Search

Tropical forests take up more carbon (C) from the atmosphere per annum by photosynthesis than any other type of vegetation. Phosphorus (P) limitations to C uptake are paramount for tropical and subtropical forests around the globe. Yet the generality of photosynthesis-P relationships underlying these limitations are in question, and hence are not represented well in terrestrial biosphere models. Here we demonstrate the dependence of photosynthesis and underlying processes on both leaf N and P concentrations. The regulation of photosynthetic capacity by P was similar across four continents. Implementing P constraints in the ORCHIDEE-CNP model, gross photosynthesis was reduced by 36% across the tropics and subtropics relative to traditional N constraints and unlimiting leaf P. Our results provide a quantitative relationship for the P dependence for photosynthesis for the front-end of global terrestrial C models that is consistent with canopy leaf measurements.

The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO2 fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO2 fertilization effect from other long‐term confounding drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO2 fertilization effect on photosynthetic carbon uptake using eddy covariance (EC) records across 38 extratropical forest sites. We find the median stimulation rate of GPP to be 3.2 ± 0.9 gC m−2 yr−1 ppm−1 (or 16.4 ± 4.2% per 100 ppm) under increasing atmospheric CO2 across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process‐based land surface models. We address additional factors, including nitrogen deposition and land management, that may impact plant productivity, potentially confounding the attribution to the CO2 fertilization effect. Assuming these site‐specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO2 fertilization effect. However, disentanglement of these long‐term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long‐term CO2 fertilization across multiple EC sites. Such observation‐based quantification is key to constraining the long‐standing uncertainties in the land carbon cycle under rising CO2 concentrations. Plain Language Summary: Through photosynthesis, plants convert CO2 and water into sugars and oxygen using solar energy, one of the most important chemical reactions on Earth. Human‐made carbon emissions are increasing atmospheric CO2 levels, impacting global photosynthesis. The additional carbon is believed to have a fertilizing effect on photosynthesis, causing vegetation to absorb a significant portion of the emitted CO2. However, the strength of this CO2 fertilization effect on photosynthesis is uncertain, but is a crucial factor in determining the future trajectory of atmospheric CO2 concentrations. In this study, we introduce a new statistical method to quantify the increase in photosynthetic carbon uptake, stimulated by rising CO2, based on measurements from 38 forest sites. Our results reveal that a 100 ppm increase in CO2 enhances photosynthesis by approximately 16%. Testing the statistical method with artificial model experiments supports the robustness of our findings. Our study improves the understanding of the impacts of human‐made CO2 emissions on the global carbon cycle. Key Points: We present a novel statistical method to disentangle the variability of photosynthetic rates related to climate and non‐climate driversThe analysis from 38 eddy covariance sites reveals a 3.2 ± 0.9 gC m−2 yr−1 increase in plant productivity per ppm rise in CO2Our statistical method is successfully tested against idealized model simulations with and without increasing CO2 [ABSTRACT FROM AUTHOR]

The response of plants to increasing atmospheric CO2 depends on the ecological context where the plants are found. Several experiments with elevated CO2 (eCO2) have been done worldwide, but the Amazonian forest understory has been neglected. As the central Amazon is limited by light and phosphorus, understanding how understory responds to eCO2 is important for foreseeing how the forest will function in the future. In the understory of a natural forest in the Central Amazon, we installed four open‐top chambers as control replicates and another four under eCO2 (+250 ppm above ambient levels). Under eCO2, we observed increases in carbon assimilation rate (67%), maximum electron transport rate (19%), quantum yield (56%), and water use efficiency (78%). We also detected an increase in leaf area (51%) and stem diameter increment (65%). Central Amazon understory responded positively to eCO2 by increasing their ability to capture and use light and the extra primary productivity was allocated to supporting more leaf and conducting tissues. The increment in leaf area while maintaining transpiration rates suggests that the understory will increase its contribution to evapotranspiration. Therefore, this forest might be less resistant in the future to extreme drought, as no reduction in transpiration rates were detected. Summary statement: The Amazonian understory plants demonstrate a remarkable response to elevated CO2, including an increase in leaf area and a higher investment in the maximum electron transport rate. These findings suggest enhanced carbon storage and water flux, stressing the important role of the understory in the overall functioning of the forest ecosystem. [ABSTRACT FROM AUTHOR]

