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Understanding the effects of intensification of Amazon basin hydrological cycling manifest as increasingly frequent floods and droughts on water and energy cycles of tropical forests is essential to meeting the challenge of predicting ecosystem responses to climate change including forest tipping points Here we investigated the impacts of hydrological extremes on forest function using 12 years of observations between 2001 2020 of water and energy fluxes from eddy covariance along with associated ecological dynamics from biometry at the Tapaj s National Forest Measurements encompass the strong 2015 2016 El Ni o drought and La Ni a 2008 2009 wet events We found that the forest responded strongly to El Ni o Southern Oscillation ENSO Drought reduced water availability for evapotranspiration ET leading to large increases in sensible heat fluxes H Partitioning ET by an approach that assumes transpiration T is proportional to photosynthesis we found that water stress induced reductions in canopy conductance G sub s sub drove T declines partly compensated by higher evaporation E By contrast the abnormally wet La Ni a period gave higher T and lower E with little change in seasonal ET Both El Ni o Southern Oscillation ENSO events resulted in changes in forest structure manifested as lower wet season leaf area index However only during El Ni o 2015 2016 we observed a breakdown in the strong meteorological control of transpiration fluxes via energy availability and atmospheric demand because of slowing vegetation functions via shutdown of G sub s sub and significant leaf shedding Drought reduced T and G sub s sub higher H and E amplified by feedbacks with higher temperatures and vapor pressure deficits signaled that forest function had crossed a threshold from which it recovered slowly with delay post drought Identifying such tipping point onsets beyond which future irreversible processes may occur at local scale is crucial for predicting basin scale threshold crossing changes

Uncertainty in how the land carbon (C) sink will change over time contributes to uncertainty in Earth system model (ESM) projections of climate change. Much of the land sink is thought to reside in old-growth tropical forests, but recent analyses suggest a diminishing C sink in these forests due to rising temperatures and drought. Thus, there is an urgent need to better understand tropical forest responses to drought and to incorporate this understanding into ESMs. Previous work with vegetation demographic models (VDMs) – which represent the dynamics of individuals or cohorts, along with hydrology and biogeochemistry − suggest that functional diversity can enhance tropical forest resilience to climate change. However, there is little understanding of how different approaches to representing trait diversity and demography affect model outcomes. To explore the potential for trait diversity to moderate tropical forest responses to drought, we explored the behavior of nine VDMs, ranging from models with detailed site-level parameterizations to more generalized land models designed as ESM components. The behavior of each model was studied using soil and meteorological data collected at each of two tropical forest sites: Paracou Research Station, French Guiana, and Tapajos National Forest, Brazil. Low and high trait-diversity scenarios were simulated for each model using historical meteorology, as well as reduced rainfall scenarios. Few models showed strong effects of trait diversity on drought resistance (short-term response of forest biomass to rainfall reduction), but most models showed positive effects of diversity on resilience (long-term recovery of forest biomass following the initial biomass loss due to rainfall reduction). Long-term recovery was always associated with shifts in community composition towards greater drought-tolerance. However, there were large differences among models in the degree and time-scale of recovery. These differences were unrelated to th

Uncertainty in how the land carbon (C) sink will change over time contributes to uncertainty in Earth system model (ESM) projections of climate change. Much of the land sink is thought to reside in old-growth tropical forests, but recent analyses suggest a diminishing C sink in these forests due to rising temperatures and drought. Thus, there is an urgent need to better understand tropical forest responses to drought and to incorporate this understanding into ESMs. Previous work with vegetation demographic models (VDMs) – which represent the dynamics of individuals or cohorts, along with hydrology and biogeochemistry − suggest that functional diversity can enhance tropical forest resilience to climate change. However, there is little understanding of how different approaches to representing trait diversity and demography affect model outcomes. To explore the potential for trait diversity to moderate tropical forest responses to drought, we explored the behavior of nine VDMs, ranging from models with detailed site-level parameterizations to more generalized land models designed as ESM components. The behavior of each model was studied using soil and meteorological data collected at each of two tropical forest sites: Paracou Research Station, French Guiana, and Tapajos National Forest, Brazil. Low and high trait-diversity scenarios were simulated for each model using historical meteorology, as well as reduced rainfall scenarios. Few models showed strong effects of trait diversity on drought resistance (short-term response of forest biomass to rainfall reduction), but most models showed positive effects of diversity on resilience (long-term recovery of forest biomass following the initial biomass loss due to rainfall reduction). Long-term recovery was always associated with shifts in community composition towards greater drought-tolerance. However, there were large differences among models in the degree and time-scale of recovery. These differences were unrelated to th

