Geophysical Research Letters RESEARCH LETTER 10.1002/2017GL076933 Key Points: • Between 1984 and 2013, spring onset advanced by 1 week across temperate forests, but changes in boreal forests were much more heterogeneous • Regional trends and variation in spring onset are consistent with contemporaneous changes in springtime temperatures • The sensitivity of spring onset to variation in springtime temperatures was uniform across all sites and ecoregions included in the analysis
Supporting Information: • Supporting Information S1 Correspondence to: E. K. Melaas,
[email protected]
Citation: Melaas, E. K., Sulla-Menashe, D., & Friedl, M. A. (2018). Multidecadal changes and interannual variation in springtime phenology of North American temperate and boreal deciduous forests. Geophysical Research Letters, 45. https:// doi.org/10.1002/2017GL076933 Received 23 DEC 2017 Accepted 4 MAR 2018 Accepted article online 9 MAR 2018
Multidecadal Changes and Interannual Variation in Springtime Phenology of North American Temperate and Boreal Deciduous Forests Eli K. Melaas1 1
, Damien Sulla-Menashe1, and Mark A. Friedl1
Department of Earth and Environment, Boston University, Boston, MA, USA
Abstract
The timing of leaf emergence is an important diagnostic of climate change impacts on ecosystems. Here we present the first continental-scale analysis of multidecadal changes in the timing of spring onset across North American temperate and boreal forests based on Landsat imagery. Our results show that leaf emergence in Eastern Temperate Forests has consistently trended earlier, with a median change of about 1 week over the 30 year study period. Changes in leaf emergence dates in boreal forests were more heterogeneous, with some sites showing trends toward later dates. Interannual variability in leaf emergence dates was strongly sensitive to springtime accumulated growing degree days across all sites, and geographic patterns of changes in onset dates were highly correlated with changes in regional springtime temperatures. These results provide a refined characterization of recent changes in springtime forest phenology and improve understanding regarding the sensitivity of North American forests to climate change.
Plain Language Summary
The timing of leaf emergence is an important diagnostic of climate change impacts on ecosystems. Here we present the first continental-scale analysis of multidecadal changes in the timing of spring onset across North American temperate and boreal forests based on Landsat imagery. Our results show that leaf emergence in Eastern Temperate Forests has consistently trended earlier, with a median change of about 1 week over the 30 year study period. Changes in leaf emergence dates in boreal forests were more variable, with some sites showing trends toward later dates. Interannual variability in leaf emergence dates was strongly sensitive to springtime accumulated growing degree days across all sites, and geographic patterns of changes in onset dates were strongly correlated with changes in regional springtime temperatures. These results provide a refined characterization of recent changes in springtime forest phenology and improve understanding regarding the sensitivity of North American forests to climate change.
1. Introduction Boreal and temperate forest phenology exerts important controls on land-atmosphere exchanges of carbon, energy, and water (Barr et al., 2004; Fitzjarrald et al., 2001). Early emergence of leaves in spring (hereafter, start of season or SOS) has been shown to increase annual net ecosystem production and decrease peak summer productivity in North American forests (Buermann et al., 2013; Keenan et al., 2014; Richardson et al., 2010) and, in some cases, can offset large decreases in midseason primary production caused by anomalous weather (e.g., drought) (Wolf et al., 2016). The timing of SOS also affects regional weather and climate through changes in surface albedo and partitioning of latent and sensible heat fluxes (Hogg et al., 2000; Penuelas et al., 2009). In temperate and boreal regions, the timing of SOS is sensitive to temperature and substantial evidence indicates that SOS has shifted earlier in many regions. In this context, a key question that has recently emerged is whether continued warming will lead to ongoing changes in SOS or whether reduced wintertime chilling or photoperiod controls will buffer the impact of warmer temperatures (Badeck et al., 2004; Fu et al., 2015; Polgar & Primack, 2011; Zohner et al., 2016)
