Terrestrial gross primary production inferred from satellite fluorescence and vegetation models

Determining the spatial and temporal distribution of terrestrial gross primary production (GPP) is a critical step in closing the Earth's carbon budget. Dynamical global vegetation models (DGVMs) provide mechanistic insight into GPP variability but diverge in predicting the response to climate in poorly investigated regions. Recent advances in the remote sensing of solar‐induced chlorophyll fluorescence (SIF) opens up a new possibility to provide direct global observational constraints for GPP. Here, we apply an optimal estimation approach to infer the global distribution of GPP from an ensemble of eight DGVMs constrained by global measurements of SIF from the Greenhouse Gases Observing SATellite (GOSAT). These estimates are compared to flux tower data in N. America, Europe, and tropical S. America, with careful consideration of scale differences between models, GOSAT, and flux towers. Assimilation of GOSAT SIF with DGVMs causes a redistribution of global productivity from northern latitudes to the tropics of 7–8 Pg C yr^?1 from 2010 to 2012, with reduced GPP in northern forests (~3.6 Pg C yr^?1) and enhanced GPP in tropical forests (~3.7 Pg C yr^?1). This leads to improvements in the structure of the seasonal cycle, including earlier dry season GPP loss and enhanced peak‐to‐trough GPP in tropical forests within the Amazon Basin and reduced growing season length in northern croplands and deciduous forests. Uncertainty in predicted GPP (estimated from the spread of DGVMs) is reduced by 40–70% during peak productivity suggesting the assimilation of GOSAT SIF with models is well‐suited for benchmarking. We conclude that satellite fluorescence augurs a new opportunity to quantify the GPP response to climate drivers and the potential to constrain predictions of carbon cycle evolution.     

Estimation of vegetation photosynthetic capacity from space‐based measurements of chlorophyll fluorescence for terrestrial biosphere models

Photosynthesis simulations by terrestrial biosphere models are usually based on the Farquhar's model, in which the maximum rate of carboxylation (V_cmax) is a key control parameter of photosynthetic capacity. Even though V_cmax is known to vary substantially in space and time in response to environmental controls, it is typically parameterized in models with tabulated values associated to plant functional types. Remote sensing can be used to produce a spatially continuous and temporally resolved view on photosynthetic efficiency, but traditional vegetation observations based on spectral reflectance lack a direct link to plant photochemical processes. Alternatively, recent space‐borne measurements of sun‐induced chlorophyll fluorescence (SIF) can offer an observational constraint on photosynthesis simulations. Here, we show that top‐of‐canopy SIF measurements from space are sensitive to V_cmax at the ecosystem level, and present an approach to invert V_cmax from SIF data. We use the Soil‐Canopy Observation of Photosynthesis and Energy (SCOPE) balance model to derive empirical relationships between seasonal V_cmax and SIF which are used to solve the inverse problem. We evaluate our V_cmax estimation method at six agricultural flux tower sites in the midwestern US using spaced‐based SIF retrievals. Our V_cmax estimates agree well with literature values for corn and soybean plants (average values of 37 and 101 μmol m^?2 s^?1, respectively) and show plausible seasonal patterns. The effect of the updated seasonally varying V_cmax parameterization on simulated gross primary productivity (GPP) is tested by comparing to simulations with fixed V_cmax values. Validation against flux tower observations demonstrate that simulations of GPP and light use efficiency improve significantly when our time‐resolved V_cmax estimates from SIF are used, with R² for GPP comparisons increasing from 0.85 to 0.93, and for light use efficiency from 0.44 to 0.83. Our results support the use of space‐based SIF data as a proxy for photosynthetic capacity and suggest the potential for global, time‐resolved estimates of V_cmax.     

