1748-9326/10/10/104008

The onset of spring plant growth has shifted earlier in the year over the past several decades due to rising global temperatures. Earlier spring onset may cause phenological mismatches between the availability of plant resources and dependent animals, and potentially lead to more false springs, when subsequent freezing temperatures damage new plant growth. We used the extended spring indices to project changes in spring onset, defined by leaf out and by first bloom, and predicted false springs until 2100 in the conterminous United States (US) using statistically-downscaled climate projections from the Coupled Model Intercomparison Project 5 ensemble. Averaged over our study region, the median shift in spring onset was 23 days earlier in the Representative Concentration Pathway 8.5 scenario with particularly large shifts in the Western US and the Great Plains. Spatial variation in phenology was due to the influence of short-term temperature changes around the time of spring onset versus season-long accumulation of warm temperatures. False spring risk increased in the Great Plains and portions of the Midwest, but remained constant or decreased elsewhere. We conclude that global climate change may have complex and spatially variable effects on spring onset and false springs, making local predictions of change difficult.

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Introduction

The onset of spring plant growth, or 'spring onset,' has shifted earlier in the year in recent decades amid rising global temperatures (Cleland et al 2007, Ault et al 2011, McCabe et al 2012). Whereas a longer growing season may increase carbon uptake and potentially mitigate climate change (Black et al 2000, Dragoni et al 2011), earlier leaf and flower emergence has led to phenological mismatches between plant resources and many of those animals depending upon them (Walther et al 2002, Schweiger et al 2008, Saino et al 2011, Burkle et al 2013, Kellermann and van Riper IIIrd 2015). Earlier spring onset can also lead to increased risk of false springs, when subsequent hard freezes damage new, vulnerable plant growth in ecological and agricultural systems (Cannell and Smith 1986, Inouye 2008, Knudson 2012). At many locations, projected future increases in global temperatures will likely result in earlier spring onset and fewer frost days overall. However, the rates and magnitude of these changes, determining the likelihood of phenological mismatches and false springs, are not known. Studies of spring plant phenology often define spring onset with one of two easily identified events: leaf emergence ('leaf out') or flower emergence ('first bloom'; Schwartz 1998, Wolfe et al 2005, Polgar and Primack 2011). The timing of these events are largely determined by temperature and photoperiod (Bernier 1988, Bernier et al 1993, Polgar and Primack 2011), but the exact phenological response varies among plant species, ecotypes, and genotypes (Schwartz 1993, Polgar and Primack 2011). Despite this variation, general phenological models, like the widely-used spring indices (SI-x) (Schwartz 1990, Schwartz et al 2013, Ault et al 2015), capture the behavior of a wide variety of plants in natural and agricultural systems (Wolfe et al 2005, Schwartz and Hanes 2010, Schwartz et al 2013). Historical studies using the SI-x (Schwartz et al 2006, Ault et al 2011, McCabe et al 2012) and other general indices (Peterson and Abatzoglou 2014) indicate that spring onset has generally shifted earlier, but there is considerable variation in the magnitude of trends, and in some areas spring onset now occurs later than in the recent past. Both leaf out and first bloom will likely continue to shift earlier in the year with rising global temperatures, but the complex nature of this process makes it difficult to make a priori estimates of how changes in temperature will affect spring onset. However, newly available daily records from climate projections allow for high resolution modeling of spring onset that may help identify spatial patterns of change, even in topographically complex areas where temperatures may vary considerably over short distances. Many plants are resistant to cold weather while dormant through the winter. However, sub-freezing temperatures after spring onset can damage vulnerable plant tissue, and reproductive growth stages later in spring typically make plants more susceptible to damage from cold (Sakai et al 1987, Augspurger 2013). Damage due to false springs is often observed in natural systems (Inouye 2000, 2008, Augspurger 2011), and lost plant productivity can negatively impact dependent animal populations (Blais et al 1955, Nixon and McClain 1969). False springs can also strongly affect agricultural systems (Paulsen and Heyne 1983, Brown and Blackburn 1987). For example, the false spring of 2012 caused $500 million in damages to fruit and vegetables in Michigan (Knudson 2012, Ault et al 2013). Broad-scale studies suggest that cold weather is diminishing more rapidly than changes in spring onset, therefore decreasing the risk of a false spring at most locations (Schwartz et al 2006, Marino et al 2011, Peterson and Abatzoglou 2014). However, several recent false springs in North America suggest that the risk remains (Gu et al 2008, Hufkens et al 2012, Knudson 2012) and has even increased in some areas (Inouye 2008, Augspurger 2013). Empirical evidence is mixed, and the future risk of false springs is still largely unknown. In this study, we asked two questions: first, how will spring onset change by 2100 in the conterminous US, and second, how will relative changes in spring onset and freezing conditions affect the likelihood of future false springs?