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Satellite remotely sensed vegetation indices such as the Normalized Difference Vegetation Index and Enhanced Vegetation Index (EVI) have been widely used to map both long-term trends and interannual variation in SOS. In particular, numerous studies have used the 30+ year record of Advanced Very High Resolution Radiometer (AVHRR) observations to characterize the nature and magnitude of changes in SOS in Eurasian and North American ecosystems. More recent studies have used instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l’Observation de la Terre
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Vegetation (SPOT-VGT) (Delbart et al., 2015; Keenan et al., 2014), which provide imagery at 250 m, 500 m, and 1 km spatial resolution. However, results from these studies are limited because time series from MODIS and SPOT-VGT are relatively short, which makes robust estimation of long-term trends difficult. Hence, most studies of long-term changes in SOS at continental scale have relied on AVHRR data. A key challenge, in terms of both quantifying the amount of change in SOS and attributing the drivers behind observed changes, is the quality and spatial resolution of AVHRR time series. In particular, the coarse spatial resolution (most commonly 8 km), lack of onboard calibration, incomplete screening of snow and clouds, and geolocation errors associated with AVHRR imagery data introduce uncertainty to estimated trends (Doktor et al., 2009; Gutman, 1999; Kross et al., 2011; Roy, 2000; White et al., 2009). Moreover, disturbance regimes (fire, insect outbreaks, logging, etc.) introduce substantial variability in land cover and ecosystem properties at scales well below the spatial resolution of AVHRR data, which complicates efforts focused on understanding how the phenology of different plant communities and functional types is responding to changes in the climate (Jeganathan et al., 2014; Peckham et al., 2008). To overcome these limitations, here we provide the first biome-scale characterization of changes in temperate and boreal forest phenology from Landsat data. Similar to AVHRR, the Landsat record begins in the early 1980s and continues today. In contrast to AVHRR, Landsat provides observations at medium spatial resolution (30 m) that are well suited for long-term analysis of forest change at landscape scales (Sulla-Menashe et al., 2016). In addition to providing well-calibrated and atmospherically corrected time series of surface reflectance, the 30 m spatial resolution of Landsat allows land use, disturbance, and plant functional types to be identified in a way that is not possible from advanced very-high-resolution radiometer (AVHRR) or even Moderate Resolution Imaging Spectroradiometer (MODIS). Leveraging these properties, a number of recent studies have demonstrated the value and utility of using Landsat imagery to study long-term changes in forest ecosystems at large spatial scales (Fraser et al., 2011). For example, Ju and Masek (2016) and SullaMenashe et al. (2017) used Landsat time series to refine and revise results from previous studies based on AVHRR data regarding boreal greening and browning trends. In this paper, we use Landsat data to characterize and quantify shifts in large-scale forest phenology caused by climate change. Specifically, our analysis examines two key questions: (1) what is the geographic pattern and magnitude of SOS changes in temperate and boreal forests of North America over the last three decades? And (2) what is the sensitivity of observed changes in SOS to changes in temperature over the same time period? To address these questions, we use Landsat to estimate SOS at annual time steps across 30 years at 75 sites spanning three geographically extensive ecoregions in North America: Taiga, Northern Forests, and Eastern Temperate Forests. We then use the results from this analysis to (1) characterize landscape-scale changes in SOS across these three ecoregions, (2) relate observed trends to regional changes in near-surface air temperature, and (3) quantify the sensitivity of SOS in temperate and boreal forests to changes in springtime temperatures.
