Climate Change & Tropospheric Temperature Trends

• Omitted a full third of the MSU record, including only that portion for which a negative lower troposphere temperature trend can be derived. Longer 2LT records show warming trends that are moving in the direction of restoring long-term agreement with the surface record.
• Allowed themselves to directly compare the UAH MSU2 record with the one record which is truly independent of MSU products and shows the best agreement with their chosen MSU product for that period, LKS (Lanzante et al., 2003). The LKS record does not extend beyond 1997.
• Allowed themselves to directly compare another radiosonde product, HadRT2.0, with the UAH 2LT record over a period where there is very good agreement between the two, yet avoid a longer period over which the agreement is much worse (see their second paper cited here, Douglass et al., 2004b).

DEA argued that using the entire record has little impact on their conclusions. To demonstrate this, they did a repeat of their analyses for the 1979-2002 over ocean regions only (which they say avoids snow cover problems) produces similar trends. But the comparison is not valid. First, land regions contribute significantly to the overall trend and cannot be ignored regardless of oceanic response. DEA’s reasoning on this point assumes that snow cover is one of the most dominant features of land based trends, if not the most important, which is incorrect. Indeed, it is enlightening to compare this argument with the MSU regional trends shown in Figure 11B. Remember that UAH Version D is cited as their only trusted authority for this record. MSU 2LT trends by global region are shown in the middle map. Note that the large majority of lower trend areas for this period are over the world’s oceans. This is not surprising, as we expect oceanic regions to have a mediating effect (we have already seen this at work in the Ocean Only vs. Ocean + Land diurnal cycles discussed earlier). Similar land-ocean trend differences can also be seen in the RSS regional trends (top map), though with higher overall values. Note also that many of the warmer regions occur in tropical or extra-tropical areas like the southeast United States and the Arabian Peninsula. The idea that snow cover could be polluting tropospheric trends over Florida and Saudi Arabia is not compelling. The agreement with their earlier results appears to be a result of their choice of oceanic regions only for comparison, which due to the moderating effects of oceanic climates are expected to produce lower trends.

The regional data and figures DEA use have issues as well. Figure 43 shows their Figure 1 (Douglass et al., 2004) which presents their regional 1979-1996 trends as determined by the surface record (Jones et al., 2001), the UAH Version D MSU record (Christy et al., 2000), and the NCEP/NCAR 2-Meter Reanalysis (Kanamitsu et al., 2002). For the period they analyzed, the surface record contained many gaps, so DEA wisely conducted their study only for areas where there were consistent records for all 3 products. However, in this figure where they report regional trends, they show cells with missing data in the same color (dark blue) as those with the minimum regional cooling rates. Though the caption mentions this in passing, the casual reader is left with an inability to discriminate between regions with observed cooling and those with no data, making the figure misleading. Likewise, Figure 44 shows their Figure 2 which presents their 1979-1996 trends for the Surface Record (Jones et al., 2001), the UAH Version D MSU 2LT Record (Christy et al., 2000), and the NCEP/NCAR 2-Meter Reanalysis (Kanamitsu et al., 2002) plotted by latitude. The first thing to notice is that the plot is not symmetric about the equator. DEA extend their trends northward beyond 60 deg. N. Latitude, stopping just short of the Arctic Circle. Yet in the Southern Hemisphere they truncate it at about 35 deg. S. Latitude without explanation. A comparison of Figure 44 with Figures 11A and 11B reveals that ending the geographic trend record here avoids the region where UAH and RSS products are most different. The region from 60 deg. S. Latitude to the South Pole is where Antarctic sea-ice and summer melt pools have the most impact on the MSU 2LT and TLT records (Swanson, 2003). These regions also significantly impact the NCEP/NCAR R2-2m record as well. Figure 45 shows zonally averaged oceanic albedo as a function of latitude in both the original NCEP/NCAR Reanalysis (Kalnay et al., 1996) and the R2-2m product used by DEA. Sharp increases beyond 60 deg. latitude at either pole reflect the heavy influence of sea-ice. The austral summer cycling of these albedos can be readily seen in the R2-2m product at higher latitudes than 60 deg. S. Note also that the R2-2m product will not reflect the effect of summer melt pools on this albedo (which will have the effect of lowering it to open ocean values). These high albedos will appear as warming trends to the UAH 2LT record, and their interaction with summer melt pools correlate strongly with lower UAH 2LT trends. The effect is much stronger in the Southern Hemisphere than in the North (Swanson, 2003). By avoiding the polar regions, DEA avoid the impact of these influences on their trends, and they avoid the regions of largest difference between UAH and RSS for MSU Channel 2.

