A Significant Bias of Tmax and Tmin Average Temperature and Its Trend

2019 ◽  
Vol 58 (10) ◽  
pp. 2235-2246 ◽  
Author(s):  
Yulian Liu ◽  
Guoyu Ren ◽  
Hengyuan Kang ◽  
Xiubao Sun
Keyword(s):  
Arid Region ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Positive Trend ◽  
Systematic Bias ◽  
Average Temperature ◽  
Mean Temperature ◽  
Daily Data ◽  
Autumn And Winter

AbstractThe systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China.

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.

Trends in intra-seasonal temperature variability in Europe

2020 ◽  
Author(s):  
Tomas Krauskopf
Keyword(s):  
Western Europe ◽  
Climate Extremes ◽  
Individual Variability ◽  
Daily Temperature ◽  
Linear Regression Method ◽  
Seasonal Temperature ◽  
Mean Temperature ◽  
Daily Data ◽  
Change In Mean

<p>While long-term changes in measures of central tendency of climate elements, i. e. mean temperature, are well acknowledged, studies of trends in measures of their variability are much less common. This is despite the fact that trends in variability can have higher effect on climate extremes than trends in mean. Here, three measures of intra-seasonal variability are examined: 1) standard deviation of mean daily temperature 2) mean absolute value of day-to-day temperature change, 3) the range between the 90th and 10th quantile of mean daily temperature. ECA&D daily data from 180 stations and linear regression method are utilized to calculate trends of these characteristics in period from 1961 to 2012. Spatial distribution of trends in individual variability characteristics in Europe together with long-term change in mean and autocorrelation of mean temperature are demonstrated in maps. Significant trends (positive and negative) in all examined variability characteristics were found with substantial differences between seasons as well as between regions. On this basis, Europe is divided into 6 regions and trends are assessed in each reagion separately. While the most significant decrease in variability is observed in Northern Scandinavia and Iceland in winter, the most substantial increase is detected in Central and Western Europe in spring. Our results are accompanied by comparing the probability density function of daily temperature between periods 1961 – 1986 and 1987 – 2012 in each region showing how the shape of distribution of daily temperature has changed and if it could affect the changing number and value of temperature extremes.</p>

scholarly journals Using Markov Analysis to Study the Impact of Temperature in Bangladesh

2019 ◽  
Vol 6 (2) ◽  
pp. 69-76
Author(s):  
Janardan Mahanta ◽  
Soumen Kishor Nath ◽  
Md. Haronur Rashid
Keyword(s):  
Historical Data ◽  
Monsoon Season ◽  
Winter Season ◽  
Surface Air Temperature ◽  
Markov Analysis ◽  
Average Temperature ◽  
Chain Analysis ◽  
The Impact ◽  
Stationary Probabilities

In this paper has been studied the temperature trend in Bangladesh. Long-term changes of surface air temperature over Bangladesh have been studied using the available historical data collected by the Bangladesh meteorological Department (BMD). Daily temperature data is collected from BMD in Dhaka and Chittagong. Then month have been divided according to season and their descriptive statistics are computed. Maximum average temperature in pre-monsoon season and minimum average temperature in winter season have been shown in the paper. This study also reveals that temperature has increased over the time. Markov chain analysis has been applied for these data so as to find the stationary probability. After 26 and 13 days stationary probabilities in Dhaka and Chittagong stations respectively have observed.

Long-Term Surface Air Temperature Trends Over Mainland China

2019 ◽  
Author(s):  
Guoyu Ren ◽  
Guoli Tang ◽  
Kangmin Wen
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Regional Warming ◽  
National Reference ◽  
The Past ◽  
Temperature Trends ◽  
Past Half Century ◽  
Autumn And Winter

Based on a dataset of national reference and basic stations, which have been quality controlled and inhomogeneity processed, updated surface air temperature (SAT) series of the past 67 (1951–2017) and 113 (1905–2017) years for mainland China are constructed and analyzed. The new temperature series show significant warming trends of 0.24°C/10yr and 0.09°C/10yr respectively for the two periods. The rapid regional warming generally begins from the mid-1980s, about a decade later than the northern hemisphere average SAT change. Warming during the period of 1951–2017 is larger and more significant in the northeast, north, northwest and the Qinghai-Tibetan Plateau, and the most significant SAT increase usually occurs in winter and spring except for the Qinghai-Tibetan Plateau where winter and autumn undergo the largest warming. The slowdown of the warming can be clearly detected after 1998, especially for autumn and winter. The effect of urbanization on trends of the region averaged annual and seasonal mean SAT as calculated from the national reference and basic stations has not been adjusted, despite it being generally large and significant. In north China, the increasing trend of annual mean SAT induced by urbanization for the national stations is 0.10°C/10yr for the period 1961–2015, accounting for at least 31% of the overall annual mean warming. The contribution of urbanization to the overall warming of the past half century in Mainland China has also been summarized and discussed referring to the previous studies.

