An Investigation into the Impact of using Various Techniques to Estimate Arctic Surface Air Temperature Anomalies*

Abstract

Time series of global and regional mean surface air temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice was investigated using reanalysis data as a test bed. Techniques that interpolated anomalies were found to result in smaller errors than noninterpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies, and simple kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, root-mean-square errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Noninterpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979–2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.

Seepage: Climate change denial and its effect on the scientific community

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Quantifying the impact of early 21st century volcanic eruptions on global-mean surface temperature

“…In VER, the 2003-2012 trend is also positive (þ0.04°C þ/À 0.07°C per decade). This trend is close to BEST and 'Cowtan and Way' , which are the most reliable observations for the analysis of the GMST, due to a better representation of the surface temperature at high latitudes (Dodd et al 2015). The difference between VER and BAK trend is determined from the time series of paired differences between VER and BAK.…”

Air temperature changes in the Arctic in the period 1951–2015 in the light of observational and reanalysis data

“…correlation coefficients (r) and root mean square errors (RMSE,°C) between observational and reanalysed values on the basis of the least-squares regression method (Wilks 2011). For spatial analysis, the ordinary kriging interpolation technique has been applied as it provides only small errors in the distribution of SAT anomalies in the Arctic (Dodd et al 2015).…”