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Abstract
Measurements made by microwave sounding instruments provide a multidecadal record of atmospheric temperature in several thick atmospheric layers. Satellite measurements began in late 1978 with the launch of the first Microwave Sounding Unit (MSU) and have continued to the present via the use of measurements from the follow-on series of instruments, the Advanced Microwave Sounding Unit (AMSU). The weighting function for MSU channel 2 is centered in the middle troposphere but contains significant weight in the lower stratosphere. To obtain an estimate of tropospheric temperature change that is free from stratospheric effects, a weighted average of MSU channel 2 measurements made at different local zenith angles is used to extrapolate the measurements toward the surface, which results in a measurement of changes in the lower troposphere. In this paper, a description is provided of methods that were used to extend the MSU method to the newer AMSU channel 5 measurements and to intercalibrate the results from the different types of satellites. Then, satellite measurements are compared to results from homogenized radiosonde datasets. The results are found to be in excellent agreement with the radiosonde results in the northern extratropics, where the majority of the radiosonde stations are located.
Abstract
Measurements made by microwave sounding instruments provide a multidecadal record of atmospheric temperature in several thick atmospheric layers. Satellite measurements began in late 1978 with the launch of the first Microwave Sounding Unit (MSU) and have continued to the present via the use of measurements from the follow-on series of instruments, the Advanced Microwave Sounding Unit (AMSU). The weighting function for MSU channel 2 is centered in the middle troposphere but contains significant weight in the lower stratosphere. To obtain an estimate of tropospheric temperature change that is free from stratospheric effects, a weighted average of MSU channel 2 measurements made at different local zenith angles is used to extrapolate the measurements toward the surface, which results in a measurement of changes in the lower troposphere. In this paper, a description is provided of methods that were used to extend the MSU method to the newer AMSU channel 5 measurements and to intercalibrate the results from the different types of satellites. Then, satellite measurements are compared to results from homogenized radiosonde datasets. The results are found to be in excellent agreement with the radiosonde results in the northern extratropics, where the majority of the radiosonde stations are located.
Abstract
Measurements made by microwave sounding instruments provide a multidecadal record of atmospheric temperature change. Measurements began in late 1978 with the launch of the first Microwave Sounding Unit (MSU) and continue to the present. In 1998, the first of the follow-on series of instruments—the Advanced Microwave Sounding Units (AMSUs)—was launched. To continue the atmospheric temperature record past 2004, when measurements from the last MSU instrument degraded in quality, AMSU and MSU measurements must be intercalibrated and combined to extend the atmospheric temperature data records. Calibration methods are described for three MSU–AMSU channels that measure the temperature of thick layers of the atmosphere centered in the middle troposphere, near the tropopause, and in the lower stratosphere. Some features of the resulting datasets are briefly summarized.
Abstract
Measurements made by microwave sounding instruments provide a multidecadal record of atmospheric temperature change. Measurements began in late 1978 with the launch of the first Microwave Sounding Unit (MSU) and continue to the present. In 1998, the first of the follow-on series of instruments—the Advanced Microwave Sounding Units (AMSUs)—was launched. To continue the atmospheric temperature record past 2004, when measurements from the last MSU instrument degraded in quality, AMSU and MSU measurements must be intercalibrated and combined to extend the atmospheric temperature data records. Calibration methods are described for three MSU–AMSU channels that measure the temperature of thick layers of the atmosphere centered in the middle troposphere, near the tropopause, and in the lower stratosphere. Some features of the resulting datasets are briefly summarized.