Uncertainty in how the land carbon (C) sink will change over time contributes to uncertainty in Earth system model (ESM) projections of climate change. Much of the land sink is thought to reside in old-growth tropical forests, but recent analyses suggest a diminishing C sink in these forests due to rising temperatures and drought. Thus, there is an urgent need to better understand tropical forest responses to drought and to incorporate this understanding into ESMs. Previous work with vegetation demographic models (VDMs) – which represent the dynamics of individuals or cohorts, along with hydrology and biogeochemistry − suggest that functional diversity can enhance tropical forest resilience to climate change. However, there is little understanding of how different approaches to representing trait diversity and demography affect model outcomes. To explore the potential for trait diversity to moderate tropical forest responses to drought, we explored the behavior of nine VDMs, ranging from models with detailed site-level parameterizations to more generalized land models designed as ESM components. The behavior of each model was studied using soil and meteorological data collected at each of two tropical forest sites: Paracou Research Station, French Guiana, and Tapajos National Forest, Brazil. Low and high trait-diversity scenarios were simulated for each model using historical meteorology, as well as reduced rainfall scenarios. Few models showed strong effects of trait diversity on drought resistance (short-term response of forest biomass to rainfall reduction), but most models showed positive effects of diversity on resilience (long-term recovery of forest biomass following the initial biomass loss due to rainfall reduction). Long-term recovery was always associated with shifts in community composition towards greater drought-tolerance. However, there were large differences among models in the degree and time-scale of recovery. These differences were unrelated to th

Tropical forests are characterized by vast biomass, complex structures and mega-biodiversity. However, the adaptation processes of these forests to seasonal water availability are less understood, especially those located in the monsoonal and mountainous regions of tropical Southeast Asia. This study used four representative tropical forests spanning from 2° N to 22° N in continental Southeast Asia to address dry-condition photosynthesis at the seasonal scale. We first provided novel and reliable estimations of ecosystem photosynthesis (gross primary production; GPP) seasonality at all four sites. As expected, both evergreen and deciduous seasonal forests exhibited higher GPPs during the rainy season than during the dry season. A bimodal pattern corresponding to solar radiation occurred in the GPP of the perhumid forest. The surface conductance (Gs) was consistently lower both in the dry season and during dry spells (DSPs) than during the wet season and non-dry spells. However, this did not prevent GPP from increasing alongside increasing irradiance in the perhumid forest, suggesting that other ecosystem physiological properties, for example, the light-saturated photosynthetic rate, must have increased, thus surpassing the effect of Gs reduction. Thus, perhumid forests could be defined as light-demanding ecosystems with regard to their seasonal dynamics. Seasonal forests are water-stressed ecosystems in the dry season, as shown by the reductions in GPP, Gs and related ecosystem physiological properties. At all four forest sites, we observed a lack of consistent adaptive strategy to fit the water seasonality due to the diversity in leaf phenology, soil nutrient availability, root depth and other potential factors.

Understanding the effects of intensification of Amazon basin hydrological cycling manifest as increasingly frequent floods and droughts on water and energy cycles of tropical forests is essential to meeting the challenge of predicting ecosystem responses to climate change including forest tipping points Here we investigated the impacts of hydrological extremes on forest function using 12 years of observations between 2001 2020 of water and energy fluxes from eddy covariance along with associated ecological dynamics from biometry at the Tapaj s National Forest Measurements encompass the strong 2015 2016 El Ni o drought and La Ni a 2008 2009 wet events We found that the forest responded strongly to El Ni o Southern Oscillation ENSO Drought reduced water availability for evapotranspiration ET leading to large increases in sensible heat fluxes H Partitioning ET by an approach that assumes transpiration T is proportional to photosynthesis we found that water stress induced reductions in canopy conductance G sub s sub drove T declines partly compensated by higher evaporation E By contrast the abnormally wet La Ni a period gave higher T and lower E with little change in seasonal ET Both El Ni o Southern Oscillation ENSO events resulted in changes in forest structure manifested as lower wet season leaf area index However only during El Ni o 2015 2016 we observed a breakdown in the strong meteorological control of transpiration fluxes via energy availability and atmospheric demand because of slowing vegetation functions via shutdown of G sub s sub and significant leaf shedding Drought reduced T and G sub s sub higher H and E amplified by feedbacks with higher temperatures and vapor pressure deficits signaled that forest function had crossed a threshold from which it recovered slowly with delay post drought Identifying such tipping point onsets beyond which future irreversible processes may occur at local scale is crucial for predicting basin scale threshold crossing changes

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.

Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land-atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia-along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry-season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%-30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%-15% of incoming solar radiation, compared to 0.15%-0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry-season ET and associated cooling). These partially compensating model-observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn ), the key driver of water and energy fluxes (LE ~ 0.6 Rn and H ~ 0.15 Rn ), though these biases varied among sites and models. A better representation of energy-related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation-atmosphere exchange and reduce uncertainty of regional and global biogeochem

Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land-atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia-along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry-season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%-30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%-15% of incoming solar radiation, compared to 0.15%-0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry-season ET and associated cooling). These partially compensating model-observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn ), the key driver of water and energy fluxes (LE ~ 0.6 Rn and H ~ 0.15 Rn ), though these biases varied among sites and models. A better representation of energy-related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation-atmosphere exchange and reduce uncertainty of regional and global biogeochem

