Climate Change & Tropospheric Temperature Trends

W(h)_FT = α₂(h) W₂(h) + α₄(h)_200-0W₄(h)(1)

where W(h)₂ and W(h)₄ are the MSU2 and MSU4 weightings respectively, as functions of altitude and α₂ and α₄ are additional factors that correct out the lower stratospheric influence on MSU2 indicated by the MSU4 signal below 100 hPa.

Apart from the obvious El Chicon and Pinatubo volcanic events, the stratosphere has displayed a more regionally and temporally stable trend history during the satellite era than the troposphere. Because of this, and the fact that MSU2 and MSU4 weighting functions have been temporally stable as well, it is possible to use equation 1 to express the 850-300 hPa layer brightness temperature trend, T^tr_850-300, as a linear sum of the observed MSU Channel 2 and Channel 4 brightness temperatures T^tr₂ and T^tr₄ factored respectively by constant coefficients a₂ and a₄, plus a constant offset term to reflect the contribution of surface emissions. Thus, T^tr_850-300 can be expressed as,

T^tr_850-300 = a₀ + a₂T^tr₂ + a₄ T^tr₄(2)

where a₂ and a₄reflect the corresponding weighting function terms in equation 1. Fu’s team used monthly averaged T^tr₂ and T^tr₄ data taken directly from MSU/AMSU data from Mears et al. (2003) and derived the coefficients a₀, a₂ and a₄ from these temperatures and their LKS based synthetic weighting profiles. Then, they derived the satellite era trend in T^tr_850-300 from equation 2 using least squares methods. Note that in equation 1, W(h)_FT is not a real weighting function as the requirement that it remove the stratospheric signal makes it go negative above some critical altitude. This is in contrast to actual brightness temperature weighting functions for T₂ and T₄ such as those shown in Figure 7, which must be everywhere positive (real heat sources cannot have negative emissivities). Fu’s team found a₂ and a₄ to be equal to 1.156 and -0.153 respectively for globally averaged anomalies. For global trends, W(h)_FT generally peaks in the middle troposphere at the same level as W(h)₂ but 15 percent higher, and goes negative at the top of the tropopause (100 hPa), exactly as we would expect for removal of the Channel 4 influence.

To check the validity of their derived values for a₂ and a₄, the team applied simulated MSU2 and MSU4 weighting functions to the LKS dataset (Lanzante et al., 2003) and derived effective globally averaged Ttr2 and Ttr4 values for the full period of 1958 to 1997. These were used to calculate an effective global T^tr_FT from equation 2 for the for the same period, which was then evaluated against the actual LKS radiosonde determined temperature profiles for the 850-300 hPa layer. They found excellent agreement between the two, with RMS error and trend difference of 0.065 deg. K and 0.001 deg. K/decade respectively, and a correlation coefficient of 0.984. This shows that globally, the vertical structure of stratospheric temperatures are very coherent and equation 2 can be used to remove nearly all of the Channel 4 signal from Channel 2, giving a well characterized measurement of T_850-300 (Fu et al., 2004). See the Appendix for more details on this method.

Figure 39 shows uncorrected MSU Channel 2 troposphere temperature trends from UAH Version 5.0 (Christy et al., 2003) and RSS Version 1.0 (Mears et al., 2003), and the Fu team’s corrected troposphere temperature trends (Fu et al., 2004). Fu and his colleagues found that the stratospheric contribution to Channel 2 during the MSU service record was about -0.08 deg. K/decade globally. Correcting the RSS Channel 2 record yields a global trend is 0.18 deg. K/decade. This represents a tropospheric warming of roughly 1.1 times the surface record for the same period. The stratospheric contribution in the tropics was somewhat less, -0.05 deg. K/decade, due to a lower tropopause and smaller stratospheric contributions there, consistent with what is expected for the moist adiabatic lapse rates which characterize these regions. These trends also bring the relative tropospheric and surface trends into the range predicted by the most up-to-date general circulation models. Santer et al. (2003) reported that their control run of the ECHAM4/OPYC replicated a 20 year trend differential of 0.08 deg. K/decade, in agreement with the observed stratospheric correction found by Fu’s team. Other AOGCM’s forced with the appropriate balance of natural and anthropogenic inputs yield similar results. AOGCM’s still differ widely in their quality and their ability to reproduce observed results, and multiple runs of the same models can produce varying results also when forced with slightly different, but equally possible scenarios. But it now seems clear that the best of these models can comfortably reproduce not only the corrected and uncorrected RSS values, but also Fu’s corrected UAH trends. Applying these corrections to UAH’s Version 5.0 TMT and TLT records yields a global tropospheric trend of 0.09 deg. K/decade, comparable to the uncorrected RSS Channel 2 trend, and a tropical trend of 0.08 deg. K/decade. While these trends represent better agreement with observed surface trends than UAH’s uncorrected numbers, they appear to be inconsistent with the UAH TLT trends. These are expected to have minimal influence from the stratosphere and receive 10 to 20 percent of their signal from the surface. But in the tropics for instance, the UAH TLT trend of -0.01 deg. K/decade bears little resemblance to the 850-300 hPa corrected trend of 0.08 deg. K/decade. Furthermore, the UAH TLT trend is cooling with respect to their Channel 2 trend by -0.04 deg. K/decade which is contrary to expectations. It is possible that this might be related to the noise sources discussed previously for TLT retrieval and differences in the UAH merging procedures with respect to those of other teams – in particular, the contributions of Antarctic sea-ice and summer melt pools may contribute to these discrepancies (Swanson, 2003). The low southern latitudes and the tropics are where we expect at least some degree of troposphere decoupling from the surface due to the possible meridional advection of heat (Trenberth and Stepaniak, 2003; 2003b).

