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Uncertainty Quantification in Data-Poor Spatial Averaging: An Update to the NASA GISS Surface Temperature Analysis (GISTEMP)
Authors: Nathan Lenssen, Reto Ruedy, Gavin Schmidt
Presented by Nathan Lenssen
The Goddard Institute for Space Studies (GISS) analysis of global surface temperature change is one of the most publicized climate data products, aimed at quantifying the observed extent of climate change. However, global temperature estimates are interpolated from weather stations which are sparsely distributed, especially in remote regions where temperature increases may be the largest. We propose an improved method for quantifying the error in spatial averaging that arises from incomplete spatial coverage of weather stations. In tandem, we compare our global temperature and uncertainty estimates with the established NCDC and HadCRU analyses as well as the newer Berkeley Earth method.
(Bring your lunch)