An Extreme Value Analysis of Wind Speed over the European and Siberian Parts of Arctic Region

Abstract

Multiyear observed time series of wind speed for selected points of the Arctic region (data of station network from the Kola Peninsula to the Chukotka Peninsula) are used to highlight the important peculiarities of wind speed extreme statistics. How largest extremes could be simulated by climate model (the INM-CM4 model data from the Historical experiment of the CMIP5) is also discussed. Extreme value analysis yielded that a volume of observed samples of wind speeds are strictly divided into two sets of variables. Statistical properties of one population are sharply different from another. Because the common statistical conditions are the sign of identity of extreme events we therefore hypothesize that two groups of extreme wind events adhere to different circulation processes. A very important message is that the procedure of selection can be realized easily based on analysis of the cumulative distribution function. The authors estimate the properties of the modelled extremes and conclude that they consist of only the samples, adhering to one group. This evidence provides a clue that atmospheric model with a coarse spatial resolution does not simulate special mechanism responsible for appearance of largest wind speed extremes. Therefore, the tasks where extreme wind is needed cannot be explicitly solved using the output of climate model. The finding that global models are unable to capture the wind extremes is already well known, but information that they are members of group with the specific statistical conditions provides new knowledge. Generally, the implemented analytical approach allows us to detect that the extreme wind speed events adhere to different statistical models. Events located above the threshold value are much more pronounced than representatives of another group (located below the threshold value) predicted by the extrapolation of law distributions in their tail. The same situation is found in different areas of science where the data referring to the same nomenclature are adhering to different statistical models. This result motivates our interest on our ability to detect, analyze, and understand such different extremes. A. Kislov, T. Matveeva 206

Analysis of Observed and Modelled Near-Surface Wind Extremes over the Sub-Arctic Northeast Pacific

“…It was assessed as 72 hours. Similar values for such aim were calculated previously [7] [13] [14] [15].…”

“…As was established earlier [7], and we can see it again (Figure 2), the tail diverges from the Weibull model starting with a certain large threshold value th U .…”

“…However, the independence is very often not the case and extreme value theory could be extended for dependent time series [1] [2]. As far as the identical distribution is concerned, our analysis showed that the set of wind speed extremes obtained from observations from the European and Siberian Arctic area is a mixture of two different subsets each neatly described by the Weibull distribution [7]. Representatives of each population were marked as "Black Swans" (hereafter BSs) and "Dragons" (hereafter Ds) based on the terminology introduced by N. N. Taleb [8] and D. Sornette [9].…”

“…In Figure 2, we plot several cumulative distribution functions (cdfs). The images are the "Weibull Plots" (see details in [7]), which represent the specific transformation of the data, and a straight line is recovered if the sample has a Weibull distribution:…”

“…In a previous paper [7] we concluded that the collection of wind extremes modelled by a general circulation model (GCM) consisted only of samples conforming to the BSs group. The same conclusion was reached after a reanalysing the datasets, confirming that they do not contain observed exceptional outliers.…”