•Seven light use efficiency models were compared at global eddy covariance towers.•Performance of seven models differed substantially among ecosystem types.•It is needed to improve LUE models by ...integrating impacts of diffuse radiation and reliable water stress equations.
Simulating gross primary productivity (GPP) of terrestrial ecosystems has been a major challenge in quantifying the global carbon cycle. Many different light use efficiency (LUE) models have been developed recently, but our understanding of the relative merits of different models remains limited. Using CO2 flux measurements from multiple eddy covariance sites, we here compared and assessed major algorithms and performance of seven LUE models (CASA, CFix, CFlux, EC-LUE, MODIS, VPM and VPRM). Comparison between simulated GPP and estimated GPP from flux measurements showed that model performance differed substantially among ecosystem types. In general, most models performed better in capturing the temporal changes and magnitude of GPP in deciduous broadleaf forests and mixed forests than in evergreen broadleaf forests and shrublands. Six of the seven LUE models significantly underestimated GPP during cloudy days because the impacts of diffuse radiation on light use efficiency were ignored in the models. CFlux and EC-LUE exhibited the lowest root mean square error among all models at 80% and 75% of the sites, respectively. Moreover, these two models showed better performance than others in simulating interannual variability of GPP. Two pairwise comparisons revealed that the seven models differed substantially in algorithms describing the environmental regulations, particularly water stress, on GPP. This analysis highlights the need to improve representation of the impacts of diffuse radiation and water stress in the LUE models.
► Mean energy balance closure at 173 FLUXNET sites is 0.84. ► Mean forest and non-forest closure does not differ. ► Significant differences in closure were found among plant functional types. ► ...Landscape-level vegetation variability should not be excluded from the interpretation.
The energy balance at most surface-atmosphere flux research sites remains unclosed. The mechanisms underlying the discrepancy between measured energy inputs and outputs across the global FLUXNET tower network are still under debate. Recent reviews have identified exchange processes and turbulent motions at large spatial and temporal scales in heterogeneous landscapes as the primary cause of the lack of energy balance closure at some intensively-researched sites, while unmeasured storage terms cannot be ruled out as a dominant contributor to the lack of energy balance closure at many other sites. We analyzed energy balance closure across 173 ecosystems in the FLUXNET database and explored the relationship between energy balance closure and landscape heterogeneity using MODIS products and GLOBEstat elevation data. Energy balance closure per research site (CEB,s) averaged 0.84±0.20, with best average closures in evergreen broadleaf forests and savannas (0.91–0.94) and worst average closures in crops, deciduous broadleaf forests, mixed forests and wetlands (0.70–0.78). Half-hourly or hourly energy balance closure on a percent basis increased with friction velocity (u*) and was highest on average under near-neutral atmospheric conditions. CEB,s was significantly related to mean precipitation, gross primary productivity and landscape-level enhanced vegetation index (EVI) from MODIS, and the variability in elevation, MODIS plant functional type, and MODIS EVI. A linear model including landscape-level variability in both EVI and elevation, mean precipitation, and an interaction term between EVI variability and precipitation had the lowest Akaike's information criterion value. CEB,s in landscapes with uniform plant functional type approached 0.9 and CEB,s in landscapes with uniform EVI approached 1. These results suggest that landscape-level heterogeneity in vegetation and topography cannot be ignored as a contributor to incomplete energy balance closure at the flux network level, although net radiation measurements, biological energy assimilation, unmeasured storage terms, and the importance of good practice including site selection when making flux measurements should not be discounted. Our results suggest that future research should focus on the quantitative mechanistic relationships between energy balance closure and landscape-scale heterogeneity, and the consequences of mesoscale circulations for surface-atmosphere exchange measurements.
Surface albedo is a key parameter in the Earth's energy balance since it affects the amount of solar radiation directly absorbed at the planet surface. Its variability in time and space can be ...globally retrieved through the use of remote sensing products. To evaluate and improve the quality of satellite retrievals, careful intercomparisons with in situ measurements of surface albedo are crucial. For this purpose we compared MODIS albedo retrievals with surface measurements taken at 53 FLUXNET sites that met strict conditions of land cover homogeneity. A good agreement between mean yearly values of satellite retrievals and in situ measurements was found (r2=0.82). The mismatch is correlated with the spatial heterogeneity of surface albedo, stressing the relevance of land cover homogeneity when comparing point to pixel data. When the seasonal patterns of MODIS albedo are considered for different plant functional types, the match with surface observations is extremely good at all forest sites. On the contrary, satellite retrievals at non-forested sites (grasslands, savannas, croplands) underestimate in situ measurements across the seasonal cycle. The mismatch observed at grassland and cropland sites is likely due to the extreme fragmentation of these landscapes, as confirmed by geostatistical attributes derived from high resolution scenes.