Thus the conclusions of DEA’s troposphere disparity paper (Douglass et al., 2004) are highly sensitive to their choice of region, temporal period, and analysis product. Its conclusions do not survive broader comparisons and are thus not robust. Their paper, in which they compare the upper-air record to the predictions of AOGCM’s (Douglass et al., 2004b) suffers from similar difficulties. Here, DEA shift their attention from claims of a surface/upper-air discrepancy to an attempt to show that state-of-the-art AOGCM’s cannot account for it. They examine results from 3 AOGCM’s and compare them to the 1979-1997 surface temperature record as determined by Jones et al. (1999) and resolved to a 5 deg. by 5 deg. (latitude vs. longitude) grid, the MSU 2LT lower troposphere temperature record as determined by UAH Version D (Christy et al., 2000), the same as determined by HadRT2.0 (Parker et al., 1997), and the NCEP/NCAR 2-Meter Reanalysis (Kisteler et al., 2001). The models they choose are Hadley CM3 (Tett et al., 2002), the Goddard Institute for Space Studies GISS SI2000 atmospheric model (Hansen et al., 2002), and the Dept. of Energy Parallel Coupled Model, or PCM (Meehl et al., 2003; 2003b).

Hadley CM3 is run for the period 1985-1995 and forced with greenhouse gas emissions, sulfates, and tropospheric and stratospheric ozone. The 1961-1980 portion of this run was removed. Once again we see a truncated record – this time one that removes certain portions of both the beginning and the end of the MSU record. An examination of the upper-air history during the satellite era reveals that the portion of the record DEA omitted in their Hadley CM3 run contains the El Chicon eruption (1982) and a large El Nino event. Hadley CM3 has the ability to capture both events and in fact, results from runs with solar and volcanic forcing were available to DEA at the time they published (Tett et al., 2002; Braganza et al., 2004). An examination of Figures 20 and 22 reveals that the combined impact of these two events was a boost in tropospheric temperatures below 300 hPa for a year or two followed by a cooling period of comparable length prior to 1985 (when their run began). The impact of including these events might well have boosted the early end of the record in this model and resulted in a lower overall trend for the period they examined, which would likely have improved the agreement between their Hadley CM3 run and the MSU record. Thus, the disparity obtained in this run is unlikely to be robust.

Similar problems limit the usefulness of their GISS SI2000 run. DEA use runs of this model that are described in Hansen et al. (2002). In particular, they draw upon results cited in Figure 16 from that reference, which is reproduced here as Figures 46A and 46B. SI2000 is a coupled ocean-atmosphere model with several alternative oceanic components and a 4 deg. x 5 deg. gridded atmospheric portion. The atmospheric portion is an update of the earlier GISS SI95 model where the number of vertical layers has been increased from 9 to 12, and the higher layers have been made higher resolution to allow for more accurate modeling of ozone and stratospheric aerosols from volcanic eruptions. Several other refinements were used to improve the performance of this model. Its higher tropopause level resolution of results in a lower 2 X CO2 forcing compared to SI95 and its climate sensitivity falls within the range of 3.5-4.1 W/m² reported by IPCC WG I (2001). SI95 also contained a programming error that caused it to misrepresents sea-ice and summer melt pool absorptivity, and SI2000 contains an update that corrects for this by fixing the Antarctic and Greenland interiors at an albedo of 0.80 (Hansen et al., 2002).

