42 research outputs found
Structural Relaxation and Frequency Dependent Specific Heat in a Supercooled Liquid
We have studied the relation between the structural relaxation and the
frequency dependent thermal response or the specific heat, , in a
supercooled liquid.
The Mode Coupling Theory (MCT) results are used to obtain
corresponding to different wavevectors. Due to the two-step
relaxation process present in the MCT, an extra peak, in addition to the low
frequency peak, is predicted in specific heat at high frequency.Comment: 14 pages, 13 Figure
Uncertainty in Signals of Large-Scale Climate Variations in Radiosonde and Satellite Upper-Air Temperature Datasets
There is no single reference dataset of long-term global upper-air temperature observations, although several
groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The
existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and
change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty
of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO
signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of
1976–77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal
estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in
any individual dataset.
The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upperair
temperature trends gives a more complete characterization of their uncertainty than reliance on a single
dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary.
However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively
encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle,
augmenting the 10 principles that have now been generally accepted (although not generally implemented) by
the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent
observing systems for measuring the variable, and multiple, independent groups analyzing the data
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Annual and seasonal global temperature anomalies in the troposphere and low stratosphere, 1958 - Summer 1986
Surface temperatures and thickness-derived temperatures from a network of 63 well-distributed radiosonde stations have been used to estimate global and zonal annual and seasonal temperatures anomalies for the period 1958 through the summer of 1986. These anomaly estimates were made using a 1958-1977 reference period mean. Anomaly estimates are provided for surface, troposphere (850-300 mb), tropopause layer (300-100 mb), and low stratosphere (100-50 mb); (100-30mb) layers and for polar (60{degrees}-90{degrees}), temperate (30{degrees}-60{degrees}), subtropical (10{degrees}-30{degrees}), and equatorial (10{degrees}N - 10{degrees}S) zones, as well as the tropics, both hemispheres, and the world
Uncertainty in Signals of Large-Scale Climate Variations in Radiosonde and Satellite Upper-Air Temperature Datasets
There is no single reference dataset of long-term global upper-air temperature observations, although several
groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The
existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and
change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty
of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO
signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of
1976–77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal
estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in
any individual dataset.
The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upperair
temperature trends gives a more complete characterization of their uncertainty than reliance on a single
dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary.
However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively
encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle,
augmenting the 10 principles that have now been generally accepted (although not generally implemented) by
the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent
observing systems for measuring the variable, and multiple, independent groups analyzing the data
Uncertainty in Signals of Large-Scale Climate Variations in Radiosonde and Satellite Upper-Air Temperature Datasets
There is no single reference dataset of long-term global upper-air temperature observations, although several
groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The
existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and
change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty
of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO
signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of
1976–77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal
estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in
any individual dataset.
The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upperair
temperature trends gives a more complete characterization of their uncertainty than reliance on a single
dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary.
However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively
encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle,
augmenting the 10 principles that have now been generally accepted (although not generally implemented) by
the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent
observing systems for measuring the variable, and multiple, independent groups analyzing the data