A comparison of some simple methods used to detect unstable temperature responses in tree-ring chronologies

Higher Winter-Spring Temperature and Winter-Spring/Summer Moisture Availability Increase Scots Pine Growth on Coastal Dune Microsites Around the South Baltic Sea

“…Moreover, specific local site conditions together with variations in local climate (e.g., coastal site characteristics, local precipitation), and varying stand age could contribute to the discrepancies in temporal stationarity of climate-growth responses documented in our and other studies. Further, the use of a greater number of intervals, different interval's length or alternative periods could also influence results (Allen et al, 2018) reported by Harvey et al (2020) (31 years moving windows lagged by 1 year over the 1943-2002 interval) and in our study (two-interval approach over the 1903-1959 and 1960-2016 intervals). Undoubtedly, each of the methods used to detect temporal instabilities has its limitation, thus should be chosen carefully depending on the aim of the study (Allen et al, 2018).…”

“…Further, the use of a greater number of intervals, different interval's length or alternative periods could also influence results (Allen et al, 2018) reported by Harvey et al (2020) (31 years moving windows lagged by 1 year over the 1943-2002 interval) and in our study (two-interval approach over the 1903-1959 and 1960-2016 intervals). Undoubtedly, each of the methods used to detect temporal instabilities has its limitation, thus should be chosen carefully depending on the aim of the study (Allen et al, 2018). Finally, in the first half of the 20th century the lower number of station climate data contributing to the gridded climate data could cause artificial climate-growth instabilities because the interpolation was based on less station data and therefore interpolated from farther away compared to the second half of the 20th century (Allen et al, 2018).…”

“…To evaluate possible shifts of the climate responses of trees over time, we correlated the RW and LBI microsite and sub-regional chronologies against climate variables over two consecutive 56 years time intervals (early: 1903-1959 and recent: 1960-2016). We decided for a two-interval comparison approach as a relatively simple and effective method in detecting general changes in climate-growth relationships, which is also a common procedure for validating the temporal stability of climate-growth responses in tree-ring based climate reconstruction (Allen et al, 2018;Babst et al, 2019).…”

“…Undoubtedly, each of the methods used to detect temporal instabilities has its limitation, thus should be chosen carefully depending on the aim of the study (Allen et al, 2018). Finally, in the first half of the 20th century the lower number of station climate data contributing to the gridded climate data could cause artificial climate-growth instabilities because the interpolation was based on less station data and therefore interpolated from farther away compared to the second half of the 20th century (Allen et al, 2018). However, Pärn (2009) and Metslaid et al (2018) documented non-stationary climate-growth relationships over the 1950 to 2000 and 1955 to 2006, respectively, and relied on station climate data in their analyses.…”

Forest responses to last‐millennium hydroclimate variability are governed by spatial variations in ecosystem sensitivity

“…By focusing on spatial variations in ecosystem variability over the last millennium, our analyses suggest spatial variation in ecosystem properties are a more important regulator than spatial variations in climate exposure. Finally, uncertainties in the proxy‐based reconstructions may lower correlations as detrending techniques used to remove non‐climatic signals such as age effects may dampen estimates of centennial‐scale variability (Allen et al ., 2018; Esper et al ., 2018). Despite lower PDSI variability in the LBDA than model drivers, we do not think that spatial variability in hydroclimate variability in the empirical data set is too low to detect effects on ecosystem variability.…”