Climate change has accelerated the frequency of catastrophic wildfires; however, the drivers that control the time‐to‐recover of forests are poorly understood. We integrated remotely sensed data, climate records, and landscape features to identify the causes of variability in the time‐to‐recover of canopy leaf area in southeast Australian eucalypt forests. Approximately 97% of all observed burns between 2001 and 2014 recovered to a pre‐fire leaf area index (±0.25 sd) within six years. Time‐to‐recover was highly variable within individual wildfires (ranging between ≤1 and ≥5 years), across burn seasons (90% longer January to September), and year of fire (median time‐to‐recover varying four‐fold across fire years). We used the logistic growth function to estimate the leaf area recovery rate, burn severity, and the long‐term carrying capacity of leaf area. Time‐to‐recover was most correlated with the leaf area recovery rate. The leaf area recovery rate was largest in areas that experienced high burn severity, and smallest in areas of intermediate to low burn severity. The leaf area recovery rate was also strongly accelerated by anomalously high post‐fire precipitation, and delayed by post‐fire drought. Finally we developed a predictive machine‐learning model of time‐to‐recover (R2: 0.68). Despite the exceptionally high burn severity of the 2019–2020 Australian megafires, we forecast the time‐to‐recover to be only 15% longer than the average of previous fire years. Plain Language Summary: Australian eucalypt forests have evolved different strategies to recover from fire. While the meteorological drivers of bushfire are reasonably well understood, the various processes explaining how long a forest takes to recover from fire are not. We investigated a range of static (landscape) and dynamic (vegetation condition or meteorological) factors that could influence how long a forest's canopy leaf area would take to recover from fire. "Time‐to‐recover" after fire is highly variable, ranging from less than 1 year to more than 5 years even within an individual burn location. More intense fires cause greater forest canopy damage and generally (but not always) lead to longer recovery times, whereas wetter conditions after the fire can accelerate recovery. Using these factors and others, we developed a model capable of predicting the time‐to‐recover and applied it to the 2019–2020 Australian megafires. Our analysis suggests the canopy damage caused by these fires was far more severe than fires in years prior. This would normally lead to a prolonged time‐to‐recover, however we predict that anomalously high rainfall in the year following the fires will shorten recovery time, compensating for the high burn severity. Ultimately we predict the time‐to‐recover will be only slightly longer than average. Key Points: Pre‐fire leaf area, burn severity, and post‐fire meteorological conditions combine to determine time‐to‐recover after fireLarge geographic variation in time‐to‐recover can be explained by mean climate and landscape differencesTime‐to‐recover can be predicted with high accuracy using information limited to the first year following fire [ABSTRACT FROM AUTHOR]

For over 40 years, Plant Functional Types (PFTs) have been used to discretize the ∼400,000 species of terrestrial plants into "similar" classes. Within Earth System Models (ESMs), PFTs simplify terrestrial biosphere modeling in combination with soil information and other site characteristics. However, in flux analysis studies, PFT schemes are often implemented as the sole analytical lens to clarify complex behavior. This usage assumes that PFTs adequately enable a mapping between climate inputs and flux outputs. Here, we show that random forest models, trained using aggregated climate and flux measurements from 245 eddy‐covariance sites, cannot accurately predict PFT groupings, regardless of the nature of the PFT scheme. Similarly, PFTs provide negligible benefit when using site climate to predict site flux regimes and vice versa. While use of PFT classifications is convenient, our results suggest they do not aid analytical skill, which has important implications for future terrestrial flux studies. Plain Language Summary: To understand how the land surface behaves, we often divide plants into a small number (20 or less) of "similar" groups, such as evergreen forests, or grasslands, known as Plant Functional Types (PFTs). The idea is that landscapes with similar large‐scale characteristics will behave in the same way. In land surface models, these PFT groups determine how the simulated plants react to the climate in combination with soil information and other characteristics, yet analysis of observations often use PFT groups alone to try to explain variations in results between different experimental sites. We use machine learning to show that while PFTs might be visually compelling, they do not necessarily represent behavior groupings and might actually hide real world behavior if used for analysis. As such, we suggest that future studies instead try to look at more specific site characteristics when trying to explain analysis results. Key Points: Plant Functional Types (PFTs), as often used in land flux studies, are not easily empirically associated with site climate and/or flux regimesA broad selection of alternative vegetation/land cover classifications do not offer greater predictabilityThe disconnect between PFTs and climate/flux regimes has implications for modeling and analysis of terrestrial systems [ABSTRACT FROM AUTHOR]

