Nature's style: Naturally trendy

Cohn, Timothy A.; Lins, Harry F.

doi:10.1029/2005gl024476

Cited by 336 publications

(334 citation statements)

References 25 publications

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“…It is known that there are various robust nonparametric trend estimators (e.g. Helsel and Hirsch, 1992;Moberg and Jones, 2005;Moberg et al, 2006) but the trend magnitude is quite similar, whereas the corresponding statistical significance is less certain (Cohn and Lins, 2005). Special attention is paid to the statistical modelling of the temporal variation of weather events represented as count data (e.g.…”

Section: Methodsmentioning

confidence: 99%

Spatial and temporal variability of climate extremes in Romania and associated large‐scale mechanisms

et al. 2014

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ABSTRACT:The simultaneous variability of several climate extremes in Romania on the one hand and the understanding of the large-scale mechanisms responsible for this variability on the other are examined. Ten indices associated with temperature and precipitation extremes computed at high spatial resolution for the period 1961-2010 are analysed. Significant increasing trends for the temperature extremes are detected in all seasons, except for autumn, with the highest increasing rate in summer and the lowest in spring. Regarding precipitation extremes, significant increasing trends over large areas in the frequency of very wet days and maximum daily amount during autumn and in the maximum duration of dry spells during summer are the only ones detected.The large-scale mechanisms responsible for these characteristics of variability, especially the simultaneous variability of several climate extremes, are identified through the canonical correlation analysis applied to a combination of various large-scale predictors and to combined climate extremes.In winter, it was found that the thermodynamic factor (represented by air temperature anomalies at 850 hPa) mainly controls the trends of temperature extremes in Romania, whereas the dynamic one (represented by the sea level pressure anomalies) controls the pattern of trend magnitude. Regarding precipitation extremes, the role of the two factors is reversed. The Carpathians' influence is noted for this season. In summertime, the thermodynamic factor is dominant for both temperature and precipitation extremes analysed in this article. For temperature extremes, the T850 alone could explain their variability characteristics, whereas for precipitation extremes (frequency and duration) the SH700 has the dominant role, except for the maximum duration of dry intervals, which is controlled by a combination of T850 and SH700 anomalies. The connections found in this study are strong and explain a great part of the total observed variance, showing that these results can be used in a future study to build skilful statistical downscaling models, simultaneously for several seasonal climate extremes, giving the results more physical coherence.

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Section: Methodsmentioning

confidence: 99%

Spatial and temporal variability of climate extremes in Romania and associated large‐scale mechanisms

et al. 2014

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“…A linear trend in climatological or hydrological records is no more than a hypothesis which can only be falsified (as it almost certainly will be) after the passage of years and possibly decades. This point has been forcefully made by Cohn & Lins (2005), whilst the weakness inherent in much of the current methodology in statistical hydrology applied to timeseries analysis has been emphasized by Koutsoyiannis (2003) and Koutsoyiannis & Montanari (2007a).…”

Section: Trendsmentioning

confidence: 99%

“…From this point of view, the failure to find a significant difference between prediction and observation is an indication of success rather than failure. Indeed the exaggerated importance given to significance-testing is widely deplored by many professional statisticians (Cohn & Lins, 2005); there is also a website (http://www.indiana.edu/∼stigtsts/refs.html) giving examples of their inappropriate use. The limitations of significance testing are also set out very clearly by Cox & Hinkley (1974, p. 81 In hydro-climatological research, the second possibility mentioned in this quotation from Cox & Hinkley appears to be more than "probably rarer in applications", since it is not difficult to find accounts of highly significant multiple regressions whose predictive potential is very low; a recent paper in Physical Review Letters presented a multiple regression equation "with a Pearson correlation coefficient r = 0.66" [corresponding to 43% explained variation] "significant to 99.99%".…”

