scholarly journals A Comprehensive Evaluation of Surface Air Temperature Reanalyses over China against Urbanization Bias–Adjusted Observations

2021 ◽  
Author(s):  
Siqi Zhang ◽  
Guoyu Ren ◽  
Yuyu Ren ◽  
Yingxian Zhang ◽  
Xiaoying Xue
Keyword(s):  
Standard Deviation ◽  
Air Temperature ◽  
Comprehensive Evaluation ◽  
Linear Trend ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Reanalysis Data ◽  
Deviation Ratio ◽  
Standard Deviation Ratio ◽  
Linear Trends

Abstract The goal of this study is to compare the differences in surface air temperature (SAT) between observational and reanalysis data in mainland China from 1961–2015 for evaluating the reliability and applicability of the reanalysis datasets, based on an observational dataset of 763 stations which has been adjusted for urbanization bias, and 8 reanalysis datasets. The time series, anomaly correlations, standard deviations, climate state, and linear trends of the reanalysis data are evaluated against the observations. The reanalysis data are consistent with the observational climate characteristics to a large extent. The correlation and standard deviation ratio between the reanalysis data and observations exhibited highly consistent inter–annual variability and dispersion, with the inter–annual SAT variability of JRA55 and ERA5 the closest to the observations for the periods 1961–2015 and 1979–2015, and the dispersions of 20CRV3 and NCEPV1 the most consistent with the observations for the two periods. The annual mean SAT of the reanalyses is generally 0–2.0°C lower than the observations, while the linear trends of all datasets exhibited clear warming. The biases in the SAT climatology of 20CRV3 and CRA40 are lower than other reanalysis datasets, and the linear trends of NCEPV1 and 20CRV3 are closer to the observations. With increasing elevation, the biases of the reanalysis data in terms of correlation, standard deviation, climate state, and linear trend all increased. Overall, in terms of the similarity of multiple measures to the urbanization bias–adjusted observations, CRA40 and JRA55 show the best performance of the products in reproducing various aspects of climatological and climate change features in mainland China for the period 1979–2015 and 1961–2015 respectively.

scholarly journals Recent Surface Air Temperature Change over Mainland China Based on an Urbanization-Bias Adjusted Dataset

2019 ◽  
Vol 32 (10) ◽  
pp. 2691-2705 ◽  
Author(s):  
Kangmin Wen ◽  
Guoyu Ren ◽  
Jiao Li ◽  
Aiying Zhang ◽  
Yuyu Ren ◽  
...  
Keyword(s):  
Air Temperature ◽  
North China ◽  
Linear Trend ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Data Series ◽  
Standard Procedures ◽  
Corrected Data ◽  
Warming Rate ◽  
Temperature Time

Abstract A dataset from 763 national Reference Climate and Basic Meteorological Stations (RCBMS) was used to analyze surface air temperature (SAT) change in mainland China. The monthly historical observational records had been adjusted for urbanization bias existing in the data series of size-varied urban stations, after they were corrected for data inhomogeneities mainly caused by relocation and instrumentation. The standard procedures for creating area-averaged temperature time series and for calculating linear trend were used. Analyses were made for annual and seasonal mean temperature. Annual mean SAT in mainland China as a whole rose by 1.24°C for the last 55 years, with a warming rate of 0.23°C decade−1. This was close to the warming of 1.09°C observed in global mean land SAT over the period 1951–2010. Compared to the SAT before correction, after-corrected data showed that the urbanization bias had caused an overestimate of the annual warming rate of more than 19.6% during 1961–2015. The winter, autumn, spring, and summer mean warming rates were 0.28°, 0.23°, 0.23°, and 0.15°C decade−1, respectively. The spatial patterns of the annual and seasonal mean SAT trends also exhibited an obvious difference from those of the previous analyses. The largest contrast was a weak warming area appearing in central parts of mainland China, which included a small part of southwestern North China, the northwestern Yangtze River, and the eastern part of Southwest China. The annual mean warming trends in Northeast and North China obviously decreased compared to the previous analyses, which caused a relatively more significant cooling in Northeast China after 1998 under the background of global warming slowdown.

scholarly journals Subseasonal relationship between Arctic and Eurasian surface air temperature

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hye-Jin Kim ◽  
Seok-Woo Son ◽  
Woosok Moon ◽  
Jong-Seong Kug ◽  
Jaeyoung Hwang
Keyword(s):  
Air Temperature ◽  
Diabatic Heating ◽  
Surface Air Temperature ◽  
Significant Negative Correlation ◽  
Cold Season ◽  
Reanalysis Data ◽  
Necessary Condition ◽  
Sea Level Pressure ◽  
Blocking High ◽  
Pressure Anomaly