The ascent of water from the soil to the leaves of vascular plants, described by the study of plant hydraulics, regulates ecosystem responses to environmental forcing and recovery from stress periods. Several approaches to model plant hydraulics have been proposed. In this study, we introduce four different versions of plant hydraulics representations in the terrestrial biosphere model T&C to understand the significance of plant hydraulics to ecosystem functioning. We tested representations of plant hydraulics, investigating plant water capacitance, and long‐term xylem damages following drought. The four models we tested were a combination of representations including or neglecting capacitance and including or neglecting xylem damage legacies. Using the models at six case studies spanning semiarid to tropical ecosystems, we quantify how plant xylem flow, plant water storage and long‐term xylem damage can modulate overall water and carbon dynamics across multiple time scales. We show that as drought develops, models with plant hydraulics predict a slower onset of plant water stress, and a diurnal variability of water and carbon fluxes closer to observations. Plant water storage was found to be particularly important for the diurnal dynamics of water and carbon fluxes, with models that include plant water capacitance yielding better results. Models including permanent damage to conducting plant tissues show an additional significant drought legacy effect, limiting plant productivity during the recovery phase following major droughts. However, when considering ecosystem responses to the observed climate variability, plant hydraulic modules alone cannot significantly improve the overall model performance, even though they reproduce more realistic water and carbon dynamics. This opens new avenues for model development, explicitly linking plant hydraulics with additional ecosystem processes, such as plant phenology and improved carbon allocation algorithms. [ABSTRACT FROM AUTHOR]

Ecosystem function can be affected directly by climate, including by meteorological extremes, and also by sustained lags and legacies on timescales that surpass those of the weather events themselves. However, important gaps remain in our understanding of the influence and timescale of persistence of antecedent climate, known as environmental memory, on terrestrial carbon and water fluxes. Identifying the interactions between the lagged response to climate and the legacies to climate extremes, and whether the influence of memory varies through time, has not been fully explored. Here, we used a novel k‐means clustering plus regression approach to examine timeseries of the sensitivity of terrestrial fluxes to antecedent precipitation at 65 eddy‐covariance sites across a range of ecosystems. Quantifying the sensitivity to past precipitation and temperature reveals that the role of memory in ecosystem fluxes varies across sites and in time. When memory was accounted for in the model, relative improvement in modeled site flux r2 compared to an instantaneous model varied between 0% and 57%, with mean of 12%. Our results show that vegetation type was a stronger predictor of memory importance than site aridity, implying a need to understand vegetation resilience conferred by physiological traits and acclimation capacity. The influence of memory varied strongly through time at many sites, with the role of different timescales exhibiting consistent non‐stationarity. Our results demonstrate the importance of accounting for time‐varying vegetation response to antecedent rainfall in land surface models to accurately predict future terrestrial fluxes. Plain Language Summary: To predict how changes in future climate and weather extremes might impact terrestrial ecosystems, we need to understand the timescales of vegetation response to antecedent climate. Prevailing methods of exploration assume such responses to be stationary, that is constant through time. We present a novel approach that shows how the memory of plants to climate conditions change through time. We show that the carbon and water fluxes of vegetation can be significantly sensitive to antecedent rainfall and importantly that this sensitivity can vary substantially through time. Plant functional type is a key indicator of the role of memory to precipitation, while the response to antecedent rainfall is not determined by site aridity. Predicting future changes in the global carbon sink requires understanding how vegetation responds to climate across timescales. Identifying these timescales at which plants respond to climate is critically important as the climate changes, especially if extremes (e.g., heatwaves) become more frequent due to compounding effects. Key Points: We use a k‐means clustering plus regression approach to explore the time‐varying response of terrestrial fluxes to antecedent climateThe role of antecedent climate in ecosystem functioning is highly site‐ and time‐dependentSite vegetation classification is a greater predictor for the precipitation memory of terrestrial fluxes than site aridity [ABSTRACT FROM AUTHOR]

Climate projections from global circulation models (GCMs) part of the Coupled Model Intercomparison Project (CMIP6) are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation, which hamper predictive capacity. In this study we examine different methods to constrain regional projections of the carbon cycle in Australia. We employ a 5 dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias correct or calculate ensemble averages over the original forcing data to constrain the uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in carbon over time, highlighting the need to account for temporal properties in correction or ensemble averaging methods. Some bias correction methods reduce the ensemble uncertainty more than others. The vegetation distribution can depend on the bias correction method used. We further find that both the weighted ensemble averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. [ABSTRACT FROM AUTHOR]

