Climate Change & Tropospheric Temperature Trends

UAH has acknowledged the difficulties these considerations present for their sonde validation studies, and have sought to address many of them in their most recent work. Since the release of UAH Ver. 5.1, they have expanded the number of stations used in their sonde validations, particularly in the southern hemisphere where comparisons are most important and datasets are weakest. The expanded network of 89 stations in the southern hemisphere and tropics still shows good agreement with their Version 5.1 MSUTLT analyses (Christy and Norris, 2004; Christy et al., 2004). Though many of their southern hemisphere stations have only 60 to 75 percent availability of monthly data for the 1979-2001 period, against this concern is the fact that there is relatively good agreement between their 60 and 75 percent availability data, and between their adjusted and unadjusted data as well, suggesting that the impact of these discontinuities on the TLT record has not been large. But the issue of coverage remains. Once again, a comparison of Figures 11A(c) and 11B(c) with Figure 41 shows that even with its expanded southern hemisphere coverage, the 89 stations are concentrated mainly on land in regions such as Australia and the South American continent. The southern oceans, the most critical regions for comparison, are still under-represented. Furthermore, though Christy and Norris have pointed out that their MSUTLT comparisons with this network are quite favorable while their MSUTMT to RSS comparisons at the same locations are not (Christy and Norris, 2004), it is not at all clear that MSUTMT trends between differing products can be evaluated based on MSUTLT comparisons. As such, agreement on this point is hardly conclusive regarding the middle troposphere.

Another problematic aspect to these sonde comparisons can be seen by comparing Figures 11A(c) and 11B(c). Figure 11A(c) shows the global discrepancy between UAH Ver. 5.0 and RSS Ver. 1.0 for the period of 1979-2001 (Mears et al., 2003). It can be seen that the largest disagreement is in the southern hemisphere and tropics, and that the disagreement is largest at the higher southern latitude oceanic regions and in the tropical Pacific Ocean (the yellow regions) – as has been previously stated. Figure 11B(c) shows the same data for updated UAH and RSS products giving trends for 1979-2002 (Mears et al., 2004). The addition of just one year’s worth of data has changed the situation dramatically. Now we see that the agreement between UAH and RSS has improved considerably over the tropics and most of the southern latitudes. The principal area of disagreement south of the equator is now concentrated mainly at the high southern latitudes and over oceanic regions. UAH and RSS show little difference in trends over the South American continent and Australia where UAH has most expanded their southern hemisphere sonde data. Thus, even though the latest global tropospheric trends from both teams still show marked differences, the areas of agreement actually expanded between 2001 and 2002, and the regions that are most crucial for discriminating between the two products has actually retreated even further into the regions of poorest sonde representation, suggesting that there is enough variability in the record to significantly alter the validity of intercomparison studies with just one or two additional years to the record.

These considerations readily show the difficulties encountered when attempting to validate MSU analysis products with radiosonde datasets. Certainly, much can be learned from such studies. Any independent dataset is likely to clarify things that other types of data might obscure and radiosonde data is no different. The increased vertical resolution of these products, and the fact that they use different methods to measure temperature can add robustness to atmospheric temperature studies. There are currently several attempts under way to expand radiosonde products. The LKS product is currently being expanded beyond 1997 to the present and refined to included better characterized data records for the 1957-1997 period. The resulting product will be the NOAA Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC). HadRT2.1 is also being upgraded using various spatial consistency checks and additional data. The work being done to develop the Global Climate Observing System Upper Air, or GUAN network also has done much to improve the quality and coverage of radiosonde data since the National Research Council published their landmark study calling for such improvements (NRC, 2000). Clearly, care must be taken when interpreting the existing MSU and radiosonde analyses, particularly as independent validation of each other. As of this writing, what can be said with reasonably high confidence is the following;