Solar‐induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO‐2 and flux tower observations

Solar‐induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite‐observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory‐2 (OCO‐2) provides the first opportunity to examine the SIF–GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO‐2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO‐2 SIF showed strong correlations with tower GPP at both midday and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R² = 0.72, p < 0.0001). Strong linear relationships between SIF and GPP were consistently found for all biomes (R² = 0.57–0.79, p < 0.0001) except evergreen broadleaf forests (R² = 0.16, p < 0.05) at the daily timescale. A higher slope was found for C₄ grasslands and croplands than for C₃ ecosystems. The generally consistent slope of the relationship among biomes suggests a nearly universal rather than biome‐specific SIF–GPP relationship, and this finding is an important distinction and simplification compared to previous results. SIF was mainly driven by absorbed photosynthetically active radiation and was also influenced by environmental stresses (temperature and water stresses) that determine photosynthetic light use efficiency. OCO‐2 SIF generally had a better performance for predicting GPP than satellite‐derived vegetation indices and a light use efficiency model. The universal SIF–GPP relationship can potentially lead to more accurate GPP estimates regionally or globally. Our findings revealed the remarkable ability of finer resolution SIF observations from OCO‐2 and other new or future missions (e.g., TROPOMI, FLEX) for estimating terrestrial photosynthesis across a wide variety of biomes and identified their potential and limitations for ecosystem functioning and carbon cycle studies.     

Dominant regions and drivers of the variability of the global land carbon sink across timescales

Net biome productivity (NBP) dominates the observed large variation of atmospheric CO₂ annual increase over the last five decades. However, the dominant regions controlling inter‐annual to multi‐decadal variability of global NBP are still controversial (semi‐arid regions vs. temperate or tropical forests). By developing a theory for partitioning the variance of NBP into the contributions of net primary production (NPP) and heterotrophic respiration (R_h) at different timescales, and using both observation‐based atmospheric CO₂ inversion product and the outputs of 10 process‐based terrestrial ecosystem models forced by 110‐year observational climate, we tried to reconcile the controversy by showing that semi‐arid lands dominate the variability of global NBP at inter‐annual (<10 years) and tropical forests dominate at multi‐decadal scales (>30 years). Results further indicate that global NBP variability is dominated by the NPP component at inter‐annual timescales, and is progressively controlled by R_h with increasing timescale. Multi‐decadal NBP variations of tropical rainforests are modulated by the Pacific Decadal Oscillation (PDO) through its significant influences on both temperature and precipitation. This study calls for long‐term observations for the decadal or longer fluctuations in carbon fluxes to gain insights on the future evolution of global NBP, particularly in the tropical forests that dominate the decadal variability of land carbon uptake and are more effective for climate mitigation.     

Elucidating climatic drivers of photosynthesis by tropical forests

Tropical forests play a pivotal role in regulating the global carbon cycle. However, the response of these forests to changes in absorbed solar energy and water supply under the changing climate is highly uncertain. Three-year (2018–2021) spaceborne high-resolution measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) provide a new opportunity to study the response of gross primary production (GPP) and more broadly tropical forest carbon dynamics to differences in climate. SIF has been shown to be a good proxy for GPP on monthly and regional scales. Combining tropical climate reanalysis records and other contemporary satellite products, we find that on the seasonal timescale, the dependence of GPP on climate variables is highly heterogeneous. Following the principal component analyses and correlation comparisons, two regimes are identified: water limited and energy limited. GPP variations over tropical Africa are more correlated with water-related factors such as vapor pressure deficit (VPD) and soil moisture, while in tropical Southeast Asia, GPP is more correlated with energy-related factors such as photosynthetically active radiation (PAR) and surface temperature. Amazonia is itself heterogeneous: with an energy-limited regime in the north and water-limited regime in the south. The correlations of GPP with climate variables are supported by other observation-based products, such as Orbiting Carbon Observatory-2 (OCO2) SIF and FluxSat GPP. In each tropical continent, the coupling between SIF and VPD increases with the mean VPD. Even on the interannual timescale, the correlation of GPP with VPD is still discernable, but the sensitivity is smaller than the intra-annual correlation. By and large, the dynamic global vegetation models in the TRENDY v8 project do not capture the high GPP seasonal sensitivity to VPD in dry tropics. The complex interactions between carbon and water cycles in the tropics illustrated in this study and the poor representation of this coupling in the current suite of vegetation models suggest that projections of future changes in carbon dynamics based on these models may not be robust.     

