Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8 km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16 000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by AE 60 days and in standard deviation by AE 20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground-or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS Correspondence: Michael A. White, tel. 1 1 435 797 3794, fax 1 1 435 797 187, trends could be detected for only 12% of North America and were divided between trends towards both earlier and later spring.

While commonplace in other parts of the world, long-term and ongoing observations of the phenology of native tree species are rare in North America. We use 14 years of field survey data from the Hubbard Brook Experimental Forest to fit simple models of canopy phenology for three northern hardwood species, sugar maple (Acer saccharum), American beech (Fagus grandifolia), and yellow birch (Betula alleghaniensis). These models are then run with historical meteorological data to investigate potential climate change effects on phenology. Development and senescence are quantified using an index that ranges from 0 (dormant, no leaves) to 4 (full, green canopy). Sugar maple is the first species to leaf out in the spring, whereas American beech is the last species to drop its leaves in the fall. Across an elevational range from 250 to 825 m ASL, the onset of spring is delayed by 2.7 AE 0.4 days for every 100 m increase in elevation, which is in reasonable agreement with Hopkin's law. More than 90% of the variation in spring canopy development, and just slightly less than 90% of the variation in autumn canopy senescence, is accounted for by a logistic model based on accumulated degree-days. However, degree-day based models fit to Hubbard Brook data appear to overestimate the rate at which spring development occurs at the more southerly Harvard Forest. Autumn senescence at the Harvard Forest can be predicted with reasonable accuracy in sugar maple but not American beech. Retrospective modeling using five decades of Hubbard Brook daily mean temperature data suggests significant trends (P 0.05) towards an earlier spring (e.g. sugar maple, rate of change 5 0.18 days earlier/yr), consistent with other studies documenting measurable climate change effects on the onset of spring in both North America and Europe. Our results also suggest that green canopy duration has increased by about 10 days (e.g. sugar maple, rate of change 5 0.21 days longer/yr) over the period of study.

In the spring of 2010, temperatures averaged ~3 °C above the long‐term mean (March–May) across the northeastern United States. However, in mid‐to‐late spring, much of this region experienced a severe frost event. The spring of 2010 therefore provides a case study on how future spring temperature extremes may affect northeastern forest ecosystems. We assessed the response of three northern hardwood tree species (sugar maple, American beech, yellow birch) to these anomalous temperature patterns using several different data sources and addressed four main questions: (1) Along an elevational gradient, how was each species affected by the late spring frost? (2) How did differences in phenological growth strategy influence their response? (3) How did the late spring frost affect ecosystem productivity within the study domain? (4) What are the potential long‐term impacts of spring frost events on forest community ecology? Our results show that all species exhibited early leaf development triggered by the warm spring. However, yellow birch and American beech have more conservative growth strategies and were largely unaffected by the late spring frost. In contrast, sugar maples responded strongly to warmer temperatures and experienced widespread frost damage that resulted in leaf loss and delayed canopy development. Late spring frost events may therefore provide a competitive advantage for yellow birch and American beech at the expense of sugar maple. Results from satellite remote sensing confirm that frost damage was widespread throughout the region at higher elevations (>500 m). The frost event is estimated to have reduced gross ecosystem productivity by 70–153 g C m−2, or 7–14% of the annual gross productivity (1061 ± 82 g C m−2) across 8753 km2 of high‐elevation forest. We conclude that frost events following leaf out, which are expected to become more common with climate change, may influence both forest composition and ecosystem productivity.

Abstract:Long-term data from the Hubbard Brook Experimental Forest in New Hampshire show that air temperature has increased by about 1°C over the last half century. The warmer climate has caused significant declines in snow depth, snow water equivalent and snow cover duration. Paradoxically, it has been suggested that warmer air temperatures may result in colder soils and more soil frost, as warming leads to a reduction in snow cover insulating soils during winter. Hubbard Brook has one of the longest records of direct field measurements of soil frost in the United States. Historical records show no long-term trends in maximum annual frost depth, which is possibly confounded by high interannual variability and infrequency of major soil frost events. As a complement to field measurements, soil frost can be modelled reliably using knowledge of the physics of energy and water transfer. We simulated soil freezing and thawing to the year 2100 using a soil energy and water balance model driven by statistically downscaled climate change projections from three atmosphere-ocean general circulation models under two emission scenarios. Results indicated no major changes in maximum annual frost depth and only a slight increase in number of freeze-thaw events. The most important change suggested by the model is a decline in the number of days with soil frost, stemming from a concurrent decline in the number of snow-covered days. This shortening of the frost-covered period has important implications for forest ecosystem processes such as tree phenology and growth, hydrological flowpaths during winter, and biogeochemical processes in soil. Published in

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