Abstract
Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is a lower-tropospheric temperature product constructed using measurements made by the Microwave Sounding Unit channel 2 and the Advanced Microwave Sounding Unit channel 5. The temperature weighting functions for these channels peak in the middle to upper troposphere. By using a weighted average of measurements made at different Earth incidence angles, the effective weighting function can be lowered so that it peaks in the lower troposphere. Previous versions of this dataset used general circulation model output to remove the effects of drifting local measurement time on the measured temperatures. This paper presents a method to optimize these adjustments using information from the satellite measurements themselves. The new method finds a global-mean land diurnal cycle that peaks later in the afternoon, leading to improved agreement between measurements made by co-orbiting satellites. The changes result in global-scale warming [global trend (70°S–80°N, 1979–2016) = 0.174°C decade−1], ~30% larger than our previous version of the dataset [global trend (70°S–80°N, 1979–2016) = 0.134°C decade−1]. This change is primarily due to the changes in the adjustment for drifting local measurement time. The new dataset shows more warming than most similar datasets constructed from satellites or radiosonde data. However, comparisons with total column water vapor over the oceans suggest that the new dataset may not show enough warming in the tropics.
Abstract
Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is a lower-tropospheric temperature product constructed using measurements made by the Microwave Sounding Unit channel 2 and the Advanced Microwave Sounding Unit channel 5. The temperature weighting functions for these channels peak in the middle to upper troposphere. By using a weighted average of measurements made at different Earth incidence angles, the effective weighting function can be lowered so that it peaks in the lower troposphere. Previous versions of this dataset used general circulation model output to remove the effects of drifting local measurement time on the measured temperatures. This paper presents a method to optimize these adjustments using information from the satellite measurements themselves. The new method finds a global-mean land diurnal cycle that peaks later in the afternoon, leading to improved agreement between measurements made by co-orbiting satellites. The changes result in global-scale warming [global trend (70°S–80°N, 1979–2016) = 0.174°C decade−1], ~30% larger than our previous version of the dataset [global trend (70°S–80°N, 1979–2016) = 0.134°C decade−1]. This change is primarily due to the changes in the adjustment for drifting local measurement time. The new dataset shows more warming than most similar datasets constructed from satellites or radiosonde data. However, comparisons with total column water vapor over the oceans suggest that the new dataset may not show enough warming in the tropics.
Abstract
Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is the midtropospheric measurements made by the Microwave Sounding Unit (MSU) channel 2 and the Advanced Microwave Sounding Unit (AMSU) channel 5. Previous versions of the Remote Sensing Systems (RSS) dataset have used a diurnal climatology derived from general circulation model output to remove the effects of drifting local measurement time. This paper presents evidence that this previous method is not sufficiently accurate and presents several alternative methods to optimize these adjustments using information from the satellite measurements themselves. These are used to construct a number of candidate climate data records using measurements from 15 MSU and AMSU satellites. The new methods result in improved agreement between measurements made by different satellites at the same time. A method is chosen based on an optimized second harmonic adjustment to produce a new version of the RSS dataset, version 4.0. The new dataset shows substantially increased global-scale warming relative to the previous version of the dataset, particularly after 1998. The new dataset shows more warming than most other midtropospheric data records constructed from the same set of satellites. It is also shown that the new dataset is consistent with long-term changes in total column water vapor over the tropical oceans, lending support to its long-term accuracy.
Abstract
Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is the midtropospheric measurements made by the Microwave Sounding Unit (MSU) channel 2 and the Advanced Microwave Sounding Unit (AMSU) channel 5. Previous versions of the Remote Sensing Systems (RSS) dataset have used a diurnal climatology derived from general circulation model output to remove the effects of drifting local measurement time. This paper presents evidence that this previous method is not sufficiently accurate and presents several alternative methods to optimize these adjustments using information from the satellite measurements themselves. These are used to construct a number of candidate climate data records using measurements from 15 MSU and AMSU satellites. The new methods result in improved agreement between measurements made by different satellites at the same time. A method is chosen based on an optimized second harmonic adjustment to produce a new version of the RSS dataset, version 4.0. The new dataset shows substantially increased global-scale warming relative to the previous version of the dataset, particularly after 1998. The new dataset shows more warming than most other midtropospheric data records constructed from the same set of satellites. It is also shown that the new dataset is consistent with long-term changes in total column water vapor over the tropical oceans, lending support to its long-term accuracy.