Regional atmospheric CO2 inversion reveals seasonal and geographic differences in Amazon net biome exchange

Understanding tropical rainforest carbon exchange and its response to heat and drought is critical for quantifying the effects of climate change on tropical ecosystems, including global climate–carbon feedbacks. Of particular importance for the global carbon budget is net biome exchange of CO₂ with the atmosphere (NBE), which represents nonfire carbon fluxes into and out of biomass and soils. Subannual and sub‐Basin Amazon NBE estimates have relied heavily on process‐based biosphere models, despite lack of model agreement with plot‐scale observations. We present a new analysis of airborne measurements that reveals monthly, regional‐scale (~1–8 × 10⁶ km²) NBE variations. We develop a regional atmospheric CO₂ inversion that provides the first analysis of geographic and temporal variability in Amazon biosphere–atmosphere carbon exchange and that is minimally influenced by biosphere model‐based first guesses of seasonal and annual mean fluxes. We find little evidence for a clear seasonal cycle in Amazon NBE but do find NBE sensitivity to aberrations from long‐term mean climate. In particular, we observe increased NBE (more carbon emitted to the atmosphere) associated with heat and drought in 2010, and correlations between wet season NBE and precipitation (negative correlation) and temperature (positive correlation). In the eastern Amazon, pulses of increased NBE persisted through 2011, suggesting legacy effects of 2010 heat and drought. We also identify regional differences in postdrought NBE that appear related to long‐term water availability. We examine satellite proxies and find evidence for higher gross primary productivity (GPP) during a pulse of increased carbon uptake in 2011, and lower GPP during a period of increased NBE in the 2010 dry season drought, but links between GPP and NBE changes are not conclusive. These results provide novel evidence of NBE sensitivity to short‐term temperature and moisture extremes in the Amazon, where monthly and sub‐Basin estimates have not been previously available.     

Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data‐model intercomparison

To predict forest response to long‐term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short‐term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry‐season intensities and lengths, to determine how well four state‐of‐the‐art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry‐season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry‐season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry‐season GPP resulted from a combination of internal biological (leaf‐flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (R_e) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry‐season declines in GPP and R_e consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light‐harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf‐level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.     

Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest

Accurate estimation of terrestrial gross primary productivity (GPP) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf‐level ChlF was linked with canopy‐scale solar‐induced chlorophyll fluorescence (SIF) in a temperate deciduous forest at Harvard Forest, Massachusetts, USA. Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R² = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively; P < 0.0001). We developed a model to estimate GPP from the tower‐based measurement of SIF and leaf‐level ChlF parameters. The estimation of GPP from this model agreed well with flux tower observations of GPP (R² = 0.68; P < 0.0001), demonstrating the potential of SIF for modeling GPP. At the leaf scale, we found that leaf F_q’/F_m’, the fraction of absorbed photons that are used for photochemistry for a light‐adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopy SIF yield (SIF/APAR, R² = 0.79; P < 0.0001). We also found that canopy SIF and SIF‐derived GPP (GPP_SIF) were strongly correlated to leaf‐level biochemistry and canopy structure, including chlorophyll content (R² = 0.65 for canopy GPP_SIF and chlorophyll content; P < 0.0001), leaf area index (LAI) (R² = 0.35 for canopy GPP_SIF and LAI; P < 0.0001), and normalized difference vegetation index (NDVI) (R² = 0.36 for canopy GPP_SIF and NDVI; P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.     

