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 19 th 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 and152 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?C(100 yr)~(-1),with a regional mean trend of 1.65?C(100 yr)~(-1);whereas,the previous results ranged from a slight cooling at two stations to considerable warming,up to 4.5?C(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.     

南海海洋观测台站海表温度资料的均一性检验与订正

采用最大惩罚T检验（Penalized Maximum T Test,PMT）方法,结合海洋台站元数据信息,选取临近气象站做为参考站,对中国南海9个海洋观测台站月平均SST资料序列进行均一性检验,在充分考虑区域性气候变化因素的影响下,对检验结果进行气候合理性分析并对不连续点进行订正。结果表明:（1）通过多种数据的相关性比较和分析发现,临近气象站的气温资料序列是南海台站SST资料订正最优的参考序列;（2）平均每个海洋台站SST资料序列存在1~2个不连续点,其中站址变迁、仪器变更和环境变化对均一性的影响较大;（3）订正后的南海SST序列质量有大幅提高,表明南海SST具有更明显的增暖趋势。      

Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality

Daily minimum temperature (Tmin) and maximum temperature (Tmax) data of Huairou station in Beijing from 1960 to 2008 are examined and adjusted for inhomogeneities by applying the data of two nearby reference stations. Urban effects on the linear trends of the original and adjusted temperature series are estimated and compared. Results show that relocations of station cause obvious discontinuities in the data series, and one of the discontinuities for Tmin are highly significant when the station was moved from downtown to suburb in 1996. The daily Tmin and Tmax data are adjusted for the inhomogeneities. The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax. Urban effects on annual mean Tmin and Tmax trends are ?0.004°C/10 year and ?0.035°C/10 year respectively for the original data, but they increase to 0.388°C/10 year and 0.096°C/10 year respectively for the adjusted data. The increase is more significant for the annual mean Tmin series. Urban contributions to the overall trends of annual mean Tmin and Tmax reach 100% and 28.8% respectively for the adjusted data. Our analysis shows that data homogenization for the stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature, and this necessitates a careful evaluation and adjustment for urban biases before the data are applied in analyses of local and regional climate change.     

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 minimum 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 calculating 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.     

资料均一化对气温变化趋势及其城市化偏差估计的影响：以北京地区为例

当前的地面气候观测资料普遍存在非气候性因素导致的非均一性,对气候变化监测和研究结论可靠性造成重要影响。结合观测台站的历史沿革数据,使用ACMANT和Pairwise Comparisons方法以及RHtest V4软件,对北京地区20个台站均一化前的月平均气温序列进行了非均一性检验和订正,最后评估了均一化对北京地区气温序列变化趋势及其城市化偏差估算的影响。结果表明：除元数据中记录的断点外,无元数据记录的断点也会对序列的趋势变化造成明显影响,其中乡村站最显著;经过订正,1958—2018年整个北京地区、乡村站以及城市站增温趋势分别为0.27℃/(10 a)、0.10℃/(10 a)和0.32℃/(10 a),较订正前分别上升了0.03℃/(10 a)、0.06℃/(10 a)和0.02℃/(10 a)。利用均一化资料估算,1958—2018年北京观象台的城市化影响为0.24℃/(10 a),城市化贡献率为70.2%,评估结果较前人结论有所降低。可见,在现有的北京地区气温资料序列中,仍可能存在较明显的非均一性和未被记录的断点,对区域平均气温趋势估算具有显著影响。     

我国地面气温参考站点遴选的依据、原则和方法

由于城市化和观测环境变化的影响,我国现有国家级气象台站网观测的地面气温资料许多已不能满足气候变化检测研究的要求。利用我国全部气象观测站网的台站信息,对地面气温资料序列质量进行了系统评价,遴选出可用于气温变化研究的参考站点。这项工作建立了地面气温参考站点遴选的原则、方法和步骤,并据此确定了138个地面气温参考站。这些台站可代表背景地面气温场,其长期气温观测资料可用作城市台站或国家级台站城市化增温评价的参考,也可直接用于我国地面气温变化的检测分析。     

