homogenization of mean monthly temperature time series of Greece

ABSTRACTDuring the last decades due to the increased interest about climate change, many studies have been conducted trying to detect shifts in climatic series. The necessity of the homogenization of meteorological observations becomes obvious to all these studies. In practice, inhomogeneities are hardly ever avoided, because the meteorological station networks are constantly changing. A myriad of methods for detecting and adjusting inhomogeneities in climate series have been developed. In this study two homogeneity methodologies, namely MASH and Climatol, were applied to 49 monthly temperature series of synoptic stations, covering almost all climatic zones of Greece, belonging to the operational weather network of the Hellenic National Meteorological Service (HNMS). Time series cover periods ranging from 35 to 45 years. Only 8.2% of the stations passed both tests successfully, another 10.2% passed the MASH homogeneity test successfully without any breakpoint, but not the Climatol test. On the other hand, 14.3% of the stations passed the Climatol test successfully but not the MASH test. The remaining stations presented one or more breaks or outliers in both homogeneity methods. Due to lack of metadata only 15% of the breaks could be explained by the stations' history. Station relocations as well as changes in observation practices caused most of the temperature anomalies. The adjustments in seasonal series were in the range from −2.54 to 1.39 °C for MASH and from −2.30 to 1.50 °C for Climatol. Seasonal and annual mean temperature trends were analyzed and their statistical significance calculated. The most pronounced seasonal trends were recorded in summer. Also, the differences of climatological normals for the period 1961–1990 between raw and homogenized annual series were computed. The absolute values of differences ranged from 0.0 to 0.8 °C for MASH and from 0.0 to 1.0 °C for Climatol.

Trends in extreme temperature indices in Austria based on a new homogenised dataset

AbstractInstrumental time series are often affected by inhomogeneities which can mask or amplify climate change signals. Various procedures for the detection and adjustment of breaks exist for monthly and annual time series. Homogenization methods on a daily basis are scarce and often disregard uncertainties accompanying the break adjustment. We present a complete homogenization procedure for daily extreme temperature series based on the break detection method PRODIGE and SPLIDHOM for break correction. Both parts of the homogenization rely on the existence of highly correlated reference stations. After the statistical comparison with neighbouring stations, detected breaks are verified and further localized by metadata. Uncertainties of the break adjustments are estimated by altering reference stations and by applying a bootstrapping technique providing an objective indication about the reliability of the homogenization.The method was tested and applied to 71 time series of daily minimum (TN) and maximum temperatures (TX) in Austria covering the period 1948–2009. For some series homogenization was not possible due to large uncertainties in the adjustments or a lack of suitable reference series. In the remaining 57 TN and 54 TX series a total number of 139 breaks were detected. Seventy‐five percent of those breaks are documented in the metadata archive, with most of them being caused by station relocations and instrumentation changes. In general, the mean over the temperature dependent adjustments of all stations show a temperature reduction. However, the majority of breaks have mean amplitudes of less than 0.5 °C. A comprehensive analysis was performed on the new homogenized daily dataset, showing a widespread warming trend in both TN and TX series. The warming trend is in general amplified due to the homogenization. However, significant changes in the trend are only observed at very few stations. In autumn, however, the trend is reversed in many temperature based ‘climate change detection indices’. Copyright © 2012 Royal Meteorological Society

Temperature Discontinuity Caused by Relocation of Meteorological Stations in Taiwan

With global warming upon us, it has be come increasingly important to identify the extent of this warming trend and in doing so be able to rank mean temperature changes in particular seasons and years. This requires a need for homogeneous climate data, which do not reflect individual anomalies in instruments, station locations or local environments (urbanization). Accurate homogeneous long-term meteorological data helps show how temperature variations have truly occurred in the climate. Many possible factors contribute to artificial abrupt changes or sharp discontinuities in long time series data, such as the impact of station relocation, changes in observational schedules and instrumentation. Homogeneity adjustments of in situ climate data are very important processes for preparing observational data to be used in further analysis and re search. Users require a well-documented history of stations to make appropriate homogeneity adjustments because precise historical back ground records of stations can provide researchers with knowledge of when artificial discontinuity has occurred and its causes. With out such de tailed historical data for each meteorological station, abrupt changes are difficult to interpret. Unfortunately, no homogeneity adjustments for temperature records have been conducted previously in Tai wan, and present avail able sources of the history of Taiwan's meteorological stations exhibit in consistencies. In this study, in formation pertaining to station history, especially relocation re cords, is pro vided. This information is essential for analysis of continuous time series data for temperature and climate warming studies. Temperature data from several stations is given in this study to show how artificial discontinuity occurs due to station relocation. Al though there is no homogeneous adjusted climate data provided in this preliminary work, the summarizing of information regarding station relocations should be of assistance to future data users wanting to determine whether or not abrupt changes in climate data are artificial.

