An investigation into the impact of using various techniques to estimate Arctic surface air temperature anomalies

Dodd, E. M. A., Merchant, C. J. ORCID: https://orcid.org/0000-0003-4687-9850, Rayner, N. A. and Morice, C. P. (2015) An investigation into the impact of using various techniques to estimate Arctic surface air temperature anomalies. Journal of Climate, 28 (5). pp. 1743-1763. ISSN 1520-0442 doi: 10.1175/JCLI-D-14-00250.1

Abstract/Summary

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 were investigated using reanalysis data as a testbed. Techniques which interpolated anomalies were found to result in smaller errors than non-interpolating 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. Non-interpolating 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/37725
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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