Removing Diurnal Cycle Contamination in Satellite-Derived Tropospheric Temperatures: Understanding Tropical Tropospheric Trend Discrepancies

2015 ◽  
Vol 28 (6) ◽  
pp. 2274-2290 ◽  
Author(s):  
Stephen Po-Chedley ◽  
Tyler J. Thorsen ◽  
Qiang Fu
Keyword(s):  
Diurnal Cycle ◽  
General Circulation ◽  
Circulation Model ◽  
Tropospheric Temperature ◽  
University Of Alabama ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The University ◽  
Diurnal Correction

Abstract Independent research teams have constructed long-term tropical time series of the temperature of the middle troposphere (TMT) using satellite Microwave Sounding Unit (MSU) and Advanced MSU (AMSU) measurements. Despite careful efforts to homogenize the MSU/AMSU measurements, tropical TMT trends beginning in 1979 disagree by more than a factor of 3. Previous studies suggest that the discrepancy in tropical TMT trends is caused by differences in both the NOAA-9 warm target factor and diurnal drift corrections. This work introduces a new observationally based method for removing biases related to satellite diurnal drift. Over land, the derived diurnal correction is similar to a general circulation model (GCM) diurnal cycle. Over ocean, the diurnal corrections have a negligible effect on TMT trends, indicating that oceanic biases are small. It is demonstrated that this method is effective at removing biases between coorbiting satellites and biases between nodes of individual satellites. Using a homogenized TMT dataset, the ratio of tropical tropospheric temperature trends relative to surface temperature trends is in accord with the ratio from GCMs. It is shown that bias corrections for diurnal drift based on a GCM produce tropical trends very similar to those from the observationally based correction, with a trend difference smaller than 0.02 K decade−1. Differences between various TMT datasets are explored further. Large differences in tropical TMT trends between this work and that of the University of Alabama in Huntsville (UAH) are attributed to differences in the treatment of the NOAA-9 target factor and the diurnal cycle correction.

scholarly journals A Bias in the Midtropospheric Channel Warm Target Factor on the NOAA-9 Microwave Sounding Unit

2012 ◽  
Vol 29 (5) ◽  
pp. 646-652 ◽  
Author(s):  
Stephen Po-Chedley ◽  
Qiang Fu
Keyword(s):  
Tropospheric Temperature ◽  
University Of Alabama ◽  
Sensing Systems ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Remote Sensing Systems ◽  
Temperature Records ◽  
The University

Abstract The University of Alabama at Huntsville (UAH), Remote Sensing Systems (RSS), and the National Oceanic and Atmospheric Administration (NOAA) have constructed long-term temperature records for deep atmospheric layers using satellite Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU) observations. However, these groups disagree on the magnitude of global temperature trends since 1979, including the trend for the midtropospheric layer (TMT). This study evaluates the selection of the MSU TMT warm target factor for the NOAA-9 satellite using five homogenized radiosonde products as references. The analysis reveals that the UAH TMT product has a positive bias of 0.051 ± 0.031 in the warm target factor that artificially reduces the global TMT trend by 0.042 K decade−1 for 1979–2009. Accounting for this bias increases the global UAH TMT trend from 0.038 to 0.080 K decade−1, effectively eliminating the trend difference between UAH and RSS and decreasing the trend difference between UAH and NOAA by 47%. This warm target factor bias directly affects the UAH lower tropospheric (TLT) product and tropospheric temperature trends derived from a combination of TMT and lower stratospheric (TLS) channels.

Sensitivity of Satellite-Derived Tropospheric Temperature Trends to the Diurnal Cycle Adjustment

2016 ◽  
Vol 29 (10) ◽  
pp. 3629-3646 ◽  
Author(s):  
Carl A. Mears ◽  
Frank J. Wentz
Keyword(s):  
General Circulation ◽  
Second Harmonic ◽  
Circulation Model ◽  
Global Scale ◽  
Alternative Methods ◽  
Tropospheric Temperature ◽  
Climate Data ◽  
Microwave Sounding Unit ◽  
Microwave Sounding

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.

scholarly journals Robustness of Tropospheric Temperature Trends from MSU Channels 2 and 4

2006 ◽  
Vol 19 (17) ◽  
pp. 4234-4242 ◽  
Author(s):  
Celeste M. Johanson ◽  
Qiang Fu
Keyword(s):  
Training Dataset ◽  
Retrieval Algorithm ◽  
Tropospheric Temperature ◽  
Temperature Trends ◽  
Combining Data ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The Mean ◽  
Stratospheric Cooling ◽  
Global Mean

