There is an English saying that you have “lies, damned lies and statistics”. Here, I would like to introduce a variation on this expression: “There are lies, damned lies, statistics … and sloppy science”.
This morning, there was lot of noise in the Dutch media (unfortunately in Dutch only) about new research that was claiming a dramatic warming of 4 degrees in 2050. The news report quoted Dutch econometricians from the University of Tilburg. They had done a statistical analysis of temperature data and the influence of CO2 and solar radiation and concluded that aerosols masked much more of the warming of greenhouse gases than previously thought. This also means there is more warming in the pipeline for the future if the trend of global brightening, that has been detected by researcher Martin Wild of ETH in Zürich, will continue in the coming decades. They also draw policy conclusions from their research stating that in order to avoid more than 2 degree warming more drastic measures are to be taken. This news was copied by many Dutch news outlets.
Although at first I could not figure out if there was a paper behind the news article and whether or not it has been accepted for publication (I still don’t know), I finally determined it had to be this paper: http://center.uvt.nl/staff/magnus/wip04.pdf
I decided to pass the paper on to Ross McKitrick, who, as many of the readers know, published two interesting papers (here and here) on the influence of different economic parameters on the pattern of warming at the surface. Within hours McKitrick came back with an interesting finding which makes any detailed discussion on the paper let’s say… irrelevant.
Remember, their study is an attribution study depending on long term trends in temperature measurements. For their study they use a rather obscure CRU dataset: CRU TS 2.1. You can find its documentation below. The webpage reads:
The CRU TS 2.1 data-set comprises 1224 monthly grids of observed climate, for the period 1901-2002, and covering the global land surface at 0.5 degree resolution. There are nine climate variables available: daily mean, minimum and maximum temperature, diurnal temperature range, precipitation, wet day frequency, frost day frequency, vapour pressure and cloud cover.
Read the documentation
There is also a peer-reviewed paper behind CRU TS 2.1: Mitchell and Jones, International Journal of Climatology, 2005, so that’s OK. However, if the authors had just cared to go through this webpage in some detail, they would have found a link to this page:
Q1. Is it legitimate to use CRU TS 2.0 to ‘detect anthropogenic climate change’ (IPCC language)?
CRU TS 2.0 is specifically NOT designed for climate change detection or attribution in the classic IPCC sense. The classic IPCC detection issue deals with the distinctly anthropogenic climate changes we are already experiencing. Therefore it is necessary, for IPCC detection to work, to remove all influences of urban development or land use change on the station data.
In contrast, the primary purpose for which CRU TS 2.0 has been constructed is to permit environmental modellers to incorporate into their models as accurate a representation as possible of month-to-month climate variations, as experienced in the recent past. Therefore influences from urban development or land use change remain an integral part of the data-set. We emphasise that we use all available climate data.
If you want to examine the detection of anthropogenic climate change, we recommend that you use the Jones temperature data-set. This is on a coarser (5 degree) grid, but it is optimised for the reliable detection of anthropogenic trends. For precipitation trends, use the Hulme data-set (5 degree grid or 2.5 x 3.75 grid). There are few alternatives to Hulme in the first half of the 20th century; later, to include the oceans use the Xie and Arkin data-set; for the last 25 years you could also use the GPCC data-set.
Yikes. This dataset is not to be used for the type of study performed by these econometricians. Never. Period. Don’t use it. Lies, damned lies, statistics and very sloppy science.