Regarding DEA’s SI2000 studies, the most relevant piece is the ocean component. In SI2000 the atmospheric component model is coupled at a common interface grid to any one of the 5 oceanic component models it uses - Ocean A through Ocean E. Each of these has strengths and weaknesses and they vary in their ability to reproduce different aspects of oceanic response. Ocean A (observed Sea Surface Temperature) is based on the HadISST1 ocean surface model (Rayner et al., 2003) and provides global representations of SST, sea-ice, and night marine air temperatures for the period 1871-2000. Reliable in-situ data for these quantities are not consistently available for all regions and periods, so data sparse regions and periods have been filled in using reduced-space optimum interpolation methods (Kaplan et al., 1997; 1998). Ocean A does not model deep ocean responses such as latent heat transport or heat content, so it cannot be used for studies of oceanic response to climate forcings. But it has the advantage of being based on “real” rather than modeled oceanic history. So to the extent that the datasets and interpolation methods it draws from are reliable, it can be said to “capture” deep ocean history. Ocean B is a “Q-flux” ocean that models surface and deep ocean responses to a dept of 1 km. It models both horizontal and vertical heat transports using, a) horizontal heat transports chosen for their overall agreement with control runs of SST, and b) mixed layer to deep layer penetration of oceanic heat anomalies based on diffusion coefficients that vary by region and are based on local climatological stability (Hansen et al., 1984; Sun and Hansen, 2003). Hansen et al. (2002) apply the model to a depth of 1000 meters. Based on observed rates of ocean mixing of tracers, Ocean B provides a good approximation of oceanic global heat uptake for climate forcing scenarios that do not fundamentally alter the deep ocean circulation (true of most multi-decadal simulations such as those done by DEA), and has proven useful for characterizing the efficacy of each of SI2000’s radiative forcings when only limited dynamical interactions are permitted. Ocean C, another deep ocean model, uses a pressure related vertical coordinate to characterize ocean heat content and transport (Russel et al., 1995). Ocean D is a deep ocean model based on the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model (MOM), and Ocean E is taken from the isopycnic coordinate based Hybrid Coordinate Ocean Model (HYCOM) as described in Bleck (1998).

Of these, Oceans A and B are the most popular, and the ones to which Hansen et al. (2002) devote the most attention. Each has its strengths and weaknesses. Ocean A is a favorite choice for studies where the historic atmosphere data assimilation and reanalysis studies. In these cases, actual ocean dynamics are less important than is a clear picture of how they forced an atmospheric response and Ocean A has the obvious advantage of being based on known rather than modeled ocean history. But there are limitations to this. The effectiveness of Ocean A hinges on the accuracy of the historic SST and sea-ice data on which it was based. Though quite good overall, this data is known to be regionally and temporally incomplete and the interpolation methods that were used to “fill in the blanks” have had mixed success - in particular, characterizations of SST and sea-ice at high latitudes have substantial uncertainties. Some of this problem is ameliorated by the fact that the most serious difficulties occur prior to the satellite era, and HadISST sea-ice records were “homogenized” so as to provide consistency between differing components. But significant uncertainties remain in both (Hansen et al., 2002). This difficulty will be particularly telling for the high southern latitudes that are most important for discriminating between competing MSU products. Ocean A can also yield unreliable ocean-atmosphere heat fluxes that regionally impact its results. In fact, for some large scale effects such as the North Atlantic Oscillation, it can even yield the wrong sign for the resultant heat flux anomalies (Bretherton and Battisti, 2000). For instance, it is known to misrepresent the North Atlantic Oscillation (NOA) heat fluxes. These generally lead to a cooling of Siberia, but their misrepresentation by Ocean A leads instead to an NOA induced cooling of Eurasia that is not observed (Hansen et al., 2002). Problems like these are regional and far less problematic for global atmospheric change studies like those being considered in this paper, but they can have an impact. Lastly, it must be remembered that Ocean A is an historic ocean model. As such, it will be of little use in evaluating future global warming – a fact that will bear directly on the question of whether a failure of AOGCM’s to reproduce upper-air trends would disprove anthropogenic greenhouse warming. On the other hand, though Ocean B lacks the data driven and largely verifiable ocean history of Ocean A, it does provide a good representation of actual deep ocean dynamics and unlike Ocean A, it can be used for predictions of future climate change on regional and global scales. Studies of this sort require the ability to reliably reproduce oceanic heat storage, transport, and mixing. Ocean B yields good estimates of global mean thermal response to a wide range of natural and anthropogenic forcings, particularly moderate ones in which ocean surface heat anomalies will penetrate to deep ocean layers like “passive tracers” (Hansen et al., 2002). The Q-flux method on which it is based is flexible enough that a wide range of transient global surface temperature responses can be modeled with an appropriate choice of diffusion coefficient. Provided that climate forcing is moderate, and the dominate modes of deep ocean circulation do not change drastically over the period being studied – conditions that are very likely to be true for the upcoming century – this flexibility allow for good approximations of the heat uptake, storage, and transport characteristics of more sophisticated ocean models and reasonably good agreement with past observational data as well (Hansen et al., 2002; Solokov and Stone, 1998).