Satellite chlorophyll fluorescence measurements reveal large‐scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests

Mid‐to‐high latitude forests play an important role in the terrestrial carbon cycle, but the representation of photosynthesis in boreal forests by current modelling and observational methods is still challenging. In particular, the applicability of existing satellite‐based proxies of greenness to indicate photosynthetic activity is hindered by small annual changes in green biomass of the often evergreen tree population and by the confounding effects of background materials such as snow. As an alternative, satellite measurements of sun‐induced chlorophyll fluorescence (SIF) can be used as a direct proxy of photosynthetic activity. In this study, the start and end of the photosynthetically active season of the main boreal forests are analysed using spaceborne SIF measurements retrieved from the GOME‐2 instrument and compared to that of green biomass, proxied by vegetation indices including the Enhanced Vegetation Index (EVI) derived from MODIS data. We find that photosynthesis and greenness show a similar seasonality in deciduous forests. In high‐latitude evergreen needleleaf forests, however, the length of the photosynthetically active period indicated by SIF is up to 6 weeks longer than the green biomass changing period proxied by EVI, with SIF showing a start‐of‐season of approximately 1 month earlier than EVI. On average, the photosynthetic spring recovery as signalled by SIF occurs as soon as air temperatures exceed the freezing point (2–3 °C) and when the snow on the ground has not yet completely melted. These findings are supported by model data of gross primary production and a number of other studies which evaluated in situ observations of CO₂ fluxes, meteorology and the physiological state of the needles. Our results demonstrate the sensitivity of space‐based SIF measurements to light‐use efficiency of boreal forests and their potential for an unbiased detection of photosynthetic activity even under the challenging conditions interposed by evergreen boreal ecosystems.     

Evaluating litter decomposition in earth system models with long‐term litterbag experiments: an example using the Community Land Model version 4 (CLM4)

Decomposition is a large term in the global carbon budget, but models of the earth system that simulate carbon cycle‐climate feedbacks are largely untested with respect to litter decomposition. We tested the litter decomposition parameterization of the community land model version 4 (CLM4), the terrestrial component of the community earth system model, with data from the long‐term intersite decomposition experiment team (LIDET). The LIDET dataset is a 10‐year study of litter decomposition at multiple sites across North America and Central America. We performed 10‐year litter decomposition simulations comparable with LIDET for 9 litter types and 20 sites in tundra, grassland, and boreal, conifer, deciduous, and tropical forest biomes using the LIDET‐provided climatic decomposition index to constrain temperature and moisture effects on decomposition. We performed additional simulations with DAYCENT, a version of the CENTURY model, to ask how well an established ecosystem model matches the observations. The results show large discrepancy between the laboratory microcosm studies used to parameterize the CLM4 litter decomposition and the LIDET field study. Simulated carbon loss is more rapid than the observations across all sites, and nitrogen immobilization is biased high. Closer agreement with the observations requires much lower decomposition rates, obtained with the assumption that soil mineral nitrogen severely limits decomposition. DAYCENT better replicates the observations, for both carbon mass remaining and nitrogen, independent of nitrogen limitation. CLM4 has low soil carbon in global earth system simulations. These results suggest that this bias arises, in part, from too rapid litter decomposition. More broadly, the terrestrial biogeochemistry of earth system models must be critically tested with observations, and the consequences of particular model choices must be documented. Long‐term litter decomposition experiments such as LIDET provide a real‐world process‐oriented benchmark to evaluate models.     

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.     

A high‐resolution approach to estimating ecosystem respiration at continental scales using operational satellite data

A better understanding of the local variability in land‐atmosphere carbon fluxes is crucial to improving the accuracy of global carbon budgets. Operational satellite data backed by ground measurements at Fluxnet sites proved valuable in monitoring local variability of gross primary production at highly resolved spatio‐temporal resolutions. Yet, we lack similar operational estimates of ecosystem respiration (Re) to calculate net carbon fluxes. If successful, carbon fluxes from such a remote sensing approach would form an independent and sought after measure to complement widely used dynamic global vegetation models (DGVMs). Here, we establish an operational semi‐empirical Re model, based only on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with a resolution of 1 km and 8 days. Fluxnet measurements between 2000 and 2009 from 100 sites across North America and Europe are used for parameterization and validation. Our analysis shows that Re is closely tied to temperature and plant productivity. By separating temporal and intersite variation, we find that MODIS land surface temperature (LST) and enhanced vegetation index (EVI) are sufficient to explain observed Re across most major biomes with a negligible bias [R² = 0.62, RMSE = 1.32 (g C m^?2 d^?1), MBE = 0.05 (g C m^?2 d^?1)]. A comparison of such satellite‐derived Re with those simulated by the DGVM LPJmL reveals similar spatial patterns. However, LPJmL shows higher temperature sensitivities and consistently simulates higher Re values, in high‐latitude and subtropical regions. These differences remain difficult to explain and they are likely associated either with LPJmL parameterization or with systematic errors in the Fluxnet sampling technique. While uncertainties remain with Re estimates, the model formulated in this study provides an operational, cross‐validated and unbiased approach to scale Fluxnet Re to the continental scale and advances knowledge of spatio‐temporal Re variability.     