2. Data and Methods 2.1. Remote Sensing Data and Study Region Our analysis used all Landsat TM/ETM+ data acquired between 1984 and 2013 for 75 Landsat sidelaps selected using a stratified random sample of sidelaps located within the U.S. Environmental Protection Agency Level I Northern Forest, Taiga, and Eastern Temperate Forest ecoregions (https://www.epa.gov/ecoresearch/ecoregions; Figure S1 in the supporting information). To provide a balanced sample representative of forested areas across these three large and heterogeneous ecoregions, the number of sites was allocated based on the forested area of each Level II ecoregion located within each of the three Level I ecoregions identified above, subject to the constraint that each sidelap included in the sample possesses at least 10% forest cover. The final set of 75 sidelaps ranged from roughly 1,900 to 15,000 km2 in area and included over 200 million Landsat pixels covering 181,000 km2 of mixed or deciduous forest (see Table S1 for more details). 2.2. Algorithm Description Our analysis exploits a recently developed algorithm that is explicitly designed for use with Landsat imagery and exploits the higher temporal density of observations available in the overlap region between adjacent Landsat scenes (i.e., where the swaths of Landsat imagery from adjacent orbits overlap; hereafter,
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“sidelaps”). The Landsat Phenology Algorithm (LPA), which was originally described in Melaas et al. (2013) and subsequently refined and validated in Melaas et al. (2016), estimates the long-term average and annual day of year (DOY) associated with leaf emergence (i.e., SOS) and autumn senescence using all available EVI observations at each Landsat pixel. In this study, we focused on retrieval and analysis of SOS detection for forest pixels because forest SOS retrievals from the LPA have been shown to be very accurate (Melaas et al., 2016) and because much of the literature on climate change-induced changes in phenology has focused on SOS in forests. To exclude areas not classified as either deciduous broadleaf forest or mixed forest, we used the National Land Cover Database (circa 2006) and the Earth Observation for Sustainable Developments of Forest Land Cover (circa 2000) maps (Wulder et al., 2008; Xian et al., 2009). In addition, to isolate pixels with sufficiently long time series and minimize identification of spurious changes unrelated to climate forcing, we used the Continuous Change Detection and Classification algorithm (Zhu & Woodcock, 2014) to identify and exclude pixels that experienced disturbance before 1999 or that had more than two disturbance events during the 30 year Landsat record. Finally, to avoid spurious trends in the timing of SOS associated with long-term greening and browning (Ju & Masek, 2016; Sulla-Menashe et al., 2017), we normalized the EVI time series at each pixel to have unit amplitude each year using the 10th and 90th percentiles at each pixel based on moving 3 year windows. 2.3. Trend Analysis and Evaluation of Temperature Sensitivity Detection of SOS was not always possible at every pixel in every year because of persistent cloud cover during the springtime at some sites in some years, especially in eastern boreal Canada, which tends to be quite cloudy. To overcome this constraint, we analyzed SOS time series based on data that were upscaled to 500 m spatial resolution using the mean of all available 30 m SOS values in each 500 m cell in each year. To ensure robust estimation of trends, we excluded 500 m cells with fewer than 5 forested Landsat pixels or for which fewer than 10 years of SOS retrievals were available. The resulting data set included nearly 1.2 million 500 m cells, where each grid cell provided a unique time series of SOS between 1984 and 2013. Using these time series, we evaluated SOS trends in two ways. First, for each grid cell we computed the Theil-Sen slope (Sen, 1968) to estimate the magnitude of long-term SOS change and then identified grid cells with statistically significant trends (p < 0.05) using the Mann-Kendall test (Mann, 1945). Second, we estimated the overall trend in SOS for each sidelap using a fixed-effects linear regression approach applied to all 500 m grid cells located within each sidelap region. This approach is commonly applied to short time series of cross-sectional data (Hsiao, 2003) and estimates the common trend across grid cells in each sidelap. In both cases, we used the results to upscale and summarize patterns in SOS to Level I and Level II ecoregions. In the final element of our analysis, we analyzed interannual covariation in the timing of SOS and springtime temperatures. Here our goal was to quantify the sensitivity of SOS derived from Landsat to variation in springtime temperature forcing. To do this, we used near-surface air temperature, precipitation, and solar radiation data from the North American Regional Reanalysis data set (NARR) (Mesinger et al., 2006) to calculate the accumulated growing degree days (AGDDs; 0°C base temperature) during an “optimal preseason” directly prior to SOS. Following Fu et al. (2015), we identified the optimal preseason as the period (using 5 day time steps between 15 and 120 days prior to SOS in each NARR grid cell) directly preceding the long-term mean SOS date in each sidelap for which the magnitude of the partial correlation between AGDD and SOS (controlling for precipitation and radiation) was maximum. To quantify temperature sensitivity (ST), we estimated linear models between annual anomalies in AGDD (based on the optimal preseason period) and SOS for each grid cell.
