There is a new paper [h/t to Dallas Staley]
Libardoni, A. G., and C. E. Forest (2011), Sensitivity of Distributions of Climate System Properties to the Surface Temperature Dataset, Geophys. Res. Lett., doi:10.1029/2011GL049431, in press
which presents evidence for a diversity of ranges with respect to the analysis of global surface temperature data sets that are used to examine multi-decadal global climate model performmance.
The abstract of this paper reads [highlight added]
“Surface temperature, upper-air temperature, and ocean heat content data are used to constrain the distributions of the parameters that define three climate system properties in the MIT Integrated Global Systems Model: effective climate sensitivity, the rate of ocean heat uptake into the deep ocean, and net anthropogenic aerosol forcing. Five different surface temperature data records are used to show that the resulting parameter distribution functions are sensitive to the dataset used to estimate the likelihood of model output given the observed climate records. Estimates of effective climate sensitivity mode and mean differ by as much as 1 K between the datasets, with an overall range of 1.2 to 5.3 K. Ocean effective diffusivity distributions are poorly constrained by any dataset. The overall range net aerosol forcing values, -0.19 to -0.83 Wm-2, is small compared to other uncertainties in climate forcings. Transient climate response (TCR) estimates derived from these distributions range between 0.87 and 2.41 K and the shapes of individual TCR distributions depend on the surface dataset. Understanding the differences in parameter distributions and climate system properties derived from them is critical for understanding the full range of uncertainty involved in climate model calibration and prediction results.”
They write in the introduction that
“This study explores the impact that the surface temperature dataset used to compare model output to observed values has on the parameter constraints. To date, few studies have investigated how the surface temperature dataset used to compare model output with observational data impacts the parameter and TCR distributions. In total, five surface temperature data records representing three well-known climate centers are used in this study. Estimates of TCR are also investigated from the parameter distributions derived from each dataset. The resulting distributions show that model calibration is sensitive to the specific surface temperature dataset.”
The article has a succinct summary of the different global surface temperature analyses. They write
We use surface temperature data from five climate data records. The first two data records are HadCRUT2 [Jones and Moberg, 2003] and HadCRUT3 [Brohan et al., 2006]. The third is the NCDC merged land-ocean dataset [Smith et al., 2008]. The remaining two records are GISTEMP 250 and GISTEMP 1200 [Hansen et al., 2010] from NASA, with the distinctions reflecting the 250 km and 1200 km radii of influence used in the interpolation algorithm. All data are reported as monthly surface temperature anomalies with respect to a given base period on a 5◦x5◦ grid. The data records differ from one another and potential reasons for these differences are now discussed briefly.
One difference between the records is the land surface data used in the analyses. All records obtain a majority of their land surface data from the Global Historical Climatology Network (GHCN) [Peterson and Vose, 1997], but each utilizes the available data differently. For example, the Hadley Centre requires stations to have sufficient data between 1961 and 1990, their climate normal period, to be used in the analysis [Jones and Moberg, 2003; Brohan et al., 2006]. Alternatively, NASA requires that stations have a period of overlap of at least 20 years with stations inside of a 1200 km radius to be used in the analysis [Hansen et al., 2010]. A second difference between the data records is that each uses a different sea surface temperature (SST) dataset. Because oceans cover 70% of the Earth’s surface, these choices lead to differences between the temperature data records [Smith et al., 2008]. In a test of the sensitivity to ocean data choice, [Hansen et al., 2010] showed that the global mean temperature calculated from GISTEMP data is affected by the choice of SST data. A last difference between the data records is the method for filling regions with missing data and how the 5×5 grid box anomalies are calculated. Specific details of infilling and grid box averaging methods for each data record can be found in corresponding references. At this stage, we have five surface temperature data records and choose to treat them each as equally plausible. We present the results derived from each of them and do not attempt to merge the results into a single posterior distribution.
The conclusion has the text
“The results presented here show that climate model parameter constraints are sensitive to the surface dataset used to compare with model output. In general, the ranges of the effective climate sensitivity parameter distributions are comparable, but are shifted relative to each other depending on which surface dataset is used. The biggest shift in effective climate sensitivity distributions is observed when the GISTEMP datasets are used. Using the 95-percent confidence intervals and considering all datasets, climate sensitivity is found to be between 1.2 and 5.3 K. Regardless of the surface data used, effective ocean diffusivity is poorly constrained by the data. Anthropogenic aerosol forcing is found to be between -0.19 and -0.83 Wm−2 when considering all datasets. TCR estimates are also sensitive to the choice of surface data. When all surface datasets are considered, transient warming is found to lie between 0.87 and 2.31 K. However, this range masks the differences that exist between the individual distributions.The TCR distribution derived from GISTEMP data is narrower and yields only minimal warming. In contrast, distributions derived from Hadley Centre datasets are wider and yield stronger warming. Given that both the parameter and TCR distributions differ when using different datasets, additional uncertainty is present in model calibration and climate projection studies. Future studies using these datasets must account for these differences to avoid overconfidence in predictions through mistreatment of the uncertainty.”
This study is of interest since it shows a new perspective on the large uncertainty that remains in climate prediction. It also highlights how poorly the ocean uptake of heat is simulated in the models.