The success of the Fu et al. method requires accurate comparisons of high resolution vertical temperature trend profiles that can be compared with MSU sensed layer trends for derivation of the appropriate corrections. For the comparisons to be meaningful, the vertical temperature profiles need to be as independent of the MSU record as possible. The most viable options available for this task are radiosonde analyses. We saw earlier that those problems face a host of coverage and accuracy issues, and these will be a concern for the Fu et al. method. Fortunately, the coverage issue is less problematic because stratospheric temperatures and trends have been less variable during the satellite era than their tropospheric counterparts. But the trend uncertainties are less easily avoided and tend to increase with altitude, making it all the more important to independently check the method across multiple products (NRC, 2000; Seidel et al., 2004). Indeed, this was one of the main concerns voiced by many critics of the Fu et al. method after it was initially published (Spencer, 2004b; Whipple, 2004). Fu realized that these criticisms should be taken seriously and with co-author Celeste Johanson he redid his analysis using other upper-air products. The new analysis was published as a letter in the December 15, 2004 issue of Journal of Climate (Fu and Johanson, 2004).

As noted earlier, the original study Fu et al. (2004) based their weighting function derivation on vertical temperature profiles derived from the LKS radiosonde analysis (Lanzante et al., 2003) which was chosen because in addition to being one of the better characterized radiosonde products in terms of its corrections for spurious changes in record and data gathering methods, it is one of the few that is completely independent of the MSU record. But no single radiosonde product today, including this one, is without its issues. Therefore conclusions based on multiple complementary radiosonde products are more robust (Seidel et al., 2004). In their revised analysis Fu and Johanson recognized the need for multiple radiosonde products in their method and they broadened their radiosonde product base. For their new analysis they used a multi-dataset vertical stratospheric trend profile that had been derived as part of the Stratospheric Processes and their Role in Climate initiative’s Stratospheric Temperature Trends Assessment program (SPARC-STTA). This profile, which was derived by Ramaswamy et al. (2001), gives 1979-1994 trends above 120 hPa at 45 deg. N. Latitude as derived from a mix of radiosonde, lidar, satellite, and reanalysis products. It is shown in their Figure 30 - reproduced here in Figure 50, and the various analysis products used to derive it are listed in Figure 51. The radiosonde datasets used included the Angell 63 network (Angell, 1988), UK RAOB (Parker and Cox, 1995) and Oort and Liu (1993) products, among others. Like the radiosonde analyses we reviewed earlier, these vary in their degrees of global coverage, number of stations per network, and the number of vertical layers sampled (and the corresponding depth of stratospheric layer covered as well). A few have been updated since this publication, Angell 63 being one (Angell, 2003). Lidar data for 44 deg. N. Latitude and altitudes above 10 hPa (Hauchecorne et al., 1991) are included, although this layer will have little relevance to MSU trends. Satellite records included MSU4 data from UAH Version C (Christy et al., 1995) through 1994, and data from 3 direct and 5 synthetic (derived) channels of the Stratospheric Sounding Unit (SSU) and High-Resolution Infrared Sounder (HIRS) detectors. These devices are passive detectors similar to the MSU’s but operating in the infrared with weighting functions that emphasize the middle and upper stratosphere. Like the MSU’s, they are carried on the NOAA TIROS-N/ATN series of satellites (see Figure 6). The lowest peak signal (SSU15X) is centered near 40 hPa (Nash and Forrester, 1986; WMO, 1990). The reanalysis piece was provided by the NCEP/NCAR Reanalysis (Kalnay et al., 1996), and analyzed stratospheric datasets from the NASA Goddard Space Flight Center (GSFC) and the UKMO/SSUANAL (Schubert et al., 1993; Bailey et al., 1993). These products use various combinations of SSU, HIRS-2, and MSU4 data in conjunction with atmospheric GCM modeling to fill in gaps in the observational record. In many ways this suite of datasets is comparable to those we have reviewed so far. The SSU and HIRS records need to be merged in much the same manner as the complete MSU record. They also have their accompanying burden of sampling and data characterization uncertainties as well, and these are not unlike those plaguing the MSU’s. The issues with the radiosonde record have already been discussed, as has the fact that these uncertainties increase with altitude. Many, but not all, of these issues have been corrected for prior to their analysis by Ramaswamy’s team. The remaining uncertainties were accommodated by weighting the various datasets according to their confidence intervals when contrasting the vertical stratospheric trend profile in Figure 50 (Ramawsamy et al., 2001).