In this work we have developed a random forest regressor to predict daily evapotranspiration in three eddy-covariance sites in Northern Australia from in-situ meteorological data and fluxes, and satellite leaf area index and land surface temperature data. The variable analysis for the random forest regressor suggests that leaf area index is the most important one at this temporal scale. A development sample corresponding to the period 2010–2013 was used, while the year 2014 has been separated for testing. Using this approach, we have obtained satisfactory performances in the three sites, with RMSE errors around 1 mm/day (and relative RMSEs ~0.3) in comparison to the measured values. With the final aim of testing the predictive capability of a model that uses remote sensing data as input, regressors that predict evapotranspiration exclusively from leaf area index and land surface temperature, have been trained resulting in reasonable performances. The RMSEs over the test set are ~1.1−1.2 mm/day. In all cases, the errors are comparable to those obtained in previous works that use similar locations and different methods. When compared to the measured values, the random forest predictions of evapotranspiration are more accurate than the global MODIS ET 8-day 1 km product, which was used as benchmark for the performance evaluation of our models, in the three selected locations.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Tropical forests may be vulnerable to climate change1-3 if photosynthetic carbon uptake currently operates near a high temperature limit4-6. Predicting tropical forest function requires understanding the relative contributions of two mechanisms of high-temperature photosynthetic declines: stomatal limitation (H1), an indirect response due to temperature-associated changes in atmospheric vapour pressure deficit (VPD)7, and biochemical restrictions (H2), a direct temperature response8,9. Their relative control predicts different outcomes-H1 is expected to diminish with stomatal responses to future co-occurring elevated atmospheric [CO2], whereas H2 portends declining photosynthesis with increasing temperatures. Distinguishing the two mechanisms at high temperatures is therefore critical, but difficult because VPD is highly correlated with temperature in natural settings. We used a forest mesocosm to quantify the sensitivity of tropical gross ecosystem productivity (GEP) to future temperature regimes while constraining VPD by controlling humidity. We then analytically decoupled temperature and VPD effects under current climate with flux-tower-derived GEP trends in situ from four tropical forest sites. Both approaches showed consistent, negative sensitivity of GEP to VPD but little direct response to temperature. Importantly, in the mesocosm at low VPD, GEP persisted up to 38 °C, a temperature exceeding projections for tropical forests in 2100 (ref. 10). If elevated [CO2] mitigates VPD-induced stomatal limitation through enhanced water-use efficiency as hypothesized9,11, tropical forest photosynthesis may have a margin of resilience to future warming.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Tropical forests may be vulnerable to climate change1-3 if photosynthetic carbon uptake currently operates near a high temperature limit4-6. Predicting tropical forest function requires understanding the relative contributions of two mechanisms of high-temperature photosynthetic declines: stomatal limitation (H1), an indirect response due to temperature-associated changes in atmospheric vapour pressure deficit (VPD)7, and biochemical restrictions (H2), a direct temperature response8,9. Their relative control predicts different outcomes-H1 is expected to diminish with stomatal responses to future co-occurring elevated atmospheric [CO2], whereas H2 portends declining photosynthesis with increasing temperatures. Distinguishing the two mechanisms at high temperatures is therefore critical, but difficult because VPD is highly correlated with temperature in natural settings. We used a forest mesocosm to quantify the sensitivity of tropical gross ecosystem productivity (GEP) to future temperature regimes while constraining VPD by controlling humidity. We then analytically decoupled temperature and VPD effects under current climate with flux-tower-derived GEP trends in situ from four tropical forest sites. Both approaches showed consistent, negative sensitivity of GEP to VPD but little direct response to temperature. Importantly, in the mesocosm at low VPD, GEP persisted up to 38 °C, a temperature exceeding projections for tropical forests in 2100 (ref. 10). If elevated [CO2] mitigates VPD-induced stomatal limitation through enhanced water-use efficiency as hypothesized9,11, tropical forest photosynthesis may have a margin of resilience to future warming.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

© 2019 John Wiley & Sons Ltd Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

© 2019 Elsevier B.V. Tropical forests are responsible for the evaporation and transpiration of large quantities of water into the atmosphere annually. Surface conductance (gs) is a poorly understood phenomenon that plays a central role in regulating this evapotranspiration. We studied the calculations, variations, and environmental factors controlling gs based on eddy flux measurements from 10 tropical forest sites that covered a wide range of water gradients across continents. We found that boundary layer conductance (gb) is comparable with aerodynamic conductance for momentum (gaM) and thus, it should not be ignored in estimations of total aerodynamic conductance for water vapor (gaV). Based on the findings, we have made some recommendations for gaM estimation both with and without measurements of turbulence. The seasonal variation of gs is low in humid sites but large in sites with a dry season. A value of 24.8 ± 13.8 mm s−1 was suggested for maximum surface conductance (gsmax) for tropical forests. Both water vapor deficit (D) and radiation (Q) play an important role in controlling gs. The model driven by both D and Q could capture the diurnal variations of gs well and could be implemented in large-scale models in future. We believe the findings of this study could contribute substantially to our understanding of tropical forest gs.

© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust Reducing uncertainties in the response of tropical forests to global change requires understanding how intra- and interannual climatic variability selects for different species, community functional composition and ecosystem functioning, so that the response to climatic events of differing frequency and severity can be predicted. Here we present an extensive dataset of hydraulic traits of dominant species in two tropical Amazon forests with contrasting precipitation regimes – low seasonality forest (LSF) and high seasonality forest (HSF) – and relate them to community and ecosystem response to the El Niño–Southern Oscillation (ENSO) of 2015. Hydraulic traits indicated higher drought tolerance in the HSF than in the LSF. Despite more intense drought and lower plant water potentials in HSF during the 2015-ENSO, greater xylem embolism resistance maintained similar hydraulic safety margin as in LSF. This likely explains how ecosystem-scale whole-forest canopy conductance at HSF maintained a similar response to atmospheric drought as at LSF, despite their water transport systems operating at different water potentials. Our results indicate that contrasting precipitation regimes (at seasonal and interannual time scales) select for assemblies of hydraulic traits and taxa at the community level, which may have a significant role in modulating forest drought response at ecosystem scales.

© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust Seasonal dynamics in the vertical distribution of leaf area index (LAI) may impact the seasonality of forest productivity in Amazonian forests. However, until recently, fine-scale observations critical to revealing ecological mechanisms underlying these changes have been lacking. To investigate fine-scale variation in leaf area with seasonality and drought we conducted monthly ground-based LiDAR surveys over 4 yr at an Amazon forest site. We analysed temporal changes in vertically structured LAI along axes of both canopy height and light environments. Upper canopy LAI increased during the dry season, whereas lower canopy LAI decreased. The low canopy decrease was driven by highly illuminated leaves of smaller trees in gaps. By contrast, understory LAI increased concurrently with the upper canopy. Hence, tree phenological strategies were stratified by height and light environments. Trends were amplified during a 2015–2016 severe El Niño drought. Leaf area low in the canopy exhibited behaviour consistent with water limitation. Leaf loss from short trees in high light during drought may be associated with strategies to tolerate limited access to deep soil water and stressful leaf environments. Vertically and environmentally structured phenological processes suggest a critical role of canopy structural heterogeneity in seasonal changes in Amazon ecosystem function.

© 2019 Elsevier B.V. In this study, the performance of the combined-source variational data assimilation scheme (CS-VDA) is assessed in detail using in situ heat fluxes (i.e. sensible heat (H) and latent heat (LE)) collected at a Eucalypt forest savanna of Northern Australia (Howard Springs). The CS VDA scheme estimates surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) and meteorological data into a surface energy balance model and a dynamic model. The main objectives of this paper were to extend previous studies to a semi-arid ecosystem and to evaluate the potential of using global meteorological forcing data (GMD) to drive the CS VDA model (rather than in-situ meteorological observations). In order to study the new errors associated with the use of GMD, the effects on LE of the uncertainty in air temperature and wind speed (the two key meteorological factors that controls the total estimation error) was quantitatively characterized. Using hourly in-situ measurements as inputs, the daily-averaged LE RMSE daily was 54 W/m 2 , which agrees with the errors previously reported in the literature. As expected, replacing local meteorological data with GMD reduces the performance of the LE estimation (GMA: RMSE daily = 82 W/m 2 , GLDAS: RMSE daily = 151 W/m 2 ). However, LE RMSE values at 8-day temporal scale for GMA are RMSE 8-days = 32 W/m 2 , similar to those reported in this area for other models (MODIS (MOD16A2) and Breathing Earth System Simulator (BESS)). The error propagation analysis indicate that the CS VDA model is very sensitive to uncertainties in wind speed measurements. Moreover, there are large discrepancies between in situ and GMD wind speed. These two factors combined can explain the degradation in LE estimations. In this context, our study is a first step towards the characterization of an operational daily LE estimation scheme using hourly LST observations.

© 2019 Watson, Restrepo-Coupe and Huete. Grasslands of the Australian Southern Tablelands represent a patchwork of native and exotic systems, occupying a continuum of C 3 -dominated to C 4 -dominated grasslands where composition depends on disturbance factors (e.g., grazing) and climate. Managing these complex landscapes is both challenging and critical for maintaining the security of Australia's pasture industries, and for protecting the biodiversity of native remnants. Differentiating C 3 from C 4 vegetation has been a prominent theme in remote sensing research due to distinct C 3 /C 4 seasonal productivity patterns (phenology) and high uncertainty about how C 3 /C 4 vegetation will respond to a changing climate. Phenology is used in northern hemisphere ecosystems for a range of purposes but has not been widely adopted in Australia, where dynamic climate often results in non-repetitive seasonal vegetation patterns. We employed time-lapse cameras (phenocams) to study the phenology of twelve grassland areas dominated by cool season (C 3 ) and warm season (C 4 ), native or exotic grasses near Canberra, Australia. Our aims were to assess phenological characteristics of the functional types and to determine the drivers of phenological variability. We compared the fine-scale phenocam seasonal profiles with field sampling and MODIS/Landsat satellite products to assess paddock-to-landscape functioning. We found C 3 /C 4 species dominance to be the primary driver of phenological differences among grassland types, with C 3 grasslands demonstrating peak greenness in spring, and senescing rapidly in response to high summer temperatures. In contrast, C 4 grasslands showed peak activity in Austral summer and autumn (January-March). Some sites displayed primary and secondary peaks dependent on rainfall and species composition. We found that the proportion of dead vegetation is an important biophysical driver of grassland phenology, as were grazing pressures and species-dependent

