by Roger Pielke Sr., Phil Klotzbach, John Christy and Dick McNider
An update is presented of the analysis of Klotzbach et al. 2009.
In 2009 we published the paper:
An alternative explanation for differential temperature trends at the surface and in the lower troposphere
Klotzbach, P.J., R.A. Pielke Sr., R.A. Pielke Jr., J.R. Christy, and R.T. McNider,
Abstract. This paper investigates surface and satellite temperature trends over the period from 1979 to 2008. Surface temperature data sets from the National Climate Data Center and the Hadley Center show larger trends over the 30-year period than the lower-tropospheric data from the University of Alabama in Huntsville and Remote Sensing Systems data sets. The differences between trends observed in the surface and lower-tropospheric satellite data sets are statistically significant in most comparisons, with much greater differences over land areas than over ocean areas. These findings strongly suggest that there remain important inconsistencies between surface and satellite records.
Published in J. Geophys. Res. [link]
A corrigendum was published shortly thereafter [link]:
Context (written by JC)
The Klotzbach et al. paper is about the expectation that the rate of warming at higher altitudes should be larger than at the surface. When forced by anthropogenic greenhouse gases, GCM climate models on average indicate that the trend of the troposphere is amplified by a factor of 1.2 over that of the surface. When confined to the tropics, the amplification factor is about 1.4. The cause of this amplification is related to the lapse rate and lapse rate feedback. This model-generated tropospheric warming in the tropics is known as the “hot spot” and has been claimed to be a signature of greenhouse warming.
However, this amplification is not corroborated by comparison between surface temperatures and atmospheric temperatures determined from satellites. Klotzbach et al. suggest that part of this difference is caused by a bias in the surface temperatures and that this difference would increase as warming increases.
Since this increased warming in the upper layers is a signature of greenhouse gas forcing in models, and it is not observed, this raises questions about the ability of models to represent the true vertical heat flux processes of the atmosphere and thus to represent the climate impact of the extra greenhouses gases we are putting into the atmosphere. It is not hard to imagine that as the atmosphere is warmed by whatever means (i.e. extra greenhouse gases) that existing processes which naturally expel heat from the Earth (i.e. negative feedbacks) can be more vigorously engaged and counteract the direct warming of the forcing. This result is related to the idea of climate sensitivity, i.e. how sensitive is the surface temperature to higher greenhouse forcing, for which several recent publications suggest models, on average, have been overly sensitive.
Update of Klotzbach et al.
Recently, we have been asked to update our analysis to the present. The figures presented below are an update of Figures 1 and 2 in Klotzbach et al. We have also updated Table 1.
Table 1: Update of Table 1 from Klotzbach et al. (2009). Global, land and ocean per decade temperature trends and ratios over the period from 1979-2008 and from 1979-2014.
Our conclusion is that not much has changed since 2008. The update of Table 1 shows that the temperature datasets have come into slightly better agreement with the UAH satellite product since 2008 but disagree slightly more with the RSS satellite product (as you can see from the changes in the ratios).
Figure 1: Update of Figure 1 from Klotzbach et al. (2009). NCDC minus UAH lower troposphere (blue line) and NCDC minus RSS lower troposphere (green line) annual land temperature difference over the period from 1979 to 2014. The expected anomaly difference given the model amplification factor of 1.2 is also provided. This amplification factor is calculated by multiplying the surface temperature anomaly for a particular year by 1.2 and assuming that that is the value the lower troposphere should be for that year. All differences are normalized so that the difference in 1979 is zero.
Figure 2: Update of Figure 2 from Klotzbach et al. (2009). CRUTEM4 minus UAH lower troposphere (blue line) and CRUTEM4 minus RSS lower troposphere (green line) annual land temperature difference over the period from 1979 to 2014. The expected anomaly difference given the model amplification factor of 1.2 is also provided. This amplification factor is calculated by multiplying the surface temperature anomaly for a particular year by 1.2 and assuming that that is the value the lower troposphere should be for that year. All differences are normalized so that the difference in 1979 is zero.
Figures 1 and 2, however, show that there is still a significant divergence between the 1.2 amplification factor expected over land and what the satellites are showing. This reinforces our conclusion that the difference between the multi-decadal surface and lower tropospheric temperature trends remains.
In the 2009 paper we postulated that part of the discrepancy might be the use of minimum temperatures to compute a long term trend over land. As shown in our 2012 paper by McNider et al. [link], we found
“that part of the observed long-term increase in minimum temperature is reflecting a redistribution of heat by changes in turbulence and not by an accumulation of heat in the boundary layer.”
In the past, maximum temperature trends have shown closer agreement with lower tropospheric measurements. However, recent land surface data sets using homogenization corrections have reduced the trend differences in Tmin and Tmax. Now Tmax may be rising like Tmin in these analyses. But, this leads to an even bigger unexplainable physical discrepancy between the lower troposphere and corresponding surface trends as seen in the analyses.
JC note: As with all guest posts, keep your comments relevant and civil. This is a technical thread, so comments will be moderated more heavily than usual for relevance.
Filed under: Data and observations