A global assessment of forest surface albedo and its relationships with climate and atmospheric nitrogen deposition

We present a global assessment of the relationships between the short‐wave surface albedo of forests, derived from the MODIS satellite instrument product at 0.5° spatial resolution, with simulated atmospheric nitrogen deposition rates (N_dep), and climatic variables (mean annual temperature T_m and total annual precipitation P), compiled at the same spatial resolution. The analysis was performed on the following five forest plant functional types (PFTs): evergreen needle‐leaf forests (ENF); evergreen broad‐leaf forests (EBF); deciduous needle‐leaf forests (DNF); deciduous broad‐leaf forests (DBF); and mixed‐forests (MF). Generalized additive models (GAMs) were applied in the exploratory analysis to assess the functional nature of short‐wave surface albedo relations to environmental variables. The analysis showed evident correlations of albedo with environmental predictors when data were pooled across PFTs: T_m and N_dep displayed a positive relationship with forest albedo, while a negative relationship was detected with P. These correlations are primarily due to surface albedo differences between conifer and broad‐leaf species, and different species geographical distributions. However, the analysis performed within individual PFTs, strengthened by attempts to select ‘pure’ pixels in terms of species composition, showed significant correlations with annual precipitation and nitrogen deposition, pointing toward the potential effect of environmental variables on forest surface albedo at the ecosystem level. Overall, our global assessment emphasizes the importance of elucidating the ecological mechanisms that link environmental conditions and forest canopy properties for an improved parameterization of surface albedo in climate models.     

Radiation budget changes with dry forest clearing in temperate Argentina

Land cover changes may affect climate and the energy balance of the Earth through their influence on the greenhouse gas composition of the atmosphere (biogeochemical effects) but also through shifts in the physical properties of the land surface (biophysical effects). We explored how the radiation budget changes following the replacement of temperate dry forests by crops in central semiarid Argentina and quantified the biophysical radiative forcing of this transformation. For this purpose, we computed the albedo and surface temperature for a 7‐year period (2003–2009) from MODIS imagery at 70 paired sites occupied by native forests and crops and calculated the radiation budget at the tropopause and surface levels using a columnar radiation model parameterized with satellite data. Mean annual black‐sky albedo and diurnal surface temperature were 50% and 2.5 °C higher in croplands than in dry forests. These contrasts increased the outgoing shortwave energy flux at the top of the atmosphere in croplands by a quarter (58.4 vs. 45.9 W m^?2) which, together with a slight increase in the outgoing longwave flux, yielded a net cooling of ?14 W m^?2. This biophysical cooling effect would be equivalent to a reduction in atmospheric CO₂ of 22 Mg C ha^?1, which involves approximately a quarter to a half of the typical carbon emissions that accompany deforestation in these ecosystems. We showed that the replacement of dry forests by crops in central Argentina has strong biophysical effects on the energy budget which could counterbalance the biogeochemical effects of deforestation. Underestimating or ignoring these biophysical consequences of land‐use changes on climate will certainly curtail the effectiveness of many warming mitigation actions, particularly in semiarid regions where high radiation load and smaller active carbon pools would increase the relative importance of biophysical forcing.     

What controls variation in carbon use efficiency among Amazonian tropical forests?

