Results 1 - 10 of 2057
Results 1 - 10 of 2057. Search took: 0.031 seconds
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[en] Full text: Empirical expressions for n-saturated surface tent ion hydrocarbons depending on molecular mass and temperature are offered in the article. With the help of these expressions it is established that at σ=0 the temperature values for each individually n-saturated hydrocarbon practically coincide with critical values of temperature that confirms correctness of obtained correlation relations
[en] The results of gamma-spectrometric determinations of 95Zr, 103Ru, 106Ru, 125Sb, 137Cs, 141Cs, 144Ce and 7Be concentrations in ground-level air are presented. The origin of the artifical radioactivity is examined, using the 141Ce : 144Ce activity ratio. Some occurrences of shortlived fresh radioactive debris are discussed separately. Also included are the results of radiochemical analyses of the long-lived natural radionuclides 210Pb, 210Po and 226Ra, performed in 1969 and 1974 as a supplement to the routine measurements. (author)
[en] In Asia, much effort is put into reducing methane (CH4) emissions due to the region’s contribution to the recent rapid global atmospheric CH4 concentration growth. Accurate quantification of Asia’s CH4 budgets is critical for conducting global stocktake and achieving the long-term temperature goal of the Paris Agreement. In this study, we present top-down estimates of CH4 emissions from 2009 to 2018 deduced from atmospheric observations from surface network and GOSAT satellite with the high-resolution global inverse model NIES-TM-FLEXPART-VAR. The optimized average CH4 budgets are 63.40 ± 10.52 Tg y−1 from East Asia (EA), 45.20 ± 6.22 Tg y−1 from Southeast Asia (SEA), and 64.35 ± 9.28 Tg y−1 from South Asia (SA) within the 10 years. We analyzed two 5 years CH4 emission budgets for three subregions and 13 top-emitting countries with an emission budget larger than 1 Tg y−1, and interannual variabilities for these subregions. Statistically significant increasing trends in emissions are found in EA with a lower emission growth rate during 2014–2018 compared to that during 2009–2013, while trends in SEA are not significant. In contrast to the prior emission, the posterior emission shows a significant decreasing trend in SA. The flux decrease is associated with the transition from strong La Ninña (2010–2011) to strong El Ninño (2015–2016) events, which modulate the surface air temperature and rainfall patterns. The interannual variability in CH4 flux anomalies was larger in SA compared to EA and SEA. The Southern Oscillation Index correlates strongly with interannual CH4 flux anomalies for SA. Our findings suggest that the interannual variability in the total CH4 flux is dominated by climate variability in SA. The contribution of climate variability driving interannual variability in natural and anthropogenic CH4 emissions should be further quantified, especially for tropical countries. Accounting for climate variability may be necessary to improve anthropogenic emission inventories. (letter)
[en] The annual averages of the maximum, minimum and mean daily temperatures measured at the Genoa University Observatory from 1833 to 1986 are examined. All maxima lie between 20.0 degrees C and 17.2 degrees C and the minima between 14.7 degrees C and 11.5 degrees C: while the mean values lie between 17.1 degrees C and 13.2 degrees C. The smallest value of every series was recorded in 1956, the coldest of the last 154 years. The courses show sequences of years with increasing and decreasing temperatures and oscillations with different amplitudes and periods. The time occurrences of the sequences and the sign of the variations agree very well with those observed in many European places while differences concerning the amount of the variations were found. The comparison between the annual mean values of the minima in Genoa and in a rural site (Mt. Cappellino) during the last 30-years period shows a smaller increase of their differences
[en] We have used EML Surface Air Sampling Program (SASP) data to analyze the long-term trend in 7Be surface concentration and address possible correlation between this long-term trend and climatic changes, namely changes in precipitation patterns and temperature. In this paper we present 7Be concentration data from 23 sites, spanning over 25 years, all over the world, and extract long-term trend parameter using two independent techniques. The 7Be concentrations in most stations show a pronounced decreasing trend, potentially corresponding to statistically significant changes in transporting 7Be from upper atmosphere source to these sites. Weak negative correlation between 7Be concentration and amount of precipitation was also observed. However, more data from more representative sites around the world are needed the statistical robustness of this trend. - Highlights: ► We used over 25 years of data from 23 sites to analyze 7Be concentration. ► We used non-linear regression method and Seasonal Kendall Test to extract long-term trend of 7Be surface air concentration. ► The surface air 7Be concentrations in most sites show a strong decreasing trend. ► There is a weak negative correlation between the 7Be concentration and the amount of precipitation.
[en] The global temperature series is a major indicator of climate change, whereas this indicator has undergone shift in trend over the twentieth century, changing from linear trend to nonlinear trend as a result of structural breaks. This paper investigates global and regional sea surface (SS) and land air surface (LS) temperature series from 1880 to 2016 by means of fractional integration technique. The results show that temperature series are described by trend stationary process, mostly in long memory range in the case of LS temperature while in the case of SS temperature, temperature series are in nonstationary mean reverting range for global and hemispheric temperature as well as for three other regional locations. By applying the multiple structural break test, the trend line is found breaking in many dates, locking up into many regimes which can be described using nonlinear trend structure. Nonlinear trend, based on Chebyshev inequality in the fractional integration framework, shows that global and regional temperature series can be represented using nonlinear trend up to the third order since this further lowers the integration order to long memory range in both SS and LS temperature series.
[en] Extreme cold winter weather events over Eurasia have occurred more frequently in recent years in spite of a warming global climate. To gain further insight into this regional mismatch with the global mean warming trend, we analyzed winter cyclone and anticyclone activities, and their interplay with the regional atmospheric circulation pattern characterized by the semi-permanent Siberian high. We found a persistent weakening of both cyclones and anticyclones between the 1990s and early 2000s, and a pronounced intensification of anticyclone activity afterwards. It is suggested that this intensified anticyclone activity drives the substantially strengthening and northwestward shifting/expanding Siberian high, and explains the decreased midlatitude Eurasian surface air temperature and the increased frequency of cold weather events. The weakened tropospheric midlatitude westerlies in the context of the intensified anticyclones would reduce the eastward propagation speed of Rossby waves, favoring persistence and further intensification of surface anticyclone systems. (letter)
[en] At thousands of stations worldwide, the mean daily surface air temperature is estimated as a mean of the daily maximum (Tmax) and minimum (Tmin) temperatures. We use the NOAA Surface Radiation Budget Network (SURFRAD) of seven US stations with surface air temperature recorded each minute to assess the accuracy of the mean daily temperature estimate as an average of the daily maximum and minimum temperatures and to investigate how the accuracy of the estimate increases with an increasing number of daily temperature observations. We find the average difference between the estimate based on an average of the maximum and minimum temperatures and the average of 1440 1-min daily observations to be − 0.05 ± 1.56 °C, based on analyses of a sample of 238 days of temperature observations. Considering determination of the daily mean temperature based on 3, 4, 6, 12, or 24 daily temperature observations, we find that 2, 4, or 6 daily observations do not reduce significantly the uncertainty of the daily mean temperature. The bias reduction in a statistically significant manner (95% confidence level) occurs only with 12 or 24 daily observations. The daily mean temperature determination based on 24 hourly observations reduces the sample daily temperature uncertainty to − 0.01 ± 0.20 °C. Estimating the parameters of population of all SURFRAD observations, the 95% confidence intervals based on 24 hourly measurements is from − 0.025 to 0.004 °C, compared to a confidence interval from − 0.15 to 0.05 °C based on the mean of Tmax and Tmin.