IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group I: The Physical Science Basis The Satellite Microwave Sounding Unit Record Summary of satellite capabilities and challenges

Satellite-borne microwave sounders estimate the temperature of thick layers of the atmosphere by measuring microwave emissions (radiances) that are proportional to the thermal state of emission of oxygen molecules from a complex of emission lines near 60 GHz. By making measurements at different frequencies near 60 GHz, different atmospheric layers can be sampled. A series of nine instruments called Microwave Sounding Units (MSUs) began making this kind of measurement in late 1978. Beginning in mid-1998, a subsequent series of instruments, the Advanced MSUs (AMSUs), began operation. Unlike infrared sounders, microwave sounders are not affected by most clouds, although some effects are experienced from precipitation and clouds with high liquid water content. Figure 3.16 illustrates the lower troposphere (referred to as T2LT), troposphere, and MSU channel 2 (referred to as T2) and channel 4 (lower stratosphere, referred to as T4) layers.

The main advantage of satellite measurements compared to radiosondes is the excellent coverage of the measurements, with complete global coverage every few days. However, like radiosondes, temporal continuity is a major challenge for climate assessment, as data from all the satellites in the series must be merged together. The merging procedure must accurately account for a number of error sources. The most important are: (1) offsets in calibration between satellites; (2) orbital decay and drift and associated long-term changes in the time of day that the measurements are made at a particular location, which combine with the diurnal cycle in atmospheric temperature to produce diurnal drifts in the estimated temperatures; (3) drifts in satellite calibration that are correlated with the temperature of the on-board calibration target. Since the calibration target temperatures vary with the satellite diurnal drift, the satellite calibration and diurnal drift corrections are intricately coupled together (Fu and Johanson 2005). Independent teams of investigators have used different methods to determine and correct for these ‘structural’ and other sources of error (Thorne et al., 2005b). Appendix 3.B.5.3 discusses adjustments to the data in more detail. Progress since the TAR

Since the TAR, several important developments and advances have occurred in the analysis of satellite measurements of atmospheric temperatures. Existing data sets have been scrutinised and problems identified, leading to new versions as described below. A number of new data records have been constructed from the MSU measurements, as well as from global reanalyses (see Section Further, new insights have come from statistical combinations of the MSU records from different channels that have minimised the influence of the stratosphere on the tropospheric records (Fu et al., 2004a,b; Fu and Johanson, 2004, 2005). These new data sets and analyses are very important because the differences highlight assumptions and it becomes possible to estimate the uncertainty in satellite-derived temperature trends that arises from different methods and approaches to the construction of temporally consistent records.

Analyses of MSU channels 2 and 4 have been conducted by the University of Alabama in Huntsville (UAH; Christy et al., 2000, 2003) and by Remote Sensing Systems (RSS; Mears et al., 2003; Mears and Wentz, 2005). Another analysis of channel 2 is that of Vinnikov and Grody (2003; version 1 – VG1), now superseded by Grody et al. (2004) and Vinnikov et al. (2006; version 2 – VG2). MSU channel 2 (T2) measures a thick layer of the atmosphere, with approximately 75 to 80% of the signal coming from the troposphere, 15% from the lower stratosphere, and the remaining 5 to 10% from the surface. MSU channel 4 (T4) is primarily sensitive to temperature in the lower stratosphere (Figure 3.16).

Global time series from each of the MSU records are shown in Figure 3.17 and calculated global trends are depicted in Figure 3.18. These show a global cooling of the stratosphere (T4) of –0.32°C to –0.47 °C per decade and a global warming of the troposphere (T2) of 0.04°C to 0.20°C per decade for the period 1979 to 2004. The large spread in T2 trends stems from differences in the inter-satellite calibration and merging technique, and differences in the corrections for orbital drift, diurnal cycle change and the hot-point calibration temperature (Christy et al., 2003; Mears et al., 2003; Christy and Norris, 2004; Grody et al., 2004; Fu and Johanson, 2005; Mears and Wentz, 2005; Vinnikov et al., 2006; see also Appendix 3.B.5.3)

The RSS results for T2 indicate nearly 0.1°C per decade more warming in the troposphere than UAH (see Figure 3.18) and most of the difference arises from the use of different amounts of data to determine the parameters of the calibration target effect (Appendix 3.B.5.3). The UAH analysis yields parameters for the NOAA-9 satellite (1985–1987) outside of the physical bounds expected by Mears et al. (2003). Hence, the large difference in the calibration parameters for the single instrument mounted on the NOAA-9 satellite accounted for a substantial part of the difference between the UAH and RSS T2 trends. The rest arises from differences in merging parameters for other satellites; differences in the correction for the drift in measurement time, especially for the NOAA-11 satellite (Mears et al., 2003; Christy and Norris, 2004); and differences in the ways the hot-point temperature is corrected for (Grody et al., 2004; Fu and Johanson, 2005). In the tropics, these accounted for differences in T2 trends of about 0.07°C per decade after 1987 and discontinuities were also present in 1992 and 1995 at times of satellite transitions (Fu and Johanson, 2005). The T2 data record of Grody et al. (2004) and Vinnikov et al. (2006) (VG2) shows slightly more warming in the troposphere than the RSS data record (Figure 3.18). See also Appendix 3.B.5.3 for discussion of the VG2 analysis.

