A generalized, bioclimatic index to predict foliar phenology in response to climate

Abstract

The phenological state of vegetation significantly affects exchanges of heat, mass, and momentum between the Earth's surface and the atmosphere. Although current patterns can be estimated from satellites, we lack the ability to predict future trends in response to climate change. We searched the literature for a common set of variables that might be combined into an index to quantify the greenness of vegetation throughout the year. We selected as variables: daylength (photoperiod), evaporative demand (vapor pressure deficit), and suboptimal (minimum) temperatures. For each variable we set threshold limits, within which the relative phenological performance of the vegetation was assumed to vary from inactive (0) to unconstrained (1). A combined Growing Season Index (GSI) was derived as the product of the three indices. Ten-day mean GSI values for nine widely dispersed ecosystems showed good agreement (r40.8) with the satellitederived Normalized Difference Vegetation Index (NDVI). We also tested the model at a temperate deciduous forest by comparing model estimates with average field observations of leaf flush and leaf coloration. The mean absolute error of predictions at this site was 3 days for average leaf flush dates and 2 days for leaf coloration dates. Finally, we used this model to produce a global map that distinguishes major differences in regional phenological controls. The model appears sufficiently robust to reconstruct historical variation as well as to forecast future phenological responses to changing climatic conditions.

Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models

“…The modeling of leaf senescence has clearly received less attention than that of spring phenophases. The design of leaf senescence models is accordingly more variable; however, temperature and/or photoperiod thresholds (White et al 1997) or running means (Jolly et al 2005) are regularly employed as triggers of leaf senescence in models (Table 1). Models of the progression of leaf senescence that are similar to Eq.…”

The annual cycles of phytoplankton biomass

“…Although the timing of these life-history transitions varies among species (Menzel & Fabian 1999;Penuelas & Filella 2001) and regions (White et al 2009), biomass of vegetation on land follows a recurrent cycle of growth and senescence with a 12-month periodicity (Myneni et al 1997;Richardson et al 2010). At mid and high latitudes, canopy greenness is controlled by temperature and photoperiod ( Jolly et al 2005), so a temperature increase of about 0.88C in Eurasia and North America has advanced spring green-up by 4 -6 days and delayed onset of senescence by 8 -11 days over the past two decades (Zhou et al 2001). Our ability to detect large-scale phenological shifts at this resolution is based on key life-history attributes of temperate-boreal plants: seasonal transitions between growth and senescence that have a fixed, 12-month periodicity; and recurrent timing of those transitions strongly cued to seasonal climate.…”

Identifying anomalously early spring onsets in the CESM large ensemble project

“…False springs create the risk for significant ecological changes for plants and other species adapting to the transition period between winter and spring (Schwartz et al 2006;Marino et al 2011;Peterson and Abatzoglou 2014). Meinshausen et al (2011) and Kay et al (2014) Comparing the first leaf correlation coefficients between the ensemble mean and individual members indicate the highest correlations for the LENS future period from 2006-2080, while the lowest correlations and greatest range are found over 1970-2005). This suggests internal climate variability contributes a greater role in early spring onset during the historical period.…”

“…1 The SI-x gridded indices are used to calculate the first leaf and last freeze indices from 1920-2013 in the BEST data set (Mueller 2013) over the Great Lakes region. A simple least squares regression is additionally plotted to note the trend in earlier than normal spring onset over the post-industrialization era (Kanamitsu et al 2002) have been introduced and used to study continental and global scale environmental change (e.g., Jolly et al 2005;Schwartz et al 2006). One such product, the "extended spring indices" Ault et al 2015b) has become particularly widely used in this regard, with recent studies employing it to understand sources of potential predictability on interannual to decadal timescales (McCabe and Ma 2006), long-term historical trends (Schwartz et al 2006McCabe and Ma 2006;McCabe et al 2013;Ault et al 2015a), and future climate model projections (Allstadt et al 2015).…”