1.1. Phenology and Climate
 Timing and magnitude of cyclic events in the terrestrial biosphere are strongly related to climate variability [Scheifinger et al., 2002] because plant physiological processes are controlled by surface climatic states like moisture, temperature and light [Jarvis, 1976; Larcher, 2003]. Seasonal and inter-annual climatic variations influence the timing of plant development called vegetation phenology.
 Phenological networks provide one of the longest sources of direct evidence of climate variability and change [van Vliet et al., 2003; Betancourt et al., 2005; Menzel et al., 2006]. Concurrent with recent warming trends bud burst or flowering have advanced by around 1–2 days per decade in temperate deciduous ecosystems [Menzel and Fabian, 1999; Menzel, 2000]. Historical records and phenological reconstructions also reveal substantial inter-annual to centennial variability of start of season (SOS) [Menzel et al., 2005; Aono and Kazui, 2008; Rutishauser et al., 2007]. End of season (EOS) events like leaf coloring, and hence their variability, are harder to detect and document but nevertheless they are strongly coupled to climate [Sparks and Menzel, 2002; Taylor et al., 2008]. In general the growing season has lengthened during the last decades of the 20th century [Intergovernmental Panel on Climate Change, 2007b].
 Modeling studies demonstrate the influence of vegetation phenology on the climate system. Regional and global temperature and precipitation patterns are both sensitive to and affect not only temporal but also spatial phenological variability [Tsvetsinskaya et al., 2001; Lu and Shuttleworth, 2002; Kim and Wang, 2005]. Piao et al.  show that modeled growing season length correlates with terrestrial CO2 uptake. White and Nemani  however find that the yearly net CO2 balance is only moderately affected by growing season length. This can be explained by both enhanced spring CO2 uptake and higher autumnal CO2 respiration rates [Schaefer et al., 2005] and leads to larger seasonal CO2 amplitudes as documented by Keeling et al. . On longer time scales phenology can affect tree competition and vegetation dynamics in response to climate variability and change [Kramer et al., 2000]. Generally, either diagnostic or prognostic parameterizations of vegetation phenology are employed in these studies.
1.2. Diagnostic Phenology
 Satellite remote sensing vegetation indices exploiting the seasonal changes in the spectral signature of vegetation photosynthetic activity have been developed during the last two decades [Tucker et al., 1985; Reed et al., 1994]. They can be used to derive global maps of biophysical and phenological parameters like FPAR or LAI. These maps then prescribe phenological variability in climate models [Sellers et al., 1996; Buermann et al., 2001; Lu and Shuttleworth, 2002; Lawrence and Slingo, 2004], a method also termed as “diagnostic phenology”. Satellite phenological observations are different from ground observations since they provide a spatially integrative view of continuous biophysical states instead of plant-specific phenological development stages. Studer et al.  demonstrate that inter-annual SOS variability of both methods are comparable even over complex terrain such as the Swiss alps when individual ground observed species are composed into a “statistical plant” [Studer et al., 2005]. However the transfer functions to detect SOS and EOS timing from satellite measurements have to be chosen carefully.
 Current satellite sensors like MODIS [Justice et al., 2002] or Medium Resolution Imaging Spectrometer (MERIS) [Rast et al., 1999] used for phenological research provide data at 1 km spatial scale with a 1–16 day revisiting frequency. However small-scale (<500 m) topographical variability in the order of 50 m can result in a 1–2 week difference in SOS [Fisher et al., 2006]. Sub-pixel land cover heterogeneity leads to substantial uncertainty in the calculation of biophysical properties from satellite radiances [Cohen et al., 2006]. In temperate ecosystems both satellite and ground phenological observations respond to large-scale climate forcing while mediterranean and tropical phenology is known to have small-scale spatial variability [Los et al., 2001; Zhang et al., 2004; Maignan et al., 2008].
 Atmospheric disturbances like clouds or aerosols as well as snow masking of vegetation limit the applicability of diagnostic phenology data sets in climate models. Figure 1 visualizes the seasonal course and uncertainty range of MODIS-derived LAI for four major global ecosystem types, namely temperate deciduous, tropical evergreen, boreal evergreen and mediterranean savanna. Only few high-quality observations (black crosses, the other curves are explained further down) are available for the tropical ecosystem and error bars are large because of clouds and aerosols. Gaps are also present in the boreal ecosystem during winter and spring because of snow cover and missing light. Quality screening [Myneni et al., 2002; Delbart et al., 2006] and gap filling by use of curve fitting algorithms [Los et al., 2000; Jonsson and Eklundh, 2002; Zhang et al., 2003; Stöckli and Vidale, 2004; Bradley et al., 2007; Gao et al., 2008] can be applied to create continuous and consistent time series needed to prescribe biophysical states in climate models.
