2018
DOI: 10.1002/joc.5875
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Persistence of observed air temperatures in Iceland

Abstract: A large data set from 40 weather stations in Iceland is explored for persistence in monthly mean temperatures. There are great seasonal and regional variations in the persistence. Extremely high values of correlation (r > 0.8) of temperatures with subsequent months are found. These values are higher than reported elsewhere in the scientific literature. The highest values are found in coastal regions in the summer, while in the early winter there is overall little correlation. In general, there are two distinct… Show more

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Cited by 3 publications
(3 citation statements)
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References 33 publications
(45 reference statements)
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“…The two temperatures were selected to approximate conditions of the larval habitats, and air temperatures that adults could be exposed to after emergence. Air temperatures during the summer in southern Iceland range from approximately 6°C (World Weather Information Service 2020) to more than 10°C (Degenhardt and Ólafsson 2019). Thus, our 6°C treatment simulated coolest summer air temperatures.…”
Section: Methodsmentioning
confidence: 91%
“…The two temperatures were selected to approximate conditions of the larval habitats, and air temperatures that adults could be exposed to after emergence. Air temperatures during the summer in southern Iceland range from approximately 6°C (World Weather Information Service 2020) to more than 10°C (Degenhardt and Ólafsson 2019). Thus, our 6°C treatment simulated coolest summer air temperatures.…”
Section: Methodsmentioning
confidence: 91%
“…Weatherhead et al (2010) characterised daily temperature stationarity in the Arctic based on lag-1 autocorrelation. Degenhardt and Ólafsson (2019) and Kolstad et al (2015) calculated lag-1 autocorrelation to highlight intra-seasonal stationarity of monthly-mean temperatures in Iceland and Europe, respectively, and Li and Thompson (2021) applied autocorrelation to daily temperature series and found it was strongly related to the average length of warm and cold episodes across the world. Kolstad et al (2017) even used autocorrelation analysis in a causal discovery framework by regressing temperature values against previous ones and including potential covariates.…”
Section: Autocorrelationmentioning
confidence: 99%
“…Weatherhead et al ( 2010) characterized daily temperature quasi-stationarity in the Arctic based on lag-1 autocorrelation. Degenhardt and Ólafsson (2019) and Kolstad et al (2015) calculated lag-1 autocorrelation to highlight intra-seasonal quasi-stationarity of monthly mean temperatures in Iceland and Europe, respectively, and Li and Thompson (2021) applied autocorrelation to daily temperature series and found it was strongly related to the average length of warm and cold episodes across the world. Kolstad et al (2017) even used autocorrelation analysis in a causal discovery framework by regressing temperature values against previous ones and including potential covariates.…”
Section: Autocorrelationmentioning
confidence: 99%