Plant transpiration dominates terrestrial latent heat fluxes (LE) and plays a central role in regulating the water cycle and land surface energy budget. However, currently Earth system models (ESM) disagree strongly on the amount of transpiration, and thus LE, leading to large uncertainties in simulating future climate. Thus it is crucial to correctly represent the mechanisms controlling the transpiration in models. At the leaf-scale, transpiration is controlled by stomatal regulation, and at the canopy-scale, through turbulence, which is a function of canopy structure and wind. The coupling of vegetation to the atmosphere can be characterized by a coefficient O. A value of O x 0 implies a strong coupling of vegetation and the atmosphere, leaving a dominant role to stomatal conductance in regulating water (H2O) and carbon dioxide (CO2) fluxes, while O - 1 implies a complete decoupling of leaves from the atmosphere, that is, the transfer of H2O and CO2 is limited by aerodynamic transport. In this study, we investigated how well the land surface model ORCHIDEE (v7266), simulates the coupling of vegetation to the atmosphere by using empirical daily estimates of O derived from flux measurements from 106 FLUXNET sites. Our results show that ORCHIDEE generally captures the O in forest vegetation types (0.27±0.10) compared with observation (0.26±0.09), but underestimates O in grasslands and croplands (0.26±0.16 for model, 0.33±0.17 for observation). The good model performance in forests is due to compensation of biases in surface conductance (Gs) and aerodynamic conductance (Ga). Calibration of key parameters controlling the dependence of the stomatal conductance to the water vapor deficit (VPD) improves the simulated Gs, and O estimates in grasslands and croplands (0.30±0.21). To assess the underlying controls of O, we applied random forest (RF) models to both simulated and observation-based O. We found that large observed O are associated with periods of low wind speed, high temperature, low VPD and related to sites with large leaf area index (LAI) and/or short vegetation. The RF models applied to ORCHIDEE output generally agree with this pattern. However, we found the ORCHIDEE underestimated the sensitivity of O to VPD when VPD is high, overestimated the impact of LAI on O, and did not correctly simulate the temperature dependence of O when temperature is high. Our results highlight the importance of observational constraints on simulating the vegetation-atmosphere coupling strength, which can help improve predictive accuracy of water fluxes in Earth system models. [ABSTRACT FROM AUTHOR]

The vegetation's response to climate change is a significant source of uncertainty in future terrestrial biosphere model projections. Constraining climate-carbon cycle feedbacks requires improving our understanding of direct, as well as long-term, plant physiological responses to climate. In particular, the timescales and strength of memory effects arising from both extreme events (i.e., droughts and heatwaves) and structural lags in the systems have largely been overlooked in the development of models. This is despite the knowledge that plant responses to climatic drivers occur across multiple timescales (seconds to decades), with the impact of climate extremes resonating for many years. Using data from 13 eddy covariance sites, covering two rainfall gradients (256 to 1491 mm yr-1) in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales and magnitude of influence of antecedent drivers on daily net ecosystem exchange (NEE) and latent heat flux (λE). Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. Latent heat was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.95. When considered at a continental scale, both fluxes were more predictable when memory effects were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. The importance of environmental memory in predicting fluxes increased as site water availability declined (ρ = -0.72, p < 0.01 for NEE, ρ = -0.62, p < 0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a k-means clustering plus regression model to confirm the suitability of the Bayesian model for modelling these sites. The k-means approach performed similarly to the Bayesian model in terms of model fit, demonstrating the robustness of the Bayesian framework for exploring the role of environmental memory. Our results underline the importance of capturing memory effects in models used to project future responses to climate change, especially in water-limited ecosystems. Finally, we demonstrate a considerable variation in individual site predictability, driven to a notable degree by environmental memory, and this should be considered when evaluating model performance across ecosystems. [ABSTRACT FROM AUTHOR]

Understanding vegetation recovery after drought is critical for projecting vegetation dynamics in future climates. From 1997 to 2009, Australia experienced a long‐lasting drought known as the Millennium Drought (MD), which led to widespread reductions in vegetation productivity. However, vegetation recovery post‐drought and its determinants remain unclear. This study leverages remote sensing products from different sources—fraction of absorbed photosynthetically active radiation (FPAR), based on optical data, and canopy density, derived from microwave data—and random forest algorithms to assess drought recovery over Australian natural vegetation during a 20‐year period centered on the MD. Post‐drought recovery was prevalent across the continent, with 6 out of 10 drought events seeing full recovery within about 6 months. Canopy density was slower to recover than leaf area seen in FPAR. The probability of full recovery was most strongly controlled by drought return interval, post‐drought hydrological condition, and drought length. Full recovery was seldom observed when drought events occurred at intervals of 3 months or less, and moderately dry (standardized water balance anomaly [SWBA] within [−1, −0.76]) post‐drought conditions resulted in less complete recovery than wet (SWBA > 0.3) post‐drought conditions. Press droughts, which are long term but not extreme, delayed recovery more than pulse droughts (short term but extreme) and led to a higher frequency of persistent decline. Following press droughts, the frequency of persistent decline differed little among biome types but peaked in semi‐arid regions across aridity levels. Forests and savanna required the longest recovery times for press drought, while grasslands were the slowest to recover for pulse drought. This study provides quantitative thresholds that could be used to improve the modeling of ecosystem dynamics post‐drought. [ABSTRACT FROM AUTHOR]