Methods

Downscaled climate data We based our spring onset and false spring calculations on daily minimum and maximum temperature data for 1950–2100 from the Coupled Model Intercomparison Project 5 (CMIP5) multi-model ensemble General Circulation Models (GCM) dataset, statistically downscaled to 1/8th degree resolution with the bias-corrected constructed analog (BCCA) technique (Maurer et al 2007, Bureau of Reclamation 2014). With the BCCA technique, the GCM data are first debiased with historical records at the coarse resolution of the GCM and then downscaled to a 1/8th degree grid using linear combinations of past historical conditions (Maurer et al 2010). We downloaded data covering much of North America from the Global Organization for Earth System Science Portals (Maurer et al 2007) but restricted our analysis to the conterminous US. We analyzed BCCA-derived data from 19 GCMs and two emissions scenarios: Representative Concentration Pathways (RCPs) 4.5 (medium-low emissions) and 8.5 (high emissions) (table 1). For conciseness, we focus the main text on the model averages and on RCP8.5, though we do include some analysis of inter-model variability. We present results for RCP4.5 in appendix A , and extensive results for individual models under both scenarios in appendix B . Table 1. Global climate models considered in this study. Group Model abbreviation Commonwealth Scientific and Industrial Research ACCESS1-0 Organization and Bureau of Meteorology, Australia Beijing Climate Center, China Meteorological bcc-csm1-1 Administration College of Global Change and Earth System BNU-ESM Science, Beijing Normal University Canadian Centre for Climate Modelling and CanESM2 Analysis National Center for Atmospheric Research CCSM4 Community Earth System Models CESM1-BGC Centre National de Recherches Météorologiques/ CNRM-CM5 Centre Européen de Recherche et Formation Avancée en Calcul Scientifique Commonwealth Scientific and Industrial Research csiro-mk3-6-0 Organization, Queensland Climate Change Centre of Excellence NOAA Geophysical Fluid Dynamics Laboratory GFDL-ESM2G GFDL-ESM2M Institute for Numerical Mathematics inmcm4 Institut Pierre-Simon Laplace IPSL-CM5A-LR IPSL-CM5A-MR Japan Agency for Marine-Earth Science and MIROC-ESM Technology, Atmosphere and Ocean Research MIROC-ESM-CHEM Institute, and National Institute for Environmental MIROC5 Studies Max Planck Institute for Meteorology MPI-ESM-LR MPI-ESM-MR Meteorological Research Institute MRI-CGCM3 Norwegian Climate Centre NorESM1-M Extended SI-x We calculated spring onset using the extended SI-x metrics (Schwartz et al 2013). The original spring index formulas were empirically derived to predict leaf out and first bloom of lilac (Syringa chinesis 'Red Rothomagensis') and two honeysuckle clones (Lonicera tatarica 'Arnold Red' and L. korolkowii 'Zabeli') in the US (Schwartz 1993). However, these formulas have proven useful as a general model of plant phenology, as the first leaf and first bloom dates are well correlated with those of many natural and agricultural plants (Wolfe et al 2005, Schwartz and Hanes 2010, Schwartz et al 2013) and with the 'green up' of ecosystems worldwide (Schwartz 1990, Schwartz et al 2006). Additionally, first bloom captures leaf out for later spring species, like many trees (Schwartz et al 2006, Schwartz and Hanes 2010). The SI-x models that we applied have been modified to estimate leaf out and first bloom in both temperate and subtropical environments (Schwartz et al 2013, Ault et al 2015). We defined spring onset using both leaf out and first bloom The use of both definitions provided 'bookends' to capture the spring phenology of a wide variety of plant species. The SI-x include an equation to predict leaf out for each of the three plant species using empirically-derived equations based on the photoperiod (represented by the day of the year), short-term growing degree hours (GDHs), and the season-long cumulative count of high-energy synoptic events, hereafter 'warm spells.' GDHs are the number of hours above 0.6 °C, summed over two three-day periods (current and 5–7 days earlier). Hourly temperatures were interpolated from daily minimum and maximum temperatures (Ault et al 2015). The season long warm spell count is defined as the cumulative number of 3 day periods with GDH > 637. The formula for each species is updated for each day of year, and the first day of the year with a SI-x score exceeding a predetermined threshold is considered the date of leaf out (see Ault et al 2015 for full models and coefficients). The average day of the year in which this