Extended Trend Viewer

I have been maintaining regularly an active trend viewer. A reduced image looks like this:



The triangle shows with color shading the trend from any starting year to end year, in a range that you can choose (from 1999, 1989, 1960 or 1900 to now). There are settings that show just trend, trend with significance masked, or CI's (upper or lower) or t-values. And there are now 15 datasets - monthly temperature series.

Each triangle has an accompanying plot of the time series. The active aspect is that you can choose a time range, and numerical information about the trend will be shown, and colored markers will show the trend line on the graph. You can choose either by clicking in the triangle, or using controls in the graph.

I have now added four new data sets. They are from Cowtan and Way, BEST Land/Ocean, NOAA SST, and a new TempLS set. I'll describe each in detail, and give links to the sources.

Cowtan and Way

Kevin Cowtan's home page is here. They took HADCRUT 4, which uses a grid but omits cells that have no data, and used different schemes for estimating the empty regions. The motivation is that recently there has been warming in the Arctic which HADCRUT was not detecting properly. They used kriging interpolation, and hybrid schemes making use of satellite data. I'm showing the kriging results here.

BEST Land/Ocean

Berkeley Earth started with land only, and that remains their prime product. They only update their land/ocean data annually. They use HADSST3 for ocean, and as with all land/ocean indices, this has a dominant effect.

NOAA SST

I'd really like to be able to show the OI V2 SST, which is kept very current. But although it's easy to download spatial data, for some reason the global index can only be got interactively. So I'm showing the regular product which comes out late in the month.

TempLS

TempLS is my least squares global temperature index  program. My recent post gives some background, and a comparison. It is basically an area weighted regression, and I have been using a traditional scheme whereby the weight is assigned according to each stations share of its gridcell area. This gives similar performance to actual gridded schemes, and that recent post was to show how well it follows NOAA.

But this has the same faults that Cowtan and Way were trying to fix. No weight is assigned to allow for empty cell area, of which there is much in the Arctic. TempLS offers various weighting scheme, one of which is essentially finite element integration on an irregular triangular mesh. This has no problems with empty cells, and no problem with cells getting small at the poles. I have felt that Cowtan and Way's kriging was overkill, and that any interpolation (here linear) would do much the same. This is an opportunity to test. I'll write more on this soon.

Sources

Here is a table of links to the data files.

HadCRUT	HadCRUT 4 land/sea temp anomaly
GISSlo	GISS land/sea temp anomaly
NOAAlo	NOAA land/sea temp anomaly
UAH5.6	UAH lower trop anomaly
RSS-MSU	RSS-MSU Lower trop anomaly
TempLSgrid	TempLS grid weighting
TempLSmesh	TempLS mesh weighting
BESTlo	BEST Land/Ocean
C&Wkrig	Cowtan/Way Had4 Kriging
BESTla	BEST Land
GISS Ts	GISS Ts Met stations temp anomaly
CRUTEM	CRUTEM CRU global mean Station anomaly
NOAAla	NOAA land temp anomaly
HADSST3	HADSST3
NOAAsst	NOAA sea temp anomaly