Statistics and Climate

Guttorp, Peter

doi:10.1146/annurev-statistics-022513-115648

Cited by 9 publications

(1 citation statement)

References 43 publications

(36 reference statements)

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“…In other words,

{normalN}_{t}

in equation is assumed to be white noise. It is worth noting that although proper modeling of the noise term is vital for properly assessing the significance of a trend (Guttorp, ), there will be uncertainty no matter what kind of noise is assumed. In sections 3.1–3.3, we first consider the case in which the noise term is assumed to be zero.…”

Section: Methodsmentioning

confidence: 99%

Investigating the Uncertainty in Global SST Trends Due to Internal Variations Using an Improved Trend Estimator

et al. 2018

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A new criterion was proposed recently to measure the influence of internal variations on secular trends in a time series. When the magnitude of the trend is greater than a theoretical threshold that scales the influence from internal variations, the sign of the estimated trend can be interpreted as the underlying long‐term change. Otherwise, the sign may depend on the period chosen. An improved least squares method is developed here to further reduce the theoretical threshold and is applied to eight sea surface temperature (SST) data sets covering the period 1881–2013 to investigate whether there are robust trends in global SSTs. It is found that the warming trends in the western boundary regions, the South Atlantic, and the tropical and southern‐most Indian Ocean are robust. However, robust trends are not found in the North Pacific, the North Atlantic, or the South Indian Ocean. The globally averaged SST and Indian Ocean Dipole indices are found to have robustly increased, whereas trends in the zonal SST gradient across the equatorial Pacific, Niño 3.4 SST, and the Atlantic Multidecadal Oscillation indices are within the uncertainty range associated with internal variations. These results indicate that great care is required when interpreting SST trends using the available records in certain regions and indices. It is worth noting that the theoretical threshold can be strongly influenced by low‐frequency oscillations, and the above conclusions are based on the assumption that trends are linear. Caution should be exercised when applying the theoretical threshold criterion to real data.

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“…In other words,