occurs among the lilac and two honeysuckle clones is considered the 'leaf out date' (Schwartz et al 2006, 2013). The first bloom calculations begin after leaf out for each species. First bloom depends on two factors: the number of days since leaf out and the accumulated GDHs (AGDH) since leaf out (Ault et al 2015). The base GDHs are calculated in the same manner as for leaf out, but the AGDH is cumulative rather than based only on recent temperatures. The date of first bloom occurs for each species when first bloom values reach 1000 (Ault et al 2015). Again, we used the average day of year of the first bloom of the three species as our first bloom date in subsequent analyses. Changes in first bloom can only result from changes in AGDH, the final value of which is relatively constant given the threshold score of 1000. Therefore, to measure changes in first bloom we examined the duration of time between leaf out and first bloom. Within each year, we considered dates through 31 July, which was sufficient to capture spring growth even at high elevations while avoiding the return of colder temperatures accompanying the onset of autumn. We compared changes in spring onset from the CMIP5 simulated historical (1950–2005), mid-century (2041–2070), and end of the century (2071–2100) time periods. We summarized changes by Omernik ecoregions (Omernik 1987), i.e., areas with relatively similar topography and weather patterns, by comparing probability density functions for during the historical and end-century time periods. We also used these results to examine differences in inter-model variability among ecoregions. For each ecoregion, we calculated coefficient of variation among model density kernels at a given day of year, and then taking the average of these values. To prevent values near zeros from overriding the signal, we removed from consideration days of the year in which the mean density was less than 1/50th of the peak value for that ecoregion. To determine if changes were robust among models at each location in the presented maps, we took the mean of each spring onset date from each model during the historical period, and compared it to the same values from future time periods using a Welch's t-test to allow for unequal variances. Non-significant changes were masked with white in the map figures. Finally, we have included more extensive results from individual models in appendix B . False springs The exact temperature at which plant tissue is damaged depends upon the species and environmental conditions, but a daily minimum temperature below −2.2 °C is likely to cause damage, and is considered a 'hard freeze' (Schwartz 1993, Marino et al 2011, Peterson and Abatzoglou 2014). Furthermore, the later the freeze occurs after plant growth begins, the more damage is likely to occur because plants are in a more susceptible phenological phase (Schwartz et al 2006, Marino et al 2011, Peterson and Abatzoglou 2014). Because we calculated two measures of spring plant phenology, we also had two measures of false springs. We defined an early false spring event as the occurrence of a hard freeze seven or more days after leaf out, following Peterson and Abatzoglou (2014). Similarly, we defined a late false spring event as a hard freeze any time after first bloom, when plants are more susceptible to freezing conditions. We summarized changes in false springs by ecoregion, and tested for changes that were significant among models, in the manner described for spring onset. To investigate spatial variation in false springs, we calculated daily minimum temperatures around the leaf out and first bloom dates at each location. For each year, we calculated the average daily minimum temperature for the 7 days before and after the date of interest, and averaged these annual values for each CMIP5 time period. Data validation We validated the leaf out, first bloom, and false springs values calculated from the BCCA downscaled climate models versus those calculated from Maurer gridded historical temperature records (Maurer et al 2002), because the latter were the basis for downscaling of the BCCA data itself (Bureau of Reclamation 2014). We compared mean values, interannual variability, and trends between Maurer and the model average BCCA values. The BCCA-derived values compared favorably with historical records (appendix C ). To increase calculation speed, we wrote code in C# based on earlier Matlab documentation and code (Ault et al 2015), with a few small modifications to fit our study (see appendix D ). The code is available from the spring index Github page.

