Using limited time period trends as a means to determine attribution of discrepancies in microwave sounding unit–derived tropospheric temperature time series

Randall, Robb M.; Herman, Benjamin M.

doi:10.1029/2007jd008864

Cited by 23 publications

(41 citation statements)

References 21 publications

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“…Figures 3,5,and 7 show that the accumulated breakpoint magnitudes for RSS dip further downward than those of UAH in this period. Evidence has been presented elsewhere that suggests RSS produces a more positive movement in the early 1990s than several other temperature products, including surface data at this point in time, and thus is more likely the source of part of the larger discrepancy (C07; Randall and Herman 2008) However, like RSS, UAH reveals the same tendency relative to the sondes in both Australian and U.S. sondes, though of a lesser magnitude. This is a period when significant adjustments related to the east-west drifting of the NOAA-11 satellite are applied, adjustments made differently by UAH and RSS (CN06; C07).…”

Section: -95mentioning

confidence: 79%

Discontinuity Issues with Radiosonde and Satellite Temperatures in the Australian Region 1979–2006

Christy

Norris

2009

Journal of Atmospheric and Oceanic Technology

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The temperature records of 28 Australian radiosonde stations were compared with the bulk-layer temperatures of three satellite products of The University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS) for the period 1979-2006. The purpose was to use the satellite data as ''reference truth'' to quantify the effect of changes in station equipment, software, and operations on the reported upper air temperatures and resulting trends. The products are lower troposphere (LT), midtroposphere (MT), and lower stratosphere (LS).Four periods of significant shifts in temperatures were found in the radiosondes relative to both satellite datasets. In the first two shifts-around 1982/83 and 1987/88-the radiosondes experienced an accumulated LT and MT warming shift of 0.5 K on average. These shifts coincided with equipment changes. If unadjusted for these shifts, the radiosondes report spurious tropospheric warming of almost 0.2 K decade 21 . For LS in the first period, there is relative warming but in the second, cooling. If unadjusted, the radiosondes overstate LS cooling by about 20.15 K decade 21 .The third (early 1990s) and fourth (1998 LT and MT and 2002 LS) shifts are less robustly connected to changes in the radiosondes. Errors in the construction methodology of the satellite products likely account for at least part of the discrepancies but cannot be attributed with confidence to a specific cause. Having opposite signs in the two periods, the last two discrepancies tend to cancel each other. The net effect of these last two shifts on the overall LT and MT trends of 60.03 K decade 21 is small.

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Section: -95mentioning

confidence: 79%

Discontinuity Issues with Radiosonde and Satellite Temperatures in the Australian Region 1979–2006

Christy

Norris

2009

Journal of Atmospheric and Oceanic Technology

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“…The shift or drift in RSS temperatures has been documented as a change relative to (a) U.S. NWS radiosonde stations which maintained VIZ instruments, (b) Australian radiosondes, (c) tropical radiosondes, (d) surface datasets and (e) ERA-I reanalyses [6,9,20,22,23]. In addition, this RSS drift has appeared as an unphysical event when compared with vertical ratios of other channels [22,24]. To demonstrate the drift, we converted each series into seasonal means (to accommodate RATPAC), then calculated, for each data series, the eight season (2-year) moving averages minus the 2-year average commencing 4 years prior to the first month of the original 2-year average (i.e., differences of 2-year averages with a 2-year gap in-between.)…”

Section: Satellitementioning

confidence: 99%

“…Based on that assumption, [26] came to the conclusion that the SRs produced from these datasets carried a fairly large range of uncertainty (±0.95 for T MT /T sfc ). We have three factors working to greatly reduce this error, (1) an observational time series that is 31 years in length, not 21, (2) the use of T LT which avoids the much wider range of error contributions from varying upper troposphere and lower stratosphere uncertainties in the datasets of T MT , and (3) published and displayed information that identifies specific errors in some of the datasets and quantifies errors for the others [6,9,20,[22][23][24]. Thus, using information not available to [26], our results will produce a narrowed range of uncertainty in T LT trends and thus a narrowed range of uncertainty on the value of SR calculated from observations.…”