Abstract Tropospheric temperature trends based on Microwave Sounding Unit (MSU) channel 2 data are susceptible to contamination from strong stratospheric cooling. Recently, Fu et al. devised a method of removing the stratospheric contamination by linearly combining data from MSU channels 2 and 4. In this study the sensitivity of the weights of the two channels in the retrieval algorithm for the tropospheric temperatures to the choice of period of record used in the analysis and to the choice of training dataset is examined. The weights derived using monthly temperature anomalies are within about 10% of those obtained by Fu et al. irrespective of the choice of analysis period or training dataset. The trend errors in the retrieved global-mean tropospheric temperatures tested using two independent radiosonde datasets are less than about 0.01 K decade−1 for all time periods of 25 yr or longer with different starting and ending years during 1958–2004. It is found that the retrievals are more robust if they are interpreted in terms of the layer-mean temperature for the entire troposphere, rather than the mean of the 850–300-hPa layer. Because large spurious jumps remain in the reanalyses, especially prior to 1979, one should be cautious when using them as training datasets and in testing the trend errors.

scholarly journals Estimation of Tropospheric Temperature Trends from MSU Channels 2 and 4

2006 ◽  
Vol 23 (3) ◽  
pp. 417-423 ◽  
Author(s):  
Roy W. Spencer ◽  
John R. Christy ◽  
William D. Braswell ◽  
William B. Norris
Keyword(s):  
Radiosonde Data ◽  
Training Dataset ◽  
Tropospheric Temperature ◽  
Ancillary Data ◽  
Temperature Trends ◽  
Time Period ◽  
Microwave Sounding Unit ◽  
Target Layer ◽  
Microwave Sounding ◽  
Temperature Measure

Abstract The problems inherent in the estimation of global tropospheric temperature trends from a combination of near-nadir Microwave Sounding Unit (MSU) channel-2 and -4 data are described. The authors show that insufficient overlap between those two channels’ weighting functions prevents a physical removal of the stratospheric influence on tropospheric channel 2 from the stratospheric channel 4. Instead, correlations between stratospheric and tropospheric temperature fluctuations based upon ancillary (e.g., radiosonde) information can be used to statistically estimate a correction for the stratospheric influence on MSU 2 from MSU 4. Fu et al. developed such a regression relationship from radiosonde data using the 850–300-hPa layer as the target predictand. There are large errors in the resulting fit of the two MSU channels to the tropospheric target layer, so the correlations from the ancillary data must be relied upon to provide a statistical minimization of the resulting errors. Such relationships depend upon the accuracy of the particular training dataset as well as the dataset time period and its global representativeness (i.e., temporal and spatial stationarity of the statistics). It is concluded that near-nadir MSU channels 2 and 4 cannot be combined to provide a tropospheric temperature measure without substantial uncertainty resulting from a necessary dependence on ancillary information regarding the vertical profile of temperature variations, which are, in general, not well known on a global basis.

scholarly journals Biases in Stratospheric and Tropospheric Temperature Trends Derived from Historical Radiosonde Data

2006 ◽  
Vol 19 (10) ◽  
pp. 2094-2104 ◽  
Author(s):  
William J. Randel ◽  
Fei Wu
Keyword(s):  
Lower Stratosphere ◽  
Radiosonde Data ◽  
Tropospheric Temperature ◽  
Satellite Measurements ◽  
Upper Troposphere ◽  
Step Functions ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The Tropics

Abstract Temperature trends derived from historical radiosonde data often show substantial differences compared to satellite measurements. These differences are especially large for stratospheric levels, and for data in the Tropics, where results are based on relatively few stations. Detailed comparisons of one radiosonde dataset with collocated satellite measurements from the Microwave Sounding Unit reveal time series differences that occur as step functions or jumps at many stations. These jumps occur at different times for different stations, suggesting that the differences are primarily related to problems in the radiosonde data, rather than in the satellite record. As a result of these jumps, the radiosondes exhibit systematic cooling biases relative to the satellites. A large number of the radiosonde stations in the Tropics are influenced by these biases, suggesting that cooling in the tropical lower stratosphere is substantially overestimated in these radiosonde data. Comparison of trends from stations with larger and smaller biases suggests the cooling bias extends into the tropical upper troposphere. Significant biases are observed in both daytime and nighttime radiosonde measurements.

scholarly journals Satellite and VIZ–Radiosonde Intercomparisons for Diagnosis of Nonclimatic Influences

