气候变化领域集成服务门户

Growing season length (GSL) indices derived from surface air temperature are frequently used in climate monitoring applications. The widely used Expert Team on Climate Change Detection and Indices (ETCCDI) definition aims to give a broadly applicable measure of the GSL that is indicative of the duration of the mild part of the year. In this paper long-term trends in that index are compared with an alternative measure calculated using a time series decomposition technique (empirical ensemble mode decomposition [EEMD]). It is demonstrated that the ETCCDI index departs from the mild-season definition as its start and end dates are determined by temperature events operating within the synoptic timescale; this raises the inter-annual variance of the index. The EEMD-derived index provides a less noisy and more realistic index of the GSL by filtering out the synoptic-scale variance and capturing the annual-cycle and longer timescale variability. Long-term trends in the GSL are comparable between the two indices, with an average increase in length of around 5 days/decade observed for the period 1965-2016. However, the results using the EEMD index display a more coherent picture of significant trends than has been previously observed. Furthermore, the EEMD-derived growing season parameters are more closely related to variations in seasonal-mean hemispheric-scale atmospheric circulation patterns, with around 57% of the inter-annual variation in the start of the growing season being connected to the North Atlantic Oscillation and East Atlantic patterns, and around 55% of variation in the end of the growing season being associated with East Atlantic/west Russia-type patterns.