3. Results 3.1. SOS Trend Analysis Long-term average SOS varied by more than 2 months across the sidelaps included in our analysis (Figure 1a): from DOY 94 (4 April) in the Southeastern United States to DOY 173 (22 June in Eastern Canada. Average SOS timing occurred significantly earlier in the Eastern Temperate Forest ecoregion (DOY 118 ± 6) than in the Northern Forest (DOY 154 ± 3) and Taiga (DOY 165 ± 7) ecoregions. The number of cloud-free observations used to estimate SOS at each site varied with latitude, ranging from a minimum of 135 in northern sites to 723
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Figure 1. (a) Average start of season (SOS) date and (b) average SOS trend in each sidelap region from the fixed-effects regression models. Boxplots show the distribution for each across Level I ecoregions: ETF = Eastern Temperate Forests, NF = Northern Forests, and T = Taiga; DOY = day of year.
in the south. Note, however, that increased observation density did not necessarily lead to higher rates of annual SOS detection (Figure S2). Most sidelap regions showed relatively uniform advancement or delay in SOS, although some exhibited a mixture of both (Figure S3). Overall, our results show that natural vegetation in the Eastern Temperate Forest ecoregion experienced larger and more widespread changes in SOS relative to vegetation in the Northern Forest and Taiga ecoregions (Figure 1b). In total, 13 sidelaps showed statistically significant negative trends (i.e., earlier SOS) in 20% or more of the 500 m grid cells located within them (with the remaining cells showing no meaningful trend), all but one of which were located in the Eastern Temperate Forest ecoregion (Figure S4). In contrast, the proportion of grid cells showing positive SOS trends (later leaf emergence) was much lower: only four sidelap regions—all located in Western Canada—showed positive trends in at least 5% of their grid cells. At ecoregion scale, Eastern Temperate Forests showed widespread trends toward earlier SOS, with a median advancement of 0.22 ± 0.11 d yr 1 during 1984–2013, equivalent to a net change of roughly 1 week over the 30 year period of this study, but with substantial variability across sites ( 0.48 d yr 1 to +0.02 d yr 1). Northern Forests, on the other hand, were more heterogeneous, with a median SOS trend of 0.05 ± 0.13 d yr 1. It is important to note, however, that sidelaps located in eastern portions of the Northern Forest ecoregion showed SOS trends similar to those observed in Eastern Temperate Forests, while SOS trends in the western half of this ecoregion were mostly neutral or positive (i.e., delayed SOS). Finally, the overall timing of SOS in Taiga Forests also advanced but at a slower rate than in Eastern Temperate Forests (median = 0.12 ± 0.06 d yr 1). Figure 2 shows the mean time series, overall trend and statistical significance, and interannual variability in SOS across all 500 m grid cells located in each Level II ecoregion, plotted as anomalies (in days) from longterm means. Note that because the time series are relatively short and net changes in SOS are small relative to interannual variation in SOS, p values associated with estimated trends were often not significant (i.e., p > 0.05). Despite this, the timing of SOS showed statistically significant trends that ranged between 0.2 and 0.31 d yr 1 in three out of five Eastern Temperate Forest Level II ecoregions. In the two remaining ecoregions—Mixed Wood Plains (j) and Ozark/Ouachita Appalachian Forest (i)—SOS advanced, but the trends were not significant (p = 0.13, 0.19, respectively). The magnitude of interannual variation in SOS for Eastern Temperate Forests is especially striking in Figure 2, with 1998, 2010, and 2012 showing particularly strong SOS anomalies across multiple Level II ecoregions. A good example is evident in the Southeastern U.S. Plains (l) in 2012, when SOS occurred 2 weeks earlier than average (Ault et al., 2013; Friedl et al., 2014).
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Figure 2. Start of season (SOS) time series across each Level II ecoregion. Solid colored lines and shaded areas represent the annual mean and standard deviation in SOS across all 500 m grids in each ecoregion, respectively. Black lines show the long-term trend based on Theil-Sen. Blue = Eastern Temperate Forests; yellow = Northern Forests; and green = Taiga.
