NOAA: Warmest June Ever! Caveat: We Made It Up (Corrected)
NOAA and GISS claim a warm Arctic region made this June a record-breaker. They don't have a single thermometer in the Arctic!
August 15, 2010 - 12:05 am
The National Oceanic and Atmospheric Administration (NOAA) claimed that this past June was the warmest ever in its temperature records, which go back to 1880. The global average temperature in June was 61.1 degrees, or 1.22 degrees Fahrenheit above the 20th century average of 59.9 degrees. At face value, this appears to be consistent with the theory that global warming is caused by mankind’s use of fossil fuels. But face value can be deceiving, and the value is not what it appears to be.
In fact, the claim that June 2010 was the warmest on record has no value at all.
NOAA gets its temperature data from the Goddard Institute for Space Studies. They calculate the average temperature of the Earth using data from land-based thermometers and ocean buoy and ship measurements of water temperature. However, there is a major problem with how GISS measures temperature in a very large region — the Arctic.
The problem is that they don’t have any thermometers there. So they make it up. No, really!
Despite this lack of arctic temperature data, GISS shows that this June the area north of eighty degrees latitude was up to four degrees warmer than the long-term average. You must be asking: how can GISS show any temperature readings at all north of eighty degrees if they don’t have any data? Really, I’m not kidding — they make it up.
GISS uses measured temperature data from lower latitudes and then extrapolates them to the Arctic. Using this method, any readings warmer than average in the lower latitudes are pushed into the Arctic by a smoothing technique. GISS uses a 1,200 kilometer smoothing for its data, meaning that the temperature reading for one thermometer is used as the temperature for a 1,200 kilometer box in all directions from that location. Where there are more thermometers, the boxes overlap, and the readings of one thermometer are averaged with others around them. This reduces the effect of each individual thermometer.
But in data-sparse regions, the value of one thermometer takes on a much greater value.