Section: Exaggerated Importance Of Statistical Tests Particularly Thmentioning

confidence: 99%

On the (mis)use of statistical methods in hydro-climatological research

Clarke

2010

Hydrological Sciences Journal

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This paper discusses four areas where, in the author's view, statistical methods are misused in reporting results of hydro-climatological research. They are: (i) the use of the same data set both to suggest a hypothesis (commonly of trend over time) and to test it; (ii) failure to use an appropriate significance level for tests in which a number of hypotheses are tested, even when the data sets used are mutually uncorrelated; (iii) failure to account for spatial correlations between variables, whether these are explanatory or response variables; and (iv) exaggerated importance given to statistical tests of significance, in particular to the 5% and 1% significance levels. Key words hydro-climatology; statistical methods; trend detection; hypothesis testingDe l'utilisation (incorrecte) de méthodes statistiques dans la recherche hydro-climatologique Résumé Cet article discute quatre domaines dans lesquels, du point de vue de l'auteur, les méthodes statistiques sont incorrectement utilisées pour rendre compte des résultats de recherche en hydro-climatologie. Ceux-ci sont: (i) l'utilisation des mêmes données à la fois pour suggérer une hypothèse (typiquement de tendance temporelle) et pour la tester; (ii) l'échec à utiliser un niveau de significativité approprié pour les tests portant sur plusieurs hypothèses, même quand les jeux de données utilisés sont mutuellement non-corrélés; (iii) l'échec à prendre en compte les corrélations spatiales entre les variables, qu'il s'agisse de variables explicatives ou conséquentes; et (iv) une importance exagérée donnée aux tests statistiques de significativité, en particulier aux niveaux de significativité 5% et 1%.

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“…Before performing these analyses, whether the lag-1 correlation is significantly different from 0 at the 5% level is checked. The presence of autocorrelation can affect the significance of the trend tests (Kulkarni and von Storch, 1995;Yue et al, 2003;Yue and Wang, 2004;Hamed, 2009), resulting in the detection of statistically significant trends even though no trend is present (Cox and Stuart, 1955;Cohn and Lins, 2005). Out of 27 stations, only one has a lag-1 value slightly outside of the 95% confidence intervals.…”

Section: Stationaritymentioning

confidence: 99%

Analyses of extreme flooding in Austria over the period 1951–2006

Villarini

Smith

Serinaldi

et al. 2011

Intl Journal of Climatology

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ABSTRACT:Analyses of extreme flooding in Austria is performed using daily discharge time series from 27 stations over the period . The main research questions revolve around: (1) temporal non-stationarities in the flood record, (2) upper tail and scaling properties of the flood peak records, and (3) relation between magnitude and frequency of flooding and the North Atlantic Oscillation (NAO). Two datasets are derived from the daily discharge time series: annual maximum daily discharge and peaks-over-threshold (POT) data. The validity of the stationarity assumption in the annual maximum discharge record is assessed by investigating the presence of abrupt and slowly varying changes using nonparametric tests. The time series are tested for abrupt changes both in the mean and variance of the flood peak distributions by means of the Pettitt test. The presence of monotonic trends is investigated by means of the Mann-Kendall and Spearman tests. Violations of the stationarity assumption are associated with abrupt rather than gradual changes. These step changes generally involve river regulation through construction of dams or other major engineering works. It is not possible to make conclusive statements about the presence of an anthropogenic climate change signal in the flood peak record. Similar conclusions are obtained when focussing on the frequency of POT floods. The Generalised Extreme Value distribution is used to study the upper tail and scaling properties of annual maximum daily discharge records. The location and scale parameters exhibit power-law behaviour as a function of drainage area. The shape parameters indicate that the flood peak distributions for Austria have a heavy tail. Non-stationary modelling of the annual maximum daily discharge and POT time series is used to explore the relation between flood magnitude and frequency and NAO. The results indicate that NAO is a significant covariate in explaining the magnitude and frequency of occurrence of flooding over a large part of Austria.

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Nature's style: Naturally trendy

Cited by 336 publications

References 25 publications

Spatial and temporal variability of climate extremes in Romania and associated large‐scale mechanisms

Spatial and temporal variability of climate extremes in Romania and associated large‐scale mechanisms

On the (mis)use of statistical methods in hydro-climatological research

Analyses of extreme flooding in Austria over the period 1951–2006

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