 While some of these methods, such as using TIMESAT [Jonsson and Eklundh, 2002] for gap filling [Gao et al., 2008] are very promising, spatial or temporal interpolation generates further uncertainty in the time series. Finally, diagnostic phenology data sets only cover the past satellite observation period and cannot be used for, for example, seasonal numerical weather forecast or future climate predictions [Gienapp et al., 2005].
1.3. Prognostic Phenology
 Models simulating the timing of phenological events have mainly been developed for linking phenological ground observations with climate variability. Hunter and Lechowicz  find that ground observed bud-burst can be predicted from spring temperatures and photoperiod in combination with a chilling requirement. White et al.  predict SOS over the continental US with an accuracy of 6–7 days and EOS with 5–6 days accuracy. They find that temperature sums can be used to predict SOS while a more complex combination of temperature, photoperiod and precipitation is needed for EOS depending on vegetation type. Chuine  integrates previous approaches into a generalized model for SOS depending on chilling and forcing temperatures.
 So-called prognostic phenology models are employed in climate models for a continuous prediction of biophysical states like FPAR and LAI (examples shown in Figure 1). TRIFFID (Top-down Representation of Interactive Foliage and Flora Including Dynamics [Cox, 2001]; component of JULES, the Joint UK Land Environment Simulator) and IBIS (Integrated BIosphere Simulator [Foley et al., 1996]; component of the NCAR Community Land Model [Levis et al., 2004]) use temperature triggers to simulate growth and decay of leaves in temperate and boreal vegetation. TRIFFID prognoses continuous LAI changes by use of a leaf turnover rate while IBIS triggers instantaneous LAI changes. In CN (prognostic Carbon-Nitrogen dynamics based on BIOME-BGC [Thornton et al., 2002]; and component of the NCAR Community Land Model [Thornton et al., 2007]) leaf growth is predicted from vegetation biochemical cycling rates coupled to the terrestrial carbon-nitrogen cycle. SOS is triggered by cumulative soil temperature and EOS is triggered by day-length. Drought deciduous phenology in CN is triggered by temperature and soil moisture. In GSI (Growing Season Index [Jolly et al., 2005]) environmental factors based on temperature, light and humidity thresholds concurrently control the phenological state without the use of trigger functions.
 Careful interpretation is needed when comparing prognostic phenology models to satellite observations since most models simulate individual (for example, deciduous or evergreen) vegetation types. This is exemplified for a deciduous broadleaf forest (DBF) in Figure 1a: satellite observations have a winter LAI of around 1.5 as a result of the evergreen vegetation fraction while the modeled DBF LAI decays to 0 in winter. Contrasting to this, CN correctly simulates a constant LAI for the boreal evergreen needleleaf forest (ENF) in Figure 1c while there is a substantial fraction of deciduous vegetation revealed by the observations during summer. Most models can simulate a mixed phenology based on the fractional cover of individual vegetation types (for example, CN, IBIS, TRIFFID). This is ultimately needed for their application in global models but it adds another level of complexity.
 Therefore, rather than focusing on magnitude, differences in timing should be analyzed when comparing models and satellite observations. In Figure 1, GSI and CN are accurate to within one week for prediction of SOS and EOS for DBF (a), but TRIFFID and IBIS predict a too long-growing season. Models capture the almost constant LAI of the evergreen tropical forest (b), although IBIS decreases LAI during the dry season and GSI displays too much variability. Models partly fail to reproduce the constant LAI of ENF (c). None of the models matches either timing or phase of the drought-deciduous mediterranean grassland phenology (d).
 Each prognostic model [see also e.g., Potter and Klooster, 1999; Arora and Boer, 2005; Gibelin et al., 2006; Dickinson et al., 2008] includes a partial set of processes required to simulate global phenological variability. Figure 1 reveals significant timing differences between models and satellite observations especially for drought-deciduous mediterranean ecosystems independent of model complexity. The highly empirical model formulations were mostly developed for temperate DBF phenology but they are currently applied as part of decadal to centennial global climate model predictions.