Oceans A and B are therefore complementary. One excels at reproducing historic ocean-atmosphere interactions, and the other provides a good basis for predictions of future climate change. Both are necessary for model based studies of a potential anthropogenic fingerprint on the global climate of the upcoming century. Furthermore, other SI2000 ocean components – Ocean E in particular – reproduce other climatic features that are missed by both Oceans A and B, giving SI2000 a suite of modeling options that allow for a wide range of surface and upper-air studies. Thus, any true test of this model’s potential will draw upon runs based on each, and using a full suite of natural and anthropogenic forcings. Indeed, Hansen et al. (2002) evaluated results from Ocean A and Ocean B, and Sun and Hansen (2003) used Ocean’s A, B, and E.

Which brings us to DEA’s use of SI2000 for their troposphere trend comparison study. They used the 6 forcing case employed by Hansen et al. (2002) for the period 1979-1998 using Ocean A only. Figures 46A and 46B show the change in annual-mean temperature profile vs. pressure altitude for the period 1979-1998 (assuming linear trends) as determined from this run along with results from a comparable run using Ocean B. Vertical trend profiles from HadRT2.0 and HadRT2.1 (radiosonde – Parker et al., 1997), and MSU Channels 2LT, 2, and 4 (Christy et al., 2000). The left-side plot gives the Ocean A results used by DEA, and the right-side gives Ocean B. It is evident that Ocean A produces the largest discrepancy between model and observation. Both regionally and globally, Ocean B provides a better fit to both the radiosonde and MSU data. Furthermore, the MSU data shown in these figures is taken from UAH Version D (Christy et al., 2000), not the larger trends given in RSS Version 1.0. Yet even so, the 6-forcing driven Ocean B case gives global responses that consistently fall within the confidence intervals of the lower UAH trends even for the 2LT layer. Regionally, confidence intervals overlap. For the middle troposphere layer (850-300 hPa) RSS Version 1.0 can be expected to run roughly 0.18 deg. K higher than the MSU trends shown for the same period and would be a better fit still across all regions. It is clear from this data that even though it is not perfect, SI2000 run with Ocean B gives a very good overall representation of regional and global temperature trends for the surfaced and troposphere when forced by well known effects.