AbstractThe subseasonal relationship between Arctic and Eurasian surface air temperature (SAT) is re-examined using reanalysis data. Consistent with previous studies, a significant negative correlation is observed in cold season from November to February, but with a local minimum in late December. This relationship is dominated not only by the warm Arctic-cold Eurasia (WACE) pattern, which becomes more frequent during the last two decades, but also by the cold Arctic-warm Eurasia (CAWE) pattern. The budget analyses reveal that both WACE and CAWE patterns are primarily driven by the temperature advection associated with sea level pressure anomaly over the Ural region, partly cancelled by the diabatic heating. It is further found that, although the anticyclonic anomaly of WACE pattern mostly represents the Ural blocking, about 20% of WACE cases are associated with non-blocking high pressure systems. This result indicates that the Ural blocking is not a necessary condition for the WACE pattern, highlighting the importance of transient weather systems in the subseasonal Arctic-Eurasian SAT co-variability.

scholarly journals An Integrated Procedure to Determine a Reference Station Network for Evaluating and Adjusting Urban Bias in Surface Air Temperature Data

2015 ◽  
Vol 54 (6) ◽  
pp. 1248-1266 ◽  
Author(s):  
Guoyu Ren ◽  
Jiao Li ◽  
Yuyu Ren ◽  
Ziying Chu ◽  
Aiying Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Historical Data ◽  
Spatial Scales ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Reference Station ◽  
Reference Network ◽  
Station Network

AbstractTrends in surface air temperature (SAT) are a critical indicator for climate change at varied spatial scales. Because of urbanization effects, however, the current SAT records of many urban stations can hardly meet the demands of the studies. Evaluation and adjustment of the urbanization effects on the SAT trends are needed, which requires an objective selection of reference (rural) stations. Based on the station history information from all meteorological stations with long-term records in mainland China, an integrated procedure for determining the reference SAT stations has been developed and is applied in forming a network of reference SAT stations. Historical data from the network are used to assess the urbanization effects on the long-term SAT trends of the stations of the national Reference Climate Network and Basic Meteorological Network (RCN+BMN or national stations), which had been used most frequently in studies of regional climate change throughout the country. This paper describes in detail the integrated procedure and the assessment results of urbanization effects on the SAT trends of the national stations applying the data from the reference station network determined using the procedure. The results showed a highly significant urbanization effect of 0.074°C (10 yr)−1 and urbanization contribution of 24.9% for the national stations of mainland China during the time period 1961–2004, which compared well to results that were reported in previous studies by the authors using the predecessor of the present reference network and the reference stations selected but when applying other methods. The authors are thus confident that the SAT data from the updated China reference station network as reported in this paper best represented the baseline SAT trends nationwide and could be used for evaluating and adjusting the urban biases in the historical data series of the SAT from different observational networks.

scholarly journals Northern Hemisphere Climatology and Trends of Statistical Moments Documented from GHCN-Daily Surface Air Temperature Station Data from 1950 to 2010

2014 ◽  
Vol 27 (14) ◽  
pp. 5396-5410 ◽  
Author(s):  
Nicholas R. Cavanaugh ◽  
Samuel S. P. Shen
Keyword(s):  
Standard Deviation ◽  
Northern Hemisphere ◽  
Spatial Patterns ◽  
Air Temperature ◽  
Probability Distributions ◽  
Temporal Trends ◽  
Surface Air Temperature ◽  
Statistical Moments ◽  
Station Data ◽  
Skewness And Kurtosis

Abstract The first four statistical moments and their trends are calculated for the average daily surface air temperature (SAT) from 1950 to 2010 using the Global Historical Climatology Network–Daily station data for each season relative to the 1961–90 climatology over the Northern Hemisphere. Temporal variation of daily SAT probability distributions are represented as generalized linear regression coefficients on the mean, standard deviation, skewness, and kurtosis calculated for each 10-yr moving time window from 1950–59 to 2001–10. The climatology and trends of these statistical moments suggest that daily SAT probability distributions are non-Gaussian and are changing in time. The climatology of the first four statistical moments has distinct spatial patterns with large coherent structure for mean and standard deviation and relatively smaller and more regionalized patterns for skewness and kurtosis. The linear temporal trends from 1950 to 2010 of the first four moments also have coherent spatial patterns. The linear temporal trends in the characterizing statistical moments are statistically significant at most locations and have differing spatial patterns for different moments. The regionalized variations specific to higher moments may be related to the climate dynamics that contribute to extremes. The nonzero skewness and kurtosis makes this detailed documentation on the higher statistical moments useful for quantifying climate changes and assessing climate model uncertainties.