• Of the radiosonde products most used for MSU comparisons, all have strengths and weaknesses, and no one product stands out as exceptionally well characterized compared to the others. There are significant differences in the methodologies each uses to detect and correct for spurious changes in historical data records, and little agreement among them as to what changes are necessary and to what degree. Yet these changes can have a significant effect on derived trends and there are statistically significant differences among the trends produced by each.
• It is generally agreed that all the radiosonde analyses considered here are currently necessary for a complete characterization of tropospheric and stratospheric temperatures. Selecting any one of them for comparisons emphasizing and one particular period (such as 1979-1997 or 1979-2001 for instance) is inappropriate. Of the sonde products most used for MSU comparisons, only the LKS product (Lanzante et al., 2003) can be said to be truly independent of MSU datasets at this time.
• None of these radiosonde analyses has truly global coverage. In general, the northern hemisphere is well covered geographically and temporally, and metadata records are relatively complete. But other regions are not so well characterized. Particularly problematic are the tropics, the high southern latitudes, the southern oceans, and northern Africa.
• Despite the differences, there is general agreement among radiosonde products that the long-term record (1958 to 2001) shows little difference between surface and tropospheric warming rates, but the shorter records are more complex. The troposphere warmed with respect to the surface between 1958 and 1978, and cooled with respect to it thereafter during the satellite era. There appears to have been a step function change in lapse rate trends sometime between 1977 and 1979.
• Radiosonde analysis products generally show more stratospheric cooling than do MSU products. Some of this may be due to sampling errors associated with the southern hemisphere being under-represented, and some may be due to shifts toward the use of Vaisala sonde devices during the 1990’s.
• Differences in trends from UAH and RSS analysis MSU2 products (RSS does not have an MSU2LT product) are mainly the result of regional variations in their data – most notably, the tropics, South Pacific, Atlantic, and Indian Oceans (in particular, the very high oceanic southern latitudes), and Northern Africa. These areas are poorly characterized in radiosonde analysis products compared to other global regions and do not clearly discriminate between UAH and RSS analyses. UAH values for MSU2LT and MSU2 tend to be closer to some radiosonde analysis products for certain regions and time periods (e.g. LKS for MSU2 from 1979-1997, and HadRT2.0 for MSU2LT from 1979-2001), but confidence intervals are usually large enough to accommodate RSS numbers also. Agreement is not preserved for other products and timeframes.
• The evolution of tropospheric temperature trends from MSU/AMSU products since 2000 has been in the direction of increased warming, gradually closing the gap between the predictions of AOGCM’s and observation for the last 25 years. The long-term radiosonde record, which predates the MSU/AMSU record, shows good agreement with the predictions of AOGCM’s. Of the extant MSU/AMSU analysis products, only UAH Version C (Christy et al., 1998), now over 6 years old, and earlier UAH analyses have shown tropospheric cooling. This is now known to have been due to uncorrected spurious cooling in the MSU record.
• Of the MSU/AMSU analysis products to date, UAH and RSS are the most mature and best characterized. UAH products show the least amount to lower and middle tropospheric warming, to a degree that cannot be accounted for by the most up-to-date versions of AOGCM’s. While there is much to be said for this product, it suffers from an analysis method that is somewhat arbitrary with its merge methodology, and yields anomalously high target factors for the NOAA-09 satellite that are not observed by other teams. The RSS methodology avoids these problems and shows more consistency and lower noise overall from various sources. While the observed means of radiosonde derived trends are closer to the UAH trends than to those of RSS products (which consistently show more warming than their UAH counterparts), the range of uncertainty in these analyses is far less than what is required to discriminate between the two products. As such, many of not most climate scientists today have more confidence in the RSS analysis products than those of UAH. Extant AOGCM’s can comfortably reproduce the RSS derived middle troposphere and stratosphere temperature trends.

In summary, radiosonde analyses can be considered as complimentary to MSU products. Where they are continuous and well characterized, they provide a higher level of vertical resolution and a longer record than is available from MSU products, allowing for better characterization of lapse rates and an independent evaluation of MSU data at isolated locations. But their issues with continuity of record, sampling error due to geographical limitations, and incomplete metadata limit their ability to act as a truly reliable independent validation of MSU products.

But Should They Agree?

It was noted earlier that under most global warming scenarios, the troposphere is expected to warm at a rate equal to or greater than the surface (IPCC, 2001). But as we have seen, for at least the last 20 to 25 years both the satellite and radiosonde records show much less warming than expected. The surface record is well characterized by a wide variety of direct and proxy indicators and can be established independent of the troposphere record. But the relationship between the two is less well understood. To date, the belief that their temperature trends should be similar has been largely due to the belief that they are strongly coupled and will exchange heat readily. AOGCM studies have tended to support this. In general, most have done a good job of reproducing observed global surface temperature trends, though they have been less reliable in regards to humidity and sea level pressure (IPCC, 2001, Chap. 8). Recent work has improved the record, but many questions remain. What role does the surface play in the radiative forcing of the troposphere vs. direct solar forcing? How much of a role does the vertical or horizontal advection of latent heat, moisture, or air mass play in surface-troposphere interactions? Have tropospheric humidity and cloud cover significantly impacted the relationship between the two over the last 25 years? These and many more questions are still up for grabs.