Section: The Scaling Ratiomentioning

confidence: 99%

What Do Observational Datasets Say about Modeled Tropospheric Temperature Trends since 1979?

et al. 2010

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Updated tropical lower tropospheric temperature datasets covering the period 1979-2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (T LT ) trend over these 31 years is +0.09 ± 0.03 °C decade −1 . Given that the surface temperature (T sfc ) trends from three different groups agree extremely closely among themselves (~ +0.12 °C decade −1 ) this indicates that the -scaling ratio‖ (SR, or ratio of atmospheric trend to surface trend: T LT /T sfc ) of the observations is ~0.8 ± 0.3. This is significantly different from the average SR calculated from the IPCC AR4 model simulations which is ~1.4. This result indicates the majority of AR4 simulations tend to portray significantly greater warming in the troposphere relative to the surface than is found in observations. The SR, as an internal, normalized metric of model behavior, largely avoids the confounding influence of short-term fluctuations such as El Niños which make direct comparison of trend magnitudes less confident, even over multi-decadal periods. OPEN ACCESSRemote Sensing 2010, 2 2149

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“…However, none of these existing long-term measurement systems for the upper-air were originally intended to be used for climate monitoring purposes. While surface temperature trends are in accordance amongst different groups (Solomon et al, 2007), the uncertainties regarding trend values for the upper-air are still substantial (Randel et al, 2009;Randall and Herman, 2008;Titchner et al, 2009). The main reasons for these uncertainties derive from demanding intercalibration and homogenization procedures.…”

Section: Introductionmentioning

confidence: 82%

An assessment of differences in lower stratospheric temperature records from (A)MSU, radiosondes, and GPS radio occultation

et al. 2011

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Abstract. Uncertainties for upper-air trend patterns are still substantial. Observations from the radio occultation (RO) technique offer new opportunities to assess the existing observational records there. Long-term time series are available from radiosondes and from the (Advanced) Microwave Sounding Unit (A)MSU. None of them were originally intended to deliver data for climate applications. Demanding intercalibration and homogenization procedures are required to account for changes in instrumentation and observation techniques. In this comparative study three (A)MSU anomaly time series and two homogenized radiosonde records are compared to RO data from the CHAMP, SAC-C, GRACE-A and F3C missions for September 2001 to December 2010. Differences of monthly anomalies are examined to assess the differences in the datasets due to structural uncertainties. The difference of anomalies of the (A)MSU datasets relative to RO shows a statistically significant trend within about (−0.2 ± 0.1) K/10 yr (95 % confidence interval) at all latitudes. This signals a systematic deviation of the two datasets over time. The radiosonde network has known deficiencies in its global coverage, with sparse representation of most of the Southern Hemisphere, the tropics and the oceans. In this study the error that results from sparse sampling is estimated and accounted for by subtracting it from radiosonde and RO datasets. Surprisingly the sampling error correction is also important in the Northern Hemisphere (NH), where the radiosonde network is dense over the continents but does not capture large atmospheric variations in NH winter. Considering the sampling error, the consistency of radiosonde and RO anomalies is improving substantially; the trend in the anomaly differences is generally very small. Regarding (A)MSU, its poor vertical Correspondence to: F. Ladstädter (florian.ladstaedter@uni-graz.at) resolution poses another problem by missing important features of the vertical atmospheric structure. This points to the advantage of homogeneously distributed measurements with high vertical resolution.

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Using limited time period trends as a means to determine attribution of discrepancies in microwave sounding unit–derived tropospheric temperature time series

Cited by 23 publications

References 21 publications

Discontinuity Issues with Radiosonde and Satellite Temperatures in the Australian Region 1979–2006

Discontinuity Issues with Radiosonde and Satellite Temperatures in the Australian Region 1979–2006

What Do Observational Datasets Say about Modeled Tropospheric Temperature Trends since 1979?

An assessment of differences in lower stratospheric temperature records from (A)MSU, radiosondes, and GPS radio occultation

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