© 2019 Elsevier B.V. Tropical forests are responsible for the evaporation and transpiration of large quantities of water into the atmosphere annually. Surface conductance (gs) is a poorly understood phenomenon that plays a central role in regulating this evapotranspiration. We studied the calculations, variations, and environmental factors controlling gs based on eddy flux measurements from 10 tropical forest sites that covered a wide range of water gradients across continents. We found that boundary layer conductance (gb) is comparable with aerodynamic conductance for momentum (gaM) and thus, it should not be ignored in estimations of total aerodynamic conductance for water vapor (gaV). Based on the findings, we have made some recommendations for gaM estimation both with and without measurements of turbulence. The seasonal variation of gs is low in humid sites but large in sites with a dry season. A value of 24.8 ± 13.8 mm s−1 was suggested for maximum surface conductance (gsmax) for tropical forests. Both water vapor deficit (D) and radiation (Q) play an important role in controlling gs. The model driven by both D and Q could capture the diurnal variations of gs well and could be implemented in large-scale models in future. We believe the findings of this study could contribute substantially to our understanding of tropical forest gs.

© 2019 Elsevier B.V. In this study, the performance of the combined-source variational data assimilation scheme (CS-VDA) is assessed in detail using in situ heat fluxes (i.e. sensible heat (H) and latent heat (LE)) collected at a Eucalypt forest savanna of Northern Australia (Howard Springs). The CS VDA scheme estimates surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) and meteorological data into a surface energy balance model and a dynamic model. The main objectives of this paper were to extend previous studies to a semi-arid ecosystem and to evaluate the potential of using global meteorological forcing data (GMD) to drive the CS VDA model (rather than in-situ meteorological observations). In order to study the new errors associated with the use of GMD, the effects on LE of the uncertainty in air temperature and wind speed (the two key meteorological factors that controls the total estimation error) was quantitatively characterized. Using hourly in-situ measurements as inputs, the daily-averaged LE RMSE daily was 54 W/m 2 , which agrees with the errors previously reported in the literature. As expected, replacing local meteorological data with GMD reduces the performance of the LE estimation (GMA: RMSE daily = 82 W/m 2 , GLDAS: RMSE daily = 151 W/m 2 ). However, LE RMSE values at 8-day temporal scale for GMA are RMSE 8-days = 32 W/m 2 , similar to those reported in this area for other models (MODIS (MOD16A2) and Breathing Earth System Simulator (BESS)). The error propagation analysis indicate that the CS VDA model is very sensitive to uncertainties in wind speed measurements. Moreover, there are large discrepancies between in situ and GMD wind speed. These two factors combined can explain the degradation in LE estimations. In this context, our study is a first step towards the characterization of an operational daily LE estimation scheme using hourly LST observations.

© 2019 Watson, Restrepo-Coupe and Huete. Grasslands of the Australian Southern Tablelands represent a patchwork of native and exotic systems, occupying a continuum of C 3 -dominated to C 4 -dominated grasslands where composition depends on disturbance factors (e.g., grazing) and climate. Managing these complex landscapes is both challenging and critical for maintaining the security of Australia's pasture industries, and for protecting the biodiversity of native remnants. Differentiating C 3 from C 4 vegetation has been a prominent theme in remote sensing research due to distinct C 3 /C 4 seasonal productivity patterns (phenology) and high uncertainty about how C 3 /C 4 vegetation will respond to a changing climate. Phenology is used in northern hemisphere ecosystems for a range of purposes but has not been widely adopted in Australia, where dynamic climate often results in non-repetitive seasonal vegetation patterns. We employed time-lapse cameras (phenocams) to study the phenology of twelve grassland areas dominated by cool season (C 3 ) and warm season (C 4 ), native or exotic grasses near Canberra, Australia. Our aims were to assess phenological characteristics of the functional types and to determine the drivers of phenological variability. We compared the fine-scale phenocam seasonal profiles with field sampling and MODIS/Landsat satellite products to assess paddock-to-landscape functioning. We found C 3 /C 4 species dominance to be the primary driver of phenological differences among grassland types, with C 3 grasslands demonstrating peak greenness in spring, and senescing rapidly in response to high summer temperatures. In contrast, C 4 grasslands showed peak activity in Austral summer and autumn (January-March). Some sites displayed primary and secondary peaks dependent on rainfall and species composition. We found that the proportion of dead vegetation is an important biophysical driver of grassland phenology, as were grazing pressures and species-dependent

