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| Titel |
A framework for benchmarking of homogenisation algorithm performance on the global scale |
| VerfasserIn |
K. Willett, C. Williams, I. T. Jolliffe, R. Lund, L. V. Alexander, S. Brönnimann, L. A. Vincent, S. Easterbrook, V. K. C. Venema, D. Berry, R. E. Warren, G. Lopardo, R. Auchmann, E. Aguilar, M. J. Menne, C. Gallagher, Z. Hausfather, T. Thorarinsdottir, P. W. Thorne |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2193-0856
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Instrumentation, Methods and Data Systems ; 3, no. 2 ; Nr. 3, no. 2 (2014-09-25), S.187-200 |
| Datensatznummer |
250115222
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| Publikation (Nr.) |
 copernicus.org/gi-3-187-2014.pdf |
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| Zusammenfassung |
| The International Surface Temperature Initiative (ISTI) is striving towards
substantively improving our ability to robustly understand historical land
surface air temperature change at all scales. A key recently completed first
step has been collating all available records into a comprehensive open
access, traceable and version-controlled databank. The crucial next step is
to maximise the value of the collated data through a robust international
framework of benchmarking and assessment for product intercomparison and
uncertainty estimation. We focus on uncertainties arising from the presence
of inhomogeneities in monthly mean land surface temperature data and the
varied methodological choices made by various groups in building homogeneous
temperature products. The central facet of the benchmarking process is the
creation of global-scale synthetic analogues to the real-world database
where both the "true" series and inhomogeneities are known (a luxury the
real-world data do not afford us). Hence, algorithmic strengths and
weaknesses can be meaningfully quantified and conditional inferences made
about the real-world climate system. Here we discuss the necessary framework
for developing an international homogenisation benchmarking system on the
global scale for monthly mean temperatures. The value of this framework is
critically dependent upon the number of groups taking part and so we
strongly advocate involvement in the benchmarking exercise from as many data
analyst groups as possible to make the best use of this substantial effort. |
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