Abstract
Over the period from 1979 to 2001, tropospheric trends derived from a widely cited analysis of the Microwave Sounding Unit (MSU) temperature record show little or no warming, while surface temperature trends based on in situ observations show a pronounced warming of ∼0.2 K decade−1. This discrepancy between trends at the surface and in the upper atmosphere has been a source of significant debate. Model predictions of amplification of warming with height in the troposphere are clearly inconsistent with the available observations, leading some researchers to question the adequacy of their representation of the water vapor greenhouse feedback. A reanalysis of the MSU channel 2 dataset, with the objective of providing a second independent source of these data, is described in this paper. Results presented herein show a global trend of 0.097 ± 0.020 K decade−1, generally agreeing with the work of Prabhakara et al. but in disagreement with the MSU analysis of Christy and Spencer, which shows significantly less (∼0.09 K decade−1) warming. Differences in the various methodologies are discussed and it is demonstrated that the principal source of these discrepancies is in the treatment of errors due to variations in the temperature of the MSU hot calibration target.
Abstract
Over the period from 1979 to 2001, tropospheric trends derived from a widely cited analysis of the Microwave Sounding Unit (MSU) temperature record show little or no warming, while surface temperature trends based on in situ observations show a pronounced warming of ∼0.2 K decade−1. This discrepancy between trends at the surface and in the upper atmosphere has been a source of significant debate. Model predictions of amplification of warming with height in the troposphere are clearly inconsistent with the available observations, leading some researchers to question the adequacy of their representation of the water vapor greenhouse feedback. A reanalysis of the MSU channel 2 dataset, with the objective of providing a second independent source of these data, is described in this paper. Results presented herein show a global trend of 0.097 ± 0.020 K decade−1, generally agreeing with the work of Prabhakara et al. but in disagreement with the MSU analysis of Christy and Spencer, which shows significantly less (∼0.09 K decade−1) warming. Differences in the various methodologies are discussed and it is demonstrated that the principal source of these discrepancies is in the treatment of errors due to variations in the temperature of the MSU hot calibration target.
Historically, meteorological observations have been made for operational forecasting rather than long-term monitoring purposes, so that there have been numerous changes in instrumentation and procedures. Hence to create climate quality datasets requires the identification, estimation, and removal of many nonclimatic biases from the historical data. Construction of a number of new tropospheric temperature climate datasets has highlighted previously unrecognized uncertainty in multidecadal temperature trends aloft. The choice of dataset can even change the sign of upper-air trends relative to those reported at the surface. So structural uncertainty introduced unintentionally through dataset construction choices is important and needs to be understood and mitigated. A number of ways that this could be addressed for historical records are discussed, as is the question of How it needs to be reduced through future coordinated observing systems with long-term monitoring as a driver, enabling explicit calculation, and removal of nonclimatic biases. Although upper-air temperature records are used to illustrate the arguments, it is strongly believed that the findings are applicable to all long-term climate datasets and variables. A full characterization of observational uncertainty is as vitally important as recent intensive efforts to understand climate model uncertainties if the goal to rigorously reduce the uncertainty regarding both past and future climate changes is to be achieved.
Historically, meteorological observations have been made for operational forecasting rather than long-term monitoring purposes, so that there have been numerous changes in instrumentation and procedures. Hence to create climate quality datasets requires the identification, estimation, and removal of many nonclimatic biases from the historical data. Construction of a number of new tropospheric temperature climate datasets has highlighted previously unrecognized uncertainty in multidecadal temperature trends aloft. The choice of dataset can even change the sign of upper-air trends relative to those reported at the surface. So structural uncertainty introduced unintentionally through dataset construction choices is important and needs to be understood and mitigated. A number of ways that this could be addressed for historical records are discussed, as is the question of How it needs to be reduced through future coordinated observing systems with long-term monitoring as a driver, enabling explicit calculation, and removal of nonclimatic biases. Although upper-air temperature records are used to illustrate the arguments, it is strongly believed that the findings are applicable to all long-term climate datasets and variables. A full characterization of observational uncertainty is as vitally important as recent intensive efforts to understand climate model uncertainties if the goal to rigorously reduce the uncertainty regarding both past and future climate changes is to be achieved.