Homogenized Daily Relative Humidity Series in China during 1960?2017

Surface relative humidity(RH)is a key element for weather and climate monitoring and research.However,RH is not as commonly applied in studying climate change,partly because the observation series of RH are prone to inhomogeneous biases due to non-climate changes in the observation system.A homogenized dataset of daily RH series from 746 stations in Chinese mainland for the period 1960–2017,ChinaRHv1.0,has been developed.Most(685 or 91.82%of the total)station time series were inhomogeneous with one or more break points.The major breakpoints occurred in the early 2000s for many stations,especially in the humid and semi-humid zones,due to the implementation of automated observation across the country.The inhomogeneous biases in the early manual records before this change are positive relative to the recent automatic records,for most of the biased station series.There are more break points detected by using the MASH(Multiple Analysis of Series for Homogenization)method,with biases mainly around?0.5%and 0.5%.These inhomogeneous biases are adjusted with reference to the most recent observations for each station.Based on the adjusted observations,the regional mean RH series of China shows little long-term trend during 1960–2017[0.006%(10 yr)^?1],contrasting with a false decreasing trend[?0.414%(10 yr)?1]in the raw data.It is notable that ERA5 reanalysis data match closely with the interannual variations of the raw RH series in China,including the jump in the early 2000s,raising a caveat for its application in studying climate change in the region.     

由最高最低气温求算的平均气温对我国年平均气温序列影响

针对研究全国近百年平均气温长期变化的实际需要，利用603个测站1961—2002年气温观测资料，比较分析了最高最低平均气温距平序列和4次观测记录平均气温距平序列的差异，讨论了最高、最低气温变化趋势。结果表明:两种统计方法得到的平均气温距平序列及增温速率的差异均不明显，在一定条件下两者可以互相替换。此外，最高、最低气温变化普遍存在不对称现象，且可分为4种类型，这种不对称性对平均气温变化速率并没有明确一致的影响。     

中国地面月气候资料质量控制方法的研究

由于气象台站信息变化以及观测规范的频繁变动, 使中国地面气象资料序列比较复杂。该文在分析中国地面历史气象资料中可能存在的非均一性和错误性、研究月气候资料序列存在的可能分布状态的基础上, 提出了中国地面月气候资料质量控制技术。通过分析认为, 中国地面月气候资料的质量控制, 应把下列3种方式结合起来, 进行综合质量检测:①把12个月的气候资料序列联合起来, 进行长时间连续性错误资料检测; ②把时间序列变换为接近正态分布的均一序列后, 进行时间域和空间域的奇异值检测; ③对检测出的连续性可疑资料和单个数据点的奇异值进行人工分析辨别。利用上述质量控制方法, 对1971—2000年中国地面700多基准基本站月统计气候资料进行了质量检查, 最后介绍了质量检查结果。     

A high-quality monthly total cloud amount dataset for Australia

A high-quality monthly total cloud amount dataset for 165 stations has been developed for monitoring and assessing long-term trends in cloud cover over Australia. The dataset is based on visual 9 a.m. and 3 p.m. observations of total cloud amount, with most records starting around 1957. The quality control process involved examination of historical station metadata, together with an objective statistical test comparing candidate and reference cloud series. Individual cloud series were also compared against rainfall and diurnal temperature range series from the same site, and individual cloud series from neighboring sites. Adjustments for inhomogeneities caused by relocations and changes in observers were applied, as well as adjustments for biases caused by the shift to daylight saving time in the summer months. Analysis of these data reveals that the Australian mean annual total cloud amount is characterised by high year-to-year variability and shows a weak, statistically non-significant increase over the 1957–2007 period. A more pronounced, but also non-significant, decrease from 1977 to 2007 is evident. A strong positive correlation is found between all-Australian averages of cloud amount and rainfall, while a strong negative correlation is found between mean cloud amount and diurnal temperature range. Patterns of annual and seasonal trends in cloud amount are in general agreement with rainfall changes across Australia, however the high-quality cloud network is too coarse to fully capture topographic influences. Nevertheless, the broadscale consistency between patterns of cloud and rainfall variations indicates that the new total cloud amount dataset is able to adequately describe the broadscale patterns of change over Australia. Favourable simple comparisons between surface and satellite measures of cloudiness suggest that satellites may ultimately provide the means for monitoring long-term changes in cloud over Australia. However, due to the relative shortness and homogeneity problems of the satellite record, a robust network of surface cloud observations will be required for many years to come.     

