By Dr. Ross McKitrick

Summary

There are three main global temperature histories: the combined CRU-Hadley record (HADCRU), the NASA-GISS (GISTEMP) record, and the NOAA record. All three global averages depend on the same underlying land data archive, the Global Historical Climatology Network (GHCN). CRU and GISS supplement it with a small amount of additional data.

Because of this reliance on GHCN, its quality deficiencies will constrain the quality of all derived products. The number of weather stations providing data to GHCN plunged in 1990 and again in 2005. The sample size has fallen by over 75% from its peak in the early 1970s, and is now smaller than at any time since 1919. The collapse in sample size has not been spatially uniform. It has increased the relative fraction of data coming from airports to about 50 percent (up from about 30 percent in the 1970s). It has also reduced the average latitude of source data and removed relatively more high-altitude monitoring sites.

GHCN applies adjustments to try and correct for sampling discontinuities. These have tended to increase the warming trend over the 20th century. After 1990 the magnitude of the adjustments (positive and negative) gets implausibly large.

CRU has stated that about 98 percent of its input data are from GHCN. GISS also relies on GHCN with some additional US data from the USHCN network, and some additional Antarctic data sources. NOAA relies entirely on the GHCN network.

Oceanic data are based on sea surface temperature (SST) rather than marine air temperature (MAT). All three global products rely on SST series derived from the ICOADS archive, though the Hadley Centre switched to a real time network source after 1998, which may have caused a jump in that series. ICOADS observations were primarily obtained from ships that voluntarily monitored SST. Prior to the post-war era, coverage of the southern oceans and polar regions was very thin. Coverage has improved partly due to deployment of buoys, as well as use of satellites to support extrapolation. Ship-based readings changed over the 20th century from bucket-and-thermometer to engine-intake methods, leading to a warm bias as the new readings displaced the old. Until recently it was assumed that bucket methods disappeared after 1941, but this is now believed not to be the case, which may necessitate a major revision to the 20th century ocean record. Adjustments for equipment changes, trends in ship height, etc., have been large and are subject to continuing uncertainties. Relatively few studies have compared SST and MAT in places where both are available. There is evidence that SST trends overstate nearby MAT trends.

Processing methods to create global averages differ slightly among different groups, but they do not seem to make major differences, given the choice of input data. After 1980 the SST products have not trended upwards as much as land air temperature averages. The quality of data over land, namely the raw temperature data in GHCN, depends on the validity of adjustments for known problems due to urbanization and land-use change. The adequacy of these adjustments has been tested in three different ways, with two of the three finding evidence that they do not suffice to remove warming biases.

The overall conclusion of this report is that there are serious quality problems in the surface temperature data sets that call into question whether the global temperature history, especially over land, can be considered both continuous and precise. Users should be aware of these limitations, especially in policy sensitive applications.

See full report here.

Ross also notes the following on another paper:

You might be interested in a new paper I have coauthored with Steve McIntyre and Chad Herman, in press at Atmospheric Science Letters, which presents two methods developed in econometrics for testing trend equivalence between data sets and then applies them to a comparison of model projections and observations over the 1979-2009 interval in the tropical troposphere. One method is a panel regression with a heavily parameterized error covariance matrix, and the other uses a non-parametric covariance matrix from multivariate trend regressions. The former has the convenience that it is coded in standard software packages but is restrictive in handling higher-order autocorrelations, whereas the latter is robust to any form of autocorrelation but requires some special coding. I think both methods could find wide application in climatology questions.

The tropical troposphere issue is important because that is where climate models project a large, rapid response to greenhouse gas emissions. The 2006 CCSP report pointed to the lack of observed warming there as a “potentially serious inconsistency” between models and observations. The Douglass et al. and Santer et al. papers came to opposite conclusions about whether the discrepancy was statistically significant or not. We discuss methodological weaknesses in both papers. We also updated the data to 2009, whereas the earlier papers focused on data ending around 2000.

We find that the model trends are 2x larger than observations in the lower troposphere and 4x larger than in the mid-troposphere, and the trend differences at both layers are statistically significant (p<1%), suggestive of an inconsistency between models and observations. We also find the observed LT trend significant but not the MT trend.

If interested, you can access the pre-print, SI and data/code archive at my new weebly page.

See how these agree with many of the findings and conclusions in the compendium Surface Temperature Records: A Policy Driven Deception by Anthony Watts, E.M. Smith and I (and many others).  See also further work on GHCN unedited here by E.M. Smith (Chiefio).