Results

Discussion

Our projections indicated that spring onset will occur earlier throughout the conterminous US by the year 2100. Spatial variation in the rates of change stemmed from differing contributions by projected temperature changes and the temperature components of the SI-x model: the short-term GDH terms around the time of spring onset, and the season-long cumulative count of warm spells. Our projections of earlier spring onset indicate that recent historical trends may continue, but not everywhere. In the Western US, leaf out has advanced at a rate of 1.5 days/decade from 1950 to 2005 (Ault et al 2011), consistent with the BCCA model average changes in the RCP4.5 scenario (appendix A ) but less than in the RCP8.5 scenario (figures 1(e) and (f)). However, in the Great Plains the historical pattern is variable, with some areas even indicating a later spring onset (Schwartz et al 2013, Peterson and Abatzoglou 2014). Similarly, our results showed a trend toward earlier spring onset in the central Eastern Temperate Forests ecoregion, but it has actually been later in the recent past (figure C1, Marino et al 2011, Schwartz et al 2013). These discrepancies are likely caused by the strong influence of internal climate variability, including large-scale modes such as El Nino-Southern Oscillation and the Pacific-North American Pattern, on historical spring temperatures (Ault et al 2011, Marino et al 2011, Schwartz et al 2013). We found similar variability in individual BCCA models (figure 5, appendix B ), but the year-to-year variability in these modelled atmospheric patterns is not expected to match historical records. Our averaging of many models canceled out the effects of the simulated internal variability and emphasized the role of greenhouse-forced climate change. The extended SI-x that we calculated are well correlated with spring phenology in ecosystems worldwide (Schwartz et al 2013), and the time from leaf out to first bloom spans the period of spring phenology for many plant species. However, the phenological response to environmental conditions varies among plant species and even among locations (Polgar and Primack 2011), and other phenological models have been developed. While the extended SI-x incorporate spring degree-above-threshold and photoperiod (day of year) measures to predict leaf out, winter chilling days are included in other models, reflecting the physiology of plant species that require cold temperatures to break dormancy (Polgar and Primack 2011). Our projections may not capture changes in spring phenology of such plants. For example, a study using a black ash (Fraxinus nigra) phenological model and a CMIP3 GCM predicted that spring leaf out will arrive later in the mid-latitudes of the Eastern US (Morin et al 2009), where we projected much earlier spring onset. Detailed phenological models may be required for species-specific models, whereas our study was designed to capture ecosystem green up in general, for which the extended SI-x are ideal (Schwartz et al 2013). Future changes in false spring risk depend on the relative change in the timing of spring onset and the last spring freeze (Cannell and Smith 1986, Peterson and Abatzoglou 2014). While both relate to changes in temperature, differences arise because spring phenology is a cumulative process throughout the spring whereas a single cold night can cause a hard freeze. Though our early definition was common enough to strain the term false spring, the shared spatial patterns indicate that future patterns of damaging false springs will likely be somewhere between our two 'bookend' scenarios (figure 4). We projected only slightly earlier spring onset along the Gulf Coast, where projected increases in temperature nearly eliminated freezing temperatures, causing a large reduction in the risk of false springs. Similarly, decreases in false springs risk in the Northern forests were the result of a projected decrease in late cold temperatures, and only a slightly earlier spring onset in a region where photoperiod remained an important component of the phenology. This difference in timescales was particularly evident in the Great Plains ecoregion, where the cumulative warm spells caused large shifts in leaf out and first bloom. These dates changed more than the projected mean temperature increases would suggest, causing temperatures around those dates to become cooler at the end of the century than historically. Although it may at first glance be counterintuitive that increasing mean temperatures could increase the risk of a false spring, the phenomenon has already been reported in historical studies (Inouye 2008, Augspurger 2013). Plants face an evolutionary tradeoff between the benefit of earlier leaf emergence, and hence, a longer growing season, and the risk of tissue damage from a false spring. Variation in internal responses to environmental cues may allow some individuals to survive the opposing selective pressures from earlier leaf out and risk of tissue damage (Leinonen and Hanninen 2002, Gömöry and Paule 2011). Though extended SI-x are static, our results suggest these selective pressures will continue. Throughout most of the US a similar or reduced risk of false springs indicates that plants could continue their present responses to environmental cues, and the resulting earlier spring growth would have little negative consequence, but it may mean missing the benefit of a longer growing season. However in the Great Plains, conditions may favor individuals that demonstrate delayed spring leaf out and avoid the increased risk of tissue damage from false springs. Given changing plant phenology, maintaining current plant-animal interactions will require evolutionary or behavioral adaptation by dependent animals (Visser and Holleman 2001, Berteaux et al 2004). However, animals may respond to different environmental cues than plants. For example, long-distance migratory birds respond to cues present in their overwintering habitat, such as day length, while plants in their summer breeding grounds respond to local environmental cues like temperature. Birds that have adapted to migrate earlier have maintained their population levels, while birds that retained historical temporal patterns in migrations have declined, at least in part due to phenological mismatches with plant-based resources (Saino et al 2011, Clausen and Clausen 2013). Increasing temperatures have led to poor synchronization between moth emergence and leaf out of host trees (Visser and Holleman 2001). Ultimately, the ability of a species to respond to rapid phenological changes will depend upon generation time, levels of genetic variability, and the plasticity of phenological and behavioral traits (Berteaux et al 2004). In summary, our projections indicate that widespread historical advances in spring plant phenology will continue into the future, albeit with considerable spatial variation in the rates of change and the risk of false springs. Extensive regional variation emphasizes the need for future predictions that are even more fine-scale and species specific, to better understand the potential effects on natural and agricultural systems. To facilitate such research, we have created an online repository of weather data. The data presented here and more can be downloaded at http://silvis.forest.wisc.edu.

