Sunday, September 16, 2012

What does Global Warming look like if you only use the highest quality data?

A repeated theme here is that the quality of the temperature databases is pretty dreadful, and much of the reported warming comes from lousy data.  To date, this has mostly been inferred based on station siting problems, data infilling (i.e. making data up) problems, and "adjustments" that are poorly explained and seemingly biased towards warming.  Now, however, this has been demonstrated by direct data measurement:
What if the climate experts conducted an actual experiment that would prove whether the global warming skeptics were right or wrong about world-wide warming being overstated?
Well, NOAA has actually conducted said experiment by building their U.S. Climate Reference Network (USCRN), which precisely, and automatically, measures temperature and weather conditions across the U.S. The USCRN effort is based on the concept that the best way to measure the impact of greenhouse gases on global temperatures is to place state-of-the-art climate stations in pristine rural areas that are little impacted by people, buildings, vehicles, equipment, asphalt and etc.
An example of one of NOAA's pristine climate measurement stations is the top image (Image #1). And the middle image depicts the location of each pristine station - there are currently 114 of them, and clearly they are well dispersed providing good U.S. coverage.
By carefully planning and maintaining these pristine stations and by using the best technology available, this large-scale experiment eliminates the following problems with the older weather measurement network:
  • There are no observer or transcription errors to correct.
  • There is no time of observation bias, nor need for correction of it.
  • There is no broad scale missing data, requiring filling in data from potentially bad surrounding stations. (FILNET)
  • There are no needs for bias adjustments for equipment types since all equipment is identical.
  • There are no need for urbanization adjustments, since all stations are rural and well sited.
  • There are no regular sensor errors due to air aspiration and triple redundant lab grade sensors. Any errors detected in one sensor are identified and managed by two others, ensuring quality data.
  • Due to the near perfect geospatial distribution of stations in the USA, there isn’t a need for gridding to get a national average temperature.
So, what has this NOAA experiment found? The bottom image (Image #3) tells that story - when compared to measurements from the old, inaccurate, non-pristine network, temperature "warming" in the U.S. is being overstated anywhere from +0.5°C on average, up to almost +4.0°C (+0.9°F to +7.2°F) in some locations during the summer months.
Add in that previous statements from NOAA that 0.5° of the 0.6° warming in the lower 48 states over the course of the 20th Century was from adjustments to data, not directly measured, and we have a case that there has been essentially no warming at all over the last century.


Is this true?  Beats me.  But this is another reason to be skeptical about the hysterical predictions of climate catastrophe that we see every day.


Anonymous said...

But that would indicate that the Aussie politicians were a bit premature in passin' their carbon tax. And the Chicago Carbon Exchange might've actually failed for a reason. And we don't need to divert corn from the food chain to make vehicle fuel. Maybe AlGore's "documentary" was all Bravo Sierra. Why, if this is true, it might almost make one believe that we've been led astray by those who stand to make some money for all that alarmist claptrap.
Nah, what's wrong with me for even thinkin' that way?

Rob J

SiGraybeard said...

I find it very encouraging that NOAA would make the effort to actually determine the truth. At least they show interest in the truth and not just torturing the data until it confesses, like Hansen at NASA or the Hadley CRU.

When I first started reading about this and saw how they adjusted data series, not just once to fill in missing entries or errors, but over and over it immediately made me pull the BS flag.