by Judith Curry
The new data call into question our understanding of observed stratospheric temperature trends and our ability to test simulations of the stratospheric response to emissions of greenhouse gases and ozone-depleting substances. – Thompson et al.
The mystery of recent stratospheric temperature trends
David Thompson, Dian Seidel, William Randel, Cheng Zhi Zhou, Amy Butler, Carl Mears, Albert Osso, Craig Long
Abstract. A new data set of middle- and upper-stratospheric temperatures based on reprocessing of satellite radiances provides a view of stratospheric climate change during the period 1979–2005 that is strikingly different from that provided by earlier data sets. The new data call into question our understanding of observed stratospheric temperature trends and our ability to test simulations of the stratospheric response to emissions of greenhouse gases and ozone-depleting substances. Here we highlight the important issues raised by the new data and suggest how the climate science community can resolve them.
Citation: Nature 491, 692–697 (29 November 2012) doi:10.1038/nature11579. [link] Link to full manuscript.
Excerpts:
The radiative effects of human emissions of ozone-depleting substances and greenhouse gases have driven marked atmospheric cooling at stratospheric altitudes. Ozone depletion is believed to have caused the preponderance of the cooling in the lower stratosphere (around 15–25km altitude); both ozone depletion and increases in greenhouse gases are believed to have driven the cooling in the middle and upper stratosphere (around 25–50km altitude). Stratospheric temperature trends play an important part in allowing us to distinguish between the climate responses to natural and anthropogenic climate forcings. Although less widely discussed in either scientific or policy circles, stratospheric cooling is as fundamental as surface warming as evidence of the influence of anthropogenic emissions on the climate system.
Continuous time series of temperatures in the middle and upper stratosphere back to 1979 are based exclusively on SSU data (the AMSUdata also sample the middle and upper stratosphere but are available only since 1998). The SSU data require correction for several unique issues before they can be used for climate studies.
The SSU data were originally processed for climate analysis by scientists at the UK Met Office in the 1980s. The data were further revised in 2008 to account for variations in the satellite weighting functions over time due to changes in atmospheric composition. However, the methodology used to develop the Met Office SSU product was never published in the peer-reviewed literature, and certain aspects of the original processing remain unknown. For this reason, the NOAA STAR recently reprocessed the SSU temperatures and published the full processing methodology and the resulting data in the peer-reviewed literature. The new NOAA SSU data provide an invaluable independent resource for assessing the reproducibility of the original Met Office SSU data. But the new data raise more questions than they answer, because they provide a strikingly different view of recent stratospheric temperature trends.
The long-term variability and trends in global-mean temperatures for the uppermost SSU channel (SSU channel 3) are relatively similar in both the Met Office and NOAA data sets. But the same cannot be said for the SSU channels that sample the middle stratosphere (SSU channels 1 and 2). The global-mean cooling in channels 1 and 2 (around 25–45 km) is nearly twice as large in the NOAA data set as it is in the Met Office data set. The differences between the NOAA and Met Office global-mean time series are so large they call into question our fundamental understanding of observed temperature trends in the middle and upper stratosphere.
The story is further muddled when the observations are compared with attempts to simulate the past few decades of stratospheric climate change using climate models. Between 40 and 50km (channel 3), global-mean temperature trends from both SSU products show more cooling than is simulated by the CCMs (atmospheric coupled chemistry models). Between about 35 and 45km (channel 2), the Met Office version of the SSU data suggests that the models overestimat the observed stratospheric cooling, whereas the NOAA SSU data suggest that the models underestimate it. The most striking discrepancies are between about 25 and 35km (channel 1). The Met Office SSU data are in reasonable agreement with the current generation of coupled CCMs at these altitudes. But, the cooling in the new NOAA SSU channel 1 data is nearly twice as large as the cooling simulated by most of the CCMs.
Are the models missing a key aspect of stratospheric climate change? Or is there an error in the newly processed NOAA data? Which SSU data set is correct? Or are both in error? If the NOAA SSU data are correct, then both the CCMVal2 and CMIP5 models are presumably missing key changes in stratospheric composition.
What might give rise to the discrepancies between observed and simulated global-mean stratospheric temperatures highlighted here? The pronounced discrepancies between simulated and observed global-mean stratospheric temperature trends are most probably due to one of the following two possibilities.