scholarly journals Analysis of Minimum temperature spatially and temporally over Iraq During the period 1980-2015

2017 ◽  
Vol 2 (5) ◽  
Author(s):  
Yassen K. Al-Timimi ◽  
Aws A. Al-Khudhairy
Keyword(s):  
High Temperature ◽  
Temporal Trends ◽  
Surface Air Temperature ◽  
Kendall Test ◽  
Temporal Analysis ◽  
Positive Trend ◽  
Seasonal Temperature ◽  
The North ◽  
Four Seasons ◽  
Autumn And Winter

Monthly Minimum surface air temperature at 23 stations in Iraq were analyzed for temporal trends and spatial variation during 1980-2015.seasonal and annual temperature was analyzed using Mann-Kendall test to detect the significant trend .The results of temporal analysis showed that during winter ,spring , summer and Autumn have a positive trend in all the parts of Iraq. A tendency has also been observed towards warmer years, with significantly warmer summer and spring periods and slightly warmer autumn and winter, the highest increase is (3.9) oC in Baghdad during the summer. The results of spatial analyze using the ArcGIS showed that the seasonal temperature can be divided into two or three distinct areas with high temperature in the south and decreasing towards north, where the trend of spatial temperature were decreasing from south to the north in all the four seasons.

Features of the distribution of long-term temperature regime on the territory of Azerbaijan

2020 ◽  
Vol 4 ◽  
pp. 110-116
Author(s):  
D. S. Guseinov ◽  
◽  
Keyword(s):  
Temperature Regime ◽  
Global Climate ◽  
Surface Air Temperature ◽  
Temperature Fluctuations ◽  
Water Area ◽  
Climate Indices ◽  
Climate Regime ◽  
Average Temperature ◽  
Using Data

The estimates of the temperature regime are given using data from 49 hydrometeorological stations operating in Azerbaijan, including 32 main stations with available regular observations for 1961-2016. Data from the other 17 stations with fragmentary observations are used to refine information from neighboring stations. Surface air temperature with monthly, seasonal and multi-year averaging is analyzed, the average values of temperature fluctuations for two periods from 1991 to 2016 and from 1961 to 1990 are compared. The study revealed changes in average temperature in 1991-2016 as compared to 1961-1990 and showed that temperature in the country increased by of 0.7°C on average over the period 1991-2016. This caused the desertification intensification and the shift of the green landscape to the higher altitudes. The presented results can be used for studying the climate regime on the territory of Azerbaijan. Keywords: global climate change, hypsometric characteristics, transformation, convergence, climate types, interpolation, climate indices, water area, temperature regime

scholarly journals Evaluations of Surface PM10 Concentration and Chemical Compositions in MERRA-2 Aerosol Reanalysis over Central and Eastern China

2021 ◽  
Vol 13 (7) ◽  
pp. 1317
Author(s):  
Xiaodan Ma ◽  
Peng Yan ◽  
Tianliang Zhao ◽  
Xiaofang Jia ◽  
Jian Jiao ◽  
...  
Keyword(s):  
Eastern China ◽  
Mainland China ◽  
Chemical Components ◽  
Chemical Compositions ◽  
Observation Data ◽  
Causative Factors ◽  
Routine Sampling ◽  
Aerosol Compositions ◽  
Modern Era

The chemical composition dataset of Aerosol Reanalysis of NASA’s Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRAero) has not been thoroughly evaluated with observation data in mainland China due to the lack of long-term chemical components data. Using the 5-year data of PM10 mass concentrations and chemical compositions obtained from the routine sampling measurements at the World Meteorological Organization the Global Atmosphere Watch Programme regional background stations, Jing Sha (JS) and Lin’An (LA), in central and eastern China, we comprehensively evaluate the surface PM10 concentrations and chemical compositions such as sulfate (SO42−), organic carbon (OC) and black carbon (BC) derived from MERRAero. Overall, the concentrations of PM10, SO42−, OC and BC from the MERRAero agreed well with the measurements, despite a slight and consistent overestimation of BC concentrations and a moderate and persistent underestimation of PM10 concentrations throughout the study period. The MERRAero reanalysis of aerosol compositions performs better during the summertime than wintertime. By considering the nitrate particles in PM10 reconstruction, MERRAero performance can be significantly improved. The unreasonable seasonal variations of PM10 chemical compositions at station LA by MERRAero could be causative factors for the larger MERRAero discrepancies during 2016–2017 than the period of 2011–2013.

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

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