© 2019 John Wiley & Sons Ltd Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust Seasonal dynamics in the vertical distribution of leaf area index (LAI) may impact the seasonality of forest productivity in Amazonian forests. However, until recently, fine-scale observations critical to revealing ecological mechanisms underlying these changes have been lacking. To investigate fine-scale variation in leaf area with seasonality and drought we conducted monthly ground-based LiDAR surveys over 4 yr at an Amazon forest site. We analysed temporal changes in vertically structured LAI along axes of both canopy height and light environments. Upper canopy LAI increased during the dry season, whereas lower canopy LAI decreased. The low canopy decrease was driven by highly illuminated leaves of smaller trees in gaps. By contrast, understory LAI increased concurrently with the upper canopy. Hence, tree phenological strategies were stratified by height and light environments. Trends were amplified during a 2015–2016 severe El Niño drought. Leaf area low in the canopy exhibited behaviour consistent with water limitation. Leaf loss from short trees in high light during drought may be associated with strategies to tolerate limited access to deep soil water and stressful leaf environments. Vertically and environmentally structured phenological processes suggest a critical role of canopy structural heterogeneity in seasonal changes in Amazon ecosystem function.

© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust Reducing uncertainties in the response of tropical forests to global change requires understanding how intra- and interannual climatic variability selects for different species, community functional composition and ecosystem functioning, so that the response to climatic events of differing frequency and severity can be predicted. Here we present an extensive dataset of hydraulic traits of dominant species in two tropical Amazon forests with contrasting precipitation regimes – low seasonality forest (LSF) and high seasonality forest (HSF) – and relate them to community and ecosystem response to the El Niño–Southern Oscillation (ENSO) of 2015. Hydraulic traits indicated higher drought tolerance in the HSF than in the LSF. Despite more intense drought and lower plant water potentials in HSF during the 2015-ENSO, greater xylem embolism resistance maintained similar hydraulic safety margin as in LSF. This likely explains how ecosystem-scale whole-forest canopy conductance at HSF maintained a similar response to atmospheric drought as at LSF, despite their water transport systems operating at different water potentials. Our results indicate that contrasting precipitation regimes (at seasonal and interannual time scales) select for assemblies of hydraulic traits and taxa at the community level, which may have a significant role in modulating forest drought response at ecosystem scales.

In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R 2 Combining double low line 0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha'1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R 2 Combining double low line 0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha'1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

© Author(s) 2018. In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R 2 Combining double low line 0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha'1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

© 2017 Elsevier B.V. This study presents the energy, water, and carbon (C) flux dynamics of a young afforested temperate white pine (Pinus strobus L.) forest in southern Ontario, Canada during the initial fourteen years (2003–2016) of establishment. Energy fluxes, namely, net radiation (Rn), latent heat (LE), and sensible heat (H) flux increased over time, due to canopy development. Annual values of ground heat flux (G) peaked in 2007 and then gradually declined in response to canopy closure. The forest became a consistent C-sink only 5 years after establishment owing in part to low respiratory fluxes from the former agricultural, sandy soils with low residual soil organic matter. Mean annual values of gross ecosystem productivity (GEP), ecosystem respiration (RE), and net ecosystem productivity (NEP) ranged from 494 to 1913, 515 to 1774 and −126 to 216 g C m−2 year−1 respectively, over the study period. Annual evapotranspiration (ET) values ranged from 328 to 429 mm year−1 over the same period. Water use efficiency (WUE) increased with stand age with a mean WUE value of 3.92 g C kg−1 H2O from 2008 to 2016. Multivariable linear regression analysis conducted using observed data suggested that the overall, C and water dynamics of the stand were primarily driven by radiation and temperature, both of which explained 77%, 48%, 28%, and 76% of the variability in GEP, RE, NEP, and ET, respectively. However, late summer droughts, which were prevalent in the region, reduced NEP. The reduction in NEP was enhanced when summer drought events were accompanied by increased heat such as those in 2005, 2012 and 2016. This study contributes to our understanding of the energy, water and C dynamics of afforested temperate conifer plantations and how these forests may respond to changing climate conditions during the crucial initial stage of their life cycle. Our findings also demonstrate the potential of pine plantation stands to sequester atmospheric CO2 in eastern North America.

© 2018 IEEE The spectral properties of plant leaves reflect the state of their photosynthetic apparatus and the surrounding environment. A well-known mechanism of photosynthetic downregulation, active on the time scale from minutes to hours, is caused by reversible changes in the xanthophyll cycle pigments. These changes affect leaf spectral absorption and are frequently quantified using the Photochemical Reflectance Index (PRI). This index can thus be used to monitor the photosynthetic status of the vegetation canopy, potentially from a large distance, and allows for a global satellite-based monitoring of photosynthesis. Such Earth observation satellites in near-polar orbits usually cover the same geographical location at the same local solar time at regular intervals. To facilitate the interpretation of these instantaneous remote PRI measurements and scale them to longer timescales, we measured the daily course of leaf PRI in two evergreen biomes: European boreal forest and Amazon rainforest. The daily course of PRI was different for the two locations. In Amazon, PRI was driven by incident Photosynthetic Photon Flux Density (PPFD). In the boreal location, PRI and PPFD were decoupled and PRI indicated downregulation only in the afternoon. This downregulation was confirmed with carbon exchange measurements. The study demonstrates the utility of biome-specific daily PRI curves for scaling instantaneous remote measurements to daily values and comparing data acquired at different times of day.

© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust Satellite and tower-based metrics of forest-scale photosynthesis generally increase with dry season progression across central Amazônia, but the underlying mechanisms lack consensus. We conducted demographic surveys of leaf age composition, and measured the age dependence of leaf physiology in broadleaf canopy trees of abundant species at a central eastern Amazon site. Using a novel leaf-to-branch scaling approach, we used these data to independently test the much-debated hypothesis – arising from satellite and tower-based observations – that leaf phenology could explain the forest-scale pattern of dry season photosynthesis. Stomatal conductance and biochemical parameters of photosynthesis were higher for recently mature leaves than for old leaves. Most branches had multiple leaf age categories simultaneously present, and the number of recently mature leaves increased as the dry season progressed because old leaves were exchanged for new leaves. These findings provide the first direct field evidence that branch-scale photosynthetic capacity increases during the dry season, with a magnitude consistent with increases in ecosystem-scale photosynthetic capacity derived from flux towers. Interactions between leaf age-dependent physiology and shifting leaf age-demographic composition are sufficient to explain the dry season photosynthetic capacity pattern at this site, and should be considered in vegetation models of tropical evergreen forests.

© Author(s) 2018. In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R 2 Combining double low line 0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha'1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

No claim to original US Government works New Phytologist © 2017 New Phytologist Trust Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun–sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate–phenology relationships in the tropics.

© 2018 Unprecedented droughts hit southern Amazonia in 2005 and 2010, causing a sharp increase in tree mortality and carbon loss. To better predict the rainforest's response to future droughts, it is necessary to understand its behavior during past events. Satellite observations provide a practical source of continuous observations of Amazonian forest. Here we used a passive microwave-based vegetation water content record (i.e., vegetation optical depth, VOD), together with multiple hydrometeorological observations as well as conventional satellite vegetation measures, to investigate the rainforest canopy dynamics during the 2005 and 2010 droughts. During the onset of droughts in the wet-to-dry season (May–July) of both years, we found large-scale positive anomalies in VOD, leaf area index (LAI) and enhanced vegetation index (EVI) over the southern Amazonia. These observations are very likely caused by enhanced canopy growth. Concurrent below-average rainfall and above-average radiation during the wet-to-dry season can be interpreted as an early arrival of normal dry season conditions, leading to enhanced new leaf development and ecosystem photosynthesis, as supported by field observations. Our results suggest that further rainfall deficit into the subsequent dry season caused water and heat stress during the peak of 2005 and 2010 droughts (August–October) that exceeded the tolerance limits of the rainforest, leading to widespread negative VOD anomalies over the southern Amazonia. Significant VOD anomalies were observed mainly over the western part in 2005 and mainly over central and eastern parts in 2010. The total area with significant negative VOD anomalies was comparable between these two drought years, though the average magnitude of significant negative VOD anomalies was greater in 2005. This finding broadly agrees with the field observations indicating that the reduction in biomass carbon uptake was stronger in 2005 than 2010. The enhanced canopy growth prece

No claim to original US Government works New Phytologist © 2017 New Phytologist Trust Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun–sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate–phenology relationships in the tropics.

© 2017 Elsevier B.V. This study presents the energy, water, and carbon (C) flux dynamics of a young afforested temperate white pine (Pinus strobus L.) forest in southern Ontario, Canada during the initial fourteen years (2003–2016) of establishment. Energy fluxes, namely, net radiation (Rn), latent heat (LE), and sensible heat (H) flux increased over time, due to canopy development. Annual values of ground heat flux (G) peaked in 2007 and then gradually declined in response to canopy closure. The forest became a consistent C-sink only 5 years after establishment owing in part to low respiratory fluxes from the former agricultural, sandy soils with low residual soil organic matter. Mean annual values of gross ecosystem productivity (GEP), ecosystem respiration (RE), and net ecosystem productivity (NEP) ranged from 494 to 1913, 515 to 1774 and −126 to 216 g C m−2 year−1 respectively, over the study period. Annual evapotranspiration (ET) values ranged from 328 to 429 mm year−1 over the same period. Water use efficiency (WUE) increased with stand age with a mean WUE value of 3.92 g C kg−1 H2O from 2008 to 2016. Multivariable linear regression analysis conducted using observed data suggested that the overall, C and water dynamics of the stand were primarily driven by radiation and temperature, both of which explained 77%, 48%, 28%, and 76% of the variability in GEP, RE, NEP, and ET, respectively. However, late summer droughts, which were prevalent in the region, reduced NEP. The reduction in NEP was enhanced when summer drought events were accompanied by increased heat such as those in 2005, 2012 and 2016. This study contributes to our understanding of the energy, water and C dynamics of afforested temperate conifer plantations and how these forests may respond to changing climate conditions during the crucial initial stage of their life cycle. Our findings also demonstrate the potential of pine plantation stands to sequester atmospheric CO2 in eastern North America.