Also in question is whether the two trends really have been all that different since the late 70’s. In their landmark Year 2000 Reconciling Observations of Global Temperature Change (NRC, 2000) the National Research Council examined the surface and troposphere records at length, including the uncertainties inherent in MSU and radiosonde products, the issues surrounding AOGCM simulations of the surface and upper atmosphere, and the degree to which natural climate variations might be playing a role. They concluded that while many open questions remain, there is a residual discrepancy surface and troposphere temperature trends that is statistically significant, and is not accounted for by state of the art AOGCM’s – the two may well be exchanging heat, mass, or radiative forcing in ways that these models are not accounting for. This has led some to claim that global warming is not happening (Singer, 1999; Douglass et al., 2004; 2004b).

Some of the difference can be attributed to natural climate variations. Interannual cycles such as ENSO (El Nino Southern Oscillation), PDO (Pacific Decadal Oscillation), and NAO (North Atlantic Oscillation) can significantly affect the surface temperature record for up to 3 years, independent of the long-term trend. Volcanic eruptions such as El Chicon in 1982 and Mt. Pinatubo in 1991 have also disturbed the troposphere and stratosphere with large injections of aerosols and particulates and caused significant climatic variation for several years after their occurrence. In fact, interannual cycles and catastrophic events have impacted more than a third of the MSU/AMSU history. While events such as these explain much of the difference between surface and upper air records, they have not explained all of it (Christy & McNider, 1994; Santer et al., 2000). Since the release of the NRC Year 2000 Report, AOGCM’s have improved considerably. Those that are forced by both natural and anthropogenic climate inputs, including volcanic forcings (a relatively recent development) can reproduce observations to a fair extent. The ECHAM4/OPYC model developed jointly by the Max Plank Institute for Meteorology (Hamburg) and the European Centre for Medium-Range Weather Forecasts is a case in point. This model, in which an atmospheric model is coupled to an isopyncal ocean model using a 2.8 deg. latitude/longitude grid, 18 atmospheric layers, and flux corrections for heat transfer and fresh water mass flow, can reproduce many of the observed surface/troposphere discrepancies including the effects on ENSO and the eruptions of Pinatubo and El Chicon. But the fit with UAH Versions D and 5.0 is near, and in some cases, beyond the limit of confidence intervals for a significant portion of the record (Santer et al., 2000). It must also be noted that the NRC report was released prior to the publication of RSS Version 1.0 (Mears et al., 2003), which has changed the picture dramatically. As of this writing, most state of the art models that include all observationally known forcings can comfortably reproduce the RSS record. Therefore, the case for a clear discrepancy between surface and troposphere trends and the validity of AOGCM representations of both, boils down to evaluations of UAH vs. RSS, and the extent to which data from various radiosonde products can be used as independent validation of either.

It must also be remembered that microwave and radiosonde temperature data are not the only indications we have of a warming troposphere. There are also independent proxy measurements of climate changes that are consistent with increasing troposphere temperatures. Santer et al. (2003b) used reanalysis data from the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF), in conjunction with several runs of both atmosphere only, and ocean-atmosphere coupled circulation models to study variations in tropopause height as characterized by tropopause lapse rate between 1979 and 2000. They found that after volcanic effects and other known natural forcings were included, the observed changes in tropopause height over this period are a robust zero-order response of the climate system to forcing by well-mixed greenhouse gases and stratospheric ozone depletion. Increasing tropospheric temperatures imply an increasing atmospheric energy content below 100 hPa, and this in turn leads to decreasing tropopause lapse rates and an increase in tropopause height that is observed. Observations of this sort are difficult to reconcile with a troposphere that is not warming.

