Variations in the probability distribution function of air temperature anomalies in winter and summer from 1961 to 2016 over China

2021 ◽  
Author(s):  
Guocan Wu ◽  
Yuan Tian ◽  
Fang Gong ◽  
Jizeng Du ◽  
Chunming Shi
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Air Temperature ◽  
Probability Distribution Function ◽  
Temperature Anomalies ◽  
Air Temperature Anomalies

scholarly journals An Online Application for ΔR Calculation

Radiocarbon ◽  
2016 ◽  
Vol 59 (5) ◽  
pp. 1623-1627 ◽  
Author(s):  
Ron W Reimer ◽  
Paula J Reimer
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Calibration Curve ◽  
Probability Distribution Function ◽  
Museum Collections ◽  
Online Program ◽  
Online Application ◽  
Radiocarbon Calibration

AbstractA regional offset (ΔR) from the marine radiocarbon calibration curve is widely used in calibration software (e.g. CALIB, OxCal) but often is not calculated correctly. While relatively straightforward for known-age samples, such as mollusks from museum collections or annually banded corals, it is more difficult to calculate ΔR and the uncertainty in ΔR for 14C dates on paired marine and terrestrial samples. Previous researchers have often utilized classical intercept methods that do not account for the full calibrated probability distribution function (pdf). Recently, Soulet (2015) provided R code for calculating reservoir ages using the pdfs, but did not address ΔR and the uncertainty in ΔR. We have developed an online application for performing these calculations for known-age, paired marine and terrestrial 14C dates and U-Th dated corals. This article briefly discusses methods that have been used for calculating ΔR and the uncertainty and describes the online program deltar, which is available free of charge.

scholarly journals Optimal Taylor–Couette turbulence

2012 ◽  
Vol 706 ◽  
pp. 118-149 ◽  
Author(s):  
Dennis P. M. van Gils ◽  
Sander G. Huisman ◽  
Siegfried Grossmann ◽  
Chao Sun ◽  
Detlef Lohse
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Angular Velocity ◽  
Probability Distribution Function ◽  
Neutral Line ◽  
Velocity Ratio ◽  
Outer Cylinder ◽  
Taylor Number ◽  
Counter Rotation ◽  
Taylor Couette

AbstractStrongly turbulent Taylor–Couette flow with independently rotating inner and outer cylinders with a radius ratio of $\eta = 0. 716$ is experimentally studied. From global torque measurements, we analyse the dimensionless angular velocity flux ${\mathit{Nu}}_{\omega } (\mathit{Ta}, a)$ as a function of the Taylor number $\mathit{Ta}$ and the angular velocity ratio $a= \ensuremath{-} {\omega }_{o} / {\omega }_{i} $ in the large-Taylor-number regime $1{0}^{11} \lesssim \mathit{Ta}\lesssim 1{0}^{13} $ and well off the inviscid stability borders (Rayleigh lines) $a= \ensuremath{-} {\eta }^{2} $ for co-rotation and $a= \infty $ for counter-rotation. We analyse the data with the common power-law ansatz for the dimensionless angular velocity transport flux ${\mathit{Nu}}_{\omega } (\mathit{Ta}, a)= f(a)\hspace{0.167em} {\mathit{Ta}}^{\gamma } $, with an amplitude $f(a)$ and an exponent $\gamma $. The data are consistent with one effective exponent $\gamma = 0. 39\pm 0. 03$ for all $a$, but we discuss a possible $a$ dependence in the co- and weakly counter-rotating regimes. The amplitude of the angular velocity flux $f(a)\equiv {\mathit{Nu}}_{\omega } (\mathit{Ta}, a)/ {\mathit{Ta}}^{0. 39} $ is measured to be maximal at slight counter-rotation, namely at an angular velocity ratio of ${a}_{\mathit{opt}} = 0. 33\pm 0. 04$, i.e. along the line ${\omega }_{o} = \ensuremath{-} 0. 33{\omega }_{i} $. This value is theoretically interpreted as the result of a competition between the destabilizing inner cylinder rotation and the stabilizing but shear-enhancing outer cylinder counter-rotation. With the help of laser Doppler anemometry, we provide angular velocity profiles and in particular identify the radial position ${r}_{n} $ of the neutral line, defined by $ \mathop{ \langle \omega ({r}_{n} )\rangle } \nolimits _{t} = 0$ for fixed height $z$. For these large $\mathit{Ta}$ values, the ratio $a\approx 0. 40$, which is close to ${a}_{\mathit{opt}} = 0. 33$, is distinguished by a zero angular velocity gradient $\partial \omega / \partial r= 0$ in the bulk. While for moderate counter-rotation $\ensuremath{-} 0. 40{\omega }_{i} \lesssim {\omega }_{o} \lt 0$, the neutral line still remains close to the outer cylinder and the probability distribution function of the bulk angular velocity is observed to be monomodal. For stronger counter-rotation the neutral line is pushed inwards towards the inner cylinder; in this regime the probability distribution function of the bulk angular velocity becomes bimodal, reflecting intermittent bursts of turbulent structures beyond the neutral line into the outer flow domain, which otherwise is stabilized by the counter-rotating outer cylinder. Finally, a hypothesis is offered allowing a unifying view and consistent interpretation for all these various results.

Temperature anomalies of a cold period on the territory of Ukraine in 2010-2019

2020 ◽  
Vol 101-102 (3-4) ◽  
pp. 19-25
Author(s):  
Olena Nashmudinova
Keyword(s):  
Air Temperature ◽  
Heat Waves ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Negative Temperature ◽  
Cold Period ◽  
Positive Anomaly ◽  
Temperature Anomalies ◽  
Gradient Fields ◽  
Air Temperature Anomalies

Regional climate change in Ukraine in recent decades is accompanied by an increase in the repetitiveness of intense waves, both heat and cold; there is a tendency to increase the frequency of warm winters, but sometimes there are periods with significant decreases in temperature. The aim of the study is to determine the specifics of the formation of air temperature anomalies in the cold period 2010–2019. According to the distribution of the average monthly air temperature at the stations Odessa, Kiev, Kharkiv, Lviv investigated positive and negative deviations from the climate norm. In January, the average monthly air temperature in most cases was above normal, except for 1–3 years. The maximum positive anomaly was 4–5°C in Kyiv and Lviv (2015), the largest negative deviations were 3.8°C. In February, the trend continues – only 2–3 years with negative anomalies, the largest deviations to 3–6°C in 2011 and 2012, and positive deviations maximum in 2016. In March, negative temperature anomalies were observed 3–4 years, with a maximum of 2–3°C in 2018, positive anomalies in 4–6°C were observed in 2014, 2017. Temperatures in November were variable, with the prevailing positive anomaly, a high of 6–8°C in 2010. The distribution of air temperature in December was characterized by positive deviations of a maximum of 5–6°C in 2011, 2015, 2017 and 2019. Months of the greatest positive and negative air temperature anomalies over Europe have been highlighted. Among the colder months, the biggest anomaly stood out in January 2010 and February 2012 to 5–6°C. Among the warm months, the temperature anomaly was observed in February 2016, positive deviations from the norm to 8°C. Heat waves formed in winter with a zonal type of circulation, when warm moist air from the Atlantic shifted across the periphery of the Icelandic low. In March, waves of heat formed in low–gradient fields. Powerful waves of cold over the European sector were mainly formed under the influence of “eastern processes” in the spread of the Siberian anticyclone to Europe. In some years, significant cooling over Ukraine is formed in cyclonic systems with a high–altitude thermobaric field characterized by polar or ultrapolar hollow.

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