In contrast, SOS trends in Northern Forests showed less pronounced trends, with modest, nonsignificant delays in SOS across the Boreal Plain (e), Mixed Wood Shield (g), and Softwood Shield (f) ecoregions. The Hudson Plain (d) shows a negative trend, but it was not statistically significant. As for the Eastern Temperate Forest, interannual variation in SOS in Northern Forests was pronounced, with all five Level II ecoregions showing ranges of SOS on the order of 20 days. Similar to Eastern Temperate Forests, the timing of SOS in Taiga Forests showed negative trends, although none were statistically significant, with the greatest magnitude ( 0.25 d yr 1) in the Taiga Cordillera (a). Once again, interannual variation in SOS was substantial in the Taiga Forest. 3.2. Temperature Sensitivity In the final element of our analysis we estimated the temperature sensitivity (ST) of SOS in each sidelap, which we define as the change in SOS associated with a change in springtime thermal forcing of 100 AGDD (Friedl et al., 2014). To account for differences in the timing of SOS across sites, we computed AGDD based on the optimal time period preceding average SOS at each site, as described in section 2.3. The resulting time period varied from 20 to 120 days, with 54 out of 75 sites exhibiting maximum sensitivity to AGDD 30 to 60 days directly prior to SOS (Figure S5a). ST ranged from 3.4 to 11.1 days/100 AGDD across the 75 sidelaps (Figure 3a), with most sites showing sensitivities of 5 to 9 days/100 AGDD and with Eastern Temperate Forests showing the strongest sensitivity among the three Level I ecoregions.
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Figure 3. (a) Temperature sensitivity (ST) in each sidelap region. (b) Long-term trends in springtime accumulated growing degree day (AGDD) in each sidelap region. All ST are significant (p < 0.05).
Consistent with observed trends in SOS, Figure 3b shows that Eastern Temperate Forests and Taiga Forests have experienced widespread increases in AGDD during the period preceding SOS. Northern Forests, on the other hand, displayed more complex springtime warming patterns, where eastern locations experienced warming and western locations experienced neutral changes (or even cooling) in springtime temperatures. Warming was strongest in northeastern Canada, where some locations exhibited warming trends of over 6 AGDD yr 1. Western Canada, on the other hand, experienced changes that were more muted, with some sidelaps actually cooling by over 3 AGDD yr 1. Reflecting these patterns, ecoregion-scale changes in SOS showed substantial sensitivity to variation in AGDD. To illustrate, Figure 4 plots mean annual anomalies in springtime AGDD (±1σ) versus mean annual anomalies in SOS (±1σ) across all grid cells in each ecoregion. Estimated ST was relatively consistent across ecoregions, ranging from 5.78 d/100 AGDD in the Hudson Plain (Figure 4d) to 7.98 d/100 AGDD across the Mixed Wood Plains (Figure 4j).
4. Discussion and Conclusions Changes in the timing of plant phenology arising from climate change have been widely reported for well over a decade (Parmesan & Yohe, 2003). At local scales, data collected in botanical gardens (Primack & Miller-Rushing, 2009), in warming experiments (Wolkovich et al., 2012), and by citizen scientists (Beaubien & Hamann, 2011) provide compelling evidence of widespread changes in the phenology of flowering and leaf development and senescence. Extrapolation of results from these studies to the scale of entire biomes, however, is challenging and until recently has relied heavily on coarse spatial resolution remote sensing data with relatively low quality. In this paper, we present the first analysis of continental-scale changes in phenology based on Landsat imagery, which provides a substantially improved basis for this type of study. Specifically, Landsat imagery provides a 30+ year record of the Earth’s surface that is well calibrated, from which clouds, snow, and atmospheric effects can be screened, and where disturbance and landscape-scale properties can be characterized in a way that is not possible from coarse spatial resolution imagery (Sulla-Menashe et al., 2016, 2017). The results from our analysis point to two main conclusions. First, we observed ecoregion-specific changes in the timing of SOS across boreal and temperate forests between 1984 and 2013 that are correlated with changes in near-surface air temperatures. In Eastern Temperate Forests, we observed a relatively uniform net change of roughly 7 days in the timing of SOS. SOS changes in Northern Forests were more heterogeneous, with sites in the eastern half of this ecoregion showing SOS changes similar to those observed in
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Figure 4. Start of season (SOS) anomalies versus accumulated growing degree day (AGDD) anomalies (±1σ) across all grid cells in each Level II ecoregion (colors indicate Level I ecoregions, cf. Figure 2).
