Phenology plays a fundamental role in regulating photosynthesis, evapotranspiration, and surface energy fluxes and is sensitive to climate change. The global mean surface air temperature data ...indicate a global warming hiatus between 1998 and 2012, while its impacts on global phenology remains unclear. Here we use long-term satellite and FLUXNET records to examine phenology trends in the northern hemisphere before and during the warming hiatus. Our results based on the satellite record show that the phenology change rate slowed down during the warming hiatus. The analysis of the long-term FLUXNET measurements, mainly within the warming hiatus, shows that there were no widespread advancing (or delaying) trends in spring (or autumn) phenology. The lack of widespread phenology trends partly led to the lack of widespread trends in spring and autumn carbon fluxes. Our findings have significant implications for understanding the responses of phenology to climate change and the climate-carbon feedbacks.
Summary
Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote ...sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated.
Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics.
We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color.
These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices.
Deforestation in mid- to high latitudes is hypothesized to have the potential to cool the Earth's surface by altering biophysical processes. In climate models of continental-scale land clearing, the ...cooling is triggered by increases in surface albedo and is reinforced by a land albedo-sea ice feedback. This feedback is crucial in the model predictions; without it other biophysical processes may overwhelm the albedo effect to generate warming instead. Ongoing land-use activities, such as land management for climate mitigation, are occurring at local scales (hectares) presumably too small to generate the feedback, and it is not known whether the intrinsic biophysical mechanism on its own can change the surface temperature in a consistent manner. Nor has the effect of deforestation on climate been demonstrated over large areas from direct observations. Here we show that surface air temperature is lower in open land than in nearby forested land. The effect is 0.85 ± 0.44 K (mean ± one standard deviation) northwards of 45° N and 0.21 ± 0.53 K southwards. Below 35° N there is weak evidence that deforestation leads to warming. Results are based on comparisons of temperature at forested eddy covariance towers in the USA and Canada and, as a proxy for small areas of cleared land, nearby surface weather stations. Night-time temperature changes unrelated to changes in surface albedo are an important contributor to the overall cooling effect. The observed latitudinal dependence is consistent with theoretical expectation of changes in energy loss from convection and radiation across latitudes in both the daytime and night-time phase of the diurnal cycle, the latter of which remains uncertain in climate models.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Methane flux (FCH4) measurements using the eddy covariance technique have increased over the past decade. FCH4 measurements commonly include data gaps, as is the case with CO2 and energy fluxes. ...However, gap‐filling FCH4 data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap‐filling method for other fluxes, to FCH4 datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH4. However, there is still no consensus regarding FCH4 gap‐filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH4 datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest RF, and support vector machine SVM) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH4 and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH4 datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA‐derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap‐filling uncertainty is much larger than measurement uncertainty in accumulated CH4 budget. Therefore, the approach used for FCH4 gap filling can have important implications for characterizing annual ecosystem‐scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes.
Methane flux (FCH4) measurements using the eddy covariance technique commonly include data gaps which should be gap filled to estimate the annual methane budget. However, there is still no consensus on best practices for gap filling. Here, we compare potential FCH4 gap‐filling algorithms including the marginal distribution sampling method, artificial neural networks, random forest, and support vector machine using multiple combinations of ancillary variables.
Forests play important roles in the Earth's climate system and global carbon cycle. Therefore, a critical need exists to improve our understanding of how the growing seasons of forests are changing, ...and by extension, how the composition and function of forests will respond to future climate change. Coarse spatial resolution satellite remote sensing has been widely used to monitor and map the phenology of terrestrial ecosystems at regional to global scales, and despite widespread agreement that the growing season of Northern Hemisphere forests is changing, the spatial resolution of these data sources imposes significant limitations on the character and quality of inferences that can be drawn from them. In particular, the spatial resolution afforded by instruments such as MODIS does not resolve ecologically important landscape-scale patterns in phenology. With this issue in mind, here we evaluate the ability of a newly developed Landsat phenology algorithm (LPA) to reconstruct a 32-year time series for the start and end of the growing season in North American temperate and boreal forests. We focus on 13 “sidelap” regions located between overlapping Landsat scenes that span a large geographic range of temperate and boreal forests, and evaluate the quality and character of LPA-derived start and end of growing season (SOS and EOS) dates using several independent data sources. On average, SOS and EOS dates were detected for about two-thirds of the 32years included in our analysis, with the remaining one-third missing due to cloud cover. Moreover, there was generally better agreement between ground observations and LPA-derived estimates of SOS dates than for EOS across the 13 sites included in our study. Our results demonstrate that, despite the presence of time series gaps, LPA provides a robust basis for retrospective analysis of long-term changes in spring and autumn deciduous forest phenology over the last three decades. Finally, our results support the potential for monitoring land surface phenology at 30m spatial resolution in near real-time by combining time series from multiple sensors such as the Landsat Operational Land Imager and the Sentinel 2 MultiSpectral Instrument.