Yet DEA make no mention of it, choosing instead to present only the Ocean A results that yield the largest surface-troposphere discrepancy. For the purposes of a study such as theirs, which seeks a satellite era comparison of modeled vs. observed results, Ocean A is a good choice and for all the reasons we have seen. But this choice must be viewed in the context of the larger objectives of their study. DEA claim to have evaluated the effectiveness of state-of-the-art AOGCM’s, both for their ability to yield past climate trends that agree well with observation, and their effectiveness as tools for predicting future ones. Indeed, they claim that the three models they examined “fail to account for the effects of greenhouse forcings” (Douglass et al., 2004b). Clearly, this is false. Ocean B does in fact, yields results which are in quite good agreement with their referenced observations. Furthermore, while it makes certainly makes sense to base a study like this on observed historical data to the greatest extent possible (as they have done), SST and sea-ice characterizations in Ocean A are not without their issues and it is far from evident that results from other components can be dismissed out of hand. This is particularly true in that DEA are claiming to have demonstrated the inability of models like SI2000 to capture future as well as past climatic changes, and between the two, only Ocean B can be used to model future climate change. DEA’s case would have been more compelling had they done the following,

• Demonstrated that these, and results from other ocean components should be dismissed outright, and only Ocean A should be used.
• Provided compelling evidence that runs based on Ocean A demonstrate that SI2000 cannot produce viable predictions of future climate change, even though Ocean A would not be used for such studies.

Neither point was addressed in their work.

These omissions becomes even more evident when we expand our evaluation of SI2000 to include its other ocean components. Oceans A and B are relatively simple component models that provide versatile and reasonably robust results, hence their popularity. But SI2000 has other ocean components that offer more thorough characterizations of many key ocean properties. An investigation of these tells even more about its capabilities. Ocean E for instance, a quasi-isopyncal Hybrid Coordinate Ocean Model (HYCOM), yields a much more complete picture of oceanic heat uptake and transport than Ocean B. It mixes heat more deeply than Ocean B, and in so doing provides a more realistic picture of oceanic heat sequestration – a feature that will be particularly telling for its ability to reproduce climate moderating effects and the amount of atmospheric warming still “in the pipe”, waiting to be released at a future date when global oceanic heat sequestration reaches its limits (Sun and Hansen, 2003). It provides a fairly good representation of oceanic heat storage profiles vs. depth and latitude as compared with observation, though specific geographic patterns often vary, and captures observed heat loss fluxes in the North Pacific and heat storage in the circum-Antarctic belt (Sun and Hansen, 2003; Levitus et al., 2000). Like Oceans A and B, Ocean E is not without its problems, at least two of which will likely be important for studies of satellite era trends. It displays a non-negligible climate drift, which if allowed to run to equilibrium would introduce an additional 8 deg. C to its results, and cannot be accounted for using flux corrections without introducing other unrealistic variations (Sun and Hansen, 2003; Neelin and Dyjkstra, 1995; Tziperman, 2000). It also fails to adequately capture equatorial “waveguide” cycles which likely contributes to its under-estimation of ENSO amplitudes. The latter has a predominately regional rather than global impact, and the former can be corrected for to a great extent by differencing control and experiment runs (Sun and Hansen, 2003). But overall, it yields a very good picture global climate during the satellite era and the longer period since the early ‘50’s.

Figure 57 shows global mean temperature trend profiles taken from Sun and Hansen (2003) for an expanded set of SI2000 runs. The results shown, which are directly comparable to those in Figures 46A and 46B, reflect 5 and 6 forcing cases applied to Oceans A, B, and E for the satellite era and the longer 1958-1998 period, as compared with radiosonde trend profiles from HadRT2.0 and HadRT2.1 (Parker et al., 1997), and MSU data for the 2LT, MSU2, and MSU4 layers from UAH Ver. D (Christy et al., 2000). Figure 58, also from Sun and Hansen (2003) shows transient temperature responses for the MSU 2LT, MSU2, and MSU4 layers, and global ocean heat content anomalies for the three same runs and the period 1951-1998, with anomalies referenced to a base period of 1984-1990. Once again we see that of the three ocean components, Ocean A consistently predicts the highest trend profiles and Oceans B and E both do surprisingly well at reproducing comparable trend profiles from the referenced radiosonde and MSU datasets. The Ocean A trends are the only ones that fall outside of the MSU confident intervals for most of the free troposphere (850-300 hPa) and are a worse fit than Oceans B and E at all layers except the surface. In fact, Ocean E actually under-represents surface trends. Likewise, Ocean B and E global transient responses are for the most part much closer to observation than their Ocean A counterparts. All three capture stratospheric response fairly well, but Oceans B and E consistently capture the MSU2 response better. Ocean A consistently over-represents observed global ocean heat content anomalies while Oceans B and E fall to either side of it. Thus, while far from perfect, Oceans B and E offer much better characterizations of many key ocean-atmosphere responses than Ocean A, and unlike Ocean A are well suited to studies of future as well as past and present climate change. Clearly, any realistic evaluation of the SI2000’s capabilities must consider all three. Yet DEA have restricted themselves to Ocean A runs only. Though not without many merits, this yields only part of the story at best and leads them to seriously under-represent SI2000’s usefulness.

