In a new article just published in the Journal of Geophysical Research-Atmospheres, Pat Michaels and I have concluded that the manipulations for the steep post-1980 period are inadequate, and the global temperature graph showing warming is an exaggeration, at least in the past few decades. Along the way I have also found that the UN agency promoting the global temperature graph has made false claims about the quality of their data. The graph comes from data collected in weather stations around the world. Other graphs come from weather satellites and from networks of weather balloons that monitor layers of the atmosphere. These other graphs didn’t show as much warming as the weather station data, even though they measure at heights where there is supposed to be even more greenhouse gas-induced warming than at the surface. The discrepancy is especially clear in the tropics.
The surface-measured data has many well-known problems. Over the post-war era, equipment has changed, station sites have been moved, and the time of day at which the data are collected has changed. Many long-term weather records come from in or near cities, which have gotten warmer as they grow. Many poor countries have sparse weather station records, and few resources to ensure data quality. Fewer than one-third of the weather stations operating in the 1970s remain in operation. When the Soviet Union collapsed in the early 1990s, more than half the world’s weather stations were closed in a four year span, which means that we can’t really compare today’s average to that from the 1980s. Read a background summary here and a technical paper published in the JGR December 2007 here.
The Abstract states:
Local land surface modification and variations in data quality affect temperature trends in
surface-measured data. Such effects are considered extraneous for the purpose of
measuring climate change, and providers of climate data must develop adjustments to
filter them out. If done correctly, temperature trends in climate data should be
uncorrelated with socioeconomic variables that determine these extraneous factors. This
hypothesis can be tested, which is the main aim of this paper. Using a new data base for
all available land-based grid cells around the world we test the null hypothesis that the
spatial pattern of temperature trends in a widely-used gridded climate data set is
independent of socioeconomic determinants of surface processes and data
inhomogeneities. The hypothesis is strongly rejected (P= 14 7.1 10− × ), indicating that
extraneous (nonclimatic) signals contaminate gridded climate data. The patterns of
contamination are detectable in both rich and poor countries, and are relatively stronger in
countries where real income is growing. We apply a battery of model specification tests to
rule out spurious correlations and endogeneity bias. We conclude that the data
contamination likely leads to an overstatement of actual trends over land. Using the
regression model to filter the extraneous, nonclimatic effects reduces the estimated 1980-
2002 global average temperature trend over land by about half.
Jennifer Marohasy BSc PhD has worked in industry and government. She is currently researching a novel technique for long-range weather forecasting funded by the B. Macfie Family Foundation.