西南地区城市热岛强度变化对地面气温序列影响

利用1961—2004年我国西南地区322个站的气温观测资料, 分析了乡村站、小城市站、大中城市站和国家基准/基本站气温变化趋势特点, 着重研究了城市化对城镇站和国家站地面气温记录的影响程度和相对贡献比例。结果显示:区域平均的各类台站年平均气温呈现不同程度的上升趋势, 城市站、国家站的增温速率均高于乡村站。大中城市站和国家站的年平均热岛增温率分别为0.086 ℃/ 10a和0.052 ℃/10a, 其增温贡献率分别达57.6%和45.3%。与大多数地区不同, 西南地区的增温速率明显偏小。因此, 尽管平均热岛强度变化比许多地区弱, 但其相对贡献明显, 表明城市化对该区域气温趋势的绝对影响较弱, 但相对影响较强。另外, 城市热岛增温有明显的季节变化, 表现为秋季最强, 春季或冬季次之, 夏季最弱。热岛增温贡献率则为春季最大 (100%), 夏季次之 (73%以上), 秋季和冬季相对较小。这主要是因为春、夏两季背景气候变凉或趋势微弱, 热岛增温在实际增温中占有更高的比例。     

Homogenization of the Daily Land Surface Temperature over the Mainland of China from 1960 through 2017

Land surface temperature(LST) is one of the most important factors in the land-atmosphere interaction process. Raw measured LSTs may contain biases due to instrument replacement, changes in recording procedures, and other non-climatic factors. This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 through 2017. The high-quality land surface air temperature(LSAT)dataset is used to correct the LST warming biases especially evident during cold months in regions north of 40°N due to the replacement of observation instruments around 2004. Subsequently, the Multiple Analysis of Series for Homogenization(MASH) method is adopted to detect and then adjust the daily observed LST records. In total, 3.68 × 103 effective breakpoints in 1.65 × 106 monthly records(about 20%) are detected. A large number of these effective breakpoints are located over large parts of the Sichuan Basin and southern China. After the MASH procedure, LSTs at more than 80% of the breakpoints are adjusted within +/– 0.5°C, and of the remaining breakpoints, only 10% are adjusted over 1.5°C.Compared to the raw LST dataset over the whole domain, the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations. Finally, we perform preliminary analysis upon the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend [0.22°C(10 yr)–1]. The homogenized LST dataset can be further adapted for a variety of applications(e.g.,model evaluation and extreme event characterization).     

Homogenization of Surface Pressure Data in Tianjin,China

Based on a hydrostatic check, a preliminary quality control procedure was applied to the hourly surface air pressure(SAP) and sea level pressure(SLP) data from 13 surface observation stations in Tianjin, China. The daily and monthly series of the two pressures were tested for homogeneity with the RHtestsV5 software package and corrected by using the surface air temperature data as a reference. The results showed that 5 and 3 of the 13 stations had significant breakpoints in their SAP and SLP data, respectively, mainly caused by station relocation. Quantile matching adjustments showed that both pressures were dominated by positive deviation corrections and adjustments, with probability densities 0.2 concentrated in the ranges 0.02 to 1.80 and-0.02 to 1.64 hPa. Comparison of the variances and trends of the two pressures before and after adjustment showed that homogeneity correction can greatly reduce the influence of abnormal discrete pressure data and make the long-term trends of the series more reasonable.Taking Baodi station(54525), which has the most significant correction results, as an example, the trend amplitude of the adjusted SAP and SLP was increased by 0.316 and 0.294 hPa(10 yr)-1(95% confidence level), respectively,clearly weakening the sudden reduction induced in the two pressure series by the relocation of this station. Comparison with similar products showed that the adjusted data were better than those from the China Meteorological Administration. Therefore, the reliability of the surface pressure data and the processing techniques have been improved.     

长江三角洲地区降水资料的均一性检验与订正试验

该文对1961—2002年我国长江三角洲地区36个地面观测站的年、月降水资料序列作均一性检验和订正试验, 以探讨我国年、月降水气候观测序列的均一化方法和思路。该文首先从与待检台站序列相关性较好、台站距离较近的台站中选取了若干站点作为待检台站的参考台站建立参考序列, 采用传统的标准正态检验 (SNHT) 方法, 对所研究的地区年降水的均一性进行了检验。检验结果显示, 该地区降水序列中存在非均一性现象。考虑到所选地区站点密度仍然不够, 文中还采用了一种不依赖于参考台站的思路, 对上述检验结果进行了进一步确认后, 最后对不连续序列进行了逐月订正调整。对比分析表明, 对序列的订正较为合理。在已有的研究基础上, 结合我国台站网络实际特点, 提出结合不同思路来研究我国降水量数据均一性是必要的和可行的, 应用该思路对于我国降水序列进行均一性研究具有一定的合理性和应用前景。     

四川省平均气温序列的均一性检验及其分析

本文应用标准正态检验(SNHT)技术,对四川省161个台站自建站以来~2005年的长序列气温资料进行了一个普查性的检验,发现四川161个台站中有64.6%的台站存在气温资料的不均一现象,盆地内站点的不均一现象略高于高原三州台站,60年代以前和2000年以后不均一的台站比其他年代少;并且结合四川省台站历史沿革资料分析发现,有21.2%的台站的不均一问题是由迁站引起的。进一步以松潘和中江站为例,初步分析了不均一性产生的原因。      