► In situ and satellite albedo are in good agreement at 53 FLUXNET sites (r2 0.83). ► MODIS albedo is systematically lower than in situ measurements for non-forest PFTs. ► The mismatch increases with the spatial heterogeneity of albedo in a 7×7km area. ► The seasonal pattern of MODIS albedo matches extremely well for forest PFTs.
► In the warmest and driest years the ecosystem was a significant source of CO
2. ► CO
2 flux IAV is largely controlled by changes in ecosystem responses to climate. ► The negative interaction of ...climate and ecosystem responses reduces NEE IAV.
Seven years of continuous eddy covariance measurements at an alpine meadow were used to investigate the impacts of climate drivers and ecosystem responses on the inter-annual variability (IAV) of the net ecosystem exchange (NEE). The annual cumulative value of NEE was positive (source) in 2003, 2005 and 2009 (50, 15 and 112
g
m
−2 respectively) and negative (sink) in 2004, 2006, 2007 and 2008 (29, 75, 110 and 28
g
m
−2 respectively). The IAV of carbon dioxide fluxes builds up in two phenological phases: the onset of the growing season (triggered by snow melting) and the canopy re-growth after mowing. Respiratory fluxes during the non-growing season were observed to increase IAV, while growing season uptake dampened it. A novel approach was applied to factor out the two main sources of IAV: climate drivers’ variability and changes in the ecosystem responses to climate. Annual values of carbon dioxide fluxes were calculated assuming (a) variable climate and variable ecosystem response among years, (b) variable climate and constant ecosystem response and (c) constant climate and variable ecosystem response. The analysis of flux variances calculated under these three assumptions indicates the occurrence of an important negative feedback between climate and ecosystem responses. Due to this feedback, the observed IAV of NEE is lower than one would expect for a given climate variability, because of the counteracting changes in ecosystem responses. This alpine meadow therefore demonstrates the ability to acclimatise and to limit the IAV of carbon fluxes induced by climate variability.
Soil moisture induced droughts are expected to become more frequent under future global climate change. Precipitation has been previously assumed to be mainly responsible for variability in summer ...soil moisture. However, little is known about the impacts of precipitation frequency on summer soil moisture, either interannually or spatially. To better understand the temporal and spatial drivers of summer drought, 415 site yr measurements observed at 75 flux sites world wide were used to analyze the temporal and spatial relationships between summer soil water content (SWC) and the precipitation frequencies at various temporal scales, i.e., from half-hourly, 3, 6, 12 and 24 h measurements. Summer precipitation was found to be an indicator of interannual SWC variability with r of 0.49 (p < 0.001) for the overall dataset. However, interannual variability in summer SWC was also significantly correlated with the five precipitation frequencies and the sub-daily precipitation frequencies seemed to explain the interannual SWC variability better than the total of precipitation. Spatially, all these precipitation frequencies were better indicators of summer SWC than precipitation totals, but these better performances were only observed in non-forest ecosystems. Our results demonstrate that precipitation frequency may play an important role in regulating both interannual and spatial variations of summer SWC, which has probably been overlooked or underestimated. However, the spatial interpretation should carefully consider other factors, such as the plant functional types and soil characteristics of diverse ecoregions.
The terrestrial carbon fluxes show the largest variability among the components of the global carbon cycle and drive most of the temporal variations in the growth rate of atmospheric CO2. ...Understanding the environmental controls and trends of the terrestrial carbon budget is therefore essential to predict the future trajectories of the CO2 airborne fraction and atmospheric concentrations. In the present work, patterns and controls of the inter-annual variability (IAV) of carbon net ecosystem exchange (NEE) have been analysed using three different data streams: ecosystem-level observations from the FLUXNET database (La Thuile and 2015 releases), the MPI-MTE (model tree ensemble) bottom–up product resulting from the global upscaling of site-level fluxes, and the Jena CarboScope Inversion, a top–down estimate of surface fluxes obtained from observed CO2 concentrations and an atmospheric transport model. Consistencies and discrepancies in the temporal and spatial patterns and in the climatic and physiological controls of IAV were investigated between the three data sources. Results show that the global average of IAV at FLUXNET sites, quantified as the standard deviation of annual NEE, peaks in arid ecosystems and amounts to ∼ 120 gC m−2 y−1, almost 6 times more than the values calculated from the two global products (15 and 20 gC m−2 y−1 for MPI-MTE and the Jena Inversion, respectively). Most of the temporal variability observed in the last three decades of the MPI-MTE and Jena Inversion products is due to yearly anomalies, whereas the temporal trends explain only about 15 and 20 % of the variability, respectively. Both at the site level and on a global scale, the IAV of NEE is driven by the gross primary productivity and in particular by the cumulative carbon flux during the months when land acts as a sink. Altogether these results offer a broad view on the magnitude, spatial patterns and environmental drivers of IAV from a variety of data sources that can be instrumental to improve our understanding of the terrestrial carbon budget and to validate the predictions of land surface models.