Why do some forests produce biomass more efficiently than others? Variations in Carbon Use Efficiency (CUE: total Net Primary Production (NPP)/ Gross Primary Production (GPP)) may be due to changes in wood residence time (Biomass/NPP_wood), temperature, or soil nutrient status. We tested these hypotheses in 14, one ha plots across Amazonian and Andean forests where we measured most key components of net primary production (NPP: wood, fine roots, and leaves) and autotrophic respiration (R_a; wood, rhizosphere, and leaf respiration). We found that lower fertility sites were less efficient at producing biomass and had higher rhizosphere respiration, indicating increased carbon allocation to belowground components. We then compared wood respiration to wood growth and rhizosphere respiration to fine root growth and found that forests with residence times <40 yrs had significantly lower maintenance respiration for both wood and fine roots than forests with residence times >40 yrs. A comparison of rhizosphere respiration to fine root growth showed that rhizosphere growth respiration was significantly greater at low fertility sites. Overall, we found that Amazonian forests produce biomass less efficiently in stands with residence times >40 yrs and in stands with lower fertility, but changes to long‐term mean annual temperatures do not impact CUE.     

Carbon emissions from agricultural expansion and intensification in the Chaco

Carbon emissions from land‐use changes in tropical dry forest systems are poorly understood, although they are likely globally significant. The South American Chaco has recently emerged as a hot spot of agricultural expansion and intensification, as cattle ranching and soybean cultivation expand into forests, and as soybean cultivation replaces grazing lands. Still, our knowledge of the rates and spatial patterns of these land‐use changes and how they affected carbon emissions remains partial. We used the Landsat satellite image archive to reconstruct land‐use change over the past 30 years and applied a carbon bookkeeping model to quantify how these changes affected carbon budgets. Between 1985 and 2013, more than 142 000 km² of the Chaco's forests, equaling 20% of all forest, was replaced by croplands (38.9%) or grazing lands (61.1%). Of those grazing lands that existed in 1985, about 40% were subsequently converted to cropland. These land‐use changes resulted in substantial carbon emissions, totaling 824 Tg C between 1985 and 2013, and 46.2 Tg C for 2013 alone. The majority of these emissions came from forest‐to‐grazing‐land conversions (68%), but post‐deforestation land‐use change triggered an additional 52.6 Tg C. Although tropical dry forests are less carbon‐dense than moist tropical forests, carbon emissions from land‐use change in the Chaco were similar in magnitude to those from other major tropical deforestation frontiers. Our study thus highlights the urgent need for an improved monitoring of the often overlooked tropical dry forests and savannas, and more broadly speaking the value of the Landsat image archive for quantifying carbon fluxes from land change.     

Risky future for Mediterranean forests unless they undergo extreme carbon fertilization

Forest performance is challenged by climate change but higher atmospheric [CO₂] (c_a) could help trees mitigate the negative effect of enhanced water stress. Forest projections using data assimilation with mechanistic models are a valuable tool to assess forest performance. Firstly, we used dendrochronological data from 12 Mediterranean tree species (six conifers and six broadleaves) to calibrate a process‐based vegetation model at 77 sites. Secondly, we conducted simulations of gross primary production (GPP) and radial growth using an ensemble of climate projections for the period 2010–2100 for the high‐emission RCP8.5 and low‐emission RCP2.6 scenarios. GPP and growth projections were simulated using climatic data from the two RCPs combined with (i) expected c_a; (ii) constant c_a = 390 ppm, to test a purely climate‐driven performance excluding compensation from carbon fertilization. The model accurately mimicked the growth trends since the 1950s when, despite increasing c_a, enhanced evaporative demands precluded a global net positive effect on growth. Modeled annual growth and GPP showed similar long‐term trends. Under RCP2.6 (i.e., temperatures below +2 °C with respect to preindustrial values), the forests showed resistance to future climate (as expressed by non‐negative trends in growth and GPP) except for some coniferous sites. Using exponentially growing c_a and climate as from RCP8.5, carbon fertilization overrode the negative effect of the highly constraining climatic conditions under that scenario. This effect was particularly evident above 500 ppm (which is already over +2 °C), which seems unrealistic and likely reflects model miss‐performance at high c_a above the calibration range. Thus, forest projections under RCP8.5 preventing carbon fertilization displayed very negative forest performance at the regional scale. This suggests that most of western Mediterranean forests would successfully acclimate to the coldest climate change scenario but be vulnerable to a climate warmer than +2 °C unless the trees developed an exaggerated fertilization response to [CO₂].     

