Surface Temperatures – How significant is the January 2008 fall?

As in the previous post about recent plummeting global temperatures, I want to look at the statistics of the drop, and determine its significance. The sort of questions of interest are, how improbable is a fall in temperatures of that magnitude of a 12 month period? After all, it is irresponsible to report alarming results without demonstrating the statistical significance. Unfortunately it is a common practice, for example, see record high temperatures from NASA.

The statistical setup for answering the question is encoded in the question. As we are only looking at falls in temperature, this should be a one-tailed test. The data we need are the twelve-month changes in global temperature anomalies, of which there are twelve every year to compare against the previous year. We then need the area of the distribution curve for these results, up to and including the value in question, -0.5906 in the case of the HadCRU data.

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Global Temperature Prediction

As reported by Anthony Watts at his blog post at Watt’s Up With That, global surface temperatures plummeted in the month of January. All four major sources of temperature anomoly data reported sharp drops, averaging -0.6405C. It is also reported that the large contribution to this value is from Northern Hemisphere land temperatures showing a huge drop of 2.4C from last January.

Here is the monthly global surface temperature anomaly data from the UK Hadley Climate Research Unit illustrating the fall.

Our paper published in Bioscience Forecasting the Effects of Global Warming on Biodiversity called for more accurate forecasting of global warming in order to assess effects of global warming. However, current methods for forecasting have limitations not recognized by those concerned with impacts, policies, the media or the general public. In general, you could say that the confidence in the accuracy of forecasts of global warming is misguided.

Reliable ways to forecast rates of extinction, both in relation
to global warming and in general, still elude us.

One approach would be to identify the most reliable sources of prediction of global temperatures.

The interesting question is – Who best predicted this fall in temperatures?

Russian Astronomers?

In 2005, Russian astronomer Khabibullo Abdusamatov predicted the sun would soon peak, triggering a rapid decline in world temperatures. Only last month, the view was echoed by Dr. Oleg Sorokhtin, a fellow of the Russian Academy of Natural Sciences. who advised the world to “stock up on fur coats.” Sorokhtin, who calls man’s contribution to climate change “a drop in the bucket,” predicts the solar minimum to occur by the year 2040, with icy weather lasting till 2100 or beyond.

Or the IPCC Climate Modellers?

On the graph you will also see the now familiar temperature records from two satellite and two surface analyses. It seems pretty clear that the IPCC in 1990 over-forecast temperature increases, and this is confirmed by the most recent IPCC report (Figure TS.26), so it is not surprising.

The public credibility of spokespeople for climate model predictions in support of political action on climate change is endangered by such nonsensical and useless pronouncements as found in the Garnaut Climate Change Review Interim Report, stating

Developments in mainstream scientific opinion on the relationship between emissions
accumulations and climate outcomes, and the Review’s own work on future “business as
usual” global emissions, suggest the world is moving towards high risks of dangerous
climate change more rapidly than has generally been understood. This makes mitigation
more urgent and more costly.

Rather, common sense would suggest that a wait-and-see attitude might be a wiser approach, as the next few years should tell us conclusively whether there will be a major upset in the ‘mainstream scientific opinion’ on global warming via contradiction by the data.

3D Modelling in R

While looking at a roadside cutting in order to evaluate options for treating it for erosion, I thought it would be interesting to
gather the data and develop a 3D model in R. The process can be confusing the first time, as the data need to be massaged to meet the requirements of the interpolative algorithm that converts scattered heights to a regular grid, which can then be visualized.

Below is the process I used, and it was relatively quick.

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