Calibrating and Evaluating Reanalysis Surface Temperature Error by Topographic Correction

2008 ◽  
Vol 21 (6) ◽  
pp. 1440-1446 ◽  
Author(s):  
Tianbao Zhao ◽  
Weidong Guo ◽  
Congbin Fu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Original Data ◽  
Reanalysis Data ◽  
Temperature Data ◽  
Temperature Error ◽  
Topographic Correction ◽  
Temperature Errors

Abstract Based on the observed daily surface air temperature data from 597 stations over continental China and two sets of reanalysis data [NCEP–NCAR and 40-yr ECMWF Re-Analysis (ERA-40)] during 1979–2001, the altitude effects in calibrating and evaluating reanalyzed surface temperature errors are studied. The results indicate that the accuracy of interpolated surface temperature from the reanalyzed gridpoint value or the station observations depends much on the altitudes of original data. Bias of interpolated temperature is usually in proportion to the increase of local elevation and topographical complexity. Notable improvements of interpolated surface temperature have been achieved through “topographic correction,” especially for ERA-40, which highlights the necessity of removal of “elevation-induced bias” when using and evaluating reanalyzed surface temperature.

scholarly journals An inkling of the relation between the monofractality of temperatures and pressure anomalies

2015 ◽  
Vol 2 (4) ◽  
pp. 1339-1353
Author(s):  
A. Deliège ◽  
S. Nicolay
Keyword(s):  
Wavelet Transform ◽  
Standard Deviation ◽  
Discrete Wavelet Transform ◽  
Air Temperature ◽  
Surface Pressure ◽  
Surface Air Temperature ◽  
Discrete Wavelet ◽  
Long Range Correlations ◽  
Hölder Exponents ◽  
Weather Stations

Abstract. We use the discrete "wavelet transform microscope" to study the monofractal nature of surface air temperature signals of weather stations spread across Europe. This method reveals that the information obtained in this way is richer than previous works studying long range correlations in meteorological stations: the approach presented here allows to bind the Hölder exponents with the standard deviation of surface pressure anomalies, while such a link does not appear with methods previously carried out.

scholarly journals Pengklasteran Kabupaten/Kota di Jawa Tengah berdasarkan Tenaga Kesehatan dengan Menggunakan Metode Ward dan K-Means

2018 ◽  
Vol 7 (2) ◽  
pp. 103-109
Author(s):  
Sri Puji Lestari ◽  
Epha Diana Supandi ◽  
Pipit Pratiwi Rahayu
Keyword(s):  
Cluster Analysis ◽  
Standard Deviation ◽  
Nursing Staff ◽  
Health Workers ◽  
Medical Personnel ◽  
Central Java ◽  
Deviation Ratio ◽  
Standard Deviation Ratio ◽  
Ward Method ◽  
Central Java Province

Analisis klaster merupakan suatu metode yang digunakan untuk mengelompokkan objek (kasus) ke dalam klaster (kelompok) yang relatif sama.  Tujuan penelitian ini untuk mengklasterkan Kabupaten/Kota di Provinsi Jawa Tengah berdasarkan tenaga kesehatan tahun 2015 seperti tenaga medis, tenaga keperawatan, tenaga kebidanan, tenaga kefarmasian dan tenaga kesehatan lainnya dengan menggunakan metode Ward dan K-Means. Hasil penelitian menunjukkan ada tiga klaster terbentuk dimana metode Ward menghasilkan nilai rasio simpangan baku sebesar 0,3019% lebih besar jika dibandingkan dengan nilai rasio simpangan baku pada metode K-Means yaitu 0,2974%. Pada kasus ini, metode K-Means merupakan metode yang lebih baik dibandingkan metode Ward. [Cluster analysis is a method used to group objects (cases) into clusters (groups) that are relatively the same. The purpose of this study is to classify districts/cities in Central Java Province based on health worker in 2015 such as medical personnel, nursing staff, midwifery staff, pharmacy personnel and health workers using the Ward and K-Means methods. The results show that there are three clusters formed where the Ward method produce a standard deviation ratio of 0.3019% greater than the standard deviation ratio in the K-Means method, which is 0.2974%. In this case, the K-Means method is a better method than the Ward method.]

Trend of Surface Air Temperature in Eastern China and Associated Large-Scale Climate Variability over the Last 100 Years

2014 ◽  
Vol 27 (12) ◽  
pp. 4693-4703 ◽  
Author(s):  
Ping Zhao ◽  
Phil Jones ◽  
Lijuan Cao ◽  
Zhongwei Yan ◽  
Shuyao Zha ◽  
...  
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Eastern China ◽  
Surface Air Temperature ◽  
Tropical Indian Ocean ◽  
Population Data ◽  
Warming Trend ◽  
Climate Indices ◽  
The Past ◽  
Linear Trends

Abstract Using the reconstructed continuous and homogenized surface air temperature (SAT) series for 16 cities across eastern China (where the greatest industrial developments in China have taken place) back to the nineteenth century, the authors examine linear trends of SAT. The regional-mean SAT over eastern China shows a warming trend of 1.52°C (100 yr)−1 during 1909–2010. It mainly occurred in the past 4 decades and this agrees well with the variability in another SAT series developed from a much denser station network (over 400 sites) across this part of China since 1951. This study collects population data for 245 sites (from these 400+ locations) and split these into five equally sized groups based on population size. Comparison of these five groups across different durations from 30 to 60 yr in length indicates that differences in population only account for between 9% and 24% of the warming since 1951. To show that a larger urbanization impact is very unlikely, the study additionally determines how much can be explained by some large-scale climate indices. Anomalies of large-scale climate indices such as the tropical Indian Ocean SST and the Siberian atmospheric circulation systems account for at least 80% of the total warming trends.

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