Spring Temperature and Snow Cover Climatology Drive the Advanced Springtime Phenology (1991–2014) in the European Alps

Abstract

The average global temperature has been reported rising in recent decades, particularly in mountainous regions (IPCC, 2018, 2018). In the European Alps, the average temperature has increased by about 1.1°C over the past 100 years (Böhm et al., 2001). The European Alps were reported to be particularly sensitive to inter-annual variations in climatic drivers such as temperature, precipitation, and solar radiation (Beniston et al., 2003; Gobiet et al., 2014), as well as, depending on elevation, snow cover extent and its depth (Jonas et al., 2008; Xie et al., 2017). Recent climatic warming was reported to have led to considerable variance and shifts in plant phenology across ecosystems (Graven et al., 2013; Parmesan, 2006; Parmesan & Yohe, 2003) and thus may threaten species with synchronized life cycles (Dozier & Painter, 2004; Post et al., 2009). The firm link between phenological changes and seasonal temperature variability suggests that broad-scale and long-term phenology observations could serve as a proxy for detecting and tracing global climate change over space and time (

Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems

“…8). This finding was consistent with previous studies indicating that more snow cover and lower winter temperature were expected to delay snowmelt and spring phenology and thus vegetation growth, particularly at high latitudes 20,[29][30][31] . Alternatively, less winter snow cover may decrease soil moisture availability and limit vegetation growth, particularly in the mid-latitudes [32][33][34] .…”

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

“…Although the close relationship between snowmelt date and SOS has been widely reported (Qi et al, 2021;Sa et al, 2021;Xie et al, 2021), the direct "growth period" effect of snow cover (low path coefficient) appears to be weak in the TP when compared with the "temperature effect" and "moisture effect." This mechanism has also been observed at low latitudes, such as Central Asia and North America .…”

In-situ Temperature Stations Elucidate Species’ Phenological Responses to Climate in the Alps, but Meteorological and Snow Reanalysis Facilitates Broad Scale and Long-Term Studies

“…When the missing data period was equal or inferior to 4 h, we used the R function "na.approx" from package zoo (Zeileis and Grothendieck, 2005) to interpolate missing values. At a given time, if sensor 1 or 2 (Figure 1B) had missing values, and sensor 3 recorded two following values inferior to 2 °C, superior to −2 °C and with a variance inferior to 0.2 °C, we estimated that the missing values equaled zero, meaning that sensor 1, 2 and 3 were under the snow.…”

“…These gridded datasets typically provide interpolated data at monthly, seasonal and annual time steps at a resolution of 1 km 2 but lack the daily or even hourly temporal resolution necessary to calculate many of the aforementioned bioclimate indices. Furthermore, most gridded climate datasets do not provide information about seasonal snow cover at the scale of mesotopographic gradients, which is an essential parameter regulating ecosystem functioning in temperate mountain environments (Xie et al, 2021). The SAFRAN-Crocus-MEPRA (S2M) reanalysis provides meteorological variables including temperature, wind, incoming solar radiation, precipitation and snow pack height (Durand et al, 1993;Vernay et al, 2021).…”