The global warming scandal keeps getting worse. Revelations over the few weeks show that many important assertions in the UN's Intergovernmental Panel on Climate Change were based on misquotes and false claims from environmental groups, not on published academic research as it was originally presented. This is on top of the recent mess regarding data, where the three most relied-on data series used by the United Nation's Intergovernmental Panel on Climate Change 2007 assessment report still have not been released. Other data simply never seem to have existed or cannot be provided to other scientists.
But probably the most damaging report has come from Joseph D’Aleo, the first Director of Meteorology and co-founder of the Weather Channel, and Anthony Watts, a meteorologist and founder of SurfaceStations.org.
In a January 29 report, they find that starting in 1990, the National Oceanic and Atmospheric Administration (NOAA) began systematically eliminating climate measuring stations in cooler locations around the world. Yes, that's right. They began eliminating stations that tended to record cooler temperatures and drove up the average measured temperature. The eliminated stations had been in higher latitudes and altitudes, inland areas away from the sea, as well as more rural locations. The drop in the number of weather stations was dramatic, declining from more than 6,000 stations to fewer than 1,500.
D’Aleo and Watts show that the jumps in measured global temperature occur just when the number of weather stations is cut. But there is another bias that this change to more urban stations also exacerbates. Recorded temperatures in more urban areas rise over time simply because more densely populated areas produce more heat. Combining the greater share of weather stations in more urban areas over time with this urban heat effect also tends to increase the rate that recorded temperatures tend to rise over time.
Their report provides examples of how the systematic elimination of stations and unexplained adjustments in temperature data caused measured temperatures to rise for Australia, New Zealand, Canada, Norway, Sweden, and the United States. Many adjustments change what would have been a drop in temperatures into an increase. Take New Zealand, where D’Aleo and Watts note: “About half the adjustments actually created a warming trend where none existed; the other half greatly exaggerated existing warming.”
D’Aleo and Watts also criticize how these data adjustments are often unexplained or poorly documented. In one case involving James Hansen at NASA a Freedom of Information Act request has been unanswered for over two years.
The report details other fascinating temperature biases. For example, Siberia has experienced one the greatest increases in recorded warming. A large drop in the number of stations and the some missing data can explain part of the change, but apparently during the Soviet-era areas with lower recorded temperatures received more fuel and money, creating a real incentive for weather stations to lie.
The D’Aleo and Watts’ report also helps answer some puzzling questions about the report. One of the major ones questions has been the divergence in temperature data recorded by satellites in space and down here on the ground. That difference was very small when satellites first started being used during the 1980s but has grown over time, with ground observations showing a rise in temperature relative to the satellite data. The urban warming effect may not only explain this, but also why land warming has been so much greater than ocean warming.
All three terrestrial global-temperature datasets (National Oceanic and Atmospheric Administration/ National Climatic Data Center, NASA Goddard Institute for Space Studies, and University of East Anglia) really rely on the same measures of surface temperatures. These three sources do not provide independent measures of how the world’s temperatures have changed over time. The relatively small differences that do arise from these three institutions result from how they adjust the raw data.
Flawed data leads to flawed conclusions. Whether these errors were made on purpose or by accident ultimately doesn’t matter. Especially with tens of trillions of dollars at stake, science should be more transparent than this. But we can only draw one conclusion: the studies based on that data, no matter the number, are ultimately unreliable.