- The observations may be in error.
- The simulated ozone trends may be in error. Uncertainties in ozone depletion in the lower stratosphere may help to account for the discrepancies between modeled and observed trends in temperatures there.
Uncertainties in the evolution of stratospheric ozone and implications for recent temperature changes in the tropical lower stratosphere
Susan Solomon, Paul Young, Birgit Hassler
Abstract. Observations from satellites and balloons suggest that ozone abundances have decreased in the tropical lower stratosphere since the late 1970s, but this long-term change is occurring in a region of large interannual variability. Three different ozone databases provide regression fits to the ozone observations, and are available for use in model studies of the influence of ozone changes on stratospheric and tropospheric temperatures. Differences between these ozone databases suggest that the estimated decreases of tropical lower stratospheric ozone in recent decades are uncertain by about a factor of two to three. The uncertainties in ozone decreases lead to similar uncertainties in cooling of the tropical lower stratosphere, a key area of focus in climate change studies.
Citation: Solomon, S., P. J. Young, and B. Hassler (2012), Uncertainties in the evolution of stratospheric ozone and implications for recent temperature changes in the tropical lower stratosphere, Geophys. Res. Lett., 39, L17706, doi:10.1029/2012GL052723 [link; article behind paywall].
Excerpts:
Understanding the factors that can influence temperatures near the tropical lower stratosphere is important for interpreting past climate change and projecting future changes. One driver of temperatures in this region is the abundance and variability of ozone, but water vapor, volcanic aerosols, and dynamical changes such as the Quasi- Biennial Oscillation (QBO) are also significant; anthropogenic increases in other greenhouse gases such as carbon dioxide play a lesser but significant role in the lower stratosphere. Due to the important role of ozone in driving temperature changes in the stratosphere as well as radiative forcing of surface climate, several different groups have provided databases characterizing the time-varying concentrations of this key gas that can be used to force global climate change simulations (particularly for those models that do not calculate ozone from photochemical principles).
Our purpose is to examine the range of the estimated ozone changes obtained from available databases for the tropical lower stratosphere and to explore implications for changes in temperature [as simulated in climate models].
While the three ozone databases all show a reduction in ozone in the lower tropical stratosphere, the magnitude of this change differs substantially between them and occurs against a backdrop of much larger interannual variability. The SPARC database that was used for many global model runs for the Climate Modelling Intercomparison Project 5 (CMIP5) displays the least interannual variability and the most conservative trends in ozone of the available databases. Uncertainties in the regression models and fits used to distinguish between periodic variations and trends in the different databases appear to be a significant source of uncertainty in the estimates of longterm trends. The three different ozone databases yield changes in tropical lower stratospheric temperatures that differ by more than a factor of two at 70 mbar, although all have qualitatively similar seasonal cycles. Therefore, the uncertainties in ozone changes in the tropical lower stratosphere and their characterization in different databases using regression fits constitute a major barrier to understanding temperature trends and radiative forcing. According to the present model, the changes in lower stratospheric ozone may also influence temperatures in the tropopause region and thereby perhaps water vapor, although we emphasize that further testing with additional models is required.
Identifying human influences on atmospheric temperatures
Benjamin Santer et al.
Abstract. We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.
Published online before printNovember 29, 2012, doi:10.1073/pnas.1210514109
PNAS November 29, 2012201210514 [link to full text].
JC comments:
Each of these papers makes an important contribution by grappling with the uncertainty in forcing and evaluation data sets. These issues were essentially ignored in the IPCC AR4 report in the context of attribution studies. Santer et al. draw an important conclusion:
These results point to the need for a more systematic exploration of the impact of forcing uncertainties on simulations of historical climate change.
I also made this argument in the Uncertainty Monster paper.
I note that Santer et al. did not use the new SSU dataset described above in the paper by Thompson et al.
Thompson et al. point out the importance of documenting the data sets (apparently the UKMO SSU analysis was not documented in the published literature). Thompson et al. also argue for a 3rd analysis of the SSU data to try to better understand and resolve the discrepancies between the two data sets.
So, how will more realistic assessment of data set uncertainty influence the IPCC AR5 conclusions and confidence levels regarding the attribution of warming since the mid 20th century?
Moderation note: This is a technical thread, comments will be moderated for relevance.