Assessing homogeneity of land surface air temperature observations using sparse-input reanalyses

Ian M. Gillespie, Leopold Haimberger, Gilbert P. Compo, Peter Thorne

State-of-the-art homogenisation approaches for any test site rely upon the availability of a sufficient number of neighbouring sites with similar climatic conditions and a sufficient quantity of overlapping measurements. These conditions are not always met, particularly in poorly sampled regions and epochs. Modern sparse-input reanalysis products which are constrained by observed sea surface temperatures, sea-ice and surface pressure observations, continue to improve, offering independently produced surface temperature estimates back to the early 19th century. This study undertakes an exploratory analysis on the applicability of sparse-input reanalysis to identify breakpoints in available basic station data. Adjustments are then applied using a variety of reanalysis and neighbour-based approaches to produce four distinct estimates. The methodological independence of the approach may offer valuable insights into historical data quality issues. The resulting estimates are compared to Global Historical Climatology Network version 4 (GHCNMv4) at various aggregations. Comparisons are also made with five existing global land surface monthly time series. We find a lower rate of long-term warming which principally arises in differences in estimated behaviour prior to the early 20th century. Differences depend upon the exact pair of estimates, varying between 15 and 40% for changes from 1850–1900 to 2005–2014. Differences are much smaller for metrics starting after 1900 and negligible after 1950. Initial efforts at quantifying parametric uncertainty suggest this would be substantial and may lead to overlap between these new estimates and existing estimates. Further work would be required to use these data products in an operational context. This would include better understanding the reasons for apparent early period divergence including the impact of spatial infilling choices, quantification of parametric uncertainty, and a means to update the product post-2015 when the NOAA-CIRES-DOE 20CRv3 sparse input reanalysis product, upon which they are based, presently ceases.

Institut für Meteorologie und Geophysik
Externe Organisation(en)
National Oceanic and Atmospheric Administration, National University of Ireland, Maynooth, University of Colorado, Boulder
International Journal of Climatology
Anzahl der Seiten
ÖFOS 2012
105206 Meteorologie
ASJC Scopus Sachgebiete
Atmospheric Science
Sustainable Development Goals
SDG 13 – Maßnahmen zum Klimaschutz
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