In this study we present an assessment of the impact of future climate change on total fire probability, burned area, and carbon (C) emissions from fires in Europe. The analysis was performed with ...the Community Land Model (CLM) extended with a prognostic treatment of fires that was specifically refined and optimized for application over Europe. Simulations over the 21st century are forced by five different high‐resolution Regional Climate Models under the Special Report on Emissions Scenarios A1B. Both original and bias‐corrected meteorological forcings is used. Results show that the simulated C emissions over the present period are improved by using bias corrected meteorological forcing, with a reduction of the intermodel variability. In the course of the 21st century, burned area and C emissions from fires are shown to increase in Europe, in particular in the Mediterranean basins, in the Balkan regions and in Eastern Europe. However, the projected increase is lower than in other studies that did not fully account for the effect of climate on ecosystem functioning. We demonstrate that the lower sensitivity of burned area and C emissions to climate change is related to the predicted reduction of the net primary productivity, which is identified as the most important determinant of fire activity in the Mediterranean region after anthropogenic interaction. This behavior, consistent with the intermediate fire‐productivity hypothesis, limits the sensitivity of future burned area and C emissions from fires on climate change, providing more conservative estimates of future fire patterns, and demonstrates the importance of coupling fire simulation with a climate driven ecosystem productivity model.
Key Points
Projected increase of C emissions from fires for the 21st century in Europe
Model performance improved by using bias corrected climate scenarios
The net primary productivity limits the sensitivity of fire to climate change
In the last decades terrestrial ecosystems have
reabsorbed on average more than one-quarter of anthropogenic emissions
(Le
Quéré et al., 2018). However, this large carbon sink is modulated by
climate ...and is therefore highly variable in time and space. The magnitude
and temporal changes in the sensitivity of terrestrial CO2 fluxes to
climate drivers are key factors to determine future atmospheric CO2
concentration and climate trajectories. In the literature, there is so far a
strong focus on the climatic controls of daily and long-term variability,
while less is known about the key drivers at a seasonal timescale and about
their variation over time (Wohlfahrt
et al., 2008). This latter temporal scale is relevant to assess which
climatic drivers dominate the seasonality of the fluxes and to understand
which factors limit the CO2 exchange during the course of the year.
Here, we investigate the global sensitivity of net terrestrial CO2
fluxes, derived from atmospheric inversion, to three key climate drivers
(i.e. global radiation and temperature from WFDEI and soil water content from
ERA-Interim) from weekly to seasonal temporal scales, in order to explore
the short-term interdependence between climate and the terrestrial carbon
budget. We observed that the CO2 exchange is controlled by temperature
during the carbon uptake period over most of the land surface (from 55 % to
52 % of the total surface), while radiation is the most widespread
dominant climate driver during the carbon release period (from 64 % to 70 %
of the total surface). As expected, soil water content plays a key role in
arid regions of the Southern Hemisphere during both the carbon uptake and
the carbon release period. Looking at the decadal trend of these
sensitivities (1985–2016) we observed that the importance of radiation as a
driver is increasing over time, while we observed a decrease in sensitivity
to temperature in Eurasia. Overall, we show that flux temporal variation due
to a specific driver has been dominated by the temporal changes in ecosystem
sensitivity (i.e. the response of ecosystem to climate) rather than to the
temporal variability of the climate driver itself over the last decades.
Ultimately, this analysis shows that the ecosystem response to climate is
significantly changing both in space and in time, with potential
repercussion on the future terrestrial CO2 sink and therefore on the
role that land may play in climate trajectories.
Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate–carbon ...cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy–covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO ₂ uptake period (CUP) and the seasonal maximal capacity of CO ₂ uptake (GPP ₘₐₓ). The product of CUP and GPP ₘₐₓ explained >90% of the temporal GPP variability in most areas of North America during 2000–2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 ( r ² = 0.90) and GPP recovery after a fire disturbance in South Dakota ( r ² = 0.88). Additional analysis of the eddy–covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPP ₘₐₓ than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPP ₘₐₓ and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.
Significance Terrestrial gross primary productivity (GPP), the total photosynthetic CO ₂ fixation at ecosystem level, fuels all life on land. However, its spatiotemporal variability is poorly understood, because GPP is determined by many processes related to plant phenology and physiological activities. In this study, we find that plant phenological and physiological properties can be integrated in a robust index—the product of the length of CO ₂ uptake period and the seasonal maximal photosynthesis—to explain the GPP variability over space and time in response to climate extremes and during recovery after disturbance.
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