Homogenization of Bulgarian temperature series

AbstractThe Standard Normal Homogeneity Test (SNHT) was applied to five sets of temperature series—the four seasonal and the annual mean temperatures, at stations representative for the lowlands of Bulgaria. Series cover periods of about 50 to about 100 years. Fourteen percent of the series passed the test successfully, the others had one or more breaks. The number of breaks in the cold seasons was less than the number of breaks in the warm seasons. On average, 38% of the breaks could not be explained by the stations' history, the percentage being the greatest (almost 50%) for the summer series. The adjustments were in the range (−1.2, 0.9)°C. The average values were negative (about − 0.1 °C) for the winter, spring and summer series, but for the autumn and annual series they were practically zero. The average absolute values for different seasons were between 0.34 and 0.44 °C. Magnitudes of the adjustments were greater in the regions closer to the mountains than in the more flat areas. Most of the inhomogeneities were caused by station relocations. Copyright © 2008 Royal Meteorological Society

Warming maximum in the tropical upper troposphere deduced from thermal winds

Climate models and theoretical expectations have predicted that the upper troposphere should be warming faster than the surface. Surprisingly, direct temperature observations from radiosonde and satellite data have often not shown this expected trend. However, non-climatic biases have been found in such measurements. Here we apply the thermal-wind equation to wind measurements from radiosonde data, which seem to be more stable than the temperature data. We derive estimates of temperature trends for the upper tropospheretothelowerstratospheresince1970.Overtheperiodofobservations,wefindamaximumwarmingtrendof0.65±0.47K per decade near the 200hPa pressure level, below the tropical tropopause. Warming patterns are consistent with model predictions except for small discrepancies close to the tropopause. Our findings are inconsistent with the trends derived from radiosonde temperature datasets and from NCEP reanalyses of temperature and wind fields. The agreement with models increases confidence in current model-based predictions of future climate change. It has long been recognized that radiosonde temperature data are a ected by non-climatic artifacts due to station relocations, observation time changes and radiosonde type or design changes 1 . Several investigators have attempted to detect and adjust (that is homogenize) these artefacts using a variety of tools, including statistical procedures, station metadata, various indicators of natural variability (such as volcanic eruptions, vertical coherence) and forecasts from a climate data assimilation system 2‐6 . Despite these attempts, most analyses of radiosondes continue to show less warming of the tropical troposphere since 1979 than reported at the surface 1 . At least one satellite dataset also implies this 7 . By contrast, theoretical and model expectations 7,8 indicate that the troposphere should warm somewhat faster than the surface. Recently, time-varying biases were shown to remain in the radiosonde temperature data, including a daytime cooling bias related to solar heating of the instrument (especially in the stratosphere) 9 . They were significantly larger than the average adjustments that had previously been made, and comparable to the above discrepancies, calling into question whether the adjustments had been adequate. A similar cooling bias was also found in night-time soundings 10,11 . Subsequent attempts to produce better homogenized records have yielded more warming than before 5,12,13 ,

The development of a new dataset of Spanish Daily Adjusted Temperature Series (SDATS) (1850–2003)

Here we present the development of a new adjusted dataset composed of the 22 longest and most reliable Spanish daily temperature records (maximum and minimum temperatures and derived daily mean temperature) covering the period 1850–2003. The paper describes the approaches followed for compiling, controlling the quality and homogenising these 22 daily Spanish records, leading to the creation of a dataset called ‘Spanish Daily Adjusted Temperature Series’ (SDATS). An assessment of the sources of data and metadata used is followed by a reliability assessment of the selected network. Data quality control (QC) procedures applied to raw daily maximum (Tmax) and minimum (Tmin) temperatures and their results are then considered. For the very first time, an empirical minimisation of the bias related to the impact of changing exposure of thermometers on the records has been undertaken. The application of the Standard Normal Homogeneity Test (SNHT) to check homogeneity of raw Tmax and Tmin data on a monthly basis is presented, together with a discussion of the causes, magnitudes and timings of the various inhomogeneities. All 22 records contained a number of inhomogeneities (2.6 on average), mainly associated with documented station relocations confirmed by the metadata available. Monthly adjustments calculated for both screen developments and from the SNHT were linearly interpolated to a daily basis following the Vincent et al. (2002) scheme. Finally, the procedures adopted for creating the regional average, the Spanish Temperature Series (STS), together with an exploratory analysis of long-term trends of each Tmax and Tmin records, are provided. The final analysis shows that over mainland Spain highly significant rates of temperature increases have occurred for Tmax and Tmin (0.12 °C/decade and 0.10 °C/decade, respectively) over 1850–2003. Copyright © 2006 Royal Meteorological Society.

Detecting and adjusting temporal inhomogeneity in Chinese mean surface air temperature data

Adopting the Easterling-Peterson (EP) techniques and considering the reality of Chinese meteorological observations, this paper designed several tests and tested for inhomogeneities in all Chinese historical surface air temperature series from 1951 to 2001. The result shows that the time series have been widely impacted by inhomogeneities resulting from the relocation of stations and changes in local environment such as urbanization or some other factors. Among these factors, station relocations caused the largest magnitude of abrupt changes in the time series, and other factors also resulted in inhomogeneities to some extent. According to the amplitude of change of the difference series and the monthly distribution features of surface air temperatures, discontinuities identified by applying both the E-P technique and supported by China’s station history records, or by comparison with other approaches, have been adjusted. Based on the above processing, the most significant temporal inhomogeneities were eliminated, and China’s most homogeneous surface air temperature series has thus been created. Results show that the inhomogeneity testing captured well the most important change of the stations, and the adjusted dataset is more reliable than ever. This suggests that the adjusted temperature dataset has great value of decreasing the uncertaities in the study of observed climate change in China.