•Modifies existing Landsat phenology algorithm to account for disturbance•Algorithm is tested over a wide range of temperate and boreal deciduous forests.•Better agreement between ground observations and Landsat during spring than in autumn•Provides a foundation for algorithm development using Landsat OLI and Sentinel 2 MSI
Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the ...relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration (RECO) in deciduous forests. Previous studies showed that empirical RECOmodels can be substantially improved by considering the biotic dependency of RECOon the short‐term productivity (e.g., daily gross primary production, GPP) in addition to the well‐known environmental controls of temperature and water availability. Here, we use a model‐data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling RECOat 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data‐oriented semiempirical model leads to an 8% decrease in root mean square error (RMSE) and a 6% increase in the modeling efficiency (EF) of modeled RECOwhen compared to a version of the model that does not consider the physiological phenology. The reduction of the model‐observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model‐error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling RECOin deciduous forests by including the phenological cycle of the canopy.
We apply and compare three widely applicable methods for estimating ecosystem transpiration (T) from eddy covariance (EC) data across 251 FLUXNET sites globally. All three methods are based on the ...coupled water and carbon relationship, but they differ in assumptions and parameterizations. Intercomparison of the three daily T estimates shows high correlation among methods (R between .89 and .94), but a spread in magnitudes of T/ET (evapotranspiration) from 45% to 77%. When compared at six sites with concurrent EC and sap flow measurements, all three EC‐based T estimates show higher correlation to sap flow‐based T than EC‐based ET. The partitioning methods show expected tendencies of T/ET increasing with dryness (vapor pressure deficit and days since rain) and with leaf area index (LAI). Analysis of 140 sites with high‐quality estimates for at least two continuous years shows that T/ET variability was 1.6 times higher across sites than across years. Spatial variability of T/ET was primarily driven by vegetation and soil characteristics (e.g., crop or grass designation, minimum annual LAI, soil coarse fragment volume) rather than climatic variables such as mean/standard deviation of temperature or precipitation. Overall, T and T/ET patterns are plausible and qualitatively consistent among the different water flux partitioning methods implying a significant advance made for estimating and understanding T globally, while the magnitudes remain uncertain. Our results represent the first extensive EC data‐based estimates of ecosystem T permitting a data‐driven perspective on the role of plants’ water use for global water and carbon cycling in a changing climate.
While transpiration (T) from plants has been studied for centuries, it is difficult to measure at ecosystem scale. We explore three new methods for estimating T from existing eddy covariance data from FLUXNET, providing insights into how plants use water at over 251 sites across the globe. Though there is still work to be done to constrain the magnitude of T, we show that this new dataset represents a significant step toward bridging the gap between individual plant measurements and global estimates of plant water use. Photo credit to Tiana Wilene Hammer.
Nitrogen (N) fertilization of forests for increasing carbon sequestration and wood volume is expected to influence soil greenhouse gas (GHG) emissions, especially to increase N2O emissions. As ...biochar application is known to affect soil GHG emissions, we investigated the effect of biochar application, with and without N fertilization, to a forest soil on GHG emissions in a controlled laboratory study. We found that biochar application at high (10%) application rates increased CO2 and N2O emissions when applied without urea-N fertilizer. At both low (1%) and high biochar (10%) application rates CH4 consumption was reduced when applied without urea-N fertilizer. Biochar application with urea-N fertilization did not increase CO2 emissions compared to biochar amended soil without fertilizer. In terms of CO2-eq, the net change in GHG emissions was mainly controlled by CO2 emissions, regardless of treatment, with CH4 and N2O together accounting for less than 1.5% of the total emissions.
•Biochar application at high (10%) application rates increased CO2 and N2O emissions when applied without urea-N fertilizer.•Both low (1%) and high (10%) biochar application rates decreased CH4 consumption when applied without urea-N fertilizer.•Biochar application with urea-N fertilization did not increase CO2 emissions compared to biochar and no fertilizer.•In terms of CO2-eq, net change in GHG emissions was mainly controlled by CO2 emissions.
Soil respiration (
) represents the largest flux of CO
from terrestrial ecosystems to the atmosphere, but its spatial and temporal changes as well as the driving forces are not well understood. We ...derived a product of annual global
from 2000 to 2014 at 1 km by 1 km spatial resolution using remote sensing data and biome-specific statistical models. Different from the existing view that climate change dominated changes in
, we showed that land-cover change played a more important role in regulating
changes in temperate and boreal regions during 2000-2014. Significant changes in
occurred more frequently in areas with significant changes in short vegetation cover (i.e., all vegetation shorter than 5 m in height) than in areas with significant climate change. These results contribute to our understanding of global
patterns and highlight the importance of land-cover change in driving global and regional
changes.
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