Surface-Troposphere Coupling

If the surface and troposphere are indeed strongly coupled to each other thermally, then discrepancies between their temperature trends are indeed puzzling. At the very least, we would have to say either that uncertainties in MSU and radiosonde records are more uncertain than we imagined, or that there are regional and/or global interactions between the two that AOGCM’s are not getting. Most of the climate simulation models used over the last decade or so assume thermal coupling across atmospheric vertical layers and have been less well characterized regarding things might interfere with this coupling (e.g. water vapor, sea level pressure, or deep convection cells). So it is not surprising that they predict similar surface and upper air temperature trends. But if the troposphere is even partially decoupled from the surface, either regionally or globally, then surface and upper air trends may well diverge (NRC, 2000). Recently, several lines of research have emerged suggesting that this may well be the case. One of the most promising has been the work of Kevin Trenberth and David Stepaniak of the National Center for Atmospheric Research (Boulder, CO) on the earth’s global radiation budget. Trenberth and Stepaniak studied the earth’s energy budget and the way solar energy input to the atmosphere and surface are redistributed globally. Among other things, they found that important zonal and poleward energy transports occur in the tropics and extra-tropics that redistribute latent heat mush more strongly in these directions than vertically, decoupling the surface from the troposphere in these regions. The finding are particularly significant because it is primarily in these regions that lapse rates are much higher than expected from models, and the surface and troposphere trends are most noticeably different, and uncertain, in the various datasets. There are two mechanisms at work here which strongly couple vertical and poleward heat transport providing an almost seamless energy balance that connects outgoing long-wave radiative cooling with annual variation of solar atmospheric heating. Radiative cooling of the earth at the top of the atmosphere is globally uniform. But because the earth’s rotational orbital plane is tilted with respect to its solar orbital path (the ecliptic plane), the weighting of solar heating will shift in a meridional (north – south) direction annually – which is, of course, why there are seasons at higher latitudes. This requires a poleward energy transfer that must balance. Trenberth and Stepaniak showed that this balance has two components which favor a poleward transfer of latent heat that largely decouples the surface from the troposphere, particularly in the tropics and extra-tropics (Trenberth & Stepaniak, 2003a,b). They found that in lower latitudes the dominant mechanism of latent heat transport if the overturning of Hadley and Walker cells. In the upward cycle of these cells the dominant diabatic heat transfer occurs from the convergence of moisture driven by the cell motion itself. This results in a poleward transport of dry static energy that is partially, but not completely balanced by an equatorial transport of latent heat, leaving a net poleward transport of moist static energy. In the subtropics, the subsidence warming in the downward branch of these cells is balanced by cooling that arises from the poleward transport of energy by transient baroclinic eddies. These eddies are broadly organized into storm tracks that covary with global stationary atmospheric waves in a symbiotic relationship where one feeds the other. The relatively clear skies in the subtropics feed this cycle by allowing for strong solar absorption at the surface which feeds the latent heat transport cycle through evaporation, and in return, this is compensated by subsurface ocean heat transport that is itself driven by the Hadley circulation winds. The relationship between these cycles and how they exchange energy is shown in Figure 35.

For their analysis of the magnitudes of these effects, Trenberth and Stepaniak used overall energy transports derived from reanalysis products for the period 1979-2001 from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) as derived by Kalnay et al. (1996) and used in Trenberth et al. (2001). These were deemed to be most consistent with the overall heat budget as determined from Top of Atmosphere (TOA) and ocean measurements (Trenberth and Caron 2001; Trenberth & Stepaniak, 2003a). Other complimentary heat budget data from the Southampton Oceanographic Centre (SOC) heat budget atlas was also used to characterize ocean surface heat transfer (Josey et al. 1998, 1999). Trenberth and Stepaniak noted that this data had considerably uncertainties due to sampling error and systematic biases from bulk flux parameterizations, but they were careful to use them only with relevant physical constraints that limited the impact of these uncertainties on their results (Trenberth et al., 2001; Trenberth and Stepaniak, 2003b). TOA data was taken mainly from Earth Radiation Budget Experiment (ERBE) satellite measurements of TOA radiation (Trenberth 1997). Precipitation estimates were taken from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) precipitation estimates (Xie and Arkin, 1997).

Figures 36 and 37 show typical zonally average annual magnitudes of the energy transfers involved in these various processes in the tropics and extra-tropics for the North Pacific (Fig. 34), and the South Pacific (Fig. 35) for the ERBE period February 1985–April 1989. It can be seen that the net effect is to give the earth’s energy budget has a strong poleward component in the tropics and extra-tropics that redistributes a significant portion of surface reradiated, convective, and latent heat poleward rather than vertically. This should at least partially decouple surface temperature trends from upper troposphere trends in these regions in ways not accounted for in previous AOGCM’s. Given that this effect is most evident in the tropics and extra-tropics, we should expect that heat transfer processes that would ordinarily bring the troposphere up to the same temperature as the surface will be at least partially diverted, leaving the troposphere cooler (or perhaps under some circumstances, warmer) in these regions than would otherwise be expected. The fact that it is the tropics and extra-tropics that display the largest discrepancies between UAH and RSS analyses lends further support to this theory. There are still considerable uncertainties in the magnitudes of some of the heat transfer budgets in this process, and more work needs to be done to fully characterize it (Trenberth & Stepaniak, 2003a,b), so the degree to which this process contributes to discrepancies between various MSU analyses and the surface record needs further examination.