Eastern Temperate Forests, while western sites showed relatively little change in SOS and in some sites changes toward later SOS. SOS in Taiga Forests also shifted toward earlier leaf emergence, but these changes were less pronounced than those observed in Eastern Temperate Forests. Significantly, observed patterns in long-term SOS were consistent with geographic patterns in springtime temperature trends during the study period. SOS trends in Western Canada have been previously linked to cyclic cooling in Western North America caused, in part, by dynamics in the Pacific Decadal Oscillation (Ault et al., 2015). This result conforms with those of Beaubien and Freeland (2000), who showed that the timing of spring flowering across central Alberta is correlated with sea surface temperature dynamics in the Pacific Ocean. Eastern North America, on the other hand, is relatively unaffected by the Pacific Decadal Oscillation, and so changes in SOS and springtime temperatures in this region reflect secular changes associated with global-scale warming. The second major conclusion from this work addresses the sensitivity of SOS in temperate and boreal forests to changes in temperature. We observed strong linear sensitivity (ST) in SOS to changes in springtime temperatures across all 12 Level II ecoregions included in our study that ranged from 6 to 8 d/100 AGDD, where more than half of interannual variation in SOS was explained by variation in AGDD during the optimal period directly preceding leaf emergence (Figure 4). In contrast to net changes in SOS, we observed no geographic pattern in the magnitude of ST. This result is important because unlike observed changes in SOS, which depend on location-specific variation in springtime air temperatures, ST provides a direct quantification of how the timing of leaf emergence is affected by changes in springtime temperatures.
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While our results identify a strong linear response in SOS to variation in springtime AGDD based on daily mean temperatures, the exact nature and strength of this relationship remains uncertain. For example, Piao et al. (2015) showed that daytime maximum temperatures were more strongly correlated with SOS than daily mean temperatures. Similarly, previous studies have suggested that decreased wintertime chilling can cause the relationship between SOS and AGDD to be nonlinear (Pope et al., 2013; Vitasse et al., 2018). Finally, identification of the optimal preseason period for AGDD is challenging and imprecise. For example, the optimal preseason length was not clearly identifiable in some of the sidelaps examined in this study (i.e., correlations between AGDD and SOS were close to maximum over a range of dates), and uncertainty in LPA-detected SOS dates (particularly in regions with fewer cloud-free observations; Figure S2a) can influence estimates of both the optimal preseason length and ST. (Hence, the literature on this topic is far from definitive and more research is clearly needed to resolve these issues. A final key result from this work is the utility and importance of moderate spatial resolution imagery from instruments such as Landsat for reconstructing and monitoring change in terrestrial ecosystems. Until recently, this type of analysis could only be accomplished at regional to continental scales using coarse spatial resolution instruments such as AVHRR and MODIS. There remains an important role for such instruments, especially MODIS (and more recently the Visible Infrared Imaging Radiometer Suite (VIIRS)), which have a growing archive and possess high radiometric and geometric quality. However, as this and other recent studies have shown (e.g., Ju & Masek, 2016; Sulla-Menashe et al., 2016, 2017) the ability to analyze multiple decades of ecosystem properties using time series of well-calibrated surface reflectance measurements that support analysis at 30 m spatial resolution is transformative and is supporting new opportunities to characterize and understand recent and long-term variability and change in terrestrial ecosystems (e.g., Hansen et al., 2013; Pekel et al., 2016). The results from this study, which document the magnitude and sensitivity of changes in boreal and temperate forest phenology to warming trends at landscape scale across three decades, provide additional evidence of how terrestrial ecosystems are changing and responding to perturbations in the climate system and refine understanding of how the growing season of these important and geographically extensive biomes are likely to change in the future. Acknowledgments This work was supported by NASA Terrestrial Ecology (grant NNX14AD57G). All remote sensing and climate data used in this study are public. Landsat TM/ETM+ data can be obtained from the U.S. Geological Survey Earth Explorer (https://earthexplorer.usgs.gov), while NARR data can be downloaded from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (ftp://ftp.cdc.noaa.gov/ Datasets/NARR/monolevel/). The 500 m spring phenology time series data are publicly archived with Oak Ridge National Laboratory on the Distributed Active Archive System (https://doi.org/ 10.3334/ORNLDAAC/1570).
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