Friday, July 13, 2012

June GISS Temp down 0.08°C - ice news

I thought I might be out on a limb when TempLS showed slight cooling from May to June. Both satellite measures showed significant warming. But GISS showed cooling, from 0.64 °C to 0.56 °C. Time series graphs are shown here

Meanwhile, Arctic ice has been melting. Still a little behind last year, but keeping pace.

As usual, I compare the previously posted TempLS distribution to the GISS plot.

Tuesday, July 10, 2012

June TempLS down slightly from May

The TempLS analysis, based on GHCNV3 land temperatures and the ERSST sea temps, showed a monthly average anomaly of 0.50°C for June, down slightly from 0.52 °C in May. UAH seems to be the only other result out, showing a 0.08 C increase. There are more details at the latest temperature data page.

I'll show here the usual spherical harmonics plot of area distribution of temperature. But I'll also show a more elaborate interactive plot of station temperatures. This gives a good guide as to what data is currently in, as well as a basis for comparing the spherical harmonics smoothing. It's the same style I showed last November

Monday, July 2, 2012

CPS proxy reconstruction - analysis and selection bias

CPS is an old and fairly simple reconstruction method for temperature proxies. The proxies are simply normalised - mean subtracted and divided by standard deviation - and averaged. Then the average is scaled to some target temperature during a calibration period - a multi-decade period where the instrumental measure is available. This can be done by regression. Finally, the reconstruction is just the average in precalibration times, scaled by that same formula.

It solves the problem of overfitting - having more proxies than years of measurement of target temperature. But it also has limitations. It can't make use of local temperature information. The notion of standard deviation is not really appropriate, since neither proxies nor the target temp are stationary random variables. And, as normally implemented, proxies that show little temperature dependence are swept in, and add only noise.

It's a convenient method to analyze, so I thought I would study the effect of selection of proxies by correlation in the calibration period - recently controversial. Not too much should be made of it, since selection is motivated by avoiding overfitting, which is not a problem in CPS. But I'm developing the methods for use in more complex algorithms.