Statistical significance of rising and oscillatory trends in global ocean and land temperature in the past 160 years
Various interpretations of the notion of a trend in the context of global warming are discussed, contrasting the difference between viewing a trend as the deterministic response to an external forcing and viewing it as a slow variation which can be separated from the background spectral continuum of long-range persistent climate noise. The emphasis in this paper is on the latter notion, and a general scheme is presented for testing a multi-parameter trend model against a null hypothesis which models the observed climate record as an autocorrelated noise. The scheme is employed to the instrumental global sea-surface temperature record and the global land temperature record. A trend model comprising both monotonic trend and non-monotonic multidecadal variability is proposed, represented by a linear plus an oscillatory trend with period around 70 yr. The statistical significance of the trends are tested against three different null models: first-order autoregressive process, fractional Gaussian noise, and fractional Brownian motion. The parameters of the null models are estimated from the instrumental record. The estimated linear trend rejects the null independent of the strength of the oscillation, but the oscillation amplitude rejects the null only if the rising trend is taken as significant. The results suggest that the global land record may be better suited for detection of the global warming signal than the ocean record.
An earlier version of this article is part of Lene Østvand's doctoral thesis which is available in Munin at http://hdl.handle.net/10037/6391
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