我国大陆大尺度气候观测网的理想密度和分布

以我国大陆2416个气象观测站点组成的站网为参考,以观测序列的相关性表示对气候变化信号的监测能力,分析不同地区单位经纬度网格内站点数对信号监测能力的影响,得到特定监测能力要求下所需的站点数,为大尺度气候观测站网的站点设置提供科学依据。对2.5°×2.5°网格来说,当要求子集站网气温序列与标准站网气温序列相关系数超过0.99和0.95时,所需的站点数分别为750和377;当要求子集站网降水量序列与标准站网降水量序列的相关系数超过0.95和0.90时,需要的站点数分别为670和417。对2.5°×3.5°和5.0°×5.0°经纬度网格的分析发现,对站点数量要求较高的区域大部分是地形过渡区,一般来说,降水量观测对站点密度的要求高于气温。     

定点观测气候序列的均一性研究

文章分析了气候资料产生非均一性的原因，介绍了近年来国内、外气候资料均一性研究的方法以及一些国家利用这些研究方法对本国或区域气候序列均一性问题的研究成果。对我国气象观测站观测站址、仪器变化等因素进行了总结，并介绍了我国气象资料均一性检验的一些结论，讨论了与气象资料均一性研究相关的一些问题。     

Resampling of network-induced variability in estimates of terrestrial air temperature change

Uneven and changing spatial distributions of air temperature stations can produce unrepresentative samples of the space-time variability of near-surface air temperature. Over the last century, station networks have varied from less than 300 stations that are largely limited to the Northern Hemisphere to nearly 1700 stations that are fairly well distributed over the terrestrial surface. As a result, estimates of air temperature change derived from historical observation networks often contain network-induced variability.Spatial resampling methods are used to estimate network-induced variability in terrestrially averaged air temperature anomalies and trends. Random resampling from the station networks allows pseudo confidence intervals and other statistics of variability to be estimated. Network-induced variability appears to be substantial during the late 1800s, especially in the Southern Hemisphere. Most networks from the 1900s and the Northern Hemisphere, however, appear to produce reliable estimates of spatially averaged air temperature anomalies and trends.A non-random, but historically appropriate, resampling method - sampling a given year's air temperature anomaly field using another year's station distribution - also is used. Using 1987 - one of the warmest years on record - as an example, station distributions from 1881-1988 are used to sample the 1987 air temperature anomaly field. This sampling procedure produces spatially averaged air temperature anomalies for 1987 that vary by over 0.3°C. A combinatorial process of resampling a given year's anomaly field is repeated for every year and every station network to produce terrestrial average air temperature anomaly estimates that vary by more than 0.3°C solely due to network changes.     

内蒙古气温序列均一性检验

通过构建参考序列、找出间断点、结果集成等步骤对内蒙古地区119个地面气象站逐日平均气温、逐日最高气温、逐日最低气温进行均一性检验。结果表明:内蒙古地区大部分地面气象站气温资料均一性状况良好;部分台站气温资料不均一的原因主要是受迁站影响,其次是观测站周围环境变化、观测仪器的更换以及观测标准的变更。不均一现象对最低气温影响较大,其次是平均气温,最高气温受影响相对最小。     

北京地区城市热岛强度变化对区域温度序列的影响

通过对北京地区20个台站1961~2000年月平均温度资料的对比分析,证实热岛效应对城市气象站记录的地表平均气温的绝对影响随时间显著增大,近20 a尤为突出,但其相对影响即热岛增温对全部增暖的贡献却呈下降趋势。近40 a来,北京地区的国家基本、基准站平均温度距平序列与被认为不受城市热岛影响的郊区站平均温度距平序列差异明显,由于热岛效应加强因素引起的国家基本、基准站平均年温度变化速率为0.16℃/(10 a),对整个时期全部增温的贡献达到71%;近20 a来热岛效应加强因素使北京地区国家基本、基准站年平均温度每10 a增暖0.33℃,对该时期全部增温的贡献达到49%。城市热岛效应加强因素对国家基本、基准站季节平均温度上升的贡献在夏、秋季高,冬季最小。本文的结果说明,目前根据国家基本、基准站资料建立的全国或较大区域平均温度序列可能在很大程度上保留着城市化的影响,有必要做进一步的检验和订正。