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within-canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%–15% and 23%–34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%–18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit–shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity (V_cmax), which together decreased photosynthesis in sunlit canopy by 5.9–7.2 PgC year⁻¹ while enhanced photosynthesis by 6.9–8.2 PgC year⁻¹ in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost photosynthesis in sunlit canopy, resulting in 1.0 ± 0.12 PgC year⁻¹ net increase in GPP. The uncertainty of GPP due to the different input CI datasets was much larger than that caused by CI seasonal variations, and was up to 50% of the magnitude of GPP interannual variations in the tropical regions. This study highlights the necessity of considering the impacts of CI and its uncertainty in terrestrial biosphere models.     

Recovery of ponderosa pine ecosystem carbon and water fluxes from thinning and stand‐replacing fire

Carbon uptake by forests is a major sink in the global carbon cycle, helping buffer the rising concentration of CO₂ in the atmosphere, yet the potential for future carbon uptake by forests is uncertain. Climate warming and drought can reduce forest carbon uptake by reducing photosynthesis, increasing respiration, and by increasing the frequency and intensity of wildfires, leading to large releases of stored carbon. Five years of eddy covariance measurements in a ponderosa pine (Pinus ponderosa)‐dominated ecosystem in northern Arizona showed that an intense wildfire that converted forest into sparse grassland shifted site carbon balance from sink to source for at least 15 years after burning. In contrast, recovery of carbon sink strength after thinning, a management practice used to reduce the likelihood of intense wildfires, was rapid. Comparisons between an undisturbed‐control site and an experimentally thinned site showed that thinning reduced carbon sink strength only for the first two posttreatment years. In the third and fourth posttreatment years, annual carbon sink strength of the thinned site was higher than the undisturbed site because thinning reduced aridity and drought limitation to carbon uptake. As a result, annual maximum gross primary production occurred when temperature was 3 °C higher at the thinned site compared with the undisturbed site. The severe fire consistently reduced annual evapotranspiration (range of 12–30%), whereas effects of thinning were smaller and transient, and could not be detected in the fourth year after thinning. Our results show large and persistent effects of intense fire and minor and short‐lived effects of thinning on southwestern ponderosa pine ecosystem carbon and water exchanges.     

Short‐term acclimation to warmer temperatures accelerates leaf carbon exchange processes across plant types

While temperature responses of photosynthesis and plant respiration are known to acclimate over time in many species, few studies have been designed to directly compare process‐level differences in acclimation capacity among plant types. We assessed short‐term (7 day) temperature acclimation of the maximum rate of Rubisco carboxylation (V_cmax), the maximum rate of electron transport (J_max), the maximum rate of phosphoenolpyruvate carboxylase carboxylation (V_pmax), and foliar dark respiration (R_d) in 22 plant species that varied in lifespan (annual and perennial), photosynthetic pathway (C₃ and C₄), and climate of origin (tropical and nontropical) grown under fertilized, well‐watered conditions. In general, acclimation to warmer temperatures increased the rate of each process. The relative increase in different photosynthetic processes varied by plant type, with C₃ species tending to preferentially accelerate CO₂‐limited photosynthetic processes and respiration and C₄ species tending to preferentially accelerate light‐limited photosynthetic processes under warmer conditions. R_d acclimation to warmer temperatures caused a reduction in temperature sensitivity that resulted in slower rates at high leaf temperatures. R_d acclimation was similar across plant types. These results suggest that temperature acclimation of the biochemical processes that underlie plant carbon exchange is common across different plant types, but that acclimation to warmer temperatures tends to have a relatively greater positive effect on the processes most limiting to carbon assimilation, which differ by plant type. The acclimation responses observed here suggest that warmer conditions should lead to increased rates of carbon assimilation when water and nutrients are not limiting.