Although the T4 from RSS has about 0.1°C per decade less cooling than the UAH product (Figure 3.18), both data sets support the conclusions that the stratosphere has undergone strong cooling since 1979. Because about 15% of the signal for T2 comes from the lower stratosphere, the observed cooling causes the reported T2 trends to underestimate tropospheric warming. By creating a weighted combination of T2 and T4, this effect has been greatly reduced (Fu et al., 2004a; see Figure 3.16). This technique for estimating the global mean temperature implies small negative weights at some stratospheric levels, but because of vertical coherence these merely compensate for other positive weights nearby and it is the integral that matters (Fu and Johanson, 2004). From 1979 to 2001 the stratospheric contribution to the trend in T2 is about –0.08°C per decade. Questions about this technique (Tett and Thorne, 2004) have led to clearer interpretation of its application to the tropics (Fu et al., 2004b). The technique has also been successfully applied to model results (Gillett et al., 2004; Kiehl et al., 2005), although model biases in depicting stratospheric cooling can affect results. In a further development, weighted combinations of T2, MSU channel 3 (T3) and T4 since 1987 have formed tropical series for the upper, lower and whole troposphere (Fu and Johanson, 2005).


Figure 3.17. Observed surface and upper-air temperature anomalies (°C). (A) Lower stratospheric T4, (B) Tropospheric T2, (C) Lower tropospheric T2LT, from UAH, RSS and VG2 MSU satellite analyses and UKMO HadAT2 and NOAA RATPAC radiosonde observations; and (D) Surface records from NOAA, NASA/GISS and UKMO/CRU (HadCRUT2v). All time series are monthly mean anomalies relative to the period 1979 to 1997 smoothed with a seven-month running mean filter. Major volcanic eruptions are indicated by vertical blue dashed lines. Adapted from Karl et al. (2006).

By differencing T2 measurements made at different slant angles, the UAH group produced an updated data record weighted for the lower and mid troposphere, T2LT (Christy et al., 2003). This retrieval also has the effect of removing the stratospheric influence on long-term trends, but its uncertainties are augmented by the need to compensate for orbital decay and by computing a small residual from two large values (Wentz and Schabel, 1998). T2LT retrievals include a large signal from the surface and so are adversely affected by changes in surface emissivity, including changes in sea ice cover (Swanson, 2003). Fu and Johanson (2005) found that the T2LT trends were physically inconsistent compared with those of the surface, T2 and T4, even if taken from the UAH record. They also showed that the large trend bias is mainly attributed to the periods when a satellite had substantial drifts in local equator crossing time that caused large changes in calibration target temperatures and large diurnal drifts. Mears and Wentz (2005) further found that the adjustments for diurnal cycle required from satellite drift had the wrong sign in the UAH record in the tropics. Corrections have been made (version 5.2; Christy and Spencer, 2005) and are reflected in Figure 3.18, but the trend in the tropics is still smaller for most periods than both those in the troposphere (using T2 and T4) and those at the surface. Mears and Wentz (2005) computed their own alternative T2LT record and found a T2LT trend nearly 0.1°C per decade larger than the revised UAH trend. After 1987, when MSU channel 3 became available, Fu and Johanson (2005), using RSS data, found a systematic trend of increasing temperature with altitude throughout the tropics.


Figure 3.18. Linear temperature trends (°C per decade) for 1979 to 2004 for the globe (top) and tropics (20°N to 20°S; bottom) for the MSU channels T4 (top panel) and T2 (second panel) or equivalent for radiosondes and reanalyses; for the troposphere (third panel) based on T2 with T4 used to statistically remove stratospheric influences (Fu et al., 2004a); for the lower troposphere (fourth panel) based on the UAH retrieval profile; and for the surface (bottom panel). Surface records are from NOAA/NCDC (green), NASA/GISS (blue) and HadCRUT2v (light blue). Satellite records are from UAH (orange), RSS (dark red) and VG2 (magenta); radiosonde-based records are from NOAA RATPAC (brown) and HadAT2 (light green); and atmospheric reanalyses are from NRA (red) and ERA-40 (cyan). The error bars are 5 to 95% confidence limits associated with sampling a finite record with an allowance for autocorrelation. Where the confidence limits exceed –1, the values are truncated. ERA-40 trends are only for 1979 to August 2002. Data from Karl et al. (2006; D. Seidel courtesy of J. Lanzante; and J. Christy).

Comparisons of tropospheric radiosonde station data with collocated satellite data (Christy and Norris, 2004) show considerable scatter, and root mean square differences between UAH satellite data and radiosondes are substantial (Hurrell et al., 2000). Although Christy and Norris (2004) found good agreement between median radiosonde temperature trends and UAH trends, comparisons are more likely to be biased by spurious cooling than by spurious warming in unhomogenised (Sherwood et al., 2005) and even homogenised (Randel and Wu, 2006) radiosonde data (see Section and Appendix 3.B.5.1). In the stratosphere, radiosonde trends are more negative than both MSU retrievals, especially when compared with RSS, and this is very likely due to changes in sondes and their processing for radiation corrections (Randel and Wu, 2006).

Geographical patterns of the linear trend in tropospheric temperature from 1979 to 2004 (Figure 3.19) are qualitatively similar in the RSS and UAH MSU data sets. Both show coherent warming over most of the NH, but UAH shows cooling over parts of the tropical Pacific and tropospheric temperature trends differ south of 45°S where UAH indicate more cooling than RSS.


Figure 3.19. Linear tropospheric temperature trends (°C per decade) for 1979 to 2005 from RSS (based on T2 and T4 adjusted as in Fu et al., 2004a). Courtesy Q. Fu.