Satellite‐based layer average stratospheric temperature (T) climate data records (CDRs) now span more than three decades and so can elucidate climate variability associated with processes on multiple time scales. We intercompare and analyze available published T CDRs covering at least two decades, with a focus on Stratospheric Sounding Unit (SSU) and Microwave Sounding Unit (MSU) CDRs. Recent research has reduced but not eliminated discrepancies between SSU CDRs developed by NOAA and the UK Meteorological Office. The MSU CDRs from NOAA and Remote Sensing Systems are in closer agreement than the CDR from the University of Alabama in Huntsville. The latter has a previously unreported inhomogeneity in 2005, revealed by an abrupt increase in the magnitude and spatial variability of T anomaly differences between CDRs. Although time‐varying biases remain in both SSU and MSU CDRs, multiple linear regression analyses reveal consistent solar, El Niño–Southern Oscillation (ENSO), quasi‐biennial oscillation, aerosol, and piecewise‐linear trend signals. Together, these predictors explain 80 to 90% of the variance in the near‐global‐average T CDRs. The most important predictor variables (in terms of percent explained variance in near‐global‐average T) for lower stratospheric T measured by MSU are aerosols, solar variability, and ENSO. Trends explain the largest percentage of variance in observations from all three SSU channels. In MSU and SSU CDRs, piecewise‐linear trends, with a 1995 break point, indicate cooling during 1979–1994 but no trend during 1995–2013 for MSU and during 1995–2005 for SSU. These observational findings provide a basis for evaluating climate model simulations of stratospheric temperature during the past 35 years.
[1] The quality of humidity measurements from global operational radiosonde sensors in upper, middle, and lower troposphere for the period 2000-2011 were investigated using satellite observations from three microwave water vapor channels operating at 183.31˙1, 183.31˙3, and 183.31˙7 GHz. The radiosonde data were partitioned based on sensor type into 19 classes. The satellite brightness temperatures (Tb) were simulated using radiosonde profiles and a radiative transfer model, then the radiosonde simulated Tb's were compared with the observed Tb's from the satellites. The surface affected Tb's were excluded from the comparison due to the lack of reliable surface emissivity data at the microwave frequencies. Daytime and nighttime data were examined separately to see the possible effect of daytime radiation bias on the sonde data. The error characteristics among different radiosondes vary significantly, which largely reflects the differences in sensor type. These differences are more evident in the mid-upper troposphere than in the lower troposphere, mainly because some of the sensors stop responding to tropospheric humidity somewhere in the upper or even in the middle troposphere. In the upper troposphere, most sensors have a dry bias but Russian sensors and a few other sensors including GZZ2, VZB2, and RS80H have a wet bias. In middle troposphere, Russian sensors still have a wet bias but all other sensors have a dry bias. All sensors, including Russian sensors, have a dry bias in lower troposphere. The systematic and random errors generally decrease from upper to lower troposphere. Sensors from China, India, Russia, and the U.S. have a large random error in upper troposphere, which indicates that these sensors are not suitable for upper tropospheric studies as they fail to respond to humidity changes in the upper and even middle troposphere. Overall, Vaisala sensors perform better than other sensors throughout the troposphere exhibiting the smallest systematic and random errors. Because of the large differences between different radiosonde humidity sensors, it is important for long-term trend studies to only use data measured using a single type of sensor at any given station. If multiple sensor types are used then it is necessary to consider the bias between sensor types and its possible dependence on humidity and temperature.Citation: Moradi, I., B. Soden, R. Ferraro, P. Arkin, and H. Vömel (2013), Assessing the quality of humidity measurements from global operational radiosonde sensors,
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