False Spring over Europe: risk and uncertainties

<p>Understanding the impact of the climate crisis on our planet, and hence, on human and natural life, is a pressing but challenging scientific endeavor. Phenological studies help to build such an understanding by analyzing changes in the timing of biological events. Such changes are, in turn, linked to changes in the likelihood of experiencing false springs. Here we examine the risk and uncertainty of false springs over Europe using the extended spring indices models and the E-OBS weather dataset (1950-onwards). False spring uncertainty is evaluated using the full ensemble of 100 daily weather realizations that accompany the E-OBS dataset. Smart computing is used to handle this relatively large amount of gridded data. Our results show that changes in false spring risk are heterogeneous in space, with increasing risks mostly found in the mid-latitudes and decreasing risks mostly found at high and low latitudes. From 1950 until 1979, there was a substantial increase in false spring risk, whereas the change in false spring risk from 1980 onwards is negligible. Our results also show that false spring uncertainty is highest in western Europe. These results highlight the importance of considering temperature uncertainty in phenological modeling, especially when examining the risk of false springs.</p><p> </p>

Gross Primary Production and False Spring: a spatio-temporal analysis

<p>Phenological information can be obtained from different sources of data. For instance, from remote sensing data or products and from models driven by weather variables. The former typically allows analyzing land surface phenology whereas the latter provide plant phenological information. Analyzing relationships between both sources of data allows us to understand the impact of climate change on vegetation over space and time. For example, the onset of spring is advanced or delayed by changes in the climate. These alterations affect plant productivity and animal migrations.</p><p>Spring onset monitoring is supported by the Extended Spring Index (SI-x), which are a suite of regression-based models for key indicator plant species. These models (Schwartz et al. in 2013) are based on daily maximum and minimum temperature from the first day of the year (January 1<sup>st</sup>). The primary products of these models are the timing of first leaf and first bloom, but they also provide derivative products such as the timing of last freeze day and the risk of frost damage day (damage index) for each year. This information helps to understand if vegetation could have suffered from environmental stressors such as droughts or a late frost events. The effects of environmental stressors in vegetation could be captured by the false spring index, which relates the first leaf day and the last freeze day. Moreover, this information could be used to understand plant productivity as well as to evaluate the economic impact of climate change.</p><p>Previous works studied the relationship between remote sensing and plant level products by means of spatial-temporal analysis between Gross Primary Production (GPP) and a spring onset index. However, they did not consider the possible impact of false spring effect in these relationships. Here, we present a spatial-temporal analysis between GPP and the damage index to better understand the effect of false springs (in annual gross photosynthesis data). The analysis is done for the period 2000 to 2015 over the contiguous US and at spatial resolution of 1 km. We used the MODIS annual sum of GPP and the damage and false spring indices derived from the SI-x models.</p>

Climate change reshapes the drivers of false spring risk across European trees

<p>Temperate and boreal forests are shaped by late spring freezing events after budburst, which are also known as false springs. Research has generated conflicting results on whether or not these events will change with climate change, potentially because---to date---no study has compared the myriad climatic and geographic factors that contribute to a plant's risk of a false spring. We assessed and compared the strength of the effects of mean spring temperature, distance from the coast, elevation and the North Atlantic Oscillation (NAO) using PEP725 leafout data for six temperate, decidious tree species across 11,648 sites in Central Europe and how these predictors shifted with climate change. Across species before recent warming, mean spring temperature and distance from the coast were the strongest predictors, with higher mean spring temperatures associated with decreased risk in false springs (–7.64% per 2°C increase) and sites further from the coast experiencing an increased risk (5.32% per 150km from the coast). Elevation (2.23% per 200m increase in elevation) and NAO index (1.91% per 0.3 increase) also increased false spring risk. </p><p>With climate change, elevation and distance from coast---i.e., the geographic factors---remain relatively stable, while climatic factors shifted in magnitude for mean spring temperature (down to -2.84% in risk per 2°C), and in direction, with positive NAO phases leading to lower risk (-9.15% per 0.3). The residual effects of climate change---unexplained by the climatic and geographic factors already included in the model---magnified the species-level variation in risk, with risk increasing among early-leafout species (i.e., <em>Aesculus hippocastanum</em>, <em>Alnus glutinosa</em> and <em>Betula pendula</em>) but a decline or no change in risk among late-leafout species (i.e., <em>Fagus sylvatica</em>, <em>Fraxinus excelsior</em> and <em>Quercus robur</em>). Our results show that climate change has reshaped the major drivers of false spring risk and highlight how considering multiple factors can yield a better understanding of the complexities of climate change.</p>