© 2018 Unprecedented droughts hit southern Amazonia in 2005 and 2010, causing a sharp increase in tree mortality and carbon loss. To better predict the rainforest's response to future droughts, it is necessary to understand its behavior during past events. Satellite observations provide a practical source of continuous observations of Amazonian forest. Here we used a passive microwave-based vegetation water content record (i.e., vegetation optical depth, VOD), together with multiple hydrometeorological observations as well as conventional satellite vegetation measures, to investigate the rainforest canopy dynamics during the 2005 and 2010 droughts. During the onset of droughts in the wet-to-dry season (May–July) of both years, we found large-scale positive anomalies in VOD, leaf area index (LAI) and enhanced vegetation index (EVI) over the southern Amazonia. These observations are very likely caused by enhanced canopy growth. Concurrent below-average rainfall and above-average radiation during the wet-to-dry season can be interpreted as an early arrival of normal dry season conditions, leading to enhanced new leaf development and ecosystem photosynthesis, as supported by field observations. Our results suggest that further rainfall deficit into the subsequent dry season caused water and heat stress during the peak of 2005 and 2010 droughts (August–October) that exceeded the tolerance limits of the rainforest, leading to widespread negative VOD anomalies over the southern Amazonia. Significant VOD anomalies were observed mainly over the western part in 2005 and mainly over central and eastern parts in 2010. The total area with significant negative VOD anomalies was comparable between these two drought years, though the average magnitude of significant negative VOD anomalies was greater in 2005. This finding broadly agrees with the field observations indicating that the reduction in biomass carbon uptake was stronger in 2005 than 2010. The enhanced canopy growth prece

© 2018 IEEE The spectral properties of plant leaves reflect the state of their photosynthetic apparatus and the surrounding environment. A well-known mechanism of photosynthetic downregulation, active on the time scale from minutes to hours, is caused by reversible changes in the xanthophyll cycle pigments. These changes affect leaf spectral absorption and are frequently quantified using the Photochemical Reflectance Index (PRI). This index can thus be used to monitor the photosynthetic status of the vegetation canopy, potentially from a large distance, and allows for a global satellite-based monitoring of photosynthesis. Such Earth observation satellites in near-polar orbits usually cover the same geographical location at the same local solar time at regular intervals. To facilitate the interpretation of these instantaneous remote PRI measurements and scale them to longer timescales, we measured the daily course of leaf PRI in two evergreen biomes: European boreal forest and Amazon rainforest. The daily course of PRI was different for the two locations. In Amazon, PRI was driven by incident Photosynthetic Photon Flux Density (PPFD). In the boreal location, PRI and PPFD were decoupled and PRI indicated downregulation only in the afternoon. This downregulation was confirmed with carbon exchange measurements. The study demonstrates the utility of biome-specific daily PRI curves for scaling instantaneous remote measurements to daily values and comparing data acquired at different times of day.

© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust Satellite and tower-based metrics of forest-scale photosynthesis generally increase with dry season progression across central Amazônia, but the underlying mechanisms lack consensus. We conducted demographic surveys of leaf age composition, and measured the age dependence of leaf physiology in broadleaf canopy trees of abundant species at a central eastern Amazon site. Using a novel leaf-to-branch scaling approach, we used these data to independently test the much-debated hypothesis – arising from satellite and tower-based observations – that leaf phenology could explain the forest-scale pattern of dry season photosynthesis. Stomatal conductance and biochemical parameters of photosynthesis were higher for recently mature leaves than for old leaves. Most branches had multiple leaf age categories simultaneously present, and the number of recently mature leaves increased as the dry season progressed because old leaves were exchanged for new leaves. These findings provide the first direct field evidence that branch-scale photosynthetic capacity increases during the dry season, with a magnitude consistent with increases in ecosystem-scale photosynthetic capacity derived from flux towers. Interactions between leaf age-dependent physiology and shifting leaf age-demographic composition are sufficient to explain the dry season photosynthetic capacity pattern at this site, and should be considered in vegetation models of tropical evergreen forests.

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In this study, we compared different remote-sensing (RS)-based land surface models (LSM) and reanalysis latent heat flux (LE) products over different forest ecosystems. We analysed the performance of three RS products, the MOD16A2, the Breathing Earth System Simulator (BESS) model, and a combined optical-microwave model (COM) in their ability to replicate eddy covariance (EC) flux observations of LE at eight southern hemisphere forest ecosystems and compared their results to simulated LE from the offline LSM (GLDAS/NOAH) and a reanalysis LE dataset (MERRA). To determine spatial uncertainties, we used the triple collocation (TC) method, which does not require a priori knowledge of the true LE value, at selected Australian EC locations and over an area without in situ measurement (the Dry Chaco Forest (DCF), Argentina). The spatial pattern of the TC results was commensurable with uncertainties calculated using EC observations, indicating that the TC method is a robust technique to estimate spatial uncertainties. As global products have been validated with EC measurement from Ozflux stations, we hypothesized and found, using the TC model, that LE products achieve a better performance over areas with EC from networks than over sites without ground-based measurements and may reflect over-calibration of models or a need for a more diverse representation of ecosystems at flux tower networks.