Confidence intervals for time averages in the presence of long‐range correlations, a case study on Earth surface temperature anomalies
Abstract Time averages, a standard tool in the analysis of environmental data, suffer severely from long‐range correlations. The sample size needed to obtain a desired small confidence interval can be dramatically larger than for uncorrelated data. We present quantitative results for short‐ and long...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-09-01
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| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/2016GL069555 |
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| Summary: | Abstract Time averages, a standard tool in the analysis of environmental data, suffer severely from long‐range correlations. The sample size needed to obtain a desired small confidence interval can be dramatically larger than for uncorrelated data. We present quantitative results for short‐ and long‐range correlated Gaussian stochastic processes. Using these, we calculate confidence intervals for time averages of surface temperature measurements. Temperature time series are well known to be long‐range correlated with Hurst exponents larger than 1/2. Multidecadal time averages are routinely used in the study of climate change. Our analysis shows that uncertainties of such averages are as large as for a single year of uncorrelated data. |
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| ISSN: | 0094-8276 1944-8007 |