Climate Change & Tropospheric Temperature Trends

These points are particularly telling because DEA have made a point of ridiculing the argument that the MSU record is too short to be of use for long-range climate change predictions. In the Aug. 2004 Tech Central Station argument where they introduce these papers to the public they state that,

“When this was noted in the first satellite paper published in 1990, some scientists objected that the record, which began in 1979, was too short. Now we have a quarter-century of concurrent balloon and satellite data, both screaming that the UN's climate models have failed, as well as indicating that its surface record is simply too hot.”

(Douglass et al., 2004c)

Yes, now we do have a quarter century long MSU record against which to evaluate troposphere temperature trends, and DEA deliberately omit almost a third of it in their study! They claim that this neglect has little impact on their results. The rationale offered is that a repeat of their analyses for the 1979-2002 over ocean regions only (which they say avoids “snow cover” problems) produces similar trends. This however, is a bogus comparison. First of all, 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. This is patently false. Indeed, it is enlightening to compare their reasoning here with the MSU regional trends shown in Figure 4B. Remember that DEA cite UAH Version D 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 extratropical areas like the southeast United States and the Arabian Peninsula. It is difficult to see how snow cover could be polluting tropospheric trends over Florida and Saudi Arabia! The truth is that DEA “tested” their MSU record truncation by choosing a comparison that anyone could have told them would produce minimal differences in trend for the 2 periods. Then, they came up with a rationale to sell it. Lastly, it is interesting that they are so worried about snow cover here when they were obviously less worried about it in regard to its effects on MSU 2LT trends in the high southern latitudes - where it is far more abundant and its seasonal variations most affect trend differences between UAH and RSS products.

Moving on to their regional data and figures, we see even more problems. Figure 17 shows DEA’s 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 so that a casual inspection of it implies more regions with satellite era cooling trends than they actually observed. Though the caption mentions this in passing, we are left with an inability to tell what regions they observed cooling in from which ones they had no data for. At best, this is misleading. Likewise, Figure 18 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. But in the Southern Hemisphere they truncate it at about 35 deg. S. Latitude. Why? A comparison of Figure 18 with Figures 4A and 4B is revealing. It can be seen that by ending their geographic trend record here they avoid the very region of the globe where UAH and RSS products are most different! The region from 60 deg. S. Latitude to the South Pole is precisely 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 19 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, DEA’s first “bombshell” paper is little more than a cherry-picking tour-de-force. They examine only two thirds of the extant MSU record and compare it to products that were carefully selected for their agreement with it at the expense of other equally valid upper-air products. Along the way, they made sure that they had picked a time period that would yield the desired discrepancy between surface and tropospheric trends. Their second “bombshell” (Douglass et al., 2004b) shows little improvement. Here, DEA shift their attention from their alleged 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, MSU 2LT lower troposphere temperatures 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 avoids 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 6 and 8 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 the examined. So the total period for which DEA run Hadley CM3 amounts to less than half of the extant MSU record, and for a portion that produces the result they desire.

The selectivity becomes even more obvious in 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 20A and 20B. 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 depth 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 absolutely no mention of it and present only the Ocean A results that yield the surface-troposphere discrepancy they desire. For the purposes of a study such as theirs, which seeks a satellite era comparison of modeled vs. observed results, Ocean A is in fact a good choice and they are right to include it. But the choice to use this ocean component alone must be viewed in the context of their larger objectives. DEA claim to have proven that state-of-the-art AOGCM’s cannot reproduce past or present climate trends that agree with observation, and are therefore useless for predicting future global change. Indeed, this more than anything else, is the basis of their “declaration of victory” over mainstream climate change science. Even a casual inspection of Figures 20A and 20B reveals that 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.

Not surprisingly they did not even attempt to do either, much less succeed, leaving the casual reader with the impression that GISS SI2000 is unable to reproduce any aspect of observed climate change.

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 32 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 20A and 20B, 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 33, 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 – particularly when an evaluation of the ability of AOGCM’s to predict future climate change is being sought, as DEA are doing. Despite these considerations, they have restricted themselves to Ocean A runs only – because only these runs yield the significant surface-troposphere disparity they desire.

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 they did in fact, detect an anthropogenic fingerprint in that model, and that while it is not a good fit with UAH Versions D and 5.0, it is a good fit with RSS Version 1.0. DEA of course, make no mention of any of this, but highlight its disagreement with the preferred UAH products.

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). Here we see the same selectivity in observational datasets that plagued the first paper, but with a few new twists. 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 in Part I 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). Once again, they appear to have chosen the former because it yields the desired larger discrepancies with modeled results for the lower troposphere. 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. The answer, of course, is that once again, DEA only report the value through 1996 omitting fully one 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) that was 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. Yet despite these issues, they use the original record in this case when the updated product was available for the same period.

So between two papers published in the same month, we essentially have a cherry-picking tour-de-force. Both have been carefully orchestrated to “prove” a disparity between observational temperature trends at the earth’s surface and those of the modeled and observed troposphere featuring,

• A complete neglect of almost one third of the extant record, including a significant ENSO event of the late 1990’s, even though such events may well be related to anthropogenic global warming, and previous events almost as large were included.
• A “validation” of this shorter record based on exactly the choice of global region that is most likely to produce minimal trend differences for both periods, followed by a rationale for this choice that assumes a major “snow cover” problem over latitude bands where snow cover is minimal.
• A neglect of 3 other upper-air MSU products, at least one of which overall is every bit as well characterized and the one they chose, and in a few respects, better.



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