2006 ◽  
Vol 23 (9) ◽  
pp. 1181-1194 ◽  
Author(s):  
John R. Christy ◽  
William B. Norris
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Lower Troposphere ◽  
University Of Alabama ◽  
High Significance ◽  
Sensing Systems ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Remote Sensing Systems ◽  
The University

Abstract Radiosonde datasets of temperature often suffer from discontinuities due to changes in instrumentation, location, observing practices, and algorithms. To identify temporal discontinuities that affect the VIZ/Sippican family of radiosondes, the 1979–2004 time series of a composite of 31 VIZ stations are compared to composites of collocated values of layer temperatures from two microwave sounding unit datasets—the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS). Discontinuities in the radiosonde time series relative to the two satellite datasets were detected with high significance and with similar magnitudes; however, some instances occurred where only one satellite dataset differed from the radiosondes. For the products known as lower troposphere (LT; surface–300 hPa) and midtroposphere (MT; surface–75-hPa layer), significant discontinuities relative to both satellite datasets were found—two cases for LT and four for MT. These are likely associated with changes in the radiosonde system. Three apparent radiosonde discontinuities were also determined for the lower-stratospheric product (LS; 150–15 hPa). Because they cannot be definitely traced to changes in the radiosonde system, they could be the result of common errors in the satellite products. When adjustments are applied to the radiosondes based independently on each satellite dataset, 26-yr trends of UAH (RSS) are consistent with the radiosondes for LT, MT, and LS at the level of ±0.06, ±0.04, and ±0.07 (±0.12, ±0.10, and ±0.10) K decade−1. Also, simple statistical retrievals based on radiosonde-derived relationships of LT, MT, and LS indicate a higher level of consistency with UAH products than with those of RSS.

Stratospheric Influences on MSU-Derived Tropospheric Temperature Trends: A Direct Error Analysis

2004 ◽  
Vol 17 (24) ◽  
pp. 4636-4640 ◽  
Author(s):  
Qiang Fu ◽  
Celeste M. Johanson
Keyword(s):  
Error Analysis ◽  
Tropospheric Temperature ◽  
Retrieval Method ◽  
The Past ◽  
Stratospheric Temperature ◽  
Retrieval Technique ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Angular Scanning

Abstract Retrievals of tropospheric temperature trends from data of the Microwave Sounding Unit (MSU) are subject to biases related to the strong cooling of the stratosphere during the past few decades. The magnitude of this stratospheric contamination in various retrievals is estimated using stratospheric temperature trend profiles based on observations. It is found that from 1979 to 2001 the stratospheric contribution to the trend of MSU channel-2 brightness temperature is about −0.08 K decade−1, which is consistent with the findings of Fu et al. In the retrieval method developed by Fu et al. based on a linear combination of MSU channels 2 and 4, the stratospheric influence is largely removed, leaving a residual influence of less than ±0.01 K decade−1. This method is also found to be more accurate than the angular scanning retrieval technique of Spencer and Christy to remove the stratospheric contamination.

scholarly journals Comments on “A Bias in the Midtropospheric Channel Warm Target Factor on the NOAA-9 Microwave Sounding Unit”

2013 ◽  
Vol 30 (5) ◽  
pp. 1006-1013 ◽  
Author(s):  
John R. Christy ◽  
Roy W. Spencer
Keyword(s):  
Noise Reduction ◽  
Sensing Systems ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Reduction Coefficient ◽  
Similar Product ◽  
Microwave Sounding ◽  
Remote Sensing Systems ◽  
The Difference ◽  
The University

Abstract Po-Chedley and Fu investigated the difference in the magnitude of global temperature trends generated from the Microwave Sounding Unit (MSU) for the midtroposphere (TMT, surface to about 75 hPa) between the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS). Their approach was to examine the magnitude of a noise-reduction coefficient of one short-lived satellite, NOAA-9, which differed from UAH and RSS. Using radiosonde comparisons over a 2-yr period, they calculated an adjustment to the UAH coefficient that, when applied to the UAH data, increased the UAH global TMT trend for 1979–2009 by +0.042 K decade−1, which then happens to agree with RSS’s TMT trend. In studying their analysis, the authors demonstrate 1) the adjustment calculated using radiosondes is inconclusive when errors are accounted for; 2) the adjustment was applied in a manner inconsistent with the UAH satellite merging strategy, creating a larger change than would be generated had the actual UAH methodology been followed; and 3) that trends of a similar product that uses the same UAH coefficient are essentially identical to UAH and RSS. Based on the authors’ previous analysis and additional work here, UAH will continue using the NOAA-9 noise-reduction coefficient, as is, for version 5.4 and the follow-on version 5.5.

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