Institutions Are Biased

Interesting paper coming out that may demolish Psychology.  And other pseudo sciences like climate science.

“How could hundreds of peer-reviewed studies possibly be so wrong? There may be a way to explain it, and it’s shaking researchers to their cores.

Every time scientists conduct an experiment, there’s a chance they’ll find a false positive. But here’s the scary thing: Psychologists are now realizing their institutions are structured so it’s more likely that false positives will make it through to publication than inconclusive results.

We’re now learning that there’s so much bias in the published literature that the meta-analyses can’t be trusted,” Simine Vazire, a professor of psychology and the editor in chief of the journal Social Psychological and Personality Science, tells me.”

 

DRAX Wood Burning Scam Unravelling

 

I’ve written about DRAX before. Because of loopholes in UK and EU “climate” laws the largest coal power plant is switching from coal to wood pellets sources from the USA.

DRAX has made the news again.

The report says it has found ‘misleading statements by Enviva about its emissions and environmental impacts’ in its prospectus when it was floated on the New York stock exchange last April.

The report says Enviva has claimed that ‘burning wood in power plants reduces carbon emissions compared to coal’. But the study says Drax’s own data shows that while burning coal leads to emissions of 1,901lb of carbon dioxide per megawatt hour (Mwh), the figure for wood is significantly higher – 2,128lb per Mwh.

Enviva’s claim is only possible because of a UK and EU ‘policy loophole’ – which does not apply in America – classing biomass fuel such as wood pellets as ‘zero carbon’.

According to the study, Enviva has not made this clear. Its claim to the SEC that using its pellets ‘reduces’ emissions only applies to making and shipping the pellets, not burning them.

The complaint calls on the SEC to launch an investigation to ‘establish and enforce clear guidelines applicable to companies that may be claiming climate benefits’.

Drax produces eight per cent of the UK’s electricity – enough to power six million homes. Half of its six 650 megawatt (MW) generators have been converted from coal to burn wood pellets from America. Drax spokesman Andrew Brown yesterday confirmed the firm wants to adapt its remaining three furnaces.

http://www.thegwpf.com/biofuel-emits-more-co2-than-coal-u-s-watchdog-to-probe-draxs-green-supplier/

Stop The Sahara Before It Kills Again!

This really isn’t funny at all.

Millions of asthmatics unable to breathe as giant cloud of Saharan sand and toxic air covers Britain in layer of smog

But the greenies like to blame everything on coal. In fact in many countries the major sources of Particulate Matter are in fact dust, agricultural and fires.

 

PM10 PM2.5

 

But coal is always blamed. The biggest man made source of PM many times is diesel soot. And diesel cars are 50-70% of cars sold in Europe.

 

Back to the Daily Mail article:

 

“One sufferer said she felt like she had ‘a baby elephant sitting on my chest’, while another said her lungs felt like they had ‘cobwebs’ inside them.

Even those without health difficulties have been told by experts to reduce outdoor exercise, with air pollution set to hit 10 out of 10 in some areas.”

http://www.dailymail.co.uk/news/article-2594730/Health-warning-smog-soars-dangerous-high-country-People-heart-lung-problems-told-avoid-exercise.html

Sahara

Canada February 2014 – Monthly Mean Temperature Anomalies Mapped

I have mapped the February 2014 mean temperature anomalies in the Environment Canada monthly summaries that have “normals”. The anomalies are calculated from selected stations based on the 1971-2000 average.

The size if the dot represents the size of the anomaly. The 5C black dot in the top left hand corner represents 5C difference from “normal”. Red dots are warmer than normal. Blue are cooler. And Green are 0.

Most of the prairies averaged 5C or more below “normal”. Click for bigger.

EC Canada Mean Temp Anomaly February 2014

 

Canada January 2014 – Monthly Anomalies Mapped

I have mapped the January 2014 anomalies in the Environment Canada monthly summaries that have “normals”. The anomalies are calculated from selected stations based on the 1971-2000 average.

The size if the dot represents the size of the anomaly. The 5C black dot in the top left hand corner represents 5C difference from “normal”. Red dots are warmer than normal. Blue are cooler. And Green are 0.

Not much sign of the Polar Vortex in BC and Alberta for January. A big change from December.

EC MonthlyNormalsTemperature2014-01

Canada 1930- Monthly Anomalies Mapped

Yesterday I mapped the anomalies for 2013 using the Environment Canada monthly summaries that have “normals”. The anomalies are calculated from selected stations based on the 1971-2000 average.

Today I thought … why not 1930. I picked 1930 because I know the dustbowl occurred in the 1930s. So I assumed it would be warm at times. Remember, this is the anomaly from the 1971-2000 averages. It started out cold, but December was 3.21C warmer!

An example of the effect of the dustbowl. “In 1928, the net Farming income was $363 million; by 1933, it dropped to $11 million; and by 1937, two-thirds of the farm population of Saskatchewan was destitute.”

Click for a bigger version. (The black dot in the top left corner represents a 5C difference. Red = hotter than 1971-2000. Blue = colder.

MonthlyNormals_1930

Canada 2013 – Monthly Anomalies Mapped

I have mapped the anomalies in the Environment Canada monthly summaries that have “normals”. The anomalies are calculated from selected stations based on the 1971-2000 average.

The 5C black dot in the top left hand corner represents 5C difference from “normal”. Red dots are warmer than normal. Blue are cooler. And Green are 0.

April was cold. December ended up very cold across the country.

There is an animated gif at the top showing all months of 2013. You may have to refresh this page or click on the gif to get the full effect.

MonthlyNormals_2013

EC MonthlyNormals 2013-01
EC MonthlyNormals 2013-02
EC MonthlyNormals 2013-03
EC MonthlyNormals 2013-04
EC MonthlyNormals 2013-05
EC MonthlyNormals 2013-06
EC MonthlyNormals 2013-07
EC MonthlyNormals 2013-08
EC MonthlyNormals 2013-09
EC MonthlyNormals 2013-10
EC MonthlyNormals 2013-11
EC MonthlyNormals 2013-12