The important point here is that the existence of such a mechanism means that we should expect at least some disconnect between surface and troposphere warming rates in these regions. Even if this disconnect proves to be of considerable magnitude, it would not present any issues for the long-term surface record, which we must remember, is robust and well characterized independent of the troposphere record (NRC, 2000; IPCC, 2001). As it is today, MSU products, and to a lesser extent radiosonde products, vary between those that predict little if any disconnect and can be comfortably reproduced by state-of-the-art AOGCM’s (Mears et al., 2003; Prabhakara et al., 2000; Vinnikov and Grody, 2003) and those that show relatively large, statistically significant disconnects (Christy et al., 2003). The truth is likely to be somewhere in-between. For our purposes, it is enough to emphasize that demonstrable differences between surface and tropospheric temperature trends do not invalidate either record.

Stratospheric Signals – Fu et al.

There is one more question that still needs to be asked – How well do MSU observations represent actual troposphere air temperatures? It was noted earlier that MSU sensors detect upwelling radiation emissions from atmospheric oxygen on discreet frequencies according to weighting functions that give radiated intensity as a function of altitude. Figure 7 shows the weighting functions of Channels 2 and 4 from 1000 to 10 hPa altitudes. The region of interest for studies of anthropogenic greenhouse gas warming is the lower to middle troposphere covering the 850 – 300 hPa layer. The radiation detected by MSU Channel 2 and AMSU Channel 5 peaks at roughly 700 hPa (about 7 km) in the lower region of this layer, and receives most of its input from it making it, as we saw, most representative of the 850 – 300 hPa layer. Globally, the tropopause falls within the 300-100 hPa layer, and the 100-50 hPa layer crosses the lower stratosphere. The MSU Channel 4 and AMSU Channel 9 signals peak at about 90 hPa and receive most of their signal from the 200-10 hPa layer making them most representative of the lower stratosphere. Even though these channels are mainly sensitive to different layers, it can be seen that Channel 2 receives a non-negligible portion of its input from the 300 hPa layer up to nearly 30 hPa altitude, amounting to roughly 15-20 percent of its signal (NRC, 2000; Fu et al., 2004). Figures 20 through 22 show why this is problematic. Figure 22 shows averaged trends for several upper-air products from both radiosonde and MSU for MSU Channels 2 and 4, and the 850-300 and 100-50 hPa layers that respectively correspond to them. Figure 20 shows MSU2 in detail for UAH and RSS products along with 2 radiosonde products. Figure 21 show similar data for MSU4. For the 1979-2002 period, the 850-300 layer has generally trended upward while the 100-50 hPa layer has trended downward. Here we can clearly see the stratospheric cooling that was discussed earlier – due mainly to ozone depletion (Bengtsson, 1999; NRC, 2000; IPCC, 2001). Therefore, the lower stratosphere will alias a spurious cooling in the MSU2/AMSU5 trend making it less representative of that layer’s actual trends. UAH chose to account for this with their synthetic 2LT and TLT Channels, which earlier we saw were weighted to lower altitudes, avoiding significant input from above 100 hPa. We also saw how this approach, while elegant and effective to a fair degree, also significantly amplified sampling noise and other signal pollution from surface emissions.

In May of 2004, a team lead by Qiang Fu of the University of Washington published a study in Nature that presented another approach to removing this “trend contamination”. Because MSU4 provides a near direct measure of the 100-50 hPa layer, it can be used to estimate the lower stratospheric cooling that is being aliased into MSU2 and remove it (Fu et al., 2004). What Fu and his colleagues did was to derive explicit vertical temperature profiles from global radiosonde data and use these to derive effective weighting functions for MSU2 and MSU4 that remove the lower stratospheric influence from MSU2, leaving behind a pure 850-300 hPa layer brightness temperature. One of Fu’s co-authors, Dian Seidel, was also a co-author of the LKS radiosonde analysis (she is the “S” in LKS). Bringing her considerable expertise with radiosonde networks to Fu’s team, Ms. Seidel used the 87 station LKS network (Lanzante et al., 2003) to provide monthly temperature profiles for the surface and 15 pressure levels from 1000 to 15 hPa for the globe, the northern and southern hemispheres, and the tropics for the period of 1958 to 1997. Known weighting profiles for MSU2 and MSU4 (Christy et al., 2003) were then superposed on this data and used to derive a new weighting function W(h)^FT that is positive up to roughly 100 hPa and negative above. The net effect is a weighting that averages to zero above 100 hPa and equal to the MSU2 weighting below. This effectively removes the stratospheric input and leaves only the desired 850-300 hPa layer brightness temperature. This can be expressed as follows,