Fu and Johanson rescaled the 1979-1994 SPARC-STTA vertical profile of Ramaswamy’s team for 45 deg. N. Latitude to the 1979-2001 global MSU4 trend of UAH Version 5.0 (Christy et al., 2003), making it more representative of global means for the longer period. Profiles were derived using distance and pressure altitudes, and trend profiles for the 200-120 hPa layer were linearly extrapolated from 120 hPa trends after rescaling. The resulting curves are shown in Figure 52 where R_H gives the distance derived profile, R_P the pressure derived one, and R_Had the one based on HadRT2.1s. Confidence intervals will be more or less similar to those shown in Figure 50 with a slight increase due to the rescaled UAH Version 5.0 MSU4 confidence interval. It can be seen that the altitude trends derived by distance and pressure are quite similar above 100 hPa, and diverge below it due to differences in the respective 120 hPa trends, with the distance altitude derived trend being in better agreement with the collective radiosonde record than the pressure altitude profile. But all are in reasonably good agreement with 100-300 hPa global mean trends from other radiosonde analyses (Seidel et al., 2004) and may be taken as representative of typical other multi-dataset profiles for these layers. Fu and Johanson used the 2 rescaled SPARC-STTA profiles and the HadRT2.1s profile to derive 3 new sets of synthetic weighting coefficients for the MSU2 and MSU4 covered layers. These were then used to derive 3 new stratospheric “contamination” signals, one for each product. The resulting contaminations in ΔT^tr₂ were,

These figures are to be compared with the original figure of -0.08 deg. K/decade (Fu et al., 2004). It is evident that the agreement between the various products is quite good, as are the confidence intervals on associated with the derivations. The small confidence intervals are largely attributable to the fact that the vertical portion of the weighting function above 200 hPa (equation 1) integrates to zero, removing most of the noise associated with these layers. We should also note that these corrections are global rather than regional. There will likely be regional differences, particularly in the tropics where highly variable lapse rates have been observed and various physical processes, such as the Hadley cell driven meridional advection of latent heat, might be at least partially decoupling troposphere and surface trends (Trenberth and Stepaniak, 2003). In fact, in the tropics the Fu et al. synthetic brightness temperature trends and weighting function (equations 1 and 2) are likely to be more representative of the entire troposphere and tropopause below 100 hPa, rather than the 850-300 hPa layer (Fu et al., 2004b). Thus, the Fu et al. method is fairly insensitive to multiple dataset differences and proves to be a robust method for removing the stratospheric contamination in MSU2.

Based on these values for ΔT^tr₂, the corrected UAH Version 5.0 TMT trends range from 0.076 to 0.093 deg. K/decade. The corresponding TLT trend for the same period is 0.045 deg. K/decade after removal of an estimated 0.01 deg. K/decade stratospheric interference on that product (Fu and Johanson, 2004). UAH reported TLT trends of 0.061 deg. K/decade for the period 1979-2002 (Christy et al., 2003) and 0.06 deg. K/decade for 1979-1998 (Christy et al., 2000). Though they are not precisely the same, the Fu derived T^tr_FT trends should be similar to TLT trends for the same period. Furthermore, we saw earlier that the TLT channel receives a significant amount of noise from surface emissions (NRC, 2000). So given the known surface trend, we might expect TLT to show spurious warming rather than cooling. In particular, we might expect warming of the TLT record with respect to the corrected TMT or DTtr2 records. TLT vs. ΔT^tr₂ trend discrepancies will need further investigation. But apart from uncertainties in the Fu et al. method, there 2 most likely candidates for spurious cooling in TLT trends are,

• The UAH “backbone” based merge method, which along with the stated goal of minimizing intersatellite trend differences, may be minimizing trends as well and not reducing RMS scatter in the residuals, which would likely provide a better overall trend estimate (Mears et al., 2003; Christy and Norris, 2004).

Whatever issues remain with the UAH TLT global trends, the corrected UAH MSU2 trends, regional and global, are still within the range of what can be reproduced by GCM’s supplemented by considerations of tropospheric decoupling mechanisms.

