Temperature series contain episodes of high and low values. About every 365 days or 1 year or fourth season the values in temperature series reach about the same magnitude. The time elapsed between two values of the same magnitude is defined as the period of a cycle. This temperature series is dominated by the annual cycle with a period of 1 year or 365 days.
The number indicating how many repetitions of a cycle occur per time interval is called the frequency.
Temperature plays an important role in electricity demand and generation; heat waves and cold waves represent potential threats to the energy system due to the sustained nature of such events. In the current climate change context, it is important to anticipate possible changes in the occurrence of the most severe events to adapt energy system planning and management. However, because such events are rare, any estimation of their frequency is uncertain and a very large data sample is required to reduce any sampling uncertainty. In this paper, a way of expanding multimodel or single-model ensembles in order to produce a much larger ensemble of temporal evolutions of a temperature based indicator, is presented. This ensemble can then be used to statistically estimate the occurrence and occurrence changes in time of the most extreme events. The generation of additional time series is made by use of a stochastic weather generator designed for the simulation of the stochastic residuals once the trends and seasonalities in mean and standard deviation of the temperature have been removed. The approach is first described and applied to an observed 32-station weighted average temperature time series in France, used as an indicator for the role of temperature in electricity demand, in a cross-validation setting. Using the observed time series of this thermal indicator over the period 1950–2017, the approach is calibrated over the period 1955–1986 to generate similar time series over the whole period 1955–2017 using different CMIP5 Global Climate Model simulations. Then the generated set of time series are validated for the mean annual cycle and distributions of heat waves and cold waves over the period 1987–2017. The choices made for generation and reconstruction are detailed and motivated. The methodology is used to generate a large set of indicator evolutions covering recent past and near future periods, which is used to identify changes in the frequencies of the most severe heat waves or cold waves. The increased frequency of very severe heat waves is found in agreement with previous studies, and estimates of the decreased frequency of the most severe cold waves are also provided.
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