Acknowledgments

We thank M Schwartz and T Ault for advice regarding the spring indices, S Schmidt for technical assistance, and B Bateman, J Gorzo, and R Behnke for helpful discussion of these ideas. Financial support was provided NASA Biodiversity Program and the Climate and Biological Response funding opportunity (NNH10ZDA001N-BIOCLIM). The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the US Fish and Wildlife Service. Any use of trade, product, or firm names are for descriptive purposes only and do not imply endorsement by the US Government. All associated data are available upon request or from http://silvis.forest.wisc.edu.

Appendix A.: Model average results for RCP4.5

Figure A1. BCCA model average day of year (DOY) for (left) leaf out and (right) first bloom date. (Top) Average leaf out and first bloom dates during the climatological base period 1950–2005. Numbers denote the Omernik ecoregions: (1) North American Desert, (2) Mediterranean California, (3) Southern Semi-Arid Highlands (4) Temperate Sierras, (5) Tropical Wet Forests, (6) Northern Forests, (7) Northwestern Forested Mountains, (8) Marine West Coast Forests, (9) Eastern Temperate Forests, and (10) Great Plains. Remaining subplots indicate the mean changes in phenological events between the (top) historical period (1950–2005) and (middle) mid-century (2041–2070) and (bottom) end-century (2071–2100) time periods for the RCP8.5 high emission scenario. Changes at all locations were statistically robust among the climate models. Download figure: Standard image High-resolution image Export PowerPoint slide Figure A2. The BCCA model average probabilities of a false spring in a given year. (Left) An early false spring (FS Early ), defined as a daily minimum temperature below −2.2 °C seven or more days after leaf out. (Right) A late false spring (FS Late ) is defined as a daily minimum temperature below −2.2 °C any time after first bloom. (Top) Likelihood of a false spring during the historical period. Remaining subplots indicate change in the probability of a false spring between the (top) historical period and (middle) mid-century and (bottom) end-century time periods for RCP4.5. White areas indicate areas where the mean change among climate models was not significantly different from zero. Download figure: Standard image High-resolution image Export PowerPoint slide

Appendix B.: Results for individual climate model

Appendix C.: Data validation with Maurer historical data

Here, we conduct a validation of leaf out, first bloom, and false springs values calculated from the BCCA downscaled climate models versus those calculated from Maurer gridded historical temperature records (Maurer et al 2002). This dataset provided a natural comparison because it was used in the downscaling of the BCCA data itself (Bureau of Reclamation 2014). Figure C1. Comparison of 1950–2005 leaf out (rows 1–3) and (bottom row) early false springs measures calculated between (left) Maurer historical gridded data and (right) model average from bias correction constructed analog (BBCA) statistically downscaled climate models. (Top) Mean leaf out date. (Second row) Inter-annual variation (standard deviation) in leaf out. For BCCA, it is the average of the standard deviations from each model. (Third row) Linear trends during this time period. (Bottom) Probability of an early false spring during this period. BCCA results are averages of 19 GCMs (table 1). Download figure: Standard image High-resolution image Export PowerPoint slide The mean values of leaf out and first bloom were very similar between the Maurer and BCCA results (figures C1 and C2 top). Differences do exist, but they are small and not readily viewable on the map. Interannual variability in leaf out and first bloom were higher in the BCCA models, though we observed similar spatial patterns of variability with the Maurer data. There was considerable spatial variation in trends of spring onset through the base period for both Maurer (figures C1 and C2) and individual BCCA models (not shown). However, the BCCA model average smoothed over most of this variability, and there was typically a slight shift earlier in these dates through time. For both definitions of false springs, spatial patterns were nearly identical between the Maurer and BCCA model average values (figures C1, C2, bottom). Figure C2. Comparison of 1950–2005 first bloom (rows 1–3) and (bottom row) late false spring measures calculated between (left) Maurer historical gridded data and (right) model average from bias correction constructed analog (BBCA) statistically downscaled climate models. (Top) Mean first bloom date. (Second row) Inter-annual variation (standard deviation) in first bloom. For BCCA, it is the average of the standard deviations from each model. (Third row) Linear trends during this time period. (Bottom) Probability of a late false spring during this period. BCCA results are averages of 19 GCMs (table 1). Download figure: Standard image High-resolution image Export PowerPoint slide

Appendix D.: Comparison of C# code against Matlab package