Summary

In light of the previous discussions, we can summarize the current state of knowledge regarding surface and tropospheric temperature trends and their relationship to anthropogenic greenhouse gases as follows;

• The global surface temperature trend is well characterized by a wide range of in situ and proxy data apart from the tropospheric record (NRC, 2000; IPCC, 2001). State-of-the-art AOGCM’s can comfortably reproduce this trend, but also predict a similar long-term trend for the upper atmosphere. The apparent disagreement between the surface and upper air records hinges on these AOGCM predictions, and the belief that the two should be well coupled (NRC, 2000; IPCC, 2001). Over the longer period for which upper air data is available (1958 to the present), the two are in excellent agreement as expected. But short-term trends have shown much variation indicating that the troposphere and surface interact in more complex ways than previously thought. There is evidence that for certain regions of the globe (particularly the tropics), the surface and troposphere may well be decoupled to some extent so that short-term differences in trend are to be expected (Trenberth and Stepaniak, 2003). Because of this, the last 25 years are highly unlikely to be representative of long-term trends in the surface-troposphere temperature differences (NRC, 2000). Because of this, short-term upper air trends cannot be considered to be an indicator of surface global warming or an anthropogenic global warming fingerprint.
• The disagreement between the tropospheric trends of the RSS and UAH teams is likely related to how each team handled the merging of intersatellite datasets and the derivation of hot target factors – particularly the NOAA-9 target factor and the shorter overlaps (e.g. NOAA-9/NOAA-10). The UAH derived value for the NOAA-9 target factor appears to be an outlier compared not only with the RSS value, but with all other RSS and UAH target factors. This anomalous factor appears to be related to UAH’s choice of a “backbone” method of merging that neglects shorter overlaps. The RSS analysis which uses all overlaps appears to result in a set of target factors and trend residuals for the merged time series, resulting in less trend error and a more consistent set of target factors. The difference between these two methods accounts for at least 65 percent of the difference between the trends of each team (Mears et al., 2003; Christy et al., 2003; Santer et al., 2003).
• Though many uncertainties still remain, the best current estimates of troposphere temperature trends for the 850-300 hPa approach the RSS team value of 0.10 deg. C/decade uncorrected for spurious stratospheric cooling, and corrected trends approach 0.18 deg. K/decade (Mears et al., 2003; Fu et al., 2004).
• UAH MSU2LT and MSUTLT products are more strongly influenced by surface radiation emissions than the MSU Channel 2 products of all teams. As such, they will be much more strongly influenced by annual and inter-annual Arctic and Antarctic sea-ice and melt pool areas, particularly the latter. These impart a distinct cooling trend to high southern latitudes (above 60 deg. S) and as such are likely to contribute to the lower trends observed by these products in the southern oceans.
• Vinnikov and Grody have independently shown tropospheric warming rates that agree with expectations, but have not been corrected for differences between land and ocean diurnal cycles or instrument body effect. If corrected for these effects, their trends appear to be more likely to approach those of the RSS team rather than the UAH team (Vinnikov and Grody, 2004).
• Radiosonde analyses are in reasonable agreement with both RSS and UAH trends for most regions where the three overlap. But the noise inherent in these datasets is large enough that they cannot be reliably used to discriminate between RSS and UAH products. In regions where the RSS and UAH products diverge (e.g. the tropical Pacific and northern Africa) radiosonde coverage is too sparse and poorly characterized to be useful. This situation has not changed appreciably in the 4 years since the National Research Council released their year 2000 report on satellite derived troposphere temperatures and global change.
• Tropospheric trends corrected for a spurious stratospheric cooling signal are within the range of what can be comfortably reproduced by the best extant general circulation models with the appropriate natural and anthropogenic forcings (Fu et al., 4004; 2004b; Fu and Johanson, 2004). These models not only capture the signal of the observed troposphere warming, they also capture natural climate variations related to ENSO, PDO, and volcanic eruptions such as those of Mt. Pinatubo and El Chicon. The remaining discrepancies between models and observation can be explained by various mechanisms of poleward energy transport in the tropics and extra-tropics that decouple the troposphere from the surface (Trenberth and Stepaniak, 2003a,b; Santer et al., 2003).

Many issues still remain regarding troposphere and surface temperature trends and their relationship to anthropogenic greenhouse gas emissions and land use activities. More work needs to be done to improve the data quality, particularly that from radiosondes. The ongoing development of the GUAN and RATPAC radiosonde products are a very positive step in this direction as is the ongoing work of the UAH and RSS teams to better characterize and expand their own datasets. But the greatest mysteries surrounding the apparent disagreement between surface and troposphere temperature trends during the last 25 years have largely been resolved. There is no longer any valid reason to dispute global warming at the earth’s surface based on tropospheric temperature trends.




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