275-287
en
Part of the debate over possible climate changes centers on the possibility that the changes observed over the previous century are natural in origin. This raises the question of how large a change could be expected as a result of natural variability. If the climate measurement of interest is modelled as a stationary (or related) Gaussian time series, this question can be answered in terms of (a) the way in which change is estimated, and (b) the spectrum of the time series. These computations are illustrated for 128 years of global temperature data using some simple measures of change and for a variety of possible temperature spectra. The results highlight the time scales on which it is important to know the magnitude of natural variability. The uncertainties in estimates of trend are most sensitive to fluctuations in the temperature series with periods from approximately 50 to 500 years. For some of the temperature spectra, it was found that the standard error of the least squares trend estimate was 3 times the standard error derived under the naïve assumption that the temperature series was uncorrelated. The observed trend differs from zero by more than 3 times the largest of the calculated standard errors, however, and is therefore highly significant.
1992-07
false
research_article
2019-04-11T13:48
1992-07-01
https://scigraph.springernature.com/explorer/license/
articles
http://link.springer.com/10.1007/BF00139727
Climate spectra and detecting climate change
Nychka
Douglas
Economics
10.1007/bf00139727
doi
Econometrics
21
readcube_id
750c1c8ddbf6a72d661f54bf43769d0023de3b4e3f09ba575d572ba7c002f06e
Bloomfield
Peter
1573-1480
0165-0009
Climatic Change
Springer Nature - SN SciGraph project
3
North Carolina State University
Department of Statistics, North Carolina State University, 27695-8203, Raleigh, NC, USA
dimensions_id
pub.1024569108