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Abstract

The onset of spring is crucial for planning agricultural practices and has influences on animal activities as well. It is no doubt beneficial to properly forecast spring onset in advance. In this work, we present a new method based on harmonic analysis of daily mean temperature to predict the date of spring onset in the next year. The algorithm of the proposed method considers the memory of the seasonal cycle and can be easily conducted using the local past records. This study is based on gridded observational surface air temperature (SAT) in Europe. The SAT data are first decomposed into harmonics by wavelet transform and each harmonic's time-dependent amplitude and phase are extracted. Then the time evolution for amplitude and phase are modelled by an AR(2) process, where we employ a new method to fit its coefficients from the data. This provides a prediction of the time dependent amplitudes and phases in the next year, from which we compose the future seasonal cycle and identify the prediction of spring onset by threshold crossing. We compare our model with a classical climatological seasonal cycle forecast. While this benchmark forecast leads to only very small variations of the predicted onset date, our method yields predictions which vary by about 15 days from year to year. We verify the correctness of our predictions by a correlation measure and the root mean squared errors.