1.4. Best of Both Worlds
 A realistic representation of seasonal to inter-annual phenological variability would be of benefit for models simulating the global carbon and water cycle. It however requires bridging the gap between knowledge available from local-scale phenological observations and their application in global-scale models [Cleland et al., 2007]. It also requires taking advantage of the wealth of data contained in diagnostic phenology data sets and applying them to reduce uncertainty in prognostic phenology models.
 This study explores whether it is possible to constrain uncertainty in model parameters by assimilating MODIS FPAR and LAI into the GSI phenology model. In the next section both the data assimilation model and the modifications to the GSI model are presented. Data assimilation and model experiments are carried out at local and regional scale covering a wide range of ecosystem types and climate zones (Table 1). This strategy is computationally efficient and can reveal advantages and deficiencies of the employed methodology prior to its application in a global scale experiment. It further allows for validation with ground observations which are only available at local scale. The results section firstly demonstrates the potential of data assimilation to constrain model parameters. Seasonal FPAR and LAI predictions using satellite-constrained parameters are then compared to model simulations with original parameters. Modeled inter-annual SOS and EOS variability is finally validated against three independent ground phenology data sets.
|No.||Site||Lon [°E]||Lat [°N]||Altitude [m]||Biome Type||Years||Climate|
|CarboEurope Sites (Europe)|
|1||Vielsalm [Aubinet et al., 2001]||6.00||50.30||450||MF||2000–2005||Temperate|
|2||Tharandt [Grunwald and Bernhofer, 2007]||13.57||50.96||380||ENF||2000–2003||Temperate|
|3||Castel Porziano [Valentini, 2003]||12.38||41.71||68||EBF||2000–2005||Mediterranean|
|4||Collelongo [Valentini, 2003]||13.59||41.85||1550||DBF||2000–2003||Mediterranean|
|5||Kaamanen [Laurila et al., 2001]||27.30||69.14||155||TUN||2000–2005||North boreal|
|6||Hyytiälä [Suni et al., 2003]||24.29||61.85||181||ENF||2000–2005||Boreal|
|7||El Saler [Ciais et al., 2005]||−0.32||39.35||10||ENF||2000–2005||Mediterranean|
|8||Puechabon [Rambal et al., 2004]||3.60||43.74||270||DBF||2001–2005||Temperate|
|9||Sarrebourg [Granier et al., 2000]||7.06||48.67||300||DBF||2000–2005||Temperate|
|LBA Sites (Brazil)|
|10||Santarem KM83 [Goulden et al., 2004]||−54.97||−3.02||130||EBF||2001–2003||Tropical|
|11||Tapajos KM67 [Hutyra et al., 2007]||−54.96||−2.86||130||EBF||2002–2005||Tropical|
|AmeriFlux Sites (USA)|
|12||Morgan Monroe [Schmid et al., 2000]||−86.41||39.32||275||DBF||2000–2006||Temperate|
|13||Boreas OBS [Dunn et al., 2007]||−98.48||55.88||259||ENF||2000–2005||Boreal|
|14||Lethbridge [Flanagan et al., 2002]||−112.94||49.71||960||GRA||2000–2004||Boreal|
|15||Fort Peck [Gilmanov et al., 2005]||−105.10||48.31||634||GRA||2000–2005||Temperate|
|16||Harvard Forest [Urbanski et al., 2007]||−72.17||42.54||303||DBF||1990–2006||Temperate|
|17||Niwot Ridge [Monson et al., 2002]||−105.55||40.03||3050||ENF||2000–2004||Sub-alpine|
|18||Wind River [Paw U et al., 2004]||−121.95||45.82||371||ENF||2000–2004||Temperate|
|19||Bondville [Meyers and Hollinger, 2004]||−88.29||40.01||213||CRO||2000–2005||Temperate|
|20||Willow Creek [Bolstad et al., 2004]||−90.08||45.81||520||DBF||2000–2005||Temperate|
|21||Tonzi Ranch [Baldocchi et al., 2004]||−120.97||38.43||177||SAV||2002–2005||Mediterranean|
|22||Vaira Ranch [Baldocchi et al., 2004]||−120.95||38.41||129||GRA||2002–2005||Mediterranean|
|23||Swiss Lowl [Rutishauser et al., 2007]||8.25||47.25||600||MF||1958–2006||Temperate|