Of the 3 AOGCM’s evaluated by DEA, the Dept. of Energy PCM model is the only one they ran using a full suite of realistic forcings and oceanic and atmospheric components for a time period that includes all significant ENSO and volcanic episodes for the satellite era. They use the “ALL” case that includes greenhouse gases, sulfate aerosols (direct effect only), stratospheric and tropospheric ozone, solar, and volcanic forcings. This is the same run that Santer et al. (2003) considered in their evaluation of the detectability of an anthropogenic fingerprint in a modeled climate. We have already seen that Santer’s team did in fact, detect an anthropogenic fingerprint in that model. While PCM does not yield a good overall fit with UAH Versions D and 5.0, it does provide a good fit with RSS Version 1.0, and it has already been shown that the differences are largely a matter of analysis method (Mears et al., 2003; 2003b; Santer et al, 2003).

Their Figure 1 presents results from the PCM “ALL” case, along with results from GISS SI2000 and Hadley CM3, as zonally averaged trends vs. latitude. Their Figure 2 presents decadal trends vs. altitude from these runs compared with observational data (Douglass et al., 2004b). As with their first paper, the disparities they observe are largely dependent on the time frame, region, and datasets chosen. The limitations of the radiosonde datasets and the Reanalysis product have already been discussed. But it is noteworthy that for their radiosonde comparison they choose HadRT2.0 when HadRT2.1 was available. We saw earlier that the latter had improved considerably on the former with updated corrections for anomalous data and discontinuous records (Free et al., 2002; Seidel et al., 2003; 2004). Note also that with the exception of the northern hemisphere, their Figure 2 shows all negative trends at 800 hPa for the MSU record (MSU 2LT). Yet their cited source is UAH Version D (Christy et al., 2000) which reports an MSU 2LT trend of 0.06 deg. K/decade for 1979-2001. This is a direct result of the fact that DEA report the value through 1996 omitting the latter third of the extant MSU record. For the NCEP/NCAR Reanalysis they use the original version (R1) in this paper rather than the later version (R2-2m) used in the first. It has already been noted that the updated version of this product corrected many problems present in the first. These included corrections for bogus data in the southern hemisphere, snow and ice cover problems for the 1974-1994 period, and snowmelt pool and oceanic albedo problems for the entire record (Kanamitsu et al., 2002) – all problems that will be of importance to MSU and model comparisons. DEA do not state why the updated Reanalysis product was not used for this comparison study, though the problems with the earlier product were known to them.

In summary, DEA’s conclusions are dependent on,

• A neglect of one third of the extant record, including a significant ENSO event of the late 1990’s.
• A validation of the shorter record that is heavily dependent on the choice of global region that is most likely to produce minimal trend differences for both periods.
• A neglect of 3 other upper-air MSU products in their study, at least one of which overall is arguably as well characterized and the one they chose, and in a few respects, better.
• A neglect of the most recent, and improved, analyses of the MSU product they did use (other than passing remarks) – most likely because the later products (Christy et al., 2003; 2004) show higher MSU TLT trends than the one they chose (Christy et al., 2000) and that one covers a time frame closer to the truncated period they analyzed.



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